IntroToMatplotlib.ipynb

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   "source": [
    "# <center>Introduction to matplotlib</center>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "2VNVDDIIKHAi"
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   "source": [
    "![](https://matplotlib.org/_static/logo2.svg)\n",
    "\n",
    ">Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms. Matplotlib can be used in Python scripts, the Python and IPython shell, the jupyter notebook, web application servers, and four graphical user interface toolkits."
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Installation\n",
    "\n",
    "```\n",
    "pip install matplotlib\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "7Rh4PUNPKHAj"
   },
   "source": [
    "## Example 1\n",
    "\n",
    "Plot a simple line."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "MI9BNMr4KHAk"
   },
   "source": [
    "- Take a graph paper! (import matplotlib)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "CFGilzClKHAl"
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "4dnqTH6bKHAn"
   },
   "source": [
    "- Specify data to plot\n",
    "\n",
    "| x | y |\n",
    "| -- |---|\n",
    "|1 | 2 |\n",
    "|2 | 4 |\n",
    "|3 | 1 |"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "kyDfz7gpKHAo"
   },
   "outputs": [],
   "source": [
    "x = [1,2,3]\n",
    "y = [2,4,1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "5hjo-_iMKHAq",
    "outputId": "b9632cae-6d24-4744-a47a-9fd7670885fb"
   },
   "outputs": [
    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Specify X and Y axis labels\n",
    "plt.xlabel('x - axis')\n",
    "plt.ylabel('y - axis')\n",
    "# Give title to your graph!\n",
    "plt.title('My first graph!')\n",
    "# Plot points on graph paper!\n",
    "plt.plot(x,y)\n",
    "# Show the plot\n",
    "plt.show()\n",
    "# Close the plot\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "FWbqXBZdKHAs"
   },
   "source": [
    "## Example 2\n",
    "\n",
    "Plot 2 lines on same plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UjyPdQY0KHAs"
   },
   "outputs": [],
   "source": [
    "# line 1 points\n",
    "x1 = [1,2,3]\n",
    "y1 = [2,4,1]\n",
    "\n",
    "# line 2 points\n",
    "x2 = [1,2,3]\n",
    "y2 = [4,1,3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 295
    },
    "colab_type": "code",
    "id": "45HaCfhjKHAw",
    "outputId": "ab843993-0e2e-4e41-f2c9-07174920df5d"
   },
   "outputs": [
    {
     "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": [
    "# plotting the line 1 points \n",
    "plt.plot(x1, y1, label = \"line 1\")\n",
    "\n",
    "# plotting the line 2 points \n",
    "plt.plot(x2, y2, label = \"line 2\")\n",
    "\n",
    "plt.xlabel('x - axis')\n",
    "plt.ylabel('y - axis')\n",
    "\n",
    "plt.title('Two lines on same graph!')\n",
    "\n",
    "# show labels for different lines in a legend\n",
    "plt.legend()\n",
    "\n",
    "plt.show()\n",
    "\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "63N2_ObEKHAy"
   },
   "source": [
    "## Example 3\n",
    "\n",
    "Customizing line plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 466
    },
    "colab_type": "code",
    "id": "MPkc4-T-KHAz",
    "outputId": "eebf7285-a00b-4710-b83c-fc278a5bcb5c"
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['bmh',\n",
       " 'classic',\n",
       " 'dark_background',\n",
       " 'fast',\n",
       " 'fivethirtyeight',\n",
       " 'ggplot',\n",
       " 'grayscale',\n",
       " 'seaborn-bright',\n",
       " 'seaborn-colorblind',\n",
