delhi-weather-prediction-using-logistic-classifier.ipynb

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   "source": [
    "<a href=\"https://www.facebook.com/codemakerz\"><img src=\"https://scontent.ffjr1-4.fna.fbcdn.net/v/t1.0-9/36189148_736466693143793_2172101683281133568_n.png?_nc_cat=107&_nc_eui2=AeHzxv3SUcQBOfijLP-cEnHkX4z9XQXdeau__2MlErWZ1x07aZ1zx1PzJUDDxL6cpr7oPqYiifggXDptgtP8W5iCoDRjcdILDBYZ5Ig40dqi8Q&_nc_oc=AQmMCNXdzelFB2rdtpk8wN8nC410Wm2yKupYfYS1FxHNejTF0Jhr1G3WIZORKRF3TvFpohMB8Puw29Txxan8CW05&_nc_ht=scontent.ffjr1-4.fna&oh=7b13627e991a4d1b508923041bd7bc22&oe=5D8A7B03\" />\n",
    "</a>\n",
    "Follow Us:\n",
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    "\n",
    "<h1>Delhi Weather Classification Using Decision Tree Classification</h1>\n",
    "<h3>Help us to increase the accuracy of model. Contact us to post your code.</h3>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "_cell_guid": "b1076dfc-b9ad-4769-8c92-a6c4dae69d19",
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/kaggle/input/testset.csv\n"
     ]
    }
   ],
   "source": [
    "# This Python 3 environment comes with many helpful analytics libraries installed\n",
    "# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python\n",
    "# For example, here's several helpful packages to load in \n",
    "\n",
    "import numpy as np # linear algebra\n",
    "import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "# Input data files are available in the \"../input/\" directory.\n",
    "# For example, running this (by clicking run or pressing Shift+Enter) will list all files under the input directory\n",
    "\n",
    "import os\n",
    "for dirname, _, filenames in os.walk('/kaggle/input'):\n",
    "    for filename in filenames:\n",
    "        print(os.path.join(dirname, filename))\n",
    "\n",
    "# Any results you write to the current directory are saved as output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"../input/testset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime_utc</th>\n",
       "      <th>_conds</th>\n",
       "      <th>_dewptm</th>\n",
       "      <th>_fog</th>\n",
       "      <th>_hail</th>\n",
       "      <th>_heatindexm</th>\n",
       "      <th>_hum</th>\n",
       "      <th>_precipm</th>\n",
       "      <th>_pressurem</th>\n",
       "      <th>_rain</th>\n",
       "      <th>_snow</th>\n",
       "      <th>_tempm</th>\n",
       "      <th>_thunder</th>\n",
       "      <th>_tornado</th>\n",
       "      <th>_vism</th>\n",
       "      <th>_wdird</th>\n",
       "      <th>_wdire</th>\n",
       "      <th>_wgustm</th>\n",
       "      <th>_windchillm</th>\n",
       "      <th>_wspdm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19961101-11:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>West</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19961101-12:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19961101-13:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
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       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19961101-14:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19961101-16:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
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       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
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      "text/plain": [
       "     datetime_utc  _conds   _dewptm   _fog   _hail   _heatindexm   _hum  \\\n",
       "0  19961101-11:00   Smoke       9.0      0       0           NaN   27.0   \n",
       "1  19961101-12:00   Smoke      10.0      0       0           NaN   32.0   \n",
       "2  19961101-13:00   Smoke      11.0      0       0           NaN   44.0   \n",
       "3  19961101-14:00   Smoke      10.0      0       0           NaN   41.0   \n",
       "4  19961101-16:00   Smoke      11.0      0       0           NaN   47.0   \n",
       "\n",
       "    _precipm   _pressurem   _rain   _snow   _tempm   _thunder   _tornado  \\\n",
       "0        NaN       1010.0       0       0     30.0          0          0   \n",
       "1        NaN      -9999.0       0       0     28.0          0          0   \n",
       "2        NaN      -9999.0       0       0     24.0          0          0   \n",
       "3        NaN       1010.0       0       0     24.0          0          0   \n",
       "4        NaN       1011.0       0       0     23.0          0          0   \n",
       "\n",
       "    _vism   _wdird  _wdire   _wgustm   _windchillm   _wspdm  \n",
       "0     5.0    280.0    West       NaN           NaN      7.4  \n",
       "1     NaN      0.0   North       NaN           NaN      NaN  \n",
       "2     NaN      0.0   North       NaN           NaN      NaN  \n",
       "3     2.0      0.0   North       NaN           NaN      NaN  \n",
       "4     1.2      0.0   North       NaN           NaN      0.0  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Problem Set"
   ]
  },
  {
   "cell_type": "raw",
   "metadata": {},
   "source": [
    "Given dataset provides the weather data for city Delhi, India. We will try to predict the weather _conds field. Like weather will be smoke, Haze, Clear.\n",
    "\n",
    "For this we will use Decision Tree Classifier. You can use any other classifier to compare its accuracy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# EDA & Data Munging"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['datetime_utc', ' _conds', ' _dewptm', ' _fog', ' _hail',\n",
       "       ' _heatindexm', ' _hum', ' _precipm', ' _pressurem', ' _rain', ' _snow',\n",
       "       ' _tempm', ' _thunder', ' _tornado', ' _vism', ' _wdird', ' _wdire',\n",
       "       ' _wgustm', ' _windchillm', ' _wspdm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# We can see all the column name has space in there names, lets assign new names with removed space.\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = map(lambda x: x.strip(), df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['datetime_utc', '_conds', '_dewptm', '_fog', '_hail', '_heatindexm',\n",
       "       '_hum', '_precipm', '_pressurem', '_rain', '_snow', '_tempm',\n",
       "       '_thunder', '_tornado', '_vism', '_wdird', '_wdire', '_wgustm',\n",
       "       '_windchillm', '_wspdm'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns # Space removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Haze                             47602\n",
       "Smoke                            20760\n",
       "Mist                              9375\n",
       "Clear                             3129\n",
       "Widespread Dust                   2856\n",
       "Fog                               2760\n",
       "Scattered Clouds                  2209\n",
       "Partly Cloudy                     2091\n",
       "Shallow Fog                       1860\n",
       "Mostly Cloudy                     1537\n",
       "Light Rain                        1302\n",
       "Partial Fog                       1031\n",
       "Patches of Fog                     901\n",
       "Thunderstorms and Rain             486\n",
       "Heavy Fog                          421\n",
       "Light Drizzle                      414\n",
       "Rain                               394\n",
       "Unknown                            383\n",
       "Blowing Sand                       378\n",
       "Overcast                           326\n",
       "Thunderstorm                       192\n",
       "Light Thunderstorms and Rain       176\n",
       "Drizzle                            112\n",
       "Light Fog                           64\n",
       "Light Thunderstorm                  64\n",
       "Heavy Rain                          28\n",
       "Heavy Thunderstorms and Rain        22\n",
       "Thunderstorms with Hail             11\n",
       "Squalls                              6\n",
       "Light Sandstorm                      6\n",
       "Light Rain Showers                   5\n",
       "Light Haze                           4\n",
       "Volcanic Ash                         4\n",
       "Sandstorm                            2\n",
       "Rain Showers                         2\n",
       "Funnel Cloud                         2\n",
       "Heavy Thunderstorms with Hail        1\n",
       "Light Freezing Rain                  1\n",
       "Light Hail Showers                   1\n",
       "Name: _conds, dtype: int64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets check the usual whether codition. We can see usually delhi's weather is either Haze, Smoke. Not good\n",
    "# for health. :()\n",
    "df._conds.value_counts(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets plot top 10 weather condition in delhi.\n",
    "plt.figure(figsize=(15, 10));\n",
    "df._conds.value_counts().head(10).plot(kind='bar');\n",
    "plt.title(\"Top 10 most common weather condition\")\n",
    "plt.plot();\n",
    "# We can clearly see that haze and smoe are the most commo weather condition in delhi."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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AAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgAhkAAMAgaw5kVbVfVX2gql63mQUBAABsFXvSQvaIJGdsViEAAABbzZoCWVUdmuQuSZ63ueUAAABsHfuv8X7PSPJbSQ7e2R2q6tgkxybJYYcdtveVAQBL66xHv3N0CWty6JNvO7qE3XrCE54wuoQ1WZQ6YV/bbQtZVf1kki9098m7ul93H9/dR3X3Udu2bduwAgEAAJbVWrosHp3kblX1ySQvS3JMVb14U6sCAADYAnYbyLr7Md19aHcfnuTeSd7a3T+36ZUBAAAsOeuQAQAADLLWST2SJN399iRv35RKAAAAthgtZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIMIZAAAAIPsNpBV1cWr6l+r6oNV9eGqOm5fFAYAALDs9l/Dfb6Z5Jju/p+qOiDJu6rq9d393k2uDQAAYKntNpB1dyf5n/nmAfNHb2ZRAAAAW8GaxpBV1X5VdWqSLyR5c3e/b3PLAgAAWH5rCmTdfX533zjJoUluUVXX3/4+VXVsVZ1UVSd98Ytf3Og6AQAAls4ezbLY3V9J8vYkd97BvuO7+6juPmrbtm0bVB4AAMDyWsssi9uq6tLz1wcm+ZEkH93swgAAAJbdWmZZvFKSF1bVfpkC3Mu7+3WbWxYAAMDyW8ssi6cluck+qAUAAGBL2aMxZAAAAGwcgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGAQgQwAAGCQ3QayqrpqVb2tqs6oqg9X1SP2RWEAAADLbv813Oe8JL/R3adU1cFJTq6qN3f3Rza5NgAAgKW22xay7v5cd58yf31ukjOSXGWzCwMAAFh2ezSGrKoOT3KTJO/bjGIAAAC2krV0WUySVNVBSV6Z5JHdfc4O9h+b5NgkOeywwzasQGBrOeM6Pzi6hN36wY+eMbqENXn2Q946uoQ1eehzjxldwpr86b1+cnQJa/Ibf/e60SUAsAfW1EJWVQdkCmMv6e5X7eg+3X18dx/V3Udt27ZtI2sEAABYSmuZZbGSPD/JGd39tM0vCQAAYGtYSwvZ0Unun+SYqjp1/viJTa4LAABg6e12DFl3vytJ7YNaAAAAtpQ9mmURAACAjSOQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADCKQAQAADLLbQFZVL6iqL1TV6fuiIAAAgK1iLS1kf53kzptcBwAAwJaz20DW3e9I8t/7oBYAAIAtZf+NeqCqOjbJsUly2GGHbdTDftfhj/6nDX/MjfbJJ99ldAlr84RLja5gbZ7w1dEVrMkNXniD0SWsyYce8KHRJQAAsJ0Nm9Sju4/v7qO6+6ht27Zt1MMCAAAsLbMsAgAADCKQAQAADLKWae9fmuQ9SY6sqrOq6hc3vywAAIDlt9tJPbr7PvuiEAAAgK1Gl0UAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBBBDIAAIBB1hTIqurOVfWxqvp4VT16s4sCAADYCnYbyKpqvyTPTvLjSa6b5D5Vdd3NLgwAAGDZraWF7BZJPt7dZ3b3t5K8LMlPbW5ZAAAAy6+6e9d3qLpnkjt39y/Nt++f5Jbd/avb3e/YJMfON49M8rGNL3dDXS7Jl0YXsUQcz43leG4sx3PjOJYby/HcWI7nxnEsN5bjubEW5Xherbu37e5O+6/hgWoH274nxXX38UmOX8PjfV+oqpO6+6jRdSwLx3NjOZ4by/HcOI7lxnI8N5bjuXEcy43leG6sZTuea+myeFaSq666fWiSz25OOQAAAFvHWgLZ+5Ncu6quXlUXTXLvJP+4uWUBAAAsv912Wezu86rqV5O8Mcl+SV7Q3R/e9Mo238J0r1wQjufGcjw3luO5cRzLjeV4bizHc+M4lhvL8dxYS3U8dzupBwAAAJtjTQtDAwAAsPEEMgAAgEEEMgDWpKouUlWHjK4DAJbJlhlDVlWXTPL17v5OVR2R5DpJXt/d3x5c2kKqqosl+ekkh2fV5DDd/fujalpEVfXru9rf3U/bV7Usg6p6ZnawTuKK7n74PixnKVTV3yZ5SJLzk5yc5FJJntbdfzK0MGDDVdVtkly7u0+oqm1JDuru/xhd16Krqh9IctXuPm10LYusqm6Y7z3vfNWwgjbQWhaGXhbvSHLb+U3xliQnJblXkvsNrWpx/UOSr2Y6Qfvm4FoW2cGjC1gyJ82fj05y3SR/N9/+mUyvVfbcdbv7nKq6X5J/TvLbmY6lQLYXVk7QcuETi1PGVbSYquroJE9IcrVMx7KSdHdfY2Rdi6iqHp/kqCRHJjkhyQFJXpzp7yl7qKrenuRumV6Xpyb5YlWd2N27vBDLjlXVC5LcMMmHk3xn3txJBLIFU939tar6xSTP7O6nVNUHRhe1wA7t7juPLmLRdfdxo2tYJt39wiSpqgcmucNKC3hVPTfJmwaWtsgOqKoDktw9ybO6+9tVtTW6VmySqvqDJA9M8olc0KLbSY4ZVdMCe36SX8t0keD8wbUsunskuUmSU5Kkuz9bVS4art+l5otZv5TkhO5+fFVpIVu/W3X3dUcXsVm2VCCrqltnahH7xXnbVvr5N9r/q6obdPeHRheyyKrqt+aLAzvsaqeL3bpdOVPr43/Ptw+at7Hnnpvkk0k+mOQdVXW1JOcMrWjx/WySa3b3t0YXsgS+2t2vH13EkvhWd/fKBZd5qAfrt39VXSnT+/13RxezBN5TVdft7o+MLmQzbKVA8ogkj0ny6u7+cFVdI8nbBte0yG6T5IFV9R+ZuiyudBO54diyFs4Z8+eTdnkv9tSTk3ygqlbe47fP1K2JPVBVF0ny+e6+yqptn05yh3FVLYXTk1w6yRdGF7Koquqm85dvq6o/ydRt6bvd53X/XJeXV9VfJrl0VT04yS8ked7gmhbZcUnemORd3f3++bzz3wfXtMhemCmUnZ0lPO/cEpN6VNV+SZ7c3b85upZlMV8l/x7d/al9XQvsSFVdMckt55vv6+6zR9azqKrqHd19u9F1LJOqOirTONzTc+EQcbdhRS2YVRdbdqS7W/fPdaiqOyX50Uwnu2/s7jcPLmkhzeedD+/up4+uZVlU1ceT/HqSD+WCMWRLc965JQJZklTVW/2B3ntVdcjcJ/oyO9rf3f+9o+3s2jyb1W9nmoji4ivbvWbXp6p2GCC6+x37upZFV1WPTfL1TBOk/O/Kdu/19auqDyf5y3zvicWJw4piy5vf63/d3Z9Zte3Y7j5+YFkLq6re1t16E2yQZT+P30qB7E+TXDvJK3Lhk4qlmJ1lX6mq13X3T85dFTvTVbQVZrZap6p6U6YT3kdlmmL8AUm+2N2/PbSwBVVVr1118+JJbpHk5GX+Y75Z5vf69rzX98I809rtR9exDKrqEZlmBDw3yV8luWmSR3e3SXz2UFV9IcmXkjy0u982bzulu2+66+9kR6rqDzMtE7L9xSzdadehqv4iU1fv1+bCPQuW4jx+KwWyE3awubv7F/Z5MbCdqjq5u29WVaet9Id20rZxquqqSZ7S3fcZXQtU1dMynVD8Y4x72itV9cHuvlFV/ViShyZ2FkzVAAAgAElEQVR5bKYZ7YSIPTTPPP1TmS5c/313/0lVfaC7bzK4tIW0k261utOu07Kfx2+ZST26+0Gja1g28zo6186Fu9jpErY+KwuUf66q7pLks0kOHVjPsjkryfVHF7GIquoSmfrtH9bdx1bVtZMc2d2vG1zaIls5wb3Vqm2mvV+flV4aP5EpiH2wqmpX38DOdfenq+r2SZ5TVa9IcuDomhaV7oobZx6Td9oyj8nbMoGsqo5I8pwkV+ju68+rfd+tu584uLSFNK+r8YhMoeHUTCcW74kTivV6YlVdKslvJHlmkkMyra3DOmy3jMBFktw407Tt7LkTMq3x9EPz7bMyXUEXyNZhnrnyOd398tG1LImT5y7fV0/ymHndrO/s5nvYsZOSpLu/keRBVfXQJDcbW9LiqqorJPmjJFfu7h+vqusmuXV3P39waQunu8+vqrslWdpAtpW6LJ6Y5DeT/OVK83tVnd7drpqvQ1V9KMnNk7y3u29cVddJclx332twaZCqesCqm+cl+WR3v3tUPYusqk7q7qNWd11a6SY2urZFZebKjTMH3BsnObO7vzJPOHUVa2QyWlW9PtMFrd+du9Xun+QD3X2DwaUtpGUfk7dlWsiSXKK7/3W7ngznjSpmCXyju79RVamqi3X3R6vqyNFFLZqdLQi9wsLQ69PdL6yqiyY5Yt70sZH1LLhvVdWBmV+nVXXNrBr3xLq8uaoeFTNX7rXu/k6SU5Lvvjbvk+Te0UV5j83dkZ+UC8/22919zXFVLbTLdffLq+oxSdLd51XV+aOLWmArvTR+f9W2penqvZUC2ZfmP9YrJxX3TPK5sSUttLOq6tJJXpPp5OLLmcY9sWdWLwh9XJLHjypkmVTVD2daRPKTmcaYXLWqHmCM47o8IckbMh3DlyQ5OskDRxa0BFYGoT901bZOYubKPVRVV0pyryT3TXLDTIHC5D3rc0Km/0FPz7T4+4Ny4ZmU2TP/W1WXzQXnnbdK8tWxJS2uZR+Tt5W6LF4jyfGZEvaXk/xHkvsty4JyI80DgC+V5PXd/e3d3Z8dM5vVxqmqk5Pct7s/Nt8+IslLu9t4iHWYTypulenk7L3d/aXBJbHFVdWDMwWvQ5O8fP74h+6++tDCFtiq2X4/tNKtrqre2d23HV3bIqqqmyX580yttacn2Zbknt192tDCFtQ8zv7xSVa6e5+Y5Pe7eylC7pZpIevuM5P8SFVdMslFuvvc0TUti+4+cW4t+60kfzi6ngW2Na6O7BsHrISxJOnuf6uqA0YWtKiq6kVJ3pHknd390dH1LIP5tfgrueDE4u2Zxje7oLV2z840kdR9u/ukJKkqf0P3zjfmMXn/XlW/muQ/k1x+cE0Lq7tPni9YH5npYtbHvMf3ygsyBdufnW/fP1Or7v8ZVtEG2kotZJ9I8t4k70zyju7+yOCSFtK8ntNjk1w5U3fFv03yB5neGC/t7kcMLG+hWYBz41TVCzIF3BfNm+6XZH/LX+y5qjomyW2S3DZTl7pTM/0N/bOhhS2wqnpekgMydatNpr+f53f3L42rarFU1eWS/EymVrIrZGohe2B3X3VoYQusqm6e5IxMi+/+QaaeL0/p7vcOLWxBVdU7M1/MSvJuDQF7p6pO7e4b727botpKgexiSW6Z6aTi6CTXSfLB7r7H0MIWzLzQ4YmZrkzeOckdk3w4ya9199kja1tEVXVuLmgZu0SSr63syjSY+pAhhS24+f3+0ExBojL9U/yL7jYZxTrMa8DcPNO4kock+Xp3X2dsVYtrR7NUmrly/arq0EwTedwn09/RV3f374ytiq1uHiqzcjHrVpkmQ3pnd1vSZh2q6j1JfrO73zXfPjrJU7v71mMr2xhbpstikvMzLb57fqY1Sj6f5AtDK1pMl+nuJ8xfv7GqPp/k5k5016e7Dx5dwzKaX49Pmz/YC1X1liSXzHQR5p2Z3u/+du6d86vqmt39ieS7J25mX1un7j4ryVOTPHWe7ffeg0taKFX12ux6tt+77cNylkZ3n1lVX0/yrfnjDkl+cGxVC+0hSf5mHkuWTPNBPGAX918oWymQnZPkQ5lO0P6qu/9rcD0Lq6p+IBfMvHR2kkvMY/NM28xQ8/p4uzqxuOE+LGdZnJZpcdjrZ5oh7CtV9Z7u/vrYshbabyZ5W1Wdmelv6dVywcyL7IV57Ohxo+tYME+dP1eSv0qi6+wGmIfKfCnT0I7nJ3nYvEwD63POvJ7bIUnS3edU1dJM4rOVuiz+VKam41tkulLx/zKNg3jL0MIWTFV9MlML446mwu3uNm0zw1TV1Xa136yq61dVB2WaBvtRSa7Y3RcbXNLCmrvUJhcM9v9o8t2WXRjGbL8bp6oekem886qZ3uMnZjrv/MTQwhbUjsbZr8wMOqqmjbRlAtmKqrpOkh9P8sgkl+/uAweXBGyQqrpWkit097u3237bJJ/1j3DPzbOt3TZTK9mncsGMi28dWtgC28mJhUl9GM7rcONtdzHr0O7eb3BJC2U+b79ekqdk6l2w4pBMY8quN6SwDbZluixW1SuT3DjJx5O8K8nPJ3nf0KKAjfaMJDsazP/1ed9d9205S+HATF29T+7u80YXs8iq6opJrpLkwKq6SS7oaXBIpsko2ENVdY8kb11Zi2heguWHu/s1YytbHFV1mVU399tuWIKhCOtUVX+aqYXsoEyzfD8u0zhc9syRSX4y0+yfq/+Hn5vkwUMq2gRbpoVsns71lO42cJrvO1X1f5L8caY1XypmWVyXqjq9u6+/k33fXeyUPVNVN8rUSpZMrWMfHFnPoqqqByR5YJKjkrw/F5z0npvkr7v7VYNKW1g7mQpbt7s9UFX/kWnsraEIG6iqfiZTF8XPj65lGVTVrbv7PaPr2CxbKZBtvxDniUmea5E+vh9U1ceT3LW7zxhdyyKrqo9397X2dB87V1UPT3JskpWwcI8kx3f3M8dVtdiq6qe7+5Wj61gGVXXa9pP1uPjC94uqultWnXd292tH1rPIquopSZ6YqcfLG5LcKMkju/vFQwvbIBcZXcA+9JxMYyD+Yv646byNdaiqy+zg44DRdS2wzwtjG+L9VfU9XRiq6heTnDygnmXwS0lu2d2P6+7HZVpPZ2m6iQxyaFUdUpPnVdUpVfWjo4taUCdV1dOq6ppVdY2qenq81/k+UFVPSvKIJB+ZPx4+b2N9frS7z8nUffGsJEfkwmPKFtpWaiGzEOcGmmdbvGqmdSAqU9/ez2Va2+3B3e0f4hrMXRWT5PZJrpjkNZkWj0yS6MK0Z6rqCklenWkm1ZXX4FFJLprkHhYv33PzUgI37+5vzLcvnuT9WiDWb+V/T1X9WKYFzB+b5ASTKey5ecmVxyb5kUz/i96U5Ind/b9DC2PLq6rTktx4Zar7qtovyQcsv7I+VfXh7r5eVf1Vkld29xuW6Tx+y0zqEQtxbrQ3JHl1d78xSearu3dO8vJMLZC3HFjbIlk9QPVrSVZfJe9c0E2MNZj76v9QVd0h07pZSfJPZgTcKyckeV9VvXq+ffdMa+qwfitjdX4iUxD7YFXtaPwOuzEHr0ePrgN24tJJViZFudSu7shuvbaqPpqpy+L/raptSb4xuKYNs5VayO6Y6cRi9UKcD+rutw0tbEFV1UndfdSOtu1okDW7VlVH72Cq9u/ZBiNU1c2SHJ3pb+c7uvsDg0taaFV1QqbZFq+eaRzEfknevizr6ewLVfWM7n5kVb02O1gMvrvvNqCshVZVL+ru++9uG2tTVfdJ8uQkb8v0t/N2SR7T3S8bWtgCm2cAPae7z59bxw9elp4vWyaQJd9djPO7C3FahHP9qupNSd6SZOUPy72S3ClTK9n7db3ZM9Yl4vvZ3NXmClnVq6K7Pz2uosVWVRfJtAzLmd39laq6bJKrdPdpg0tbGFV1s+4+uapuv6P93X3ivq5p0W3/P2d+33+ou687sKyFVlVXSnLzTOed71uW8LAvrRrasUPLMrRjK3VZTKZJPQ7P9HPfqKrS3X8ztqSFdd8kj8805qkyre1230xXen92YF0LpapuneSHkmyrql9fteuQTMcShqqqh2V6r38+UzfvytQiYRzEHqqq7S+wXENPxfVZGacseO29qnpMpvUbD6yqc1Y2ZxqLe/ywwpbDRZJ8KdN55xFVdUR3v2NwTYtmZWjH5TOdL60MQbhDkrdnSYZ2bJkWsqp6UZJrJjk1F4wd6+5++Liq2Ormq7s/nOQhSZ67ate5SV7b3f8+oi5YMS/JcMvu/q/RtSy6qlrpIn/xTBcIT8t04nvDTFfPbzOqtkVVVUcneUKmYQj754I1HK2dtYeq6knd/ZjRdSyLqvrjTL2HPpzkO/Pm1p12farqdZkmjfvcfPtKSZ7d3btsQVsUWymQnZHkur1VfuBNVlVHJHlULmhxTJJ09zGjalpkVXW17v7U6DoWXVWdmwvGk6w0PawseGqh7XWYQ8Sduvu80bUsi6p6WZI/7O4Pzbevn+RR3f3AoYUtoHmQ/69lmlX1uxN1uYCwPlV1lVwQbpMkWnTWp6o+luSGhsdsjKo6vbuvv+r2RZKctnrbIttKXRZPzzSt+OdGF7IkXpGpRed5MVvluq0MTE/yrKoyMH0vdffBo2tYFqu60J6Z5O1V9U+58JIMTxtS2HK4zkoYS5LuPr2qTIS0Pl/t7tePLmIZVNWTk9w705pZ3+1JlEQgW58zkxyQVX832Stvr6o3JnlpptflvTNNmLIUlj6QrZqB6eAkH6mqf82FTyqc8K7Ped1tYe2996L581OHVrGEquo2Sa7d3SdU1eUyzcb0H6PrWiAr4fbT88dF5w/23hlV9bwkL870/+nnklgYfn3eVlV/kmkcyer/7aeMK2lh3SPJkVp09k5VPTPT+/prSU6tqrfkwq9NQ2XWobt/dZ7g47bzpuO7+9W7+p5FsvRdFnc2A9MKA4LXp6qekGkR6Ffnwn9o/ntn38POVdUxSd7b3V8bXcsyqKrHZ1oQ+sjuPqKqrpzkFd199ODSFto85fBXdP3eO/Pi2r+SaRrsZGqBeM7K4tus3apxeau17vN7rqpen+Rnuvt/RteyyKrqAbva390v3Fe1sDiWPpBtb55e+HZJPr0ySxN7rqp21NJgIPU6VdXfJLlVkv9K8s75413d/eWhhS2oqjo1yU2SnNLdN5m3ndbdZgZco6p6XJKXd/dH5yVDXp9pqvbzkty3u/9laIHAhljVonOVTOviadHZQFV1QJLrJ/nP7v7C6HoW1dw69seZZlusLNnY8K3QZfF1SR4999G/UpJTkpyU5JpVdXx3P2NshYupu68+uoZl0t0/nyRzS849kzw7yZWzBd6jm+Rb3d0r4/LmBSTZM/dK8gfz1w/INH3ztiRHJHlhEoFsnXYwM2CSxAWt9amquyS5XqbZK5Mk3f374ypaOCfNn09O8o8jC1kGVfXcJM/s7g9X1aWSvCfTmLzLVNWjuvulYytcWE9JctfuXsru3VvhZO/q3X36/PWDkry5u3++qg5O8u4kAtkeqKpjuvutO1uob1kW6NvXqurnMvWLvkGmNUuelamVjPV5eVX9ZZJLV9WDk/xCkr8aXNOi+daqrok/luSl3X1+pvFPW+F/x2Z6fnYwMyB7bj75vUSmNYmel+mC1r8OLWrB6EK34W7b3Q+Zv35Qkn/r7rtX1RUz9TQQyNbn88saxpKtEci+verrO2Y+Kevuc6vqOzv+Fnbh9pkW5bvrDvZ1lmSBvgGekeQTmWaufFt3f3JsOYutu59aVXdKck6SI5M8rrvfPLisRfPNeTr2z2c62X3Uqn2XGFPS0jAz4Mb5oe6+4dwl+biq+tP4P7QuVfWhXLBsyIqvZmpBe6KlBNbsW6u+vlOmWanT3WdbCH6vnFRVf5fkNblwl9qleL9vhUD2map6WJKzktw0yRuSpKoOzDQdKXugux8/f37Q6FqWSXdfrqqul2l84x9W1bWTfKy77z+4tIU1BzAhbP0ekeTvM3VTfPrKDJVV9RNJPjCysCVgZsCN8/X589fmLt//lUSX+vV5faYW2//f3r0HS17Wdx5/f4YFuQwD0aDlIpDgeomGq4DIEBMwJq4R48aqCMW4MZGwiUkQqVUXKwY0JliyElysSEiCGG5mWZONEvES44IKyM4gwyCQxVJB3BUXWC6FBQp894/fr6Vn5gzMOdPdT3ef96vq1On+9UzVZ86cc/p5fs/3+T6X9M+Ppduncz9wAQvfiNXm7kvyGuC7wGrgzQB9ZcFOLYPNuFV0nSt/aeja3CwELIcJ2ZuB9wK/CLyhqu7rrx8OfLRZqhmX5DHgTODUQVlTkuur6uC2yWZTklXA3nR7Sn4K2A1wBXeJ5n3z7yRU1VeBFy5w/dPApyefaK68tP98yNC1AuwMuHiXJ9md7v3oerqv41+1jTSzVm/SiXZDkq9U1eq+rF5b5z8A/4Xu7NuTq+p7/fVXAP/YLNWMm/eFgGXXZVGjkeRGutXGg+gmuvcm+dqgo50Wp/96frn/uKqq7mwcaaYl+QZzvPlX0ub6bqA7VtX9rbPMoiTrgRP7mzEkOQz4y6o6wPd3tdYfF/JmNm/g81vNQo3Qclgh03g8WlXvSPLrwJeS/Hs2rz3XVrId+8jN9eZfzT47A45Gkt8DLq6q+6rqkSQ7J3lLVf1562wz6ATg/CQr6aoKHgBO6LvUntE0mQQXArfSNZl6L3A8MDfv866QaUmG75b1e58uBfauqt3bJpMgyYfoykXmcvOvZtuWOgNW1ZubBptBSW6oqgM3ueZqzjboW7VnaIuH1Nzg53pwpmh/vttn5+UQeFfItFQnDB70Z20cCbyuYR5p2Fxv/p20JEfQ7W0cPjPrb5oFmn12BhydFUkytJd5O2CHxplmSpI1VXVRklM2uQ5AVZ3VJJi0sUHX9Pv6DsDfo3tfmgtzPyEbOoF+QZ5Av2S3JHk33arYbwPPAu5tnEkC5n/z7yQluRB4LnADT5yZVYATsqXbtDPgvdgZcKk+S3fu4Ll035e/Q99NWVttl/7zrgu8ZhnVEvV7Gl/P5jezLE1emvOS/ATwbroDzFf2j+fC3E/IeOIEeo3WR+kONX1Z//xOurM2Lm+WaIYl+QDwPrqB2meAA+i6M13UNNiMSrIH8Nts/kY4F5t/J+wQ4EVlffsoDToDfoDu9yjYGXCp3knX1e536fY9fQ6/lotSVX/Rf37Ppq8lOXnyiebGP9AdGbCOodJ5LU1VDX6urwT2bZllHNxDpiVJsraqDtlkL9n6qjqgdbZZNNgHkeTf0ZV+vo3ugGi/nkuQ5GrgS3RvhINVHarqE81CzagklwEnVdX/aZ1l1iU5FPjOoA123wxpDd1G9dOryioDTZUkd1TV3q1zzKIkN1XVz7bOMeuSHAPcWFW398//iG7l8XbgrYMzMmfd3K+QJTm7qk5O8ikWWHqvqtc2iDUPftgfrj2o238u3gHaFoNDyl8NXNofI9Ayz6zbuare2TrEnPhJ4OYk17FxgxR/dy7eX9CdiUmSlwPvB/4AOBA4j665h7ZCkg08+XYEO9eOhm9ES3d1kv2qakPrIDPuT+jODqY/cHsNcBzdsUvn0nVdnHlzPyGja5MJ8J+bppg/p9GV1u2V5GK60+jf1DTRbPtUklvpShbf0pfcPdw40yy7PMmr+0OMtW1Obx1gjmw3tAr2BuC8ftX2E0luaJhrFr2mdYBlwjKqpTsSeFOSb9HdzApQ3ixYtKqqH/SPfw3466paB6xL8paGuUbKkkUtWZJn0N21CHBtVd3dONJM6zerPlBVjyXZGVg1KG3S4iR5kG6j+iN0nZkGb4SrmgbTspbkJuDAqnq0vwFzYlVdNXjN8qalSfIs4ND+6XVV9f2WeWZN//tyocFggJ2qajncvB+5JPssdH1Qeqetk+RG4Ai6zsnfAl5fVWv7126uqhe1zDcqy+aHLMnz6A42fBEbH8Q5dxsDJyFdPd2/Bfatqvcm2TvJYVV1Xetss6hv1fxzwE8lGf65tN3wElTVQt3CtAhJvlxVRy4wWHNyu3SXAlcmuZtuNfxLAEn+Dd3mfy1Skl8HzgT+B9335jlJ3l5V/61psBni78vRSrKqqh4AHmydZU6cTdfl9wHglqHJ2EHA3OxtXjYrZEm+TFdm92fAMcBv0v37T2sabEYl+QjwOHB0Vf1Mv7rzuao69Cn+qhaQ5NN0JYob6L6uwMJdr/TU+v05mxmsRkitJDkceDbd78uH+mvPB1ZW1fVNw82gJOuBVw5Wxfpy73+yIZJaSXJ5Vb2mL1UsNt6HVy4ELF6SPYFnAuur6vH+2rOB7avqjqbhRmQ5TcjWVdVLkmyoqv36a1+qqp9rnW0WJbm+qg62y+JoDE6eb51jXvRNfAZ2BA4D1lXV0Y0izbwkz2Tj6oK5eBPUbBt+T++fr6AbtO33JH9NkqbKsilZBB7uf1HfluT3ge/Szba1ND/qy+wGXRb3YGhlR4t2RZJfqqrPtQ4yD6rqmOHnSfaiO/NJi5TktcAHgX8NfB/YB7gFeHHLXFLvM0k+S1cOCl2zFJv5aCr01UPPY+ObWVZqaDPLaYXsULpBxO7AHwO7AR+oqmubBptRSY6ne+M7GPgYXbvmP6yqy5oGm1H9+WMXASuwCcXI9Xseb/Su+eL1JWFH05WBHZTkKOC4qjqxcTQJgCS/RtfRLsBVVfX3jSNJJDkBeCvwHLo9UIcD11ipoYUsmwmZRi/JC4FX0L0JfqGqbmkcaWYl+SbdgdAbyh/KbZbkHJ5oRLGC7pynb1fVmnapZtPQIfDrgYOq6vEk11XVYa2zaflK8mHgkqq6unUWaSH9WXmH0nWhPrAfM72nqt7QONpMSnJhVb3xqa7NqrkvWdzSgdADHm66eH3p5419i+ZbW+eZE7cBNzkZG5m1Q48fpTts+yutwsy4+5KsBK4CLk7yfbqvqdTSbcAH+439f0v3M+5ZbpomD1fVw0lI8rSqujXJC1qHmmEblcn322Ze0ijLyM39ClmSnx88BP4SOGH49aq6cuKh5kB/GPSpbuwfjSQXAPsCV9CdnQVAVdn2fhGS7O335Ggl2YWuRfsK4Hi6cu+Lq+qepsEkfnzW07H9x450e8k+XlX/q2kwLXtJ/p6uo/fJdGXf/4+uK+CrmwabMUlOBd4F7ER3Fhl0Y/ofAudV1amtso3S3E/Ihg13BNS2SfLPdEvx1wEPDa674rg0SRY8fsG294sz6P7ZP/5EVb2+daZ509+VPLaqLm6dRRrWn0t0PrB/VW3XOo800C8O7AZcUVU/ap1nFiU5Y14mXwuZ+5LFTSyf2ef4OVEYkX6Au7Kq3t46yxwYPu/Fs162QZJVwO8BewKfBD7fP3873QZ1J2RqLsn2wKvoVsheAVyJ70+aMlV1ZZLdgXcAf9I6zyyqqlP788j2YWj+Mi9dK+d+Qpbk6UNPt+tbkP540FZV904+1exKcnhVXWup5+hU1WNJDm6dY07UFh5r8S6kK7G5hq7U++3ADsCvuldHrSV5JXAc8Ct0lRofB04cHLYttdIfs/JuuqNC/jtwCV137zfyxPEMWqQk76e78XIz8Fh/uej2N8+8uS9Z3MJJ6QOemL5Im5SEXVNVL2udaR4k+SDdWSWXsXEJ6N81CzWDkjxG9/ULm9ebe4zAIgwfuNuv4t4N7F1VD7ZNJkGSL9INdD/hjVVNk/5780q6m1mvolu5/Trwtqr6XstssyzJv9CVIz/ylH94Bs39CllV/XTrDHNmeGK74xb/lBbr6cA9dBt/BwpwQrYI7hsZqR/vc+hXcb/lZEzToqqOap1B2oKnV9Xp/ePPJrkLOHReJxIT9E1ge4Yan82TuZ+QaeRW9GWfK4YeWwK6jarqN1tnkDZxQJIH+scBduqfu9ooSU9ik7HR94Cd+461jpMWaehc0R8ANyT5Aht3oz6pVbZRmvuSRY1Wkm8Dj2MJ6EgleQ5wDrCa7hfPl4G3VtWdTYNJkqSt5jhptJL8xpO9XlUfm1SWcXJCJk2BJJ+n2w9xYX9pDXB8Vb2yXSpJkiSN27KZkCW5sKre+FTXpBaS3FBVBz7VNUmSpOUmyQY27558P7AWeF9V3TP5VKOznPaQvXj4Sd817CWNskibujvJGp5oiXscXZMPSZKk5e4Kunb3l/TPj6UrC70fuAA4pk2s0Zj7CVmSU4F38cSGdOj+A38InNcsmLSx3wI+DPwZ3R2gqwEbfUiSJMHqqlo99HxDkq9U1er+hvZMW9E6wLhV1RlVtStwZlWt6j92rapnVNWprfPNqiQXbs01bbW9quq1VbVHVT2zql4H7NU6lCRJWjzHSSO3MslLB0+SHAas7J8+2ibS6Mz9CtlAVZ2aZE9gH4b+3VU1Fyd8N2AJ6GidAxy8FdckSdL0c5w0WicA5ydZSVfp9gBwQn+cwBlNk43AspmQJXk/Xb3pzXQ1qNCVhjkhWwRLQEcrycuAI4A9kpwy9NIqwEOOJUmaIY6TxqOq/iewX5Ld6JoS3jf08n9tFGtkllOXxX8B9vek9NFIcoYln9suyc8DvwD8DnDu0EsPAp+qqtta5JIkSUvnOGk0kqypqos2uWn9Y1V11qQzjcOyWSEDvglsz9Dp3lo6S0BHo6quBK5MckFV3Q6QZAWwsqoeePK/LUmSppHjpJHZpf+86wKvzc2q0tyvkCU5h+4/bE/gAOALDE3KquqkRtFm2pZKQKvqte1Sza4kl9Ctkj0GrAN2A86qqjObBpMkSYvmOGn8kpxcVWe3zjEKy2FC9htP9npVfWxSWeaJJaCjNTgEOsnxdJt+3wmsq6r9G0eTJEmL5Dhp/JLcUVV7t84xCnNfsuiEa2wsAR2t7ZNsD7wO+HBV/SjJfN8tkSRpfjlOGr+0DjAqcz8hG0iygc1rTe8H1gLvq6p7Jp9q9gyVgP4AuCGJJaCjcS7wbWA9cFWSfehaukqSpBnhOGmi5ubG9bKZkAFX0LuJk1IAAAdvSURBVNXwXtI/P5ZuZn0/cAFwTJtYM2dt/3kd8MmWQeZF38Tjrqrac+jaHcBR7VJJkqQlcJw0QkkeZOGJV4CdJhxnbOZ+D9lAkq9U1eqFriXZUFX7tcomJbmqql7eOockSZImazmtkK1M8tKq+ipAksOAlf1rj7aLNZssAR25zyf5j8DfAg8NLlbVve0iSZKkpXCcpMVYTitkhwLn003CQrc/5wTg68CvVNXMn/I9SUk+wJZLQI+sKktAFyHJtxa4XFW178TDSJKkbeI4SYuxbCZkA0l2o/t339c6yyyzBFSSJGlhjpO0GHNfsphkTVVdlOSUTa4DUFVnNQk2+ywBHaEkOwOnAHtX1YlJnge8oKoubxxNkiQtnuMkbbW5n5ABu/Sfd13gteW1PDhaJwDnJ9moBDTJLsAZTZPNpo/SdWQ6on9+J3AZ4IRMkqTZ4zhJW23ZlSwOS3JyVZ3dOscsswR0NJKsrapDknytqg7qr62vqgNaZ5MkSUvjOElbYzmskD2ZUwAnZItgCejY/DDJTvSrtkmey9BBkpIkafo5TtJSLPcJWVoHmEGWgI7HacBngL2SXAysBt7UNJEkSVosx0latOVesnhHVe3dOse8sAR02yR5BnA43Y2Ca6vq7saRJEnSiDhO0pbM/YQsyYMsfEciwE5VtdxXCUfGCe7iJTn4yV6vqusnlUWSJI2P4yRtydxPRqpqoSVjjYcloIv3wf7zjsAhwHq6r+P+wFeBIxvlkiRJo+U4SQta0TqA5sp8L7eOQVUdVVVHAbcDB1fVIVX1EuAg4Btt00mSpBFynKQFzf0KmUbrqUpAJxxnnrywqjYMnlTVTUkObBlIkiQtjuMkLcXc7yGTZkGSS4GHgIvofpGvAVZW1XFNg0mSJGmsnJBJUyDJjsDvAi/vL10FfKSqHm6XSpIkSePmhEySJEmSGnEPmTQFkqwGTgf2Yejnsqr2bZVJkiRJ4+cKmTQFktwKvA1YBzw2uF5V9zQLJUmSpLFzhUyaDvdX1RWtQ0iSJGmyXCGTpkCS9wPbAX8HPDK4XlXXNwslSZKksXNCJk2BJF9c4HJV1dETDyNJkqSJcUImSZIkSY24h0yaAkn+aKHrVfXeSWeRJEnS5Dghk6bDQ0OPdwReA9zSKIskSZImxJJFaQoleRrwyar65dZZJEmSND4rWgeQtKCdAQ+FliRJmnOWLEpTIMkGYLBcvR2wB+D+MUmSpDlnyaI0BZLsM/T0UeCuqnq0VR5JkiRNhhMyaUok2Q54FkMr11V1R7tEkiRJGjdLFqUpkOQPgNOAu4DH+8sF7N8slCRJksbOFTJpCiT5BvDSqrqndRZJkiRNjl0WpenwHeD+1iEkSZI0Wa6QSQ0lOaV/+GLgBcA/Ao8MXq+qs1rkkiRJ0mS4h0xqa9f+8x39xw79hyRJkpYBJ2RSW0+rqne1DiFJkqQ23EMmtfWq1gEkSZLUjitkUlvbJfkJIAu9WFX3TjiPJEmSJsimHlJDSR4BvsvCE7Kqqn0nHEmSJEkT5AqZ1NbNVXVQ6xCSJElqwz1kkiRJktSIEzKprQ+1DiBJkqR23EMmSZIkSY24QiZJkiRJjTghk6ZAkqe3ziBJkqTJc0ImTYevJrksyauTLHgmmSRJkuaPEzJpOjwfOA94I/CNJH+a5PmNM0mSJGnMbOohTZkkRwEXAbsA64H/VFXXtE0lSZKkcXBCJk2BJM8A1tCtkN0F/DXwSeBA4LKq+umG8SRJkjQm/6p1AEkAXANcCLyuqu4cur42ybmNMkmSJGnMXCGTpkCSg6vq+tY5JEmSNFlOyKQpkOSLwLOBy4CPV9XXG0eSJEnSBNhlUZoCVXUU8AvA/wXOS7IhyR+2TSVJkqRxc4VMmjJJ9gPeAbyhqnZonUeSJEnj4wqZNAWS/EyS05PcBHwYuBp4TuNYkiRJGjNXyKQpkORa4FK6Fvf/u3UeSZIkTYYTMkmSJElqxHPIpCmQ5HnAGcCLgB0H16tq32ahJEmSNHbuIZOmw0eBjwCPAkcBf0N3ULQkSZLmmBMyaTrsVFVfoCsjvr2qTgeObpxJkiRJY2bJojQdHk6yArgtye8D3wWe2TiTJEmSxsymHtIUSHIocAuwO/DHwCrgzKq6tmkwSZIkjZUTMmmKJNmlqh5qnUOSJEmT4R4yaQokeVmSm+lWyUhyQJI/bxxLkiRJY+aETJoOZwO/DNwDUFXrgZc3TSRJkqSxc0ImTYmq+s4mlx5rEkSSJEkTY5dFaTp8J8kRQCXZATiJvnxRkiRJ88umHtIUSPKTwIeAXwQCfA54a1Xd0zSYJEmSxsoJmSRJkiQ1Ysmi1FCSc4At3hWpqpMmGEeSJEkT5oRMamvt0OP3AKe1CiJJkqTJs2RRmhJJvlZVB7XOIUmSpMmx7b00Pbw7IkmStMw4IZMkSZKkRixZlBpK8iBPrIztDPxg8BJQVbWqSTBJkiRNhBMySZIkSWrEkkVJkiRJasQJmSRJkiQ14oRMkiRJkhpxQiZJkiRJjTghkyRJkqRGnJBJkiRJUiP/H6D5eAnpsyAOAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Lets see top 10 least condition\n",
    "plt.figure(figsize=(15, 10));\n",
    "df._conds.value_counts(ascending=True).head(10).plot(kind=\"bar\");\n",
    "plt.title(\"Top 10 least whether condition in delhi\");\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "North       19034\n",
       "West        11888\n",
       "WNW          7464\n",
       "East         7233\n",
       "NW           7117\n",
       "WSW          5466\n",
       "ESE          5336\n",
       "ENE          3721\n",
       "SE           3513\n",
       "SW           3200\n",
       "NNW          2696\n",
       "NE           2500\n",
       "SSE          2052\n",
       "NNE          1752\n",
       "SSW          1715\n",
       "South        1543\n",
       "Variable        5\n",
       "Name: _wdire, dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# common wind direction\n",
    "df._wdire.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15, 10));\n",
    "plt.title(\"Common wind direction in delhi\");\n",
    "df._wdire.value_counts().plot(kind=\"bar\");\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "average temprature in delhi: 25.45\n"
     ]
    }
   ],
   "source": [
    "# Average temprature\n",
    "print(\"average temprature in delhi:\", round(df._tempm.mean(axis=0),2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we can see there is datetime column, We can extract year from it. Year can ve an important feature\n",
    "# for us to calculate how temprature is changing according to year\n",
    "def extract_year(value):\n",
    "    return (value[0:4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime_utc</th>\n",
       "      <th>_conds</th>\n",
       "      <th>_dewptm</th>\n",
       "      <th>_fog</th>\n",
       "      <th>_hail</th>\n",
       "      <th>_heatindexm</th>\n",
       "      <th>_hum</th>\n",
       "      <th>_precipm</th>\n",
       "      <th>_pressurem</th>\n",
       "      <th>_rain</th>\n",
       "      <th>_snow</th>\n",
       "      <th>_tempm</th>\n",
       "      <th>_thunder</th>\n",
       "      <th>_tornado</th>\n",
       "      <th>_vism</th>\n",
       "      <th>_wdird</th>\n",
       "      <th>_wdire</th>\n",
       "      <th>_wgustm</th>\n",
       "      <th>_windchillm</th>\n",
       "      <th>_wspdm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19961101-11:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>West</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19961101-12:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19961101-13:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19961101-14:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19961101-16:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datetime_utc _conds  _dewptm  _fog  _hail  _heatindexm  _hum  _precipm  \\\n",
       "0  19961101-11:00  Smoke      9.0     0      0          NaN  27.0       NaN   \n",
       "1  19961101-12:00  Smoke     10.0     0      0          NaN  32.0       NaN   \n",
       "2  19961101-13:00  Smoke     11.0     0      0          NaN  44.0       NaN   \n",
       "3  19961101-14:00  Smoke     10.0     0      0          NaN  41.0       NaN   \n",
       "4  19961101-16:00  Smoke     11.0     0      0          NaN  47.0       NaN   \n",
       "\n",
       "   _pressurem  _rain  _snow  _tempm  _thunder  _tornado  _vism  _wdird _wdire  \\\n",
       "0      1010.0      0      0    30.0         0         0    5.0   280.0   West   \n",
       "1     -9999.0      0      0    28.0         0         0    NaN     0.0  North   \n",
       "2     -9999.0      0      0    24.0         0         0    NaN     0.0  North   \n",
       "3      1010.0      0      0    24.0         0         0    2.0     0.0  North   \n",
       "4      1011.0      0      0    23.0         0         0    1.2     0.0  North   \n",
       "\n",
       "   _wgustm  _windchillm  _wspdm  \n",
       "0      NaN          NaN     7.4  \n",
       "1      NaN          NaN     NaN  \n",
       "2      NaN          NaN     NaN  \n",
       "3      NaN          NaN     NaN  \n",
       "4      NaN          NaN     0.0  "
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "# function to get month\n",
    "def extract_month(value):\n",
    "    return (value[4:6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets check our method\n",
    "df[\"year\"] = df[\"datetime_utc\"].apply(lambda x:extract_year(x))\n",
    "df[\"month\"] = df[\"datetime_utc\"].apply(lambda x:extract_month(x))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime_utc</th>\n",
       "      <th>_conds</th>\n",
       "      <th>_dewptm</th>\n",
       "      <th>_fog</th>\n",
       "      <th>_hail</th>\n",
       "      <th>_heatindexm</th>\n",
       "      <th>_hum</th>\n",
       "      <th>_precipm</th>\n",
       "      <th>_pressurem</th>\n",
       "      <th>_rain</th>\n",
       "      <th>...</th>\n",
       "      <th>_thunder</th>\n",
       "      <th>_tornado</th>\n",
       "      <th>_vism</th>\n",
       "      <th>_wdird</th>\n",
       "      <th>_wdire</th>\n",
       "      <th>_wgustm</th>\n",
       "      <th>_windchillm</th>\n",
       "      <th>_wspdm</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19961101-11:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>West</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19961101-12:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19961101-13:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19961101-14:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>41.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19961101-16:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>47.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 22 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     datetime_utc _conds  _dewptm  _fog  _hail  _heatindexm  _hum  _precipm  \\\n",
       "0  19961101-11:00  Smoke      9.0     0      0          NaN  27.0       NaN   \n",
       "1  19961101-12:00  Smoke     10.0     0      0          NaN  32.0       NaN   \n",
       "2  19961101-13:00  Smoke     11.0     0      0          NaN  44.0       NaN   \n",
       "3  19961101-14:00  Smoke     10.0     0      0          NaN  41.0       NaN   \n",
       "4  19961101-16:00  Smoke     11.0     0      0          NaN  47.0       NaN   \n",
       "\n",
       "   _pressurem  _rain  ...    _thunder  _tornado  _vism  _wdird  _wdire  \\\n",
       "0      1010.0      0  ...           0         0    5.0   280.0    West   \n",
       "1     -9999.0      0  ...           0         0    NaN     0.0   North   \n",
       "2     -9999.0      0  ...           0         0    NaN     0.0   North   \n",
       "3      1010.0      0  ...           0         0    2.0     0.0   North   \n",
       "4      1011.0      0  ...           0         0    1.2     0.0   North   \n",
       "\n",
       "   _wgustm _windchillm  _wspdm  year  month  \n",
       "0      NaN         NaN     7.4  1996     11  \n",
       "1      NaN         NaN     NaN  1996     11  \n",
       "2      NaN         NaN     NaN  1996     11  \n",
       "3      NaN         NaN     NaN  1996     11  \n",
       "4      NaN         NaN     0.0  1996     11  \n",
       "\n",
       "[5 rows x 22 columns]"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head() # So we can see a new column with year added"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "max, min:  2017 , 1996\n"
     ]
    }
   ],
   "source": [
    "# lets check out data range\n",
    "print(\"max, min: \", df.year.max(), \",\", df.year.min())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# So our given data is from 1996 to 2017. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2003    13186\n",
       "2004     9151\n",
       "2002     8503\n",
       "2001     8403\n",
       "2016     6634\n",
       "1997     6513\n",
       "1999     6300\n",
       "2000     5391\n",
       "1998     3579\n",
       "2014     2973\n",
       "2015     2936\n",
       "2005     2920\n",
       "2007     2865\n",
       "2006     2858\n",
       "2008     2857\n",
       "2009     2851\n",
       "2010     2768\n",
       "2011     2738\n",
       "2012     2438\n",
       "2013     2428\n",
       "2017     1420\n",
       "1996     1278\n",
       "Name: year, dtype: int64"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Number of records for paticular year\n",
    "df.year.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year\n",
       "1996    16.518636\n",
       "1997    24.444635\n",
       "1998    24.847371\n",
       "1999    27.335731\n",
       "2000    23.865647\n",
       "2001    25.582460\n",
       "2002    26.481948\n",
       "2003    26.176556\n",
       "2004    26.004396\n",
       "2005    24.712715\n",
       "2006    25.449245\n",
       "2007    24.800211\n",
       "2008    24.525744\n",
       "2009    25.355360\n",
       "2010    25.553338\n",
       "2011    24.966362\n",
       "2012    25.024661\n",
       "2013    24.679222\n",
       "2014    25.175735\n",
