pandas-plotting-post.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
   "execution_count": 1,
   "metadata": {
    "collapsed": true,
    "jupyter": {
     "outputs_hidden": true
    }
   },
   "outputs": [],
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'dsutil'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-7d7995de0fe3>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mget_ipython\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrun_line_magic\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'matplotlib'\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m'inline'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 7\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mdsutil\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mplotting\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'dsutil'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "from dsutil import plotting\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "execution_count": 18,
   "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>name</th>\n",
       "      <th>age</th>\n",
       "      <th>gender</th>\n",
       "      <th>state</th>\n",
       "      <th>num_children</th>\n",
       "      <th>num_pets</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>john</td>\n",
       "      <td>23</td>\n",
       "      <td>M</td>\n",
       "      <td>california</td>\n",
       "      <td>2</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mary</td>\n",
       "      <td>78</td>\n",
       "      <td>F</td>\n",
       "      <td>dc</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>peter</td>\n",
       "      <td>22</td>\n",
       "      <td>M</td>\n",
       "      <td>california</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>jeff</td>\n",
       "      <td>19</td>\n",
       "      <td>M</td>\n",
       "      <td>dc</td>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>bill</td>\n",
       "      <td>45</td>\n",
       "      <td>M</td>\n",
       "      <td>california</td>\n",
       "      <td>2</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>lisa</td>\n",
       "      <td>33</td>\n",
       "      <td>F</td>\n",
       "      <td>texas</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>jose</td>\n",
       "      <td>20</td>\n",
       "      <td>M</td>\n",
       "      <td>texas</td>\n",
       "      <td>4</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name  age gender       state  num_children  num_pets\n",
