Notebooks/Day24-Cleaning Data,matplotlib/Data Cleaning,Matplotlib.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Day Agenda\n",
    "- Filtering a Data\n",
    "- Sorting a Data Frame\n",
    "- Cleaning the Data Frame\n",
    "- Matplotlib Introduction\n",
    "- Types of Plots\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 1.Read the dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s.no</th>\n",
       "      <th>day</th>\n",
       "      <th>temparature</th>\n",
       "      <th>winspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1/4/2019</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1/5/2019</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1/6/2019</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1/7/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1/8/2019</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1/9/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019         30.0      12.0     rain\n",
       "1      1   1/2/2019          NaN      14.0      NaN\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0       NaN  fullair\n",
       "4      4   1/5/2019         66.0      10.0      NaN\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019          NaN      17.0  fullair\n",
       "7      7   1/8/2019         89.0       NaN     cold\n",
       "8      8   1/9/2019          NaN       9.0  fullair\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "10    10  1/11/2019         65.0       NaN      NaN\n",
       "11    11  1/12/2019          NaN      20.0  fullair"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df=pd.read_csv(\"https://raw.githubusercontent.com/AP-State-Skill-Development-Corporation/Data-Science-Using-Python-Internship-EB1/main/Notebooks/Day23-Pandas%20continue/weather.csv\")\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(12, 5)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Rows ,columns\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 12 entries, 0 to 11\n",
      "Data columns (total 5 columns):\n",
      " #   Column       Non-Null Count  Dtype  \n",
      "---  ------       --------------  -----  \n",
      " 0   s.no         12 non-null     int64  \n",
      " 1   day          12 non-null     object \n",
      " 2   temparature  8 non-null      float64\n",
      " 3   winspeed     9 non-null      float64\n",
      " 4   event        9 non-null      object \n",
      "dtypes: float64(2), int64(1), object(2)\n",
      "memory usage: 608.0+ bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s.no</th>\n",
       "      <th>temparature</th>\n",
       "      <th>winspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>12.000000</td>\n",
       "      <td>8.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.583333</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>13.444444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.476109</td>\n",
       "      <td>22.940918</td>\n",
       "      <td>3.503966</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>9.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.750000</td>\n",
       "      <td>40.750000</td>\n",
       "      <td>11.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.500000</td>\n",
       "      <td>60.000000</td>\n",
       "      <td>13.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>8.250000</td>\n",
       "      <td>71.750000</td>\n",
       "      <td>15.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>11.000000</td>\n",
       "      <td>90.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            s.no  temparature   winspeed\n",
       "count  12.000000     8.000000   9.000000\n",
       "mean    5.583333    59.000000  13.444444\n",
       "std     3.476109    22.940918   3.503966\n",
       "min     1.000000    30.000000   9.000000\n",
       "25%     2.750000    40.750000  11.000000\n",
       "50%     5.500000    60.000000  13.000000\n",
       "75%     8.250000    71.750000  15.000000\n",
       "max    11.000000    90.000000  20.000000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
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       "      <th>s.no</th>\n",
       "      <th>day</th>\n",
       "      <th>temparature</th>\n",
       "      <th>winspeed</th>\n",
       "      <th>event</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>rain</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>rain</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1/4/2019</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1/5/2019</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
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      ],
      "text/plain": [
       "   s.no       day  temparature  winspeed    event\n",
       "0     1  1/2/2019         30.0      12.0     rain\n",
       "1     1  1/2/2019          NaN      14.0      NaN\n",
       "2     2  1/3/2019         43.0      13.0     rain\n",
       "3     3  1/4/2019         55.0       NaN  fullair\n",
       "4     4  1/5/2019         66.0      10.0      NaN"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     30.0\n",
       "1      NaN\n",
       "2     43.0\n",
       "3     55.0\n",
       "4     66.0\n",
       "5     34.0\n",
       "6      NaN\n",
       "7     89.0\n",
       "8      NaN\n",
       "9     90.0\n",
       "10    65.0\n",
       "11     NaN\n",
       "Name: temparature, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['temparature']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['temparature'].count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "65.0    1\n",
       "90.0    1\n",
       "89.0    1\n",
       "34.0    1\n",
       "66.0    1\n",
       "55.0    1\n",
       "43.0    1\n",
       "30.0    1\n",
       "Name: temparature, dtype: int64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['temparature'].value_counts() # findout the element count in a column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Filtering a Data\n",