       " 'seaborn-dark-palette',\n",
       " 'seaborn-dark',\n",
       " 'seaborn-darkgrid',\n",
       " 'seaborn-deep',\n",
       " 'seaborn-muted',\n",
       " 'seaborn-notebook',\n",
       " 'seaborn-paper',\n",
       " 'seaborn-pastel',\n",
       " 'seaborn-poster',\n",
       " 'seaborn-talk',\n",
       " 'seaborn-ticks',\n",
       " 'seaborn-white',\n",
       " 'seaborn-whitegrid',\n",
       " 'seaborn',\n",
       " 'Solarize_Light2',\n",
       " 'tableau-colorblind10',\n",
       " '_classic_test']"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "plt.style.available"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "vCXmL4XGKHA1"
   },
   "outputs": [],
   "source": [
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "iPsd1uYPKHA5"
   },
   "outputs": [],
   "source": [
    "x = [2,2,3,4,5,6]\n",
    "y = [2,4,3,5,2,6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 305
    },
    "colab_type": "code",
    "id": "DN08ux17KHA7",
    "outputId": "76e4808a-5830-4ae6-9736-fefcc3813a4e"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y, color='green', linestyle='dashed', linewidth = 3,\n",
    "         marker='o', markerfacecolor='blue', markersize=8)\n",
    "\n",
    "# setting x and y axis range\n",
    "plt.ylim(1,8)\n",
    "plt.xlim(1,8)\n",
    "\n",
    "plt.xlabel('x - axis')\n",
    "plt.ylabel('y - axis')\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "OxD3com8KHA-"
   },
   "source": [
    "## Example 4\n",
    "\n",
    "Plotting bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "dboscw3vKHA_"
   },
   "outputs": [],
   "source": [
    "# x-coordinates of left sides of bars \n",
    "x = [1, 2, 3, 4, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eDAWHmUDKHBB"
   },
   "outputs": [],
   "source": [
    "# heights of bars\n",
    "y = [10, 24, 36, 40, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "RQiuXO5NKHBD"
   },
   "outputs": [],
   "source": [
    "# labels for bars\n",
    "tick_label = ['one', 'two', 'three', 'four', 'five']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 321
    },
    "colab_type": "code",
    "id": "yIkkprGTKHBE",
    "outputId": "b44d214b-a9bf-4f65-f537-0de03b9d0e6b"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a bar chart\n",
    "plt.bar(x, y, color = ['green'],tick_label = tick_label,\n",
    "        width = 0.8, )\n",
    "\n",
    "plt.xlabel('x - axis')\n",
    "plt.ylabel('y - axis')\n",
    "\n",
    "plt.title('My bar chart!')\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "KlocyGZ0KHBG"
   },
   "source": [
    "## Example 5\n",
    "\n",
    "Plot histogram."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HP30BCGLKHBH"
   },
   "outputs": [],
   "source": [
    "ages = [2,5,70,40,30,45,50,45,43,40,44,\n",
    "        60,7,13,57,18,90,77,32,21,20,40]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "eBpKEJB-KHBJ"
   },
   "outputs": [],
   "source": [
    "# setting the ranges and no. of intervals\n",
    "range = (0, 100)\n",
    "bins = 10 "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 321
    },
    "colab_type": "code",
    "id": "07iJ772MKHBM",
    "outputId": "0b2160ad-d9c5-4672-a9d7-6353c95735c2"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting a histogram\n",
    "plt.hist(ages, bins, range, color = 'green',\n",
    "        histtype = 'bar', rwidth = 0.8)\n",
    "\n",
    "plt.xlabel('age')\n",
    "plt.ylabel('No. of people')\n",
    "\n",
    "plt.title('My histogram')\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZZZrxo_YKHBP"
   },
   "source": [
    "## Example 6\n",
    "\n",