       "0   john   23      M  california             2         5\n",
       "1   mary   78      F          dc             0         1\n",
       "2  peter   22      M  california             0         0\n",
       "3   jeff   19      M          dc             3         5\n",
       "4   bill   45      M  california             2         2\n",
       "5   lisa   33      F       texas             1         2\n",
       "6   jose   20      M       texas             4         3"
      ]
     },
     "execution_count": 5,
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'name':['john','mary','peter','jeff','bill','lisa','jose'],\n",
    "    'age':[23,78,22,19,45,33,20],\n",
    "    'gender':['M','F','M','M','M','F','M'],\n",
    "    'state':['california','dc','california','dc','california','texas','texas'],\n",
    "    'num_children':[2,0,0,3,2,1,4],\n",
    "    'num_pets':[5,1,0,5,2,2,3]\n",
    "})\n",
    "df[['name','age','gender','state','num_children','num_pets']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x13a5cf50>"
      ]
     },
     "execution_count": 6,
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f31b09e2438>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot num_children vs num_pets\n",
    "df.plot(kind='scatter',x='num_children',y='num_pets',color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x14b41370>"
      ]
     },
     "execution_count": 7,
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f31b03c89e8>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind='bar',x='name',y='age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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4Le+suUTpBgxWSqUB3wC9lVKzrj3IMIwphmFEG4YRHRoaaucwS1Cnjr7bL4m74iqsbzvyxmRRha2B06Y553zCLcxPPszZnDxGxoQ75XylJm7DMF42DCPMMIxw4F7AYhjGCIdHZq34eNi8GfbsMTsSYQaLRf8D7qyNXTt00LO+J03SA61EhWcYBtOS9tG6XhWiGzlndrt79nEXFRenH+fNMzcO4XyGoRN37966ZOYs48bB7t3w44/OO6dwWav2nmTnsXM8FBN+3U7zjmJT4jYMY5lhGAMdFUyZhIfrBRJSLql4/vgDjh51Tn27qGHDoF49PTVQVHjTEtOoXsmPwW3rOe2c7n/FDfqNtGoVHD5sdiTCmSwW/eis+nYhX18YM0bvxPTHH849t3ApB06d59ftx7i3YwMCfB1/U7KQZyTuwm3MpFxSsVgs0KgRmDG+dPRoaQ0UzFq9H6UUI7o0cup5PSNxt2gBLVtKuaQiKSjQHSW9ejm3vl2odm245x7dXXL2rPPPL0x3ITefb9Yd4PbWtalXNdCp5/aMxA36qnv5cj3uVXi+rVvh1Cnnl0mKGjcOzp2T1sAKav6mQ5y5cImRMc7/xOdZiTs/X+9HKTxfYX3b2Tcmi+rYUe8oL62BFY5hGExPSqNl3Sp0DHdOC2BRnpO427XT9U4pl1QMFgs0bar3hDTT+PF67MJPP5kbh3Cq1XtPseNoFg87sQWwKM9J3Erpq+6ff4asLLOjEY6Ul6d3dDezTFJo2