    " - Accessing a particular data or information from given dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s.no</th>\n",
       "      <th>day</th>\n",
       "      <th>temparature</th>\n",
       "      <th>winspeed</th>\n",
       "      <th>event</th>\n",
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       "      <td>1/2/2019</td>\n",
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       "      <td>rain</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>43.0</td>\n",
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       "      <td>rain</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1/4/2019</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1/5/2019</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
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      ],
      "text/plain": [
       "   s.no       day  temparature  winspeed    event\n",
       "0     1  1/2/2019         30.0      12.0     rain\n",
       "1     1  1/2/2019          NaN      14.0      NaN\n",
       "2     2  1/3/2019         43.0      13.0     rain\n",
       "3     3  1/4/2019         55.0       NaN  fullair\n",
       "4     4  1/5/2019         66.0      10.0      NaN"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     False\n",
       "1     False\n",
       "2     False\n",
       "3     False\n",
       "4      True\n",
       "5     False\n",
       "6     False\n",
       "7      True\n",
       "8     False\n",
       "9      True\n",
       "10     True\n",
       "11    False\n",
       "Name: temparature, dtype: bool"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['temparature']>55 # Filterning"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1/5/2019</td>\n",
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       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1/8/2019</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed event\n",
       "4      4   1/5/2019         66.0      10.0   NaN\n",
       "7      7   1/8/2019         89.0       NaN  cold\n",
       "9      9  1/10/2019         90.0      11.0  cold\n",
       "10    10  1/11/2019         65.0       NaN   NaN"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[df['temparature']>55]"
   ]
  },
  {
   "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>s.no</th>\n",
       "      <th>day</th>\n",
       "      <th>temparature</th>\n",
       "      <th>winspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1/6/2019</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1/7/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "1      1   1/2/2019          NaN      14.0      NaN\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019          NaN      17.0  fullair\n",
       "11    11  1/12/2019          NaN      20.0  fullair"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Filter the windspeed data >13\n",
    "df[df['winspeed']>13]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>s.no</th>\n",
       "      <th>day</th>\n",
       "      <th>temparature</th>\n",
       "      <th>winspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1/6/2019</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   s.no       day  temparature  winspeed event\n",
       "5     5  1/6/2019         34.0      15.0  cold"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Filetr the data,temp>40 and windspeed>15\n",
    "df[(df[\"temparature\"]>=30)  & (df['winspeed']==15)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>s.no</th>\n",
       "      <th>day</th>\n",
       "      <th>temparature</th>\n",
       "      <th>winspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1/4/2019</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1/5/2019</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1/6/2019</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1/8/2019</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0       NaN  fullair\n",
       "4      4   1/5/2019         66.0      10.0      NaN\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "7      7   1/8/2019         89.0       NaN     cold\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "10    10  1/11/2019         65.0       NaN      NaN"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Filetr the data,temp>40 or windspeed>15\n",
    "df[(df[\"temparature\"]>=40)  | (df['winspeed']==15)]\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Sorting a Data Frame\n",
    "- ascending order\n",
    "- dscending order\n",
    "    - sort_index\n",
    "        - to sort the index of dataset\n",
    "    - sort_values\n",
    "        - to sort values in side dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>s.no</th>\n",
       "      <th>day</th>\n",
       "      <th>temparature</th>\n",
       "      <th>winspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1/4/2019</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1/5/2019</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   s.no       day  temparature  winspeed    event\n",
       "0     1  1/2/2019         30.0      12.0     rain\n",
       "1     1  1/2/2019          NaN      14.0      NaN\n",
       "2     2  1/3/2019         43.0      13.0     rain\n",
       "3     3  1/4/2019         55.0       NaN  fullair\n",
       "4     4  1/5/2019         66.0      10.0      NaN"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "      <th>s.no</th>\n",
       "      <th>day</th>\n",
       "      <th>temparature</th>\n",
       "      <th>winspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5</td>\n",
       "      <td>1/6/2019</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1/4/2019</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>4</td>\n",