       "2015    25.141832\n",
       "2016    27.315734\n",
       "2017    20.204369\n",
       "Name: _tempm, dtype: float64"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby(\"year\")._tempm.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mean = df.groupby(\"year\")._tempm.mean().reset_index().sort_values('_tempm', ascending=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year       object\n",
       "_tempm    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_mean.year = df_mean.year.astype(\"float\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "year      float64\n",
       "_tempm    float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_mean.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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Aj3xvHwBwIrOcXfOyJM9O8q6qumlc9sNJXpXkVVX17iSfTnJla63NMAdAl3xvHwBwPDMrea21dyT5rGPtRs+a1fUCAACsZWdldk0AAADODiUPAACgI0oeAABAR5Q8AACAjih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwAA0BElDwAAoCNKHvAP9u4/lJtvuzN79x+adxQAAFZp3bwDAIvhTTfdnmt27c76paUcXl7Oziu25/IdF847FsBc7d1/KHv2Hci2zRuzZdOGeccBWBElD8je/Ydyza7dOXh4OQeznCS5etfuXHbJVm9qgDXLh1/AucpwTSB79h3I+qWjnw7WLy1lz74Dc0oEMF+TH37ddejuHDy8nKt37TacHTgnKHlAtm3emMPLy0ctO7y8nG2bN84pEcB8+fALOJcpeUC2bNqQnVdsz3nrl3L+hnU5b/1Sdl6x3VBNYM3y4RdwLnNMHpAkuXzHhbnskq0mGKArJs1gtY58+HX1Mcfk+TsCzgVKHvAPtmza4A0M3TBpBmfKh1/AuUrJA6A7ZoxlWnz4BZyLHJMHQHdMmgHAWqbkAdAdk2YAsJYpeQB0x4yx56a9+w/l5tvu9F10AGfIMXkAdMmkGecWE+UATI+SB0C3TJpxbjBRDsB0Ga4JAMyViXIApkvJAwDmykQ5ANOl5AEAc2WiHIDpckweADB3JsoBmB4lDwBYCCbKAZgOwzUBAGBGfP8j82BPHgAAzMAifv/j3v2HDIteA5Q8AACYskX8/sdFLJ3MhuGaAAAwZYv2/Y+TpfOuQ3fn4OHlXL1rt2GknVLyAABgyhbt+x8XrXQyW0oeAABM2aJ9/+OilU5myzF5AAAwA4v0/Y9HSufVxxyTZ/KVPil5AACsipkaT22Rvv9xkUons6XkAQBw2szUeG5apNLJ7DgmDwDgHLEoX6xtpkZYbPbkAQCcAxZpz9mRmRqPfP9bcs9MjfYSwfzZkwcAsOAWbc+ZmRphsSl5AAALbtG+42zRvh4AOJrhmgAAC24R95yZqREWlz15AAALblH3nG3ZtCGPvuh+c88BHM2ePGbGd+cAwPTYcwaslJLHTCzSDGAA0AvfcQashOGaTN2izQAGsCgW5TvOAOibPXlMne/OAfhsRjgAcLbYk8fULeIMYADzZIQDAGeTksfULeoMYADzsmjfcQZA3wzXZCbMAAZrk1l1j88IBwDOJiWPmTEDGKwtjjk7sSMjHK4+5v7xHAnT5YMmGCh5AJyxyWPOjky6dPWu3bnskq3eaI2McIDZ8kET3MMxeQCcMcecrcyWTRvy6Ivup+DBlJncCI6m5AFwxhxzBsyTD5rgaEoeAGfMrLrAPPmgCY7mmDwApsIxZ8C8mNwIjqbkATA1ZtUF5sUHTXAPJQ8A1ijTzdMbHzTBQMkDgDXIdPMA/TLxCgCsMaabB+ibkgcAa4zp5gH6puR1ZO/+Q7n5tjt9EgvASZluHqBvjsnrhGMrAFgp080D9E3J68DksRUHM3wye/Wu3bnskq1esAE4LtPNA/RLyevAkWMrjhS85J5jK7xoA3AippsH6JNj8jrg2AoAAOAIJa8DR46tOG/9Us7fsC7nrV9ybAUAAKxRhmt2wrEVAABAouR1xbEVAACA4ZoAAAAdUfIAAAA6ouQBAAB0RMkDAADoiJIHAADQESUPAACgI0oeAABAR5Q8AACAjih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6MjMSl5VXVRVb6+qW6vqlqp67jHn/0BVtaraOqsMAAAAa826GW777iTPb63dWFXnJ7mhqt7aWntPVV2U5GlJPjzD6wcAAFhzZrYnr7X20dbajePpu5LcmuTC8ez/luTqJG1W1w8AALAWnZVj8qrq4iSPSXJdVV2e5PbW2s2nuMxVVXV9VV1/xx13nIWUAAAA576Zl7yq2pRkV5LnZRjC+SNJfvRUl2utvaK1dmlr7dILLrhgxikBAAD6MNOSV1XrMxS817TW3pDkC5J8fpKbq+qDSbYlubGqHjTLHAAAAGvFzCZeqapK8sokt7bWXpIkrbV3JXnAxDofTHJpa+0Ts8oBAACwlsxyT95lSZ6d5ClVddP487UzvD44p+zdfyg333Zn9u4/NO8oAAB0ZGZ78lpr70hSp1jn4lldPyyyN910e67ZtTvrl5ZyeHk5O6/Ynst3XHjqCwIAwCmcldk1gXvs3X8o1+zanYOHl3PXobtz8PByrt612x49AACmQsmDs2zPvgNZv3T0f731S0vZs+/AnBIBANATJQ/Osm2bN+bw8vJRyw4vL2fb5o1zSgQAQE+UPDjLtmzakJ1XbM9565dy/oZ1OW/9UnZesT1bNm2YdzQAADows4lXgBO7fMeFueySrdmz70C2bd6o4AEAMDVKHszJlk0blDsAAKbOcE0AAICOKHkAAAAdUfIAAAA6ouQBAAB0RMkDAADoiJIHAADQESUPAACgI0oeAABAR5Q8AACAjih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwAA0BElDwAAoCNKHgAAQEeUPAAAgI4oeQAAAB1R8gAAADqi5AEAAHREyQMAAOiIkgcAANARJQ8AAKAjSh4AAEBHlDwAAICOKHkAAAAdUfIAAAA6ouQBAAB0RMkDAADoiJIHAADQESUPAACgI0oeAABAR5Q8AACAjih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwAA0BElDwAAoCNKHgAAQEeUPAAAgI4oeQAAAB1R8gAAADqi5AEAAHREyQMAAOiIkgcAANARJQ8AAKAjSh4AAEBHlDwAAICOKHkAAAAdUfIAAAA6ouQBAAB0RMkDAADoiJIHAADQESUPAACgI0oeAABAR5Q8AACAjih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwAA0BElDwAAoCNKHgAAQEeUPAAAgI6sO9UKVfX5Sf5Nkosn12+tXT67WAAAAKzGKUtekl9P8sokv5lkebZxAAAAOBMrKXkHW2s/M/MkAAAAnLGVlLyXVtULk7wlyaEjC1trN84sFQAAAKuykpL3xUmeneQpuWe4Zht/BwAAYIGspOR9Q5KHtdY+PeswAAAAnJmVfIXCzUnuN+sgAAAAnLmV7Ml7YJK/rKp35uhj8nyFAgAAwIJZScl74cxTAAAAMBWnLHmttT+sqgcleVyGCVfe2Vr72MyTAQAAcNpOeUxeVX1Xkj9P8o1JvinJtVX1nFkHAwAA4PStZLjmDyZ5TGttb5JU1ZYkf5rkVbMMBgAAwOlbyeyae5LcNfH7XUluO9WFquqiqnp7Vd1aVbdU1XPH5f+lqv6yqnZX1RurysydAAAAU7KSknd7kuuq6kVV9cIk1yZ5f1X9+6r69ye53N1Jnt9ae2SSJyT5vqp6VJK3JvnHrbXtSf4qyQvO7CYAAABwxEqGa35g/DniTeO/55/sQq21jyb56Hj6rqq6NcmFrbW3TKx2bYbj/AAAAJiClcyu+WNneiVVdXGSxyS57piznpPk105wmauSXJUkD3nIQ840AgAAwJqwktk1Lx2PnbtxPI5ud1XtXukVVNWmJLuSPK+19qmJ5T+SYUjna453udbaK1prl7bWLr3gggtWenUAAABr2kqGa74mwwyb70qyfDobr6r1GQrea1prb5hYfmWSr0vy1NZaO51tAgAAcGIrKXl3tNZ+43Q3XFWV5JVJbm2tvWRi+dOTXJPkSa21vz/d7QIAAHBiKyl5L6yqX0jytiSHjiyc3DN3ApcleXaSd1XVTeOyH07yM0k2JHnr0ANzbWvte043OAAAAJ9tJSXvXyZ5RJL1uWe4Zkty0pLXWntHkjrOWb99OgEBAABYuZWUvEe31r545kkAAAA4Yyv5MvRrxy8xBwAAYMGtZE/elyW5sqr+T4Zj8ipJa61tn2kyAAAATttKSt7TZ54CAACAqTjlcM3W2oeSXJTkKePpv1/J5QAAADj7TlnWquqFGb7X7gXjovVJ/ucsQwEAALA6K9kj9w1JLk/yd0nSWvtIkvNnGQoAAIDVWUnJ+3RrrWX4brxU1X1mGwkAAIDVWknJe11VvTzJ/arqu5P8XpJfmG0sAAAAVuOUs2u21n6qqp6W5FNJvijJj7bW3jrzZAAAAJy2U5a8qvrJ1to1Sd56nGUAAAAskJUM13zacZZ9zbSDAAAAcOZOuCevqr43yb9O8rCq2j1x1vlJ/mRivc2ttX2ziwgAAMBKnWy45q8k+Z0kP57khyaW39Va++TE729L8tgZZAMAAOA0nbDktdb+NsnfJnnmKbZRU00EAADAqq3kmLxTaVPYBgAAAFMwjZIHAADAgphGyTNcEwAAYEFMo+Q9dQrbAAAAYArOuOQdM9MmAAAAc+SYPAAAgI4oeQAAAB1R8gAAADqi5AEAAHREyQMAAOiIkgcAANARJQ8AAKAjSh4AAEBHlDwAAICOKHkAAAAdUfIAAAA6ouQBAAB0RMkDAADoiJIHAADQESUPAACgI0oeAABAR5Q8AACAjih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwAA0BElDwAAoCNKHgAAQEeUPAAAgI4oeQAAAB1R8gAAADqi5AEAAHREyQMAAOiIkgcAANARJQ8AAKAjSh4AAEBHlDwAAICOKHkAAAAdUfIAAAA6ouQBAAB0RMkDAADoiJIHAADQESUPAACgI0oeAABAR5Q8AACAjih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwAA0BElDwAAoCNKHgAAQEeUPAAAgI4oeQAAAB1R8gAAADqi5AEAAHREyQMAAOiIkgcAAJBk7/5Dufm2O7N3/6F5Rzkj6+YdAAAAYN7edNPtuWbX7qxfWsrh5eXsvGJ7Lt9x4bxjrYo9eQAAwJq2d/+hXLNrdw4eXs5dh+7OwcPLuXrX7nN2j56SBwAArGl79h3I+qWjq9H6paXs2XdgTonOjJIHAACsads2b8zh5eWjlh1eXs62zRvnlOjMKHkAAMCatmXThuy8YnvOW7+U8zesy3nrl7Lziu3ZsmnDvKOtiolXAACANe/yHRfmsku2Zs++A9m2eeM5W/ASJQ8AACDJsEfvXC53RxiuCQAA0JGZlbyquqiq3l5Vt1bVLVX13HH5/avqrVX1vvHfzbPKAAAAsNbMck/e3Ume31p7ZJInJPm+qnpUkh9K8rbW2hcmedv4OwAAAFMws5LXWvtoa+3G8fRdSW5NcmGSZyT5pXG1X0ry9bPKAAAAsNaclWPyquriJI9Jcl2SB7bWPpoMRTDJA05wmauq6vqquv6OO+44GzEBAADOeTMveVW1KcmuJM9rrX1qpZdrrb2itXZpa+3SCy64YHYBAQAAOjLTkldV6zMUvNe01t4wLv6bqnrweP6Dk3x8lhkAAADWklnOrllJXpnk1tbaSybO+o0kV46nr0zyplllAAAAWGtm+WXolyV5dpJ3VdVN47IfTvITSV5XVd+Z5MNJvnmGGQAAANaUmZW81to7ktQJzn7qrK4XAABgLTsrs2sCAABwdih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwAA0BElDwAAoCNKHgAAQEeUPAAAgI4oeQAAAB1R8gAAADqi5AEAAHREyQMAAOiIkgcAANARJQ8AAKAjSh4AAEBHlDwAAICOKHkAAAAdUfIAAAA6ouQBAAB0RMkDAADoiJIHAADQESUPAACgI0oeAABAR5Q8AACAjih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwAA0BElDwAAoCNKHgAAQEeUPAAAgI4oeQAAAB1R8gAAADqi5AEAAHREyQMAAOiIkgcAANARJQ8AAKAjSh4AAEBHlDwAAICOKHkAAAAdUfIAAAA6ouQBAAB0RMkDAADoiJIHAADQESUPAACgI0oeAABAR5Q8AACAjih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwAA0BElDwAAoCNKHgAAQEeUPAAAgI4oeQAAAB1R8gAAADqi5AEAAHREyQMAAOiIkgcAANARJQ8AAKAjSh4AAEBHlDwAAICOKHkAAAAdUfIAAAA6ouQBAAB0RMkDAADoiJIHAADQESUPAACgI0oeAABAR5Q8AACAjiitD4LNAAASAUlEQVR5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwAA0BElDwAAoCNKHgAAQEeUPAAAgI4oeQAAAB2ZacmrqldV1cer6t0Ty3ZU1bVVdVNVXV9Vj5tlBgAAgLVk1nvyfjHJ049ZtjPJj7XWdiT50fF3AAAApmCmJa+19kdJPnns4iT3HU9/bpKPzDIDAADAWrJuDtf5vCRvrqqfylAy/8nxVqqqq5JclSQPechDzl46AACAc9g8Jl753iT/rrV2UZJ/l+SVx1uptfaK1tqlrbVLL7jggrMaEAAA4Fw1j5J3ZZI3jKdfn8TEKwAAAFMyj5L3kSRPGk8/Jcn75pABAACgSzM9Jq+qXpvkyUm2VtWeJC9M8t1JXlpV65IczHjcHQAAAGdupiWvtfbME5z1JbO8XgAAgLVqHsM1AQAAmBElDwAAoCNKHgAAQEeUPAAAgI4oeQAAAB1R8gAAADqi5AEAAHREyQMAAOiIkgcAANARJQ8AAKAjSh4AAEBHlDwAAICOKHkAAAAdUfIAAAA6ouQBAAB0RMkDAADoiJIHAADQESUPAACgI0oeAABAR5Q8AACAjih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwAA0BElDwAAoCNKHgAAQEeUPAAAgI4oeQAAAB1R8gAAADqi5AEAAHREyQMAAOiIkgcAANARJQ8AAKAjSh4AAEBHlDwAAICOKHkAAAAdUfIAAAA6ouQBAAB0RMkDAADoiJIHAADQESUPAACgI0oeAABAR5Q8AACAjih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwAA0BElDwAAoCNKHgAAQEeUPAAAgI4oeQAAAB1R8gAAADqi5AEAAHREyQMAAOiIkgcAANARJQ8AAKAjSh4AAEBHlLxV2rv/UG6+7c7s3X9o3lEAAAD+wbp5BzgXvemm23PNrt1Zv7SUw8vL2XnF9ly+48J5xwIAALAn73Tt3X8o1+zanYOHl3PXobtz8PByrt612x49AABgISh5p2nPvgNZv3T03bZ+aSl79h2YUyIAAIB7KHmnadvmjTm8vHzUssPLy9m2eeOcEgEAANxDyTtNWzZtyM4rtue89Us5f8O6nLd+KTuv2J4tmzbMOxoAAICJV1bj8h0X5rJLtmbPvgPZtnmjggcAACwMJW+VtmzaoNwBAAALx3BNAACAjih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwAA0BElDwAAoCNKHgAAQEeUPAAAgI4oeQAAAB1R8gAAADqi5AEAAHREyQMAAOiIkgcAANARJQ8AAKAjSh4AAEBHZlryqupVVfXxqnr3Mcv/TVW9t6puqaqds8wAAACwlsx6T94vJnn65IKq+sokz0iyvbX2j5L81IwzAAAArBkzLXmttT9K8sljFn9vkp9orR0a1/n4LDMAAACsJfM4Ju/hSb68qq6rqj+sqi893kpVdVVVXV9V199xxx1nOSIAAMC5ad2crnNzkick+dIkr6uqh7XW2uRKrbVXJHlFklTVHVX1obOe9NS2JvnEvEMcY9EyyXNyi5YnWbxM8pzcouVJFi+TPCcnz6ktWiZ5Tm7R8iSLl0mek1u0PEc8dKUrzqPk7UnyhrHU/XlVLWe4I0+4u661dsHZCnc6qur61tql884xadEyyXNyi5YnWbxM8pzcouVJFi+TPCcnz6ktWiZ5Tm7R8iSLl0mek1u0PKsxj+Gav57kKUlSVQ9Pcu8sZlMGAAA458x0T15VvTbJk5Nsrao9SV6Y5FVJXjV+rcKnk1x57FBNAAAAVmemJa+19swTnPWsWV7vWfSKeQc4jkXLJM/JLVqeZPEyyXNyi5YnWbxM8pycPKe2aJnkOblFy5MsXiZ5Tm7R8py2shMNAACgH/M4Jg8AAIAZUfIAAAA6ouQdo6peVVUfHyeGObLs0VX1Z1X1rqr6zaq677j83lX16nH5zVX15InL3LuqXlFVf1VVf1lVV8wrT1WdX1U3Tfx8oqp+es73zzPH5bur6nerautq8kw50z8f89xSVTvPIM9FVfX2qrp13NZzx+X3r6q3VtX7xn83j8urqn6mqt4/Xv9jJ7Z15bj++6rqygXI87tVdWdV/da875+q2jE+xreMy//5nPM8tKpuGP+P3VJV3zPPPBPbu29V3V5VL1tNnmlnqqrP1D3PRb+xAHkeUlVvGbf1nqq6eF55quor6+jn6oNV9fVzvn92jtu4dVynTjfPDDL9ZFW9e/w5W//vH1HD882hqvqBY7b19Kp675j1hxYgz2e9Js4rz4m2M8c851XVn9fw+n9LVf3YvO+jie3dq6r+olb5+jrlv6EP1vA+6aaqun4B8tyvqv5XDe+nb62qJ84rT1V9UR39PP2pqnreau6jmWut+Zn4SfIVSR6b5N0Ty96Z5Enj6eck+Y/j6e9L8urx9AOS3JBkafz9x5L8p/H0UpKt88xzzDZvSPIV88qTYcKfjx+5T5LsTPKieT5mSbYk+XCSC8bzfinJU1eZ58FJHjuePj/JXyV51Hg7f2hc/kNJfnI8/bVJfidJJXlCkuvG5fdP8tfjv5vH05vnlWc876lJ/p8kv3UGj9e07p+HJ/nC8fTnJflokvvNMc+9k2wYT29K8sEknzfPx2s8/6VJfiXJy+b9mI3n7V9tjhnl+YMkT5t43D5n3o/ZuM79k3xynnmS/JMkf5LkXuPPnyV58jwfsyT/LMlbM7yO3CfJ9UnuexbyPCDJlyZ5cZIfmNjOvZJ8IMnDMjwH3JzkUfPKM573Wa+Jc7x/jrudOeapJJvG0+uTXJfkCfO8jya29+8zPFev6vV1yn9DH8wq37fOKM8vJfmu8fS9c3Ze60/6eI3r3CvJx5I89Ezuq1n9zD3AIv4kuThHF4ZP5Z5Jai5K8p7x9M8medbEem9L8rjx9G1J7rMoeSaWfeGYreaVJ8MT6x1JHprhCffnk1w1z/to/I/8exPLn53k56b0+L0pydOSvDfJg8dlD07y3vH0y5M8c2L9947nPzPJyyeWH7Xe2c4z8fuTcwYlb9p5JpbfnLH0zTtP7vnQ4LRL3jTzJPmSJL+a5DtyBiVvypnOuORNK0+GF/h3LEqeY7ZxVZLXzPn+eWKGD8I2JvmcDIXqkXPO9INJ/sPE8lcm+ZZZ55lY70U5ujQ8McmbJ35/QZIXzCvPxPKLcwYlb9p5jt3OIuQZ/6ZvTPL4ed9HSbZleD/ylEzp9fUM83wwZ1jyppUnyX2T/J+cwfvWGf4NfXWSP5lmrmn+GK65Mu9Ocvl4+pszlIZkeFP5jKpaV1Wfn+EN1UVVdb/x/P9YVTdW1eur6oHzynPMZZ+Z5Nfa+Nc5jzyttcNJvjfJu5J8JMMbrVdOMc9pZ0ry/iSPqKqLq2pdkq/PZ993p62GoV+PyfBp4QNbax9NkvHfB4yrXZiheB+xZ1x2ouXzyjN108pTVY/L8OneB+aZZxwOsns8/ydbax+ZV56qWkryXzO8KZ6aKTxm51XV9VV1ba1iKOKU8zw8yZ1V9YZxmNR/qap7zTHPpG9N8tozyXKmeVprf5bk7Rn2kn80Q5m5dZ6ZMjyHf01VfU4Nw/y/Mmf4XL3CPCcyr+fps2ZaeY7ZztzyjMMib8owouitrbUzyjONTEl+OsnVSZbPNMuU8rQkb6nhEISr5pznYRl2DLx6fJ7+haq6zxzzTJrK8/SsKHkr85wk31dVN2TYxfvpcfmrMjyhX5/hP+ifJrk7wzCSbRna/WMzDHH5qTnmmTSLP8jTylNV6zOUvMdkGGa3O8Onn3PL1FrbN2b6tSR/nOFTrGPvu9NSVZuS7EryvNbap0626nGWtZMsn1eeqZpWnqp6cJL/keRfttZW/QI5jTyttdtaa9uTXJLkyjP5cGcKef51kt9urd12nPPnlSlJHtJauzTJv0jy01X1BXPMsy7Jlyf5gQx78x+WYa/nvPIc2c6Dk3xxkjevNss08lTVJUkemeH17MIkT6mqr5hnptbaW5L8dobn7tdmeH1d9XP1aeQ5rZxzzDNV08qzSNtprX2mtbYjw9/146rqH682zzQyVdXXJfl4a+2GM8kxrTyjy8b3r1+T4b3Uqv/fTyHPugzDj/+/1tpjkvxdhmGV88pzZDv3zrAz4fWr3casKXkr0Fr7y9baV7fWviTDi8oHxuV3t9b+XWttR2vtGUnul+R9SfYm+fskbxw38foMf6DzypMkqapHJ1k3rSeSM8izYzz/A+MexddlOPZjnpnSWvvN1trjW2tPzLD7/n0n2v6pjEV2V4bhVm8YF//N+ObtyJu4j4/L9+ToT6K3ZdjDeaLl88ozNdPKU8OEOv87w/Cta+ed54hxD94tGQrEvPI8Mcn3V9UHM3zI9O1V9ROryTPFTEfum7TW/jrD8XCPmWOePUn+orX21621u5P8elb5XD3lv6FvSfLGcdTDqkwpzzckuba1tr+1tj/DMXJPmHOmtNZePD6HPy1DyVrVc/Vp5jmReT1Pz9y08pxgO3PLc0Rr7c4Mz0FPn3Omy5JcPj5X/2qGD1P+5xzzTD5PfzzDe9nHzTHPniR7Jva4/q+cnefpU/maJDe21v5mNVnOBiVvBarqAeO/S0n+Q4ZjyDIOF7nPePppGfYIvWcsLr+Z4dilZJis4j3zyjNx0WdmBruVV5Hn9iSPqqoLxk08LckZDwE6w0yTl9mcYS/IL6zyuivD8NNbW2svmTjrN5JcOZ6+MsN48CPLv70GT0jyt+OQgTcn+eqq2jxm+uqs4pP9KeaZimnlGT9Fe2OSX26trfqTtCnm2VZVG8dtbs7wwv3eeeVprX1ba+0hrbWLM+yp+uXW2mpn/pvWfbS5qjaM29ya4T467efGKf5NvzPJ5onnoqfMOc8RZ/RcPcU8H07ypBqGt69P8qSs8rl6in9D96qqLeM2tyfZnuQtZyHPibwzyRdW1eePz0nfOm5jXnmmYlp5TrKdeeW5oMZDasbn669K8pfzzNRae0Frbdv4XP2tSX6/tfaseeWpqvtU1flHTmd473HaM7VO8f75WJLbquqLxkWrek89g/9jM3lPPVVtAQ4MXKSfDA/YR5MczvDpwXcmeW6GWXj+KslPJP8wocfFGd7E3Zrk9zIxu06GSUX+KMNQxLdlGKI0tzzj+X+d5BELcv98z7h8d4ZCvGUBMr02wxPHe5J86xnk+bIMw3V2J7lp/PnaDJNxvC3Dp85vS3L/cf3KMCHMBzIcp3jpxLaek+F4wfdnGI447zx/nGFs/IHxvv6n88qT5FnjY37TxM+OOeZ52riNm8d/VzWZ0DQfr4ltfkfObHbNad1H/2T8/ebx3++c93008bi9K8kvJrn3nPNcnOGDsM+aGXkOj9e9MkyAcmuG58WXLECm83LP8/S1WcX/+VXmeVCG57xPJblzPH3f8byvzfBa84EkP7IAeT7rNXFeeU60nTnm2Z7kL8btvDvJj57Fv+kTPmYT23xyVj+75rTuo4dleI6+OcOIlEX4m96R4TCb3RlGXKxmpvFp5vmcDKP2Pne1fz9n4+fIG18AAAA6YLgmAABAR5Q8AACAjih5AAAAHVHyAAAAOqLkAQAAdETJAwAA6IiSBwBTVFX3mncGANY2JQ+ANauq/mNVPXfi9xdX1b+tqh+sqndW1e6q+rGJ83+9qm6oqluq6qqJ5fur6v+tquuSPPEs3wwAOIqSB8Ba9sokVyZJVS0l+dYkf5PkC5M8LsmOJF9SVV8xrv+c1tqXJLk0yb+tqi3j8vskeXdr7fGttXeczRsAAMdaN+8AADAvrbUPVtXeqnpMkgcm+YskX5rkq8fTSbIpQ+n7owzF7hvG5ReNy/cm+UySXWczOwCciJIHwFr3C0m+I8mDkrwqyVOT/Hhr7eWTK1XVk5N8VZInttb+vqr+IMl549kHW2ufOVuBAeBkDNcEYK17Y5KnZ9iD9+bx5zlVtSlJqurCqnpAks9Nsm8seI9I8oR5BQaAk7EnD4A1rbX26ap6e5I7x71xb6mqRyb5s6pKkv1JnpXkd5N8T1XtTvLeJNfOKzMAnEy11uadAQDmZpxw5cYk39xae9+88wDAmTJcE4A1q6oeleT9Sd6m4AHQC3vyAAAAOmJPHgAAQEeUPAAAgI4oeQAAAB1R8gAAADqi5AEAAHTk/wI0bmvNNUJz+QAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df_mean.plot(kind=\"scatter\", x=\"year\", y=\"_tempm\", figsize=(15, 10))\n",
    "\n",
    "plt.xticks(df_mean.year);\n",
    "plt.title(\"Average temprature change\");\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "So u can see there was a big change in year 1996-1997. It may be because of many reasons:\n",
    "1. New industries started in the city.\n",
    "2. People started purchasing more vehicles.\n",
    "or any other reasons."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Missing Values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime_utc         0\n",
       "_conds              72\n",
       "_dewptm            621\n",
       "_fog                 0\n",
       "_hail                0\n",
       "_heatindexm      71835\n",
       "_hum               757\n",
       "_precipm        100990\n",
       "_pressurem         232\n",
       "_rain                0\n",
       "_snow                0\n",
       "_tempm             673\n",
       "_thunder             0\n",
       "_tornado             0\n",
       "_vism             4428\n",
       "_wdird           14755\n",
       "_wdire           14755\n",
       "_wgustm          99918\n",
       "_windchillm     100411\n",
       "_wspdm            2358\n",
       "year                 0\n",
       "month                0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will make copy of original dataset and will take only relevant columns."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['datetime_utc', '_conds', '_dewptm', '_fog', '_hail', '_heatindexm',\n",
       "       '_hum', '_precipm', '_pressurem', '_rain', '_snow', '_tempm',\n",
       "       '_thunder', '_tornado', '_vism', '_wdird', '_wdire', '_wgustm',\n",
       "       '_windchillm', '_wspdm', 'year', 'month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_filtered = df[['datetime_utc', '_conds', '_dewptm', '_fog', '_hail',\n",
    "       '_hum', '_pressurem', '_rain', '_snow', '_tempm',\n",
    "       '_thunder', '_tornado', '_vism', '_wdird', '_wdire'\n",
    "       , '_wspdm', 'year', \"month\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime_utc</th>\n",
       "      <th>_conds</th>\n",
       "      <th>_dewptm</th>\n",
       "      <th>_fog</th>\n",
       "      <th>_hail</th>\n",
       "      <th>_hum</th>\n",
       "      <th>_pressurem</th>\n",
       "      <th>_rain</th>\n",
       "      <th>_snow</th>\n",
       "      <th>_tempm</th>\n",
       "      <th>_thunder</th>\n",
       "      <th>_tornado</th>\n",
       "      <th>_vism</th>\n",
       "      <th>_wdird</th>\n",
       "      <th>_wdire</th>\n",
       "      <th>_wspdm</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>19961108-01:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>19961113-02:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>280.0</td>\n",
       "      <td>West</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>19961116-06:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>19961126-04:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>19961129-18:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>19961210-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>18.5</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>19961210-17:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>19961211-07:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>19961212-19:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>19961218-00:00</td>\n",
       "      <td>Shallow Fog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.6</td>\n",
       "      <td>230.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>19961218-02:00</td>\n",
       "      <td>Shallow Fog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>230.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>19961221-00:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>19961221-01:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>19961223-03:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.7</td>\n",
       "      <td>230.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>19961224-13:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>19961226-02:00</td>\n",
       "      <td>Shallow Fog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>19961226-07:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>270.0</td>\n",
       "      <td>West</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>19961230-12:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>19961231-18:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>19970101-03:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>220.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1388</th>\n",
       "      <td>19970106-02:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1559</th>\n",
       "      <td>19970113-23:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>19970114-06:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1581</th>\n",
       "      <td>19970114-21:00</td>\n",
       "      <td>Shallow Fog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.4</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1806</th>\n",
       "      <td>19970126-01:00</td>\n",
       "      <td>Fog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1813</th>\n",
       "      <td>19970126-08:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>WSW</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>19970130-13:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>19970130-14:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2061</th>\n",
       "      <td>19970207-08:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.0</td>\n",
       "      <td>WSW</td>\n",
       "      <td>25.9</td>\n",
       "      <td>1997</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2129</th>\n",
       "      <td>19970210-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>9.3</td>\n",
       "      <td>1997</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74019</th>\n",
       "      <td>20090128-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1013.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>24.1</td>\n",
       "      <td>2009</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76072</th>\n",
       "      <td>20091018-03:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>West</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2009</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77701</th>\n",
       "      <td>20100514-12:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>996.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2010</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80228</th>\n",
       "      <td>20110422-18:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80398</th>\n",
       "      <td>20110516-18:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80660</th>\n",
       "      <td>20110611-21:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>997.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>110.0</td>\n",
       "      <td>ESE</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2011</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81279</th>\n",
       "      <td>20110831-06:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>70.0</td>\n",
       "      <td>ENE</td>\n",
       "      <td>16.7</td>\n",
       "      <td>2011</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81671</th>\n",
       "      <td>20111023-15:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2011</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86610</th>\n",
       "      <td>20131108-09:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2013</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>88880</th>\n",
       "      <td>20140813-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>998.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>West</td>\n",
       "      <td>25.9</td>\n",
       "      <td>2014</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>90594</th>\n",
       "      <td>20150316-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2015</td>\n",
       "      <td>03</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91285</th>\n",
       "      <td>20150610-21:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>West</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2015</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92014</th>\n",
       "      <td>20150906-15:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92015</th>\n",
       "      <td>20150906-18:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>West</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2015</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92016</th>\n",
       "      <td>20150906-21:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93361</th>\n",
       "      <td>20160204-02:00</td>\n",
       "      <td>Fog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.8</td>\n",
       "      <td>220.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2016</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94528</th>\n",
       "      <td>20160423-06:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2016</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94890</th>\n",
       "      <td>20160516-10:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2016</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95095</th>\n",
       "      <td>20160528-01:30</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>West</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2016</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95248</th>\n",
       "      <td>20160607-03:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>243.0</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>ESE</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2016</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95303</th>\n",
       "      <td>20160610-13:00</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>998.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.5</td>\n",
       "      <td>120.0</td>\n",
       "      <td>ESE</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2016</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95547</th>\n",
       "      <td>20160626-11:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>East</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2016</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95622</th>\n",
       "      <td>20160701-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2016</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95640</th>\n",
       "      <td>20160702-11:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>998.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>350.0</td>\n",
       "      <td>North</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2016</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96696</th>\n",
       "      <td>20160825-13:00</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.5</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2016</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96965</th>\n",
       "      <td>20160907-08:30</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.5</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2016</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97259</th>\n",
       "      <td>20160924-12:30</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98049</th>\n",
       "      <td>20161025-07:30</td>\n",
       "      <td>Widespread Dust</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.5</td>\n",
       "      <td>280.0</td>\n",
       "      <td>West</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99347</th>\n",
       "      <td>20161221-05:30</td>\n",
       "      <td>Haze</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>West</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99716</th>\n",
       "      <td>20170108-03:00</td>\n",
       "      <td>Patches of Fog</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>621 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         datetime_utc           _conds  _dewptm  _fog  _hail   _hum  \\\n",
       "124    19961108-01:00            Smoke      NaN     0      0    NaN   \n",
       "240    19961113-02:00            Smoke      NaN     0      0    NaN   \n",
       "303    19961116-06:00          Unknown      NaN     0      0    NaN   \n",
       "520    19961126-04:00            Smoke      NaN     0      0    NaN   \n",
       "584    19961129-18:00            Smoke      NaN     0      0    NaN   \n",
       "812    19961210-09:00             Haze      NaN     0      0    NaN   \n",
       "820    19961210-17:00          Unknown      NaN     0      0    NaN   \n",
       "830    19961211-07:00          Unknown      NaN     0      0    NaN   \n",
       "858    19961212-19:00            Smoke      NaN     0      0    NaN   \n",
       "969    19961218-00:00      Shallow Fog      NaN     1      0    NaN   \n",
       "971    19961218-02:00      Shallow Fog      NaN     1      0    NaN   \n",
       "1032   19961221-00:00          Unknown      NaN     0      0    NaN   \n",
       "1033   19961221-01:00          Unknown      NaN     0      0    NaN   \n",
       "1082   19961223-03:00            Smoke      NaN     0      0    NaN   \n",
       "1113   19961224-13:00            Smoke      NaN     0      0    NaN   \n",
       "1147   19961226-02:00      Shallow Fog      NaN     1      0    NaN   \n",
       "1150   19961226-07:00          Unknown      NaN     0      0    NaN   \n",
       "1243   19961230-12:00            Smoke      NaN     0      0    NaN   \n",
       "1272   19961231-18:00          Unknown      NaN     0      0    NaN   \n",
       "1281   19970101-03:00          Unknown      NaN     0      0    NaN   \n",
       "1388   19970106-02:00          Unknown      NaN     0      0    NaN   \n",
       "1559   19970113-23:00          Unknown      NaN     0      0    NaN   \n",
       "1566   19970114-06:00            Smoke      NaN     0      0    NaN   \n",
       "1581   19970114-21:00      Shallow Fog      NaN     1      0    NaN   \n",
       "1806   19970126-01:00              Fog      NaN     1      0    NaN   \n",
       "1813   19970126-08:00            Smoke      NaN     0      0    NaN   \n",
       "1910   19970130-13:00            Smoke      NaN     0      0    NaN   \n",
       "1911   19970130-14:00            Smoke      NaN     0      0    NaN   \n",
       "2061   19970207-08:00          Unknown      NaN     0      0    NaN   \n",
       "2129   19970210-09:00             Haze      NaN     0      0    NaN   \n",
       "...               ...              ...      ...   ...    ...    ...   \n",
       "74019  20090128-09:00             Haze      NaN     0      0    NaN   \n",
       "76072  20091018-03:00            Smoke      NaN     0      0    NaN   \n",
       "77701  20100514-12:00             Haze      NaN     0      0    NaN   \n",
       "80228  20110422-18:00            Smoke      NaN     0      0    NaN   \n",
       "80398  20110516-18:00             Haze      NaN     0      0    NaN   \n",
       "80660  20110611-21:00          Unknown      NaN     0      0    NaN   \n",
       "81279  20110831-06:00             Haze      NaN     0      0    NaN   \n",
       "81671  20111023-15:00             Mist      NaN     0      0    NaN   \n",
       "86610  20131108-09:00            Smoke      NaN     0      0    NaN   \n",
       "88880  20140813-09:00             Haze      NaN     0      0    NaN   \n",
       "90594  20150316-09:00             Haze      NaN     0      0    NaN   \n",
       "91285  20150610-21:00             Haze      NaN     0      0    NaN   \n",
       "92014  20150906-15:00             Haze      NaN     0      0    NaN   \n",
       "92015  20150906-18:00             Haze      NaN     0      0    NaN   \n",
       "92016  20150906-21:00             Haze      NaN     0      0    NaN   \n",
       "93361  20160204-02:00              Fog      NaN     1      0    NaN   \n",
       "94528  20160423-06:00             Haze      NaN     0      0    NaN   \n",
       "94890  20160516-10:00          Unknown      NaN     0      0    NaN   \n",
       "95095  20160528-01:30             Haze      NaN     0      0    NaN   \n",
       "95248  20160607-03:00             Haze      NaN     0      0  243.0   \n",
       "95303  20160610-13:00       Light Rain      NaN     0      0    NaN   \n",
       "95547  20160626-11:00             Haze      NaN     0      0    NaN   \n",
       "95622  20160701-09:00             Haze      NaN     0      0    NaN   \n",
       "95640  20160702-11:00             Haze      NaN     0      0    NaN   \n",
       "96696  20160825-13:00    Mostly Cloudy      NaN     0      0    NaN   \n",
       "96965  20160907-08:30             Haze      NaN     0      0    NaN   \n",
       "97259  20160924-12:30             Haze      NaN     0      0    NaN   \n",
       "98049  20161025-07:30  Widespread Dust      NaN     0      0    NaN   \n",
       "99347  20161221-05:30             Haze      NaN     0      0    NaN   \n",
       "99716  20170108-03:00   Patches of Fog      NaN     1      0    NaN   \n",
       "\n",
       "       _pressurem  _rain  _snow  _tempm  _thunder  _tornado  _vism  _wdird  \\\n",
       "124        1014.0      0      0     NaN         0         0    2.0     0.0   \n",
       "240        1012.0      0      0     NaN         0         0    NaN   280.0   \n",
       "303       -9999.0      0      0     NaN         0         0    NaN     0.0   \n",
       "520        1017.0      0      0     NaN         0         0    1.6     0.0   \n",
       "584       -9999.0      0      0     NaN         0         0    0.7     0.0   \n",
       "812       -9999.0      0      0     NaN         0         0    5.0   290.0   \n",
       "820        1017.0      0      0     NaN         0         0    1.2     0.0   \n",
       "830       -9999.0      0      0     NaN         0         0    NaN     0.0   \n",
       "858       -9999.0      0      0     NaN         0         0    1.0     0.0   \n",
       "969        1018.0      0      0     NaN         0         0    0.6   230.0   \n",
       "971        1019.0      0      0     NaN         0         0    0.8   230.0   \n",
       "1032       1020.0      0      0    10.0         0         0    NaN     0.0   \n",
       "1033       1020.0      0      0    10.0         0         0    NaN     0.0   \n",
       "1082       1021.0      0      0     NaN         0         0    0.7   230.0   \n",
       "1113       1017.0      0      0     NaN         0         0    NaN     0.0   \n",
       "1147       1019.0      0      0     NaN         0         0    0.2     0.0   \n",
       "1150       1018.0      0      0     NaN         0         0    2.5   270.0   \n",
       "1243       1018.0      0      0     NaN         0         0    3.0     0.0   \n",
       "1272       1021.0      0      0     NaN         0         0    NaN     0.0   \n",
       "1281      -9999.0      0      0     NaN         0         0    0.4   220.0   \n",
       "1388       1019.0      0      0     NaN         0         0    NaN     0.0   \n",
       "1559       1017.0      0      0     NaN         0         0    1.0     0.0   \n",
       "1566      -9999.0      0      0     NaN         0         0    1.2     0.0   \n",
       "1581       1017.0      0      0     NaN         0         0    0.4     0.0   \n",
       "1806       1017.0      0      0     NaN         0         0    1.2     0.0   \n",
       "1813       1016.0      0      0     NaN         0         0    3.0   250.0   \n",
       "1910      -9999.0      0      0     NaN         0         0    NaN     0.0   \n",
       "1911       1012.0      0      0     NaN         0         0    1.2     0.0   \n",
       "2061      -9999.0      0      0     NaN         0         0    NaN   250.0   \n",
       "2129       1012.0      0      0     NaN         0         0    5.0   290.0   \n",
       "...           ...    ...    ...     ...       ...       ...    ...     ...   \n",
       "74019      1013.0      0      0     NaN         0         0    2.0   320.0   \n",
       "76072      1011.0      0      0    12.0         0         0    2.0   270.0   \n",
       "77701       996.0      0      0    41.0         0         0    4.0   320.0   \n",
       "80228      1007.0      0      0    24.0         0         0    1.0     NaN   \n",
       "80398      1006.0      0      0    31.0         0         0    2.0     NaN   \n",
       "80660       997.0      0      0     NaN         0         0    3.5   110.0   \n",
       "81279      1003.0      0      0    34.0         0         0    2.0    70.0   \n",
       "81671      1012.0      0      0    23.0         0         0    1.0     NaN   \n",
       "86610      1014.0      0      0     NaN         0         0    1.0    50.0   \n",
       "88880       998.0      0      0    36.0         0         0    4.0   270.0   \n",
       "90594      1016.0      0      0     NaN         0         0    4.0   140.0   \n",
       "91285      1001.0      0      0    34.0         0         0    2.0   270.0   \n",
       "92014      1005.0      0      0    32.0         0         0    2.0     NaN   \n",
       "92015      1006.0      0      0    29.0         0         0    2.0   270.0   \n",
       "92016      1006.0      0      0    28.0         0         0    2.0     NaN   \n",
       "93361      1017.0      0      0     NaN         0         0    0.8   220.0   \n",
       "94528      1002.0      0      0    34.0         0         0    2.0   320.0   \n",
       "94890      1000.0      0      0     NaN         0         0    4.0   290.0   \n",
       "95095      1002.0      0      0     NaN         0         0    2.0   280.0   \n",
       "95248      1003.0      0      0    32.0         0         0    2.0   110.0   \n",
       "95303       998.0      1      0     NaN         0         0    1.5   120.0   \n",