DC9AEimBlYo05PSqBbky+Ao57UAFuU5iRt04s7NhaVLzY5EONLGjfqGoCskbj8/PTXwhx/0THDh8Q6ePs/PqUe5t1NDp7YAFuVZibtrV323X8olnq2wvh0ba2oYV4werXu7J082OxLhBLNWpwM4vQWwKM9K3N7eegn8kiVw4YLZ0QhHSUiAiAg9rc8V1KmjWwOnTpUynYfLuZTPN+vSub11Heo7uQWwKM9K3KDLJdnZ8MsvZkciHCE3F1ascI0ySVHjxumkPX262ZEIB1qw6RCZ5y/xkJOmAJbE8xJ3bCxUrSrlEk+1Zo3+NOVqibtTJ+jcWVoDPZhhGExNTKNFnWA6N65uaiyel7h9fWHwYFi4EC5dMjsaYW8Wi+4guuUWsyO53rhxenaJfNrzSGv36RbAkSa1ABbleYkbdLnk9Gm9klJ4loQEPQ2ymvMXPZTqrrukNdCDTUtKIyTQlyFR9c0OxUMTd9++EBQk5RJPc/68ngLpamWSQn5+8Pjjuh11926zoxF2dCjzAj+nHuPeTg0I9DOnBbAoz0zcgYFwxx16WqDUGz1HUpK+OWnmMvfSPP44+PhIa6CHmbV6P4Zh8ICJLYBFeWbiBl0uOXoUVq82OxJhLwkJOil27252JCWrW1eXTL74Qg+gEm4v51I+36xNp0+r2oRVCzI7HMCTE/eAAfqj65w5Zkci7MVi0d0bwcFmR3Jj48frlZ0zZpgdibCDhZsOc9oFWgCL8tzEXaUK9Omj69yGYXY0oryysmDdOtcukxTq3FlPDpw4UX723JzeCDiN5rWD6XpTDbPDucJzEzfocklaGmzaZHYkorxWrNBje131xuS1xo+HHTvg11/NjkSUw7q006QeOevUjYCt4dmJe/Bg8PKS7hJPYLGAv7+eR+MO7rpLL8mXDYXd2vTLLYBD25kzBbAknp24a9aEnj0lcXsCiwViYnTHkDvw99cdJkuWwJ49ZkcjyuBw5gV+3HaUezo2IMjPx+xwruLZiRt0uSQ1VX9sFe7p1Cld7nKH+nZRY8bowWfSGuiWvlzjWi2ARXl+4h46VD/KDvDua/lyfZPPXerbherVgzvvlNZAN5RzKZ+v1x7g1pa1aVDdNVoAi/L8xB0Wpu/yS7nEfVksUKmS7tRwN+PHw5kzMHOm2ZEIGyzafJhT2bk87EItgEV5fuIGXS5Zvx7S082ORJSFxaIX3fj5mR2J7bp0gQ4dpDXQjRS2ADarXZmuN7tOC2BRFSdxg1x1u6Njx/Q9CncrkxRSSl91b98Ov/1mdjTCChv2n2bbYddrASyqYiTuJk0gMlIStztKSNCP7pq4Qe+OExoqUwPdxLSkNKoE+BDXzvwpgCWpGIkb9FX3ypX6Ck64D4sFQkKgXTuzIym7wtbARYtg716zoxE3cPRMDj+kuGYLYFEVK3EbBixYYHYkwhYJCboX39v8UZrlUtga+OGHZkcibuDLNfspMAwe6BJudig3VHESd0SELplIucR9pKfrudbuXCYpVL8+DBsGn3+u90QVLifnUj5frUnn1ha1aVjD9VoAiyo1cSulApRSa5VSm5VS25RSbzgjMLtTSl91//YbZGaaHY2whifUt4saN07/7M2aZXYkohhLthzhZHYuI120BbAoa664LwK9DcNoC0QB/ZRSXRwbloPEx0NeHixebHYkwhoJCXpsQevWZkdiHzExets1aQ10OYUtgE1qVaZbE9dsASyq1MRtaIXLvnwv/+eeP3UdO+qPrFIucX2GoW9M9uqlB4XZQerhs6QcOmOX1yoTpfRV97Ztf36aEC5hY3omWw+dKV8LYEoKfPqpfQMrgVXvCKWUt1JqE3Ac+MUwjDXFHDNaKbVeKbX+xIkT9o7TPry8IC4OfvxR6oyubs8eOHDAbmWS3LwCHpq6liGTE/n0970YZl3x3nuv/hQhUwNdyrSkNIIDfIgvawvgV1/pFdpvvOGU8QZWJW7DMPINw4gCwoBOSqmIYo6ZYhhGtGEY0aGhofaO036GDYMLF3TyFq7LYtGPdhos9UPKEU5kXaR1vSq8vXQ7Y7/ayLmLeXZ5bZsEBMDo0bo1cN8+559fXOfY2Rx+2HqEu6MbUMnfxhbA3Fy9wOr++/UK2XXroHJlxwRahE2fQQ3DyASWAf0cEo0zdO+ur3ikXOLaEhL0kKZmzezyctOS0mhcsxLznuzGy/1b8GPKUYZMWsnu41l2eX2bPPGELptIa6BL+HL1fvINgwe72jgF8NAhfWExcSI8+6xufKhb1zFBXsOarpJQpVTVy78OBG4D3HdGqo8PDBmib1BevGh2NKI4hfXt3r11giunzQcySU7P5KGujfD2Ujze82ZmjerMmQuXGDwpkcVbDtshaBuEhekb5Z99JiU7k13My+erten0bl6LRjUqWf/EZcv0jebNm2H2bHjvPfD1dVic17LmirsukKCU2gKsQ9e43bstIz5eb+Za+HFcuJbUVDh+3G5lkulJaVTy82ZYh7Ar/y/m5posHteDFnWCeeqrZN5anMql/AK7nM8q48fr1sAvv3TeOcV1lmw5Qsa5XEZ2C7fuCYYB774Lt90G1avD2rVw990OjbE41nSVbDEMo51hGJGGYUQYhvGmMwJzqFtv1TuFS7nENdmxf/tE1kUWbTnMXdENCA64+oqoTkgA34zuysiYcD5fuY/7P13D8bM55T6nVbp1g6goaQ00UWEL4M2hlejepGbpT8jK0lvS/fWvuslh7Vpo1crxgRaj4qycLMrfHwYOhPnz9Qa0wrVYLNC4MYSHl/ulvl6bzqX8kuuXfj5evD64Nf+9N4qth84wYOJK1qWdKvd5S1U4NTAlRX/sFk6XfCCTLQfPMNKaFsDUVN1OPH++vuL+9lt98WeSipm4QZdLMjL04CnhOgoKdCKzw9V2bl4Bs1bvp2ezUG4KvfGd/iFR9Zk3NoZKft4Mn7Kaz1fuc3zL4L33Qo0aMjXQJNOT0gj29yG+fdiND/z2W+jUCU6f1jcgn3/eLvdeyqPiJu5+/XRr1pw5Zkciitq8Wb9B7FDf/nHbUY5nXbR6CXOLOlVYOK47vVrU4q3FqYz7OplsR7YMBgbq1sAFC2D/fsedR1zn+Nkclmw5wl03agG8dAmee06P5W3bFpKT9cAzF1BxE3flynD77brOXeDEm1LixuzYvz0tcR/hNYLo2cz6dQVVAnz5ZEQHXujXnKVbjzB0ciK7jztwQYW0BpriyzXpN24BPHJE3wv7z390SauwPdVFVNzEDXoxzqFDelsz4RosFmjRotxvki0HM9mYnsmDXcPx8rLtY62Xl+LJ2CbMfLQzJ7NzGTJpJT9sPVKueErUoIG+0fXpp3D+vGPOIa6Sm1fAl2vS6dW8FuE1i2kBXLlSt/pt2KBXRP73vy63bV7FTtwDB+q+bukucQ2XLsHvv9vnavtyC+Cd0aXUL2+gW5OaLB7Xnaa1g3niy438a+l28hzRMjhunC4PffWV/V9bXGfp1iNknLvIQ9eW0AwD3n9f//wFB8OaNTB8uCkxlqZiJ+5q1fRNsDlzpCXLFWzYoOc8lPPGZMa5iyzefIRhHcKoElC+RRH1qgYy+/EuPNClEVN+38uIz9dwIsvOC7d69NA11A8+kJ9DJ5ialMZNoZXoUbQF8Nw5naSffRYGDdJL1yOum+zhMip24gbdXbJ7t57YJsxVWN+OjS3Xy3y9Jp3c/AIe7Bpe7pAA/H28eWtoBO/d3ZZNBzIZOHEFG/bbsWWwcGrg1q36E4dwmOT002w+kMlDRUtoO3boAVHffQfvvKMv5EJCzA20FJK4hwzRbxwpl5jPYtGbOte0YjFECS7lFzBrzX56NK1Jk1r2HfYT3z6MuU90w9/Hm3s+Wc30pDT7tQzed59eiSdTAx1qelIalf19/lxFO2eO7s8+cQJ++QVeeMH0Vj9rSOKuU0evYpPEba6LFyExsdxlkh9TjnLs7EUetnYJs41a1avCoqe607NZKP9YuI1nZm/ifK4dWgYDA+Gxx/QCj/T08r+euM7xrByWbD3CnR3CqOyNXgF55516o46NG91qpyVJ3KDLJZs36xnQwhyrV0NOTrnfPNOT0mhUI4jYZrXsFNj1QoJ8+fTBaP7StxkLNx8mbnIS+zLsMCzqySf1o7QGOsRXa/Qq2oebBEKfPnoF5JNPwvLlevCXG5HEDbodC+Sq20wWi97o4pZbyvwSKYfOsH7/6TK1ANrKy0vxVO+mTH+4E8ezchg8cSU/bTtavhdt2BCGDtWtgRcu2CdQAfzZAjjK+wiN+vTQHSMzZsDkyXoEhpuRxA16Jkb79pK4zZSQoAfRl+Om0LSkNIL8vLmrHC2AtrqlWSiLxnWncWglHp+5gXd+3FG+lsHx4+HUKWkNtLMfth6m/7Lv+dv/PKHLUqtXwwMPmB1WmUniLjRsmP7LPHTI7Egqnuxs/WdfjjLJyXMXWbj5MMPal78F0FZh1YL49vGuDO/UkI+W7eHBL9aSca6MLYO33AJt2sjUQHvKzqbq6Ed489dPUP366wV3kZFmR1UukrgLxcfrx/nzzY2jIkpM1ItvypG4v1l3QO8rGWPjLiZ2EuDrzb/j2/A/d0ayfv9pBk1cSXL6adtfqHBq4ObNsGKF/QOtaHbt4kKHjnTf8BsbRj+PWjAfqlY1O6pyk8RdqEULaNlSyiVmsFj07iHdupXp6ZfyC5i5qrAF0LxRmwB3Rzdg7hMxeHsp7v5kFTNX77e9ZfC++/TiMJkaWD7z50N0NPmHj/D4fW/R7L//1vdRPIBnfBf2Eh+v7zBnZJgdScWSkKAXQFSyYeuoIn7edoyjZ3N4yE4Lbsoron4Ii8d1p1uTmrw6P4Xnv9vMhVwb5r4HBenWwHnz9E73wjZ5efDyyxAXx6Umzbjjwf8QdveQ6zbScGeSuIuKj9cbKyxaZHYkFceZM7rmWI4yybSkfTSsHkSvFo5rAbRV1SA/vnioI8/c1pR5yYeI+zCR/SdtaBl88kld4/7oI8cF6YmOH9dTPydMgMcfZ8o/p5FeOdT2jYBdnCTuotq1g0aNpFziTL//rsfqlnGwVMqhM6xLO82DlzcCdiVeXopnbmvGFyM7cuRMDgMnruTX1GPWPblRI72qd8oUaQ201po1ujMpKQmmTiV30odM33jEqo003I0k7qKU0lfdP/+sNxMWjpeQoDe06NKlTE+fnpRGoK83d0U3sHNg9tOreS0Wj+tOoxpBjJqxnnd/+oP8Aivq3uPGwcmT8M03jg/SnRV+MunRQ98rWbUKRo60eSMNdyKJ+1rx8ZCbC0uXmh1JxWCx6JuSAQE2P/VUdi4LNh8mvn19QgJdu37ZoHoQ34+J4e7oMCYl7Gbk1LWcys698ZNiY/WEOmkyPOn1AAAeoklEQVQNLNn58zBypC4t9emjJ0xGRQFl20jDXUjivlZMjJ5fIuUSx8vI0G1vZSyTfL02ndy8Are5ogrw9eZ/7mzLhPg2rNl3ikETV7L5QGbJTyicGpicrFsmxdX27IGuXWHmTHjzTX1vqlo1oHwbabgDSdzX8vLSy46XLpXaoqMtX64fy3BjMi9fbwTcrUkNmtY2twXQVvd2asj3Y7oCcNfHq/h6bXrJLYP33y+tgcVZtEjXsw8c0O/VV1+9qtXPHhtpuDJJ3MWJj9er+X75xexIPJvFovf+jI62+ak/px7jyJkcRsY0dkBgjhcZVpVF47rT+abqvDx3Ky98v4WcS8W0DFaqBI8+qsePHjzo/EBdTX4+vPIKDB4MN9+sp/r163fVIfbcSMNVSeIuTmysXl0l5RLHslj0Em9f299c05LSCKsWSG8XagG0VfVKfkx7uBPjezfhuw0HGfZREgdOFbPv5Nixusb98cfOD9KVZGRA//7w9tv6H7PERD1n6Br23kjDFUniLo6vr/4XfeFCvRRb2N/hw3rnkTLUt1MPn2XtvlM81DXc5VoAbeXtpXiub3M+fyiaA6fOM3DiShJ2HL/6oPBwvZ3WJ5/o0bcV0bp1ujTy++96euJnnxV7Q9uRG2m4EkncJYmP1xu4FtZhhX0tW6Yfy1DfLmwBvNuFWwBtdWvL2iwa1516VQN5ZPo6/vPLTgqKtgyOH6+vOGfPNi9IMxiGTtTdu+ubtYmJMGpUiYc7eiMNVyGJuyR9++qlx3PmmB2JZ7JY9E23tm1tetrp7FzmbzpEXPv6hAR5Vv2yUY1KzH0ihvh2Yfz3t108PG0dmecvtwz26qV3aqlIGwpfuKBLIqNH6+9/wwZ91X0DzthIwxVI4i5JYCDccYeeF5Fvw5wJYR2LBXr2BG9vm572zboDXMwrcJm5JPYW6OfNu3dF8nZcBEl7Mhg4cSUph8782Rq4caNeYOLp9u3T/f1Tp8Jrr8GSJVCjxg2f4syNNMwmiftG4uPh2DE9K1rYT1qafmPaWCYpbAGMubkGzeu4VwugLZRS3N+5Ed8+3pX8AoP4j5L4dt0BGDFC3zT39A2Fly7VV9b79sHixfDGG1b9A2/GRhpmKTVxK6UaKKUSlFLblVLblFJPOyMwlzBgAPj5SXeJvSUk6EcbE/ev249xKPMCD7nJgpvyatewGovHdadjeDVemLOFl3/aQ97DD+vynSdu+FFQAK+/DgMH6m3cNmzQ70ErmLmRhhmsueLOA543DKMl0AUYq5Rq5diwXESVKnoZ7dy5Faeu6AwWC9SqBa1s+zGalpRG/aqB3NaytoMCcz01Kvsz45HOPBl7M1+vPcCY4C4Y+fme1xp46pRO0m+8AQ8+qMtBN91k9dPN3kjD2UpN3IZhHDEMY+PlX2cB24H6jgjmYl5++fbrc4T4eP3RftMmsyPxDIahr7h79dJ1WyttP3KW1XtPueQUQEfz9lK80K8FUx7owBpCWNasM7kffgwXy7g9mqvZuFGXRiwW/Q/S1Kn6HpOVXGkjDWfxseVgpVQ40A5YY+9Azpy/xJ0fJzGsQxhjet5s75cvu8GD9VLauXP12FdRPrt26Y/5NpZJZqxKI8DXi3s6ek4LoK36tq7DwnHBTDl+F70+fp4LteoQEBSA2/8zlpEBdevqrdo6dbL56YUbafxzaIQDgnNNVidupVRlYA7wjGEY1808VUqNBkYDNGzY0OZAQoJ8uSm0Ev/5ZSf9WtchvGbZdkOxu5o1dffD3Lnw1ltmR+P+LBb9aMPCm8zzuXozgnb1qRrk56DA3EPjmpV49f1xLD25m8yd+6hXNZCuN9fA38eN+wxCQuCvf4XQsk3xc8WNNBzNqsStlPJFJ+0vDcMo9k6dYRhTgCkA0dHRZSoIvzkkgtv+bzkvz93KV491RtnwUdqh4uN1K9aOHXpvSlF2FguEhUGTJlY/Zfa6A+RcKqgwNyVLE+TvS//Zk5m5ej+PLU6lbkggH4/oQKt6VcwOzekKN9J4ZUDLClVCs6arRAGfA9sNw3jPkcHUrhLAy3e0ZNXek3y33oUG6gwdqh+lu6R8Cgr0isneva2ub+cXGMxYtZ8uN1WnRZ2Kl5hKopTiwa7hfDO6Kxfz8on7MJE5G1zoPeMk7rCRhiNY8/mqG/AA0Fsptenyf3c4KqB7OzagU+Pq/HNJKsezXGQuQ1iY3sxWEnf5bNsGJ07YVN8ubAF0l5nbztahUTUWj+tBu4ZVef67zbwyfysX8yrGgjF32kjD3qzpKllpGIYyDCPSMIyoy/85bHsYLy/FhPg25OQV8PrCbY46je3i43Vf6f79ZkfivspQ356WWPFaAG0VGuzPrEc783jPm5i1Op27P1nN4UzPnyXvbhtp2JNL3tG4KbQyT9/alKVbj/LTtqNmh6PFx+vHefPMjcOdJSToGcpW3rz+42gWq/aeZESXRvh4u+SPqsvw8fbi5f4t+XhEe/YcP8fAiStJ3J1hdlgO484badiDy74bRt9yEy3qBPPaghTO5rjAaNUmTSAyUsolZZWf/2d920rTktLw9/Hi3grcAmirfhF1WfBUN2pU8uOBz9cwOWH31VMGPYS7b6RRXi6buH29vfifOyM5kXWRd37YYXY4Wnw8rFyp55cI2yQnw5kzVpdJzpy/xLzkgwyNqk+1ShW7BdBWN4dWZv7YbgyIrMf//vQHj8/a4BoXP3bkCRtplIfLJm7Q2zs92r0xX65JZ83ek2aHoxO3YcCCBWZH4n4K55NYmbhnr0+XFsByqOTvwwf3RvGPQa1I2HGcwRNXsuPodcsv3JInbaRRVi6duAGe7dOMBtUDeXnu1uL35HOmiAhdMpFyie0sFj2bpE6dUg8tbAHs1Lh6hexNthelFA93a8zXo7uQnZvP0MmJzE92/+FUnriRhq1cPnEH+fnwr7g27M3IZpJlt7nBKKWvun/7Te+OI6yTm6uXM1t5tf3b9mMcPH2Bh+Vq2y46hldnybjuRNavyjOzN/GPBSnk5rnYTCArefJGGrZw+cQN0KNpKMPah/Hx8j1sP2Lyx734eMjL03OChXXWrYPsbKtvTE5flUa9kAD6tJIWQHupVSWALx/rzKjujZm+aj/3TlnF0TMusk7CBp6+kYa13CJxA7wyoCVVg3x5ac4W8s28S96xI9SvL+USWyQk6E8rPXuWeujOY1kk7j7JiK7SAmhvvt5evDKwFZPua8eOo1kMnLiCVXtc4N6RlfLyC5i5Ks3jN9Kwhtu8M6pV8uMfg1qz+eAZpibuMy8QLy+Ii4Mff9RXkaJ0FgtERZW69RTo+qWfjxf3drR9UJmwzsDIeix8qhshgb6M+HwNnyzfg+EG8+Z/3X6Mw2dy5IY1bpS4AQZG1uXWFrX4v593cuDUefMCGTYMcnJ08hY3lpMDSUlW1bfPnL/E3I2HGBpVj+rSAuhQTWoFs+Cp7tzeujb//mEHT8zaSJaLtwxOlVW0V7hV4lZK8dbQCLy9FH+bt9W8q4Tu3fW4VymXlG7VKj3w34r69ncbDnDhUr5cUTlJZX8fJt/XnlcGtOSX7ccYMjmRnceyzA6rWNuPnGXNvoq5kUZx3CpxA9SrGsiL/ZqzYlcGczea1Nrk4wNDhugblJ6yC4mjWCx6o9cePW54WH6BwfRVaXQKr07reiHOiU2glGJUj5v4clRnzl7IY+jkRBZtPmx2WNeZniQbaRTldokb4P7OjejQqBpvLUkl45xJiTM+Hs6e/XNwkiiexQLR0Xr/zhtI2HGcA6cqzkbArqbLTTVYMr47LetWYdzXyby5KJVLLrKN4JUWQNlI4wq3TNyFEwTPX8znzUWp5gRx660QHKx33BbFO3cO1q61qkwyLSmNuiEB9G0t9Uuz1K4SwNePdWFkTDhfJO7jvk9Xc/ys+S2Ds9fLRhrXcsvEDdC0djBjezVh4ebDWHaYMDvE3x8GDtTL3/PynH9+d7Bypf6zKSVx7zqWxcrdGYzo0ghfaQE0lZ+PF68Pbs1/740i5dBZBkxcydp9p0yLJ+/yRsCykcbV3Ppd8kTszTSrXZlX5qVw7qIJyTM+Xm90unKl88/tDiwW8PWFmJgbHjZ9VWELoNQvXcWQqPrMH9uNYH8fhn+6ms9W7DWlGeDX7ccvb6RRMacAlsStE7efjxcThkVy5GwO//ujCRME+/WDgADpLimJxQJdu0JQUImHnLmgWwAHt61Hjcr+TgxOlKZ5nWAWPNWN21rW4p9LtvPU18lkO/kCaXpSYQtgxZwCWBK3TtwA7RtW46Gu4cxYvZ8N+538ka5yZZ28587V+ymKP50+rUe5llIm+W79Ac7n5lfIXUzcQXCALx+P6MBL/Vvww9YjDJmcyO7j55xy7h1Hz7Jq70kekFW01/GIP42/3t6ceiGBvDjHhP324uPh0CFYv96553V1v/+u/zG7QeIunAIY3agaEfWlBdBVKaUY0/NmZj3amdPZuQyZtJIfth5x+HmnJ+3H38eLeyrwFMCSeETiruTvwz/jIth9/BwfLdvj3JMPHKj7uqVccjWLBQIDoVOnEg9Z9sdx0k+dZ2S3cOfFJcospklNFo/vTtPawTzx5Ub+tXQ7eQ5qGcw8n8u85IPEtZONNIrjEYkboFfzWgyNqsfkhN3OXf1VrZq+qpwzR2+yILSEBL3C1L/kuvW0pDTqVAng9talz+gWrqFuSCCzH+/CA10aMeX3vdz/2RpOZNl/LcW30gJ4Qx6TuAFeHdiKyv4+vOjsCYLx8bB7N6SkOO+cruz4cdi69YZlkt3Hz7FiVwYjujSUFkA34+/jzVtDI3jv7rZsPpjJwIkr7Hp/qbCE1rlxdVrWlRbA4njUO6ZGZX9eG9SK5PRMZq3e77wTDxmix5ZKuURbtkw/3mCw1IxVafh5e3FvJ5kC6K7i24cx78luBPh6c88nq5mWuM8uLYOFG2nIDeuSeVTiBhgaVZ+ezUL5nx93cCjzgnNOWqcOdOsmibtQQoJeVdqhQ7FfPptzie83HGRQ23rUlBZAt9aybhUWPtWd2OahvL4olae/2cT53PK1DE5Lko00SuNxiVspxdtxERjAK86cIBgfD1u26JJJRWex6E0TfHyK/fL36w9KC6AHCQn0ZcoD0fz19uYs3nKYuMlJ7D1RtpbBnceySNojG2mUxiP/ZMKqBfGXvs1J+OMEC5016SwuTj/Om+ec87mqgwdh584SyyQFBQYzVqXRoVE12oRJC6Cn8PJSjO3VhOmPdOJ4Vg5DJiXy07ajNr/OtKQ0/GUjjVJ5ZOIGeCgmnLYNqvLGolROZec6/oTh4bo0UNHLJQkJ+rGEG5PLd54g7eR56RbwUD2ahrJ4fA9uCq3E4zM3MOGHHVa3DJ45f4l5Gw8xRDbSKJXHJm5vL8U7w9pw9sIl/rnESRME4+Nh9Wq9IKeiSkiA6tUhMrLYL09NSqN2FX/6R0gLoKeqXzWQb8d05b7ODfl4+R4e/GKtVeOXv10vG2lYy2MTN0CLOlV4MvZm5m48xPKdJxx/wvh4/Th/vuPP5YoMA377DWJj9d6c19hz4hy/7zzB/Z1lCqCn8/fx5l9xbfjfOyPZsP80gyauJDn9dInH5xcYzFgtG2lYy+PfPWN7N+Hm0Er8be5Wxw/IadECWrasuOWSffsgPb3EMsmMJN0COFxaACuMu6IbMPfJGHy8FXd/soqZq/cX2zBgubyRhqyitY7HJ25/H28mDIvkUOYF3vtlp+NPGB8Py5frca8VzQ3q21mXWwAHRtYlNFhaACuS1vVCWPxUD7o3qcmr81N4/tvNXMi9eqbQ9MKNNKQF0CqlJm6l1BdKqeNKKbddFtgxvDojujRkauI+Nh3IdOzJ4uMhPx8WLnTseVyRxaJ72lu0uO5L3284SHau1C8rqpAgXz5/qCPP9WnGvE2HiPswkf0ns4GrN9KQFkDrWPOnNA3o5+A4HO7Ffi2oFRzAS3O2OHYvvXbtoFGjilcuMQyduHv10qtIiyi4vIS5XcOqtG1Q1aQAhdm8vBTjb23K1JEdOXo2h4ETV/Jr6rErG2lICc16pSZuwzB+B8zbu8hOggN8eWtoBDuOZvHJcgdOEFRKX3X/8gucOeO487iaP/6Ao0eLLZMs33WCfRnZsuBGABDbvBaLnupOoxpBjJqxntnrDjCkrbQA2sJun0uUUqOVUuuVUutPnHBCB0cZ9GlVmwGRdfngt92OHQY/fDjk5sLtt8OBA447jysp3O2+mMQ9PSmN0GB/+kfUdXJQwlU1qB7E92NiuCe6AQrFw91kazJb2C1xG4YxxTCMaMMwokNDQ+31snb3+qDWBPp587e5Wylw1ATBjh3h++8hNRXat9ctcp7OYoGGDaHx1W/AvSfOseyPE4zo3Ag/H6lfij8F+Hrzzp2RbP5HX1rVkymAtqhw76TQYH9eGdCStWmn+HpduuNONGwYrFsHtWpB374wYYLnzusuKNAdJb17X1ffnrFqP77eiuGdZRcTUbxAP2+zQ3A7FS5xA9zZIYxuTWowYekOjp7JcdyJmjeHNWvgrrvg5Zd17dsT695bt8KpU9eVSc5dzLvcAliPWsEBJgUnhOexph3wa2AV0FwpdVAp9ajjw3IspRT/imvDpYICXpmf4tgJgpUrw9dfw/vvw+LFuoyydavjzmeGwvr2NYOl5mw4yLmLedICKISdWdNVMtwwjLqGYfgahhFmGMbnzgjM0RrVqMRzfZrx6/Zj/JBi+xQzmygFTz+tywlZWdClC3z1lWPP6UwWCzRtCmFhV/5XQYHB9FVpRDWoSpS0AAphVxWyVFLokW6NaVM/hNcWbOPM+UuOP2H37rBxo54ieP/9MH687j5xZ3l5ekf3a8okK3ZnsPeEtAAK4QgVOnH7eHsxYVgbTp/P5e2lTpogWLeu7jJ59lmYOFEPZHLnaYIbN8LZs9cl7sIWwDvaSAugEPZWoRM36DkKo2+5iW/XHyRxt5Pmi/j6wnvvwezZetec9