       "      <td>1/5/2019</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>1/8/2019</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>9</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>1/7/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>1/9/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019         30.0      12.0     rain\n",
       "1      5   1/6/2019         34.0      15.0     cold\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0       NaN  fullair\n",
       "4     10  1/11/2019         65.0       NaN      NaN\n",
       "5      4   1/5/2019         66.0      10.0      NaN\n",
       "6      7   1/8/2019         89.0       NaN     cold\n",
       "7      9  1/10/2019         90.0      11.0     cold\n",
       "8      1   1/2/2019          NaN      14.0      NaN\n",
       "9      6   1/7/2019          NaN      17.0  fullair\n",
       "10     8   1/9/2019          NaN       9.0  fullair\n",
       "11    11  1/12/2019          NaN      20.0  fullair"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"temparature\",ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>winspeed</th>\n",
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       "    <tr>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
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       "      <th>3</th>\n",
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       "      <td>NaN</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>3</td>\n",
       "      <td>1/4/2019</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fullair</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5</td>\n",
       "      <td>1/6/2019</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>rain</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>6</td>\n",
       "      <td>1/7/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>1/9/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "0      9  1/10/2019         90.0      11.0     cold\n",
       "1      7   1/8/2019         89.0       NaN     cold\n",
       "2      4   1/5/2019         66.0      10.0      NaN\n",
       "3     10  1/11/2019         65.0       NaN      NaN\n",
       "4      3   1/4/2019         55.0       NaN  fullair\n",
       "5      2   1/3/2019         43.0      13.0     rain\n",
       "6      5   1/6/2019         34.0      15.0     cold\n",
       "7      1   1/2/2019         30.0      12.0     rain\n",
       "8      1   1/2/2019          NaN      14.0      NaN\n",
       "9      6   1/7/2019          NaN      17.0  fullair\n",
       "10     8   1/9/2019          NaN       9.0  fullair\n",
       "11    11  1/12/2019          NaN      20.0  fullair"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(\"temparature\",ascending=False,ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
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       "    s.no        day  temparature  winspeed    event\n",
       "8      8   1/9/2019          NaN       9.0  fullair\n",
       "4      4   1/5/2019         66.0      10.0      NaN\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "0      1   1/2/2019         30.0      12.0     rain\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "1      1   1/2/2019          NaN      14.0      NaN\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019          NaN      17.0  fullair\n",
       "11    11  1/12/2019          NaN      20.0  fullair\n",
       "3      3   1/4/2019         55.0       NaN  fullair\n",
       "7      7   1/8/2019         89.0       NaN     cold\n",
       "10    10  1/11/2019         65.0       NaN      NaN"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "s1=df.sort_values(\"winspeed\")\n",
    "s1"
   ]
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  {
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   "execution_count": 35,
   "metadata": {},
   "outputs": [
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       "   s.no        day  temparature  winspeed    event\n",
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       "4     4   1/5/2019         66.0      10.0      NaN\n",
       "9     9  1/10/2019         90.0      11.0     cold\n",
       "0     1   1/2/2019         30.0      12.0     rain\n",
       "2     2   1/3/2019         43.0      13.0     rain"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
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    "s1.head()"
   ]
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   "execution_count": 37,
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       "      <th>1</th>\n",
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      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "11    11  1/12/2019          NaN      20.0  fullair\n",
       "10    10  1/11/2019         65.0       NaN      NaN\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "8      8   1/9/2019          NaN       9.0  fullair\n",
       "7      7   1/8/2019         89.0       NaN     cold\n",
       "6      6   1/7/2019          NaN      17.0  fullair\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "4      4   1/5/2019         66.0      10.0      NaN\n",
       "3      3   1/4/2019         55.0       NaN  fullair\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "1      1   1/2/2019          NaN      14.0      NaN\n",
       "0      1   1/2/2019         30.0      12.0     rain"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "s1.sort_index(ascending=False)"
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  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
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       "      <td>9.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1/7/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019         30.0      12.0     rain\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0       NaN  fullair\n",
       "10    10  1/11/2019         65.0       NaN      NaN\n",
       "4      4   1/5/2019         66.0      10.0      NaN\n",