       "95547       999.0      0      0     NaN         0         0    3.0    80.0   \n",
       "95622      1100.0      0      0    36.0         0         0    2.0   290.0   \n",
       "95640       998.0      0      0     NaN         0         0    3.0   350.0   \n",
       "96696      1004.0      0      0     NaN         0         0    2.5   320.0   \n",
       "96965      1005.0      0      0     NaN         0         0    4.5   320.0   \n",
       "97259      1002.0      0      0     NaN         0         0    3.0     0.0   \n",
       "98049      1009.0      0      0     NaN         0         0    3.5   280.0   \n",
       "99347      1018.0      0      0     NaN         0         0    3.0   270.0   \n",
       "99716      1016.0      0      0     NaN         0         0    0.5     NaN   \n",
       "\n",
       "      _wdire  _wspdm  year month  \n",
       "124    North     0.0  1996    11  \n",
       "240     West     5.6  1996    11  \n",
       "303    North     NaN  1996    11  \n",
       "520    North     0.0  1996    11  \n",
       "584    North     0.0  1996    11  \n",
       "812      WNW    18.5  1996    12  \n",
       "820    North     0.0  1996    12  \n",
       "830    North     NaN  1996    12  \n",
       "858    North     0.0  1996    12  \n",
       "969       SW     3.7  1996    12  \n",
       "971       SW     5.6  1996    12  \n",
       "1032   North     0.0  1996    12  \n",
       "1033   North     0.0  1996    12  \n",
       "1082      SW     5.6  1996    12  \n",
       "1113   North     NaN  1996    12  \n",
       "1147   North     0.0  1996    12  \n",
       "1150    West     7.4  1996    12  \n",
       "1243   North     0.0  1996    12  \n",
       "1272   North     NaN  1996    12  \n",
       "1281      SW     5.6  1997    01  \n",
       "1388   North     NaN  1997    01  \n",
       "1559   North     0.0  1997    01  \n",
       "1566   North     0.0  1997    01  \n",
       "1581   North     0.0  1997    01  \n",
       "1806   North     0.0  1997    01  \n",
       "1813     WSW    11.1  1997    01  \n",
       "1910   North     NaN  1997    01  \n",
       "1911   North     0.0  1997    01  \n",
       "2061     WSW    25.9  1997    02  \n",
       "2129     WNW     9.3  1997    02  \n",
       "...      ...     ...   ...   ...  \n",
       "74019     NW    24.1  2009    01  \n",
       "76072   West     5.6  2009    10  \n",
       "77701     NW     1.9  2010    05  \n",
       "80228    NaN     0.0  2011    04  \n",
       "80398    NaN     0.0  2011    05  \n",
       "80660    ESE    11.1  2011    06  \n",
       "81279    ENE    16.7  2011    08  \n",
       "81671    NaN     0.0  2011    10  \n",
       "86610     NE     7.4  2013    11  \n",
       "88880   West    25.9  2014    08  \n",
       "90594     SE    14.8  2015    03  \n",
       "91285   West     5.6  2015    06  \n",
       "92014    NaN     0.0  2015    09  \n",
       "92015   West     3.7  2015    09  \n",
       "92016    NaN     0.0  2015    09  \n",
       "93361     SW     3.7  2016    02  \n",
       "94528     NW    14.8  2016    04  \n",
       "94890    WNW    14.8  2016    05  \n",
       "95095   West     7.4  2016    05  \n",
       "95248    ESE     9.3  2016    06  \n",
       "95303    ESE     7.4  2016    06  \n",
       "95547   East     7.4  2016    06  \n",
       "95622    WNW     9.3  2016    07  \n",
       "95640  North     5.6  2016    07  \n",
       "96696     NW     5.6  2016    08  \n",
       "96965     NW     9.3  2016    09  \n",
       "97259  North     0.0  2016    09  \n",
       "98049   West    14.8  2016    10  \n",
       "99347   West    11.1  2016    12  \n",
       "99716    NaN     0.0  2017    01  \n",
       "\n",
       "[621 rows x 18 columns]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Lets replace missing values in _dewptm. We can take an avrgae of that year\n",
    "df_filtered[df_filtered._dewptm.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will try to replace value with average value of that year\n",
    "for index,row in df_filtered[df_filtered._dewptm.isnull()].iterrows():\n",
    "    mean_val = df_filtered[df_filtered[\"year\"] == row[\"year\"]]._dewptm.mean()\n",
    "    df_filtered.at[index, \"_dewptm\"] = mean_val\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime_utc</th>\n",
       "      <th>_conds</th>\n",
       "      <th>_dewptm</th>\n",
       "      <th>_fog</th>\n",
       "      <th>_hail</th>\n",
       "      <th>_hum</th>\n",
       "      <th>_pressurem</th>\n",
       "      <th>_rain</th>\n",
       "      <th>_snow</th>\n",
       "      <th>_tempm</th>\n",
       "      <th>_thunder</th>\n",
       "      <th>_tornado</th>\n",
       "      <th>_vism</th>\n",
       "      <th>_wdird</th>\n",
       "      <th>_wdire</th>\n",
       "      <th>_wspdm</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [datetime_utc, _conds, _dewptm, _fog, _hail, _hum, _pressurem, _rain, _snow, _tempm, _thunder, _tornado, _vism, _wdird, _wdire, _wspdm, year, month]\n",
       "Index: []"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered[df_filtered._dewptm.isnull()] # We replaced null values fof _dewtmp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100990, 18)"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime_utc        0\n",
       "_conds             72\n",
       "_dewptm             0\n",
       "_fog                0\n",
       "_hail               0\n",
       "_hum              757\n",
       "_pressurem        232\n",
       "_rain               0\n",
       "_snow               0\n",
       "_tempm            673\n",
       "_thunder            0\n",
       "_tornado            0\n",
       "_vism            4428\n",
       "_wdird          14755\n",
       "_wdire          14755\n",
       "_wspdm           2358\n",
       "year                0\n",
       "month               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.isnull().sum()\n",
    "# so now we have only relevant columns. Lets handle them one by one."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime_utc</th>\n",
       "      <th>_conds</th>\n",
       "      <th>_dewptm</th>\n",
       "      <th>_fog</th>\n",
       "      <th>_hail</th>\n",
       "      <th>_hum</th>\n",
       "      <th>_pressurem</th>\n",
       "      <th>_rain</th>\n",
       "      <th>_snow</th>\n",
       "      <th>_tempm</th>\n",
       "      <th>_thunder</th>\n",
       "      <th>_tornado</th>\n",
       "      <th>_vism</th>\n",
       "      <th>_wdird</th>\n",
       "      <th>_wdire</th>\n",
       "      <th>_wspdm</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>19961108-01:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1014.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>240</th>\n",
       "      <td>19961113-02:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>280.0</td>\n",
       "      <td>West</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>303</th>\n",
       "      <td>19961116-06:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>520</th>\n",
       "      <td>19961126-04:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.60</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>584</th>\n",
       "      <td>19961129-18:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>812</th>\n",
       "      <td>19961210-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.00</td>\n",
       "      <td>290.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>18.5</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>820</th>\n",
       "      <td>19961210-17:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>830</th>\n",
       "      <td>19961211-07:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>858</th>\n",
       "      <td>19961212-19:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>969</th>\n",
       "      <td>19961218-00:00</td>\n",
       "      <td>Shallow Fog</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>230.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>971</th>\n",
       "      <td>19961218-02:00</td>\n",
       "      <td>Shallow Fog</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>230.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1032</th>\n",
       "      <td>19961221-00:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1033</th>\n",
       "      <td>19961221-01:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1020.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1082</th>\n",
       "      <td>19961223-03:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.70</td>\n",
       "      <td>230.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1113</th>\n",
       "      <td>19961224-13:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1147</th>\n",
       "      <td>19961226-02:00</td>\n",
       "      <td>Shallow Fog</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1150</th>\n",
       "      <td>19961226-07:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>270.0</td>\n",
       "      <td>West</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1243</th>\n",
       "      <td>19961230-12:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1272</th>\n",
       "      <td>19961231-18:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>5.992851</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1021.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1281</th>\n",
       "      <td>19970101-03:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>17.052394</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>220.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1388</th>\n",
       "      <td>19970106-02:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>17.052394</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1559</th>\n",
       "      <td>19970113-23:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>17.052394</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1566</th>\n",
       "      <td>19970114-06:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>17.052394</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1581</th>\n",
       "      <td>19970114-21:00</td>\n",
       "      <td>Shallow Fog</td>\n",
       "      <td>17.052394</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1806</th>\n",
       "      <td>19970126-01:00</td>\n",
       "      <td>Fog</td>\n",
       "      <td>17.052394</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1813</th>\n",
       "      <td>19970126-08:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>17.052394</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>250.0</td>\n",
       "      <td>WSW</td>\n",
       "      <td>11.1</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1842</th>\n",
       "      <td>19970127-15:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1910</th>\n",
       "      <td>19970130-13:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>17.052394</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1911</th>\n",
       "      <td>19970130-14:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>17.052394</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.20</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1997</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2061</th>\n",
       "      <td>19970207-08:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>17.052394</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>250.0</td>\n",
       "      <td>WSW</td>\n",
       "      <td>25.9</td>\n",
       "      <td>1997</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92219</th>\n",
       "      <td>20151002-18:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>81.5</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92222</th>\n",
       "      <td>20151003-03:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.40</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>96.3</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92342</th>\n",
       "      <td>20151018-09:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1012.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.60</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>85.2</td>\n",
       "      <td>2015</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93353</th>\n",
       "      <td>20160203-03:00</td>\n",
       "      <td>Patches of Fog</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1019.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>10.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>93361</th>\n",
       "      <td>20160204-02:00</td>\n",
       "      <td>Fog</td>\n",
       "      <td>18.274320</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>220.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2016</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94322</th>\n",
       "      <td>20160409-12:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>14.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1007.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>100.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94527</th>\n",
       "      <td>20160423-03:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>15.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1003.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.80</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>133.3</td>\n",
       "      <td>2016</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94528</th>\n",
       "      <td>20160423-06:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>18.274320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2016</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94777</th>\n",
       "      <td>20160509-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>144.5</td>\n",
       "      <td>2016</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94890</th>\n",
       "      <td>20160516-10:00</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>18.274320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1000.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.00</td>\n",
       "      <td>290.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2016</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95095</th>\n",
       "      <td>20160528-01:30</td>\n",
       "      <td>Haze</td>\n",
       "      <td>18.274320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>280.0</td>\n",
       "      <td>West</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2016</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95104</th>\n",
       "      <td>20160528-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>20.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>998.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>18.5</td>\n",
       "      <td>2016</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95303</th>\n",
       "      <td>20160610-13:00</td>\n",
       "      <td>Light Rain</td>\n",
       "      <td>18.274320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>998.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.50</td>\n",
       "      <td>120.0</td>\n",
       "      <td>ESE</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2016</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95547</th>\n",
       "      <td>20160626-11:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>18.274320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>80.0</td>\n",
       "      <td>East</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2016</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95622</th>\n",
       "      <td>20160701-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>18.274320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1100.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>290.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2016</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95640</th>\n",
       "      <td>20160702-11:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>18.274320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>998.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>350.0</td>\n",
       "      <td>North</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2016</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96221</th>\n",
       "      <td>20160808-06:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>50.0</td>\n",
       "      <td>NE</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2016</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96696</th>\n",
       "      <td>20160825-13:00</td>\n",
       "      <td>Mostly Cloudy</td>\n",
       "      <td>18.274320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.50</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2016</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96831</th>\n",
       "      <td>20160830-15:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>25.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1001.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>148.2</td>\n",
       "      <td>2016</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96965</th>\n",
       "      <td>20160907-08:30</td>\n",
       "      <td>Haze</td>\n",
       "      <td>18.274320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1005.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.50</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2016</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97063</th>\n",
       "      <td>20160915-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1004.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>92.6</td>\n",
       "      <td>2016</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97259</th>\n",
       "      <td>20160924-12:30</td>\n",
       "      <td>Haze</td>\n",
       "      <td>18.274320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1002.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97490</th>\n",
       "      <td>20161003-18:00</td>\n",
       "      <td>Scattered Clouds</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1006.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>97603</th>\n",
       "      <td>20161008-06:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1008.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.00</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98049</th>\n",
       "      <td>20161025-07:30</td>\n",
       "      <td>Widespread Dust</td>\n",
       "      <td>18.274320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1009.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.50</td>\n",
       "      <td>280.0</td>\n",
       "      <td>West</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2016</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98641</th>\n",
       "      <td>20161117-07:30</td>\n",
       "      <td>Unknown</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1017.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99268</th>\n",
       "      <td>20161217-09:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.00</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>25.9</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99347</th>\n",
       "      <td>20161221-05:30</td>\n",
       "      <td>Haze</td>\n",
       "      <td>18.274320</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1018.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>3.00</td>\n",
       "      <td>270.0</td>\n",
       "      <td>West</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99567</th>\n",
       "      <td>20161231-15:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>13.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.05</td>\n",
       "      <td>10.0</td>\n",
       "      <td>North</td>\n",
       "      <td>59.3</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99716</th>\n",
       "      <td>20170108-03:00</td>\n",
       "      <td>Patches of Fog</td>\n",
       "      <td>12.451022</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1016.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.50</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2017</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>757 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         datetime_utc            _conds    _dewptm  _fog  _hail  _hum  \\\n",
       "124    19961108-01:00             Smoke   5.992851     0      0   NaN   \n",
       "240    19961113-02:00             Smoke   5.992851     0      0   NaN   \n",
       "303    19961116-06:00           Unknown   5.992851     0      0   NaN   \n",
       "520    19961126-04:00             Smoke   5.992851     0      0   NaN   \n",
       "584    19961129-18:00             Smoke   5.992851     0      0   NaN   \n",
       "812    19961210-09:00              Haze   5.992851     0      0   NaN   \n",
       "820    19961210-17:00           Unknown   5.992851     0      0   NaN   \n",
       "830    19961211-07:00           Unknown   5.992851     0      0   NaN   \n",
       "858    19961212-19:00             Smoke   5.992851     0      0   NaN   \n",
       "969    19961218-00:00       Shallow Fog   5.992851     1      0   NaN   \n",
       "971    19961218-02:00       Shallow Fog   5.992851     1      0   NaN   \n",
       "1032   19961221-00:00           Unknown   5.992851     0      0   NaN   \n",
       "1033   19961221-01:00           Unknown   5.992851     0      0   NaN   \n",
       "1082   19961223-03:00             Smoke   5.992851     0      0   NaN   \n",
       "1113   19961224-13:00             Smoke   5.992851     0      0   NaN   \n",
       "1147   19961226-02:00       Shallow Fog   5.992851     1      0   NaN   \n",
       "1150   19961226-07:00           Unknown   5.992851     0      0   NaN   \n",
       "1243   19961230-12:00             Smoke   5.992851     0      0   NaN   \n",
       "1272   19961231-18:00           Unknown   5.992851     0      0   NaN   \n",
       "1281   19970101-03:00           Unknown  17.052394     0      0   NaN   \n",
       "1388   19970106-02:00           Unknown  17.052394     0      0   NaN   \n",
       "1559   19970113-23:00           Unknown  17.052394     0      0   NaN   \n",
       "1566   19970114-06:00             Smoke  17.052394     0      0   NaN   \n",
       "1581   19970114-21:00       Shallow Fog  17.052394     1      0   NaN   \n",
       "1806   19970126-01:00               Fog  17.052394     1      0   NaN   \n",
       "1813   19970126-08:00             Smoke  17.052394     0      0   NaN   \n",
       "1842   19970127-15:00             Smoke  10.000000     0      0   NaN   \n",
       "1910   19970130-13:00             Smoke  17.052394     0      0   NaN   \n",
       "1911   19970130-14:00             Smoke  17.052394     0      0   NaN   \n",
       "2061   19970207-08:00           Unknown  17.052394     0      0   NaN   \n",
       "...               ...               ...        ...   ...    ...   ...   \n",
       "92219  20151002-18:00              Mist  20.000000     0      0   NaN   \n",
       "92222  20151003-03:00               NaN  19.000000     0      0   NaN   \n",
       "92342  20151018-09:00               NaN  22.000000     0      0   NaN   \n",
       "93353  20160203-03:00    Patches of Fog   8.000000     1      0   NaN   \n",
       "93361  20160204-02:00               Fog  18.274320     1      0   NaN   \n",
       "94322  20160409-12:00              Haze  14.000000     0      0   NaN   \n",
       "94527  20160423-03:00               NaN  15.000000     0      0   NaN   \n",
       "94528  20160423-06:00              Haze  18.274320     0      0   NaN   \n",
       "94777  20160509-09:00              Haze  20.000000     0      0   NaN   \n",
       "94890  20160516-10:00           Unknown  18.274320     0      0   NaN   \n",
       "95095  20160528-01:30              Haze  18.274320     0      0   NaN   \n",
       "95104  20160528-09:00              Haze  20.000000     0      0   NaN   \n",
       "95303  20160610-13:00        Light Rain  18.274320     0      0   NaN   \n",
       "95547  20160626-11:00              Haze  18.274320     0      0   NaN   \n",
       "95622  20160701-09:00              Haze  18.274320     0      0   NaN   \n",
       "95640  20160702-11:00              Haze  18.274320     0      0   NaN   \n",
       "96221  20160808-06:00              Haze  26.000000     0      0   NaN   \n",
       "96696  20160825-13:00     Mostly Cloudy  18.274320     0      0   NaN   \n",
       "96831  20160830-15:00              Mist  25.000000     0      0   NaN   \n",
       "96965  20160907-08:30              Haze  18.274320     0      0   NaN   \n",
       "97063  20160915-09:00              Haze  22.000000     0      0   NaN   \n",
       "97259  20160924-12:30              Haze  18.274320     0      0   NaN   \n",
       "97490  20161003-18:00  Scattered Clouds  26.000000     0      0   NaN   \n",
       "97603  20161008-06:00              Haze  21.000000     0      0   NaN   \n",
       "98049  20161025-07:30   Widespread Dust  18.274320     0      0   NaN   \n",
       "98641  20161117-07:30           Unknown  10.000000     0      0   NaN   \n",
       "99268  20161217-09:00             Smoke  11.000000     0      0   NaN   \n",
       "99347  20161221-05:30              Haze  18.274320     0      0   NaN   \n",
       "99567  20161231-15:00               NaN  13.000000     0      0   NaN   \n",
       "99716  20170108-03:00    Patches of Fog  12.451022     1      0   NaN   \n",
       "\n",
       "       _pressurem  _rain  _snow  _tempm  _thunder  _tornado  _vism  _wdird  \\\n",
       "124        1014.0      0      0     NaN         0         0   2.00     0.0   \n",
       "240        1012.0      0      0     NaN         0         0    NaN   280.0   \n",
       "303       -9999.0      0      0     NaN         0         0    NaN     0.0   \n",
       "520        1017.0      0      0     NaN         0         0   1.60     0.0   \n",
       "584       -9999.0      0      0     NaN         0         0   0.70     0.0   \n",
       "812       -9999.0      0      0     NaN         0         0   5.00   290.0   \n",
       "820        1017.0      0      0     NaN         0         0   1.20     0.0   \n",
       "830       -9999.0      0      0     NaN         0         0    NaN     0.0   \n",
       "858       -9999.0      0      0     NaN         0         0   1.00     0.0   \n",
       "969        1018.0      0      0     NaN         0         0   0.60   230.0   \n",
       "971        1019.0      0      0     NaN         0         0   0.80   230.0   \n",
       "1032       1020.0      0      0    10.0         0         0    NaN     0.0   \n",
       "1033       1020.0      0      0    10.0         0         0    NaN     0.0   \n",
       "1082       1021.0      0      0     NaN         0         0   0.70   230.0   \n",
       "1113       1017.0      0      0     NaN         0         0    NaN     0.0   \n",
       "1147       1019.0      0      0     NaN         0         0   0.20     0.0   \n",
       "1150       1018.0      0      0     NaN         0         0   2.50   270.0   \n",
       "1243       1018.0      0      0     NaN         0         0   3.00     0.0   \n",
       "1272       1021.0      0      0     NaN         0         0    NaN     0.0   \n",
       "1281      -9999.0      0      0     NaN         0         0   0.40   220.0   \n",
       "1388       1019.0      0      0     NaN         0         0    NaN     0.0   \n",
       "1559       1017.0      0      0     NaN         0         0   1.00     0.0   \n",
       "1566      -9999.0      0      0     NaN         0         0   1.20     0.0   \n",
       "1581       1017.0      0      0     NaN         0         0   0.40     0.0   \n",
       "1806       1017.0      0      0     NaN         0         0   1.20     0.0   \n",
       "1813       1016.0      0      0     NaN         0         0   3.00   250.0   \n",
       "1842       1018.0      0      0     NaN         0         0   0.80     0.0   \n",
       "1910      -9999.0      0      0     NaN         0         0    NaN     0.0   \n",
       "1911       1012.0      0      0     NaN         0         0   1.20     0.0   \n",
       "2061      -9999.0      0      0     NaN         0         0    NaN   250.0   \n",
       "...           ...    ...    ...     ...       ...       ...    ...     ...   \n",
       "92219      1010.0      0      0     NaN         0         0   2.00    20.0   \n",
       "92222      1012.0      0      0     NaN         0         0   0.40    20.0   \n",
       "92342      1012.0      0      0     NaN         0         0   0.60    30.0   \n",
       "93353      1019.0      0      0     NaN         0         0   0.50    10.0   \n",
       "93361      1017.0      0      0     NaN         0         0   0.80   220.0   \n",
       "94322      1007.0      0      0     NaN         0         0   2.00    30.0   \n",
       "94527      1003.0      0      0     NaN         0         0   0.80    20.0   \n",
       "94528      1002.0      0      0    34.0         0         0   2.00   320.0   \n",
       "94777      1005.0      0      0     NaN         0         0   2.00    30.0   \n",
       "94890      1000.0      0      0     NaN         0         0   4.00   290.0   \n",
       "95095      1002.0      0      0     NaN         0         0   2.00   280.0   \n",
       "95104       998.0      0      0     NaN         0         0   2.00    40.0   \n",
       "95303       998.0      1      0     NaN         0         0   1.50   120.0   \n",
       "95547       999.0      0      0     NaN         0         0   3.00    80.0   \n",
       "95622      1100.0      0      0    36.0         0         0   2.00   290.0   \n",
       "95640       998.0      0      0     NaN         0         0   3.00   350.0   \n",
       "96221      1001.0      0      0     NaN         0         0   2.00    50.0   \n",
       "96696      1004.0      0      0     NaN         0         0   2.50   320.0   \n",
       "96831      1001.0      0      0     NaN         0         0   2.00    20.0   \n",
       "96965      1005.0      0      0     NaN         0         0   4.50   320.0   \n",
       "97063      1004.0      0      0     NaN         0         0   2.00    30.0   \n",
       "97259      1002.0      0      0     NaN         0         0   3.00     0.0   \n",
       "97490      1006.0      0      0     NaN         0         0   2.00    30.0   \n",
       "97603      1008.0      0      0     NaN         0         0   2.00   320.0   \n",
       "98049      1009.0      0      0     NaN         0         0   3.50   280.0   \n",
       "98641      1017.0      0      0     NaN         0         0    NaN     0.0   \n",
       "99268      1016.0      0      0     NaN         0         0   1.00    20.0   \n",
       "99347      1018.0      0      0     NaN         0         0   3.00   270.0   \n",
       "99567      1016.0      0      0     NaN         0         0   0.05    10.0   \n",
       "99716      1016.0      0      0     NaN         0         0   0.50     NaN   \n",
       "\n",
       "      _wdire  _wspdm  year month  \n",
       "124    North     0.0  1996    11  \n",
       "240     West     5.6  1996    11  \n",
       "303    North     NaN  1996    11  \n",
       "520    North     0.0  1996    11  \n",
       "584    North     0.0  1996    11  \n",
       "812      WNW    18.5  1996    12  \n",
       "820    North     0.0  1996    12  \n",
       "830    North     NaN  1996    12  \n",
       "858    North     0.0  1996    12  \n",
       "969       SW     3.7  1996    12  \n",
       "971       SW     5.6  1996    12  \n",
       "1032   North     0.0  1996    12  \n",
       "1033   North     0.0  1996    12  \n",
       "1082      SW     5.6  1996    12  \n",
       "1113   North     NaN  1996    12  \n",
       "1147   North     0.0  1996    12  \n",
       "1150    West     7.4  1996    12  \n",
       "1243   North     0.0  1996    12  \n",
       "1272   North     NaN  1996    12  \n",
       "1281      SW     5.6  1997    01  \n",
       "1388   North     NaN  1997    01  \n",
       "1559   North     0.0  1997    01  \n",
       "1566   North     0.0  1997    01  \n",
       "1581   North     0.0  1997    01  \n",
       "1806   North     0.0  1997    01  \n",
       "1813     WSW    11.1  1997    01  \n",
       "1842   North     0.0  1997    01  \n",
       "1910   North     NaN  1997    01  \n",
       "1911   North     0.0  1997    01  \n",
       "2061     WSW    25.9  1997    02  \n",
       "...      ...     ...   ...   ...  \n",
       "92219    NNE    81.5  2015    10  \n",
       "92222    NNE    96.3  2015    10  \n",
       "92342    NNE    85.2  2015    10  \n",
       "93353  North     0.0  2016    02  \n",
       "93361     SW     3.7  2016    02  \n",
       "94322    NNE   100.0  2016    04  \n",
       "94527    NNE   133.3  2016    04  \n",
       "94528     NW    14.8  2016    04  \n",
       "94777    NNE   144.5  2016    05  \n",
       "94890    WNW    14.8  2016    05  \n",
       "95095   West     7.4  2016    05  \n",
       "95104     NE    18.5  2016    05  \n",
       "95303    ESE     7.4  2016    06  \n",
       "95547   East     7.4  2016    06  \n",
       "95622    WNW     9.3  2016    07  \n",
       "95640  North     5.6  2016    07  \n",
       "96221     NE    11.1  2016    08  \n",
       "96696     NW     5.6  2016    08  \n",
       "96831    NNE   148.2  2016    08  \n",
       "96965     NW     9.3  2016    09  \n",
       "97063    NNE    92.6  2016    09  \n",
       "97259  North     0.0  2016    09  \n",
       "97490    NNE     0.0  2016    10  \n",
       "97603     NW    11.1  2016    10  \n",
       "98049   West    14.8  2016    10  \n",
       "98641  North     NaN  2016    11  \n",
       "99268    NNE    25.9  2016    12  \n",
       "99347   West    11.1  2016    12  \n",
       "99567  North    59.3  2016    12  \n",
       "99716    NaN     0.0  2017    01  \n",
       "\n",
       "[757 rows x 18 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Handle _hum column.\n",
    "df_filtered[df_filtered._hum.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [],
   "source": [
    "# We will use the same logic o replace as we did before.\n",
    "# We will try to replace value with average value of that year\n",
    "for index,row in df_filtered[df_filtered._hum.isnull()].iterrows():\n",
    "    mean_val = df_filtered[df_filtered[\"year\"] == row[\"year\"]]._hum.mean()\n",
    "    df_filtered.at[index, \"_hum\"] = mean_val\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime_utc</th>\n",
       "      <th>_conds</th>\n",
       "      <th>_dewptm</th>\n",
       "      <th>_fog</th>\n",
       "      <th>_hail</th>\n",
       "      <th>_hum</th>\n",
       "      <th>_pressurem</th>\n",
       "      <th>_rain</th>\n",
       "      <th>_snow</th>\n",
       "      <th>_tempm</th>\n",
       "      <th>_thunder</th>\n",
       "      <th>_tornado</th>\n",
       "      <th>_vism</th>\n",
       "      <th>_wdird</th>\n",
       "      <th>_wdire</th>\n",
       "      <th>_wspdm</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [datetime_utc, _conds, _dewptm, _fog, _hail, _hum, _pressurem, _rain, _snow, _tempm, _thunder, _tornado, _vism, _wdird, _wdire, _wspdm, year, month]\n",
       "Index: []"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered[df_filtered._hum.isnull()] # replaced"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime_utc        0\n",
       "_conds             72\n",
       "_dewptm             0\n",
       "_fog                0\n",
       "_hail               0\n",
       "_hum                0\n",
       "_pressurem        232\n",
       "_rain               0\n",
       "_snow               0\n",
       "_tempm            673\n",
       "_thunder            0\n",
       "_tornado            0\n",
       "_vism            4428\n",
       "_wdird          14755\n",
       "_wdire          14755\n",
       "_wspdm           2358\n",
       "year                0\n",
       "month               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.isnull().sum() # Now lets handle _pressurem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>datetime_utc</th>\n",
       "      <th>_conds</th>\n",
       "      <th>_dewptm</th>\n",
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       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>60703</th>\n",
       "      <td>20040625-21:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>62.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60723</th>\n",
       "      <td>20040628-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>20.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2004</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60728</th>\n",
       "      <td>20040629-00:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>63.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>60921</th>\n",
       "      <td>20040723-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61255</th>\n",
       "      <td>20040829-18:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61270</th>\n",
       "      <td>20040831-15:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2004</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>61394</th>\n",
       "      <td>20040911-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>21.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>230.0</td>\n",
       "      <td>SW</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2004</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62409</th>\n",
       "      <td>20050114-03:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>7.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>WSW</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2005</td>\n",
       "      <td>01</td>\n",
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       "    <tr>\n",
       "      <th>62501</th>\n",
       "      <td>20050125-15:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>84.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63218</th>\n",
       "      <td>20050426-15:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63267</th>\n",
       "      <td>20050502-18:00</td>\n",
       "      <td>Widespread Dust</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>49.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63312</th>\n",
       "      <td>20050508-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2005</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63329</th>\n",
       "      <td>20050510-12:00</td>\n",
       "      <td>Widespread Dust</td>\n",
       "      <td>4.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>25.9</td>\n",
       "      <td>2005</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63793</th>\n",
       "      <td>20050708-03:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>1.9</td>\n",
       "      <td>2005</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63857</th>\n",
       "      <td>20050716-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2005</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>63861</th>\n",
       "      <td>20050716-21:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>East</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2005</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64037</th>\n",
       "      <td>20050808-03:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>61.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2005</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64100</th>\n",
       "      <td>20050816-00:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>85.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2005</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64134</th>\n",
       "      <td>20050820-06:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>18.5</td>\n",
       "      <td>2005</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64160</th>\n",
       "      <td>20050823-12:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2005</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64219</th>\n",
       "      <td>20050830-21:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64271</th>\n",
       "      <td>20050906-12:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>43.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>ESE</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2005</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64508</th>\n",
       "      <td>20051006-06:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>16.7</td>\n",
       "      <td>2005</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64649</th>\n",
       "      <td>20051023-21:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>75.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>18.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>64670</th>\n",
       "      <td>20051026-18:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>15.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>19.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2005</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65299</th>\n",
       "      <td>20060110-21:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>5.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2006</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65327</th>\n",
       "      <td>20060114-09:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>20.4</td>\n",
       "      <td>2006</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65347</th>\n",
       "      <td>20060116-21:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>12.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2006</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65450</th>\n",
       "      <td>20060130-06:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2006</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>65489</th>\n",
       "      <td>20060204-03:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>74.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>West</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2006</td>\n",
       "      <td>02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91530</th>\n",
       "      <td>20150711-15:00</td>\n",
       "      <td>Rain</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91531</th>\n",
       "      <td>20150711-18:00</td>\n",
       "      <td>Drizzle</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>96.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91549</th>\n",
       "      <td>20150714-00:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>87.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91634</th>\n",
       "      <td>20150724-15:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91637</th>\n",
       "      <td>20150725-00:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2015</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91658</th>\n",
       "      <td>20150727-15:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>82.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91783</th>\n",
       "      <td>20150808-12:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>East</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2015</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91802</th>\n",
       "      <td>20150810-21:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91863</th>\n",
       "      <td>20150818-12:00</td>\n",
       "      <td>Thunderstorms and Rain</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91888</th>\n",
       "      <td>20150821-15:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91904</th>\n",
       "      <td>20150823-15:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>67.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>210.0</td>\n",
       "      <td>SSW</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91965</th>\n",
       "      <td>20150831-12:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>42.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>West</td>\n",
       "      <td>11.1</td>\n",
       "      <td>2015</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>91974</th>\n",
       "      <td>20150901-15:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>33.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>250.0</td>\n",
       "      <td>WSW</td>\n",
       "      <td>13.0</td>\n",
       "      <td>2015</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>94523</th>\n",
       "      <td>20160422-15:00</td>\n",
       "      <td>Light Drizzle</td>\n",
       "      <td>13.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>NNE</td>\n",
       "      <td>18.5</td>\n",
       "      <td>2016</td>\n",
       "      <td>04</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95154</th>\n",
       "      <td>20160531-12:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>35.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>37.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>340.0</td>\n",
       "      <td>NNW</td>\n",
       "      <td>3.7</td>\n",
       "      <td>2016</td>\n",
       "      <td>05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95309</th>\n",
       "      <td>20160611-03:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>West</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2016</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95402</th>\n",
       "      <td>20160617-03:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>ESE</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2016</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95405</th>\n",
       "      <td>20160617-06:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>38.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>36.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>180.0</td>\n",
       "      <td>South</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2016</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95555</th>\n",
       "      <td>20160627-03:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>69.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>East</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2016</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95883</th>\n",
       "      <td>20160717-21:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>95960</th>\n",
       "      <td>20160722-18:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>76.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>140.0</td>\n",
       "      <td>SE</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2016</td>\n",
       "      <td>07</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96473</th>\n",
       "      <td>20160817-09:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>59.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>320.0</td>\n",
       "      <td>NW</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2016</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96488</th>\n",
       "      <td>20160818-03:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>83.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96549</th>\n",
       "      <td>20160820-06:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>64.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>34.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>East</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2016</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96564</th>\n",
       "      <td>20160820-15:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>27.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>66.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>90.0</td>\n",
       "      <td>East</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2016</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96567</th>\n",
       "      <td>20160821-00:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>81.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>110.0</td>\n",
       "      <td>ESE</td>\n",
       "      <td>9.3</td>\n",
       "      <td>2016</td>\n",
       "      <td>08</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>96907</th>\n",
       "      <td>20160902-18:00</td>\n",
       "      <td>Mist</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>79.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>West</td>\n",
       "      <td>7.4</td>\n",
       "      <td>2016</td>\n",
       "      <td>09</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98402</th>\n",
       "      <td>20161108-06:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>14.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>39.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>25.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>270.0</td>\n",
       "      <td>West</td>\n",
       "      <td>22.2</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>98820</th>\n",
       "      <td>20161126-09:00</td>\n",
       "      <td>Haze</td>\n",
       "      <td>8.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>290.0</td>\n",
       "      <td>WNW</td>\n",
       "      <td>14.8</td>\n",
       "      <td>2016</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>99069</th>\n",
       "      <td>20161207-12:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>16.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>60.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>22.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.5</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2016</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>232 rows × 18 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "         datetime_utc                  _conds  _dewptm  _fog  _hail  _hum  \\\n",
       "60703  20040625-21:00                    Haze     21.0     0      0  62.0   \n",
       "60723  20040628-09:00                    Haze     20.0     0      0  27.0   \n",
       "60728  20040629-00:00                    Haze     21.0     0      0  63.0   \n",
       "60921  20040723-09:00                    Haze     24.0     0      0  37.0   \n",
       "61255  20040829-18:00                    Haze     27.0     0      0  75.0   \n",
       "61270  20040831-15:00                    Haze     24.0     0      0  54.0   \n",
       "61394  20040911-09:00                    Haze     21.0     0      0  38.0   \n",
       "62409  20050114-03:00                   Smoke      7.0     0      0  64.0   \n",
       "62501  20050125-15:00                    Haze     10.0     0      0  84.0   \n",
       "63218  20050426-15:00                    Haze     16.0     0      0  31.0   \n",
       "63267  20050502-18:00         Widespread Dust     16.0     0      0  49.0   \n",
       "63312  20050508-09:00                    Haze     15.0     0      0  18.0   \n",
       "63329  20050510-12:00         Widespread Dust      4.0     0      0   7.0   \n",
       "63793  20050708-03:00                    Haze     27.0     0      0  89.0   \n",
       "63857  20050716-09:00                    Haze     27.0     0      0  64.0   \n",
       "63861  20050716-21:00                    Haze     26.0     0      0  90.0   \n",
       "64037  20050808-03:00                    Haze     22.0     0      0  61.0   \n",
       "64100  20050816-00:00                    Haze     26.0     0      0  85.0   \n",
       "64134  20050820-06:00                    Haze     23.0     0      0  44.0   \n",
       "64160  20050823-12:00                    Haze     24.0     0      0  44.0   \n",
       "64219  20050830-21:00                    Haze     24.0     0      0  66.0   \n",
       "64271  20050906-12:00                    Haze     24.0     0      0  43.0   \n",
       "64508  20051006-06:00                    Haze     19.0     0      0  37.0   \n",
       "64649  20051023-21:00                    Mist     14.0     0      0  75.0   \n",
       "64670  20051026-18:00                    Mist     15.0     0      0  74.0   \n",
       "65299  20060110-21:00                    Mist      5.0     0      0  92.0   \n",
       "65327  20060114-09:00                   Smoke      1.0     0      0  13.0   \n",
       "65347  20060116-21:00                    Mist     12.0     0      0  90.0   \n",
       "65450  20060130-06:00                   Smoke      2.0     0      0  16.0   \n",
       "65489  20060204-03:00                    Mist     10.0     0      0  74.0   \n",
       "...               ...                     ...      ...   ...    ...   ...   \n",
       "91530  20150711-15:00                    Rain     25.0     0      0  97.0   \n",
       "91531  20150711-18:00                 Drizzle     25.0     0      0  96.0   \n",
       "91549  20150714-00:00                    Mist     26.0     0      0  87.0   \n",
       "91634  20150724-15:00                    Haze     26.0     0      0  65.0   \n",
       "91637  20150725-00:00                    Mist     25.0     0      0  76.0   \n",
       "91658  20150727-15:00                    Mist     26.0     0      0  82.0   \n",
       "91783  20150808-12:00                    Mist     27.0     0      0  79.0   \n",
       "91802  20150810-21:00                    Mist     27.0     0      0  89.0   \n",
       "91863  20150818-12:00  Thunderstorms and Rain     24.0     0      0  67.0   \n",
       "91888  20150821-15:00                    Mist     28.0     0      0  90.0   \n",
       "91904  20150823-15:00                    Haze     26.0     0      0  67.0   \n",
       "91965  20150831-12:00                    Haze     24.0     0      0  42.0   \n",
       "91974  20150901-15:00                    Haze     22.0     0      0  45.0   \n",
       "94523  20160422-15:00           Light Drizzle     13.0     0      0  24.0   \n",
       "95154  20160531-12:00                    Haze     23.0     0      0  35.0   \n",
       "95309  20160611-03:00                    Haze     23.0     0      0  60.0   \n",
       "95402  20160617-03:00                    Haze     24.0     0      0  60.0   \n",
       "95405  20160617-06:00                    Haze     23.0     0      0  38.0   \n",
       "95555  20160627-03:00                    Mist     27.0     0      0  69.0   \n",
       "95883  20160717-21:00                    Mist     25.0     0      0  90.0   \n",
       "95960  20160722-18:00                    Mist     27.0     0      0  76.0   \n",
       "96473  20160817-09:00                     NaN     24.0     0      0  59.0   \n",
       "96488  20160818-03:00                    Mist     26.0     0      0  83.0   \n",
       "96549  20160820-06:00                    Haze     27.0     0      0  64.0   \n",
       "96564  20160820-15:00                    Haze     27.0     0      0  66.0   \n",
       "96567  20160821-00:00                    Mist     25.0     0      0  81.0   \n",
       "96907  20160902-18:00                    Mist     25.0     0      0  79.0   \n",
       "98402  20161108-06:00                   Smoke     14.0     0      0  39.0   \n",
       "98820  20161126-09:00                    Haze      8.0     0      0  15.0   \n",
       "99069  20161207-12:00                   Smoke     16.0     0      0  60.0   \n",
       "\n",
       "       _pressurem  _rain  _snow  _tempm  _thunder  _tornado  _vism  _wdird  \\\n",
       "60703         NaN      0      0    26.0         0         0    2.0     NaN   \n",
       "60723         NaN      0      0    37.0         0         0    4.0     0.0   \n",
       "60728         NaN      0      0    27.0         0         0    2.0     NaN   \n",
       "60921         NaN      0      0    37.0         0         0    2.0     NaN   \n",
       "61255         NaN      0      0    31.0         0         0    2.0     NaN   \n",
       "61270         NaN      0      0    31.0         0         0    2.0     NaN   \n",
       "61394         NaN      0      0    34.0         0         0    4.0   230.0   \n",
       "62409         NaN      0      0    12.0         0         0    1.0   250.0   \n",
       "62501         NaN      0      0    12.0         0         0    1.0     NaN   \n",
       "63218         NaN      0      0    30.0         0         0    2.0     NaN   \n",
       "63267         NaN      0      0    25.0         0         0    2.0     NaN   \n",
       "63312         NaN      0      0    37.0         0         0    2.0   320.0   \n",
       "63329         NaN      0      0    35.0         0         0    4.0   320.0   \n",
       "63793         NaN      0      0    28.0         0         0    2.0   140.0   \n",
       "63857         NaN      0      0    33.0         0         0    2.0   140.0   \n",
       "63861         NaN      0      0    28.0         0         0    2.0    90.0   \n",
       "64037         NaN      0      0    29.0         0         0    2.0   290.0   \n",
       "64100         NaN      0      0    28.0         0         0    2.0   140.0   \n",
       "64134         NaN      0      0    33.0         0         0    4.0   320.0   \n",
       "64160         NaN      0      0    35.0         0         0    4.0   340.0   \n",
       "64219         NaN      0      0    29.0         0         0    2.0     NaN   \n",
       "64271         NaN      0      0    36.0         0         0    4.0   110.0   \n",
       "64508         NaN      0      0    32.0         0         0    4.0   320.0   \n",
       "64649         NaN      0      0    18.0         0         0    1.0     NaN   \n",
       "64670         NaN      0      0    19.0         0         0    2.0     NaN   \n",
       "65299         NaN      0      0     6.0         0         0    1.0     NaN   \n",
       "65327         NaN      0      0    24.0         0         0    1.0   320.0   \n",
       "65347         NaN      0      0    14.0         0         0    1.0     NaN   \n",
       "65450         NaN      0      0    23.0         0         0    2.0   290.0   \n",
       "65489         NaN      0      0    13.0         0         0    1.0   270.0   \n",
       "...           ...    ...    ...     ...       ...       ...    ...     ...   \n",
       "91530         NaN      1      0    26.0         0         0    1.0     NaN   \n",
       "91531         NaN      1      0    25.0         0         0    1.0     NaN   \n",
       "91549         NaN      0      0    28.0         0         0    2.0     NaN   \n",
       "91634         NaN      0      0    32.0         0         0    2.0     NaN   \n",
       "91637         NaN      0      0    29.0         0         0    2.0   140.0   \n",
       "91658         NaN      0      0    29.0         0         0    2.0     NaN   \n",
       "91783         NaN      0      0    30.0         0         0    1.0    90.0   \n",
       "91802         NaN      0      0    29.0         0         0    2.0     NaN   \n",
       "91863         NaN      1      0    29.0         1         0    2.0     NaN   \n",
       "91888         NaN      0      0    29.0         0         0    2.0     NaN   \n",
       "91904         NaN      0      0    32.0         0         0    2.0   210.0   \n",
       "91965         NaN      0      0    35.0         0         0    4.0   270.0   \n",
       "91974         NaN      0      0    33.0         0         0    2.0   250.0   \n",
       "94523         NaN      1      0    31.0         0         0    2.0    20.0   \n",
       "95154         NaN      0      0    37.0         0         0    2.0   340.0   \n",
       "95309         NaN      0      0    30.0         0         0    2.0   270.0   \n",
       "95402         NaN      0      0    31.0         0         0    4.0   110.0   \n",
       "95405         NaN      0      0    36.0         0         0    4.0   180.0   \n",
       "95555         NaN      0      0    32.0         0         0    2.0    90.0   \n",
       "95883         NaN      0      0    26.0         0         0    2.0     NaN   \n",
       "95960         NaN      0      0    31.0         0         0    2.0   140.0   \n",
       "96473         NaN      0      0    31.0         0         0    2.0   320.0   \n",
       "96488         NaN      0      0    28.0         0         0    2.0     NaN   \n",
       "96549         NaN      0      0    34.0         0         0    2.0    90.0   \n",
       "96564         NaN      0      0    32.0         0         0    2.0    90.0   \n",
       "96567         NaN      0      0    28.0         0         0    2.0   110.0   \n",
       "96907         NaN      0      0    28.0         0         0    2.0   270.0   \n",
       "98402         NaN      0      0    25.0         0         0    1.0   270.0   \n",
       "98820         NaN      0      0    30.0         0         0    4.0   290.0   \n",
       "99069         NaN      0      0    22.0         0         0    0.5     NaN   \n",
       "\n",
       "      _wdire  _wspdm  year month  \n",
       "60703    NaN     0.0  2004    06  \n",
       "60723  North     3.7  2004    06  \n",
       "60728    NaN     0.0  2004    06  \n",
       "60921    NaN     0.0  2004    07  \n",
       "61255    NaN     0.0  2004    08  \n",
       "61270    NaN     0.0  2004    08  \n",
       "61394     SW     7.4  2004    09  \n",
       "62409    WSW     3.7  2005    01  \n",
       "62501    NaN     0.0  2005    01  \n",
       "63218    NaN     0.0  2005    04  \n",
       "63267    NaN     0.0  2005    05  \n",
       "63312     NW    14.8  2005    05  \n",
       "63329     NW    25.9  2005    05  \n",
       "63793     SE     1.9  2005    07  \n",
       "63857     SE     9.3  2005    07  \n",
       "63861   East     3.7  2005    07  \n",
       "64037    WNW    11.1  2005    08  \n",
       "64100     SE     5.6  2005    08  \n",
       "64134     NW    18.5  2005    08  \n",
       "64160    NNW     3.7  2005    08  \n",
       "64219    NaN     0.0  2005    08  \n",
       "64271    ESE     9.3  2005    09  \n",
       "64508     NW    16.7  2005    10  \n",
       "64649    NaN     0.0  2005    10  \n",
       "64670    NaN     0.0  2005    10  \n",
       "65299    NaN     0.0  2006    01  \n",
       "65327     NW    20.4  2006    01  \n",
       "65347    NaN     0.0  2006    01  \n",
       "65450    WNW    11.1  2006    01  \n",
       "65489   West     5.6  2006    02  \n",
       "...      ...     ...   ...   ...  \n",
       "91530    NaN     0.0  2015    07  \n",
       "91531    NaN     0.0  2015    07  \n",
       "91549    NaN     0.0  2015    07  \n",
       "91634    NaN     0.0  2015    07  \n",
       "91637     SE     5.6  2015    07  \n",
       "91658    NaN     0.0  2015    07  \n",
       "91783   East     5.6  2015    08  \n",
       "91802    NaN     0.0  2015    08  \n",
       "91863    NaN     0.0  2015    08  \n",
       "91888    NaN     0.0  2015    08  \n",
       "91904    SSW    13.0  2015    08  \n",
       "91965   West    11.1  2015    08  \n",
       "91974    WSW    13.0  2015    09  \n",
       "94523    NNE    18.5  2016    04  \n",
       "95154    NNW     3.7  2016    05  \n",
       "95309   West     9.3  2016    06  \n",
       "95402    ESE     9.3  2016    06  \n",
       "95405  South     7.4  2016    06  \n",
       "95555   East    14.8  2016    06  \n",
       "95883    NaN     0.0  2016    07  \n",
       "95960     SE     7.4  2016    07  \n",
       "96473     NW     7.4  2016    08  \n",
       "96488    NaN     0.0  2016    08  \n",
       "96549   East     7.4  2016    08  \n",
       "96564   East     5.6  2016    08  \n",
       "96567    ESE     9.3  2016    08  \n",
       "96907   West     7.4  2016    09  \n",
       "98402   West    22.2  2016    11  \n",
       "98820    WNW    14.8  2016    11  \n",
       "99069    NaN     0.0  2016    12  \n",
       "\n",
       "[232 rows x 18 columns]"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered[df_filtered._pressurem.isnull()]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime_utc</th>\n",
       "      <th>_conds</th>\n",
       "      <th>_dewptm</th>\n",
       "      <th>_fog</th>\n",
       "      <th>_hail</th>\n",
       "      <th>_hum</th>\n",
       "      <th>_pressurem</th>\n",
       "      <th>_rain</th>\n",
       "      <th>_snow</th>\n",
       "      <th>_tempm</th>\n",
       "      <th>_thunder</th>\n",
       "      <th>_tornado</th>\n",
       "      <th>_vism</th>\n",
       "      <th>_wdird</th>\n",
       "      <th>_wdire</th>\n",
       "      <th>_wspdm</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19961101-11:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>West</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19961101-12:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19961101-13:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>-9999.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19961101-14:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19961101-16:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datetime_utc _conds  _dewptm  _fog  _hail  _hum  _pressurem  _rain  \\\n",
       "0  19961101-11:00  Smoke      9.0     0      0  27.0      1010.0      0   \n",
       "1  19961101-12:00  Smoke     10.0     0      0  32.0     -9999.0      0   \n",
       "2  19961101-13:00  Smoke     11.0     0      0  44.0     -9999.0      0   \n",
       "3  19961101-14:00  Smoke     10.0     0      0  41.0      1010.0      0   \n",
       "4  19961101-16:00  Smoke     11.0     0      0  47.0      1011.0      0   \n",
       "\n",
       "   _snow  _tempm  _thunder  _tornado  _vism  _wdird _wdire  _wspdm  year month  \n",
       "0      0    30.0         0         0    5.0   280.0   West     7.4  1996    11  \n",
       "1      0    28.0         0         0    NaN     0.0  North     NaN  1996    11  \n",
       "2      0    24.0         0         0    NaN     0.0  North     NaN  1996    11  \n",
       "3      0    24.0         0         0    2.0     0.0  North     NaN  1996    11  \n",
       "4      0    23.0         0         0    1.2     0.0  North     0.0  1996    11  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py:5890: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self._update_inplace(new_data)\n"
     ]
    }
   ],
   "source": [
    "# if you see pressure column, there are few -9999 values. Which is obviously bad values and it can affect your\n",
    "# calculations very badly. So we will consider this also missing values. Lets convert them first to the nan\n",
    "df_filtered._pressurem.replace(-9999.0, np.nan, inplace=True)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>datetime_utc</th>\n",
       "      <th>_conds</th>\n",
       "      <th>_dewptm</th>\n",
       "      <th>_fog</th>\n",
       "      <th>_hail</th>\n",
       "      <th>_hum</th>\n",
       "      <th>_pressurem</th>\n",
       "      <th>_rain</th>\n",
       "      <th>_snow</th>\n",
       "      <th>_tempm</th>\n",
       "      <th>_thunder</th>\n",
       "      <th>_tornado</th>\n",
       "      <th>_vism</th>\n",
       "      <th>_wdird</th>\n",
       "      <th>_wdire</th>\n",
       "      <th>_wspdm</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>19961101-11:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>280.0</td>\n",
       "      <td>West</td>\n",
       "      <td>7.4</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>19961101-12:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>19961101-13:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>19961101-14:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1010.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>19961101-16:00</td>\n",
       "      <td>Smoke</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1011.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.0</td>\n",
       "      <td>North</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     datetime_utc _conds  _dewptm  _fog  _hail  _hum  _pressurem  _rain  \\\n",
       "0  19961101-11:00  Smoke      9.0     0      0  27.0      1010.0      0   \n",
       "1  19961101-12:00  Smoke     10.0     0      0  32.0         NaN      0   \n",
       "2  19961101-13:00  Smoke     11.0     0      0  44.0         NaN      0   \n",
       "3  19961101-14:00  Smoke     10.0     0      0  41.0      1010.0      0   \n",
       "4  19961101-16:00  Smoke     11.0     0      0  47.0      1011.0      0   \n",
       "\n",
       "   _snow  _tempm  _thunder  _tornado  _vism  _wdird _wdire  _wspdm  year month  \n",
       "0      0    30.0         0         0    5.0   280.0   West     7.4  1996    11  \n",
       "1      0    28.0         0         0    NaN     0.0  North     NaN  1996    11  \n",
       "2      0    24.0         0         0    NaN     0.0  North     NaN  1996    11  \n",
       "3      0    24.0         0         0    2.0     0.0  North     NaN  1996    11  \n",
       "4      0    23.0         0         0    1.2     0.0  North     0.0  1996    11  "
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.head() # so now -9999.0 is Nan.Lets again get the number of missing values in _pressurem"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "983"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered._pressurem.isnull().sum() # So u can see previsously it was 232 and now its 983. \n",
    "# We need to check the data for this kin of errors.\n",
    "# So we will use the same idea as before We will replace missing values with the mean values of _hum column\n",
    "# for that partcular year."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "for index,row in df_filtered[df_filtered._pressurem.isnull()].iterrows():\n",
    "    mean_val = df_filtered[df_filtered[\"year\"] == row[\"year\"]]._pressurem.mean()\n",
    "    df_filtered.at[index, \"_pressurem\"] = mean_val\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime_utc        0\n",
       "_conds             72\n",
       "_dewptm             0\n",
       "_fog                0\n",
       "_hail               0\n",
       "_hum                0\n",
       "_pressurem          0\n",
       "_rain               0\n",
       "_snow               0\n",
       "_tempm            673\n",
       "_thunder            0\n",
       "_tornado            0\n",
       "_vism            4428\n",
       "_wdird          14755\n",
       "_wdire          14755\n",
       "_wspdm           2358\n",
       "year                0\n",
       "month               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.isnull().sum() # pressurem is also resolved. Lets apply same for other columns. I will make\n",
    "# it quickly. Process will be the same as above."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index,row in df_filtered[df_filtered._tempm.isnull()].iterrows():\n",