    "Plotting scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "_Q-YJCRmKHBQ"
   },
   "outputs": [],
   "source": [
    "x = [1,2,3,4,5,6,7,8,9,10]\n",
    "y = [2,4,5,7,6,8,9,11,12,12]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 321
    },
    "colab_type": "code",
    "id": "1HgkoQBOKHBR",
    "outputId": "b3c24241-1a1b-45b2-ba57-17953febff79"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting points as a scatter plot\n",
    "plt.figure(figsize = (5,5))\n",
    "plt.scatter(x, y, label= \"stars\", color= \"blue\", \n",
    "            marker= \".\", s=300)\n",
    "\n",
    "plt.xlabel('x - axis')\n",
    "plt.ylabel('y- axis')\n",
    "\n",
    "plt.title('My scatter plot!')\n",
    "\n",
    "plt.legend(loc=5)\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "wuMaJ8iJKHBX"
   },
   "source": [
    "## Example 7\n",
    "\n",
    "Plotting pie-chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "gZS2XregKHBX"
   },
   "outputs": [],
   "source": [
    "# defining labels\n",
    "activities = ['eat', 'sleep', 'work', 'play']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "xnETFX9cKHBa"
   },
   "outputs": [],
   "source": [
    "# portion covered by each label\n",
    "slices = [3, 7, 8, 6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "TsHe5miuKHBc"
   },
   "outputs": [],
   "source": [
    "# color for each label\n",
    "colors = ['r', 'm', 'g', 'b']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "collapsed": true,
    "id": "DGBDwSZbPpFo"
   },
   "outputs": [],
   "source": [
    "#help(plt.legend())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 544
    },
    "colab_type": "code",
    "id": "OJn4sb2ZKHBf",
    "outputId": "ededb5ff-251c-4200-c3ab-6985fd831ab1"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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JRBJJN7phwDuztoSoEBebNQteflntKupH06fwgfjXyuPxMPvN2bT4vAV6lzcob+ImHufxmjYzmAHAfdX/XXE/eLidi9dq0Z9MYXXKNDmdF6Laq69Cly7e66JaID1QHxbNWET0v6IxlfkernQlduw8zuPcwA20pOVlr0tPVIhaigIPPwxbt6pdSd3IfKDXsGPNDnZP3E3yhmS/H8uTclJ6oo1M5gPVrrZtYf16SPb/X73rIj3Qqzhx/ASbX98ckPAE6YkKcaEjR7yzNzmdaldybRKgV2CvsjP3z3NpMb9FQI8rISpEreXL4Te/UbuKa5MAvYSiKHz9969J+ToFg80Q8ONLiApR6803Ye1atau4OgnQS6z5fg3KbIWoI+qN6A3mEF0L3Fr99RYgu/r724ATV2h/c/XrtwITqn/2T6AP8NQF7e4Hyhq5VqF9brf3plKwzmYvAXqBE8dPsGPmDpoub6p2KUEZom8APwXO/y7/AvgrsBS4B5h6Sfvz7ZZWf3xY/f0nwCogHyjFu9RzNhDrl6qF1u3ZA7/7ndpVXJkEaDWX08XcaXNpM7cNOnc957bzk2AL0RuAWRd8PwPoWv21C4i4pP1W4BwwDBgErKn+eSTecHXi/QX8F/CYf0oWIeKtt4LzVF4CtNrizxcTNzcOS7FF7VIuEkwhOgq4cDTs+RmlVgHvAs9d0j4S+CUwH/gAGI83aCcD4/D2Wv8DPIK3d/sksNdPtQttC9ZTeQlQIG9HHse/PU7CxgS1S7miYArRS80EnsB7Gn7pgK9M4AFAV/11ElAI9AdmA/cBy4F2QAHwKvBKQKoWWhSMp/JhH6C2czZyP82l1cJW6AiOU/crCcYQ/RRvz3MpkH6F1/8FPF/9dQHem0QXzoP6/4AX8J7mG/AGbYWfahWhIdhO5cM+QOd9Mo/ENYmYT5nVLsWnYApRN/AMUI73VPxW4PfVrz0EHAUeBc7g7XGOwRuo5ydfOFz9Wlfgpur2twNPB6J4oVnBdiof1o9yHtx+kEXvLKLdR+1qJgrRAnnss27kUc7Q9atfwRtvqF1FGPdAXU4XS75cQqslrTQVnhBcPVEh1BAsp/LaSo5G9MPsH7ButhJ5MFLtUhpEQlSEM7cbJkwAl0vdOsIyQE8VnmLPsj00XaT+gPnrISEqwtnu3fB//6duDWEXoIqiMP/T+TTf0BxjpabnkwYkREV4e+UVOHdOveOHXYBu/mEzlQcriVsfOoEjISrCVWEhvP22escPqwC12+ysy11Hs9XN0LtD661LiIpwNXUqlJSoc+zQShEfln+zHHOhmZhtMWqX4hcSoiIcnT0Lr7+uzrHDJkArzlawd+Nemi1tFtRPHF0vCVERjv76V8jPD/xxwyZAF3+xmNjjsUTmaXPYUn1IiIpwY7OpsyRyWARocUExR3YfIWVxitqlBIyEqAg3H37onXAkkMIiQJd8sYSkI0lYioJrqjp/kxDVKifwIN5ppnsB3wCbgJbUzu8/85JtqvBOOJiNd1aB4uqf/wHoC7xW/b0LuBfvbAahxe2GyZMDe8yQD9Aje45w4ugJElclql2KKiREtehTvJP/LQfm4Z1iZRMwidr5/cdcss3fgC7V2zwE/LH65wuB1cD31d//He80L4Ff7ysQZs2CdesCd7yQDlBFUVjx7QoSTiQQUXDpfOnhQ0JUa0bjnR31PCOwEe+sqwPwBmD5JdusAIZXf/0jvMEJ3imwHXgD8yywsvr10PXii4E7VkgH6LH9xzhddJrEleHZ+7yQhKiWRAMxeEPyXry9yV7An4Af8M6++odLtikDzv+/jcEblgATq/fxHPA68Cu8s7A+zZWXAdS+JUtg4ULf7RpDSAfoqjmriD8TT+Sh0L/zXhcSolpyDBiI91ro/cBIoHv1ayOBzZe0j6W2V1oOxF/Q9hu8p/dlwEm8awc8DLzjn9KDQKCeTgrZAD1x7AQn809K7/MSEqJacALvUnxT8a4YBd6Fo89f3FtEbZiedwswt/rreXhvJl3oj8BvCJf5/+fOhSNH/H+ckA3Qld+uJLYilqi96q3vHqwkRIPda3gXfH6V2rvubwHPVn+9Em8YgjdoHXiX5NuJd/7/adSuDwDem0ht8S6oMhT4Fu8p/KP+fBOq8njggwDMpR2SM9KXFpfy6Ruf0mZRG+I2S0hcTajPbC8z0oe35GQ4fhzMflytJyR7oMu/WY4VKzE7QvOZ98YiPVERyoqL4fPP/XuMkAvQyvJKju09RtzOOPTOkHt7jU5CVISy99/37/5DLmE2LNyA3qgndmOs2qVohoSoCFWrVsHWrf7bf0gFqMfjYf/W/cQUxWA5GV6PbV4vCVERqvzZCw2pAM3bmUdVRZX0PhtIQlSEov/8B8rK/LPvkArQzUs2E6mPJHpXtNqlaJaEqAg1lZXw8cf+2XfIBGjF2QpOHDtB7LZYza3zHmwkREWo8ddpfMgkzdoFazGajSG7XEegSYiKULJnDyxe3Pj7DYkA9Xg8HNp5COtZKxGF4TvrUmOTEBWh5KOPGn+fIRGgh/cc5lz5ORk47wcSoiJUzJnjnXS5MYVEgG5bsQ1rtJXoHXLzyB8kREUoKCmBlSsbd5+aD1CX00XRkSIsJy2YT/vxodcwJyEqQsE33zTu/jQfoPu37Mdpd8rQpQCQEBVaJwF6iV3rdxERFSEBGiASokLL9u9v3JU7NR2gLqeL4uPFmEvMmE/J6XugSIgKLWvMXqimA/TAtgM4HU4iD8iSHYEmISq06ttvG29fmg7Q3et3ExEVQeRBCVA1SIgKLVq9Gk6dapx9aTZAPR4PpwpPofPosB62ql1O2JIQFVrjdnvHhDYGzQZocX4xVRVVWI9aZeJklUmICq1prOugmk2e3et3Y7aa5fQ9SEiICi1ZsADs9uvfj2YDtPBQIUajUQI0iEiICq2oqGicyUU0GaAOu4PS4lIMFQbMRTJ8KZhIiAqtWLDg+vehyQA9sucIbpebiCMR6NCpXY64hISo0IING65/H5oM0L0b9xIRFUFEvkxdF6wkREWw27wZPJ7r24cmA/R00Wl0Op0EaJCTEBXBrLLy+h/r1FyA2qvsVJytAA9YCmTlzWAnISqC2caN17e95gK08HAhbqcb8wmzrH2kERKiIliFXYAe3HFQrn9qkISoCEZhF6ClRaXoDXoijkuAao2EqAg213sjSVMBqigKpcWlANID1SgJURFMrvdGkqYCtKykDNs5G7jAdNqkdjmigSRERTC5ntN4TQXo0X1H0el1mEvM6BQZQK9lEqIiWIRNgB4/cByL1YLplPQ+Q4GEqAgGYROglWcq0el0snxHCJEQFWq7nhtJmgrQirMVABKgIUZCVKipshL27m3YtpoJUJfLxbmKcwByCh+CJESFmg4fbth2mgnQstNluJwuAMynpQcaiiREhVoKCxu2nWYC9MSxE+j0OgxlBvQOzZQt6klCVKihoKBh22kmiQoOFWCxWjCWGdUuRfiZhKgItJDvgZaXlKPX6zGWS4CGAwlREUgh3wM9fwNJAjR8SIiKQAn5Hqi9yruEnqHCoHIlIpAkREUghHSAKorifQYe6YGGIwlR4W9FRaAo9d9OEwHqsDlwu9yA9EDDlYSo8CeHA06frv92mgjQyvJKPG7vs1bSAw1fEqLCnxpyI0kTAVpxpgJP9cOq0gMNbxKiwl8ach1UEwF6pvgMJrP38U29XRMlCz+SEBX+ELI90NKTpRjNRlCQheQEICEqGl/I9kCrKqswGAzonDKJsqglISoa08mT9d9GEwF6/g683qmJckUASYiKxmK3138bTSTS+QCVHqi4EglR0Rhcrvpvo6kAlVmYxNVIiIrr5XTWfxtNJJL0QEVdnA/RCoOEqKi/0O2BuqsD1CUBKq5NfzKFxVEfcM6UoHYpQmNCN0Cre6BC1EVsWTOWJXyI3RCldilCQxoSoJp4LtLtrA5QTcS9CAbWk3E4uw7mjq5r1S5FBLkmTWLp27cDqan131YTAargnSZF0TdguhQRltxGDxU55XSPP6N2KSLItW2bwoQJHRq0rSYCVK+v7npKD1TU0eFeZZRZbdCAsX0ivJjNDY9BTQSoTue9eSQ9UFEXxmZGHs69E0OkTDwj/EsTfTqdXgJU1F3GmxkSniIgtBGg1T1QbVQr1BTVK4qm9zdVuwwRJjQRSTU9UIP0QMU16CDrvSy1qxBhRFPXQD0RHpUrqR8XLt7gDYoowomTB3iAZJL5X/4XEyba0Y6neRr9Bf+OVVDBa7xGJZW4cPEUT9GJTsyp/i+DDJ7jOQBe5VUmMYkoZLwjQMqDKcT2iFW7DBFGtBWgFg+KTkGnaOOJpFxyiSWWl3iJs5zlcR4nnngmMpHOdOaf/JNFLGIoQ2u2+ZzP6UY37uVejnKUP/JHpjGNBSzgXd7lt/yWcsrZwQ5u5EYJz2q6SB03TL1B7TJEmNHEKbzRWJ3zOvBYtdMLvZVbeYRHar43YKCYYjrTGYDOdGY72y/aZjSjuZM7AXDjxowZgAgicODAjRsdOuYxjzu4I0DvJPil/jYVSzOL2mWIMKOJHqjJYqr52m11YzinjTusVqwAnOMcL/Myj/AIX/M1W9hCV7qyilXYsF20TTTRAJRQwmu8xs/5OQAP8ACv8irZZLOQhfyIHzGd6RRTzChG0YY2gX1zQcTU1kTrSa3r3H7OvjmsL1jvx4pEKBnQdgCD0gZd8TXNBKiiKOh0OtyRbmjA8qNqOclJfstvGcEIhjCETDJ5l3eZwQza076mh3mhPPJ4hVd4kifpSlcAulT/V0klb/EW3ejGOtbxCI/wV/7Kb/hNoN9a0Mj8SyZ6c91Opoorixk/azxn7Wf9XJUIFb8d8FttB2hkTCQetweD0aCpU/gSSvgVv+IZnqE73QFYwxr+h/+hCU14h3foRa+LtjnMYV7mZX7H72hHu8v2+V/+yzjGYcdec/Opiir/v5kgFTswluS7k322c7lcLFu/jD/v+LOEp6gXg+7qZ7yaCNCYhBhcLhcGo8HbA9WI//Afyinn39X/AdzHfbzIi0QQQVe60oc+APyKX/Ear/EP/oEDB+/yLgBRRDGFKQAUUUQFFbSjHR48nOQkL/Iij/KoOm9QbXrI+mvdhi0tWLGAGatmMN85389FiVCj11397EanKErQD67cv20/8z6eR2RMJEkLkkhYLXM9Cmj+ZHOy3vcdoBWVFUz5YArTbdM54joSgMpEKJkyaAovZb90xdc0cRc+Jj4GxePNeVdsAybtEyFHF6cj/Y/pdWr72fefsc+9T8JTNIjVaL3qa5o4hbdYLTVPI7niJUAF3PDKDZgSTT7bHS04yvb921nkXBSAqkQoio+Iv+pr9e6BLl26lLFjx15XQfUVGROJwei9kOuMa8DKTyKkWLIstHiqhc92iqIwc+5Mtui2cNYjN45EwyRYr37JUBOn8JYICyazt7chPVCR9W4WeqPvX911W9dx8PRBVtpWBqAqEaoSIq4jQPft20e/fv3Iyclh8ODB5Ofn17z2+eef07dvX/r378+LL74IwNmzZ7n33nsZOHAgAwcOZPt275M26enpjBs3jl69evHII4/g8dRvOJI12nsdwmP14I7Qzp140bgSfpxA4pBEn+2cTidzf5jLKs8qnMhZi2i46+qB5ubm0r17dxYuXMjkyZMpLS0FoKSkhN///vcsWrSIFStWkJ+fT25uLq+99hqDBw9myZIlTJs2jSeffBKA48eP8+qrr7Ju3ToqKir4+uuv6/UmrFG1F3KdifIXIiyZIOMvGXVqOmfZHPJseWx3bPfdWIhruFYP1OdNpEcffZSpU6cyfPhw4uLiGDZsGAAHDhyguLiY22+/HYDy8nLy8vLYvn07ixcvZubMmQA1gdumTRvatfMODO/Xrx979+6t15uwxlgpLS5Fp9PhTHQSURBRr+2F9rX6RSsi20X6bHem7AyrNq1isWNxAKoSoe66biLNnj2b7OxsFi1axOjRo5k6dSoAaWlptG7dmtzcXJYuXcrEiRPp3bs37du357nnnmPp0qV89tlnjB8/HoD8/HyKiooAWLlyJZ06darXm0hMScTl9F7/lB5o+NEn6Un9XWqd2s6YM4M97KHAXeDfokTIM+qNxFhirv409h4vAAAgAElEQVS6rx306NGDBx54AKPRiF6vZ+LEiaxbt47k5GQmTZpETk4Obreb1NRU7rvvPiZPnsyjjz7KtGnTKCsr4+WXXwbAYrHw9NNPc+zYMfr06cOdd95ZrzfSPK0563LXYTKbsDeVlcLCTcYbGRhjfI+62394P7uP7GaJY0kAqhKh7lq9Twjgk0jNmjWr6YE2RFlJGR+/9jHWaCvGUiOp76Q2XnEiqEV2jaTnpp61S7tchaIovD7tdb4r/Y5V9lUBqk6Esg5NOrDr57uu+romhjEBRMdH10xr54p34bbInfhwkfVels/wBFi+YTmHSg+x1r42AFWJcJCWkHbN1wMWoNfT+wTv2vCxCdXLNejA0czRCFWJYNfkvibE9Yvz2c7usLNgxQKWe5bjRv5xFY0jPf7ajwtrpgcKEJMYw/krDvZmch005EVAuz9fPqXflXyV+xV5jjz2OPf4uSgRTtITQihAm7VphtPhvQNvby4BGuravtCWiNa+h6udKjnFhh0b5Hl30ehCKkDbZLXBaa8OUOmBhjRjCyNtXqjbMiXT50xnp24nJ90n/VyVCDchFaBJzZJqnol3JDvwGLUzO72on8y3MjFYfa99tevALvYe38sy27IAVCXCTUgFqNFkJDrOu+gaerC1sl17A6FJ0f2iSRmT4rOd2+1m1oJZrNetp0oJ32VNhH8kRyYTZb72suGaClDw3kg6rypV/tKEnHos07F49WIOlR9ig32Dn4sS4chX7xM0GKAt0lrUXAeVAA09TX/SlJhuV3907rxzVedYsm4Jy1zL8CCXckTjy0zK9NlGcwGa0TWj5k68rZVNroOGEF2Ujhtev6FObb+c/yX7Xfs56Dro56pEuOrWvJvPNpoL0ITkhJq5QTGArbVcBw0Vab9Pw5xi9tmu4EQBm/duZrFdZlsS/tO9eXefbTQXoDqdjqRmSTXfV7WV0/hQYEoz0erZVj7bnV+mY7tuOyWekgBUJsKRXqfn5uY3+24XgFoaXat2rXDYvY9yynXQ0JD1dhZ6k+9fx827NrP/5H6W25YHoCoRrjKTMok2R/tsp8kAzeiagcvhnRvU1sqGxyzXQbUsbkgcTe5s4rOdy+Xi2yXfstqzGrsiD1II/6nL6TtoNEDjm8RfdB20sl2lugWJhjNA5ju+73YCfL/8e/Iq89jq2OrnokS4C+kAPX8d9PzEIpXtJUC1qsVTLYjqcO3BygDlFeWs2LiCJa4lKARkClsRxrq3COEABWh3YzvsVd7TuHMZ51D08pdKa/TxetL+cO35Fs+bOW8mez17OeY65ueqRLjToePmZr5vIIGGAzSrW+0ku54Ij9xM0qD0P6ZjSjD5bHck/wg7Du6QYUsiIDomd7zmOkgX0myAWqyWi4YzVbSvULEaUV8RHSJo+URLn+3OD1varNtMmacsAJWJcDckfUid22o2QAFSO6bWDGeqzKqUa2MakvVuFjqD72U6Vm9ZTd7pPFbZZI0jERhD04fWua2mA7Rzn864nd7lG9yxbuwtZWiLFiTelUjCoASf7RwOB/OXz2eFsgIXrgBUJsKdSW8iJzWnzu01HaDRcdHEJ9cuO1reqVzFakSdmCDjLxl1avrtkm85aDvITsdOPxclhFefVn3qNID+PE0HKHifSnK5vL2T8hvL5W58kGs9qTXWNKvPdqVnS1m7dS2LHXLjSAROfa5/QggE6E3ZN+Go8l4H9UR5qMyQMaHBypBsoO1v2tap7Yw5M9jFLgrdhX6uSoha9bn+CSEQoIkpiSQ2Taz5vryrnMYHq4w/ZWCMNvpst+/QPnYd3cVS21L/FyVEtVhLLL1a9qrXNpoPUIDMmzNrBtVXZlbiipIbDsEmslskTR9q6rOdx+Phi/lfsJGNVCpyNiECZ2DqQAx63+twXSgkAvSm/jeho3pIjB4qusiY0GCi6BSy3qt98OFaflj/A4fPHmadfV0AKhOi1h2Zd9R7m5AIUIvVQtM2TWuejS/rKgOug0nK2BTi+sT5bGez21i4eiHL3Mtw4w5AZUJ4GfVG7ulwT723C4kABbj51pupqvA+zulo6sDWQmaqDwpWaPendnVq+tWCrzhgP8A+5z4/FyXExYakDyHRmui74SVCJkBTO6QSGR1Z8/2Z3mdUrEacl/rrVCwtLT7bnTx9ko27NrLYKcOWROCN6TSmQduFTIDq9XratG+D2+U99avoVIErRm4mqcnYykjrX7WuU9sZ381gh24Hxe5iP1clxMXMBjN3t7+7QduGTIAC9Bnep2bJYwxwppf0QtWU+b+ZGCJ839Xcvnc7+wr3scy2LABVCXGx2264jfiIeN8NryCkAjQ2IZbmqc1rbyZ1L8NjkuU+1BDTP4aUe1N8tnO73Xy98GvWshabItetReA19PQdQixAAfrc3qfmZpLH6qH8JhlYH3B672xLdbFw1UIOVRxio32jn4sS4nIRxgjuyrqrwduHXIC2SG1x0ZNJZ/qckWnuAqzZI82Ivsn3hAyV5ypZum4pS11L5f+RUMXtGbfXefLkKwm5ANXpdNx8683YznlPB51JTs5lnlO5qvChi9GR/v/S69T2i++/YL97P4dch/xclRBX9tObf3pd24dcgAJ06NEBa1TtjD8lA0pUrCa8pL+cjrmJ2We7/KJ8tuzfIst0CNWkJ6QzvN3w69pHSAaoXq+nQ88OOGzeWZrsLe1UZMnjnf5mvsFMy4l1W6Zj+pzpbNNto9RTGoDKhLjcE92fqNPjxdcSkgEK0H1QdwyG2iE0JQNL5Dqbn2W9k4Xe5PtXauOOjRwsPsiKqhUBqEqIy0UYI3jk5keuez8hG6CWCAsde3es6YU6mjqo6Ci9UH+JGxZH0u1JPts5nU6+W/odq5XVOHAEoDIhLje642iSIn3/vvoSsgEK3oH1RlPt/JMlt5ag6KQX2uiM3t5nXcz7YR6Hzh1iq2Orn4sS4uqe6vlUo+wnpAPUZDbRpV+XmhnrnclOyrvIuNDG1vLnLYnMivTZrqyijFWbV7HYJTeOhHq6Ne9Gn1Z9GmVfIR2gAD2H9MRoqe2FluaUyrpJjUifqCf15dQ6tZ0xZwZ7PHs47jru36KEuIYnezzZaPsK+QA1mox0ze5a2wtNdHK251mVqwod7V5rhyne5LPdoWOH2HloJ4tt0vsU6kmJSuH+Lvc32v5CPkABug3shtlaOzax5NYSWfajEUR0iqD5Y819tlMUhZnzZrJZt5lyRS6hCPU83/d5Ik2+LzfVVVgEqNFopMfgHjXrJnkiPJwedFrlqrSv/Xvt0el9j6NbuWkleSV5rLatDkBVQlxZkjWJn/f8eaPuMywCFLzrJsUmxtbM1FR+c7nMWn8dEkcmEp/jewowh8PB/OXzWamsxIX0+oV6JvWdRJQ5qlH3GTYBqtPpGDJmCPZz9uofQPGPimVwfUOYIeOtjDo1/WbxN+Q58tjl2OXnooS4uviIeCb2mtjo+w2bAAVontqcth3a4nJ5e0L2VnZZR74BWv+yNdZUq892JWdKWLdtHYsdcuNIqGtSn0nXNevS1YRVgAIMvm8wXDDH8unBp3FbZAXIujI0NZD6Umqd2k6fM52d7KTIXeTfooS4hjhLHM/0fsYv+w67AI2MjqRrTteaG0ruaDenh8oNpbrK+HMGhijfy3TsydvD3mN7WWpb6v+ihLiGX/T+BXERvpfVboiwC1DwDq6PjIm8aOmPc2kyZ6gvUT2iaDq+qc92Ho+HL+d/yXrWc06RP1ehnjhLHM/2edZv+w/LADUYDAwaPaj2hhJw8s6Tsn7Stegg672sOk3/tXTtUg6dPcQ6+7oAFCbE1f0u53ckWBP8tv+wDFCAtlltSe+cjtPhXcXTleDi9BA5lb+a5PHJxPaK9dmuylbFotWL+MHzAx7kHyShnrS4NJ7u9bRfjxG2AQowZOwQjMba5+TP9jwrp/JXoIvU0e6NdnVqOyt3FgdcB9jv3O/nqjTGA3wN/BP4F1ACnKz+/p/Ad3DZvzdX2gZgPzANmHnBNnMAmZv6In++7c+YDb5XR7geYR2gZouZQaMHYausHlCvg5MjTspd+UukTk7F0tzis92JUyfYuHOjLNNxJXurPz8KDATmA4uAwdU/c17Q5lrbAKwHHgRigRPVHxbAf2eqmtO/dX/u6XCP348T1gEKcEOXG0jrlFZ7Kh/n4tTwUypXFTxMbUy0fr61z3bnl+nYqdvJKY/8+V2mA3Bn9ddngShgDJAKuICK6p/52gbAjDdwHYAJWAH091PdGqRHz3s/fi9AxxIMHTcUo8lY+5hn13LKbipTuargkPmXTPQW378m2/ZuY3/hfn6w/xCAqjTKAHwFzAU64v3bdwZ4HzgHNKnDNgA5wDy8Pc4SoDWwHfgWOOa/8rXiiR5PcGPTGwNyLAlQvKfyw+4fdtFd+eIfF2NPtl9jq9AXkxND8shkn+3cbjezF81mLWuxKTK/wDWNBCbiDTsHEA88A/Sg9hTd1zbJeHuv/YHNQBfgIHA7sMyPtWtAgiWBKYOnBOx4EqDV2ma1pVPvTjXzhiomhaLRReE7tEkPWX+t2zIdC1Yu4FDFITbZN/m5KA3bCiyv/toE6IAZwPmBH5bqn/na5sI2G4Gu1V8r1a85G7VqzfnzbX8mPsL3JDeNRQL0Ajn35BCfHI/H7Q1NZ7KTk3ecVLkqdTR7rBnRXaJ9tqs8V8kP639gqWupTMxyLR2AQrx30z8FhgO34r3L/hHesBxc3XYW3lP7K21zfu5qG3AYyAKsQDTeu/U3+/l9BLGBbQY2ykqb9aFTzl/4EwBUlFXw79f/jclcO8t68jfJxG32z6NgwUgXq6NfXj9MSb5nmv/wyw+Zc3AOX1Z9GYDKhLiySEMk+57ZR8vYlgE9rvRALxEdG81t42+rHdoEnLr9FPam4XM9NP2V9DqF57HCY2zbv41F9kUBqEqIq/vL8L8EPDxBAvSK0julc+MtN9ZeDzUqFI4tDItlQCwZFlr+3PcvoqIozJwzk626rZzxnAlAZUJcWU7LHB7r8Zgqx5YAvYr+d/UnoWkCbrd3UL0r3kXRmCI8xtC+qZT1bhZ6o+9fiw3bN5B3Oo8VthUBqEqIK4s0RPKf+/6j2vElQK9Cr9cz4vER6NDVjA+1tbZxcsTJkL1ZEv+jeBKHJfps53Q6mbNsDis9K3GG+21foaq3h7+tyqn7eRKg1xAZHcmIn43AaasNiYrOFZTcWnKNrTTKBJlvZ9ap6dxlc8mrymObY5ufixLi6m5tdSs/7fFTVWuQAPWhaaumDBk3BNu52ptKpTmllHcJraVAWk5sSWSG7+Vez5afZeWmlbJMh1BVnDGO6WOmq12GBGhdZHbNpOeQntiqakP0xF0nqGpdpWJVjUefpCft92l1ajtzzkz2spd8d76fqxLiyvTo+XLMlzSLbqZ2KRKgddVraC/adWmHw+a9M48RCscVYk/R/vCmdq+3wxhr9Nnu4NGD7Di8gyW2JQGoSogre7778wxuN9h3wwCQAK0jnU7H0HFDSWyWiMvpHc7ksXooeLAAR6JD5eoaznqjleaPNPfZTlEUPpv3GZt0myhXQuvyhdCOPkl9eOOON9Quo4YEaD0YDAZGPjESi9VSM7zJHe2m4KECnHHavBud9V4WOr3vZTpWbFjB4TOHWWNbE4CqhLhcE2MT5j06T+0yLiIBWk+WCAtjnh2DwWDA4/GOCXXFuch/KB9XtLYG2ieNTiK+v++JF+wOOwtWLmC5ezluZLJpEXhGjHz7wLfEWwM3UUhdSIA2QGR0JGOeHQMKtSGa6A1Rd6RGAsbiXaK4LmYvnM1B+0F2O3f7uSghrmzKgCn0adtH7TIuIwHaQDHxMdz3i/tQ3ErNQHtnspP8B/JxRwR/iLb9n7ZEtInw2e506WnW71jPYqcMWxLquKPVHfzPwP9Ru4wrkgC9DvFN4hk1cRQuh6smRB3NHeT/JD+on5s3NjfS5sU2dWo7/bvp7GQnJ9wn/FyVEJfrEt2Frx7+Su0yrkoC9Do1adaEkU+NxGl31oZoMwf5E/KD9sZSxpsZGCINPtvtPribPfl7WGYL82nOhSpaGFuw7IllGA2+h9ipRQK0ETRr3YwRj4+4qCfqTHKSPyEfR1JwDXGK6h1F03FNfbbzeDx8Of9LNrCBc4os9SwCK04Xx6IJi0iICu6lRiVAG0nL9JaMenoUbqe7JkRdcS6OTziOrVmQrBOkg/bvta9T08VrFnO4/DDr7ev9XJQQF4sggln3zqJ9i7r9rqpJArQRpbRMYcxzY1A8Ss3deU+Uh4KfFFDVRv3HPlMeSiGme4zPdueqzrFkzRKWupfiIbSn7xPBxYCB9we9z6COg9QupU4kQBtZQnIC9//yfgwGA26X9268J8JDwQMFVLSvUK0uXaSOdlPb1antrAWz2O/az0HnQT9XJcTFXrrpJSZkT1C7jDqTAPWD6Lho7v/l/URERtQ89qmYFIruK6KkvzpT4aX+LhVzU7PPdkXFRWzas0mW6RAB92jao7xy9ytql1EvEqB+Yo2yMnbSWGITYmsnINFByeASikYW4TEE7tTYlGqi9XOtfbZTFIXpc6azne2UeEJwzlMRtO5vdj//ePAfapdRbxKgfmSJsHDfs/fROqM19nO1szZV3FhBwU8KAjZWNOsvWejNvv9Xb92zlf1F+1luX+6zrRCNZWTCSD756SfodL7nZAg2EqB+ZjQaueORO+ia0/WilT5trW0cf+y431f7jB0US5MRTXy2c7lczF40mzWswa5of4o+oQ0jE0Yy44kZGAy+xyUHIwnQANDpdNzy41sYOm4o9nP2i4c5PXKc8o5+mh7OAFnvZNWp6YIVCzhceZgt9i3+qUWIS5wPT7PZ97X5YCUBGkBZ3bK495l7UTxKzXR4ilnhxOgTnPzxyUZf8bP5E82J6hTls11FZQXLNy5nsWtxyC6YJ4JLKIQnSIAGXNNWTXnghQeIjImsvbkElPUo4/hPjzfak0u6OB3pr6TXqe1n8z5jr3svR11HG+XYQlxLqIQnSICqIjI6kvufv592N7W76Lqoo6mDY48fo+zGsus+xg2v3oAp0eSz3dGCo2w7sI3FdpltSfiXAQMPJT0UMuEJEqCqMRgMDBs3jGH3D8Nhd9Q8uaSYFU6OPMmJESfwmBp2Sm9pb6HlU77XylYUhZlzZ7JFt4WznrMNOpYQdRFBBE+mPMk/Hv9HyIQnSICqLvPmTB584UGiYqJwVNWevpd3LefY48catPJn+3fbozP4HhKydstaDp4+yCrbqnofQ4i6itPF8Xzr53nrp2+FVHiCBGhQiImPYdzz4+jQs8NFp/TOJt4ZnYpvK65zbzThjgQSBvuewcbhcDBv+TxWelbiJDin3RPa11TflCkdp/DKw69gMvm+pKQ1OuX8mBoRFPJ25pE7PRdFUTCaaudBNJYaSfkmhcjDkVff2AS9d/fGeoPV53FmLZjFV1u+4tOqTxujbCEuk65P542+b3DP4Hs0OUi+LqQHGmTSO6Xz8OSHaZnekqqK2tN3V4KLgocKvMOdzFfujbZ6tlWdwvNM2RnWbFnDYofcOBL+0dXQlU/u+IRRQ0aFbHiC9ECD2oFtB1jyxRJcLhcmc+3pj/GskeS5yUTtqx3jaWhioG9eX4wxvmfv/tv0vzH32Fy+q/rOL3WL8GXEyDDzMN4a8xZZ6XV7iEPLpAcaxNrd2I6fTP4JbbPaYqu0XfQEU+G4QgrGFeBI9N54avdGuzqF5/7D+9l9ZDdLbUv9WboIQ4n6RCbETOCfP/1nWIQnSA9UMw7tPsSimYtw2B2YLbV3Mh0VDm5rfRvpz6T7PFXyeDxM/cdUviv9jlV2ufMuGk+mIZMHUx7k2fHPEh0VrXY5ASMBqiFOh5Pl3yxn17pdmCPMuJ1uMrtlMnj04Dptv2zdMj5d/Ckf2j/ETfAvvSyCnx49A4wDePLmJ7l3+L3o9eF1Uhu8y92Jy5jMJgbdO4juA7sz/z/zKSkqIfuu7Dpta7PbyF2Zy3LPcglP0ShidDHcZbmL5378HN07d1e7HFVID1TD7FV2LFZLndrOmDOD2TtmM8M2w89ViXCQYcxgVPwonh/3PE0SfU+XGKqkB6phdQ3P4tPFbNixgUVOWaZDXB+rzsog4yBGtx/N/XfeH5KD4+tDAjQMzJg7g526nRS7i9UuRWhYpjGTIaYhjB00luwedbt0FOokQEPczv072Zu/l2WOZWqXIjQqQhfBYNNgcpJzmHDPhLA+Zb+UBGgIc7vdfJX7FetYR5Wi/rr0QnsyjBkMNQ3lrr53Maz/sJB+qqghJEBD2OLVi8kry2OjfaPapQiNidZFk2PKISc5h4dHPkxyUrLaJQUlCdAQda7qHEvWLmGZexkeAreEstA2PXp6WXrRW9+b4X2HM+yWYWE3trM+JEBD1Offf06hUkieK0/tUoRGpBvTyTHk0KFJB+l11pEEaAgqOFHAjn07aBvZlof1D7Pg3AIK3AVqlyWCVBN9E2613EqWKYuhtwxlQM8Bcq2zjmQgfQh6819vUlZRVvOXQFEUtjm2sbhqMWWe619vSYQGq85KTkQOHT0d6daxG6NuG0WEJULtsjRFAjQEfbfkO1ZsXIHRaMRoqD3JcCkuNtk3saJqBeWKn9aiF0HPqrPSN6IvnT2dSW2aypjbx9CiaQu1y9IkCdAQVV5RzufzP2f3gd1EWiMvOiVzKS422zez0rZSFpMLI5G6SPpF9KMznUmMSmTE4BF0yeoip+vXQQI0xOUX5fPlgi85WniUKGvURX9Z3IqbLfYtrLCt4IznjIpVCn+K1kV7e5x0JiYihuwe2QzsPRCDwaB2aZonARomjhYcZfai2RwpOHJZkHoUD1sdW1letZxST6mKVYrGFKOLoZ+1Hx2VjsRaY+nfvT+39roVo1HuHTcWCdAwc7zoOLMXzubQ8UNEWiMvGuPnUTzsc+5jvW29DH/SsNbG1vSw9CDVnUpcZBzZPbIZ0HOABKcfSICGqYITBXy98GvyjucRGRF52WDp0+7TbLBtYItjCzbFdpW9iGBhwkQXSxd6WnoS64wlOjKanF459O/eX07V/UgCNMwVFRfxzaJvyDueh06nw2K+eIo8p+Jkh2MH623rKXQXqlSluJom+ib0iOjBTeabcFW5SIxPJLtHNv1u7hdUwXn48GHGjh3LmjVr1C6lUUmACgAqz1WyaPUituzeQllFGVGRUZfdnc135bPJvondjt0yOYmKzJjJMmdxs+VmWiotcXvcpLVKY+gtQ0lv7XttLDVIgIqw4PF42Ll/J0vWLuFo4VEiLBEXjSUF7937PGceuxy72O3cjV2xq1Rt+DBhItOcSSdzJ9oZ22G32YmJiqFr+64M7js4IAu5devWje+//56EhASSkpJYtmwZN998M926dWPMmDF88cUXGI1GBgwYwNSpU3n55ZdZtWoVFRUV/POf/2TChAmsXLmShx9+mM6dO/PCCy/4vWZ/k6vK4iJ6vZ4uWV3oktWFkjMlzF8xn10Hd1FVVVXTKzXoDGSYM8gwZ3CHcgcHnAfY6djJXsdeHDjUfgshw4iRDFMGHc0dyTRnggscTgdNkpow6LZBdMnsEtCJPu6++27mz59Pq1atSEtLIzc3F4vFQlpaGrNmzWLVqlUYjUZGjRrFd999B0CHDh14++23OXz4MC6Xi/HjxzNgwACeeuqpgNXtTxKg4qoS4xMZd8c4XC4X2/dtZ82WNRwvOo7D6agZnG/QGcgyZ5FlzsKluNjv3M8+xz7yXHny2GgDROmiSDOlkWHKIMuchd6tx2a3ERcbR/v09gzoOYCEuARVarvnnnuYMmUKbdq0YcqUKbzzzjt4PB7Gjh3LihUrapb3yM7OZufOnQBkZdWuD79161ZiY2OpqKhQpX5/kFN4US8Oh4Ote7eybts6jhcdx+V2ERkRecXrbqfcp8hz5pHnzOOw67Cc6l+BESNtTW1JN6aTbkqnqaEpbrcbm91GUnwS7W9oz4AeA0hKSFK7VAD69OmD1Wpl0aJF9O/fH0VRmDZtGo899hgrVqzAYDBwzz338NBDD7F161aaNWvGE088UXMNdMmSJfTq1Yv//Oc/3HjjjWq/nesmPVBRL2azmZ5detKzS0/sDjubd25mw84N5Bfl43A5iI6MrgnTJoYmNDE0oVdELzyKh3xXPnkub6AWuApw4VL53QSeAQPNDM1IM6WRbkqntbE1Rp0Ru8OOw+7AHGemfVZ7sntkk5KUona5l8nJyeHQoUPo9XpycnLYtWsXXbp04b777uOWW27B4/HQv39/7r77brZu3XrZ9larlQ8++ICHHnqItWvXYrHUbWHEYCU9UNEo7A47+w7tY9POTeSfzKe0rBQdOqwR1iv2Tj2Kh2J3MYXuQgpcBRS6CylyFYVUqBow0NTQlGbGZrQwtKC5sTlNDU0x6Ay43C6qbFVYI6w0T25ORpsMunXqRlJCUlDeRRdXJgEq/KKsooyte7ay88BOioqLKK8ox2KxYDaZr7qNR/Fwyn2KAncBha5CTntOU+ou5aznLG7cAay+fvToidPHkaBPINGQSHNDc5obm5NiSMGg847F9Hg8VJ6rxGwy0ySxCW1btKVnl560bt5aZnzXMAlQ4XeKonDi1Ak2797MscJjnD5zmrNlZ3G4HERYIq4Zque3L/OUUeop5YznDKXu0pqvKz2VVClV2BQbCo3/q6xDh0VnwaqzEqmLJMGQQLw+ngR9Qs3Xcfo49LoLHon1eKisqkSHjihrFEnxSSQnJdMlswsZqRmXPawgtEsCVKjC4XBQcLKAXQd3cbzoOKfPnOZM2RmcLidGg5EIS0S9emaKouDAQZXHG6bnQ9Wm2HAqThQUPHhQFO9nPXp06NDpdOjRY8RIhD4Cq85KhK72s0VnueoptaIoOJwObA4bRr2RyIhIkhKSSE5Mpn16e9JapREXEyen5Mj56JQAAAEFSURBVCFMAlQEDYfDQXFpMflF+RwtOsqZs2eoOFdB+blyKs9V4nK5cCtuLCYLJqMJvV7v13BSFAWXy4XdacftcWMymIiwRBAdGU10VDSxUbE0a9KM1s1b0yy5GbHRsRKWYUYCVGiC2+3mbPlZikuKyT+Rz5nyM1TZqnA4Hdgddu+H047T6cThdOBwOlA8Cuf/06FDUZSLAk6n02EwGLCYLVhM3uuzFrP3s9lkxmKxEBsVS3JiMi2btiQ+Nh5rhFXFPwURbCRARUg6/2vt8VSftnu8SzvrdLqanuv5DyEaSgJUCCEaSMZPCCFEA0mACiFEA0mACiFEA0mACiFEA0mACiFEA/1/wryqVnLmkLQAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plotting the pie chart\n",
    "plt.figure(figsize = (5,5))\n",
    "plt.pie(slices, labels = activities, colors=colors, \n",
    "        startangle=90, shadow = True, explode = (0, 0, 0.1, 0),\n",
    "        radius = 1.2, autopct = '%1.1f%%')\n",
    "\n",
    "plt.legend(loc=2)\n",
    "\n",
    "plt.show()\n",
    "plt.close()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "gSMBZWXuKHBq"
   },
   "source": [
    "## Subplots\n",
    "\n",
    "Subplots are required when we want to show two or more plots in same figure. We can do it in two ways using two slightly different methods.\n",
    "\n",
    "## Example 8\n",
    "\n",
    "Plotting subplots using method 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 379
    },
    "colab_type": "code",
    "id": "nv2erzBYKHBr",
    "outputId": "07b3dcba-eb6f-424d-f373-a3a759522735"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize = (8,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.bar(left, height, tick_label = tick_label,\n",
    "        width = 0.8, color = ['red', 'blue'])\n",
    "\n",
    "plt.xlabel('x - axis')\n",
    "plt.ylabel('y - axis')\n",
    "plt.title('My bar chart 1!')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.bar(left, height, tick_label = tick_label,\n",
    "        width = 0.8, color = ['green', 'yellow'])\n",
    "\n",
    "plt.xlabel('x - axis')\n",
    "plt.ylabel('y - axis')\n",
    "plt.title('My bar chart 2!')\n",
    "plt.img\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "jpWGw_grKHBt"
   },
   "source": [
    "Figure acts as a top level container for all plot elements."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "BLHm7l03KHBu"
   },
   "source": [
    "![](https://indianpythonista.files.wordpress.com/2016/12/sub1.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "YBqOMWXUKHB7"
   },
   "source": [
    "### Other data visualization libraries\n",
    "\n",
    "- [seaborn](https://seaborn.pydata.org/)\n",
    "\n",
    "- [plotly](https://plot.ly/)\n",
    "\n",
    "- [bokeh](https://bokeh.pydata.org/en/latest/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "\n",
    "[Notebook Source](https://github.com/nikhilkumarsingh/PythonForDataScience) from [Nikhil Kumar Singh](https://github.com/nikhilkumarsingh) on Github\n",
    "\n"
   ]
  }
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