       "7      7   1/8/2019         89.0       NaN     cold\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "8      8   1/9/2019          NaN       9.0  fullair\n",
       "1      1   1/2/2019          NaN      14.0      NaN\n",
       "6      6   1/7/2019          NaN      17.0  fullair\n",
       "11    11  1/12/2019          NaN      20.0  fullair"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values([\"temparature\",\"winspeed\"])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Cleaning the Data Frame\n",
    "- To check unwanted data/misiing data\n",
    "    - isnull()\n",
    "    - notnull()\n",
    "\n",
    "- To remove unwanted data\n",
    "    - dropna()\n",
    "- Handling unwanted data\n",
    "    - fillna()\n",
    "    - replace()\n",
    "\n",
    "    \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "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>s.no</th>\n",
       "      <th>day</th>\n",
       "      <th>temparature</th>\n",
       "      <th>winspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1/4/2019</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1/5/2019</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   s.no       day  temparature  winspeed    event\n",
       "0     1  1/2/2019         30.0      12.0     rain\n",
       "1     1  1/2/2019          NaN      14.0      NaN\n",
       "2     2  1/3/2019         43.0      13.0     rain\n",
       "3     3  1/4/2019         55.0       NaN  fullair\n",
       "4     4  1/5/2019         66.0      10.0      NaN"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Preprocessing\n",
    "- checking null values\n",
    "- remove null values\n",
    "- fill null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "s.no           0\n",
       "day            0\n",
       "temparature    4\n",
       "winspeed       3\n",
       "event          3\n",
       "dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum() # count of null values in each column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>temparature</th>\n",
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       "      <th>event</th>\n",
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       "  <tbody>\n",
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       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>rain</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1/6/2019</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
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       "      <td>11.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   s.no        day  temparature  winspeed event\n",
       "0     1   1/2/2019         30.0      12.0  rain\n",
       "2     2   1/3/2019         43.0      13.0  rain\n",
       "5     5   1/6/2019         34.0      15.0  cold\n",
       "9     9  1/10/2019         90.0      11.0  cold"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1=df.dropna() # remove the null values\n",
    "d1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "s.no           0\n",
       "day            0\n",
       "temparature    0\n",
       "winspeed       0\n",
       "event          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d1.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
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       "      <td>10.0</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
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       "      <td>1/6/2019</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1/7/2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1/8/2019</td>\n",
       "      <td>89.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1/9/2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>0.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019         30.0      12.0     rain\n",
       "1      1   1/2/2019          0.0      14.0        0\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0       0.0  fullair\n",
       "4      4   1/5/2019         66.0      10.0        0\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019          0.0      17.0  fullair\n",
       "7      7   1/8/2019         89.0       0.0     cold\n",
       "8      8   1/9/2019          0.0       9.0  fullair\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "10    10  1/11/2019         65.0       0.0        0\n",
       "11    11  1/12/2019          0.0      20.0  fullair"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th></th>\n",
       "      <th>s.no</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>10.0</td>\n",
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       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>cold</td>\n",
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       "      <th>6</th>\n",
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       "      <td>1/7/2019</td>\n",
       "      <td>1.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1/8/2019</td>\n",
       "      <td>89.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1/9/2019</td>\n",
       "      <td>1.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>1.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019         30.0      12.0     rain\n",
       "1      1   1/2/2019          1.0      14.0    sunny\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0       2.0  fullair\n",
       "4      4   1/5/2019         66.0      10.0    sunny\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019          1.0      17.0  fullair\n",
       "7      7   1/8/2019         89.0       2.0     cold\n",
       "8      8   1/9/2019          1.0       9.0  fullair\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "10    10  1/11/2019         65.0       2.0    sunny\n",