    "    mean_val = df_filtered[df_filtered[\"year\"] == row[\"year\"]]._tempm.mean()\n",
    "    df_filtered.at[index, \"_tempm\"] = mean_val\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index,row in df_filtered[df_filtered._vism.isnull()].iterrows():\n",
    "    mean_val = df_filtered[df_filtered[\"year\"] == row[\"year\"]]._vism.mean()\n",
    "    df_filtered.at[index, \"_vism\"] = mean_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index,row in df_filtered[df_filtered._wdird.isnull()].iterrows():\n",
    "    mean_val = df_filtered[df_filtered[\"year\"] == row[\"year\"]]._wdird.mean()\n",
    "    df_filtered.at[index, \"_wdird\"] = mean_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [],
   "source": [
    "for index,row in df_filtered[df_filtered._wspdm.isnull()].iterrows():\n",
    "    mean_val = df_filtered[df_filtered[\"year\"] == row[\"year\"]]._wspdm.mean()\n",
    "    df_filtered.at[index, \"_wspdm\"] = mean_val"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime_utc        0\n",
       "_conds             72\n",
       "_dewptm             0\n",
       "_fog                0\n",
       "_hail               0\n",
       "_hum                0\n",
       "_pressurem          0\n",
       "_rain               0\n",
       "_snow               0\n",
       "_tempm              0\n",
       "_thunder            0\n",
       "_tornado            0\n",
       "_vism               0\n",
       "_wdird              0\n",
       "_wdire          14755\n",
       "_wspdm              0\n",
       "year                0\n",
       "month               0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "# As we can see _wdire is a categorical feature so we can not apply mean here. We have to get the most frequent\n",
    "# value of _wdire for a year and then replace missing value with the most frequent value.\n",
    "for index,row in df_filtered[df_filtered._wdire.isnull()].iterrows():\n",
    "    most_frequent = df_filtered[df_filtered[\"year\"] == row[\"year\"]]._wdire.value_counts().idxmax()\n",
    "    df_filtered.at[index, \"_wdire\"] = most_frequent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime_utc     0\n",
       "_conds          72\n",
       "_dewptm          0\n",
       "_fog             0\n",
       "_hail            0\n",
       "_hum             0\n",
       "_pressurem       0\n",
       "_rain            0\n",
       "_snow            0\n",
       "_tempm           0\n",
       "_thunder         0\n",
       "_tornado         0\n",
       "_vism            0\n",
       "_wdird           0\n",
       "_wdire           0\n",
       "_wspdm           0\n",
       "year             0\n",
       "month            0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "# now we can see,  _conds which is again acategorical feature.\n",
    "# so we will apply again the same strategy as above(_wdire)\n",
    "for index,row in df_filtered[df_filtered._conds.isnull()].iterrows():\n",
    "    most_frequent = df_filtered[df_filtered[\"year\"] == row[\"year\"]]._conds.value_counts().idxmax()\n",
    "    df_filtered.at[index, \"_conds\"] = most_frequent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime_utc    0\n",
       "_conds          0\n",
       "_dewptm         0\n",
       "_fog            0\n",
       "_hail           0\n",
       "_hum            0\n",
       "_pressurem      0\n",
       "_rain           0\n",
       "_snow           0\n",
       "_tempm          0\n",
       "_thunder        0\n",
       "_tornado        0\n",
       "_vism           0\n",
       "_wdird          0\n",
       "_wdire          0\n",
       "_wspdm          0\n",
       "year            0\n",
       "month           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.isnull().sum()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [],
   "source": [
    "## So finally ..... WE HAVE REPLACED ALL THE MISSING VALUES. Phew... Thats a whole big task.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.6/site-packages/pandas/core/generic.py:4405: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n",
      "  self[name] = value\n"
     ]
    }
   ],
   "source": [
    "df_filtered.year = df_filtered.year.astype(\"object\")\n",
    "df_filtered.month = df_filtered.month.astype(\"object\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "datetime_utc     object\n",
       "_conds           object\n",
       "_dewptm         float64\n",
       "_fog              int64\n",
       "_hail             int64\n",
       "_hum            float64\n",
       "_pressurem      float64\n",
       "_rain             int64\n",
       "_snow             int64\n",
       "_tempm          float64\n",
       "_thunder          int64\n",
       "_tornado          int64\n",
       "_vism           float64\n",
       "_wdird          float64\n",
       "_wdire           object\n",
       "_wspdm          float64\n",
       "year             object\n",
       "month            object\n",
       "dtype: object"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>month</th>\n",
       "      <th>01</th>\n",
       "      <th>02</th>\n",
       "      <th>03</th>\n",
       "      <th>04</th>\n",
       "      <th>05</th>\n",
       "      <th>06</th>\n",
       "      <th>07</th>\n",
       "      <th>08</th>\n",
       "      <th>09</th>\n",
       "      <th>10</th>\n",
       "      <th>11</th>\n",
       "      <th>12</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>year</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1996</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>19.415046</td>\n",
       "      <td>13.865403</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1997</th>\n",
       "      <td>13.048083</td>\n",
       "      <td>15.825284</td>\n",
       "      <td>21.173384</td>\n",
       "      <td>26.300805</td>\n",
       "      <td>29.900929</td>\n",
       "      <td>31.735553</td>\n",
       "      <td>31.076391</td>\n",
       "      <td>29.450078</td>\n",
       "      <td>29.695443</td>\n",
       "      <td>24.130558</td>\n",
       "      <td>19.957239</td>\n",
       "      <td>13.698195</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1998</th>\n",
       "      <td>13.144290</td>\n",
       "      <td>16.510567</td>\n",
       "      <td>19.882767</td>\n",
       "      <td>28.698630</td>\n",
       "      <td>33.991784</td>\n",
       "      <td>32.450593</td>\n",
       "      <td>29.923938</td>\n",
       "      <td>29.179850</td>\n",
       "      <td>29.119385</td>\n",
       "      <td>25.276475</td>\n",
       "      <td>20.306736</td>\n",
       "      <td>14.682166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999</th>\n",
       "      <td>12.594269</td>\n",
       "      <td>16.743446</td>\n",
       "      <td>22.465106</td>\n",
       "      <td>31.419503</td>\n",
       "      <td>34.269136</td>\n",
       "      <td>34.281680</td>\n",
       "      <td>31.998110</td>\n",
       "      <td>32.903209</td>\n",
       "      <td>30.738490</td>\n",
       "      <td>27.700867</td>\n",
       "      <td>22.663247</td>\n",
       "      <td>15.271864</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2000</th>\n",
       "      <td>13.803659</td>\n",
       "      <td>15.104891</td>\n",
       "      <td>NaN</td>\n",
       "      <td>35.000000</td>\n",
       "      <td>26.000000</td>\n",
       "      <td>32.442863</td>\n",
       "      <td>30.245092</td>\n",
       "      <td>31.881618</td>\n",
       "      <td>30.125521</td>\n",
       "      <td>28.188217</td>\n",
       "      <td>22.112297</td>\n",
       "      <td>16.154194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2001</th>\n",
       "      <td>12.658686</td>\n",
       "      <td>18.287586</td>\n",
       "      <td>23.564530</td>\n",
       "      <td>29.666763</td>\n",
       "      <td>32.988538</td>\n",
       "      <td>30.981903</td>\n",
       "      <td>30.912933</td>\n",
       "      <td>31.112729</td>\n",
       "      <td>31.174088</td>\n",
       "      <td>27.831502</td>\n",
       "      <td>21.620347</td>\n",
       "      <td>15.959586</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2002</th>\n",
       "      <td>14.277785</td>\n",
       "      <td>17.470228</td>\n",
       "      <td>24.534641</td>\n",
       "      <td>32.058416</td>\n",
       "      <td>35.373743</td>\n",
       "      <td>34.219867</td>\n",
       "      <td>35.355093</td>\n",
       "      <td>30.697186</td>\n",
       "      <td>28.428300</td>\n",
       "      <td>27.166094</td>\n",
       "      <td>21.377793</td>\n",
       "      <td>16.904873</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2003</th>\n",
       "      <td>11.931473</td>\n",
       "      <td>17.958429</td>\n",
       "      <td>23.538990</td>\n",
       "      <td>31.716751</td>\n",
       "      <td>34.950594</td>\n",
       "      <td>35.455894</td>\n",
       "      <td>30.293639</td>\n",
       "      <td>29.966644</td>\n",
       "      <td>28.951836</td>\n",
       "      <td>26.453855</td>\n",
       "      <td>20.371110</td>\n",
       "      <td>15.211904</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2004</th>\n",
       "      <td>13.232766</td>\n",
       "      <td>18.734445</td>\n",
       "      <td>26.633420</td>\n",
       "      <td>31.858427</td>\n",
       "      <td>34.314077</td>\n",
       "      <td>33.072721</td>\n",
       "      <td>32.657179</td>\n",
       "      <td>29.602128</td>\n",
       "      <td>29.948052</td>\n",
       "      <td>24.262331</td>\n",
       "      <td>19.474359</td>\n",
       "      <td>15.534730</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2005</th>\n",
       "      <td>13.468473</td>\n",
       "      <td>16.835250</td>\n",
       "      <td>23.676773</td>\n",
       "      <td>28.602970</td>\n",
       "      <td>32.276423</td>\n",
       "      <td>33.983122</td>\n",
       "      <td>30.176230</td>\n",
       "      <td>31.415323</td>\n",
       "      <td>28.786611</td>\n",
       "      <td>25.068215</td>\n",
       "      <td>19.516949</td>\n",
       "      <td>13.674414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2006</th>\n",
       "      <td>13.987500</td>\n",
       "      <td>21.123698</td>\n",
       "      <td>22.526431</td>\n",
       "      <td>29.582979</td>\n",
       "      <td>33.137097</td>\n",
       "      <td>32.070598</td>\n",
       "      <td>30.977527</td>\n",
       "      <td>30.555575</td>\n",
       "      <td>29.118214</td>\n",
       "      <td>26.059218</td>\n",
       "      <td>20.211207</td>\n",
       "      <td>15.464956</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2007</th>\n",
       "      <td>13.394309</td>\n",
       "      <td>17.619822</td>\n",
       "      <td>21.736067</td>\n",
       "      <td>30.457983</td>\n",
       "      <td>31.761019</td>\n",
       "      <td>32.875951</td>\n",
       "      <td>31.134146</td>\n",
       "      <td>30.601621</td>\n",
       "      <td>29.179916</td>\n",
       "      <td>24.446222</td>\n",
       "      <td>19.454784</td>\n",
       "      <td>14.552893</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2008</th>\n",
       "      <td>12.937759</td>\n",
       "      <td>15.565611</td>\n",
       "      <td>25.061475</td>\n",
       "      <td>28.560000</td>\n",
       "      <td>30.258333</td>\n",
       "      <td>30.246862</td>\n",
       "      <td>30.721992</td>\n",
       "      <td>29.591837</td>\n",
       "      <td>28.508403</td>\n",
       "      <td>26.453441</td>\n",
       "      <td>19.598291</td>\n",
       "      <td>16.202692</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2009</th>\n",
       "      <td>14.837522</td>\n",
       "      <td>18.292793</td>\n",
       "      <td>23.450820</td>\n",
       "      <td>29.570213</td>\n",
       "      <td>32.356552</td>\n",
       "      <td>34.567568</td>\n",
       "      <td>32.032922</td>\n",
       "      <td>30.914286</td>\n",
       "      <td>28.885200</td>\n",
       "      <td>25.073171</td>\n",
       "      <td>19.283898</td>\n",
       "      <td>14.995781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2010</th>\n",
       "      <td>12.861224</td>\n",
       "      <td>18.135008</td>\n",
       "      <td>25.935906</td>\n",
       "      <td>32.769231</td>\n",
       "      <td>34.521186</td>\n",
       "      <td>34.273531</td>\n",
       "      <td>31.038529</td>\n",
       "      <td>29.586543</td>\n",
       "      <td>27.670493</td>\n",
       "      <td>26.013333</td>\n",
       "      <td>20.392778</td>\n",
       "      <td>14.008584</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2011</th>\n",
       "      <td>12.320175</td>\n",
       "      <td>17.025000</td>\n",
       "      <td>22.810185</td>\n",
       "      <td>27.770714</td>\n",
       "      <td>32.952586</td>\n",
       "      <td>32.012592</td>\n",
       "      <td>30.631579</td>\n",
       "      <td>29.899563</td>\n",
       "      <td>29.079812</td>\n",
       "      <td>25.607759</td>\n",
       "      <td>20.815166</td>\n",
       "      <td>14.486364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2012</th>\n",
       "      <td>12.776256</td>\n",
       "      <td>16.130000</td>\n",
       "      <td>22.683962</td>\n",
       "      <td>28.222222</td>\n",
       "      <td>33.425339</td>\n",
       "      <td>36.033252</td>\n",
       "      <td>31.873355</td>\n",
       "      <td>29.180123</td>\n",
       "      <td>29.207921</td>\n",
       "      <td>24.699588</td>\n",
       "      <td>18.783784</td>\n",
       "      <td>15.157609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2013</th>\n",
       "      <td>12.255078</td>\n",
       "      <td>16.918938</td>\n",
       "      <td>23.030457</td>\n",
       "      <td>29.000000</td>\n",
       "      <td>33.673015</td>\n",
       "      <td>32.347073</td>\n",
       "      <td>30.701923</td>\n",
       "      <td>29.462728</td>\n",
       "      <td>29.839573</td>\n",
       "      <td>26.035176</td>\n",
       "      <td>18.885122</td>\n",
       "      <td>15.288221</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2014</th>\n",
       "      <td>13.550621</td>\n",
       "      <td>15.805140</td>\n",
       "      <td>21.594262</td>\n",
       "      <td>28.067227</td>\n",
       "      <td>31.412955</td>\n",
       "      <td>34.770833</td>\n",
       "      <td>32.233013</td>\n",
       "      <td>31.036970</td>\n",
       "      <td>29.709319</td>\n",
       "      <td>26.484627</td>\n",
       "      <td>20.104167</td>\n",
       "      <td>14.780488</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2015</th>\n",
       "      <td>12.762669</td>\n",
       "      <td>18.794358</td>\n",
       "      <td>21.569120</td>\n",
       "      <td>27.989273</td>\n",
       "      <td>33.344130</td>\n",
       "      <td>32.737500</td>\n",
       "      <td>30.364947</td>\n",
       "      <td>30.235507</td>\n",
       "      <td>30.665254</td>\n",
       "      <td>26.796826</td>\n",
       "      <td>20.754167</td>\n",
       "      <td>14.975709</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016</th>\n",
       "      <td>15.007752</td>\n",
       "      <td>19.563948</td>\n",
       "      <td>25.696391</td>\n",
       "      <td>32.535145</td>\n",
       "      <td>34.691920</td>\n",
       "      <td>34.907087</td>\n",
       "      <td>30.882036</td>\n",
       "      <td>30.993334</td>\n",
       "      <td>31.500809</td>\n",
       "      <td>28.958941</td>\n",
       "      <td>23.045124</td>\n",
       "      <td>17.777697</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2017</th>\n",
       "      <td>15.783680</td>\n",
       "      <td>18.414062</td>\n",
       "      <td>23.553459</td>\n",
       "      <td>30.775120</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "month         01         02         03         04         05         06  \\\n",
       "year                                                                      \n",
       "1996         NaN        NaN        NaN        NaN        NaN        NaN   \n",
       "1997   13.048083  15.825284  21.173384  26.300805  29.900929  31.735553   \n",
       "1998   13.144290  16.510567  19.882767  28.698630  33.991784  32.450593   \n",
       "1999   12.594269  16.743446  22.465106  31.419503  34.269136  34.281680   \n",
       "2000   13.803659  15.104891        NaN  35.000000  26.000000  32.442863   \n",
       "2001   12.658686  18.287586  23.564530  29.666763  32.988538  30.981903   \n",
       "2002   14.277785  17.470228  24.534641  32.058416  35.373743  34.219867   \n",
       "2003   11.931473  17.958429  23.538990  31.716751  34.950594  35.455894   \n",
       "2004   13.232766  18.734445  26.633420  31.858427  34.314077  33.072721   \n",
       "2005   13.468473  16.835250  23.676773  28.602970  32.276423  33.983122   \n",
       "2006   13.987500  21.123698  22.526431  29.582979  33.137097  32.070598   \n",
       "2007   13.394309  17.619822  21.736067  30.457983  31.761019  32.875951   \n",
       "2008   12.937759  15.565611  25.061475  28.560000  30.258333  30.246862   \n",
       "2009   14.837522  18.292793  23.450820  29.570213  32.356552  34.567568   \n",
       "2010   12.861224  18.135008  25.935906  32.769231  34.521186  34.273531   \n",
       "2011   12.320175  17.025000  22.810185  27.770714  32.952586  32.012592   \n",
       "2012   12.776256  16.130000  22.683962  28.222222  33.425339  36.033252   \n",
       "2013   12.255078  16.918938  23.030457  29.000000  33.673015  32.347073   \n",
       "2014   13.550621  15.805140  21.594262  28.067227  31.412955  34.770833   \n",
       "2015   12.762669  18.794358  21.569120  27.989273  33.344130  32.737500   \n",
       "2016   15.007752  19.563948  25.696391  32.535145  34.691920  34.907087   \n",
       "2017   15.783680  18.414062  23.553459  30.775120        NaN        NaN   \n",
       "\n",
       "month         07         08         09         10         11         12  \n",
       "year                                                                     \n",
       "1996         NaN        NaN        NaN        NaN  19.415046  13.865403  \n",
       "1997   31.076391  29.450078  29.695443  24.130558  19.957239  13.698195  \n",
       "1998   29.923938  29.179850  29.119385  25.276475  20.306736  14.682166  \n",
       "1999   31.998110  32.903209  30.738490  27.700867  22.663247  15.271864  \n",
       "2000   30.245092  31.881618  30.125521  28.188217  22.112297  16.154194  \n",
       "2001   30.912933  31.112729  31.174088  27.831502  21.620347  15.959586  \n",
       "2002   35.355093  30.697186  28.428300  27.166094  21.377793  16.904873  \n",
       "2003   30.293639  29.966644  28.951836  26.453855  20.371110  15.211904  \n",
       "2004   32.657179  29.602128  29.948052  24.262331  19.474359  15.534730  \n",
       "2005   30.176230  31.415323  28.786611  25.068215  19.516949  13.674414  \n",
       "2006   30.977527  30.555575  29.118214  26.059218  20.211207  15.464956  \n",
       "2007   31.134146  30.601621  29.179916  24.446222  19.454784  14.552893  \n",
       "2008   30.721992  29.591837  28.508403  26.453441  19.598291  16.202692  \n",
       "2009   32.032922  30.914286  28.885200  25.073171  19.283898  14.995781  \n",
       "2010   31.038529  29.586543  27.670493  26.013333  20.392778  14.008584  \n",
       "2011   30.631579  29.899563  29.079812  25.607759  20.815166  14.486364  \n",
       "2012   31.873355  29.180123  29.207921  24.699588  18.783784  15.157609  \n",
       "2013   30.701923  29.462728  29.839573  26.035176  18.885122  15.288221  \n",
       "2014   32.233013  31.036970  29.709319  26.484627  20.104167  14.780488  \n",
       "2015   30.364947  30.235507  30.665254  26.796826  20.754167  14.975709  \n",
       "2016   30.882036  30.993334  31.500809  28.958941  23.045124  17.777697  \n",
       "2017         NaN        NaN        NaN        NaN        NaN        NaN  "
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(df_filtered.year, [df_filtered.month], values=df_filtered._tempm, aggfunc=\"mean\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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UUkoppU6IiISJyEIRWS4iK0XkIXv5WyKySUSW2beOx9qXtswopZRSSikV4FzVq2WmCOhjjMkVkWBggYh8Za+7wxjz0fHuSCszSimllFJKqSpjrOmUc+2HwfatQrOtBWxl5qyxfzgdwWead0pxOoJPnds7xOkIPtW95AenI/hM0LKfnI7gU7kbj2dWeP8VHBXhdASfCquf6HQEnzKlgXt189TTRjsdwafCQho7HcGnSgP4/6Y/EVfVtcyIyNXA1WUWTTLGTDpkGzewGGgKvGSM+U1ErgMeE5H7gdnA3caYoqO9VsBWZpRSSimllFJVz664TDrGNqVARxGJBWaISFtgArAbCLGffxfw8NH2o5UZpZRSSimlApy4q+e8X8aYTBH5ARhojHnKXlwkIpOB24/1/OpZKqWUUkoppVRAEpF4u0UGEQkHzgTWiEg9e5kAQ4E/j7UvbZlRSimllFIqwFWz2czqAW/b42ZcwHRjzCwRmSMi8YAAy4Brj7UjrcwopZRSSimlqowx5g+g02GW9ynvvrQyo5RSSimlVICrytnMqpKOmVFKKaWUUkr5Ja3MKKWUUkoppfySdjMrp1svr0/3jjFkZnu4buI6AC45vw6ndIrBayAr28PTr28jI9PjcNKKuejMMNqkuMnNNzz5fv6B5ad1COa0DiF4vYZVm0r57KejXr+o2tq3dzdvv3gv2Zl7EXHR88wL6H3OxSz55Vu+mP4yqTs2cscTH9CoSRuno5bb7vR9PPjyu6Rn5iAinNfnVC46qxcA076Zx/Rvf8TtctGzUxtuHjXE2bAVUOQp5YoP5lBcWkqp13BmiwZc17MtOzJzufvzX8gqKKZVnZo8Ougkgt1up+OWX1AwNa+aAO4gxOWmaOXv5M2eSczwawhKSgZvKSXbN5Iz823w+uEF6NxBRF883i6fi+K1Syn88QtCu5xBaLfeuGsmkPnsHZiCPKeTVsjurDwmfvYT6bkFiAgXdG7G6O6tWJuawWNf/kZ+sYfE2EgeH9qTqFD/ujBwkaeUK977juJS78HP3untrc/ezJ/IKiyiVd1aPHruKX752UtLS+M/Tz/Nvn37EHFx9sCBDB168Dvyo48/5vU33mTalA+oUaOGg0krJjN9Fx9PupvcrL2ICF17j+DU/mPIz81k2v/Gk7l3B7G1kxh5wzOER/pf+W4cnUDXtpFk5ZQy7vG/X9h4SN9Yxp4Xz5i7NpCT53UoYfVRzSYAqDQ+q8yIyJvAIGCPMaatvawD8AoQBWwGRhtjskUkBHgV6Ap4gXHGmB9EJBr4scxu6wPvGWNu8VXuY/luwT4+m53O7Vc1OLDs4y/TePeTVAAGnxnHqCF1ePHtHU5FPCELV5Xw4/JiLu4fdmBZ0/pu2jUO4l/v51FaClHh/vthcLndnD/mNho2bk1hQR7/umskLdufQmKDplx9+3+ZMukRpyNWWJDLxS2jz6NlSgPyCgoZc+9/OKldCzKycpi3aAVTnryLkOBgMrJynI5aISFuF5NG9iIiJJiSUi+XfzCbHo3r8t7v6xjdtQUDWzXk0W8WMeOPTYzo1NTpuOXnKSHzjX9hiovA5abm1fdQtG4Fhct/ofjDVwGIGXEt4V1Pp2DhXIfDVkCph5wPnoOSInC5iL7kNko2rMSzfQMlf60gatStTic8IW6XcNuZXWhVL468ohIueuMLTk6px0OzfmX8mV3o2qgOM5f9xdu/rOKGXh2djlsuIW4Xk0b1PfjZe/c7ejRJ5L2FaxjdvQUDWyfz6NcLmbF8IyM6N3M6brm53G6uuvJKmjVtSn5+PjfdPI5OnTvRqGFD0tLSWLJ0GQnx8U7HrDC3281ZF91JYnIbigry+N8DF9C0zaksWTCDxq1P4YxBVzFv1mvMn/UaAy485iU9qp05v2bz5bwsxo2p87flcbFBdGgZwZ6MEoeSqariy25mbwEDD1n2OnC3MaYdMAO4w15+FYC9vB/wtIi4jDE5xpiO+2/AFuATH2Y+pj/X5ZGT9/dWl/zCg7X9sFAXmKpOVXk27Cwlv/DvBejZLpjvFxVTap8Mzi3w3wLWqBlPw8atAQgLj6ROUgqZGXuoW78xdZJSHE53YmrXrEHLFKuSHRkeRnJSHdL2ZfHx9wu4dHA/QoKDAahVI9rJmBUmIkSEWGXweL14Sr0Iwu9bUzmzRX0Azm2bzA/r/fNEAmBVZADcbutmDMXr/jiwvmT7Rlw1ajmUrhKU2OVzua0bUJq6HW9WhoOhKkd8dASt6sUBEBkaTOPaNdiTk8+W9Gy6NEwA4OSUesxes/Vou6mW/vHZ83oR4PctqZzZsiEA57ZN4Yd12xxMWXFxtWrRrKl1AiQiIoIGDRuQvjcdgFcnvcaVl18G4r8n8aJjE0hMtnobhIZHEp/YhOx9qaxZMofOPa0WqM49h7B6yWwnY1bYqg2F5OT/s7X68gtq887MvX59TFbZxC1VdqtKPmuZMcbMF5HkQxa3AObb978DvgHuA1oDs+3n7RGRTKxWmoX7nygizYAE/t5SU21cekEd+p5ak7wCL3f/a4PTcSpVfE0XTZLcnHNqKB6P4dMFRWxN9f/m2vQ9O9i+aQ3Jzdo5HaXS7UxLZ+3mHbRp0ojnPviUZWs38PL0WYQEBzFu9FDaNGnkdMQKKfV6GfXOd2zbl8uFnZpSPzaK6NAQglzWeZk60RHsyc0/xl6qMRFq3vAQ7loJFPw2G8/2jQfXudyEdTqV3FkfOJfvRIkQfdnduGvGU7R4PqU7NzudyCd2ZOayZncG7ZJq0yQhlh/Wbad3iwZ8t3oLu7P9sxtdqdfLqMlfW5+9Ls2oXzOK6NDgg5+9mAj25BQ4nPLE7U5NZcOGjbRo2YJffv2VuLg4Gjdu7HSsSrMvbQe7tqymfpMO5GanEx1rVbSjYxPIzfb/kwr7dWsXSUamh807ip2OoqpAVU8A8Ccw2L4/HNjfV2s5MEREgkQkBehSZt1+FwHTjDFHrGOLyNUiskhEFm1b91ElRz+6tz9OZcxta5j7yz7O7Vu7Sl/b19wC4aHCM9Py+XRBEWPPCnc60gkrLMjntafGM+yyOwmPiHI6TqXKLyzirmfeYPwl5xMVEU5pqZecvHwmPzyecaOGcs/zkznKx6hac7tcTBs7gG+uO5c/d2WwKT37H9sI/nsGFWPY9+L9pP97PMH1G+NOSDqwKnrwGEo2raNkyzoHA54gY8h58wmyXrwXd2Iyrtr1nE5U6fKLS7j9o3nc0b8bUaEhPDToFKYtWstFr39BXnEJwW7/nHfH7XIx7Yqz+ebGofy5M/3wnz0//ugBFBQU8Ohjj3HN1VfhdrmYOnUaYy652OlYlaaoMI8pL9zM2aPvJiw8sH73ygoJFoYNqMWULwKnclZZxOWqsltVqupv1cuBG0RkMRAN7K8yvwlsBxYBzwI/A4eOoB8JTDnazo0xk4wxXY0xXRs0H1apwY/XD79m0qOr/w2gO5rMXMMff1l/jq2pXoyBSD8eN1PqKeH1p8fT7bRz6HjSmU7HqVQeTyl3PfMGA3t0pU/3DgAk1KpB724dEBHaNG2EiJCZk+tw0hMTHRZC14bxrNiZTk5RMR6v1VKYmpNPfJT/V7ZNYT7Fm9YQ0txqNYzoMwRXZDS5Xx31K9BvmKICPFvXEdzY/ybaOJqSUi+3fTSPs9um0NfufpVSuwavjD6TKVeew1ltUqhf0z+7ee5nffbqsGLHXnKKSg5+9rL9+7Pn8Xh45LHH6d2rNz179GDXrt3sTk3luhtuZMzYy9i7dy833jyOjAz/PEAu9ZQw5YVxdDj1XNp07Q9AVEwcOZl7AMjJ3ENUjB93YS2jbnwwdeKCeGZCQ159KJm42CCevqshsdH+NzmFOj5VWpkxxqwxxvQ3xnTBqphssJd7jDG32mNjhgCxwPr9z7MnDggyxiyuyrzHK7HOwZlpTu4Uw/ZdhQ6mqXwrNnpo1sD6EoiPFdxuyPPTcTPGGN57+QHqJqXQ99wxTsepVMYYHpn0AclJdRh9zsEL6Pbq2p7fV1pn87fs2kOJp5TYaP87K5eRX0hOoXX+o7DEw29bUkmJi6FrwwS+X7sdgM//3EyvZolOxqwwiYhGwiKsB0HBhDRpTWnaLsK6nk5o07ZkTXsZ/LRFDUDCo5BQ+2A3KJig5JZ4M3Y7G6oSGWN4aNYvpNSuwSUntz6wPCPP6nrlNYbXFqxgeOfmTkWssH989jbvJiWuBl0bJfC9PQbo8z830atZfSdjVpgxhmeefY6GDRpwwfnnAZCSksy0KR/wzluTeeetydSuXZsXn3+OWrX874DfGMOMNyYSn9iYHgPHHljeslMfliz4FIAlCz6lZedyX3i9Wtq6s5ixEzZxzQObueaBzaRnerjtX1vJzPHDWSArmbikym5VqUqnZhaRBHtMjAuYiDWzGSISAYgxJk9E+gEeY8yqMk+9iGO0ylSVu65tSPuWkcREBfHuf1vy7sxUurWPoX7dUIwx7Ekv4YW3tjsds8LGDAyjaX03UWHCQ5dH8tVvxfy6soRR/cK4e3QEHi+8/63/VtY2rFnKwvmzSGzYjMdvHw7A4FE34ykp5sM3nyA3ex8vP3ED9ZNbcuPEVxxOWz7L127kywW/07RBIqMm/AuAG0YMYnCvk3n41Q+48M4nCA5y8+B1FyN+2B9kb24h93/5G15j8BpDvxYNOb1pIo1rx3D3Z7/wvx9X0KJOLEPb+Wf/dld0DWKGXWU1z4tQuGIhxWuXE//wG3gz06l57X0AFK1cRP7czxxOW36uqBpEDBoDLhciQvHqxZT89SehXXsRdlI/JCqGmCvupWTDSvK/et/puOW2bFsas1ZspFlCLCNemwXATb07sTUjm2mL1gLQt2VDhnRo4mTMCtmbW8D9s37F67U/e60acnqzJBrXrsHdny7gf/P+oEXdmgz1w7IBrFy1itlz5pCcnMz1N94IwNhLL6V7t24OJ6scW9YvYdnPn1GnfnNevM+qrPUbdgunD7qSqS+NZ8n8j6gRl8jIG55xOGnFjB9blzbNwomJcvPaI8lM/TKD2b/8sxukClziq77zIjIF6AXUBlKBB7CmZL7B3uQTYIIxxtgTBXyDNS3zDuAKY8yWMvvaCJxtjFlzvK9/1tg//PcU5jE07+Tfs24dy7m9/esaDOXVveQHpyP4TNCyn5yO4FO5G/1vJqryCI6KcDqCT4XV989Wu+NlSgP3zHPqaaOdjuBTi9L88yTM8frgvcCaGOlQM15s5hdnCJf1P63Kjo07fvtjlb0nvpzN7KIjrHruMNtuxprp7Ej7CuxPuVJKKaWUUqrcqrSbmVJKKaWUUqrqVfVYlqrin3NEKqWUUkoppf7f05YZpZRSSimlAlxVX/+lqgRmqZRSSimllFIBT1tmlFJKKaWUCnA6ZkYppZRSSimlqpGAbZkJ1NonQHhEsNMRfCojL7DLF7LruC+X5HfWfTzf6Qg+VatJgtMRfCoy2T+v4H68JCiwv1skJHCv0RVVlOF0BJ8KcgX29eNiakU7HUEFsICtzCillFJKKaUsLndgnujXbmZKKaWUUkopv6QtM0oppZRSSgW4QB2CoS0zSimllFJKKb+kLTNKKaWUUkoFOL1oplJKKaWUUkpVI9oyo5RSSimlVIDTMTNKKaWUUkopVY1oy0w53XJZEt07xJCZ7eH6+9cDcMl5dTi5YzReA1nZHv775nYyMj0OJ62YYacH0bKhm9wCw7MfFwNwUZ9g4mOt2nx4iFBQbHj+k2InY1ZYVvouPn7tbnKy9iIidOs1glP6jyE/N5PpL49n394d1KydxIXXP0N4ZA2n45bL7swc7v1wNuk5+YgIw7q3ZnSPDqzdtZdHZ8wjv7iExJrRPHFhP6LC/O/iehIcQpN/P4cEByNuN1kL5pH6/lsH1ideezM1+53FygvOcizjCQkKJuGWhyAoCHG5KVj2K9lffkitMTcR3LAJlHoo3rKBfVMngbfU6bTlVuQp5cqpcyku9VLqNfRtXp/rerRhR2YeE2b9SlZhMS3rxPLo2ScR7Pa/82y7s3K5d8Z80nMLrM9flxaMPrkNa3en8+isn8kv9pAYG8UT55/hd5+/3Vm53PvxfNJz7e+Wri0YfUpbq2yf/XSwbMN6+V2sZSTRAAAgAElEQVTZAFL3pvPoc6+SsS8LcQmD+/VmxLkDeGPqJ3z+3Q/ExlgXfLzm4uGc0qWjw2nLLzN9F9NfnXDgd6977xH0HHAJ+bmZfPDibQd+90bd9F8i/Ox3D+CKoTF0bB5Kdp6Xe19KB2Bo7yh6dQknO88LwEff5/DHev88bqlMgdoy47PKjIi8CQwC9hhj2trLOgCvAFHAZmC0MSZbREKAV4GugBcYZ4z5wX7ORcA9gAF2AhcbY/b6KvexfP/TPj6fnc5tVzY4sOyjr9J4d0YqAIPPjGPUuQm8+O5OpyKekMXrSvl5ZSkjeh28UvaUOSUH7p9zUhCFxcaJaJXC5XYzcOSdJCa3oaggj5cfvIAmbU5l6YIZNG51CqcPuor5s15j/hevMWDE7U7HLRe3y8XtZ/egVVI8eUXFjHzhQ05u2oCHPp7L+LNPpWvjJGYsWs1b85dyY/+TnI5bbqakmI0TxuMtLAC3m6ZPvUDOooXkr11FeLMWuKOinI54YjwlpD3/EKa4CFxuEm59mMJVy8hftIDCd14AoNbYcUSe2oe8Bd85HLb8QtwuXh3Ri4iQIEpKvVwxZS49Uury/qJ1jO7ajAEtG/LYd4uZuWITwzs2cTpuubldLm7v351WibXJKyph5KufcnLjRB767CfG9+9G1+R6zFiyjrd+XsGNfbo4Hbdc3C4Xtw/cX7ZiRr7yKSc3SeKhmQsYP6A7XVPssv20ghv7+lfZANwuNzeOHUWLJsnkFxRw+W33061jWwBGnDuAUUPPcTjhiXG5gzhn1J0kJbemqCCPF+4fRrO2p7B4/kyatjmZXudexQ+fv8a8z1/nrJG3OR233BYsLeD73/K5+vy/V8S++SWPr37KdyiVqkq+PP31FjDwkGWvA3cbY9oBM4A77OVXAdjL+wFPi4hLRIKA54Dexpj2wB/AjT7MfEx/rssnJ+/vZ0ULCr0H7oeFuPDfQ33YtNtQUHTk9e0au1m2wXvkDaq56NgEEpPbABAaHkl8YhOy96WyeukcOvUcAkCnnkNYvWS2kzErJD4mklZJ8QBEhobQOKEme7Lz2Lw3ky4piQCc0rQ+s1dudDLmCfEWFgAgQUGIOwiDAZeLepdfy643XnE43YkzxdaHT9xucLvBGApXLT2wvnjLX7hj45yKd0JEhIgQ6/yZx+vF4/UiAr9v20Pf5vUBGNQmmbl/7XAyZoXFR0fQKrE2AJGhwTSOj2VPTj6b92bRpVFdAE5pksjsVVucjFkhfy9biFW27Hw2p2fRJbls2TY7mLLiateKpUWTZAAiwsNJrp/I3vQMZ0NVopjYeJKSWwP7f/cak52xh1VL5tD5tKEAdD5tKCsX+9/vHsDaLSXkFfjzkVfVEZdU2a0q+awyY4yZDxz6bdACmG/f/w64wL7fGphtP28PkInVSiP2LVJEBIjBap2pdsacX4e3n2pBr5NjeXdmqtNxfCKlrpBbYEjPDowvjX1pO9i1ZTX1m3QgLyud6NgEwKrw5GX79w/Zjn3ZrNm5l3YN6tC0Ti1+WL0ZgG9XbGB3Zq6z4U6Ey0WzF16n9QczyVm6iIK1q4k79zyyf/sJzz7//psBIELCXf+m3hOvU7RmBcVb/jq4zuUmottpFK5e5ly+E1TqNYx8+1vO/N9nnNSoDvVrRBEVGkyQPV1onahw0nIKHE554nbsy2HNrnTaJcXTNKEmP6zdCsC3KzezO9uPP3+UKVt9u2xr7LL9uYndWXkOpztxu/aksW7TFlo3bwrAJ19+z6W33MPjL7xGdq7/ly8jbQc7t6ymQdP25GanExNrnQCLiY0n189/9w7Vt3skj14fxxVDY4gIC8zuVcpS1R2T/wQG2/eHA/v7ai0HhohIkIikAF2ABsaYEuA6YAVWJaY18MaRdi4iV4vIIhFZtHXtR74qw2G980kql96+lh9+zeTcPv555vRYOjRxs3yD//XVP5yiwjymvngzZ426m7BwP++edIj8ohJue+8b7hjUg6iwEB66oA9Tf1nByBc+JL+oxC/HIxzg9bL+pitZPWY4Ec1bEdm2PbE9e7H3sxlOJ6scxrDnX3ey675rCW7UhKB6B7uzxl54JUV/raZ4wxoHA54Yt0uYeml/vr5mECt3Z7ApI/sf24ifH3PkF5Vw2/Q53DHwJOvzN6QnUxeuZuSrn5JfXEKw2+10xArLLyrhtqmzueOsk62yDT2NqQtXMfLlmXbZ/Pi7BcgvKOTefz3PuMtHExkRznkD+zLt5aeZ/N9HiasZy4uTP3A64gkpKszj/efHce7oCQH3u3eoOQvzuePZNO57OZ3MHC8XDYx2OlK1IC5Xld2qUlV/81wO3CAii4FoYP9orDeB7cAi4FngZ8AjIsFYlZlOQCJWN7MJR9q5MWaSMaarMaZrwxbDfFeKo/jht0x6dPG/AXTH4hJok+xm+Ub/r8yUekqY+uI42p9yLm269gcgskYcOZl7AMjJ3ENkTC0nI1ZYSWkp49//mrM7NuPMtta4g5SEmrx6xWCm3jScgR2aUT/O//9/evNyyV2xjMj2nQipl0TLN96n5eSpuEJDafH6+07HO2GmIJ+iv1YR1soabBx91jDcUTFkzXjH4WSVIzoshC4N4lmxM4PcohI8XqvrampuAbWjwh1OV3ElpV7GT5/D2e2acGbrZABS4mN5dcxApl4zhIFtG1O/pn8eVJWUehk/dTZntz+kbJeexdTrhjKwXRPq1/LPsgF4PB4m/vt5+p9+Kmec0g2AWrE1cLtduFwuBvfvxer1GxxOWXGlnhLee/4WOp46iLbd+gEQFRNHdmYaANmZaUT56e/e4WTneTEGjIF5iwtonBR87Ccpv1WllRljzBpjTH9jTBdgCrDBXu4xxtxqjOlojBkCxALrgY72+g3GGANMB06tyszHIzHh4OwtJ3WMYfvuoww68VNNk1ykZRmy/byV3RjDjDcnEl+vMT0Gjj2wvGXHPixd8CkASxd8SqtOfRxKWHHGGB78eC6N42sy5rSDM+6k51oDIL1ew2tzFzH8pDZORTwh7pgauCKts4kSEkJ0xy4U/LWO1Refz5rLRrLmspF4i4pYe+Voh5NWjCsqGgmPsB4EBxPWoh2e1B1EnNKHsJYdSH/rWeuX2U/tyy8ip9A6f1VYUspvW/aQEhdN1wYJzF63HYBZKzfTq0mikzErzBjDg5/+SOPaNRhzatsDy9NzrW5zXq/htfnLGN61pVMRK8wYw4Mzf6RxfCxjerQ7sPxvZZu3jOHdWjkV8YQYY3jipddpVD+RkUMOzoa4NyPzwP35vy6icaP6TsQ7YcYYPnr9PhISG3PaWWMPLG/duTdLfpwJwJIfZ9K6s//97h1JjaiDh7ddWoWyfY9/zjBb2VxuqbJbVarSqZlFJMEYs0dEXMBErJnNEJEIQIwxeSLSD/AYY1aJSCLQWkTijTFpWJMDrK7KzIe685oGtG8RSUxUEO881ZL3Pk2lW7tokuqGYoxhT3oJL77jnwNYAUb2DqZxoovIMJhwUSjfLfGwaG1pwHQx27p+Cct//ow69Zvz0n3nAdBv2C2cPuhKpr00nsU/fkRsrUQuvOEZh5OW39Itu5m1dB3N6tZixPPTALip/8lsTc9k6i9/AtC3bWOGdvG/gymA4FpxNLhtArhciLjI/HEuOQt/cTpWpXHH1KTmxTfY5RPyl/5C4colJD07hdKMNBLGPwZAwfLfyPn6Y4fTll9aXgEPfPU7pV6DMYZ+LRpwepNEGsfFMGHWr7y04E9aJtRkaLsUp6NWyNKtqcz6YwPNEmoy4mXrAPGmvl3YmpHN1IXWz1bfVo0Y2qmZkzErZOnWVGYt/4tmdWoy4n9Wl86bzuzK1vSsMmVL9suyAfyxeh3f/PATTRo1YOyt9wLWNMzf//gr6zdtQUSom1CbO6693OGkFbNl3RKW/vQZdRs057l7rd+9AcNv4YxBV/HBi7fy+7yPiY2rx+ib/O93D+C6YTVomRJCVISLZ26LZ8bcXFomh9CwXhAY2JtZyuTP/tmlVQUOMT460yciU4BeQG0gFXgAa0rmG+xNPgEmGGOMiCQD32BNy7wDuMIYs8Xez7XAOKAE2AKMNcakH+v1z758hf+ewjyG9qf45w/G8erc1v+uU1Aeg3e94HQEn1n3eoCMXTmCWk0SnI7gUzXbN3c6gk+5o/y3G9RxCdBrSADktOvldASfWpDrf1Nal8fMWWlOR/Cptx+u6xcfvg1jzqmyY+Mm73xRZe+Jz1pmjDEXHWHVc4fZdjPWTGeH288r2C04SimllFJKqfIL1Itm+vfUI0oppZRSSqn/t6p0zIxSSimllFKq6lX1lMlVJTBLpZRSSimllAp42jKjlFJKKaVUgNMxM0oppZRSSilVjWjLjFJKKaWUUgEuUFtmArYyU1xQ5HQEnwkKDuwGtca1spyO4FN53610OoLPeIoC+yrLe1btdDqCTwWFBfY1ngr35TgdwadqtfbPC44ej+jaa5yO4FONGjRxOoJPhUUE9neLclbAVmaUUkoppZRSFp3NTCmllFJKKaWqEW2ZUUoppZRSKsAF6pgZbZlRSimllFJK+SVtmVFKKaWUUirA6ZgZpZRSSimllKpGtGVGKaWUUkqpQCc6ZkYppZRSSimlqg1tmSmn269J5qRONcjM9nDVndbFD8cOT+TUrrF4vZCZXcJ/XtlM+r4Sh5NWzHk93LSo7yKv0PDCp9YFEC88w03tGlZtPixEKCw2vPSZf14cMT0tlZeffYisfemIuOgzYCgDB1/IB5NfYMnCBQQFBVGnXn2uvnkikVHRTsctH3cQNcbeAe4gcLkpXr2YgnmfE9atN2En9cVdK4GM/4zHFOQ6nbRCJDiE5s++iASHIG43mfPnsuvtNw+sr3/jLcQNPJvlg/o7mLLiAr18BAVT67qJSJD1/7NoxUJyv/2EGhddR3D9xhivh5KtG8n++E3wljqdttwkKJi6dz2OBAWDy03+4p/J/GwKcZfeSGhyUxChZPdO9k5+DlNU6HTc8nEHEXnhTdZ3i7jwrF9O0S9fE9yxJ6Gdz8AVG0/O/+7FFOY5nbRCdmdkc+/bn5GenYu4hGE9OjG6T3fWbNvNo1O+otjjwe1ycc/IgbRLTnI6brmlp6Xyv2ceIdP+3es7cDBnDb6Q6e9NYtFvP+ISFzE1Yrn2lonUiot3Om65jTk7knZNgsnJ9/LwG9kADOoZTs8OoeTmewGYOa+APzf653GZOjafVWZE5E1gELDHGNPWXtYBeAWIAjYDo40x2SISArwKdAW8wDhjzA/2cy4E7gXcwBfGmDt9lfl4fDNvLzO/2cNd1x+80vL0Wbt560PryuBDByRw8fn1eO6NrU5FPCFL//Ly62ovw05zH1g2bd7BA4uBXd0UlRgnolUKl9vN6MtvJqVJSwry85g4fixtO3anbcfuXDjmOtzuIKa89SKfffQ2F4290em45VPqIeud/0JJEbjcxFx2JyV//UnJtr8oXvcHMZfe5nTCE2JKill/2zi8hQXgdtPiuZfJWvgb+atXEtG8Be6oKKcjnpBALx+eEva9+jim2Pr/WeuG+whes5yCpT+TNeVlAGqMuoHwk3pR8Mtsh8OWn/GUsPup+6yKittNvbuepODPxWRMewNTWABAzRGXE9PnHLK++tjhtOVU6iHvw5egpBhcLiIvHIdn82pKd2wib+MqIof72XflIdxu4fYL+tKqYT3yCosY+eSbnNwqhWdmzOHac06jZ5um/PjnXzw7Yw5v3HqJ03HLzeV2c/HlN5HStAUF+Xncc+vltOvYnUHnj2bExVcD8PVn0/lk6mSuvMHRQ6wK+WVFEXMXF3LZoMi/LZ/9eyHfLfSzEwc+plMzl99bwMBDlr0O3G2MaQfMAO6wl18FYC/vBzwtIi4RiQP+A/Q1xrQB6ohIXx9mPqYVa3LJyf17q0R+gffA/fAwF/jvsT6bUw0FxUcuQLsUF39s9B5xfXVXs1ZtUpq0BCA8IpLE+snsS99D+04n4XZbdfumLdqSkb7HyZgVV1Jk/etyIy6rQlq6exverHQHQ1Uer31QKEFBSJAbjAGXi6RrbmDHpJcdTnfiAr18ptj+/+l2I64gMFC8ZvmB9SXbNuCuUcuhdCduf4uLuN3gdmMMByoyABISgjF++gNRUmz963KDy/qd86btwGRnOJurEsTXiKZVw3oARIaF0rhuHHsycxARcguscucWFBFfw89a6201a9UmpWkLwPrdS2rQiIz0NCIiDh78FxYVIn46nmL9Ng/5hX76uVKVwmctM8aY+SKSfMjiFsB8+/53wDfAfUBrYLb9vD0ikonVSmOAdcaYNPs53wMX7N+2OrlsRBL9To8jL7+U2x9Z63Qcn0iuI+QWGNJznE5SOdJSd7Jl4zqatGj7t+Xzvv+ck3ue6VCqEyRCjasm4q4VT+HvP+DZscnpRJXL5aLly28QmpRE2qczyF+zivjzh5P18wI8GQFQYQv08okQd8ujuOPqkP/zd5Rs23BwnctNeOeeZH/2rnP5TpS4SLzvaYIS6pEz90uKN60DIO6ym4lo14XindvYN/3NY+ykmhIhcvTtuGJrU7x8AaW7tzidyCd2pGeyZlsq7ZKTuHNYP657cQr//eR7vMbwzu1jnY53wtJSd7F5w3qatmgDwLR3XmH+3K+JiIjkvsdfdDhd5erVJZST24awZXcpH83OJ79IKzw6NXPl+BMYbN8fDjSw7y8HhohIkIikAF3sdX8BLUUkWUSCgKFlnvMPInK1iCwSkUU7/vrEZ4U4nMnTdzDqxj+Y81M6QwYkVOlrV5V2KS7+2OS/rTJlFRbk8+yTE7jkylv+dnZq5vTJuN1B9Oh1aKOinzCGrEmPsO+ZuwhKSsEdn+h0osrl9bLmmsv488LziWzZiqh2Hah5em/2zPCzbjtHEujlM4b0Z+4l7dGbCW7QhKA69Q+sijl/LMWb1lCyyY9PBhkvOx++le13XEFISnOCExsCkD75ebbddhklu7YR2e00h0NWkDHkvfcfcl57EHfdhrji6jqdqNLlFxZz26SPuWNYP6LCQ5n+42LuGNaPbx+/mTuG9ePB92Y5HfGEFBbk88wT9zDmqnEHfvcuHHMtL02eSY9eA/hmVoB8zwDzlhQy8ZUsHn0zm6xcL8P6RjgdSflQVVdmLgduEJHFQDRgt1vzJrAdWAQ8C/wMeIwx+4DrgGnAj1jjbI448twYM8kY09UY0zWp6fk+K8TRzP4pg9O613TktX3JJdCmkYsVAVCZ8Xg8PPvkBHqcMYBup/Y+sHz+7C9Y+vtPXH/bQ37b3L6fKSqgZPNagpu2cTqKT5Tm5ZKzbClRHTsTmpREm3en0ub9D3GFhtH6nalOxzthgV4+U5hP8cbVhLRsD0Bkv/NwRUaT8/n7DierHN6CPArXriC8beeDC42XvN8XENH5FOeCVYaiAjzb/iIouZXTSSpVSWkp41/7mLO7t+XMTlZX5M9/XUHfjlb3rP6dW/Hnlp1ORjwhHo+HZ564hx69+tP91F7/WN/jjH4s/Hlu1QfzkZx8Y3XzBBYsLyK5nvuYz/n/QFxSZbeqVKWVGWPMGmNMf2NMF2AKsMFe7jHG3GqM6WiMGQLEAuvtdZ8bY04yxpwCrN2/vDpJqht64P6pXWLZtrPgKFv7pyaJQlqWITvf6SQnxhjDay88RlL9ZM4eOurA8uWLf+HzT97lton/ITQ0zMGEFScRUUhouPUgKJjgxq0o3bvb2VCVKKhGLO5IaxC8hIQQ06Ur+evXsmL4EFaOHs7K0cPxFhWyasxIh5NWTKCXTyKjkTD77GhQMKFN21K6Zyfh3XsR2rwdme+/ZI0R8lOuqBhc4dbZbgkOIbxVB0pSdxCUcLAFI6JDN0p2b3cqYoVJeCSU+W4Jatgcb0aqs6EqkTGGB9/9gsZ14xjT96QDy+NrRLFovTWZz8K1m2kY75/juYwxTHr+cRIbJHPO0IsOLN+1c9uB+4t/W0Bi/UZOxPOJmMiDB9MdmwezM83/ZkhUx69Kp2YWkQR7TIwLmIg1sxkiEgGIMSZPRPphtcqsOuQ5NYHrgRFVmflQ99yUQodW0dSIDmLKi+15+6OdnNSxBvUTwzDGkJpWzLNv+G9f4hGnu0mp6yIiDO4YHsycZaUsXu8NmC5m61YvZ8Hcr2jQqAkTxlmz0lx4yXW8M+m/lHiKeeL+mwFrEoArrr/Lyajl5oqqQdSQy6zBuSIUr1pEyfoVhHXvQ9ipA3BFxRB77f0Ur19B3iz/G5cQHBdHozvvRdwuEBf75s0h+9efnY5VaQK9fO6YWGpceM2B/5+Fy3+jaPUy6jz5NqWZe4m76UEAClf8Tt73M50NWwHu2JrUvvwWq0+6CHm//0TBH4uoe9cTuMLCrc/kts2kv+d/EzlIZAwRA0eDWGUrWbcMz6ZVhHQ6nZCufZDIaCLH3Iln0yoKv5vmdNxyW7phO7MWrqBZYgIjHn8NgJsG9+b+0efw7w+/pdTrJSQ4iPtHn+1w0opZu+oPfpz7NQ2Sm3D3zZcCcOGYa/jh21ns3LEFcbmIj6/LFX44kxnAFYMjadEwmKhw4cnrY/l8QT7NGwbTIMGNAdKzvLz3tX9OG17ZAnXMjPhqZhURmQL0AmoDqcADWFMy32Bv8gkwwRhj7IkCvsGalnkHcIUxZkuZ/XSwn/OwMea4+licedEi/z3FdwwnD2jvdASfGnpKYH/ppEzzr0pSeWyZt8rpCOoEJHZOdjqCTxXuC5DZS46gVuuUY2/kp0I6dD72Rn5sVYNznI7gU69+4t9dt4/l1btr+UUBd99xcZUdG9f9z3tV9p74cjazi46w6rnDbLsZa6az8uxHKaWUUkopdRz0OjNKKaWUUkopVY1U6ZgZpZRSSimlVNXTlhmllFJKKaWUqka0ZUYppZRSSqlAF6CzmQVmqZRSSimllFIBT1tmlFJKKaWUCnAigTlmJmArMzHxsU5H8JndO7KdjuBTr3wE1w5zOx3DZ1Z/vMjpCD7T9q2nnI7gU7lvvuJ0BJ+KSqnvdASfcgXtdDqCT7kiwp2O4DtFhU4n8Klib8AejgEQFRPY5VPO0v9dqlrq2qKm0xF8ZoHTAZRSSin1/47omBmllFJKKaWUqj60MqOUUkoppZTyS9rNTCmllFJKqQCnF81USimllFJKqWpEW2aUUkoppZQKdDoBgFJKKaWUUkpVH9oyU043jEqga5sIsnJKueXJbX9bN6RPLJcOrc2lEzaSk+d1KOGJGTsoivZNQ8jJ8/LAa5kADD4tgtM6hZKTbwCYMTePFRtKnIypDkNCQmg/+XVcwSEQ5Cb9u9lsffkVmj38IDW6dsGTkwvA+vsfIG/tOofTll/q3gwe/N/bZGRmIy5haJ+ejDy7DwDTv57Lh9/8gNvtpkenttw0+nyH01ZAUDAJtzwEQUGIy03Bsl/J/vJDao25ieCGTaDUQ/GWDeybOgm8pU6nLbciTylXffQjxaWllHoNfZsmce0prdiRlceEr34nu7CYlgmxPDKgK8FuPzzPFhRMzavvgaBgxOWi6M/fyft+BjEXXktQUgqUllKyfSM5Myb73d+vyFPKVdPnU1zqpdTrpW+zJK49tbX1t/ty4cG/3cBufvm3270vm3s/+Jr07DxEhGGntGf0GZ1Zs2MPj374PcUlHtwuF/cM60u7RvWcjltu6WmpTHr2QbIy0xEReg84j/7njmTq5OdZ9vuPuIOCSaibxJU3309kVLTTccvtwt4htGoURG6B4alpBQeW92wXRI+2wXgNrN7iYdYvetwSqGNmfFaZEZEGwDtAXcALTDLGPCcitYBpQDKwGRhhjNkn1mVJnwPOBvKBscaYJfa+LgUm2rt+1Bjztq9yH8vc37L5an4WN1+c8LflcbFBtG8RQVqGf39YflpeyJxFBVxx7t+/0L77rZBvfys4wrNUdWCKi1lx5TV4CwqQoCDav/UG+xb8BMCm/z5L+vezHU54YtxuN+MuuYCWKQ3JKyjk0glP0L19KzKyspm/aDnv/3siIcHBZGT56UVlPSWkPf8QprgIXG4Sbn2YwlXLyF+0gMJ3XgCg1thxRJ7ah7wF3zkctvxC3C5eOb8nESFBlJR6ueLD+fRIrsN7S/9idKemDGhRn8dnL2Xmys0Mb9/Y6bjl5ykh8/UnD/z9al47kaK1f1C47GeKp1kXW40ZeR3h3c6g4Lc5DoctnxC3i1eGnXbwbzd9Hj1S6vLe4vX8H3v3Hd9U1fhx/HNyk3Rv2gLdpQXKHmUIyhAQRBFEQQUEREVUUBRUxIWKPm5BfZygoihDUUScgCCgglSG7D0KFFq6dzPO74+Ego8odKSX5Hfer1detOfeJN/DheSee8Yd3i6Jvk1ieHb5JhZvO8SQ1u537DSDgcnXdCclJpLisgpufGUunZvE8eqS1YzrewmXpiSwZscBZny9mtnjb9A7bpVpmsZNY+4lvlFTSkuKeWLSSJq37kjzNh0ZMvIuNM3Igjmvs3TRh9wwaoLecatswy4ra7dauamXV2VZo4YGmscbeWlBKTY7+Hvw/WQV1w4zswKTpJQpQGfgbiFEM2AKsEJKmQyscP4OcCWQ7HyMBd4CcDZ+ngA6AR2BJ4QQut1Rccf+MgpL/n5Vbczgenz81Smk1CFULdqbbqW41M0r8f+YvdTR4BRGI8JoROI5x7JeSBBNE2IB8PPxJj6qPlk5eXyxbDUjB/bFbDIBEBoUqGfMGpEV5QAITQNNAykp27GpcnvF4X1owWF6xasRIQS+Zsf1M6vdjtVuBwEb0rPoldwQgKubxbJqf4aeMWvk9PFD08CgAZKK3X9WbrekH8AQFKpPuBo457Hj9LGLAk4fu+O6ZayJ8CB/UmIiAfDzNpMYGUpmfiFCQFGZ45gWlZUTHuSvZ8xqCw6tR3yjpgD4+PrRMDqB3JwsWrbtjKY5jmujxi3IPZWpZ8xqOzqCYnwAACAASURBVJBhp6T8r991XVqY+GlTBTbnIJkidS0WACEMdfaoSy7rmZFSZgAZzp8LhRA7gShgINDDudscYBXwkLP8IymlBNYJIYKFEA2c+y6TUuYACCGWAf2Aea7KXlUdWviSnWfl0PEKvaO4zOWp3nRp6cWhE1YWLi+mpMxzTpI9isFAm3mf4BMbQ8aChRRt3QZDriduwt3E3jGWvPW/c2jma0iLe/cgHs/MZs+hdJonxfP6J1+wedc+3p6/BLPZxD0jBtOsUbzeEatHCCIefB5jeH2KV/9AxeF9Z7YZNHw7XEbeog91i1dTNrtkxLyVpOcXMbRVItFBfgR4mTA6J6VG+PuQVezGZx1CEDL+KbSwSErXLceafuDMNoOGd9uuFC2dq1++GrDZJSM+/Yn0vCKGtm5EdPD/HLsAH7KKynROWXPHcvLZdTSTlnENePDantz59iJeWfIzdgkf3XOT3vFqLOvkcQ4f2E2jxs3/Ur5mxdd0vLSPTqlqX3iwILGBxpWdzFit8PVvFaRnuufwf+X86qTpJISIB9oC64FIZ0PndIPn9HitKODsSShHnWX/VH6u9xkrhEgTQqQd3Da/Nqvwj8wmwXVXhDL/25w6eT89rNpYysNv5vLkrDzyi+wM7e2ndyTln9jtbL7hJn6/oh/+LZrjm9SIQ6+9wcaBg9k8bATGoECix4zWO2WNlJSVMeXVd7hv1BD8fX2w2WwUFpcwe/qDTBg+mKkzZiHdtYtUSjKff5CMx8ZhimuEsUFM5abgG26jfN9OKvbv0jFgzWgGwbzhl/Pdrf3YdjKXQzmF59jLjcd0S0nu64+R/dxETNGJaJFnvqoCBo7Ccmg3lkPuN18NnMduRC++u+1Ktp3I4VD2uY6deyspr2DSB0t44Nqe+Ht7sfCXLTwwqAc/PnEHDwzswbT5P+gdsUbKSkt4/fkpDL/tfnx8z/QyLVn4PgaDRpfu/XRMV7sMQuDjJXhtURlf/1bBzVd4nf9J/x8YRN096rJarn4DIYQ/sAiYKKX8t8Hs56q5/JfyvxdK+a6UMlVKmZrQ4saqh62G+vVMRIYZeeWhGN5+Io6wYCMvPRBDcIBWJ+9fFwqKJVI6/tJXbyojoYFaN+JiZyssIn/DH4R06YLl1CkApMVC5ldLCGjRQud01We12pjyyrv0u7QjPTu2BSAiLIQeHdoihKB5UjwGIchzLnbgrmRpCeX7duCd0gaAgCuvR/MPJP/Lj3ROVjsCvMykRtVj64kcCsstlcOWMotKCffz1jldzcmyEioO7sLcuBUAvr0GYfALoOibT3VOVnMB3mZSo8P/fuwKSwn3d99jZ7HZuP+DJfRvn0LvVskAfL1hO72cP1/RpjHbjpzQM2KNWK1WXn/uIbp070vqJT0ry9f+tJTNaWsZN+lpHFOXPUN+sZ2tB6wApGfakRI84KNF+QcubcwIIUw4GjKfSCm/cBafdA4fw/nn6UGaR4GYs54eDRz/l/KLwpGMCm555BDjnjzMuCcPk51nZfKL6eQVutdqNf8myP/MB1y7JmaOZXlO3TyJMSQYLcBxtc3g5UVw506UHDqEqV69yn1Ce/akeN++f3qJi5qUkunvfEx8VH2GXdW7srx7amvStu8G4Mjxk1isNoID3G9su8E/AOHj6/jFZMK7SUusJ4/he8nleDdtTfaHM3DnSXm5JeUUljuG4pZZbaxPzyI+JIDU6Hqs2Ov4SF+64wjdE91vtSgA4ReA8HYeP6MJc6Pm2LIy8E7tjldyS/Lnv+m2xy+3pJzCsrOO3ZFM4kMDSI0JZ8XeY4Dz2DVyz2MnpWTa/B9JjAxjZI/UyvLwQH/S9h8F4Pe9R4gND9YrYo1IKZn9+tM0jEmg38DhleV/bvyNbxZ9zMRHXsbLy7PO9LcdtJEU5bioXC9IYNSg2P1HQdaYMBjq7FGXXLmamQBmAzullK+ctWkJMAp4zvnnV2eVjxdCzMcx2T9fSpkhhPgBePasSf9XAA+7Kvf53DcqkhZJPgT4a7z3VDzzv81mxTrP6W6/fVAATeJM+PsIXpgQwpLVJTSJMxETaQQJp/JtfPyde1/19lTmeuE0nv4kwqCBQXDqx2Xkrl5Di/fewRQSDEJQvHsP+55+Ru+o1bJl936+W7OepNgoRjzkqMOdNw5kQM8uTH/7Y26a/BQmo5En7hrpllcYtcAQQkbcDQYDQghKNv1G2faNRM2Yhy0ni4j7HXUu3bKewu8X6Zy26k4Vl/HEsj+w2SUSSe/kaLolNiAxLJCp323gzd920CQ8iEHN4/SOWi2GgGACh4x1/NsTBsq2rqdi12bCp3+APe8UIXc+DkD59jRKfvrqPK92cTlVXMYTP6Rhk45e+t6NoxzHLjSQqd/+zpu/7KBJRDCDmsfrHbVaNh08xtK0HSQ3qMfQFx29nxOuupTHb+jDC1+uxGaXmI0ajw+9Quek1bN35xZ+XfUd0XFJPDbR0Zi5fsRdzH3vZayWCl58YjzgWARg9F26nV5V24g+XjRqaMDPW/DYSB9+2GDh951Wbrjci8k3+GCzS+atKNc7puJCwlVjy4UQlwJrgK04lmYGmIpj3sxCIBY4AgyRUuY4Gz9v4JjcXwLcIqVMc77WGOdzAZ6RUn5wvvcffM8+97wEdgFCI93z6lBVzHqk3vl3clNrW7fTO4LLtPjwJb0juFTR+2/rHcGlglPcb1ndqihJv2g69V3CN+6c00k9gjE2Qe8ILrU50f2WfK6Kz37y7OHpL9/l5xZX0PJfnFBn58ZBD7xeZ38nrlzNbC3/PJOz1zn2l8Dd//Ba7wPv1146RVEURVEURVHcnWc3lRVFURRFURRFgTq+/0td8cxaKYqiKIqiKIri8VRjRlEURVEURVEUt6SGmSmKoiiKoiiKhxN1fDPLuqJ6ZhRFURRFURRFcUuqZ0ZRFEVRFEVRPF0d38yyrnhsY6YgK0/vCC7Ts28jvSO41BVNj7J7f7reMVym3X0D9Y7gMidfn6F3BJcKaRKrdwSXshUX6x3BpUyB/npHcClLtud+7xkbmfWO4FLlNpPeEVzKZrOffydFqSaPbcwoiqIoiqIoiuLguD+95/HM/iZFURRFURRFUTye6plRFEVRFEVRFE/noXNmPLNWiqIoiqIoiqJ4PNUzoyiKoiiKoigeTt1nRlEURVEURVEU5SKiemYURVEURVEUxdMJz+zD8MxaKYqiKIqiKIri8VTPTBVNviOeTm2DyCuwcvuD2wEYPaQhXVKDsdshr8DCi28fIjvXonPS6lkxfyqHdqzCxz+MYQ9+DUDWsZ2s+mwaNms5wqDR47oniIxrpXPS6snKymTGy8+Tm5uLEIK+/a7imkGDK7d/uWghH8x+l7nzFhEYFKRj0qort9q4de4yKmx2bHZJ7yYx3NmtFcfyipiy+Bfyy8pJqR/K9AGXYNI0veNWmTCaaDDlP2AyIQwaxWm/kPfVPOrdMgFzfBICgeXkMbJmz0SWl+kdt+o0I/7DJiKMRjBoWHZvomztt5jbdcMrtSdaSDj5rz2ELHXTG1tqRvxumojQjGAwYNmzmfJfvsXcthvm9j3QQsIpeGOKW9cvYMT9oBkRBgMVuzdRtuYbvNp3x6tDT7SQCPJmPOCe9dOMBIycdKZuuzZRtnopXqnd8epwOVpoBHmvTHbPugEncgt45KOvyS4oRgjB9V3bMLxnB3YdPcn0+d9TYbGiGQxMvaEvLeMb6h23ynJOneD91x6jIDcbYRB063Mdva4exudzXmVL2mqMRhPhkdGMnvAkvn4Besetspt6edEsXqOoVPL8p6WV5Ze1MnFZKxM2u2THIRtf/1qhY8qLhIfOmXFZY0YIEQN8BNQH7MC7UsqZQohQYAEQDxwChkopc4XjTj4zgf5ACTBaSrnR+VrfA52BtVLKq12V+UL88PMpFv+QyUN3JVSWLVx6gg8/Ow7AoL4RjBjcgJmzj+gVsUaadriWlpcOZ/mnUyrLfv36RTr2vZu4lG4c2vEzvyx9kcF3f6xjyurTNI0xt42jUVIyJSUl3H/PnbRp157Y2DiysjLZvOkPwsMj9I5ZLWbNwLvDeuFrNmGx2Rnz8TK6NmrI3N93MbxjE/o1i2f697/z5ZYDDG2XrHfcKpNWCxkvPupoqGgaDR5+jtKtG8meNwtZ5vgCC71hDIG9riL/20U6p60Gm5Wi+a+BpQIMBvyH3492YAe2owco3rcN/2H36p2wZmxWihecqZ/fTfdhPbAD67EDWPZvw//Ge/ROWDM2K4WfzgRLORgMBNw8Ccv+7ViP7seybyv+w+7TO2H12awUzp1xpm4jJ2PZtx1r+n4se7fiP+J+vRPWiGYwMHlwL1Ji6lNcVs6Nz39A56YJvLr4J8ZdeSmXNm/Emu37mLF4JbMnDtc7bpUZDBpDRt1PXKMUykqLmT55GCmtO5HSujPXjpiAphlZ9NFMvlv0PteNdL/PmfU7Laz508LwPl6VZUlRGi0SNZ7/tASbHfx9PPMk3p0JIbyB1YAXjvbI51LKJ4QQCcB8IBTYCNwspfzXlqgrh5lZgUlSyhQcDZG7hRDNgCnACillMrDC+TvAlUCy8zEWeOus13oRuNmFWS/Y1l1FFBZZ/1JWUmqv/NnH2wCyrlPVnqhGHfD2/Z8eCSGoKCsCoKKsEL9A9zzZBwgNDaNRkuNE3tfXl+jYWLJPnQJg9rtvMXrMWLe9Q64QAl+zCQCr3Y7VbkcAGw6fpHfTWAAGtEhg1Z50HVPWzOkeF6Fpjiv8yMqGDIAwe4F04/+AFufntUFzPKTElnkUe0GOvrlqy1n1E5oGSOyZR5EeU79yx5+njx9gO3kUe74H1O/sujmPnafULTzIn5SY+gD4eXuRWL8emXmFCARFZY56F5WWEx7kr2fMagsODSeuUQoA3j5+NIhOIC87i+ZtLkHTHNe0Exu3JDf7pJ4xq+3AcTslZX/93O/a0siKPyzYnKdnRaVu/L1Qi4Qw1NnjApQDl0spWwNtgH5CiM7A88CrznZCLnDr+V7IZT0zUsoMIMP5c6EQYicQBQwEejh3mwOsAh5yln8kpZTAOiFEsBCigZQyQ0q5QgjRg4vYLUOj6NMtjOISG5Of3q13nFp12aCpLHnnNn75+gWk3c5198zTO1KtOHnyBAf276NJ06asX/crYWH1SEhspHesGrHZ7Qz74HvSc4u4oX0y0SH+BHiZMDpvlBUZ6EtmYel5XuUiJgw0fOIVTBENKPjpW8oP7AGg3ph78G2ZSsXxI+QsmK1zyBoQgoBRD2EICad842psGYf1TlS7hMB/5IMYgsOp2OSZ9Qu4ZQpaSDjlf6zGdvyQ3olqjxAE3Pqwo25pP3tW3c5yLDuPXUdP0jK+IQ9e35s7/7uAV778CbuUfDRppN7xauxU5nGOHNxNQuMWfyn/5aevSO16hU6pal9EsIHEhhpXdTZjscFXa8tJz7Sf/4lKnXGe7xc5fzU5HxK4HBjmLJ8DTOOvHRx/UycLAAgh4oG2wHog0tnQOd3gOX2ZPwo4+5LxUWdZVd5nrBAiTQiRdmzfFzWNXSUfLDzGsPF/8tMv2Qzs6749F+ey7Zd5XDpwCqMfX8Wlgx7mpwWP6h2pxkpLS3numSe5bexdaAaNz+Z/yrCbR+kdq8Y0g4EFt/bnh/GD2HY8m4PZBX/bx007nhyknePTJpI+aQxeCcmYohw9Tqfef40j94/GknEUv46X6RyyBqSk8MPnKHjzUbQGcRjqNdA7Ue2SkqI5z1Pw9mMeW7/C9/9D/huPoDWM96z6SUnhrGfJf22qo27h7jd35HxKyiuYNOtLHriuN/4+Xixcs5EHBvfix+njeeC63kz75Fu9I9ZIWWkJb78wmRvGTMbH90wv0zefz8Jg0OjUrb+O6WqXwQC+XvDqZ6Us+aWc0f289Y70/87Z5+TOx9hz7KMJITYDmcAyYD+QJ6U8PQTqgtoCLm/MCCH8gUXARCnl38+sztr1HGVV6heUUr4rpUyVUqZGJQ0+/xNcYMUvOVzWMUSX93aVXWmLadTKccUmqXU/Th75U+dENWO1WnnumWl079GLLl0vIyPjOCdPnuDeu+/gttHDOXUqi4n3jCM3x32HTwR4m0mNjWTrsVMUlluw2h1XpE4WlBDu76NzupqzlxZTtnsbPi3anSmUdop/X4Nf+y76BaslsrwUa/peTInN9I7iGuWlWNP3YUxI0TuJS8jyUqxH9mBKbK53lFrnqJvn/du02Gzc/94X9E9tTu82TQD4ev02ejl/vqJtU7YdPq5nxBqxWi28/eJkOnW7knade1WW/7pyCVvTVnPrfc+47RDrc8krkvy53wbAkZN2JOCn2jOOBQDq6HH2Obnz8e7/xpFS2qSUbYBooCNwri+F87YFXNqYEUKYcDRkPpFSnu4qOSmEaODc3gBHawwcra+Ys54eDbjFJ0dU/TOTzrq0Dyb9uBsP4zkHv8AIju3/HYCje9cRHB6nc6Lqk1Ly+oyXiI6JY9Dg6wGIT0jk43mfM+vDT5j14SfUqxfOjNfeJiQ0VOe0VZNTUkZhmWNOQpnFyvpDJ0gICyI1LoLluxwLUny97SA9kqP1jFlthoBADD5+AAiTGZ9mrbGcOIYx4szVb982HbFkHNUrYo0IH3+El7OhaTRhimuCzU3HsJ+L8PGHs+pnjGuC3cPqd/bxM8Y3xZ5zQt9QtUT4nqNu2Z5RN3B8L0z75FsS64cxslfHyvLwIH/S9jo+O3/fc5jYcPf6TjhNSslH/32SBlEJ9LnmzPTjbRt/4YcvP+Tuh2fg5eX+F7nOtvWAleRox7y18GCBZoBiN1zk8v8LKWUejmknnYFgIcTpaTAX1BZw5WpmApgN7JRSvnLWpiXAKOA5559fnVU+XggxH+gE5J8ejnYxmTohgdYpAQQFGJn3RivmfH6cTm2CiG7ojZSSk1kVzJjtvuPAf/j4fo7t20BZcS4fPNmdTn0n0HPo06xZ/Ax2mw2jyYueQ57SO2a17dyxjZU/LScuPoF7x98BwM2jxpDaoZPOyWruVFEpjy9dh90usUtJn5RYuiVHkVgviClfreXNn/+kSf0QBrV2z3lBWlAo4bdORBgMIATFG9ZS+mcaDaY8h8HHBxBUpB/k1Mf/OrT2oiX8A/G96mbHxEkhqNi1Eev+bZjbd8e7U2+EXyABt0zFcmA7pd9/qnfcKhP+gfhdOcIx/gOBZfcmrAe2Y27XHa+OvRB+gfiPfhjrge2U/uB+8/IM/kH4Xj0SDAaEEFTs/APLvm14pfbAu1MfhH8ggbc+gmX/dkq++0TvuFVi8A/Cd8AoEAIhDGfVrSfelzjrdvujjrp9M1fvuFW26cBRlv6+jeSG4Qz9j2PO3YRruvP4sCt54fPl2Ox2zEaNx2/qp3PS6tm3azPrfv6GqLhknrr/BgCuHT6e+bNfxGqp4NUn7wQciwCMGOd+w8hH9vWiUZSGv7dg2i2+fLe+gvU7rNzUy4uHhvlgtcGny8v1jnlREIaL5/aSQohwwCKlzBNC+AC9cUz+Xwlcj2NFs7PbCf/8WtJFK/8IIS4F1gBbcSzNDDAVx7yZhUAscAQYIqXMcTZ+3gD64Via+RYpZZrztdYATQF/IBu4VUr5w7+9f++b0jx26YqBI9rrHcGlrmjqnlfWL1TMmvf1juAyJ1f/oXcElwppEqt3BJcSHnoPgtPsVpveEVzL7rFfe/h0dP8LTv9mfcOhekdwqS9/8uzJ9zMm+LvFh2fJ7Mfr7EPC99an/vXvRAjRCscEfw3HSLGFUsqnhBCJnFmaeRMwQkr5r61RV65mtpZzz4MB6PW/Bc5VDe7+h9dy4xm9iqIoiqIoiqKzi2helJTyTxyLg/1v+QEc82cu2MXT36QoiqIoiqIoilIFLuuZURRFURRFURTlInERzZmpTZ5ZK0VRFEVRFEVRPJ7qmVEURVEURVEUT3cRzZmpTapnRlEURVEURVEUt6R6ZhRFURRFURTFw11M95mpTR7bmAmPidA7gssUFnn2eu2+9kK9I7hUwfa9ekdwmUNr3PeGsRci/2iu3hFcKvdggd4RXKr9nT31juBS5qRkvSO4jN3bT+8ILmW3e+bwn9MCA816R1A8mMc2ZhRFURRFURRFcRKe2TPjmbVSFEVRFEVRFMXjqZ4ZRVEURVEURfF0Bs8czqh6ZhRFURRFURRFcUuqMaMoiqIoiqIoiltSw8wURVEURVEUxcMJtQCAoiiKoiiKoijKxUP1zCiKoiiKoiiKp/PQBQBUY6aK7hgSStsUHwqKbDz4yom/bLuqWwAjrg5h7LSjFJa4540tr+ogSGooKCmH97531GHQJYKwAMd/AC8zlFfA7B/ds36ZWad4/pWZ5ObmIgwGrurbh8EDBzDnk/l8+8MygoMCARgzcgSdOrTXOW0VGU2E3vkowmgEg0b51t8p+vELgm66E1N0ItJuxXLkAAWL3ge7Te+0VWYwm2n76QcYzCaE0Ujm98s49NpbNH3+KYI7pGItctxsdddDj1O0c7fOaatOmMwkvfw6BpMZNI38Nas48fH7lduj7ppIaN8r2Tqwr44pq8/Tjx+aEb8bJoBmBGHAuncL5b99j6nNpXi1644hOJzCNx9BlhXrnbTKTuQV8sj8H8kuLEEIwfWdWjD8sjbsOpbF9C9+osJiQ9MMTL22By1j6+sdt8pO5OTx2OxFZOcXIQyC67qlMqx3Fx56ez6HTp4CoLCkjABfbxY8MV7ntFWXc+oEH77+KAV52QghuLTPdfS6ajh//PojSxe+zYljB5nyn7nEJTXXO2q1DOqi0TjaQHGZ5L9LrAAM6aZRL8hx3uJtFpRVSN762qpnTMWFXNaYEULEAB8B9QE78K6UcqYQIhRYAMQDh4ChUspcIYQAZgL9gRJgtJRyoxCiDfAWEAjYgGeklAtclft8fk4r5odfC7nrhrC/lIcGabRM9iYr173/s/x5SJK2T3JNpzMjEBf/JgEJQK82gvIKncLVAk0zMO7W0SQnNaKkpJQ7J06ifds2AFw3aABDBw/SN2BNWC3kvvMssqIcDBqhdz+GadcWSjf9Sv68twAIGnY3Pp16UPrbCp3DVp29ooLNI2/DVlKKMBppN/9DclavBWD/C6+Q9f1ynRPWjLRUsP/BidjLSkHTSH71TQo2rKNk1w58kpug+fvrHbFGPP34YbNS/Nl/wVIBBgN+N9yL9dBObMcOUnxgB35D3O8k+DTNYGDy1ZeREh1BcVkFN86cT+fGMbz6zVrG9enEpU3jWbPzEDO++YXZd16nd9wq0wwa9w+9kpS4hhSXlTPs6Tfp1CyJ58fdWLnPywu+w9/XS8eU1adpGtePmkRsYgplpcU8++BNpLTqTMPYJO544BU+eedpvSPWyKb9dtbvsjP4Uq2y7LPVZy7Y9U3VKK+QekS7+Kg5M1VmBSZJKVOAzsDdQohmwBRghZQyGVjh/B3gSiDZ+RiLowEDjobNSCllc6AfMEMIEezC3P9q18Fyis7R6zJyQAiffpt3+pzfbaVnQVn5P29PiRFsP+K+lQwLDSU5qREAvr4+xMZEcyo7W+dUtUdWOA+epiEMRpBQsWtL5XZL+n60oFCd0tWcraQUAGE0Onqg3Pef4jnZy86qn+a81mQw0PD2uzg+661/eaZ78PTjh8V5pceggcEAEuxZx5AFOfrmqqHwQD9SoiMA8PM2kxgRQmZ+MUIIisocdS4qKyc80E/PmNUWHhxASlxDAPy8vUhoEE5WbkHldikly9K20q9jK70i1khQSDixiSkAePv4UT8qkbycTBpEJ1I/Kl7fcLXg8ElJafk/f5i0iDfw50H3HE2iXBiXNWaklBlSyo3OnwuBnUAUMBCY49xtDnD6UvhA4CPpsA4IFkI0kFLukVLudb7OcSATCHdV7upo38yHnAIbRzIsekdxqZhwKC6D3CK9k9SOEycz2XfgIE2bNAbgq6Xfcvv4ibw443UKi9y0kkIQdt8zRDzxJuV7t2JJ339mm0HDp92llO/+U798NWUwkLpkAV3XrSTnl3UUbNkKQOJ9E+jw9WckTZ2MMJt0DlkDBgNN3nqfFguXULhxAyW7dlDvmsEUrPsFa44HNLo9/fgJgd+IBwgYNx3rkT3YThzWO1GtO5ZTwK7jWbSMjeTBa7rx6jdruWL6+7y8dC339O+id7waO34ql91HMmiRGF1ZtnHvIUID/YmLrKdjstpxKvMY6Yd2kZDcUu8odSIuUlBUKskp1DvJRUKIunvUoTrpbxJCxANtgfVApJQyAxwNHiDCuVsUkH7W0446y85+nY6AGdjPOQghxgoh0oQQafu2fFqbVfhHZpNg0OWBfPZjXp28n56ax7p3r8zZSktLefLZ57nr9jH4+fpyTf9+fPTeW7zz2iuEhYbw9qwP9I5YPVKS/eojZE2/B1NMI4yRZ76QAwePpuLgLiwH3XA+wml2O2nX3MBvl11BYKsW+CUnceCl11jfdyBp1w3DGBRE3NgxeqesPrud3XeOYcew6/BtkoJfy9YEd+tJ1uJFeierHZ5+/KSkeO6LFL43Da1+LIYw95s/8m9KyiuY9NE3PHBNN/y9vVj421YeGNCNHx8dwwPXXMa0he43fPVsJWXlTH5zHpNv6I+/j3dl+ffr3bdX5mxlpSW8+9Jkho5+AB9f9x62eqFaJhjYqnplPJ7LGzNCCH9gETBRSlnwb7ueo6zyzFkI0QD4GLhFSnnOf5lSynellKlSytSk1sNqEvuCRYYZCQ818vzEBrw2pSGhQRrP3lufIH/PGpcoBDSJFuz0gMaM1Wpl2rMv0KtHNy7rcgkAISHBaJqGwWCgf98r2L1nr84pa0aWlVBxYCfmpo4vYL8+12LwC6Dw6090TlY7rIWF5K3fQGi3LlRkOSboygoLJxZ9RUCrFjqnqzlbcRFFf27Cv3VbvBpG0ezDeTT7aCEGL29SPpind7wa8/TjR3kpW2ntEQAAIABJREFU1vR9GONT9E5Sayw2G/d/9C392zahd8skAL7+Yye9WjqG7V7RKplt6Sf+7SUuaharjclvzePKzq3p1f7MRHirzcZPG7fTt4N792TYrBbefWkSHS/rT9vOvfSOUycMAprFGth2SDVmKhkMdfeoy2q58sWFECYcDZlPpJRfOItPOhsmpxsomc7yo0DMWU+PBo479wsEvgEedQ5Bu2ikn7Aw7qlj3PPcce557jg5+TamzjxBfpFn/edJiITsAigs1TtJzUgpeWnmf4mLieb6awdWlmfnnBnTvva3dcTHxekRr0aEXwDC29fxi9GEV1ILbJnH8enYA6/GLcn75L8g3bcxagoNwRgQAIDBy4uQLp0pOXAIc/iZoR/1+vSkeM8+vSLWiBYUjObnuFoqzGYC2qZSuncP228cxI6RQ9kxcij28jJ23nKTzkmrx9OPn/DxAy8fxy9GE8bYxthzTuobqpZIKZm2cAWJEaGM7N6usjw80I+0A8cA+H3fUWLr6TadtUaklDw550sSGoRz8xVd/7Jt/c79xDcIJzI0SKd0NSel5KM3n6R+dAK9B9ysd5w6k9hAcCpfUlCidxLF1Vy5mpkAZgM7pZSvnLVpCTAKeM7551dnlY8XQswHOgH5UsoMIYQZ+BLHfJrPXJX3Qk0YFkZKojcBfgbemNqQz5fls2qD+y21+U8GdhbERQh8vGD8AANrtkm2HJQ085AhZtt27GT5ylUkxMdxx4T7AMcyzCtXr2HfgYMIIagfEcHE8eN0Tlp1WmAwQTfc4bgiIgRlW9ZTvnMzkc/NwZZ3irAJ0wAo27qB4uWL9Q1bDebweqS8MB3hvOqT9d2PZK9cTZuP3sMUGgJCULRzN3sed8+VeUyhYcQ+MBVh0MAgyPt5JQXrf9U7Vq3x9OMn/ALx7TfcsVqQEFj2bMZ6cAfmtt0wp16O8AvAb+SDWA/uoGyZbgtyVsumQxks3biL5PphDH3FMYR7wpVdePz6Xrzw1c/Y7BKzUePx693ziv/mfYf55rfNJEdFcsOTbwAw/to+XNaqCT/87v5DzPbv2sz61UuJik1m+uShAAwcNgGrxcKC2c9RVJDLG/+ZQEx8E+55zP0WGrm+m0ZCpAFfb5h0vYmVm21s3GenZYKa+P83HrqamZAuulIrhLgUWANsxbE0M8BUHPNmFgKxwBFgiJQyx9n4eQPHimUlOIaTpQkhRgAfANvPevnRUsrN//b+Nz3oAWfe/6Bl+6jz7+TGbm7nxnM6LoDp3Wf1juAyOxdv1TuCS4UkBOodwaVyD/7bSGD31/7OnnpHcClzUrLeEVzGHhJx/p3c2Prgq/WO4FIrN2rn38mNPTXK7BZ3oyz7YmadnRt7D763zv5OXNYzI6Vcy7nnwQD87fKNdLSq7j5H+Vxgbu2mUxRFURRFUZT/Rwxu0eaqMs/sb1IURVEURVEUxeO5rGdGURRFURRFUZSLhIfOmfHMWimKoiiKoiiK4vFUY0ZRFEVRFEVRFLekhpkpiqIoiqIoiqcTagEARVEURVEURVGUi4bH9syk7zyodwSX6dA1Ru8ILlViCNA7gkuFmD32vx1mf8+tG0DOnny9I7hUxwf66R3BpYTZrHcE1yov0zuBy9i8/fSO4FL5ZZ79bzPjWJ7eEVwsTO8AF8bgmX0YnlkrRVEURVEURVE8nmdfRlUURVEURVEURc2ZURRFURRFURRFuZionhlFURRFURRF8XTqppmKoiiKoiiKoigXD9UzoyiKoiiKoiieTq1mpiiKoiiKoiiKcvFQPTOKoiiKoiiK4uk8dDUz1ZipoofvaUyXDmHk5lsYOT4NgLtuSaRrxzAsFjvHT5Tx7MxdFBXbdE5aPasWTuXwzlX4+IcxdNLXAJw6vpM1X0zDZilHGDQuu/YJImJb6Zy0erKyMpnx8vPk5uYihKBvv6u4ZtDgyu1fLlrIB7PfZe68RQQGBemYtBqMJkJufxg0I8KgUb59A8UrFhM45A6MUfFgt2E5eoDCxXPA7n7/PoXZTMvZ72EwmxGaxqnlK0h/+x2SnpxGUPt2WIuKANj3+DSK9+zROW3VGbzMtFvwAcLLUb+s75Zz8NU3SXnpaYI7pWItLARg5+THKNqxW+e0VVdutXH7orVU2OzY7JJeSQ0Z17kpx/KLefj7NArKLDSNCOLpK9pj0txv0EC51cZt81eeqV/jaO7s2pxjecU8vHQd+WUVNI0MZnr/Tm5XvxN5RTyyaCXZRaUIIbg+tSnDu7Rkd0Y205esoaTCQsPgAP4z5HL8vd3v5o8nsnN54u1Pyc4vwCAE1/a8hJv6dQdg/o+rWfjjWoyaga5tmnHvTdfonLbq8rIzWPD2wxTmn0IIQaeeQ7m0382UFOXxyRuTyM06Rkh4FMMnvIKvn5t97wGjrvKjVZKZwhI7095z3Nh4wGU+XNbGm6ISOwBfrCph236LnjEVF3JZY0YIEQN8BNQH7MC7UsqZQohQYAEQDxwChkopc4UQApgJ9AdKgNFSyo1CiDjgC0ADTMDrUsq3XZX7fL5dcZJF3xzn0fuaVpZt2JzLO3MOYLPDnaMSuPn6WN6ac1CviDXSOPVamncZzsoFUyrL1n/zIu17301s024c2fkz6759kWvGfaxjyurTNI0xt42jUVIyJSUl3H/PnbRp157Y2DiysjLZvOkPwsMj9I5ZPVYLebOfR1aUg0EjZOxUyvdspWzLb1R89g4AgUPH4ZPajdLfV+octupkRQXbxo7DXlqKMBpp+f5scn/5BYBDM2aSvXyFzglrxl5ewaZht2ErcdSv3edzyF61FoB9z75C1nfLdE5YM2bNwNvXdsXXbMRis3Pr52voGhfB3E37Gd62EX0bR/PsT1tYvP0wQ1ol6B23ysyagXeG9jhTv3kr6ZpQn0/S9jA8NZm+TWN5ZtkfLN56kCFtGukdt0o0zcDkKy8hpWE9issruPHNL+mcFM2Ti1dzf79OpCY05Ms/dvHh2i2M791B77hVZjQYuG/YNTRNiKG4tIybH3uFTi2bkJNfyOo/tjH/Pw9iNhnJyS/UO2q1GAxGrh72IFEJzSgvLea1x64nueUl/LF6MUnNOtPzmttZueQ9Vn09i/43TtI7bpX9+mc5K9PKGHON/1/Kl/9eyo/ry3RKdZFSq5lVmRWYJKVMAToDdwshmgFTgBVSymRghfN3gCuBZOdjLPCWszwD6CKlbAN0AqYIIRq6MPe/2rI9n4LCv7buN2zKxeZo/LN9dwHh9bx0SFY7GiZ2wNv3f67MCEFFmeOqd0VZIX6BbnqyD4SGhtEoKRkAX19fomNjyT51CoDZ777F6DFjEW7cDSsryh0/aJrjISUVe/6s3G45egBDUKhO6WrOXloKgDAaEUYjSJ0D1TJbyZn6GYxGkJ5TQSEEvmbH9TOr3Y7VLkHAhqOn6JXk+Ei/OiWGVQcy9IxZbX+vnx0hYEN6Jr0aRwNwdfN4Vu47pmfMagkP8CWlYT0A/LzMJIYHk1lQzKFTebSPbwDAJY2iWbHdPS/i1QsJomlCDAB+Pt7EN4wkMyefz5f/wqgBvTCbHMc1NChAz5jVFhgSTlRCMwC8fPyIaJhIfk4m2//4ifaXDQKg/WWD2J7mnheE9qZbKS7znM9Kpepc1piRUmZIKTc6fy4EdgJRwEBgjnO3OcAg588DgY+kwzogWAjRQEpZIaV0nqHh5crMteGqPg1Y90eO3jFqVZcBU1n/7YvMfaYHv33zAh2vvF/vSLXi5MkTHNi/jyZNm7J+3a+EhdUjIdG9rpj+jRCEjH+Keg+/RsW+7ViPHjizzaDh3bYLFXu26pevpgwGWs//lI4rlpG3bh1F27YBEHf3XbRZMJ+ESfcjTCadQ9aAwUCHbxdy6R+ryFn7GwWbHccqcfIEOn73OUmPPYAwu2/9bHbJTZ+upM+s7+kcG050kB8BXiaMzhV2Ivx9yCpy3yupNrvkxjk/0vvNJXSKiyQ6yB//s+oX6e9DVmGpzilr5lhuIbsyTtEyOoKkiFBW7ToMwI/bD3Aiv1jndDV3PCuH3YeP0qJRHEdOZLF59wFGPfEqY6e/wfb9R/SOV2M5Wcc4dngnsY1aUVSQTWBIOOBo8BQXeNa5S8/23jxxWxCjrvLD19t9L1LWKiHq7lGH6qRhIISIB9oC64FIKWUGOBo8wOnL/FFA+llPO+osQwgRI4T407n9eSnl8X94n7FCiDQhRNqJw1+7oir/auTQWGw2yY+rMuv8vV1px7p5XDJgCiMeWUWXAQ/z82eP6h2pxkpLS3numSe5bexdaAaNz+Z/yrCbR+kdq+akJPeNx8l+4X5M0YloEVGVmwKuGYnl4B4sh91vPkklu50tNw5jQ98rCWjRAt9GjTj8+htsvPY6toy4GWNQENG3jNY7ZfXZ7WzoP5RfL+lDYOsW+DVOYv/zM1nf6xo2DLwJU3AQcePG6J2y2jSDYN6wnnw3pi/bTuRxKKfo7zu58TmHZhDMH3UF399xNdtP5HAwp+Bv+7hxxy8l5RYmzVvGA/274O9t5snB3Zm/bjs3vvkFJeUWt5sL9L9Kysp5cOYHTBpxLf6+3ljtdgqKS/lw2kTuuWkAD78xB+nGvaXlZcXMnXkv14x4GG9f//M/wY2t2ljG1LfyeGpWPvlFdob08tU7kuJCLv/kEUL4A4uAiVLKv3+yn7XrOcokgJQyXUrZCkgCRgkhIs/1AlLKd6WUqVLK1PpxA2oavUr6XR5Jlw5hPPnyzjp937qw54/FJLS4AoDEVv3ITP/zPM+4uFmtVp57Zhrde/SiS9fLyMg4zsmTJ7j37ju4bfRwTp3KYuI948jNcd+rVLKshIqDuzA3bgmA7+UDMfgFUPTdPJ2T1Q5bURH5aWkEd+mCxTlMUFosnPxqCf7Nm+ucruasBYXkrksjtHtXKrKc9auwkPHZYgJbt9A5Xc0FeJlIjQ5j64kcCsstWO2OcbqZRaWE+3nrnK7mArzNtI8JZ+vxHIrOqt/JolLq+fvonK56LDY7989bRv/WSfRu7pjTlBAezDu3XMX8uwbTr1UjokMDdU5ZfVarjQdnfkC/Lu25vINjgZvIkGB6prZCCEGLRnEIIcgrdM/eJ5vVwsczJ9Kmy9W06NAHAP/AMApyswAoyM3CL9B9hyD/r8JiiZSOk8g1m8tJaKjWu/JkLm3MCCFMOBoyn0gpv3AWnxRCNHBubwCc7sY4CsSc9fRo4C89MM4eme3AZa7MXVWd2oUw/LoYpjy9jfJyu95xap1vYAQZB34H4Ni+dQTVi9M5UfVJKXl9xktEx8QxaPD1AMQnJPLxvM+Z9eEnzPrwE+rVC2fGa28TEupeH+zCNwDh7bz6ZDRhbtQMW1YG3qnd8EpqQf6Ct9x6DoYxJBjN33E10eDlRVCnTpQeOoSpXr3KfcJ69qBk/369ItaIKTQEY6BjTL7By4vQrp0p2X8Qc/iZ+oVfcTnFe/bpFbFGckvKKSx3zDcss9pYn55FfGgAqdH1WLHP8VG/dGc63RMb6Bmz2nJLyiksqwCgzGJj/eFMEsICSI2JYMWeowAs3X6IHo10m/JZbVJKpn35M4nhwYzsemYly+wix5A5u13y3qpNDOmYolfEGpFS8tSs+SQ0jGRE/x6V5d1TW5C2Yy8AhzMysVptBAf46ZSy+qSUfD7rMSIaJtKt/+jK8mbtevLHmsUA/LFmMc3bX65TwtoX5Hfm+njbxmaOZbnfCp4uYTDU3aMOuXI1MwHMBnZKKV85a9MSYBTwnPPPr84qHy+EmI9jon++lDJDCBENZEspS4UQIUBX4OzXq1PTJqfQpmUQwYEmvvigM7M/PcTN18diMglefdrxIb99dwEvvblXr4g1svyT+8k4sIGy4lzmPtOd1D4T6Hbd0/y65BnsdhtGoxfdrntK75jVtnPHNlb+tJy4+ATuHX8HADePGkNqh046J6s5Q0AQgdffjjAYQAjKtv5Oxe4thD81G3teNiHjHgOgfHsaJSuX6Jy26sz16pH81JMIgwYGQfay5eSuWUPzd97GFBICAop372H/M8/qHbVazBH1aPbydGf9DGR+8wPZP62m7aezMIWGgBAU7djF7kee1jtqtZwqKeOJHzdhkxIpJb2To+iWUJ/E0ACmfp/Gm7/tokl4EIOaxeodtVqyikt54rsN2OyO+vVpEkO3Rg1JDAvk4aXr+O/abTSNCGFQS/dbqW3T4ZMs3byX5MhQhr6xCIAJfTpwJDuf+et3ANCrWTyD2jXRM2a1bdlzkG/XppEU04BhU18E4K6hVzGweyeeenc+Q6c8j0nTmHbHMLdcIObQno1sXLuE+jGNmTH1WgD6DZ1IjwG388nr97Hh50UEhzVgxD2v6py0em4f6E/jOBP+PoIXxgezZE0pjWNNxERqAJzKszP3u3MMaVU8hnDV+E8hxKXAGmArjqWZAabimDezEIgFjgBDpJQ5zsbPG0A/HEsz3yKlTBNC9AFextFbKIA3pJTvnu/9Lx3ws/tegj6Pwbd10zuCS13V4qjeEVwq5P3H9I7gMnu/dePFBS5Aea5n36eg4wP99I7gUsLsfvdAqQotOETvCC5jiXfPXp8LtYK+ekdwqW+W5ekdwaXemxrmFq3c0p8+rrNzY5/Lb66zvxOX9cxIKdfyz1M5e51jfwncfY7yZYB73qFRURRFURRFURSXUTOiFEVRFEVRFMXTqZtmKoqiKIqiKIqiXDxUz4yiKIqiKIqieDrVM6MoiqIoiqIoinLxUD0ziqIoiqIoiuLhpBsuLX4hPLYxExQZpncEl/Hz8cx/jKdFndqsdwSXKrF73o1VT/MJdc+7m1+oBq3d84aOFypnq3vekPNC1bukjd4RXMqWl6t3BJfJDknSO4JLFWV49kAZs1nTO4LiwTy2MaMoiqIoiqIoipOaM6MoiqIoiqIoinLxUD0ziqIoiqIoiuLpPHTOjOqZURRFURRFURTFLameGUVRFEVRFEXxdAbP7MPwzFopiqIoiqIoiuLxVGNGURRFURRFURS3pIaZKYqiKIqiKIqHUzfNVAC4d3QUHVsFkFdo5e4nHDeYGzEwgs5tA5F2SV6hjVffP0pOvlXnpNXzwycPc2DbKnwDwhg1dSkAWUd3sXzBE1SUlxAUFsWVI1/Cy8df56TVcyI7j8ffnUd2XiEGg+DaHp0Z1vcyprzxMYdPZAFQWFJKgK8P86bfr3PaKjKaCBk7FYwmhMFA+bYNFC//ksAbxmGMSgCbDcvRAxR++QHYbXqnrTJhMtN4xhsIkxmhaeStXknGnPcrt0ePn0hYv/5sufoKHVNWnzCZaPDw8wijCaEZKN7wC7mLP6XemHvwik8GAZYTx8ma9SqyvEzvuFVnNBE5eTrCaAKDgdKNv5G/dAGhN9+FOc5xQ0RrZgbZc153y/qVW23c+vEyKmw2bHZJ76ax3NmtFcfyipiyeC35pRWk1A9h+jVdMGnudQPBcquN2+avpMJmx2aX9GoczZ1dm3Msr5iHl64jv6yCppHBTO/fCZPmfgM+MrOyePHlV8nNzUUYBP379ePagddUbv9s0RfMev8DFn46l6CgIB2TVs+SD6ay989V+AWEMe6prwE4kb6Lbz92fK8Hh0Vx7e3u+70+oq8PLRqZKCyRPPNhYWV597Zmurf1wm6HbQcsLF7tfp8ryoVxWWNGCBEDfATUB+zAu1LKmUKIUGABEA8cAoZKKXOFEAKYCfQHSoDRUsqNZ71eILAT+FJKOd5Vuc9n+S+5LP0pm/tvja4sW/TDKeZ+lQnAgF6h3DQggv/OPa5XxBpp3mkwbbqN4PuPH6os+3HeI3Qb9BAxyR3Z9tvnpK2YRderJ+qYsvo0zcB9Nw0gJT6a4tIyRjw+g84tknlu/M2V+7zy6RL8fb11TFlNVgt5s55DVpSDQSNk3KOU7/6Tss2/UrHgbQACb7wTnw7dKV3/k85hq05aKtg76V7sZaWgaTSZ+Rb5v6+nZOd2fBs3QfN3zy/i06TFQsbzUx0n8ppGw6kvULL1D7I/fQ9ZVgpA6I23Edj7avK/+VzntNVgtZD56hOO+hk0Ih94htLtm8j97IPK+gVfP5qAHldS8MOXOoetOrNm4N3hvfA1m7DY7Iz5+Ee6NmrI3PU7Gd6hKf2axzP9u/V8uXk/Q9s31jtulZg1A+8M7YGv2YjFZufWeSvpmlCfT9L2MDw1mb5NY3lm2R8s3nqQIW0a6R23yjRNY+xtY0hOSqKkpITx995Hu7ZtiIuNJTMri02bNxMRHq53zGpr3fVaOlw+nK9mT6ksWzrnUfoMeZC4Jh3ZvHYRv/4wm56D7tUxZfWt217Bz5sqGNnft7IsOcZIqyQTz84pxGoDf1/P7JGoMnXTzCqzApOklClAZ+BuIUQzYAqwQkqZDKxw/g5wJZDsfIwF3vqf13sa+NmFeS/I9r0lFBb/9ap2aZm98mdvswGJrOtYtSY6qQPevn+98pSbeZDopA4AxDXtyt4tP+oRrVaEBweSEu9oiPr5eJPQMJLM3ILK7VJKlv++hX6d2+oVsUZkRbnjB00DgwZIKnb/Wbndkn4AQ1CoPuFqgd150iuMRoRRAynBYCDqjrs59u7/fmS4n9M9EkIzIjRH/U6f6AMYzGZHnd3UmfppCM34t/oJk9ltqyeEwNdsAsBqt2O12RHAhsMn6Z0SC8CAloms2nNUx5TV46ib49qn1W7HarcjBGxIz6RXY8fn6dXN41m575ieMastLDSU5CRH76Cvry8xMTGcys4G4J33ZnHrLbcg3Hh4TlzjDvj4/fV7PfvEQWIbO77XE5p1Ydcf7vu9vu+ojeKyv35wdGtj5sf15Vidp2tFJW76waJcEJf1zEgpM4AM58+FQoidQBQwEOjh3G0OsAp4yFn+kZRSAuuEEMFCiAZSygwhRHsgEvgeSHVV5poYeW0El18SQnGpjYdfPKh3nFoV1qAx+7euIKlVb/Zs+p7C3Ay9I9WK41k57Dp8jBaNYivLNu0+QGhgALH13fQqnBCEjH8KLSyS0nXLsaYfOLPNoOHdtitFS+fql6+mDAaavjUbr6gosr76kpJdOwgfPIT8X9dizcnWO13NCQNRT87AFNGAghXfUH5gDwDht96LT6tULMfTyZ4/W+eQNSAM1J/6Isbw+hT9/D0Vh/YCEDpyPD4t2mHJSCfv8w/1zVgDNrudYe9/T3puITe0b0x0iD8B3iaMzuVQIwN9ySws0Tll9djskuEfLyM9r4ihbZKIDvLH3+usuvn7kFVYep5XufidOHmS/Qf207RJE35bt556YWE0SkzQO1ati4hKZs/mn2jSthc7076nIMczvtdPiwjRSIo2cs1l3lis8MXPpRw54X7Dq2ubVD0z1SeEiAfaAuuBSGdD53SDJ8K5WxSQftbTjgJRQggD8DLwwAW8z1ghRJoQIu3Irs9qrwIX4KMvMxn94G5WrctjwOVhdfrertZ32DNsWfMpc18YTEVZMZpm1jtSjZWUlfPA63OYPHwg/j5nhpR9v24zfS9po2OyGpKS3NcfI/u5iZiiE9Eioyo3BQwcheXQbiyH9ugYsIbsdnbdcQvbbhiMX9MU/Fu2JqRbTzK/XKR3stoh7Rx7/B7+j737Do+iWvw//j6z2fTegBRIo3cIHamigAqIgCiIIGK5FBHs2PXa7lXEn4qiICJIk6JiQxGkiBQpUgLSQk1Cetsk287vj12KXiSkLMPme17Ps0+SM7Ozn0mys3PmlDkxZTReCQ0wRtcDIHP2DE5MvhvzmZP4tb9O55BVIO2k/3sqp58ch2dcEsYox4WEnHnvcvrxe7Gkn8Y3uavOISvPoGksvrc/P0y8lb1nsjmWVfA/67jrBX6DJlh09w18f//N7EvP4VhOzdm3c0pKSnjp36/ywLhxGDSNhYuXMGrkCL1jucQto19h+9oFfPTiYMpKizF4GPWOVK00DXy9Bf9ZUMSKX0oYe4tv+U9S3JbLKzNCCH9gGTBZSvm/R7+LVr1EmQT+BXwrpTx5ieV/XVnKWVLKZCllct1GQysXuIrWbcmnc9tAXV7bVUJrJ3Lb+DmMfGw5jZJvIig8Vu9IVWKx2nj0nU/p16kNvdo1P19utdlYu30PN3Rw48qMkyw1YT52AM8GLQDw7T0IzS+Aom8+1zlZ9bAVF1G4ayf+rdrgFR1N088W0XTBUjQvb5rMW6R3vCqzm4opObAH3+ZtLhRKO8VbN+CX3Fm/YNVElpgo/XMf3k0v6s4p7Zi2b8S3dUf9glWTAG9PkutFsudMFoWlFqx2R1fkjAITEf7ufVIV4O1J29gI9pzJoajson0rKiHc30fndJVntVp56ZVX6dWzB127dCYtPZ30jAwenDCJUWPGkpmVxfiHJpOTk6t31GoRXieBEVPmMO7Z5TRrfxMhEXXLf5IbySu0s+uQBYDj6TakBH8fN69tVwchrt7jKnJpZUYIYcRRkVkgpVzuLM4QQtRxLq8DnHWWnwIuPkuOAc4AnYAJQohU4L/AKCHEa67MXVFRkRdaKjq2CuBUWpmOaaqfqdDRfUfa7fz2/Uxadh2uc6LKk1Ly0uwlxEfVYmS/7n9ZtnXfIeLqRFIrNFindFUj/AIQ3s4TJQ8jnolNsWWm4Z3cHa/6zclf9L5bj7fwCArG4OcY5C88PQlsm4zp0EH2DB3IvhFD2TdiKPayUvaPcs//Ty0gEM3XD3CMHfFp0gpL+mk8IuucX8e3VXssae435gJA8w9E+Dj+P4XRE+9GLbCmn8Yjovb5dXxatMOS4Z7jLnKKSyksNQNQarGy5Vg68WGBJNerxU8pJwD4es9RejSIudxmrkm5prKL9s3GluNniQ8LIDk2kjXOMUCr9qXSIzFKz5iVJqXkrRnvEBsby223DgIgPi6OJZ/PZ94ns5n3yWwiwsN5b8bbhIaG6Jy2ehQXXPhc3/DNB7Tt4Z7HzX+y+7CFBnUdIykiQzQ8NEHrYYd2AAAgAElEQVRRift+/imX58rZzAQwG0iRUr510aKvgLuB15xfv7yofIIQYhHQAch3dkMbcdE2RwPJUson0Mlj42Jo3tCPQH8PPn2jIQu+Oktyc3+ia3shJZzNNvPeZ+45kxnAN59M4dThrZQU5TLrmW506j8RS5mJXesdV/Trt+xD04636Zyy8nb9mco3m34nKbYOdzzt+LccP7QfXVs25gc372KmBQQTOPQ+x0BVoVG6ZwvmA7uIePkT7HlZhDz4LABl+7Zj+vnLcrZ27TGGhVHvsWkIgwZCI/eXnyn47Ve9Y1Ubj6BQIsY9DJqGEBpFWzdg2r2NqKdeR/P2BSEoO3mMrE/f0ztqpRiCQgi7e6Kj/4fQMP2+iZK9v1PrkX8jvH0AgeV0Kjmff6h31ErJKi7h2a83Y7dL7FLSp3E9utWPISE8iCdWbuL99btpWCuUQS3db7avzOISnvtuGza7REpJn4axdEuMIiEskCdX/cZ7G/fSKDKEQc3dc2zJvv37WfPzWuLj4nhwwiQAxtw9ivbtrskhuhW2fNYUjh/chqkol7cf7U73ARMxl5nYvnYBAI1a30DLLoN1Tll5Y27ypX6sB/4+gpfvD+SbTaVs3mNmZF9fpo0OwGqTzPvOPceqVbeaOmZGSBddqRVCdAU2AHtwTM0M8BSOcTNLgLrACWColDLHWfl5F+iLY2rmMVLK7X/b5mgclZlyp2a+6d69NbYKPmBYM70juNSIoK/1juBSppVXdzzX1XRq69HyV3JjwbHu2Wp3pTy83X883OWEu/HFiishzWa9I7hMZs9RekdwqY1p9fWO4FKbt+TrHcGl3nsk2C36sBVu/eaqnRsHtL/pqv1OXDmb2UYuPQ4GoPcl1pfA+HK2OReYW9VsiqIoiqIoivJ/irvP0vEPamZ7k6IoiqIoiqIoNZ7LWmYURVEURVEURblG1NAxMzVzrxRFURRFURRFqfFUy4yiKIqiKIqi1HBSjZlRFEVRFEVRFEW5dqjKjKIoiqIoiqIobqnGdjPzMBr1juAyp9Nq7r0EAM4kttQ7gkvV7XBM7wguU8tUpncEl8o+lKF3BJdKGNBJ7wguVXL8lN4RXMqvaWO9I7iMj6VQ7whKFZxJzdQ7gou5yT3I1AQAiqIoiqIoiqIo144a2zKjKIqiKIqiKIqD/Md72bs31TKjKIqiKIqiKIpbUi0ziqIoiqIoilLDSTVmRlEURVEURVEU5dqhWmYURVEURVEUpaZTLTOKoiiKoiiKoijXDtUyoyiKoiiKoig1nBQ1czYzVZmpoIl31Sa5uR/5hTYmvZQKwJ23hNGhRQB2KckvtPHOvDRy8m36Bq2kQZ0NNIjRKC6VvPeVFYCh3QyEBzneAN6eglKzZObXVj1jVlpm5lmmv/kGubk5CKHRt29/BgwafH758mVL+WT2LOYv/IKgoCAdk1Zcel4h0xavIbvQhBAwpENTRnRtycEzWby8Yh0ms4WokEBeHd4Hf29PveNWnIeRiEnPIzyMoGmU7N5C4XdLCblrIsbYBLDbMB8/TN7ij8Dufu8/YfQk8Y0ZCKMRYTCQv/EXMhbMPb886oFJhPTpx77b+umWsSrKrDbGLVmP2WbHZrfTu340D3Ruwun8Yp78disFpWYaRQbzUt92GA1u2GnA4EHQ6EfB4AGaAXPK75T88jXe7Xri3aE3htBIcv4zBVlSpHfSCkvPK2La0p/JLjIhhGBIu8aM6NKCg2lZvLxyg/PYEsCrw3q75bElIzObV2bMJDsvD00IbrmhF0Nv6cechV+w6se1BAcGAjBu5DA6JbfWOW3F5eek8eXsxynKz0JoGm26DaPD9aMoKcpj2YdTyM8+TVBYNLc9MB0fP/f63AOYMCKS5GaO87KHXjnxl2UDewcz+tYIRj1+hMJiu04JFVdzWWVGCBELzANqA3ZglpRyhhAiFFgMxAGpwDApZa4QQgAzgP6ACRgtpdzh3JYN2OPc9Akp5QBX5S7Pms35fLMul8mj65wvW/FjLp9/nQ3AzT2Dub1/ODMXuuedwncesbPlgJ3BXQ3ny5auv3BieGOygTKz1CNatTAYDNxz7/0kJdXHZDLx8KR/0apNW+rWrUdm5ll27fydiIhIvWNWikHTeOTmLjSOjqC4zMzwd5bQsX4sLyxby5SbOpOcEM2KbfuZ+8tOJtzYQe+4FWe1kPXui0hzGWgGIh56gdL9uzD9voGyz/4fACGjJuHXqRfFm37UOWzFSYuZo09OwV5aAgYDSf/9fxRu34rp4H586jfE4O+vd8Qq8TRofDDkOnw9PbDY7Ixd8gtd4msz//dDjGiTxI0NY3nlp52s3JvK0JYJesetOJuV/HlvgcXx/xk45jEsh/diOXkY859/EHj3VL0TVppBEzzSv9OFY8u7y+iYFMMLy39hSr9OJCdEsWL7AeZu2MWEPu31jlthBoPGv8aMoGFiPKaSEu6dOo12rZoDMHRAP+4YdLPOCatG0wz0GfY4deo1pay0iI9fuo2EJp3ZvWkF8Y070qX/fWz6dhabvvuI64c8onfcCvv5twK+/SWfh0bV+kt5WLAHLRv5cjbHolOya4+azazirMBUKWVjoCMwXgjRBHgCWCOlrA+scf4M0A+o73zcB8y8aFslUspWzoduFRmA/YdLKCr+61XfktILtX0vTw33PdWH4xmSkrJ/3oNmcRp/HHPfqxuhoWEkJdUHwNfXl9i6dcnOygLg41kfMOaecQg3bYaNCPSjcXQEAH5eniREhnA2v5jUzFzaxkcB0Kl+LGv2HtEzZpVIcxkAwmBwXAFHUrZ/1/nl5hOHMQSH6pSu6uylJQAIDw+EwQOJBE2jzj0PkDb7A53TVY0QAl9Px/Uzq92O1e44jmw7mUnv+tEA3NykLuuOnNEtY5VZHP+faAaE5rggZEs/iT0/W8dQVXfJY0tBMalZebSNd1zY65QUw5q9x/SMWWnhoSE0TIwHwNfHh3ox0WRm5+qcqvoEBEdSp15TALy8/Qmvk0hhbgYHd62hRedBALToPIiDO3/SM2al7T9SSqHpf1vj77ktnHkrs3DrkzLlirisZUZKmQakOb8vFEKkANHAQKCHc7VPgXXA487yeVJKCfwmhAgWQtRxbueaN3JAOD07BFJcaufp6Sf1juMS9WoJikokOYV6J6keGRnpHDlymIaNGrHlt18JCwsjPiFR71jV4nROAQdOZ9G8bi2SaoWxbv8xejZNYPUfR0jPc79uLucJQcQjr+ERUZviDT9gOX74wjLNgG9yN/KXz9UtXpVpGvVnzMIzKprsVSsoOZhC2MDbKNiyCWtujt7pqsxml4z8/GdO5hUxrGUiMcF+BHgZ8dAc19UiA3zILCrVOWUVCEHQuKcxhEZQum0d1tPueXJ/OadzCzhwJovmsbVIqhXKupRUejaJZ/XeI6Tnu/GxxSktI5NDR1Np0iCRPSkHWfHNan5Yu4FGSQmMHzOCADdvIc3LOkX6iRSiE1pSXJBNQLCjJ0JAcCSmQvc/xpzTrrkfOXlWUk+b9Y5ybXHTi7XluSrtTUKIOKA1sAWoda6C4vx6rk9PNHBxLeCUswzAWwixXQjxmxBi0GVe5z7nettT9y+u5r24vPlfZTF22lF+2VrATT2Cr+prXy3N4zX2uHGrzMVKSkp49d8vMu6+B9E0A0sWLWTEXaP1jlUtTGVmps7/nkcHdMXf25MXhvZi0ea9DH9nCaYyM0YPN25mlpLM/zxO+nMP4lkvCY86secXBQ8di/lICuajB3QMWEV2O4cm3kvKqKH4NmiMX7MWBHftQdZXK/ROVi0MmmDhyN58d28/9qbnkJpdQ66MnCMl+bNeInf643hEx2OIiNI7UbUylVmYumA1j97U2XFsGdyDRb/tY/i7X2Aqs7jnWKeLmEpKeeb16Uwcexd+vr4M6teHhR+8zZzprxIWEsx7nyzQO2KVmEuLWfr+JG64/Um8fNy7UnY5nkbBkBtDWfhNzamcKZfn8iOPEMIfWAZMllIWXG7VS5SdaxysK6VMBu4E3hZCXPLyuZRylpQyWUqZHNfk9irlrqz12wro1DpAl9d2JU1Ak7oae1PdvzJjtVp59d8v0KNHLzp3uY70tDQyMtKZNP5+xo4eSVZWJpMnPUhujvsdCC02G1M++57+rRpwfTPH2yQ+MoQP7x3AoknD6NuqATGh7jfA8+9kiYmyw/vxbtQSgIC+Q9D8A8lfOU/nZNXDXlxE0Z5d+LVojWedaBrNXkCjTxaheXnR8GP3PqECCPD2JDkmgj3pORSWWc53OTtbWEKEv7fO6apOlpVgST2IMamp3lGqjcVmY8rnP9C/VX2ub+YY0xQfGcKH99zMoglD6NsyiZiwQJ1TVp7VauWZ16fTp3sXundyjPsJDQ7CYNDQNI2b+/Qi5ZD7dtG1WS0snTmJ5h1voXHbGwDwCwyjMO8sAIV5Z/ENcN8uuherHWGkVpgH05+sy4cvxBEW7MGbj9clOMBQ7nNrOim0q/a4mlz6akIII46KzAIp5XJncYYQoo5zeR3grLP8FBB70dNjgDMAUspzX4/i6JZ2TU0nUifCeP779i38OZ1e85o1E+oIsvIlBSa9k1SNlJJ33n6T2Ni6DBo8BIC4+HjmL1zK7LnzmT13PuHhEbz9zkxCQt3rwC6l5Pkv1pIQGcKobq3Ol2cXOf5odrvko5+3M7Sje55gaX4BCB9fxw9GI14NmmE9ewbfjr3watSCnHkzQLpv52hDYBCan+NqqfD0JKBVW0oO/0nKyMEcGDOcA2OGYy8r4+C9I3ROWjm5pjIKSx3HxlKrjS0nzhIXGkBybARrDp0GYNX+E3RPrHO5zVyzhK8/wsvH8YOHEWNCY2xZ6fqGqiZSSp5f/gsJESGM6tryfHl2kWOMl90u+WjtDoa2d89ji5SS19+dRb2YaG4feNP58qycC+NmNmzZRnzdGD3iVZmUkq8/fZrwOol0vGHM+fKGrXrxx68rAfjj15U0bNVbr4jV6sQZM6OfPMb9z6Vy/3OpZOdZmfr6CfIK3W+WS+XKuHI2MwHMBlKklG9dtOgr4G7gNefXLy8qnyCEWAR0APKllGlCiBDAJKUsE0KEA12AN1yVuzxT76lDswa+BPobmP1KAgtXZdO2mR/RtTyRdjibY2Hm5+45kxnAkG4G4mtp+HrD1CFG1u6yseOwnebx7j3w/5z9+/ex9uefiIuLZ9KE+wEYdfc9JLdzw9m9/mZnahqrdhykfu0whr29CICJfTtyIiufRZsdkwH2bpbIoOTGesasNC0ohJAR/0JoGgiNkp2bKd23g6i3PseWm0nE5JcBKP1jK4U/LNM5bcUZQ8OInfokaBpCaORtWEvh1s16x6o2WcWlPPfDdmxSIiVc3yCabgl1SAgN5Klvt/L+pv00jAxmUNM4vaNWiuYfhP/AMaBpIATm/duxHNqDd/teeHe+Ec0/kOAHnsV8aA/Fqz7TO26F7Dyezqqdf1K/dijD/t9SACbe0N5xbPltHwC9m8YzqG1DPWNW2p6Ug/ywbiMJ9WK5Z/KTgGMa5jUbNnPo2HGEgNqRETzy4Fidk1bOycM72LP5SyKjGzDrBUdP/Z63PkznfuNY9sHD7Nq4jMDQOgx54G2dk1bOlNG1aVrfh0B/Ax+9FMeib3NYs/lyHYGUmkZIF13JFEJ0BTbgmFL53FnwUzjGzSwB6gIngKFSyhxn5eddoC+OqZnHSCm3CyE6Ax86t6EBb0spZ5f3+gMfPOi+l2jL0bJjvN4RXGpk15pxNfOf1N2zUu8ILpO9dpPeEVwq+5D7Xqi4EgkDOukdwaXKzrr3rGLl8WvqnhcqrkR+k256R3CpH7Pa6B3BpZYtPlz+Sm5sxbv13WJkfdbezVft3Di8Waer9jtx5WxmG7n0OBiA/2nLdM5iNv4S5b8Czas3naIoiqIoiqIo7s5llRlFURRFURRFUa4N6qaZiqIoiqIoiqIo1xDVMqMoiqIoiqIoNZ26aaaiKIqiKIqiKMq1Q7XMKIqiKIqiKEoNJ2toG0bN3CtFURRFURRFUa5JQohYIcRaIUSKEGKfEOIhZ/nzQojTQohdzkf/8rZVY1tmPL099Y7gMte1qrG30AGg0OavdwSXsufn6R3BZUpzi/SO4FKRTd3zDuBXyppfqHcEl/IMCtA7gmtZLXoncBnvkly9I7iUr1fN/lyvXS9C7wgKIK+tMTNWYKqUcocQIgD4XQjxo3PZdCnlf690QzW2MqMoiqIoiqIoyrVHSpkGpDm/LxRCpADRldmW6mamKIqiKIqiKDWcFNpVewgh7hNCbL/ocd8/5RJCxAGtgS3OoglCiD+EEHOEECHl7ZeqzCiKoiiKoiiKUm2klLOklMkXPWZdaj0hhD+wDJgspSwAZgKJQCscLTdvlvdaqpuZoiiKoiiKotRwkmtqzAxCCCOOiswCKeVyACllxkXLPwJWlbcd1TKjKIqiKIqiKMpVI4QQwGwgRUr51kXldS5a7VZgb3nbUi0ziqIoiqIoilLDSXFNtWF0Ae4C9gghdjnLngLuEEK0AiSQCtxf3oZUZUZRFEVRFEVRlKtGSrkRLtnv7duKbuuaqqIpiqIoiqIoiqJcKdUyU0EPDg+nbRNf8otsTH3j9F+W3dIjkFEDw7jn6eMUFtt1Slg1uVnpzHtvGgV5WQih0eX62+jZfyQ7Nq/m26UzyTh9lEde+Zx6iU31jlop2ZkZzJz+Inm52Qih0avvQPoNuJ0l8z/k9y0b0IRGYFAID0x+mpAw97rJV5nVxtjPfsRss2GzS65vVJcHu7XgdF4RT6zcSH6Jmca1Q3h5QGeMBoPecStMGI1EPfU6wmhEaAaKtm0id8UCIsY+hFd8EiCwpJ/m7EfTkWWlesetOA8joQ8+jfDwAM1A2Z6tFK1eTtAdD2KMSUDarVhOHKVg2Ryw2/ROW3EGD/zvnHx+/ywHd1K68Vs823TDK7knhpAI8t95HFlSrHfSyjF44DtkPBg8QNOwHv4D828/YGzRBc/W3dCCwyn68FlkqfvtX3p+EdOWrSe7yIQQgiHJDRnRqRkH07N5+atNmMxWooL9eXVID/zd8IbVGdm5PP/+p2TnFSCE4NbeXRnerycAi79fx9LVv2DQNLq0bsakEbfqnLbi8rLTWPrhExTmZyGEoH3PYXS5cRSmojwWvjuF3KzThIRHc+fE6fj4Bekdt8Lu6utD8wQjhSbJS3MdN/69qbM3XVt4UljiuBnpl+tL2HfMqmfMa8I1dtPMauOyyowQIhaYB9QG7MAsKeUMIUQosBiIw9EXbpiUMtc5EGgG0B8wAaOllDuc26oLfAzE4uhD119Kmeqq7JezbmsR328sYMKdfz3RDQs20KKhD5k57v1m0QwGBt81ldiEJpSWFPP6E8Np1KITUbFJjHvkLRbOeknviFWiGQyMuGcS8UkNKTEVM+3hMTRv1Z6bB49k2EhHt8zvv1rC8kVzGDv+cZ3TVoynQWPWiN74ehqx2Ozc89lquiRGMX9LCiPaNaJv0zhe/m4LK3YdYVjbBnrHrTBpsXDmtaccFRWDgein/4Ppj+1kLZiFLC0BIOzOewnqcwt5q5bqnLYSrBZyP3wFaS4DzUDo+GcwHthNyc5fyV84E4CgO8fj06EHJZvX6By2EmxWiha9AxYzaBr+I6ZgOLof26mjFB/ei/+dD+mdsGpsVkzLZ57fP9+hE7CmpmBLS8V0bD++Q/6ld8JKM2gaj/RtT+OocIrLzAz/4Es6JkbzwsqNTLmxPcnxdVix40/mbtrDhN5t9Y5bYQZN46GRg2kUX5fiklJGPfU67Zs3Iie/kPW//8Hnrz+Fp9FITn6h3lErRTMY6H/nY0THNaWspJj/9+xtJDXrzI71K0hs2oket4xj3dcfse7rj+g3/BG941bY5r1m1u0wM7q/71/K1/xexk/bynRKpVxNruxmZgWmSikbAx2B8UKIJsATwBopZX1gjfNngH5AfefjPhzzTJ8zD/iPc1vtgbMuzH1ZKUdLKbpEq8voQWHM/zoXidQhVfUJCokgNqEJAN4+ftSOjicv5yy1YxKoFRWvc7qqCwkNJz6pIQA+vn5Ex8aRm52Jr6/f+XXKykrADa9eCCHw9TQCYLXbsdrsCGDb8Qyub1wXgFuaJ7Duz1M6pqyacy0uwuCBMBhAcr4iAyCMniDd9z0ozc4PXoMBoXmABPOB3eeXW04ewRAUqlO6amAxO75qBsdDSmxnT2EvyNE3V3X5n/0De+ZpZGGuvrmqKCLAl8ZR4QD4eXmSEBHM2QITqdn5tI2rDUCnxCjW7E/VMWXlhYcE0SjecYz08/EmProWmTl5LPtxPXcPuAFPo+O4GhoUoGfMSgsMjiQ6ztGbwsvHj8ioRApyMti/42faXDcQgDbXDWT/7254kQQ4fMpGcan7HvevJom4ao+ryWUtM1LKNBw3u0FKWSiESAGigYFAD+dqnwLrgMed5fOklBL4TQgR7JyeLQTwkFL+6NxWkasyV1ZyU19y8q0cP2PWO0q1yj57mlPHDhCX1FzvKC6RmZFG6pE/SWzoOMgvnvcBG9Z+h6+vP0+/8q7O6SrHZrdz55zvOZlbyO1tGxAT4k+AtxEPzXHdolagL2cLTTqnrAKhEfPiDIy16pD/0zeUHT0IQMS9k/FtmYzl9EmyF87WOWQVCEHY5JcxhNXC9OuPWE4eubBMM+DTpisFX32mX76qEoKAux9HC4mgbMd6bGnH9U5UvYTA946H0YLCMf+xCXvGCb0TVbvTuYUcSMumeUwESZEhrDtwgp6N67F67zHS892vC93fncnM5mDqKZomxfHO5yvYdeAwMxd/hafRyEMjB9MksZ7eEaskN/M0Z46nEJvUkqKCbAKDIwFHhaeoplxUcOrR2osOTT05kW5l2dpSTGWqwlNTXZUJAIQQcUBrYAtQy1nROVfhiXSuFg2cvOhpp5xlDYA8IcRyIcROIcR/hBCX7PAvhLhPCLFdCLH96J6FrtmZv/E0Cgb3CWLxd+595e3vykpNfPzmFG4b/Rg+vv56x6l2pSUmpr/6JHeNm3y+Veb2UQ/w7idf0qXHDaxe9YXOCSvHoGksvrc/P0y8lb1nsjmWVfA/67hho9MF0s6pZyZyfPLdeCc0wDPacWKR+fHbHJ80CnPaSfw7XKdzyCqQkuzp08h8eRLG2EQ8asWcXxQ4eDTmYwewHDuoY8AqkpLCua9R8P7TGOrUQwuvU/5z3ImUmD5/i6LZL2KoVRctrLbeiaqVqczC1EVreLRfR/y9PXlh0HUs2rqf4TNXYjJbMBrce04hU2kpT0z/iCmjhuDv64PNZqeg2MSclx5l0ohbeXLGbKQbt/yWlRYz/51J3DziCbx9at7n+sXW7yrjmY8KeGVuIflFktt6eusd6ZoghXbVHleTy19NCOGP4+6ek6WU/3tmddGqlyiTOFqPrgMeAdoBCcDoS21ASjlLSpkspUxOaH5HlXJfqdrhHkSGGvnPo9G890wMYUEevDE1muAA9xtgfY7NauGjN6eQfN1NtOpwvd5xqp3VamX6q0/RpceNtO/c43+Wd+5+A1t/XXe1Y1WrAG9PkutFsudMFoWlFqx2R9fIjAITEf6+5Tz72mc3FVNy4A98WlzUP1/aKfptPX7tuugXrJrIUhPmoyl4NmoBgF+fW9H8Aij8eoHOyaqHLCvBevIQRmeX1hrHXIrt9BEM9RrpnaTaWGx2pixaQ/8WiVzfJA6A+IhgPry7H4seHETf5onEhLpnNywAq9XG49M/5sYu7ejZvhUAkaHB9GzfCiEETZPi0IQgr/Ca6xxyRWxWCwveeYhWnW+hWbsbAPAPDKMgz9FrvyDvLP6BbtyF9W8KTRIpHSeRG/8wE1dbzXdVk7m0MiOEMOKoyCyQUi53Fmecu7un8+u58S+ncAzwPycGOOMs3ymlPCqltAIrgTauzF0RJ9Is3PvsCca/dIrxL50iO9/KY2+eJq/QDWcbAqSULPjgOWpHx9P75lF6x6l2UkpmvfNvomPrcdOgCxXetDMXGgV3bNlIVIz7dSXIKS6lsNTR1bHUYmXLsXTiwwJJrleLn1Ic3V2+3nOUHg1iLreZa5YWEIjmbEUTRk98mrbCknYKj8gLV/f9WnfAcsY9xwQJvwCEt7Oi6WHEK6kZtrNn8GnfA68Gzclb8J5bjwcSPv4ILx/HDx5GjPUaYsvO0DdUNRI+fuDpvPpr8MAQWx97bs3YPyklz6/cQEJEMKO6XOh2nF3kGK9mt0s++mUXQ9s11itilUgpeWnWfOKjajPipt7ny7snt2T7vj8BOJ6WgcVqJTjA/Vo0pJQs+/hpIqISuK7f6PPljdv0YseGLwHYseFLmrTppVPC6hfod+H6eKv6Rs5kuec5WXX7PzlmRgihAR2llL9WdMPO2clmAylSyrcuWvQVcDfwmvPrlxeVTxBCLAI6APlSyjQhxFkgRAgRIaXMBHoB2yuap7o8dFcETZO8CfAz8MFzsSz5Ppeft7jnlZpLOXpwJ1vXryKqbn1efXQoAAPumITVambpnFcpKsjlg9fGEx3XiAnTPtA5bcUd3P8HG9d+T2xcIk9OclTWho16gHWrvybt9AmEJgiPqM3Y8Y/pnLTisopLePbrzdjtEruU9Glcj271Y0gID+KJlZt4f/1uGtYKZVDLRL2jVopHcCiR900BoSE0QdGWjZh2byNq2htoPr4IAWUnjpE59z29o1aKITCYoNvvB00DISjdvYWylF3Ueu1TbHlZhE18HoDSPdso/mmlvmErQfgH4nvTXQjh2D/zgR1Yj+zFs213vDtcj/ALJGDMU1iO7qPk+8/1jlthwi8Qnz53gCYAgfXQbmzHUjC27Ipn254IvwB8R0zFmnqAsjVL9I5bITtPZLBq92Hq1wph2PsrAJh4fTInsvNZtDUFgN6N4xjUur6eMStt98EjfLdhK0mxUYx44hWM0A0AACAASURBVBUA/nX7AAb07MRLH8xn+KMvY/Tw4LkHRyHcsJ/u8T93sHPTV9SObcA70xxTS98wdDLdb76Xhe9OYfsvXxAcFsWdE6frnLRy7rnZlwaxHvj7CF55IJBVm0ppEOtBTKQBCeTk21mw2o3HiirlEuX1/xRCbJZSdqrwhoXoCmwA9uCYmhngKRzjZpYAdYETwFApZY6z8vMu0BfH1MxjpJTbndvqA7yJoyva78B9UsrLjrYf+vAx972EWY77RkfpHcGlQn3cfxDp5TTe7J6TC1yJtDVb9I7gUn61QvSO4FLe4cF6R3Apg7eX3hFcyli7lt4RXKas/jXTIcMl1lhrTqvIpfz4y+VGGbi/mY8Gu0Ut9/jhg1ft3LheUsOr9ju5kk6Eq4UQtwHLZQVGvkkpN3LpcTAAvf9e4Nz2+H/Y1o9Aiyt9bUVRFEVRFEVRar4rqcxMAfwAqxCiFEcFRUopA12aTFEURVEURVGUanG1x7JcLeVWZqSU7js9iaIoiqIoiqIoNdYVzVUnhAgB6gPnJ+qWUq53VShFURRFURRFUarP1b7/y9VSbmVGCHEv8BCOqZJ3AR2BzThmFVMURVEURVEURdHFlVTRHsJxs8rjUsqeQGsg06WpFEVRFEVRFEWpNjX1PjNXUpkplVKWAgghvKSUB4CGro2lKIqiKIqiKIpyeVcyZuaUECIYWAn8KITIBc64NlbVlZpK9Y7gMjsOGfWO4FJdmtTse0HYCgr1juAyAbERekdwKVNGrt4RXMortGZPUlmBuwu4J3vN3b/8gGi9I7hUcVrNHMtwTmFeTb9ppXvco0u64U1fr8SVzGZ2q/Pb54UQa4Eg4HuXplIURVEURVEURSnHlc5m1hWoL6X8RAgRAUQDx1yaTFEURVEURVEU5TKuZDaz54BkHONkPgGMwHygi2ujKYqiKIqiKIpSHaSsmd3MrqST5q3AAKAYQEp5BlA30lQURVEURVEURVdX0s3MLKWUQggJIITwc3EmRVEURVEURVGqkbyiNgz3cyV7tUQI8SEQLIQYB/wEfOTaWIqiKIqiKIqiKJd3JS0zZTgqMAU4xs08K6X80aWpFEVRFEVRFEWpNlf7ZpZXy5VUZmoBDwE7gDk4Kjb/Z00aVYd2zf3JL7Qy4UXHhG4jBkTQoaU/UkJ+oZW356aRk2/VOWnlbFg2jZMH1+HtF8rgh74GIPtMCr9+9Tw2ixmhGeg84FkiYlvonLRycrLS+XjGsxTkZiE0jW59BtPnljtZMnc6u7dvwMPDg4jasdwz8Xl8/dxsaJjBA9/bHgSDBwgN65E9mLesRgSG4HPjSIS3D7bM05SuXgR2m95pK87DSMi4J8HggdAMlO3bRvGalQQOvR+P6Diw27CcOkrhyk/dcv+Eh5Haj7+C8DCCZsD0+6/kfbWQsLsn4BWXBEJgST9D1iczkGVueB8tgwcBI6c4/34a5oM7Kd3wDV5tu+PVrieGkEjy3n4UWVKsd9LKMXjgN3SC4/2naVgP7abstx8wtuyKV+tuaMHhFH7wDLLU/fYvPb+IaSvWk11UghCCIW0bMqJjUw6mZ/Pyql8xma1EBfvz6uDu+Ht76h23ws5mZvLGWzPIyc1D0wT9b7yBwQNvOb986fKVzJozly8WzCMoyP3uxfT13Cc59Mc6/ALCuP+FVQBknDzAt/Ofw1xmIjgsmkH3/hcvH3+dk1bOuMHBtGroRUGxnSffyQRgcK8AerTzpbDYDsCS1QXs/rNMz5iKC13JfWaeFkI8A9wAjAHeFUIsAWZLKY/80/OEELHAPKA2YAdmSSlnCCFCgcVAHJAKDJNS5gohBDAD6A+YgNFSyh1CiJ7A9Is23QgYLqVcWeG9rQZrNufxzdpcHh5T53zZ8tXZLPjK8Qa6pWcIw28K5/3P0/WIV2X12wyiccc7Wf/FE+fLtv3wX1r1HE9sw26cPPgL2374L/3vnadjysrTNAO3j36YeomNKSkp5qWpI2jaqiNNWnXktrsmYjB4sHTeDL5ZNoehox7SO27F2KyYVnwIFjNoGr63jceaegDP1t0w71qP9dBuvHoMxtikPZa9m/VOW3FWC3mzX0eay0AzEHLfU5T9uYfS3ZsxL/0QgMBhD+CT3I2SrWt1Dltx0moh/b/POCoqBgN1Hn+Nkr2/k7N4NrK0BICQYfcQ2Osm8r9bpnPaSrBZKfx8BljKQNMIuGsqliP7sJ46guXwHvzvfFjvhFVjs1K87P3z7z+/YROxph7AduYYxcf24TdkvN4JK82gaTxyQ3saR4VTXGZh+Idf0jEhihe+2sSUG9qRHFeHFTv+ZO6ve5jQq63ecSvMYDBw/9gx1E9KxGQq4V+Tp9K2dSvq1Y3lbGYmv+/cRWSE+94QuEXnwST3HMlXcx4/X7bq02lcP/Rx6jVsz66NX7D5h4/pMWiyjikrb/0OEz/+Vsz9Q/5648rvNxXx7Ub3u3jgSjW1ZeaKRgJJx22T050PKxACfCGEeOMyT7MCU6WUjYGOwHghRBPgCWCNlLI+sMb5M0A/oL7zcR8w0/naa6WUraSUrYBeOCo6qyu0l9Vo36ESCk1/vepbUmo//72Xl4Y734O5dnw7vHz/ekAQCCxlRQCYS4vwDYjUI1q1CA6NoF5iYwB8fPyoExNPbvZZmrXqhMHgqNsnNmhObvZZPWNWnsXs+KoZQNMAiSEmCevhPY7FB37HI6GpfvmqSJqdV9YMBsdDSsx//nF+ueXUUbSgUJ3SVd25Fhfh3D8pOV+RARCenu59F3uL8++nGRwPwJZxCnt+jo6hqtFf3n8GQGLPPI0syNU1VlVFBPjSOCocAD8vIwkRwZwtNJGalU/berUB6JQYxZr9x/WMWWlhoaHUT0oEwNfXh7qxMWRlZwPwwUdzGDfmbtz5xun1GrTDxy/oL2XZGceo26AdAPFNunBgh26nVVV2MNVMkcle/opKjXUl95mZBNwNZAEfA49KKS1CCA04BDx2qedJKdOANOf3hUKIFBw32xwI9HCu9imwDnjcWT7PWXH6TQgRLISo49zOOUOA76SUporuqKvdNTCCnh2DMJXYeOqtE3rHqVYdbnqSH+aOY9v3/0Ha7dx8/+d6R6oWWWfPcOLYQRIaNPtL+cY1X9Kuyw06paoiIfC9fTJaUBjmPb8i87OhrASk40Avi/IQ/kHlbOQaJgQh41/AEBpJyZY1WE8dvbBMM+DdujNFq9z4/1NoRD3zJh6RdShc+y3mY38CEDZmEr7N22I+c5LcJXN0DlkFQhAw5gkMIRGU/b4e25lUvRNVLyHwu3MKWlA45j82YUuvWZ8FAKdzCzmQlk3z6AiSIkNYd/AEPRvVY/W+VNILivSOV2XpGRkcPnqURg0b8OuWrYSFhZGYEK93rGoXGd2AP3evoWGr60nZ/j0FOWnlP8nN9OnoR9fWvhw7bWHBt/mYSt34QlA1+b/cMhMODJZS3iilXCqltABIKe3AzVfyIkKIOKA1sAWoda6C4vx67jJ/NHDyoqedcpZdbDiw8DKvc58QYrsQYvvxlCVXEq3afPZlJvc8eZh1Wwu4uWfIVX1tVzuwdREd+j/B7Y+tpf1NT7BhxdN6R6qy0hIT77/+CMPvmYqP74V+wquWfoxm8KBj9/46pqsCKTEtmk7RJy9jqBWLFlrrkuu4LSnJffdZst+YgjEmAUPkhUNEwIBRWI79ieX4nzoGrCJp58yLD3Pq0bF4xjfAGFUXgOxP3uHk1DFY0k7i1+46nUNWgZQUznmV/HenYYiKQwuvU/5z3ImUFC94k8LZL2CoVRctrLbeiaqVqczC1CU/82jfDvh7e/LCwK4s2prC8A+/xGS2YDQY9I5YJSUlJbz4yus8OG4sBs3AwsVLGT3yDr1jucTNd/+b7Ws/5+OXBmMuLcbg4X5jnS7npy3FTHnzLNPezSSv0MaI/m58EU8pV7mVGSnls1LKS7YdSylTynu+EMIfWAZMllIWXG7VS73ERdupAzQHfrhM1llSymQpZXK9xsPKi+YSv2zNp3NrNxs4Xo5DO1ZSr2kfAOKb9SXr1B6dE1WN1Wrh/TceoUO3/rTt1Pt8+aafv2b39g2Me/hlhDv3KQAwl2I7fRStVl3w8gHheKsL/2Bk8eXehu5BlpowHzuAZ4PmAPj2GojmF0DRd/94rcOt2EuKKT24B59mbS4USjvF2zbi26aTfsGqiSwrwXriT4xu3OXxsspKsZ46jEe9RnonqTYWm50pS36mf/NErm8SB0B8RDAfjurLovsH0rdZAjEh7vvZZ7VaeeGV1+nVozvXde5EWnoa6RlnuX/iZEbeM47MrGwenDyFnFz37jJ4TnidREY8PId7n1lO0/Y3ERIRq3ekalVQbHd005WwdpuJhBij3pGuCRJx1R5Xk0vvniOEMOKoyCyQUi53Fmc4KybnKijnBiecAi5+N8UAZy76eRiw4lzL0LWkTuSFN0mHlgGcSjfrmKb6+QZGkn5sGwBpR38jMKyezokqT0rJ3PdepE5MPDcOHHm+fM+OTXy3Yi6TnnobLy8fHRNWnvD2A09vxw8GDwyxSdhzz2I7dRiPJMdJv7FRW6zH9umYsvKEbwDC29fxg4cRz8Qm2DLT8E7uhldSM/IXz3TrVifNPxDNx3FPYmH0xKdxSywZp/GIvHB137dlOyzpp/SKWCXCxx9x7r3lYcQjrhH2HPecKOVShI8feJ17/xnxqNsAe66bjr37Gyklz3+5gYTwIEZ1vtAtN7vIMZ7Lbpd8tH4XQ5Pds/ImpeTNGe9SNzaGIbcOBCA+Lo6lCz5l/pyPmD/nIyLCw5j59luEhtSMnhfFBY4xQdJuZ+M3M2nTfbjOiapXcMCF09vkJt6cynDPGWaVK3MlUzNXinN2stlAipTyrYsWfYVjDM5rzq9fXlQ+QQixCOgA5P9tvMwdwJOuynulHhkbRfOGfgT6G/jktSQ+/zqT5Gb+RNfyxC4hM8fCewvc9wN67eKppB/dSqkpj0Wv96BN7wl0GfQiW755BbvdhsHDiy6DXtQ7ZqUdTtnF5nXfEFMviecfdhy8B4+cwMKP38BisfDm8w8CkNCgOaMenKZn1AoTfoH49Lnd0QojBNZDu7GlpmDPycCn7wi8OvbFlnkay76teketFC0giMAh4xCaY/9K92zFfHA3ES/Oxp6XTcgDzwBQtm87prVf6Zy24gzBIYTfM/n8/hVv20TJH9up/firaN4+IATmk6lkz5+pd9RK0fyD8L15FGgaQgjMKb9jObwXr+QeeHfog/APJHDsNCxH9mH6boHecStM+AXie8Md599/lkO7sR7bj2er6/Bs2xPhF4DfyEewpqZQ+tPV7QZdVTtPZLDqjyPUjwxh2EzHRKITe7flRE4Bi7Y6Omj0blyPQa3r6xmz0vbtT+GnteuIj6vH/RMdM3rdM2okHdol65yseiyfNYUTf27FVJTLjEe70W3ARCxlJravdYwvbNSmDy273KZzysobPyyYxgle+PtqvPNYLZatKaRxvCf16hiRQFaujTlf5ukd85ogpZv3OvkHwlUz4wghugIbgD04pmYGeArHuJklQF3gBDBUSpnjrPy8C/TFMWPZGCnldue24oBNQKxzrE65brk/xX0v0Zaja5+GekdwqS5NSspfyY21XPO83hFcpiQ9U+8ILmXKqBldTP5JUOLfhynWLAYfb70juJRnrUuMkashziYP0DuCS/2S1ljvCC71ww9nyl/Jjc3/d5Rb1BL2Hz5z1c6NmyRdvd+Jy1pmpJQbufQ4GIDefy9wzmJ2yYn4pZSp/O9kAIqiKIqiKIqiXIH/y7OZKYqiKIqiKIqiXHNUZUZRFEVRFEVRFLfksm5miqIoiqIoiqJcG1Q3M0VRFEVRFEVRlGuIaplRFEVRFEVRlBpOtcwoiqIoiqIoiqJcQ2psy4y5tEzvCC5zIrVA7wguNbrpEb0juJSxbj29I7iMMBj0juBSvtG19Y7gUvbSUr0juJRHSLDeEVzL10/vBC4TkndM7wguZdAa6R3BpWrH1PD3npuoqTfNVC0ziqIoiqIoiqK4pRrbMqMoiqIoiqIoioNdjZlRFEVRFEVRFEW5dqiWGUVRFEVRFEWp4dRsZoqiKIqiKIqiKNcQ1TKjKIqiKIqiKDWcms1MURRFURRFURTlGqJaZhRFURRFURSlhqupY2ZUZaaCpoyNpUOrQPIKrNw/7SAAowbXplObIKQd8got/PejE+TkWXVOWjkjb/ShWaKRQpPk33MLz5d3b+1J99Ze2O2w96iFlevd8+Z6GZnZvDJjJtl5eWhCcMsNvRh6Sz/mLPyCVT+uJTgwEIBxI4fRKbm1zmkrJj2vkGmLfiS7yIQQgiEdmjKiaysOnsnk5eXrMJktRIUE8OodN+Lv7al33Aors9oY98UGzDYbNrukd1I0D3RqzOn8Yp78bhsFpWYaRQbz0o3JGA3u1+hc0/cPgwd+t08EgwcIDeuh3ZRt/h5jq654temOFhxB4fvTkKXFeietlDKrjbGf/Xj+73d9o7o82K0Fp/OKeGLlRvJLzDSuHcLLAzpjdLOby9b0Y0t6di7PffA52fkFaEJwa89O3NG3OwCLVq9nyeqNeBg0urRqwkN3DNA5bcXl56Sx8uPHKSrIQgiNNt2G0bHPKEqK8vjiwynkZZ0mODyaIQ9Mx8cvSO+4FTashydN4gwUlUj+u/jCuUmXZh50ae6B3Q4px21885tFx5SKK7msMiOEiAXmAbUBOzBLSjlDCBEKLAbigFRgmJQyVwghgBlAf8AEjJZS7nBu6w3gJhzd4n4EHpJSSldlv5zVG3P46qcsHr2v7vmyL749y7zl6QAM7BPOyIG1eefTU3rEq7Lf9pn5ZaeZUf19z5fVj/WgRZKRVz4txGoDf1/3rdkbDBr/GjOChonxmEpKuHfqNNq1ag7A0AH9uGPQzTonrDyDpvHIzV1pHBNJcamZ4e8spmP9urzwxc9MuakryYnRrNi2n7m/7GDCjR31jlthngaNDwZ3xdfTA4vNztil6+kSV4v5Ow8zonUSNzaM4ZU1O1m5L5WhLRL0jlthNX3/sFkpXvoeWMygafjd/hDW1BRsp49RfHQ/fkMn6J2wSjwNGrNG9MbX04jFZueez1bTJTGK+VtSGNGuEX2bxvHyd1tYsesIw9o20DtuhdT0Y4uHpvHwnQNoFB9LcUkpdz3zFh2aNyQnv5D1v+9l0auP4Wn0ICe/sPyNXYM0zcANtz9OnXpNKSspYtZLt5HYtDO7Nq0gvnFHuva/j43fzmLjtx/RZ+gjesetsO0HrWzaa+GO3l7nyxKjNJrGG3hzcSk2O/j76BjwGqLGzFScFZgqpWwMdATGCyGaAE8Aa6SU9YE1zp8B+gH1nY/7gJkAQojOQBegBdAMaAd0d2Huy9p7sJjCYttfykyl9vPfe3tp6FLLqiaHT9koLv3rHnRr5cnqLWVYnbtdZHLfPQwPDaFhYjwAvj4+1IuJJjM7V+dU1SMi0I/GMZEA+Hl7khAZwtn8IlIzc2mbEAVAp/qxrNlzWM+YlSaEwNfTcf3FardjtdtBwLaTmfSu79i/m5vUZd2RND1jVlpN3z/AUZEB0AygaSDBnnkaWZCjb65q4Pj7GQHn38/muD3dtuMZXN/YcfHrluYJrPvT/S501fRjS3hIEI3iYwHw8/EmLqoWZ3Py+eKnTdx9S288jY73ZWhQgJ4xKy0gOJI69ZoC4OXjT0SdRApyMzi4cw0tOw8CoGXnQRzc+ZOeMSvtaJodU9lfyzo39WDtDgs25+lZUcnVz6VcPS5rmZFSpgFpzu8LhRApQDQwEOjhXO1TYB3wuLN8nrPF5TchRLAQog4gAW/AExCAEchwVe7KGn1bba7vEkpxiY3HXnPPA/o/iQwxkBTjwYDrvLFYYfkvJZxIt5X/xGtcWkYmh46m0qRBIntSDrLim9X8sHYDjZISGD9mBAH+/npHrLTTOQUcOJNJ87q1Saodxrr9x+jZNIHVfxwmPa9I73iVZrNLRi5cy8n8Ioa1SCAmyI8ALyMemuO6TKS/D5nF7vupVdP3DyHwG/EIWnA45t0bsaUf1ztRtbLZ7dw553tO5hZye9sGxIT4E+B94e9XK9CXs4UmnVNWTU09tpxzJjOHg8dP0SyxHu8s/IpdB4/y/tJv8TIaeeiOATRNrFv+Rq5heVmnSDuRQkxCS4oKsgkIdlRSA4IjKS50/4sK54QHa8RHGejXwYjFBqt+tXAy017+ExW3dFU6Xgsh4oDWwBaglrOic67CE+lcLRo4edHTTgHRUsrNwFocFaM04AcpZco/vM59QojtQojtp/5c5opd+Udzl6Uzcsp+ft6cy4DrI67qa7uapoGvt+A/C4pY8UsJY2/xLf9J1zhTSSnPvD6diWPvws/Xl0H9+rDwg7eZM/1VwkKCee+TBXpHrDRTmZmpn33Lo7dch7+3Jy8M7c2iX/9g+IxFmMrMGD3cq7/+xQyaYOGIXnw3ti97M3JJzblUtw/3bUav6fuHlBTP/w+FHz2PoXZdtLDaeieqVgZNY/G9/flh4q3sPZPNsayC/1lHuPGfryYfWwBMpWU8NuMTpo68FX9fb6x2OwXFJcx9fjKT7riFJ9/9FJ16uFcLc2kxS96fRN/hT+Ll474X666EQQMfT3hneRmrNlu46wb3G8vlChJx1R5Xk8srM0IIf2AZMFlK+b9H9otWvUSZFEIkAY2BGBwVnl5CiG6X2oCUcpaUMllKmRzT4LaqRq+UtZtz6ZrsfgPoLiev0M6uQ46Bc8fTbUgJ/j7u+4lstVp55vXp9Onehe6d2gMQGhyEwaChaRo39+lFyqEjOqesHIvNxpTPvqN/64Zc3zwJ+P/s3Xd4FNXi//H32ZLeCyGFNAIkNCmhihQpIqgg0qTaFQHFhggW9KLiFbFeECwoXKQoRUEFkSJVOkjvPSGB9GSzyZbz+2Nj0O9FMWUz2fzO63n2STIzmf0MS2bm1IG4WkHMergvC58cTM9m9YkK9tM4ZcX5uruRHBnCgcuZ5BVZHF2ygPT8QkK9PTROV3E1/fgoKsR64SSG2CStkziFr4cbyTG1OJBylTzztc8vLddEqI9rVgbV9HOL1Wpj/Ptz6Nm+Jbe2agpAWGAAXZKbIoSgcd0YhBBk57nmBBU2q4XFM56gSZs7SWrZAwAfv2DystMByMtOx9s3SMuIlSo7X3LwjKMHyYV0O3YJNeHUqVyfUwszQggjjoLMfCnl0pLFaSXdxyj5ml6y/CJQ5w+/HgWkAHcDv0op86WU+cCPOMbgVBsRYddK/G2b+3MhtehvtnY9+09aqB/t6JFYK1CHQSfIL3TN2ikpJW99NJuYqEgG9elduvxq5rVxM5u27yQuOkqLeBUipWTy12uJrxXIiI7XZmLLyHd0a7HbJZ+s3cmAtk20ilghWaYi8oocYy7MVhvbL1whNtCX5KgQ1p5IAWDl4fN0ig/XMma51fTjE57e4F4yCtdgxBBdH3tmtesxXG6ZBWbyzCWfn8XK9jOXiQv2IzkmjJ+PnAdgxYHTdK6vzi3VjZSS1z5dSFxEGMN6dS5d3im5MbsOnwDgXGo6VquNAF9vjVKWn5SS7754kZDwurS77f7S5fWb3cr+rcsB2L91OQ2ad9UqYqU7dMZGQqSjpTDEX2DQQ4FrTsJaqaQUVfaqSsJZTaYls5N9CWRKKcf9YfnbQIaUcqoQYgIQJKUcL4ToDYzBMZtZG+ADKWVrIcQg4GGgJ47Wm1XAe1LKFX/3/reN3OeUA5swKoamiT74+xjIyrUwb9llWjf1IyrcHbuE9KvFfPDlRTKynDcFYEKTWKft+/7eXtSrY8DHU5Brkny/xcyOw8UM6+lFVC09Vptk2QYzxy84b+rpl3s7r1Xkt8NHGTPxNeJj6qATjrL8w8MGsnbTNk6cOYcQULtWKM+OepCQoECnZPA/vs0p+91zJoX7Zy6hXu1gdCV9Wcb2bMf5jGwWbj0AQNfG8Tx5e3uEk/q6WC85b3DziSs5vLJmNza7RCLpVi+KR9okcjGngIk/7iTHXEyDUH+m3JaMmwt2d6kOx2c3O+9qrwsJx7PnUBA6EALL8X0U/7oat+YdcUu+FeHtizTlYz1zGPOaRU7JYAh2Xs3z8fQsXl6xDbtdYpeS7kkxPHpLEy5m5TFh+RZyzUU0CAvi9bvaO+3z0wU45/iqw7nFEuG8Gfz2HTvNQ//6kIQ64aXH9/jA3rRpXJ/XZi/k2PlLGPV6xg3pQ6tG9ZySYUXR7U7ZL8D5E7uZM3UotaLqI0que137PUVkfFO+mfkUOZmp+AeFM2DUe3j6BDglw54DzhvrN7SbG3Uj9Hh7QF6h5KedFnYftzGwixuRITqsNli5rZiTl5w3ZmbaKNeY5nXH0Zwqq4lunehfZf8mzizMdAA2AQdwTM0MMBHHuJnFQDRwHhggpcwsKfx8hKPQYgLul1LuEkLogRlARxyTAaySUj59o/d3VmGmOnBmYaY6cGZhpjpwVmGmOnBmYUZxPmcWZqoDZxZmqgNnFWaqA2cWZqoDZxZmqgNnFmaqA1cpzPxahYWZtlVYmHHmbGab+euRqv/Tllkyi9no6yy3AY9WbjpFURRFURRFUVyd0woziqIoiqIoiqJUD+qhmYqiKIqiKIqiKNWIaplRFEVRFEVRlBquqp//UlVUy4yiKIqiKIqiKC5JtcwoiqIoiqIoSg2nxswoiqIoiqIoiqJUIzW2ZSY8zjWfkv1PJCb5ax3BqbLcwrSO4FS+2ZlaR3CanGNntI7gVCm7z2kdwanqtEvQOoJTedpsWkdwKncv13s6/T9l8qnZ1wWrqWbWmP8u62qB1hGczEvrAP+IGjOjKIqiKIqiKIpSjdTYlhlFURRFURRFURzsUusEzqFaZhRFURRFURRFcUmqMKMoiqIoiqIoiktS3cwURVEURVEUpYZTEwAoiqIoiqIommHIxAAAIABJREFUiqJUI6plRlEURVEURVFqOPXQTEVRFEVRFEVRlGpEtcyU0QN9/GhW353cAjsvzsgAoG9nbzq18CTPZAfgm7X5/HaiWMuY5bZ24UTOHt6Ap08wQ8avAODKpSNs+HoyNmsRQqen8z2vEBbTVOOk5XPlSjrvvTOV7KwshBDc1rM3d/a9h/lz57D91y3odDr8/QN44unxBAeHaB23TIqsNh6ct4Zimw2bXdItMZpRHZtyKTufCcs3k1NYTFLtQKbc1R6jXq913LIzGAl9YjLCYASdjsL928n78WsCh4/FWCce7DaKz50ke9EnYHe9hyMKNzcSP5qJzs0NodeTuX4dKZ9/StzEl/Bt1hxbQT4Ap1//F4UnT2icthwMRoJGvYgwGECnp+jADvJ/Wor/vaMwRsUj7VYs50+Tu+Rzl/z80BvwvnccQm8AnQ7L8X0UbfkBt+YdcWvZGX1gKLkfTUAWut7DAy9n5zNpyXoy8gsRQtA/OZGh7ZtwLDWDKd9twlRsISLAlzcH3IqPh5vWccss7UoGb7w/k4zsbHRCcGePWxlw5+18vuAbVq5ZT4CfHwAPDxtIu+TmGqctuxVfvMDJAxvw9g3mkckrAUi7cJQf579CsdmEf0gkfR+chrunj8ZJy+f+O31oWs+dvAI7L8/KAuCujl50bO5BnskxF/HS9QUcOOma92WVSdbQqZmdVpgRQtQB5gK1ATswW0r5vhAiCFgExAJngYFSyiwhhADeB3oBJuA+KeWekn29BfQu2fW/pJSLnJX7RjbvK2TtDhMP3+3/p+WrfzWxaqtJo1SVJ7HV3TTpMJSfv5pQumzrirdpfdtoYpI6cvbwL2xZ+Tb9Rs/TMGX56fV6HnjoMeom1MdkMvHME49xU4uW3N1/IENH3A/Aim+XsuireTw+9imN05aNm17H7KFd8XIzYrHZeWDeT9xcN4L/bj/C0FaJ9GwUy5Qft7Ns3ykGtqyvddyys1q4+tFryOIi0OkJffJVzIf3Ydq9iaJ5HwIQOOIJvNvdSsGWNRqHLTtZXMyxJ8dgLyxE6PUkzpxNzvZtAFyY8SFZG9ZrnLCCrBayZr1R+vkFjX4J49H9FO7dSs6CmQD4DxmNZ5vOFG5bq3HYcrBZKVj0AViKQafD+96nsJ4+jPXSaSynDuIz+AmtE5abXq/j2dvbkRQRQkFRMYNnLKNtQhSvLt/I0z3bkBwXwbLdR/li837GdGulddwy0+t1PH7/UBrUjcNUWMhDz0yiVbMmAAy463bu7XuHxgkr5qb2/UjuMowVc54vXfb93El07f88MQ1as2/zN2z76VM69xmnYcry27K/iLU7zTzUx/dPy9dsL2T1r4UapVKqkjO7mVmBZ6SUSUBbYLQQoiEwAVgrpawHrC35GeB2oF7J6xFgJoAQojfQAmgGtAGeE0L4OTH33zp+zkJBoV2rt3e6yLqt8PD6c0ENISg2O2qFi815ePvV0iBZ5QgKCqZuguNG3svLi6joGDKvXsXLy7t0myKzGUfZ2rUIIfByMwJgtdux2uwIYOe5NLolRQNwZ5N4Nhy/qGHKipHFRQAIvR70BkBSdHhf6fri8yfRBwRplK7i7IWOC68wGBw1/DWsFu33zw+9HqFzHF/x0f2l6y0XTqH3d93PD0tJza9O7/g/isSefhGZm6lprIoK9fUiKcLRUu3t7kZ8aADpuQWcvZpNy9hwANrVjWLtoTNaxiy3kKBAGtSNA8DL05OYqEiuZGRpnKryRNdvhaf3n6/rGWlniK7vKHjGN7yZY3t+0iJapTh+vmbfl1UmO6LKXlXJaS0zUspUILXk+zwhxBEgEugDdC7Z7EtgA/B8yfK5UkoJ/CqECBBChAMNgV+klFbAKoTYD/QEFjsre3l0a+3FzTd5cCbFysLVeZjMNecu5Ja+E/lu1kNsWfFvpN3OPU8s0DpSpUhLu8zpUyepn5gEwLwvP2P92jV4e3szZeo7GqcrH5vdzpDPV3EhK49BLesTFeiDr4cRg85RbxHm50V6ngu3IApB6LNTMYTWpmDTaiznTl5bp9PjldyRnKVfaBavwnQ6Gn32Be6RUaQvW0LB4UPQtx+RjzxGxH0Pkrt7Jxc/noG0WLROWj5CEDxuCvrgMExb12C5cOraOp0ezxYdyP3ONVt9ARACnxHj0QWEUrx3I7bUc1onqnSXsvI4mnqVJlG1SKgVxIaj5+iSFMtPh05zOcf1utD9X6lpVzhx+iwN69flwJFjLPv+J1av30RiQjyj7x+Kr49rdsX6v0Ij6nN8/1oaNOvGkd2ryM1M1TpSpbu1lSftmnpwLtXCojUFNeq+TPmzKpkAQAgRCzQHtgNhJQWd3ws8v1fzRwIX/vBrF0uW7QduF0J4CSFCgC5Anb94n0eEELuEELuO7666C+K6nYU89/5VXv44k5w8G4Nv873xL7mQg1sW0KHPBO57eQMd+r7AukUvah2pwgoLC3nr9ck89Mjjpa0yw0c+yOdzF9Kpc1e+X7Fc44Tlo9fpWPRQL1aPvZuDKRmcuZr7P9u4YKPTNVJy5e3nufzKKNxiEjCEXzsVBAx4kOJTRyg+fVTDgBVkt3Po/hHs73cX3kkN8YyL5+KsGRwcMojDD9+Pwc+P8KHDtU5ZflKS8e4krkx5AmOduhjCokpX+fW7j+IzR7GcOaZhwAqSkvwv3yL345fQh8egCwnXOlGlMhVZeGbBGp7r1R4fDzde7deJhb8eYvCMpZiKLBj1rj2nkKnQzEtvvcvYB4fj7eVF39u7s+Dj9/j83TcJDgzgP3Pmax2x0twx8nV2r/+Kz6b0o8hcgN7gemOd/s6G3YVM+CiTV2dnkZ1vZ1B37xv/0v8HpBRV9qpKTj/zCCF8gCXAOCnl/95Z/WHT6yyTUsqfgB+ArcACYBuOLmzX23i2lDJZSplcv2XVXfBzC+xI6RhY9cueQuIjjVX23lXh6K7l1G3aA4CEm3qSdv43jRNVjNVqZerrk+nUuSvtbr7lf9Z37NyVbVs2aZCs8vh6uJEcU4sDKVfJM1uw2h1N8Gm5JkJ9vDROV3Gy0ETRycN4JN4EgG/P/uh8/MhZPlfjZJXDlp9P3t49+LdtiyXDMdGItFi4+sP3eCc11DhdxUmzieLTR3BLdEwk4t39bnTevuStqCE3i0WFWC+cxBCXpHWSSmOx2Xl6wRp63ZRAt0aOLllxoQHMur83Cx/vR8+mdYkK0qwHeIVZrVZeeutdune6mU7tWgMQFOCPXq9Dp9NxR/dbOXLi1A324jpCwusy5KnPefDFpTRq3ZuA0OvWEbus3ALpuC8DNu4xExdRs+7LlD9zamFGCGHEUZCZL6VcWrI4raT7GCVf00uWX+TPLS5RQAqAlPJ1KWUzKWV3HIWeajWVj7/PtX/GFokeXEq/blnLZXn71eLSqR0AXDzxKwGhMRonKj8pJR++N406daLp029A6fKUS9fGkezYvpXIKNc7sWcWmMkzO/rsmy1Wtp+5TFywH8kxYfx85DwAKw6cpnP9qL/bTbWl8/ZFeJYUxIxG3Os3xpqeglfbW3FPbErm3PddeqoWQ0AA+pIuLMLNHb/kVhSeO4cxOLh0m4BbOlJ45rRWEStEePsiPEo+P4MR94TG2NJT8GzdGff6Tcie/x+X/vyEpw+4ezp+MBgxxDTAnpGmbahKIqVk8rJfiA8NYMTN12ayzMh3jPGy2yWfbNjLgNauWXiTUvLWR7OJiYpkUJ/epcuvZl4bN7Np+07iol3z3Hk9BbkllSR2O1u+n0mLjoM1TlS5/nxf5s6lKzXrvqy8fq94r4pXVXLmbGYC+Aw4IqWc/odV3wEjgaklX7/9w/IxQoiFOAb650gpU4UQeiBASpkhhGgKNAU0G6n22D3+JMYa8fHSMf3pEJavzycx1o06tR3/lFez7Xyx4u8aoKq31fOe5tLJnZgLspjzaifa3DaWLgP/xablr2O32TAY3eky4DWtY5bbkcMH2bBuDTGxcYwb8wgAw0Y+yM+rf+TSpQsIIahVK4xRY1xvVperBYW8vGIbdrvELiXdk2LoWC+K+BB/JizfwoyN+2kQFkTfm+pqHbVcdP6BBA59HKHTgdBRuHcb5kN7iJj+FbasK4SOmwKA+bcd5K1eonHasjMGhxA36SWETg86Qda6teRs3UKD9z/CEBAAQlB44gRnp72lddRy0fsF4D/oUdDpQAjM+7dTdGQfYVO/xJZ9leCxkwEwH9hJwc+u181T+Pjhffswx/EhsBzbi/X0IdxadMK9dVeEtx8+972A9fQhCle71rjDvefSWLnvBPXCghj4keNva2z3VpzPyGHh9sMAdG0YS98WDbSMWW4Hjhxj9YbNxMfU4YFxLwCOaZjXbtrGiTPnEAJq1wrl2VEPapy0fJZ98jTnju2gMD+LD8Z3pONdYykuMrF7/VcANGjRnZtuvkfjlOX3yN2+NIhx3Je9/WQQ3/5iIjHGSJ3aBqSEjBwbc7/P1zqm4kRCOqn4JIToAGwCDuCYmhlgIo5xM4uBaOA8MEBKmVlS+PkIx+B+E3C/lHKXEMID2FPy+7nAY1LKa9MX/YX7Jqe5bhXfDbRs5bqzif0T3RMvaR3BqaI3f651BKfJ2nNI6whOlbK75g3o/qM67RK0juBUnrUCtY7gVO6xsVpHcJqcxp21juBUq9Nbah3BqX7ZeEXrCE712UuhLjEa9af9xVV2b9zjJrcq+zdx5mxmm7n+OBiArtfZXgKjr7PcjGNGM0VRFEVRFEVRlFJOK8woiqIoiqIoilI92GtonyXXnkdRURRFURRFUZT/b6nCjKIoiqIoiqIoLkl1M1MURVEURVGUGq6qH2ZZVVTLjKIoiqIoiqIoLkm1zCiKoiiKoihKDefCzyX+WzW2MJNyKkXrCE7TrEXNfs5M7byTWkdwqitb9tx4Ixd15VjNeOL5X9G719hTJgAeIQFaR3AqY61QrSM4V1DNPT53c47WEZzKz8umdQSnyssu0DqCk9Xcvz1XULOvzIqiKIqiKIqiYP/Lxz+6NjVmRlEURVEURVEUl6RaZhRFURRFURSlhqupY2ZUy4yiKIqiKIqiKC5JtcwoiqIoiqIoSg2nnjOjKIqiKIqiKIpSjaiWGUVRFEVRFEWp4ew1dMyMKsyU0dMPRdO2uR/ZuVYeeeEoACPvCaddC3+klGTnWnl79jkys60aJy2f9YsncvbwBjx9ghn87AoArl46wi9LJ2OzFKHT67nl7lcIi26qcdLySbuayeT/zCEzOxehE/TteguDe3UFYPGP6/h69Qb0eh03N2/C2GH3aJy2jAxGaj/3OsJgBL0e0+6t5KxYSPCIMbjF1AUhsKSlkPHFB8gis9Zpy0y4uZH4wUyE0YjQ68n6ZT0pcz4ldsKL+DZrji0/H4AzU6dQePKExmnLThjdqP/eRwijG0KvJ3vjelK//Lx0fdSYcQT37MX+O3pomLIC9AZ8hoxDGAyg02M5thfz5h9wa9ER9+Qu6ANDyfngeWShaz6Poshq48F5ayi22bDZJd0SoxnVsSmXsvOZsHwzOYXFJNUOZMpd7THq9VrHLZPLmblM+vI7MnLzETpB/5ubM/TW1hy9cJkpC36k2GpFr9MxcXBPmsRGah23zNKuZjJ5xpfXrgu3dmBwr1sBWLxqfcl1Qc/NzRszdmg/jdOWXXZGKos+foG8nKsIIWjTZSAdeg7HlJ/N/I+eIevKJQJDIxk6djpe3v5axy2zUYOCaZHkRU6+jWen/fkZg3d29mP4nUE8+PJ58grsGiVUnM1phRkhRB1gLlAbsAOzpZTvCyGCgEVALHAWGCilzBJCJAJzgBbAJCnltD/sqyfwPqAHPpVSTnVW7htZsymD79ZcYfxjMaXLvv4+jS+XpALQt0cow/qG88EXF7SKWCENku+mcfuhrF04oXTZtu/fJrn7aGISO3LuyC/8+v3b9Bk1T8OU5afX63ly+AAS46MpKDQz8oXXad00iczsPDbu2s/8t1/CzWgkMydX66hlZ7WQNv1lR0FFr6f2+DcpPLiHzMWfIc2FAAQOuB/fLr3IXbVU47BlJ4uLOfbUGOyFhQi9ngYfzSJn+zYALs78iKxf1mucsGKkpZgTzzyJ3VwIej0N3p9Jzo7tmI4cwqt+A/Q+PlpHrBiblfyFH4ClGHQ6fIY+jf70YWwXT1Nw8iA+Q57UOmGFuOl1zB7aFS83IxabnQfm/cTNdSP47/YjDG2VSM9GsUz5cTvL9p1iYMv6WsctE71e8Ow9XUmKDqfAXMTgqZ/TNimOd5et47Het9ChUQKbDp7kvWXr+Oyp4VrHLTPHdeEeEuN+vy686bgu5OQ6rgv/ftF1rwuATmfgjiHjiYxrSFFhAR+81J96Tdqxe+NyEhq2pctdD7P+u0/YsOJTeg1+Ruu4ZbZhZz6rNucx+t6QPy0PDtDTpL4nVzJds3LZGdRsZmVnBZ6RUiYBbYHRQoiGwARgrZSyHrC25GeATOAJYNofdyKE0AP/AW4HGgL3luxHEweOFZBX8Ocn9ZrM10r7Hu46JK77vyUivhXuXn+umRFCYDE7ar2LzXl4+dXSIlqlCAn0JzE+GgBvTw9iI8O5kpnN0jW/MKJPT9yMRgCC/P20jFluv7e4CL0e9HpAlhZkwFH778pnM3uh41iEweCo4XfhY7keu/mPx6d3HJ9OR+Sjo7k0e6bG6SqBpdjxVad3vKTEln4Re26mtrkqgRACLzfH+cNqt2O1OR5Pt/NcGt2SHOecO5vEs+H4RQ1Tlk+ovy9J0eEAeHu4E187mPTsPIQQ5Bc6PtP8wiJC/X21jFluIYH+JMb98bpQu+S6sJERfW5z+euCX2AokXGO2yZ3T29qRcSTk5nOod3raHlLXwBa3tKXQ7vWahmz3I6cLiLf9L+tLiPvCmL+ikwXviNT/imntcxIKVOB1JLv84QQR4BIoA/QuWSzL4ENwPNSynQgXQjR+//sqjVwUkp5GkAIsbBkH4edlb087usfTvcOQRQU2njujZNax6lUN981kZWfPsTWlf8GaefuMQu0jlQpUtKvcvzMeRolxPHhf5ew7+gJPl60HDejkSeG9adhQqzWEctO6Ah/8R0MobXJ2/AjxWcc3a2CR47Fs0lLLKkXyPpmjsYhK0Cno+HsObhHRpG+fAkFRw4T2qcfkQ89SvjIB8jbvYuLs2cgLRatk5aPTkfizM9wj4zkyrfLMB09TGi/AeRs3Yw1M0PrdBUnBL4jn0cXGErRno3YUs9pnahS2ex2hny+igtZeQxqWZ+oQB98PYwYdI56wzA/L9LzTBqnrJhLGdkcvZBGk9hIxvfvzqiPFjB96c/YpWTus/dpHa/CUtIzOH72Ao0SYvlw/lL2HT3Jxwu/w83NyBPD+tGwbqzWESsk88olLp07QnTdpuTnZuAXGAo4CjwFNaBS4XctG3mSmWPjXKqLXgucRKJmMys3IUQs0BzYDoSVFHR+L/DcqJo/Evhjn62LJcuu9z6PCCF2CSF2XTyxpKKxy+SLb1IZOu4Q67ZmcVf3kBv/ggs5tG0B7e+cwIgXN9D+rhdYv/hFrSNVmMlsZsL0WTw1ciA+Xp7YbHbyCkx8NmUCY4fdw8T3ZiNdsdZf2kn911NcfP4h3OPqYYxw1DZmfPkhF597AEvqRbySO2gcsgLsdg4/NJLfBvTBO6khHnHxXJo9k4PDB3Pk0QfQ+/lRe4jrdXMpZbdz9NH7OTioH96JSfg0uYnAjl1IX1a15zOnkZK8L6aSO+NF9OEx6ELCtU5UqfQ6HYse6sXqsXdzMCWDM1f/t1uScOF7CZO5mGdmL+G5/t3x8XRn8abdPNe/Oz+98QTP9e/O5P+u1DpihZjMZia8O4unRg4ouS7YSq4L4xk7tB8T3/vUNa8LJYrMBfz3/Se5a9gLeHi5eLfVv+FmFPTrGsCi1VlaR1GqiNMLM0IIH2AJME5KWZ4Op9c79V/3bCKlnC2lTJZSJkfV02bw9rqtmdzSKkCT93aWY7uXE9/EMei4btOepF/4TeNEFWO12pjwzix6dmhNlzYtAKgVHEDn1s0RQtAoIQ6dTpCdl69x0vKThQWYjx3Es1HzPyy0U7BrM14t2mkXrJLY8vPJ27sH/9ZtsZS0WEiLhYwfV+KdqFkv1EpjK8gnb99efJq1wD0ykkbzFtJo/tfo3D1oOHeh1vEqTBYVYr1wAmO8639W1+Pr4UZyTC0OpFwlz2zBand0gUnLNRHq46VxuvKx2Gw8/ckSerVuTLfmiQCs+PUAXZs1AKBHiyQOnkv5u11Ua1arjQnTZzuuC60d581awYF0bvX7dSEWnXDd64LNamHe++No1v4OGrfqDoCPXzC5WVcAyM26grdfkJYRK01YsIFaQQbefiaSjyZFEeyv562nIvD3da2JN2o6IUQdIcR6IcQRIcQhIcSTJcuDhBBrhBAnSr4G3mhfTi3MCCGMOAoy86WUv484ThNChJesDwfSb7Cbi0CdP/wcBVSrM2ZEmHvp9+1a+HMhxfVmivo7Xn61SDm9A4BLJ3/FPyTmBr9RfUkpmfLxXGIjazPkju6lyzu1asauQ8cAOJ+ShsVqI8DXtWqudD5+CE9vwDE2xiPpJixpKRhCa5du49m0FZbLl7SKWCEG/4DSQfDCzR2/5FaYz5/DGBRcuk1Ah04UnjmlVcQKMfgHoPf+/fjc8GuZjOnEMQ4M6MOhoQM4NHQA9iIzh0cM1jhp+QhPH4S7p+MHgxFjTANsGWnahqpEmQVm8syO8SNmi5XtZy4TF+xHckwYPx85D8CKA6fpXD9Ky5jlIqVk8rzvia8dzIiubUqXh/r7sOuE49h2HDtLdKhr3gxLKZkya57jutC7W+nyTsk3ufx1ARzH982nL1ErIp6Ove4rXd6wRRd2b1oOwO5Ny2nU8laNElauC5ctPDz5AmNev8iY1y+SkWPj+XdTyMmz3fiXazi7rLrXP1DWsfV/yZmzmQngM+CIlHL6H1Z9B4wEppZ8/fYGu9oJ1BNCxAGXgMHAkMpP/M+88HgsTZN88PcxMP/9Rsxbmkqrm/ypE+6O3Q7pGcW8P8c1ZzIDWDP/aVJO7cRckMXcKZ1o1WMsnfv/i83fvo6029Ab3Onc/zWtY5bb/mOn+HHTryRERzJs/L8AGHVvX+7scjNTZn7Jvc+8itGg55XH70O4WH8QvX8gIfc/CTodCIFp1xYKD+wi7Lk30Hk6aoMtF8+SMf9jjZOWjzE4mLiJL4NOhxCCzA3ryNm2hfrvfoghIBABmE6e4Nz0f2sdtVyMwcHEjJ+E0OtA6Mj6ZR25v27VOlalET5+ePUejhCO/5/FR/dgPXUQt5ad8GjTDeHth+/9E7GcPkThqq+0jltmVwsKeXnFNux2iV1KuifF0LFeFPEh/kxYvoUZG/fTICyIvjfV1Tpqme09dZGVOw5QL6IWA9/4BICxd3Xh5aG9+ffXP2Gz23EzGnh5aC+Nk5aP47qw3XFdeP51AEYN7sOdXdoz5eN53PvsaxgNBl55fITLXRcAzh7fw57N31G7Tn3em3g3AD0HjqPznQ8z/8On2PnLEgKCwxn2xLsaJy2fJ4eF0LCuB77eema+FMXi1dms3+GaLWj/Pynr2Pq/25dwVv9PIUQHYBNwAMfUzAATcYybWQxEA+eBAVLKTCFEbWAX4FeyfT7QUEqZK4ToBbyHY2rmz6WUr9/o/XsM3+u6HVtvoNeAZlpHcKr7on/ROoJT5cx4T+sITnPlWM2pab8enaFmd1OI71Gzzy1utV13JsZ/QhcVq3UEpzEHuV6LVllssHbSOoJTfbXQdSt5/4nF78S6RCn361+r7rGZA9vpHwUe+cOi2VLK2dfbtmRs/UagMXBeShnwh3VZUsq/7WrmzNnMNnP98S4AXa+z/WUcXciut68fgB8qL52iKIqiKIqiKM5QUnC5buHlj/7v2PrytH46rTCjKIqiKIqiKEr1UN0m4/u7sfVSytR/OLa+aqZmVhRFURRFURRFgX80th7+2dh61TKjKIqiKIqiKDWdXVaroT03A8OBA0KIfSXLJuKYIGyxEOJBSsbW32hHqjCjKIqiKIqiKEqVKevY+r+jCjOKoiiKoiiKUsNVtzEzlUWNmVEURVEURVEUxSXV2JaZ4kKz1hGcJifXqnUEpzrr3UjrCE6VOKCv1hGcxr5g6Y03cmGewX5aR3Aqnbub1hGcy1qzz51cvax1Aqex14rXOoJTuZc+jq9mcvOo4ecWF6FaZhRFURRFURRFUaqRGtsyoyiKoiiKoiiKg121zCiKoiiKoiiKolQfqmVGURRFURRFUWo4Wb2eM1NpVMuMoiiKoiiKoiguSRVmFEVRFEVRFEVxSaqbmaIoiqIoiqLUcGpqZkVRFEVRFEVRlGpEtcyU0fjH69KuZSDZORbuf3o/AI8Nj6F9ciAWq52Uy0W89Z+T5JtsGictn7va6qkfJSgww8yVjgfM3dNBT4ifY9CYhxuYi2HWD6758LmrV9L4z/QpZGdlotMJut52F736DGTRvE/YtX0zQgj8AwIZNW4SQcEhWsctk8tZuUyau4KM3AKEEPS/uRlDu7Ti6MU0pixcRbHFil6nY+Kg22gSG6F13DITBiPhE94EoxGh01OwawvZ3y4g5P6xuMUmIBBY0i5x5bP3kUUu+NBcg5GgUS8iDAbQ6Sk6sIP8n5bif+8ojFHxSLsVy/nT5C75HOwueH7RG/AeMAb0BtDpsJ7YT9GvqzHe1AH35h3RBYSQ9/FLSHOB1knLpchq48Gv1lFss2GzS7o1qMOoDo25lJ3PhBXbyCksJikskCl3tMGo12sdt0wu5+QzaclGMvJNjnNLcgOGtmvMscsZTPluC6ZiKxEBPrzZvzM+LvhwxLSrmbz24adkZOeiE4I+3TsyqHd3AL7+YS3frFqLXqenfcumjBk+QOO0ZZd1NZX5MyaSm30VnU5Hu1v706nXcPacvRQmAAAgAElEQVT9uppV38wg7dJpnpqygOi6jbWOWi4P9wugWQN3cgvsvPDBlT+t69XBmyG3+/PY65fJN9XsB5P+EzV1amanFWaEEHWAuUBtwA7MllK+L4QIAhYBscBZYKCUMksIkQjMAVoAk6SU0/6wr8+BO4B0KaWmf22r1qez7MfLTBybULps12/ZfDL/HDY7PDIsmiH9Ipn93/Mapiy/faft7Dguubv9tf8aSzZfu3Hq0UKH2aJFssqh1+sZ/uAY4hMaUGgy8cK4B2javBV33jOEQcMfBuDH775myYI5PDzmOY3Tlo1ep+PZfl1JqlObAnMRg9+aQ9vEON5dvo7Hbu9Ah0Z12XToJO8tX89n44ZqHbfMpNVC6tsvOgoqej3hL0yl8MAeMhZ8ijQXAhA06AH8uvYm54clGqctB6uFrFlvIIuLQKcnaPRLGI/up3DvVnIWzATAf8hoPNt0pnDbWo3DloPNSsGSGWApBp0O74FjsZ49ii3lDAVnDuHdf7TWCSvETa9j9uDOeLkZsdjsPPDVWm6Or81/dx5naHIDeiZFM2X1Lpb9doaBzRNuvMNqRK/T8WzP1iRFhFBQVMzgj7+lbd1IXl2+madva01yXDjL9hzniy0HGNO1pdZxy0yv1/HEyEE0iI+hoLCQ+8f/i9ZNG5GZk8vGnXuZ986ruBmNZObkah21XHR6A32GP0eduIaYCwt454WBNGjantp1Erj/6fdY/MmrWkeskI17TKz5tYBH+wf8aXmQv47GCe5czXLNylfln3NmNzMr8IyUMgloC4wWQjQEJgBrpZT1gLUlPwNkAk8A066zry+Ank7M+o/9diSPvPw//2Hs2p+DraTAf/h4PqHBrlcz9bvz6ZLCor9e3zBGx8Gzrlu7ERgUQnxCAwA8vbyIrBNLZsZVvLy8S7cxm80I4XrTF4b6+5BUpzYA3h7uxNcOIT07D4Eg3+z4UPMLiwj199EyZoX83uIi9HqE3gDI0oIMgHBzd+lOwbK45I9Pr0foDCCh+Oj+0vWWC6fQ+wdplK4SWIodX3V6xwuJ/colZG6WprEqgxACLzcjAFa7HavNjkCw83wa3RpEAXBn41g2nLikZcxyCfX1IinC0VLt7e5GfGgA6bkmzmbk0DLWcc5pVzeCtYfPapiy/EICA2gQHwOAt6cnsZHhXMnMYunq9Qy/uxduRsfnGuTvp2XMcvMPDKVOXEMAPDy9CYuMJyczjdqRdQmLiNM4XcUdO1t83VaXYb38WbgqF9e9IlQ+KavuVZWc1jIjpUwFUku+zxNCHAEigT5A55LNvgQ2AM9LKdOBdCFE7+vsa6MQItZZWStTr1tDWb8lQ+sYThFdS1BglmTmaZ2kcqSnpXLm9HESGjhO8gvnzmLjutV4ennzypsfaJyuYi5lZHP0YhpNYiMY378bo/6ziOnL1mGXkrnPjNA6XvkJHRGvTMdYK5zcdT9QdPo4ACEPPIFXk2SKU86TuegzjUNWgBAEj5uCPjgM09Y1WC6curZOp8ezRQdyv5unXb6KEgLvIU+j8w+h+Lct2C67Zgv2X7HZ7QyZu4YLWfkMap5AVIAPvu5uGHSOesMwXy/S800ap6yYS1l5HE3NoElUKAm1Atlw9DxdkmL46eAZLue4ZhfBP0pNv8rxs+dpVC+ej+Z9zf4jx5n11VLc3IyMHTGQhgmuffOfkX6Ji2ePEJPQVOsoTtUi0Z2sXBvnL6tWmf8fVMkEACUFkebAdiCspKDze4GnViW+zyNCiF1CiF0pp5dX1m7/sWH9IrHZYM2mq1X+3lWhSazg4NmaUcdhLjQx/Y1JjHz4ydJWmcEjHmXGF0vp0LkHq1Yu1Thh+ZmKinnm02U8d083fDzdWbxpD8/168pPU8bw3D3dmDz/B60jlp+0kzJ5HBeeeQD3uHoYI6MBuPr5B5x/+j4sqRfxbn2LxiErQEoy3p3ElSlPYKxTF0NYVOkqv373UXzmKJYzxzQMWEFSUjD/HfI+exV9WDS64NpaJ6pUep2ORffdxupRd3IwNZMzGf/bLUngeq2+vzMVWXhm4Vqeu70tPh5uvNr3FhbuOMzgmcsxFVsw6l17TiFToZkXps1g3H2D8fbyxGazkZdv4tM3JzFm+ABenP4x0oVbfovMJua8+xR3j3weDy/XbaG/ETej4K7Ovnzzcw2pea1ENbVlxulnHiGED7AEGCeldGqHUynlbCllspQyOSK+rzPf6n/c1imUdi0DmfL+iSp936oiBCTW0XHwnOt2Mfud1WrlnTdepEPnHrRp3+l/1nfo3J3tWzZUfbBKYLHZePqTpfRKbkS3Zo7udCu2H6Rryfc9midy8FyKlhErhb2wAPOxg3g2bnFtobRTsGMT3i3baxeskkizieLTR3BLdNSeene/G523L3kr5mucrJIUmbFePIkhJlHrJE7h6+FGcnQoB1IyyCsqxmp3nDfT8kyE+nhqnK58LDY7Ty9cS6+mdenWMBaAuNAAZo28nYWj+tKzSV2igny1DVkBVquVidNmcNstbejc1jHuJzQ4iM5tWiCEoFG9eHRCkJ2br3HS8rFZLXw+fRwtO/TmptbdtY7jVLWC9IQG6nljbCjvPluLID89U0aH4O/j2oVt5a859ZMVQhhxFGTmSyl/r+pOE0KEl6wPB9KdmaEqtG4WwL19I5j41lGKil3/Zv964msLruZK8ly7hwRSSj5+/00i68Rwx92DS5enXrpQ+v2u7ZuJjIrRIl6FSCmZPP8H4msHM6Jr69Llof4+7Drh6M6z4/g5okNdc8yFztcPnaejFU0Y3fBseBOWy5cw1Aov3carWWssqRe1ilghwtsX4eHl+MFgxD2hMbb0FDxbd8a9fhOy5//HpccDCU9vcPdw/KA3Yoiujz3L5U//pTJNZvLMjjFBZouV7efSiAv2Izm6Fj8fc/yfXHHwLJ3rud5MglJKJi/fRHxoACNublK6PCPfMV7Nbpd88ss+BrRK0ipihUgpeX3GF8REhXPvnbeVLu/Yqjm7Dh4F4HzKZSxWKwF+rteiIaVkwayXCYuMp0vvkVrHcbqLaVZGv5nGU9PSeWpaOpm5Nl78z1Vy8mvm/VlZ2GXVvaqSM2czE8BnwBEp5fQ/rPoOGAlMLfn6rbMyOMNL4+rRrJEf/r4Gvp7VgjmLLjL07kiMRsE7LznGXhw+kcf02Wc0Tlo+/TroiQ0TeLnDU3cb2PCbjb2nJI1jdTWii9mxw7+xaf1qomPrMn7sfQDcO+JR1q1ZScrF8+h0OkJCw3h4tGvNZAaw9/RFVu44SL2IUAa+6Rg3MvauTrw85Hb+/c3P2Ox23Ax6Xr63WsylUWZ6/yBCHxyH0OlACAp2bqbwt12ET5iKztMTEBRfOMPVeTO1jlouer8A/Ac9CiXHZ96/naIj+wib+iW27KsEj50MgPnATgp+rvputBUlvP3w6nEvCMfxWU7sx3rmMG7NbsGtZReEty/ew57FevYI5p8Xax23zK7mm3n5h+3YpcQuJd0bRNMxIYL4ED8mfLeNGZsO0CAsgL5N4rWOWmZ7z6excv9J6oUFMnDGMgDGdkvmfEYOC3ccAaBrUix9m9fTMma5/Xb0JKs2bqNudBQjnp0MwGND+nHnrR14fcYchj71EgaDgZfGPOiSk8OcObaXXZtWEB5dj38/fw8Adwx+EqulmCVfvEl+biaz//04kTGJjJo4W+O0ZTd6YABJ8e74eOn4YHwYS9bm8ctuF695VcpEOKv/pxCiA7AJOIBjamaAiTjGzSwGooHzwAApZaYQojawC/Ar2T4faCilzBVCLMAxaUAIkAa8IqX821G+nftvc/0777/QpW+y1hGcqk+bbK0jOFXi2e+1juA0qQtcd6zRP+EZ7JqzGf1TXpGVNoSxWjL4ul6telnofGru8Zkaun730b+z3dpG6whONf+bKzfeyIX99/UIlyjlfvJz1U3u9nC3qhsg6MzZzDbDXx5I1+tsfxmIus62SCnvrcRoiqIoiqIoiqLUAE4rzCiKoiiKoiiKUj3Ya+iwITW1g6IoiqIoiqIoLkm1zCiKoiiKoihKDefCE2L+LdUyoyiKoiiKoiiKS1KFGUVRFEVRFEVRXJLqZqYoiqIoiqIoNVxN7WZWYwszRnc3rSM4TW5usdYRnEpW3dTk2ig2a53AaYoLirSO4FSH5+3UOoJTtXq2Zj/rwi8sTOsIzlXVj92uQoXu/lpHcKrzqUatIziVp7e71hGUGqzGFmYURVEURVEURXGoqfUdasyMoiiKoiiKoiguSbXMKIqiKIqiKEoNJ6t00EzVDRlQLTOKoiiKoiiKorgk1TKjKIqiKIqiKDVcTZ3NTLXMKIqiKIqiKIriklTLjKIoiqIoiqLUcHa71gmcQ7XMKIqiKIqiKIriklTLTBk983AMbZr7k51r5ZEJhwEY2T+C9i39kRKyc628/fFZMrItGictn0Fd3EiKMZBfKJm2qLB0eYcmBm5ubMQu4cg5Kyu3uebxXb2Sxozp/yI7KxOdTnDrbX3o1Wcgi+bNZvf2zQgh8AsIZNS4SQQFh2odt0wuZ+Ux6atVZOSZEAL6t2vC0I4tOHopnSlfr6XYakOvE0y8pytNYmprHbfMhNFIncnvIIxGhE5P3vZNZHw9j7BHn8Kjbn0EUJx6idQZ05BFrvdgUp27G+3Wz0fn7obQ60ldupoTr30IQIPXxlH7np5gs3Nu9gLOfjRP47TlYDAS+MhEMBgROh1FB3dS8PMy/AY9hiEyDmw2LBdPk7dsDthtWqcts8s5+UxatpGM/EKEEPRv2YChbRtx7HIGU1ZuxVRsJSLAhzf7dcLHw7Ue6lyTjw0g/cpVpr77IZlZ2QghuKNnd+65qzdffLWI71evJcDfD4AHRwyhbXILjdOW3er5L3D64Aa8fIMZOXElAFcuHuXnRa9QXGTCPziS20dMw93TR+Ok5TOytzdNE9zIM9mZ/EkOAHfe4sktzTzINzmaIpZuMHHwlGvet1SmmjpmxmmFGSFEHWAuUBuwA7OllO8LIYKARUAscBYYKKXMEkIkAnOAFsAkKeW0v9uPs3LfyE+bMvh2TTrjH4srXfb195f58psUAPreFsqwfuG8//l5rSJWyM6jVjYfsHJv12tP660boaNRrIFpiwqx2cHHU8OAFaTX6xn+4FjiEhpQaCrghXEP0rR5K+68ZyiDhj8CwI/ffc3SBXN4aMx4jdOWjV4veLZPR5KiwigwFzP43fm0rR/Duys28dhtbemQFMemw2d4b+UmPhs9QOu4ZSYtFi68Nt5RUNHriX51OgX7dnJl7izshSYAQoc/QmDPu8j8drHGacvOXlTMr91HYiswIQwG2v3yFVdWb8QnsS4edcL5pfHtICVuoUFaRy0fq4XsT6cii4tApyfwsRcpOvYb5n1bKV70MQB+g0fh2aoThdvXaRy27PQ6Hc/2aE1SRAgFRRYGz/qWtvERvPrdFp7u0Yrk2HCW7TnOF1sPMObWllrHLZOafGzguC489sBI6ifEYzIV8thT42nZrCkA/fv0ZlC/PhonrJhGbfrRrOMwVs17vnTZTwsm0bHv89Sp15qD275h19pPufmOcRqmLL+tvxWxfpeZB+76c2Hs5x2F/LTd9Sq2lLJzZjczK/CMlDIJaAuMFkI0BCYAa6WU9YC1JT8DZAJPANP+4X40ceBoPnn5f641NBVe64To4a536ZLv6VQ7pqI/H0D7xkbW7S3GVnKY+YXX+UUXERgUQlxCAwA8vbyJrBNDZsYVvLy8S7cpMheCqLr50StLqJ8PSVFhAHh7uBFfK4j0nHyEEOSbiwHINxcR6uf9d7up1n5vcRF6A8KgBylLCzIAOjd3cOG/P1uB41iE0YDOaAApiXn0Xk5M+U9plVrxlUwtI1aILC5yfKPXg04PSIqP/Va63nLhNDp/1yyshfp6kRQRAoC3u5H40ADS80ycvZpDy5KW0HZ1I1h7+JyWMculJh8bQHBQIPUT4gHw8vIkuk4kVzNc9+/s/4pKaIWHl/+flmWlnyEqoRUAMYk3c2L/T1pEqxQnLlgpMLvwib8K2WXVvaqS01pmpJSpQGrJ93lCiCNAJNAH6Fyy2ZfABuB5KWU6kC6E6P0P93PYWdnL4/4BEXS7JZgCk43nXj+udZxKFRogiA/Xc3sbN6xWWLGtmAvprj+KLD0tlbOnT5DQoBEAC+fOYuO6VXh5efPymx9qnK5iLmXmcPTSFZrE1GZ8306MmrWM6Ss2YrdL5j4xWOt45Sd0xEz9CLfaEWStXoH55DEAao96Bu9mrSi+dJ70ebM1DlkBOh0ddizFu24052Z+RfaO3/CKr0PEgF6E9e1O8ZVMDj01BdNJ17xpRAgCx7yGPjiMwl9/xnrh9LV1Oj0ezW8mf+V/tctXSS5l5XE0NYMmkaEk1Apkw7HzdEmM4adDZ7mcm691vAqpyccGcDktnZOnzpLUoB4Hjxxl+ferWLP+F+on1GXUgyPx9XHNrlj/V3B4fU4dWEtC024c37uKvKxUrSNVui4tPWjXxJ2zqVa+XmvCpAo8NVaVTAAghIgFmgPbgbCSAsrvBZVa5dzP9dY/IoTYJYTYdfHk0oqFLqM5X6cw9IkDrNuaSZ8erjXW4kZ0QuDpLvhgiZkV24oZ3sP9xr9UzZkLTbz7xiRGPvxEaavM4BGPMuOLZXTo3IPVK5donLD8TEXFPPPFSp7r2wkfD3cWb/mN5/p04qeXH+a5vp2YvMh1a+CQds49/zinRg3FM6EBbnViALg88x1OPTaEokvn8W3fSeOQFWC3szm5L2tjOxHQqik+jeqhc3fDZi5iS9t7uPDZYm765A2tU5aflGR9+BIZU8dhjIpHHxZZusq3z0gsZ49hOevalUGmIgvPLF7Hcz3b4OPhxqt9OrBwxxEGz/oWU7EFo16vdcRyq8nHBlBYWMgrb07j8Yfvw9vLi7tuv43/zv6I2e9PIzgwkJmffal1xEpz25DX2b/pK/77734UmwvQ611vrNPf2bDHzMSZ2bz2aQ45+XYGdPXSOlK1IGXVvaqS0wszQggfYAkwTkqZ68z9SClnSymTpZTJUQn9yvtWFbJuayYdWgVq8t7OklNg58BpKwAX0u1ICd4eGoeqAKvVyvQ3JtGhcw9at+/8P+tv7tyD7Vs2VHmuymCx2Xj6i5X0apFIt6b1AFix6zBdmyYA0OOm+hw8n6ZlxEphNxVgOrwf75taXVso7eRt/QXf1h20C1ZJrDl5ZPyynVo9bsF8MY3LyxwF0MvL1+DbpIHG6SpOmk0UnzmKW33HuASvrn3RefuS//1XGierGIvNztOL19GrSV26NYwFIC40gFkjerLw0T70bBxPVKCvtiHLqSYfGziuC6+8OY1unW+hY/u2AAQFBqDX69HpdPS+rRtHj5/UOGXlCapdl3tGf86w8UtJTO6Nf0gdrSNVqrwC6bipBjbtKyIuQs13VZM5tTAjhDDiKIDMl1L+3lSSJoQIL1kfDqSXcz/VRmTYtZaKdi38uZBaswacHTxjIyHSUeMW4i8w6KHARQ9RSsms998ksk4Mve++1t0q9dKF0u93b99ERFSMFvEqRErJ5EVriK8VxIjO1wbhhvr5sOvURQB2nLhAdGiAVhErRO/rj66kFU0Y3fBq3ILilAsYwyJKt/Fp2ZbilAt/tYtqzS0kEIO/42ZQ5+FOSNf25B87zeXvfia4S8nNVcfWFJw4q2HK8hPevgiPktpRgxG3uo2wXUnFI7kT7vWakLNwhktPtSOlZPK3m4gP8WdE+8alyzNKBhna7ZJPNu5jQHKiVhHLrSYfGziO7+0PZhBdJ4oBfe8sXZ6RmVX6/aZt24mLqTk3/Ka8DACk3c6vq2ZyUwcX7n58Hf7e18a9Nq/vxqUrrjdDovLPOXM2MwF8BhyRUk7/w6rvgJHA1JKv35ZzP5qYODqOpkm++Psa+OrDJsz9JoXWzfyJCvdASkna1WKXnckMYFh3d+pG6PD2ELw0wpPVOy3sOGJl0K3uPDvIE5tdsmBtkdYxy+3Y4d/YtH4V0bF1eX7sSMDRvWz9mpWkXDyPTqcjJLQ2D41+TuOkZbf3TAordx2hXngIA6c5xh2M7XUzLw/sxr+Xb8Bms+NmNPDygG4aJy0fQ2AQtR9/FqHTgU5H3raNFOzdQfSr76Dz9AIhKDp3mrRPXXO8k3t4LW76fCpCr0cIQco3q0j/YQOZW3bTfO404p4ciS3fxG+PTtI6arnofAPwG/AIQggQOswHtlN8dB+hU+Zgz75K4KiXASg6tAvTur+9LFRLe8+nsfK3U9SrFcjAmcsBGNu1Jeczc/l/7N13dFTV2sfx756S3iGEkEZCDVUgCCjSERQVQUGl2SvYEAVRESz3etWr14YKNrCAiKhgARWkKmBogvQOoQTS2yRT9vvHxIiKQsrkMPM+n7Wykpw5c/LbaXP22Xs/Z/babQD0Tk3iynZNjIxZJb7cNoAtW7fz3Q/LSWmYyK33jAPcZZiXLFvJnn37UQpi6tVj7OjbDU5aNV+9O5bDu9dSUpjDtMe60eXSu7GXFrNxuXsktEnbvrTsfJXBKavu1oEhNE2yEhKoeHZMBPNXlNA00UpCjPsi7MlcFx984/3ruWqCrtWV+bVXSElpD10JU0p1BVYAm3GXVAaYiHu9yxwgETgIDNFaZyul6gPpQFj5/oVAC6DN6Y6jtf76n75+3+HrvPcS3xm0udA7r36drRF9vbhc2llI3TnX6Agec2DmZ0ZH8Kjd8/YbHcGjOo7rZHQEjwpr09LoCKKKsjpcanQEj/pyf2ujI3hUenqW0RE8avrEOl5RAvX5ebXXmxk32FRr3xNPVjNbyd93y3qfZv9jQPxp9v2n4wghhBBCCCHOoLZLJteWWqlmJoQQQgghhBA1Tco7CCGEEEII4eO8uMbKP5KRGSGEEEIIIYRXkpEZIYQQQgghfJzLRxfNyMiMEEIIIYQQwivJyIwQQgghhBA+zlfXzPhsZ6asxHtv7HgmwcFWoyN4lEX5+M2tiny3fS6H68w7ebHz7jrP6AgeFRAdZXQEj9IOu9ERPEqXlRkdwWOCi04YHcGjTD4+T8ZUe7ccEf8P+WxnRgghhBBCCOHmqyMzPn4tQAghhBBCCOGrZGRGCCGEEEIIH+fy0aEZGZkRQgghhBBCeCUZmRFCCCGEEMLHaR+t0SMjM0IIIYQQQgivJJ0ZIYQQQgghhFeSaWZCCCGEEEL4OO2jBQCkM1NJ40c3oktaFDl5dm68byMAd4xK4oK0SBwOzZHjNp55ZTeFxU6Dk1bNlReYaRpvosimeW2+A4Ah3czUDXff8CrAT2Er07y+wGFkzCo7eeI4r/z3X+TmZKFMJvr2v5wBA4cw6/23+Hn1SkzKRFhEBGPun0hUnbpGx62UY3mFPPLpUrIKSlAKru6YyvAurdhxNIunFqykuNROg8hQ/n11T0IC/IyOW2nKaiXpyRdQVivKbCb/pxWc/HhmxeMxN48momc/doy4wsCU1WCxEn3PZJTFCiYTJZvWUPDNJ0SOvBtrQgq4nJQd2E3ux9PB5YX/X8wWgq+5G8wWUCYcuzZR+tNCrOd1xb99d0wR0RRMfQRtKzI6aZUcyyvi0fmryCosQSnFVe2bMPz8VHYcz+bpr9dQXOagQUQw/7qyKyH+3vX3V+pwcvNHSyhzOnG6NH2aJXBn11Zk5BYyYcFP5JWUkRoTyVOXdcJqNhsdt9KOn8xm8tQZZOfmo0yKK3t15dpLewEwZ+EPfLJoKWazmQvbteLu4YMNTlt5C99/mD1blhIUWocbH/0SgMzD2/lu9uPYS4sJi4pjwA3P4x8YYnDSqhl1aTCtG1kpKHbxxNv5AFzWNZCubf0pLHYvEvl8WQlb9vr2TXP/P/NYZ0YplQDMBOoDLmCa1volpVQU8DHQENgPDNVa5yilmgPvAu2BR7TWz5cfJwBYDviX552rtX7cU7nP5JsfTjDvm2NMvKdJxbb0TblM/+AAThfcPjKJ4VfF8+b7B4yKWC0b9rhYs93F4K6/vyB9svz3E6d+aWZKy7y3Z282m7n+lrtIadyMkuJiHrr3Ftq068jAq67jupG3APDV/Ll8Mus9bh8zzuC0lWM2mRjXvzOpDepSVFrGta9/RudGcUz5Yjlj+3UmLTmWz9bt4L2VvzCmT5rRcStN2+0cmPwg2mYDs5mGT71I4fqfse3aRkCjppiDvfOFuILDzslXn0CXlYLJTPS9U7Bt3UjxuhWUvv8KAJGj7iG4Sy+KVn1ncNgqcDoo+uQ1sJeByUTwNffi2L8NZ8Y+ivZuJXjIGKMTVovZpHigTwdSY+tQVGrnure/onNyLFO+XM3YPh1IS4rh8427mfHTVkb3OM/ouJXiZzYx7doeBPlZsTtd3PTRYi5Mqc8HP+9keFoz+qcm8tSidD77ZR9D2zU2Om6lmc1m7h15Fc2TEykqsXH9w//m/DapZOflszx9Ex8++yh+VivZeflGR62Slp0H0677CL6eOb5i26IPH6HH4PEkNDmfzT/O5efv36Lr5fcZmLLqftpcyg/rbNx4WfAfti/+2cZ3a20GpTo3uaQAQKU5gAe01qlAZ2C0UqoFMAFYrLVuAiwu/xwgG7gHeP5PxykFemmt2wLnAf2VUp09mPsf/bI1n4KCP45KpG/Kw1n+C7J1ZwHRdbzrqtupDhzXlJT+fWelVUMTv+zz3r+GyKi6pDRuBkBgUBBxCUlkZ50gKOj3f4KlNhtKKaMiVll0aBCpDdyjScH+fqRER5KZX8T+k3l0aFgfgC6N41i8dZ+RMatF29wvTMpsQVksgAaTiXqjbiVz5nRjw9UAXVYKgDKb3SMYaEq3bqx4vOzgbswRUQalqwH2Mvd7kxlMJtDgOpGBzs82NlcNiA4NIjW2DgDB/lZS6oaTWVDMgax8OiTWA6BzciyLtx80MmaVKKUI8rMC4HC5cDhdKKD0ERwAACAASURBVBQ/HzxOn2bxAFzeqiFLd2UYGbPK6kaG0zw5EYDgwAAaxtXnRHYu875bzqiB/fCzutseFR5mZMwqS2jSkYDg8D9sy8ncR3zjjgAkpV7Izo3fGhGtRuw65KDY5r0XWUX1eWxkRmt9FDha/nGBUmobEAcMBHqU7zYDWAqM11pnAplKqQF/Oo4GCss/tZa/nbO/tZf2qseSVSeNjuERSTGKwhJNdoHRSWpG5vGj7N+7iybNWgDw0YzpLFuykKDgECb/+yWD01VPRk4B24+epHV8PRrXi2Tp9gP0TG3It1v2cizPO6fxAGAykfzsVPzqNyB74Xxsu7YTOWAQhT//hCPX+0+IUYrocc9gia5P0YpF2A/s/v0xk5mgtG7kzXvPsHjVphTBw8dhiqhL2aaVOI955wj2mWTkFrL9WDat4+rSqF4ES3cepmezBL7bdoBj+d759+d0uRg28zsO5RRyTbvGxEeEEOrvh8XkviYaExpEZmGxwSmr70hmFjv3H6Jl44a88uE8Nm7fzRuz5+PnZ+WeEYNp0aih0RFrRN3Ypuz5ZTGN2/Zh5/qFFOQcNTpSjevRwZ/Orfw4cMzJ3MXFFP/Dhdr/L3x1zUytVDNTSjUE2gFrgJjyjs5vHZ56Z/F8s1JqI5AJfKe1XvM3+92mlEpXSqUf3fdFTcU/ayOuisPp0ny33Dc7M62TTWz24lGZU5WUFPP8049xw613V4zKDLv+Vt6c8SkX9ejLwgXzDE5YdcWldh6Y/T0PXtKFkAA/pgzqzuw1W7n29c8oLrVjNXtxEUOXi33j7mDXbdcR2KQZgS1aE9alG9lff250spqhNSeeG8+xx+/EL6kxltiEiocihtxM2Z5tlO3dbmDAatKaog+eo2D6ZMz1EzHVqW90ohpXXGZn3NxlPHhxR0L8/ZhyWRc+Tt/BdW99RVGZ9/79mU0mPr6hH4vuvJwtR7PZl/XXKVcK7xvRPlWxzcaEF9/k/uuHEBIUiNPppKComLefeoi7hw9m4v/e8pmTwX4jnmbD8o94/5nBlNmKMFu8d0bJ6Sxbb+PRN/J46p188gpdXN07yOhIwoM8/l9VKRUCfArcp7Wu0oRTrbVTa30eEA+cr5Rq9Tf7TdNap2mt02KTB1Y9dBX06xHNBWlRPPnirlr9urXFpKBFookt+72/M+NwOHj+X49xUc++dL6w+18ev6hHH1b/uMyAZNVnd7oYO/s7Lm3TiD4tkwFIjo7gzRsuZfadg+jfphHxUd45VeJUruIiirdsIrhlW/zqN6DRazNo9Pr7KH9/Gr36ntHxqk2XFFO6eysBzdsCENr/akwhYeR9PvMMz/QSpSU4Du3G0jDV6CQ1yu508cDcZVzaKpnezd3TlpLrhvPG8D7MumUAl7RMJj4y1OCU1RMa4EdaYjSbj2RRUFqGo3wS/vGCYqJDAg1OV3UOh5MJL0yjf9fz6Xl+OwDq1YmkR8d2KKVo2bghJqXILSg8w5G8Q536jRhy9zuMnDCP5mkDiKibcOYneZGCYo3W7mk8KzeV0jDW+wpTeIJL195bbfJoZ0YpZcXdkflQa/3bpe7jSqnY8sdjcY+2nBWtdS7uaWn9azhqtZzfLoJhg+J4+N/bKC3z/pP900mJVZzM0+R7+SwCrTVTX/oP8QlJXD7omortRzMOVXz88+pVxMUnGhGvWrTWTP5sGSnRkYy6sE3F9qzCEgBcLs30pRsY0tE7TyDNYeGYykfRlJ8fwW3aY9u7i123XMOeO0ey586R6NJS9oy5wdigVWQKDkUFll89tFrxb9oKR+YRgjr3wr95G7JnvgRefFVYBQaDf/nJrsWKJbEpruzjxoaqQVprpnz5E8l1wxnZuUXF9uyi8r8/rZm+cjND2jc1KmKVZRfbKLC51zvZ7A7WHDhOcp0w0hLr8f2OwwAs2LKfHk0aGBmzyrTWPPXm+zSMq8+wAX0qtndPa0v6rzsAOHjkOHaHk4hQLy80Uq6oIAsA7XKxeuHrtO16rcGJalZY8O+jhOc1tXLkhBdWgBRnzZPVzBTwNrBNa/3CKQ/NB64Hnil//4/zwZRS0YBda52rlAoE+gD/8UzqM5t0fxPOaxVOeKiFT6Z34N3Zhxg+OA4/q4n/Pt4ScBcBeOHNvUZFrJaru5lJjjERFAAPXG3lh41O1u920TrZuxf+/2b71s0sX7KIxIYpjBtzE+CeXrb42684knEIpRTR9epz2+gHDE5aeRsOHufLTbtpEhPF0Nc+BeDuvh05mJXP7DW/AtC7RTJXeuHJFIAlMooGYx4CswmUouDH5RSuO+2MU69kCo8kcvhdKJMJlImSDT9h+3U9DV74CGfOCaLvewoA2y9rKVj0qcFpK08FhxHUfzgo98/PvnMjjn1b8WvXDb+0XqjgUIJHPYRj31Zs331sdNxK23joBF9u3kuTehEMne4uf3t3z3YczM7n43T3CXHv5okMbNvIyJhVcrLQxqSv1+DSGpfW9G2WSLfGDUipG8aE+T8xdcVmmsVEcGXrFKOjVsmmHXv4ZsUaGifGMWL80wDcee1ALu95AU+98T7XjXsCq8XC43eN8sriMF++M5ZDu9ZSUpjDG49048IBd1NWWszG5R8B0KRtX1p1ucrglFV38xXBNEu0EhKoeOauCBasLKZpopWEemY0kJXn4oOF3rlWrabp2h4yqSXKU/M/lVJdgRXAZtylmQEm4l43MwdIBA4CQ7TW2Uqp+kA6EFa+fyHQAncJ5xmAGfdI0hyt9RNn+vrdB//omz8xoPtA7yurWxlDLswxOoJHNVn/vtERPGbfx4uMjuBRYfFeXEnsLIQmxRodwaOs0d5176jK0mVlRkfwmLL2PYyO4FGfZPUyOoJHpaf7QIGWf/DmhCiv6OU+8k7tVUF4+ib/WvueeLKa2Ur429WAvU+z/zHca2L+7BfcxQOEEEIIIYQQVeDFM5X/kXeWVRFCCCGEEEL8v+exkRkhhBBCCCHEucHlo2tmZGRGCCGEEEII4ZVkZEYIIYQQQggf5ys3ff0zGZkRQgghhBBCeCWfHZmJrO+7JTjzckuNjuBR2aXefYfsMwqLMDqBxyTfeg1Fa9caHcNjdn210egIHpVo8u3rW0F2h9ERPMovJtroCB7jt2kFzpYdjY7hMZGh3n8ft39SarMbHUEA2kd/zXz7lUsIUat8uSMjhDCOL3dkhBDVI50ZIYQQQgghhFfy2WlmQgghhBBCCDeXFAAQQgghhBBCiHOHjMwIIYQQQgjh46Q0sxBCCCGEEEKcQ2RkRgghhBBCCB/ncsnIjBBCCCGEEEKcM2RkppLGjIghrXUweQVO7n3qwB8eG9gnkhsHRzPywd0UFHnnnYmu6xNAy2QzhcWaZz4srth+UVsrF7X1w+XSbN3nZP4q77xxZ/bJY7zz8mPk52ShTIpufa+i92XDmDvjRTalL8disRIdE88Nd08hKNi7bt55LCefRz74mqyCIpRSXN2lLcN7dGD74eM8Nec7yhwOzCYTE4f0pXVSrNFxK89sIfyGB8FsAZOZsm3rKFm2gICOPQno1BtzVD2ynxuLLik0OmmVKD8/Wr7xJsrPD2U2k71kMYenT6fRY5MIbd8eZ6G7XXuemELxrl0Gp60Ci5W6YyahLFYwm7FtWkPBwrlEjBiNX0IK2unEfnAPuXPeApfT6LSVZ7YQMuw+lMX9+2nfsQHbyq/xa98N/7SemCOjyXt5PLqkyOiklVbqcHLzB99R5nThdGn6NEvgzm5tyMgtZMLnq8izlZJaP4qnLu+C1Ww2Om6lHcvK4fE3PiIrLx+TUgzq2YXr+ncHYPa3y5nz7UosZhMXnteCe6+7wuC0lZebdZS50yZQmHsSZVJ07DGUC/qNorgwl9mvjSX3ZAYRdeO4bsyLBAaHGx230m4aGMZ5Tf3JL3Lx6NQsAK7sEUz39oEUFLvPxeYuLuSXXWVGxjwn+OiSGc91ZpRSCcBMoD7gAqZprV9SSkUBHwMNgf3AUK11jlKqOfAu0B54RGv9/J+OZwbSgQyt9WWeyn0mS1bn8/WyXO69vv4ftteNtHBe8yAys7z7Lrdrt9pZsamMERcHVGxrHG+mdYqF/3xYhNMJIYHKwITVYzKZGXL9WJIapWIrKeKpccNIbduJ1LadGTTibsxmC5/OfIlvPn2Hq0bda3TcSjGbTIy7siepCTEU2cq49vmZdG6exIvzl3FH/wvo2iKFFb/u5X/zl/H23dcaHbfynA7yZr4A9lIwmQm78SHsu7dgP7Sbsp2/EHb9A0YnrBZdVsbW0XfhKilBmc20nDad3J9+AuDgKy+TvWSJwQmryWEna+pT6DL3z6/uPZOxbdtIybpV5H7wGgARI+8mqHNPin/83uCwVeB0UDj7ZbCXgclEyPCxmPduxXl4L0W7txAyzLv+n5zKz2xi2rDeBPlZsTtd3PT+d1zYqAEfrN3O8POb0b9FQ55auJbPNu1laPsmRsetNIvJxP3DrqB5cgJFJTZGPvYCnVo3IzuvgOXrtjD73w/hZ7WQnVdgdNQqMZnNXHLdQ8Q1bElpSRGvTbqKxq0uYP2Kz2jUogvdL7+VZQums+zL6fS/ZpzRcStt5cYSFq8t5tZBf+yILVpdzMIfi//mWcKXeHKamQN4QGudCnQGRiulWgATgMVa6ybA4vLPAbKBe4DnT3cw4F5gmwfznpWtu0soLPrrVcObropmxmcnDEhUs/YccVJs+2PXvWtrK9+nl+Esb3Zhifd27SOioklqlApAQGAwsfHJ5GadoOV5XTCb3X37lKatyck6bmTMKokODyE1IQaA4AA/UmLqkJlbiFKKQpv7ilShrZTosBAjY1aPvXxE0GRGmdxXgJ3HDuHKyzIwVM1xlZQAoCwW9xV+H7uMpsvcPz9lNoPZDFpTum1jxeP2g7sxR0QZFa/67OVXfk1m95vWODMP48rPNjZXNSmlCPKzAuBwuXC4XCjg5wPH6dM8EYDLWyWzdOchA1NWXd3IcJonJwAQHBhAwwYxZGbnMff7VVx/eW/8rO7Xhqhw7xqt/01YRD3iGrYEwD8wmOgGjcjPOc629Utod9FAANpdNJBt6xYbGbPKdh6wU1TinbNhapt26Vp7q00eG5nRWh8FjpZ/XKCU2gbEAQOBHuW7zQCWAuO11plAplJqwJ+PpZSKBwYATwNjPZW5qjq2DiYrz8H+DN8cwoyONNEozsyAC/xxODRfrCzl4HHv/8dxMvMIB/ftILlpqz9sX7XkC9IuvNigVDUjIyuP7YeP07phLA8N6sWdr3/CC18sxaU1M+8bZnS8qlOK8FsfxRwVje3npTgy9hmdqGaZTLSeMZOA+HiOz51L4a+/EjP4KhLuuJO4m24mPz2dg6+9irZ76QiwUkQ/8C/MdetTtPJb7Af3/P6YyUxg2kXkfzbDuHzVpRSh14/HFBlN6frlOI8eOPNzvITT5WLYuws5lFPINR2aEB8ZQqi/FYvJfU00JiyIzIISg1NW35ET2ew4cJhWjZJ4edZ8Nu7Yy9RPvsbfauXe666gZaNEoyNWS86JDI4e2EZ8o7YU5mcRFlEPcHd4Cr280/1nfc4P4sK2Aew74mD2ooK/XKgVvqNWCgAopRoC7YA1QEx5R+e3Dk+9szjE/4CHcE9X+6evc5tSKl0plb5/68fVyny2/KyKIf2jmLXAN64Mn45ZQaC/4sWPi/liZSk3XBJodKRqs5UU88az47jmpnEEBv0+UvHV3Lcwmcx06napgemqp7i0jAfe+YIHB/ciJMCfOas28uCgnnw75Q4eHNSTybMWGh2x6rQmb9qT5Lw4HktcMuboBkYnqlkuF5tHjmD95ZcR3LIFgSkpHJz6GpuGDmHLjTdgCQujwahRRqesOq058fzDHJ88Gr/ERljqx1c8FH71TZTt2U7Z3h0GBqwmrSl47xnypz6KOTYJU10vXJv2N8wmEx/ffCmLxlzJliNZ7MvK/8s+yntnIANQbCvloZfe5YERgwgJCsDhcpFfVMJ7k+/jnusu5+FXZ3j1fTpKbUV89Mo9DBg+gYBALx6hPwtLfi7hwZdOMumNbPIKnFzbzztH1WqaS+tae6tNHu/MKKVCgE+B+7TWf/3vd+bnXwZkaq3XnWlfrfU0rXWa1jqtYYtrqpC28mKjrdSra+V/jyQx7clk6kRYeOHhJCLCvG8R5N/JLdT8stsBwMHjLrSGYC9eN+Nw2HnjuXF06nYJ7Tv3rtj+4w/z2Zy+nJvvfxrlpa/KdqeTse98waVpqfRp2xSABWu30Lv844vPa8aWA8eMjFgjdGkJ9v07sDZuaXQUj3AWFpK/bj0RXbpgz3JfKNF2Oye+XEBIC+9vs7YVU7pnG/7N2wIQ0u8qTCGh5H/xvsHJaoYuLcFxaBfWlBZGR6lxoQF+pCXGsDnjJAWldhwu9zXG4/nFRId474Uuh8PJQy+9S/8LOtCrYxsAYiIj6JnWBqUUrRoloZQit8D7CjgAOB12Pnr5Xtp2uZyWHd0zD0LC6pCfmwlAfm4mIWFePMXzT/KL3OcqWsOy9SWkxFmNjiQ8yKOdGaWUFXdH5kOt9bzyzceVUrHlj8cCmWc4zIXAFUqp/cBsoJdS6gMPRa60A0fKuGH8Xm57bB+3PbaPrFwHY/99gNx8L6zG8zc273XQJMHdOYuOUJjNUOSl62a01sx8bQqxccn0vWJkxfYt61ex6LP3GP3w//D3984XZK01k2ctJCWmDqN6dqzYHh0eQvpu91z2tTsPkhgdaVTEalFBIajffjYWK9aUVJwnvb9j9htLRATmEPfVUuXvT/j551Oy/wDWOnUq9ons3p3iPXv+7hDnNFNwKCogyP2J1Yp/01Y4Mo8Q1KknAc3akPP+K169RkgF/un3M6kZTi9ce3c62cU2CsrX3dnsDtbsP0ZynXDSkurx/faDACzYso8eTeL/6TDnLK01T7w1m+QGMYy4tEfF9u5prUjf6q4ceOBoJg6Hk4jQYINSVp3WmnlvP0q9Bil0veSGiu3N2/Viw4ovANiw4gtS2/cyKGHNCw/5/fS2ffMAMjIdBqY5d8iamUpS7kvbbwPbtNYvnPLQfOB64Jny91/803G01g8DD5cfswcwTms9whOZz8bYG+vTqmkQYSFm3no6mdlfZfH9j5UecDpnjeofQON4MyEBiik3BfPNmjJW/2pnWN8AJgwPwuGCD7+1GR2zynZv38jqZV8Rl9SEJ8a6R+8GDR/D7Lefw2Ev48UpdwLuIgAj7njUyKiVtmFvBl/+vJUmsXUZ+ux7ANw9oBuTrunHs/OW4HS58LNamHStd64HMoWEEzLwRjCZQCnKtqZj37WZgPN7EXBBP0whYUTcMYmyXZsp+tL7rvD71a1Lo0mPg8mEMpnIWvw9uatWkvraVKwREaAURTt3su8/zxgdtUpMYZFEDruz4udXsnE1pVs3EPv8BzhzThJ97xMAlPzyM4XfzjvD0c49KiSMoAEjUar893P7ehx7tuDXoTsBnfqggsMIvXEi9r2/UrLwI6PjVsrJwhImfbkal8s9faRvaiLdmsSRUjecCV+sZOqyX2hWP5Ir2zYyOmqVbNq5j69XptM4IZZhE58D4K6hAxjYvRNPTJvN0An/wWo2M/n2YV45an9g53o2rppPTEJTXnl0EAAXD7mP7pfdwqzXxrJu+VzC6zTgujEvGpy0au64KpzmDa2EBJl4YWxdPv+hkOYN/Uio7z7FPZnr4r0FvnOeJv5KeWr+p1KqK7AC2Mzva10m4l43MwdIBA4CQ7TW2Uqp+rhLL4eV718ItDh1atopnZkzlma+8q6d3nuJ7wySmvnOPOzTGdzHd6bonU6nQ951IlMZRWvXGh3Bo3Z9tfHMO3mxxAu882T0bAXVr3PmnbyYX0y00RE8xtmy45l38mKLXP2NjuBRX37j/dVe/8l7k2O8opc7+vncWjs3fm1cRK19TzxZzWwl8HcN6f3nDVrrY8A/jlFrrZfirn4mhBBCCCGE+H+uVqqZCSGEEEIIIURN89jIjBBCCCGEEOLcUMvr8muNjMwIIYQQQgghvJKMzAghhBBCCOHjartkcm2RkRkhhBBCCCGEV5KRGSGEEEIIIXycp27HYjSf7cwU5RUYHcFjWqQ2NTqChxUbHcCzvPCma2cra9tBoyN4VIN23nmH87MV1jjB6AiepXx7MoIjN8/oCB5jcvr2HdyD/JxGR/Aos9m3//aEsXy2MyOEEEIIIYRwc8maGSGEEEIIIYQ4d0hnRgghhBBCCB+nta61tzNRSr2jlMpUSm05ZdtkpVSGUmpj+dulZ9Mu6cwIIYQQQgghatN7QP/TbH9Ra31e+dvXZ3MgWTMjhBBCCCGEjzuX7jOjtV6ulGpYE8eSkRkhhBBCCCFEjVFK3aaUSj/l7bazfOoYpdQv5dPQIs/mCTIyI4QQQgghhI+rzZEZrfU0YFoln/Y68CSgy9//F7jpTE+SkRkhhBBCCCGEobTWx7XWTq21C5gOnH82z5ORmUp64NYkOrULJzffwW0TtgJw/dUNuKBDOFpDbr6D597YT1au3eCkVbPow4fZu2UpQaF1uH7ilwCcOLyd7z9+nLLSYsLrxHHJqOfxDwwxOGnVZJ88xjsvP0Z+ThbKpOjW9yp6XzaMuTNeZFP6ciwWK9Ex8dxw9xSCgkONjlspx3LyeeT9r8gqKEIpxdUXtGV4jzS2Hz7OUx9/S5nDidmkmDj0YlonxRodt9KU1UrCpOdRViuYzRSuWUHW3A+Iue1+AlKagFKUHT3Msdf/iy61GR238ixWYsY9hbJYwWSiZP1P5H35MVEj78IvqTEAjsyjZM14xSvbV+pwcuunKylzunC6NL0bN+COzs3JyCvi4YXp5NvsNK8XzpMXd8DqhTfYK3U4uXXuCsqczvL2xXFHl1R3+775mXxbGc3rRfBkvzTva5/ZQvCQMWC2gMmEY9cmSlcvwtq2K/7tumGKqEvBG4+hbUVGJ62SY1m5TJo2m6y8AkxKMahnJ4ZdfBETXvuAA8cyASgothEaFMCsJ8canLbycrKO8tHUieTnnkQpE116X033S0aycfUiFs6dSuaRvdz35CwSG7UyOmqV3Hh5CG2a+FNQ5GLSmzkAXNEtiG7tAigodo9EzPuhiM27y4yMeU5wnUWVMSMppWK11kfLPx0EbPmn/Suedzbl06oYKAGYCdQHXMA0rfVLSqko4GOgIbAfGKq1zlFKNQfeBdoDj2itnz/lWPuBAsAJOLTWaWf6+n2Hr/NIw1o3D6HE5uShO5IrOjNBgSaKS1wAXNkvmqS4QF56x3N3Qr/6+g4eO/bh3T9j9Q9i4fvjKzozHz53Fd2uHE9Ck/PZ8tNc8rIOc+Fl93ksQ/O4Yo8dOzf7BHk5J0lqlIqtpIinxg3jrgkvkJOVSfPWHTGbLXw68yUArhp1r0cydDo8yyPHPZFXyMn8QlIT6lNkK+Xa52byv1sG8ey8JYzsmUbXFims+HUP7y1ey9v3XOeRDAff+8Qjx/2N8g9wn8ibzSRM/i8nZrxBWcZBXCXu35noEbfhyM8lZ/4cj3z9gPAgjxz3NxXtM5mJefBpcua8g/3oIbStBICIq2/AVZBH/qLPPPL1o1o39shxwV0StMTuJMjPgt3p4ua5K3iwW2s+2LCHXo1j6dc0nn8t2USTumEMaZPsmRDKc52Iv7Tvk+U82L0NH2zYTa9GDejXLJ5/Ld5Ak+hwhrRJ8UgGV5kHT9asfmAvA5OJ4KF3Y1v6OdrpQJcWE3z1aIo+etGjnRlT5x4eO/aJ3HxO5uaT2jCeohIbIx5/if/eewMpcTEV+7wwawEhgQHcdmVfj2RY7neJR44LkJdzgvzcEyQkt8BWUsQLE4dy0wMvoxQoZWLOW1O4Yvg4j3ZmPv0q12PHbppoxVamuWVg6B86M6VlmkWrSzz2dU/19mPRqla+UDXdMPl4rfVm3psc84/fE6XULKAHUBc4Djxe/vl5uKeZ7QduP6Vz87c8eXnIATygtU4FOgOjlVItgAnAYq11E2Bx+ecA2cA9wPOnOxjQs7xM2xk7Mp60eXshBYXOP2z7rSMDEOBv5hzv+P6j+MYdCQgK/8O2nMx9xDfuCEBS8wvZtelbI6LViIioaJIapQIQEBhMbHwyuVknaHleF8xm90BlStPW5GQdNzJmlUSHh5CaUB+A4AB/UmLqkJlXiFJQaCsF3O+jw71zVA2oGJFQZgvKbAGtKzoyAMrPD2/+A/y9feaK9v3WkQFQVj+vbZ5SiiA/99+Yw+XC4dKg4OfDJ+nduAEAl6UmsHTvGV+3zkl/bZ/L3b5DJ+jdpLx9LRJZusc724e9vKNkMrvf0LhOZKDzcwyNVROiI8JIbRgPQHBgAMkN6pGZk1fxuNaa79duon/n84yKWC3hkdEkJLcA3K97MXEp5GUfJyauEfUaeOjCQS3aedBO0SnnYcI7aK2v01rHaq2tWut4rfXbWuuRWuvWWus2WusrzqYjAx6cZlYe4Gj5xwVKqW1AHDAQd88LYAawFBivtc4EMpVSAzyVyZNuHNKAPhfVoajYyYNP7zQ6To2qE9uUPZsX07hNH3ZuWEhBjpe+GP/JycwjHNy3g+Smf7watWrJF6RdeLFBqWpGRlYe2zOO0zoplocG9+bO1+fwwudLcWnNzPuHGx2v6pSJxH+9gl/9BuR+uwDbnh0AxNw+luB2HSk7fJATH0w3OGQ1KBP1Jz6HJbo+hcsWUrZ/FwBRo8YQ2Ko99qOHyJ37nrEZq8Hp0oyYvZRDeUUMbZNMfHgwof5WLCb3dbV6IYGcKPS+KXS/cbo0I2b9wKG8Qoa2STl9+4pq50pxjVOK4GFjMYXXpeyXVTiPeW72gZGOnMhm+4EjtGqUWLFtw459RIWFklg/2sBkNSP7RAaH928jqXEbo6N4XK+OgXRpE8CBo3Y+/q6IYpuXXgmqQedSaeaaVCsTd8vrSLcD1gAxLZAa4gAAIABJREFUv/W0yt/XO4tDaOBbpdS6fyrtdmoZuMO751U/eCW8+8kRht+zmSU/ZjPwYu//h3eqfsOeZtOKj/jg2cGU2Yowm/2MjlRttpJi3nh2HNfcNI7AoN9HKr6a+xYmk5lO3c7qprPnpOLSMh54+3MeHNybkEB/5qzcwIODevHtE3fy4KBeTP5oodERq067OPjwaPaOHkFAo2b4xScBcPzNF9h753DKjhwktEs3g0NWg3Zx7OkHyHj4VvwaNsbawH1ClT3zVTLG34L9WAZBaV0NDll1ZpNi1rCefHNTP7Ycy2V/duFfd/KKyRqnZzYpZg3vxTc392fL8Rz2ZxecZi8vbaDWFH34XwrenoI5JhFTnfpGJ6pxxbZSHnxlJuOGX0FIYEDF9oWrN9DPS0dlTlVqK+bdF+9n0KjxBAR57wj92Vi6roQJr2YzZVoOuYUurukbbHQk4UEe78wopUKAT4H7tNb5VTzMhVrr9sAluKernfZsRWs9TWudprVOi288uIpfqnqW/JhN145nVRbba0TVb8RVo99hxEPzaJ42gPC6CUZHqhaHw84bz42jU7dLaN+5d8X2H3+Yz+b05dx8/9Mo5Z0nHHank7Fvf86laS3o07YpAAvWbqF3+ccXt2vGlgPeP7LmKi6ieNsvBLc9ZdapdlHw03JCzvfek/3f6JJibDt/JaBlu1M2uihOX0lQu87GBashof5W0uLrsPlYNgWldveULCCzsITo4IAzPPvcF+rvR1pcXd9sX6kNx+HdWJKaG52kRtkdTh58ZSaXXNCOXmmtK7Y7nE5+WLeFizu1NTBd9Tkddt598T46XDiANud7Zt3PuSS/SKO1+0r48vU2khtYjY50TtBa19pbbfJoZ0YpZcXdkflQa/3bUMlxpVRs+eOxQOaZjqO1PlL+PhP4jLMs1VZb4mL8Kz7u0j6cQ0e9d5rE6RQXZAGgXS5WL3ydtl2vNThR1WmtmfnaFGLjkul7xciK7VvWr2LRZ+8x+uH/4e8faGDCqtNaM/mjhaTE1GFUr44V26PDQ0jffQiAtTsPkhjtnZ1tc2g4piD31TVl9SOoVTvKjh7GGvN7Zbbg9p0oO3LIqIjVYgoJQwW6Cwwoqx8BzdvgOJaBJfr3K+CBbTpiP55hVMRqySkupaDUXeXR5nCy5tAJGkaFkhZfl8W7jwDw5bZDdE/xvkp78Fv73OtKKtoXWd6+XeXt23rQK9unAoPBv7wTZrZiSWyKK+eML91eQ2vNk2/PIblBPUb07/6Hx9b+uouGsfWIiYowKF31aa2ZPW0SMQ1S6DHgeqPj1IrwkN9Pb9s39yfjhMPANMLTPLZmRrkvbb8NbNNav3DKQ/OB64Fnyt9/cYbjBAOm8nU3wcDFwBOeSX1mE0cn0yY1lPBQCx+90pqZc49w/nnhxMcGoLXm+Mkyj1Yy87Sv3h3L4d1rKSnMYdpj3ehy6d3YS4vZuPwjAJq07UvLzlcZnLLqdm/fyOplXxGX1IQnxl4DwKDhY5j99nM47GW8OOVOwF0EYMQdjxoZtdI27M3gy59/pUmDaIb+5z0A7r7sIiZd259nP12M0+XCz2ph0rX9jA1aRebIKOrf+QDKZAalKFi9nKINa0l4/HlMgUGgFKUH9pL5zqtGR60Sc3gkda6/G0wmUCaK162iZMs6YsY9jQoIBBT2jP1kf/Sm0VGr5GSxjce/3YCz/KpdnyZxdEuuT0pUKBMXpjP1p+00iw7nyhaJZz7YOehkkY3Hv1uH06XRaPo0iadbSiwpdcKY+M3PTP1pq7t9LZOMjlppKjiMoIuvc1eDUwr7rk049m3F77yL8OvQExUcSvCIcTj2b8P2vWcqCXrSxl37+erH9TSOr891j7lPV0ZffQld26ayaM1Gr59itm/HBtJXLCA2oQnPTXC/fg+45l4cjjLmvfdvCvOzmf7sXcQ1bM4dD1f2HofGu21QKM2SrIQEmXju3ii+WFZM8yQrCfUtaA1ZeU5mfnWaKa3/D7l8dM2MJ0szdwVWAJtxl2YGmIh73cwcIBE4CAzRWmcrpeoD6UBY+f6FQAvcJdt+q0NqAT7SWj99pq/vqdLM5wJPlmY+F3iyNPO5wFOlmc8Fni7NbDRPl2Y2midLM58TPFia+Vzg0dLMBvNkaeZzgSdLM58LPFma+VzgLaWZRzxypNbOjT94ukGtfU88Wc1sJX+/0rH3nzdorY8B8afZNx/w7smqQgghhBBCGEiqmQkhhBBCCCHEOcRjIzNCCCGEEEKIc0NtVxmrLTIyI4QQQgghhPBKMjIjhBBCCCGEj9Mu15l38kIyMiOEEEIIIYTwSjIyI4QQQgghhI/z1fvM+GxnJrG5d9547WyEBvnmL+NvWjvXGR3Bo04uWGh0BI/J2Z9jdASPcthOGB3Bo8IbxRkdwaN8tSzpb5wlNqMjeEzwoV1GR/CoxHbnGx3Bo1Jb1jM6gvBhPtuZEUIIIYQQQrhJNTMhhBBCCCGEOIdIZ0YIIYQQQgjhlWSamRBCCCGEED7OV9cNysiMEEIIIYQQwivJyIwQQgghhBA+TkZmhBBCCCGEEOIcIiMzQgghhBBC+DiXdhkdwSOkM1NJN14eQpsm/hQUuZj0pvsGgVd0C6JbuwAKit3Dd/N+KGLz7jIjY1ZZXvZRPn9rPIX5J1HKRPtuQ+ncdxQlhbnMfXMsuScziKgbx9V3vEhgcLjRcSvt+Mlsnnj1bbJy8zApEwP7dOOaAX0A+OSbxcz9Zglms5kL2rdmzMghBqetJIuVevc9ARYLymymZMNq8r+eQ+SwO7EmpoBSODKPkvP+a+gy77u5nvLzo8Xrb6CsfiizmewflpDx1nRSHn2M0HbtcRYWArD3qSco3uV9N9hTfn60fns6Jj93+05+v5hDb7xJ4ymTCe/QHkd5+3ZPmkzRzp0Gp60Cs4Xg6+5DmS1gMmHfuZHSVV/j164bfh16YI6MJv/VCeiSIqOTVo3ZQsiw+1AWC5jM2HdswLbya/zad8M/rSfmyGjyXh7vne2zWAi/cTyU/+zKtq6jZOl8QgbfgqVBQ7TLiSNjH0UL3geX0+i0lXYst5BHPv2BrMISlFJcndac4Re0ZsfRLJ6av4LiMjsNIkL595BehAT4GR230k6eyOTl/z5Nbk42ymSib//LuWzg1cx6/23Wrl6JSZkIj4hgzP0PE1WnrtFxK23Z3Ec4uH0pgSFRXH3fAgCyjmxj5eeTcTjKMJnMXDhwEvUS2hicVHiKxzozSqkEYCZQH3AB07TWLymlooCPgYbAfmCo1jpHKdUceBdoDzyitX7+lGNFAG8BrQAN3KS1/slT2f/Jqk2lLP7Zxi0DQ/+w/bs1JSxaXWJEpBplMpm5+JrxxCa1pLSkkGlPXkWjlhewcdVnJKd2puult7Hy62ms/Ho6fYeMMzpupZnNJu4ZNZRmKUkUldi4cfyTnN+mBdl5+Sz/eSPv/3cyflYr2Xn5RketPIedEy9PcXdUTGbqjX0S29YN5M57D21z/26GD76ekO79Kfjuc4PDVp4uK2PbmNG4SkpQZjMt3pxG3k/ufwOHXn2F7B+WGJywenRZGVtuu8PdPouF1u+8Tc6qVQDs/99LZH2/2OCE1eR0UPTxy2AvA5OJ4Ovux7F3K46Mvdj3bCHk2nuMTlg9TgeFs39vX8jwsZj3bsV5eC9Fu7cQMuxeoxNWncNB3oznoawUTGbCbhqPffcWSjevoXDeWwCEXHUr/u0vojR9qbFZq8BsNjHuki6kNqhLUWkZ1079jM6N45ny+XLG9u9EWnIDPlu3nfdWbmJMn45Gx600s9nMDbeMJqVxU0qKi3nw3ltp2y6NgVddy3Ujbwbgq/lz+WTWDG4f84DBaSuvaYcradllGEs/mVCxbc03z9O+92gSmnXj4PZlrP3meS67baaBKc8Nsmam8hzAA1rrVKAzMFop1QKYACzWWjcBFpd/DpAN3AM8f5pjvQQs1Fo3B9oC2zyY+x/tPGinqMQ3h+kAQiPqEZvUEgD/wBCiYxuRn3OcHRsW0/aCKwFoe8GV7NjwvZExq6xuZATNUpIACA4MoGFcLCeyc5j37VJGXnkJflYrAFHhYUbGrLLfRlyU2QxmM2hd0ZEBUFY/8OI7ALtK3G1RFgvKYvG5uxn/uX34VvPcJ/oAJrP7dxSNK/MwOj/b0Fg15pT2YXL//TkzD+PyhfaVlbrfm8t/dlpj37W54mFHxn5MYZEGhaue6NAgUhu4RySC/f1IiY4gM7+I/Sdz6dAwFoAujeJZ/Os+I2NWWWRUHVIaNwUgMCiI+IQksrNOEBQUXLFPqc0GyqiE1ROb3BH/oIg/blSKslL3aHaZrZCgsHoGJBO1xWMjM1rro8DR8o8LlFLbgDhgINCjfLcZwFJgvNY6E8hUSg049ThKqTCgG3BD+bHKgHNuDlevjoF0aRPAgaN2Pv6uiGKb95+F5J48zNGD24hPaUthfhahEe5/BqER9Sgq8P4X56OZJ9m57yAtm6Tw6vtz2bRtF2/O+gw/q5W7Rw2hReNkoyNWnjJRb/x/sETXp2j5QsoO7AYgcsRdBLRoh/3YYfLmzTA4ZDWYTLR6dwYB8fEc/3QuRVt/hcGDib/9DuJuuom89HQOTX0NbbcbnbRqTCbafvQBgQkJHP14DoVbtsCQq0kafRcJt95K3tq17H/5Fe9tn1KEjHoIU0Q0ZRuW4zx6wOhENUspQq8fjykymtL1PtY+pQi//THMUfWwrf0BR8YpJ/YmM/5tO1P0zWzj8tWQjJwCth89Sev4ejSuF8XS7QfomdqQb3/dy7E8L5wi+CeZx4+yb+8umjRrAcCHM6azbMkigoJDmPLv/xmcruZ0uexhvnnnVtZ8/Rxau7jijo+MjnROkJGZalBKNQTaAWuAmPKOzm8dnjN1l1OAE8C7SqkNSqm3lFLBp9tRKXWbUipdKZW+Pb32hhOXrithwqvZTJmWQ26hi2v6njaeVymzFTFn6j30v/Zh/ANDjI5T44pLbDz8/FTuu/EagoMCcbqcFBQV8da/JjJm5NU8+sKb3nnVX7vIfOZBjj56O9akxlhiEwDI+WAqRx+5HcexDAI7XGBwyGpwudhy/Ug2DLyckBYtCUxJ4dDrU/nl2qFsuelGLGFhNBg5yuiUVedysenaYfzc7xJCW7UiqFEjDrzyKusHXcWmESOxhIcTf+MNRqesOq0pnPEf8t94DHNsEqa6sUYnqllaU/DeM+RPfdT32qc1eW88Qc4LD2KJS8Zcr0HFQ8EDhmM/sBPHQe9bq3aq4lI7D8z6jgcvvYCQAD+mDO7O7NW/cu3UeRSX2rGavbsAbElJMc89PYkbb727YlRm+PW3Mm3GXLr16MM3C+YZnLDmbFs9my6XTWDYhB/oPGACyz991OhIwoM8/peplAoBPgXu01pXZSGCBfc6mte11u2AIn6fmvYHWutpWus0rXVa87TaO6HJL9Jo7Z4Rsny9jeQG1lr72p7gdNiZM/UeWne6nNQOFwMQElaHgtxMAApyMwkOjTIyYrU4HA4m/vd1+l3UmR6dOgAQHRVJj07tUUrRskkKJpMiN7/Q4KRVp0uKKd31KwEtzjtlo4vi9T8SeF5n44LVEGdhIfnr1xHeuQv2rCwAtN3OiS+/JLhFC4PTVZ+zsJC89HQiLrgA+8mTgLt9x7+YT0jLlganqwGlJTgO7caSnGp0Eo/QpSU4Du3CmuL9v4t/pm0l2PfvwNq4FQCB3S/HFBxK8aI5BierHrvTxdhZ33Fp28b0aekelU+OjuDNGwcw+67B9G/TiPgo75x+DO7Xvef+NYmLevah84Xd/vJ41x59WP3jcgOSecbO9Z/TsGVfAFJa9+fE4c1neMb/D1rrWnurTR7tzCilrLg7Mh9qrX/r8h9XSsWWPx4LZJ7hMIeBw1rrNeWfz8XduTlnhIf8/m1s39yfjBMOA9NUj9aa+e89St3YRnTpd2PF9qbn9WLTj+5F45t+/Jxm7XobFbFatNY8/foMkuJiue7yiyu2dzu/HembtwNw8Mgx7A4HEWHeNSJlCglDBQa5P7H6EdCsDY7jRzDXrV+xT2DrDjiOZxiUsHosERGYQ9w/E+XvT1jH87Ed2I+1Tp2KfSK7d6dkzx6jIlaLJfL39pn8/Qnv1ImS/fux1v29ulCdnj0o9tL2qcAQ8A90f2KxYklqhivruLGhapAKDEGd0j5rUjOcPtI+FRSCCjilbSmpOE8ew7/9RVgbt6Rg7jSvXountWbyZ8tIiY5g1IW/V7zKKnSvYXO5NNOXbmDI+d7Z+dZaM/Wl/xCfkMQVg66p2H4k43DFx+mrVxEXn2hEPI8IDqvH0X0/A3Bkz2rC6yQZnEh4kiermSngbWCb1vqFUx6aD1wPPFP+/ot/Oo7W+phS6pBSqpnWegfQG9jqodhndNugUJolWQkJMvHcvVF8sayY5klWEupb0Bqy8pzM/Mp7r+gf2r2eX376gnrxTXljsnvBf+/B99P10luZ+/r9bFjxKeFRsQy50zvn1v6yfTcLl/9Eo8Q4Ro2bAsAdwwZxec+uPP36uwwfOwmLxcJjo2/C/SvsPcxhEUSOHAMmE0opitf/hO3X9UTf9wSm8k6OPeMAOR9PNzhp1Vjr1KXRpEkokwmUiewli8ldtYrmr7yGNTICUBTv2sm+Z/9jdNQq8atblyZPTEGZzGBSZH33PTkrVtDyzTewRkaCgqIdO9nz9L+MjlolKiSM4EtGgMkEKOw7NuDY+yt+7bvjf35vVHAYITc8jGPvr5QsmmV03EpTIWEEDRiJUib34uPt63Hs2YJfh+4EdOqDCg4j9MaJ2Pf+SslC75q/bwqNIOTKm9w/O6Uo+/Vn7Dt/IWrSm7hyswi/5WEAyratp2TZlwanrbwNB47z5cZdNImJYuirnwJwd9+OHMzKY/Ya9+lG7xYNubJ9MyNjVtn2rZtZtuRbEhum8MAYd/WyYdffyuJvv+JIxiGUUkTXi+H20d5XyQxgyawHOLJvLbaiXD76dw/a9xnDRYOf4KcF/8LlcmK2+NN18BNGxzwnuFy+WcBKeWooSCnVFVgBbMZdmhlgIu51M3OAROAgMERrna2Uqg+kA2Hl+xcCLbTW+Uqp83CXZvYD9gI3aq1z/unr3/zkCe+9THQGvXt6Xx34yugfttLoCB5VPO1loyN4TMa6g0ZH8CiHzXtHXc9Gq2FevJ7qLPjq4tffOEu87/5RZyu4pXeOipyt3e2GGx3Boxb+4tvVxMYNNnnF1c/Lb99Wa/8EF7yZWmvfE09WM1vJ3xf6+8scJa31MSD+b461EUiruXRCCCGEEEL8/+GrF3S8uzSHEEIIIYQQ4v8t6cwIIYQQQgghvJLHppkJIYQQQgghzg1a+2YBABmZEUIIIYQQQnglGZkRQgghhBDCx0kBACGEEEIIIYQ4h/jsyExQqL/RETwmO9/oBJ51Irah0RE8qn7DBkZH8JjmDRuw/dPVRscQQgghxJ/46siMz3ZmhBDG6PTTGqMjCCGEV2lldAAPa9XY6ATCl0lnRgghhBBCCB/nkmpmQgghhBBCCHHukJEZIYQQQgghfJyvrpmRkRkhhBBCCCGEV5KRGSGEEEIIIXycdsmaGSGEEEIIIYQ4Z8jITCUN6xtAq2QLBcWaf39QVLG9W1sr3dr64dLw6z4HX6wsNTBl1X0/ayL7ty4lMKQOw8cvAOBExjZ++GQyTnspJpOZ7lc/Tv2kNgYnrZoTJzL533//Q05ODkop+vUfwBVXDq54/LNP5/Du29P4YNanhIWHG5i0CswWgq+9B2W2gMmEfecmSn/8hv9r7+6jpKrvO46/P7sryIOCIIgVZeVB0eZQNIitilqp8aHWh1SjhlJ8oMQHoonNiWh6qrXHc0iwRo9tTG19LvEhGpFGTWKxFHxAsBEQxAdAVBRBgi0ij8t++8e9cFa6d4HZmbne8fM6Z87MXH575/Plzs7Od+7v3ulwxAg6HHkC9fv0Yu0/XU9s+Gzn6zIzM7OaUqvHzFSsmZF0IPAA0AdoBu6KiNsl9QAeARqBZcA3IuITSYOBe4EjgR9ExC3peg5Nx2/TH/jbiLitUtnb8vLrW5gxdzOjT+m0fdmgvvUMGbAHEyd/RtNW6NpJeUQri8OGn8OQ40bx7M8mbF/2wtRJDD/lShoPO55lr/8XL/77JL4+/sEcU5auvr6eS8ZexoCBg1i/fj3XXHU5Q4/8Kgcd1I+PP17F3Ff/m169eucdszRbm/js0X+ELZuhro4uF15N0zuv0/TBUrYsWUjX88fnndDMzMysrCo5zawJ+OuIOAz4Q+BKSYcDE4BpETEImJbeB1gDXAXc0nIlEfFmRAyNiKHAV4H1wBMVzN2mJR9sZf2mz3e2xw3pwLNzNtG0Nbm/bkNxO98DBhzFnl0+v0dCEps3rgNg88ZP6dKtoG/2gR49ejJg4CAAOnfuTN+DDuJ3q1cDcPddd3LRJeOQituMsmVzcl1Xj+rqIaB51QfE2jX55jIzM7NcRTRX7VJNFdszExErgBXp7U8lLQIOAM4CTkyH3Q9MB66NiFXAKkl/2sZqRwJLIuLdSuUuRe996hhwQANnHLsnW5qCKTM38t7K2jnIasQ51/PkT8fywtQfEdHMuVc9lHeksli58iOWLlnMoYMH8/KsF+nZc18O7j8g71jtI9F19Peo696LzXNnsvWjL9SvipmZmVlZVeUEAJIagSOAl4H90kZnW8OzOx/zXwBkvpOWNE7SK5JeWfDivaUH3k11gk4d4R8e/ownZ27kktM7V+2xq+G1Fx5ixNkTuPiG6Yw46zqmPfw3eUdqtw0bNjDx5r9j7LgrqK+r5+cP/4xvjh6Td6z2i2DdA5NY+883UN+nH3X77p93IjMzM7OKqXgzI6kr8DjwnYhY2471dADOBH6eNSYi7oqIYREx7CvHXFzqQ+22/1kXzFvSBMC7K5tpjmIfN7OjN+ZMYcCQrwEwcOiprHxvfs6J2qepqYmJN9/ICSeO5JhjR7BixYesXPkRV1/5LcZeNIrVqz/mO1ddxidrCjw1a9MGmt5fTEPj4LyTmJmZ2RdAc3NU7VJNFT2bmaQ9SBqZyRHxi3TxSkn7R8QKSfsDq3ZxdacBv42IlZXI2h7zl2zhkL4NLF6+lV7d62ioL/ZxMzvqsndvPlgym74Dj2b527Po3qtf3pFKFhHccdst9D2wH2d//VwAGg/uz4MPPbZ9zNiLRnHr7T8p3NnM1KlLcg75TRugYQ8a+h3CptnT8o5lZmZmVjGVPJuZgLuBRRFxa4t/mgqMASam10/u4iovpI0pZtVy0WmdGNi3nq57ipsu7crTszYxa+EWRp28J9f9RRe2NsO//XpD3jFL9qsHruGDxXPY+Nkn3HPjCRx96rc56fy/Z8YTN9PcvJWGho6c9I2b8o5ZskWvL+A/n/sP+jUezNXjvwXA6DGXMOyoo3NO1n7q0o0up42CujqQ2PLmqzQtXUiHI46n4/CRqMtedB1zLU1LX2fDbx7OO66ZmZlVUa1+aaYiKrMHQdJxwEzgNZJTMwNcT3LczKPAQcB7wHkRsUZSH+AVYO90/Drg8IhYK6kz8D7QPyL+d1ce/9u3ra2dXSM7OPSQvfKOUFEnH7o87wgV1eeJW3Y+qMC6fe/2vCOYmZlVUyGOLTjx3Jeq9t54+mN/VLX/k0qezex5sjfuyFbGfwT0zVjXeqBn+dKZmZmZmX151OqXZlblbGZmZmZmZmblVtETAJiZmZmZWf6q/WWW1eI9M2ZmZmZmVkjeM2NmZmZmVuN8zIyZmZmZmdkXiPfMmJmZmZnVuFr9nhnvmTEzMzMzs0Kq2JdmftlIGhcRd+WdoxJquTZwfUXn+oqrlmsD11d0rq+4ark2+/+8Z6Z8xuUdoIJquTZwfUXn+oqrlmsD11d0rq+4ark224GbGTMzMzMzKyQ3M2ZmZmZmVkhuZsqnludm1nJt4PqKzvUVVy3XBq6v6FxfcdVybbYDnwDAzMzMzMwKyXtmzMzMzMyskNzMmJmZmZlZIbmZKYGkUyW9KWmxpAnpsvHp/ZC0b94Z2yOjvsnpsgWS7pG0R945S5VR392S5kmaL+kxSV3zzlmK1mpr8W93SFqXV7ZyyNh290l6R9Lc9DI075ylyqhPkm6W9JakRZKuyjtnqTLqm9li230oaUreOUuVUd9ISb9N63te0sC8c5Yio7aT0toWSLpfUkPeOUuV/l1bJWlBi2U9JD0r6e30ep88M5Yqo7bzJC2U1CxpWJ752iujvkmS3kj/pj8hqXueGa3CIsKX3bgA9cASoD/QAZgHHA4cATQCy4B9885ZgfpOB5ReHgIuzztrmevbu8WYW4EJeWctV23pvw0DHgTW5Z2zAtvuPuDcvPNVsL6LgQeAunRc77yzlrO+HcY8Dvxl3lnLvP3eAg5Lx1wB3Jd31jLW9j5wSDrmJuDSvLO2o8bjgSOBBS2W/Wjb3wJgAvDDvHOWsbbDgEOB6cCwvDNWoL6vAQ3p7R8Wddv5smsX75nZfcOBxRGxNCI2Aw8DZ0XEqxGxLN9oZZFV39ORAmYDfXNNWbqs+tZC8ik40Ako4pkxWq1NUj0wCfh+runar9X6cs5UTln1XQ7cFBHNABGxKseM7dHm9pO0F3ASUNQ9M1n1BbB3OqYb8GFO+dqjtdr+HNgUEW+lY55NlxVSRMwA1uyw+Czg/vT2/cDZVQ1VJq3VFhGLIuLNnCKVVUZ9v4mIpvTuLIr7nsV2gZuZ3XcAyadR2yxPl9WKNutLp5eNBn5V5VzlklmfpHuBj4DBwB3Vj9ZuWbWNB6ZGxIpcUpVPW8/Nm9PpBD+W1LH60coiq74BwPmSXpH0jKRBuaRrv529dp4DTNv2wUIBZdU3Fnha0nKS186JOWRrr9Zq6wPs0WKK0rnAgdUOVmH7bXvdTK9755zHSnMJ8EzeIaxy3MzsPrWyrIif4mfZWX3A/IyYAAAEgElEQVQ/AWZExMwq5Sm3zPoi4mLg94BFwPnVDFUmrdXWETiPYjZnO8radteRNKBHAT2Aa6sZqoyy6usIbIyIYcC/APdUNVX57Oy15UKSKaxFlVXfd4HTI6IvcC/JNNaiaa22ZuAC4MeSZgOfAk2tjDPLjaQfkDwvJ+edxSrHzczuW87nP33qSzGnDWTJrE/SDUAv4JoccpVLm9svIrYCj1DM6RKt1bYMGAgslrQM6CxpcfWjlUWr2y4iVqQzIDeRvFkcnku69st6bi4nOZYE4AlgSJVzlUtbry09SbbbUznkKpfW6lsF/EFEvJwuewQ4ptrByiDrd++liBgREcOBGcDbuaSrnJWS9gdIr4s6xfNLSdIY4AxgVDpF3mqUm5ndNwcYJOlgSR1IPpmamnOmcmq1PkljgVOAC7fN3S+orPoGwvZjZv4MeCPHjKVqrbYpEdEnIhojohFYHxGFPJsS2dtu25sNkcxpX9DGOr7Isl5bppAcSwJwAskB5UXU1mvnecAvI2JjbunaL6u+bpIOScecTLLnt2iyfvd6A6RTO68FfppjxkqYCoxJb48Bnswxi+0GSaeSPCfPjIj1eeexyirsaRTzEhFNksYDvyY5w8s9EbEwPV3q90nmEc+X9HREjM0zaynaqG8e8C7wUvKekV9ExE05Ri1Ja/WRvLmYKWlvkukU80gOui6UrG2Xc6yyaeO5+ZykXiTbbi5wWZ45S9VGfROByZK+C6wjOQajcHby/LyAYh5Lsl1GffMk/RXwuKRm4BOS+fuF0sZzc5KkM0g+GL0zIp7LNWg7SHoIOBHYNz2+6QaS5+Sjki4F3iNpugsno7Y1JNOPewFPSZobEafkl7J0GfVdRzJF99n0PcusiCjk3wbbOXnPm5mZmZmZFZGnmZmZmZmZWSG5mTEzMzMzs0JyM2NmZmZmZoXkZsbMzMzMzArJzYyZmZmZmRWSmxkzM2uTpO6Srmhx/0RJv8wzk5mZGbiZMTOznesOXLHTUWZmZlXmZsbMrIZIapT0hqR/lbRA0mRJfyLpBUlvSxouqYekKZLmS5olaUj6szdKukfSdElL0y8DhuTLAwdImitpUrqsq6TH0searPSb6czMzKqpIe8AZmZWdgNJvq18HDAH+CZwHHAmcD3wPvBqRJwt6STgAWBo+rODgT8G9gLelHQnMAH4SkQMhWSaGXAE8PvAh8ALwLHA89UozszMbBvvmTEzqz3vRMRrEdEMLASmRUQArwGNJI3NgwAR8RzQU1K39GefiohNEbEaWAXsl/EYsyNiefoYc9P1mpmZVZWbGTOz2rOpxe3mFvebSfbItzYlLFr52a1k78Hf1XFmZmYV42bGzOzLZwYwCrZPGVsdEWvbGP8pybQzMzOzLxR/kmZm9uVzI3CvpPnAemBMW4Mj4nfpCQQWAM8AT1U+opmZ2c4pmUZtZmZmZmZWLJ5mZmZmZmZmheRmxszMzMzMCsnNjJmZmZmZFZKbGTMzMzMzKyQ3M2ZmZmZmVkhuZszMzMzMrJDczJiZmZmZWSH9H8VhLjZnt4tAAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1080x720 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Heatmap for year and average temprature across the month. More red more heat, more blue less heat\n",
    "plt.figure(figsize=(15, 10));\n",
    "sns.heatmap(pd.crosstab(df_filtered.year, [df_filtered.month], values=df_filtered._tempm, aggfunc=\"mean\"),\n",
    "            cmap=\"coolwarm\", annot=True, cbar=True);\n",
    "plt.title(\"Average Temprature 1996-2016\")\n",
    "plt.plot();"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now our dataset doesn;t have any missing values in it. Now we should observe one thing. That our _windre is\n",
    "a categorical column and it is also important to predict a whether but the thing is your model does not understand a text value. So we need to encode this categorical column so that we can change it to integer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Haze                             47674\n",
       "Smoke                            20760\n",
       "Mist                              9375\n",
       "Clear                             3129\n",
       "Widespread Dust                   2856\n",
       "Fog                               2760\n",
       "Scattered Clouds                  2209\n",
       "Partly Cloudy                     2091\n",
       "Shallow Fog                       1860\n",
       "Mostly Cloudy                     1537\n",
       "Light Rain                        1302\n",
       "Partial Fog                       1031\n",
       "Patches of Fog                     901\n",
       "Thunderstorms and Rain             486\n",
       "Heavy Fog                          421\n",
       "Light Drizzle                      414\n",
       "Rain                               394\n",
       "Unknown                            383\n",
       "Blowing Sand                       378\n",
       "Overcast                           326\n",
       "Thunderstorm                       192\n",
       "Light Thunderstorms and Rain       176\n",
       "Drizzle                            112\n",
       "Light Fog                           64\n",
       "Light Thunderstorm                  64\n",
       "Heavy Rain                          28\n",
       "Heavy Thunderstorms and Rain        22\n",
       "Thunderstorms with Hail             11\n",
       "Squalls                              6\n",
       "Light Sandstorm                      6\n",
       "Light Rain Showers                   5\n",
       "Light Haze                           4\n",
       "Volcanic Ash                         4\n",
       "Sandstorm                            2\n",
       "Rain Showers                         2\n",
       "Funnel Cloud                         2\n",
       "Heavy Thunderstorms with Hail        1\n",
       "Light Freezing Rain                  1\n",
       "Light Hail Showers                   1\n",
       "Name: _conds, dtype: int64"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered._conds.value_counts()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Feature & Target Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import LabelEncoder, OneHotEncoder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['datetime_utc', '_conds', '_dewptm', '_fog', '_hail', '_hum',\n",
       "       '_pressurem', '_rain', '_snow', '_tempm', '_thunder', '_tornado',\n",
       "       '_vism', '_wdird', '_wdire', '_wspdm', 'year', 'month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_filtered.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Lets create a new dataset, so that we dont change in our filtered dataset\n",
    "# We will create dataset in such a way, _wdire(categorical feature in starting position & target variable\n",
    "# at last which is _conds\n",
    "df_final = df_filtered[['_wdire', '_dewptm', '_fog', '_hail', '_hum',\n",
    "       '_pressurem', '_rain', '_snow', '_tempm', '_thunder', '_tornado',\n",
    "       '_vism', '_wdird', '_wspdm', 'year', 'month', '_conds']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>_wdire</th>\n",
       "      <th>_dewptm</th>\n",
       "      <th>_fog</th>\n",
       "      <th>_hail</th>\n",
       "      <th>_hum</th>\n",
       "      <th>_pressurem</th>\n",
       "      <th>_rain</th>\n",
       "      <th>_snow</th>\n",
       "      <th>_tempm</th>\n",
       "      <th>_thunder</th>\n",
       "      <th>_tornado</th>\n",
       "      <th>_vism</th>\n",
       "      <th>_wdird</th>\n",
       "      <th>_wspdm</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>_conds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>West</td>\n",
       "      <td>9.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>1010.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>280.0</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "      <td>Smoke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>North</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>32.0</td>\n",
       "      <td>1015.429612</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.198981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.052735</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "      <td>Smoke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>North</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>1015.429612</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.198981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.052735</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "      <td>Smoke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>North</td>\n",
       "      <td>10.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>41.0</td>\n",
       "      <td>1010.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.052735</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "      <td>Smoke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>North</td>\n",
       "      <td>11.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1011.000000</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "      <td>Smoke</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  _wdire  _dewptm  _fog  _hail  _hum   _pressurem  _rain  _snow  _tempm  \\\n",
       "0   West      9.0     0      0  27.0  1010.000000      0      0    30.0   \n",
       "1  North     10.0     0      0  32.0  1015.429612      0      0    28.0   \n",
       "2  North     11.0     0      0  44.0  1015.429612      0      0    24.0   \n",
       "3  North     10.0     0      0  41.0  1010.000000      0      0    24.0   \n",
       "4  North     11.0     0      0  47.0  1011.000000      0      0    23.0   \n",
       "\n",
       "   _thunder  _tornado     _vism  _wdird    _wspdm  year month _conds  \n",
       "0         0         0  5.000000   280.0  7.400000  1996    11  Smoke  \n",
       "1         0         0  2.198981     0.0  5.052735  1996    11  Smoke  \n",
       "2         0         0  2.198981     0.0  5.052735  1996    11  Smoke  \n",
       "3         0         0  2.000000     0.0  5.052735  1996    11  Smoke  \n",
       "4         0         0  1.200000     0.0  0.000000  1996    11  Smoke  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "_wdire         object\n",
       "_dewptm       float64\n",
       "_fog            int64\n",
       "_hail           int64\n",
       "_hum          float64\n",
       "_pressurem    float64\n",
       "_rain           int64\n",
       "_snow           int64\n",
       "_tempm        float64\n",
       "_thunder        int64\n",
       "_tornado        int64\n",
       "_vism         float64\n",
       "_wdird        float64\n",
       "_wspdm        float64\n",
       "year           object\n",
       "month          object\n",
       "_conds         object\n",
       "dtype: object"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "West        19970\n",
       "North       19714\n",
       "NW          13110\n",
       "WNW          7464\n",
       "East         7233\n",
       "WSW          5466\n",
       "ESE          5336\n",
       "ENE          3721\n",
       "SE           3513\n",
       "SW           3200\n",
       "NNW          2696\n",
       "NE           2500\n",
       "SSE          2052\n",
       "NNE          1752\n",
       "SSW          1715\n",
       "South        1543\n",
       "Variable        5\n",
       "Name: _wdire, dtype: int64"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final._wdire.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "wdire_dummies = pd.get_dummies(df_final[\"_wdire\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final = pd.concat([wdire_dummies, df_final], axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ENE</th>\n",
       "      <th>ESE</th>\n",
       "      <th>East</th>\n",
       "      <th>NE</th>\n",
       "      <th>NNE</th>\n",
       "      <th>NNW</th>\n",
       "      <th>NW</th>\n",
       "      <th>North</th>\n",
       "      <th>SE</th>\n",
       "      <th>SSE</th>\n",
       "      <th>...</th>\n",
       "      <th>_snow</th>\n",
       "      <th>_tempm</th>\n",
       "      <th>_thunder</th>\n",
       "      <th>_tornado</th>\n",
       "      <th>_vism</th>\n",
       "      <th>_wdird</th>\n",
       "      <th>_wspdm</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>_conds</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
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       "      <td>30.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>280.0</td>\n",
       "      <td>7.400000</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "      <td>Smoke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.198981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.052735</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "      <td>Smoke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.198981</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.052735</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "      <td>Smoke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
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       "      <td>24.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5.052735</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "      <td>Smoke</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>23.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1996</td>\n",
       "      <td>11</td>\n",
       "      <td>Smoke</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   ENE  ESE  East  NE  NNE  NNW  NW  North  SE  SSE   ...    _snow  _tempm  \\\n",
       "0    0    0     0   0    0    0   0      0   0    0   ...        0    30.0   \n",
       "1    0    0     0   0    0    0   0      1   0    0   ...        0    28.0   \n",
       "2    0    0     0   0    0    0   0      1   0    0   ...        0    24.0   \n",
       "3    0    0     0   0    0    0   0      1   0    0   ...        0    24.0   \n",
       "4    0    0     0   0    0    0   0      1   0    0   ...        0    23.0   \n",
       "\n",
       "   _thunder  _tornado     _vism  _wdird    _wspdm  year  month  _conds  \n",
       "0         0         0  5.000000   280.0  7.400000  1996     11   Smoke  \n",
       "1         0         0  2.198981     0.0  5.052735  1996     11   Smoke  \n",
       "2         0         0  2.198981     0.0  5.052735  1996     11   Smoke  \n",
       "3         0         0  2.000000     0.0  5.052735  1996     11   Smoke  \n",
       "4         0         0  1.200000     0.0  0.000000  1996     11   Smoke  \n",
       "\n",
       "[5 rows x 34 columns]"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ENE', 'ESE', 'East', 'NE', 'NNE', 'NNW', 'NW', 'North', 'SE', 'SSE',\n",
       "       'SSW', 'SW', 'South', 'Variable', 'WNW', 'WSW', 'West', '_wdire',\n",
       "       '_dewptm', '_fog', '_hail', '_hum', '_pressurem', '_rain', '_snow',\n",
       "       '_tempm', '_thunder', '_tornado', '_vism', '_wdird', '_wspdm', 'year',\n",
       "       'month', '_conds'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_final.drop(\"_wdire\", inplace=True, axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['ENE', 'ESE', 'East', 'NE', 'NNE', 'NNW', 'NW', 'North', 'SE', 'SSE',\n",
       "       'SSW', 'SW', 'South', 'Variable', 'WNW', 'WSW', 'West', '_dewptm',\n",
       "       '_fog', '_hail', '_hum', '_pressurem', '_rain', '_snow', '_tempm',\n",
       "       '_thunder', '_tornado', '_vism', '_wdird', '_wspdm', 'year', 'month',\n",
       "       '_conds'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_final.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {},
   "outputs": [],
   "source": [
    "X = df_final.iloc[:, 0:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = df_final.iloc[:, -1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {},
   "outputs": [],
   "source": [
    "label_encoder = LabelEncoder()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {},
   "outputs": [],
   "source": [
    "y = label_encoder.fit_transform(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100990,)"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {},
   "outputs": [],
   "source": [
    "# SO now our Feature Matrix(X) and target matrix y is ready"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train & Test Split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import train_test_split"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=.25, random_state=0) # test size =0.25 or 25%"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Shape of X_train (75742, 32)\n",
      "Shape of X_test (25248, 32)\n",
      "Shape of y_train (75742,)\n",
      "Shape of y_test (25248,)\n"
     ]
    }
   ],
   "source": [
    "print(\"Shape of X_train\", X_train.shape)\n",
    "print(\"Shape of X_test\", X_test.shape)\n",
    "print(\"Shape of y_train\", y_train.shape)\n",
    "print(\"Shape of y_test\", y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Create Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier(criterion=\"entropy\", random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Train Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DecisionTreeClassifier(class_weight=None, criterion='entropy', max_depth=None,\n",
       "                       max_features=None, max_leaf_nodes=None,\n",
       "                       min_impurity_decrease=0.0, min_impurity_split=None,\n",
       "                       min_samples_leaf=1, min_samples_split=2,\n",
       "                       min_weight_fraction_leaf=0.0, presort=False,\n",
       "                       random_state=0, splitter='best')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred = clf.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5,  5, 31, ..., 15, 24, 20])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y_pred"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "DecisionTrees's Accuracy:  0.7853691381495564\n"
     ]
    }
   ],
   "source": [
    "from sklearn import metrics\n",
    "import matplotlib.pyplot as plt\n",
    "print(\"DecisionTrees's Accuracy: \", metrics.accuracy_score(y_test, y_pred))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Congrats your model is ready wth 78% of accuracy, Please provide your suggestions to increase the accuracy of the\n",
    "# model"
   ]
  }
 ],
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