       "11    11  1/12/2019          1.0      20.0  fullair"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna({'temparature':1,'winspeed':2,'event':'sunny'},inplace=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
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   "execution_count": 93,
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       "   s.no       day  temparature  winspeed    event\n",
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       "3     3  1/4/2019         55.0       NaN  fullair\n",
       "4     4  1/5/2019         66.0      10.0      NaN"
      ]
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     "execution_count": 93,
     "metadata": {},
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    "df1=pd.read_csv(\"https://raw.githubusercontent.com/AP-State-Skill-Development-Corporation/Data-Science-Using-Python-Internship-EB1/main/Notebooks/Day23-Pandas%20continue/weather.csv\")\n",
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     "execution_count": 79,
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   "execution_count": 80,
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   "source": [
    "df1['temparature']=df1['temparature'].fillna(df1['temparature'].mean())"
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   "execution_count": 81,
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       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019         30.0      12.0     rain\n",
       "1      1   1/2/2019         59.0      14.0      NaN\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0       NaN  fullair\n",
       "4      4   1/5/2019         66.0      10.0      NaN\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019         59.0      17.0  fullair\n",
       "7      7   1/8/2019         89.0       NaN     cold\n",
       "8      8   1/9/2019         59.0       9.0  fullair\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "10    10  1/11/2019         65.0       NaN      NaN\n",
       "11    11  1/12/2019         59.0      20.0  fullair"
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       "13.0"
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     "execution_count": 87,
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       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019         30.0      12.0     rain\n",
       "1      1   1/2/2019          NaN      14.0      NaN\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0      13.0  fullair\n",
       "4      4   1/5/2019         66.0      10.0      NaN\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019          NaN      17.0  fullair\n",
       "7      7   1/8/2019         89.0      13.0     cold\n",
       "8      8   1/9/2019          NaN       9.0  fullair\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "10    10  1/11/2019         65.0      13.0      NaN\n",
       "11    11  1/12/2019          NaN      20.0  fullair"
      ]
     },
     "execution_count": 86,
     "metadata": {},
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    "df1['winspeed']=df1['winspeed'].fillna(df1['winspeed'].median())\n",
    "df1"
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   "execution_count": 88,
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019         30.0      12.0     rain\n",
       "1      1   1/2/2019         11.0      14.0       11\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0      13.0  fullair\n",
       "4      4   1/5/2019         66.0      10.0       11\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019         11.0      17.0  fullair\n",
       "7      7   1/8/2019         89.0      13.0     cold\n",
       "8      8   1/9/2019          NaN       9.0  fullair\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "10    10  1/11/2019         65.0      13.0      NaN\n",
       "11    11  1/12/2019          NaN      20.0  fullair"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(11,limit=2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "</style>\n",
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>s.no</th>\n",
       "      <th>day</th>\n",
       "      <th>temparature</th>\n",
       "      <th>winspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>30.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>30.0</td>\n",
       "      <td>14.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1/4/2019</td>\n",
       "      <td>55.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1/5/2019</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1/6/2019</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1/7/2019</td>\n",
       "      <td>34.0</td>\n",
       "      <td>17.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1/8/2019</td>\n",
       "      <td>89.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1/9/2019</td>\n",
       "      <td>89.0</td>\n",
       "      <td>9.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019         30.0      12.0     rain\n",
       "1      1   1/2/2019         30.0      14.0     rain\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0      13.0  fullair\n",
       "4      4   1/5/2019         66.0      10.0  fullair\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019         34.0      17.0  fullair\n",
       "7      7   1/8/2019         89.0      13.0     cold\n",
       "8      8   1/9/2019         89.0       9.0  fullair\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "10    10  1/11/2019         65.0      13.0     cold\n",
       "11    11  1/12/2019         65.0      20.0  fullair"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.fillna(method='ffill')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "30.0\n",
      "nan\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-94-3a64a0a9d46d>:3: 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: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df1['temparature'][0]=np.nan\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "print(df1['temparature'][0])\n",
    "df1['temparature'][0]=np.nan\n",
    "print(df1['temparature'][0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>5</th>\n",
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       "      <td>1/6/2019</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1/7/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1/8/2019</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1/9/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019          NaN      12.0     rain\n",
       "1      1   1/2/2019          NaN      14.0      NaN\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0       NaN  fullair\n",
       "4      4   1/5/2019         66.0      10.0      NaN\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019          NaN      17.0  fullair\n",
       "7      7   1/8/2019         89.0       NaN     cold\n",
       "8      8   1/9/2019          NaN       9.0  fullair\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "10    10  1/11/2019         65.0       NaN      NaN\n",
       "11    11  1/12/2019          NaN      20.0  fullair"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## To replace any value\n",
    "- replace\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <td>NaN</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1/5/2019</td>\n",
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       "      <td>NaN</td>\n",
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       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   s.no       day  temparature  winspeed    event\n",
       "0     1  1/2/2019          NaN      12.0     rain\n",
       "1     1  1/2/2019          NaN      14.0      NaN\n",
       "2     2  1/3/2019         43.0      13.0     rain\n",
       "3     3  1/4/2019         55.0       NaN  fullair\n",
       "4     4  1/5/2019         66.0      10.0      NaN"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>cold</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1/7/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1/8/2019</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1/9/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019          NaN      12.0    sunny\n",
       "1      1   1/2/2019          NaN      14.0      NaN\n",
       "2      2   1/3/2019         43.0      13.0    sunny\n",
       "3      3   1/4/2019         55.0       NaN  fullair\n",
       "4      4   1/5/2019         66.0      10.0      NaN\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019          NaN      17.0  fullair\n",
       "7      7   1/8/2019         89.0       NaN     cold\n",
       "8      8   1/9/2019          NaN       9.0  fullair\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "10    10  1/11/2019         65.0       NaN      NaN\n",
       "11    11  1/12/2019          NaN      20.0  fullair"
      ]
     },
     "execution_count": 98,
     "metadata": {},
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    "df1.replace('rain','sunny')"
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       "      <th>11</th>\n",
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      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019          NaN      12.0     rain\n",
       "1      1   1/2/2019          NaN      14.0      NaN\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0       NaN  fullair\n",
       "4      4   1/5/2019         66.0      10.0      NaN\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019          NaN      17.0  fullair\n",
       "7      7   1/8/2019         89.0       NaN     cold\n",
       "8      8   1/9/2019          NaN       9.0  fullair\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "10    10  1/11/2019         65.0       NaN      NaN\n",
       "11    11  1/12/2019          NaN      25.0  fullair"
      ]
     },
     "execution_count": 99,
     "metadata": {},
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   "source": [
    "df1.replace(20,25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['s.no', 'day', 'temparature', 'winspeed', 'event'], dtype='object')"
      ]
     },
     "execution_count": 100,
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      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019          NaN      12.0     rain\n",
       "1      1   1/2/2019          NaN      14.0      NaN\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0       NaN  fullair\n",
       "4      4   1/5/2019         66.0      10.0      NaN\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019          NaN      17.0  fullair\n",
       "7      7   1/8/2019         89.0       NaN     cold\n",
       "8      8   1/9/2019          NaN       9.0  fullair\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "10    10  1/11/2019         65.0       NaN      NaN\n",
       "11    11  1/12/2019          NaN      20.0  fullair"
      ]
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   "source": [
    "df1"
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   "execution_count": 103,
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       "      <th>10</th>\n",
       "      <td>10</td>\n",
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      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event  n1\n",
       "0      1   1/2/2019          NaN      12.0     rain  11\n",
       "1      1   1/2/2019          NaN      14.0      NaN  11\n",
       "2      2   1/3/2019         43.0      13.0     rain   5\n",
       "3      3   1/4/2019         55.0       NaN  fullair   6\n",
       "4      4   1/5/2019         66.0      10.0      NaN   6\n",
       "5      5   1/6/2019         34.0      15.0     cold   9\n",
       "6      6   1/7/2019          NaN      17.0  fullair   2\n",
       "7      7   1/8/2019         89.0       NaN     cold  11\n",
       "8      8   1/9/2019          NaN       9.0  fullair   9\n",
       "9      9  1/10/2019         90.0      11.0     cold   7\n",
       "10    10  1/11/2019         65.0       NaN      NaN   2\n",
       "11    11  1/12/2019          NaN      20.0  fullair  10"
      ]
     },
     "execution_count": 103,
     "metadata": {},
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    }
   ],
   "source": [
    "df1['n1']=np.random.randint(1,12,12)\n",
    "df1"
   ]
  },
  {
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   "execution_count": 107,
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   "outputs": [
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1/9/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>cold</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>fullair</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed    event\n",
       "0      1   1/2/2019          NaN      12.0     rain\n",
       "1      1   1/2/2019          NaN      14.0      NaN\n",
       "2      2   1/3/2019         43.0      13.0     rain\n",
       "3      3   1/4/2019         55.0       NaN  fullair\n",
       "4      4   1/5/2019         66.0      10.0      NaN\n",
       "5      5   1/6/2019         34.0      15.0     cold\n",
       "6      6   1/7/2019          NaN      17.0  fullair\n",
       "7      7   1/8/2019         89.0       NaN     cold\n",
       "8      8   1/9/2019          NaN       9.0  fullair\n",
       "9      9  1/10/2019         90.0      11.0     cold\n",
       "10    10  1/11/2019         65.0       NaN      NaN\n",
       "11    11  1/12/2019          NaN      20.0  fullair"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "## Remove the column\n",
    "df1.drop('n1',axis=1,inplace=True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['s.no', 'day', 'temparature', 'winspeed', 'event'], dtype='object')"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>s.no</th>\n",
       "      <th>day</th>\n",
       "      <th>temparature</th>\n",
       "      <th>winspeed</th>\n",
       "      <th>event</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>e1</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>e1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1/4/2019</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1/5/2019</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>e1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1/6/2019</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1/7/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1/8/2019</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1/9/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed event\n",
       "0      1   1/2/2019          NaN      12.0    e1\n",
       "1      1   1/2/2019          NaN      14.0    e1\n",
       "2      2   1/3/2019         43.0      13.0    e1\n",
       "3      3   1/4/2019         55.0       NaN    e1\n",
       "4      4   1/5/2019         66.0      10.0    e1\n",
       "5      5   1/6/2019         34.0      15.0    e1\n",
       "6      6   1/7/2019          NaN      17.0    e1\n",
       "7      7   1/8/2019         89.0       NaN    e1\n",
       "8      8   1/9/2019          NaN       9.0    e1\n",
       "9      9  1/10/2019         90.0      11.0    e1\n",
       "10    10  1/11/2019         65.0       NaN    e1\n",
       "11    11  1/12/2019          NaN      20.0    e1"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1['event']='e1'\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "\n",
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       "        text-align: right;\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>s.no</th>\n",
       "      <th>day</th>\n",
       "      <th>temparature</th>\n",
       "      <th>winspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1/2/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>14.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1/3/2019</td>\n",
       "      <td>43.0</td>\n",
       "      <td>13.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>1/4/2019</td>\n",
       "      <td>55.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>1/5/2019</td>\n",
       "      <td>66.0</td>\n",
       "      <td>10.0</td>\n",
       "      <td>e1</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>1/6/2019</td>\n",
       "      <td>34.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>1/7/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>17.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>1/8/2019</td>\n",
       "      <td>89.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>1/9/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>9</td>\n",
       "      <td>1/10/2019</td>\n",
       "      <td>90.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>1/11/2019</td>\n",
       "      <td>65.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11</td>\n",
       "      <td>1/12/2019</td>\n",
       "      <td>NaN</td>\n",
       "      <td>20.0</td>\n",
       "      <td>e1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    s.no        day  temparature  winspeed event\n",
       "0      1   1/2/2019          NaN      12.0    e1\n",
       "1      1   1/2/2019          NaN      14.0    e1\n",
       "2      2   1/3/2019         43.0      13.0    e1\n",
       "3      3   1/4/2019         55.0       NaN    e1\n",
       "4      4   1/5/2019         66.0      10.0    e1\n",
       "5      5   1/6/2019         34.0      15.0    e1\n",
       "6      6   1/7/2019          NaN      17.0    e1\n",
       "7      7   1/8/2019         89.0       NaN    e1\n",
       "8      8   1/9/2019          NaN       9.0    e1\n",
       "9      9  1/10/2019         90.0      11.0    e1\n",
       "10    10  1/11/2019         65.0       NaN    e1\n",
       "11    11  1/12/2019          NaN      20.0    e1"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df1.plot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}