u3/3KfR3RTWt2Njr/yvtIxsEv44zn2dGkoLoBAOIO8q4Olbm9K4ZiX+Nm/rdcNvHOruu2HtWj2/+rbb4N133a9lMCEBIiJ02+NlM1btx8dLcX9nWcIshCNI4kYvBPh3fBv2nzzP+785YYJgUa1a6eQdFwd//atuHTx71rkxlFVuLqxYcVWZJPtiHt+tP8AdbepSq4q0AArhCJK4L+tyUw2Gd2rAZyv2kXLIyb3WwcHw7bf6inv+fOjUSa+6dHVr1sCFC1e1Ac7deJCsi3lyU1IIB5LEXcRL/VtSvZIfL3zv4AmCxVEKnn9e37g8fVon79mznRuDrSwWHXfPngAYhsG0pDTahoXQrmE1k4MTwnNJ4i4iJNCXt4a0JvXIWT5fuc+cIHr2hORkaNsW7r1Xd59cckKrYlkkJOgbq9V0kl65O4M9J7JlFxMhHEwS9zX6RdSlX+s6/OeXnaRlZJsTRL16OimOH69XXPbuDUeOmBNLSc6fh1WrriqTTEtMo2ZlP2kBFMLBJHEX440hrfHz8eLluVsduxz+Rvz84L//1SssN27UV7YrVpgTS3GSkvTNycs3JvefzMbyx3Hu69wIfx8ZGiSEI0niLkbtKgH87Y6WrNp7km/XmzxPe/hwfRMwOFhf3b7/vmu0DCYkgI+PXg2KbgH0VtICKIQzSOIuwT3RDejcuDpvL9nO8bMOnCBojYgIPSJ20CBd8x4+HM45cCMIa1gs+gZqcDDZF/P49nILYG1pARTC4SRxl8DLSzFhWCQ5eQW8vmib2eFASIjex3LCBPjuO500d+wwJ5asLP0PyeX69tzkQ2TlyBRAIZxFEvcNNK5ZiWdua8rSrUf5aZuDJwhaQyl48UW9n2VGhh4RO2eO8+NYsULvZN+7N4ZhMD0pjciwENo3lCmAQjiDJO5SPNbjJlrWrcJrC1I4m+MibXm9e+sblq1bw5136hWXeXnOO7/FAv7+0LUribtPsvv4OR7qGo66ZvcbIYRjSOIuha+3F+8Ma8OJrItM+MGk0kRxwsJg+XJ48km94rJPHzh2zDnntliga1cIDGRakm4BHNhWWgCFcBZJ3FaIDKvKqB438dWadNbsPWl2OH/y94fJk2HGDN150r69btNzpFOnYNMm6N2b9JPn+W3HMYZ3aigtgEI4kSRuKz17WzMaVg/i5blbybnkxAmC1njgAVi9GgID9crLiRMd1zK4fLl+7d69mbk67XILYCPHnEsIUSxJ3FYK9PPmX3Ft2JuRzUTLLrPDuV5kJKxfD/376xWXI0ZAtgNWflosEBTE+bbtmL3uAP0i6lAnRFoAhXAmSdw26N60Jnd2COOT5XtJPeyCo1erVtXTBd9+W29Q3KUL7LTzmFqLBXr0YN62E5zNyeNhmUsihNNJ4rbRKwNaUjXIl5fmbiG/wAVWMF7Lywv+9jf46Sc936RjR53M7eHYMUhNxejVi+lJaUTUr0J7mQIohNNJ4rZR1SA/Xh/cmi0HzzA10aQJgtbo00e3DDZrpjdpeOml8rcMJiQAsLVpe3YekxZAIcwiibsMBrSpy20ta/F/P+/kwKnzZodTsoYNYeVKePxxeOcduP12OH687K9nsUBICJPPBFO9kh+D2tazX6xCCKtJ4i4DpRRvDY3A20vxt3kmThC0hr8/fPwxTJ2qWwU7dNCtg2WRkMCFrt35ZedJhndqQICvtAAKYQZJ3GVUNySQF/s1Z8WuDOZuPGR2OKUbOVInbl9f6NEDPvrItpbB9HTYvZuVDSNRSjGii7QACmEWSdzlcH/nRkQ3qsZbS1LJOHfR7HBK166dbhns00evuHzoIb0hgjUu17c/9mpAv9Z1qBsS6MBAhRA3Iom7HPQEwTacv5jPm4vcYHNfgOrVYdEieOMNmDVLL13fs6f05yUkkFO1OhurhMnWZEKYTBJ3OTWpFcxTvZuwcPNhLDucNCukvLy84LXXYOlSOHBA170XLSr5eMPAsFhY2yiSlvWqEt1IWgCFMJMkbjsY0/NmmtcO5u/zUjh30YlT+sqrXz/YsAFuvhkGD4ZXXtHjWq+1Zw/qwAF+rt2Kkd2kBVAIs0nitgM/Hy8mDGvD0bM5/O+PLjRB0BqNG0NiIjz6qF5x2b+/nvVdlMUCQErz9gyWFkAhTGdV4lZK9VNK/aGU2q2UesnRQbmjdg2rMTImnBmr97Nh/ymzw7FNQAB89hl8+in8/rsunaxbd+XL53/6haOVqxPTP0ZaAIVwAaUmbqWUNzAZ6A+0AoYrpVo5OjB39Je+zakXEsiLc7ZyMc/FJghaY9QovWBHKb0J8KefXq5vJ7C6USQjuoabHaEQAuuuuDsBuw3D2GsYRi7wDTDEsWG5p0r+PrwdF8Hu4+f4MMGKTg1XFB2t6969esHo0eQPGEilzJNkxfSgXlVpARTCFfhYcUx94ECR3x8EOjsmHPcX27wWce3qMzlhN0u3HjE7nDLzuu0FHiiozYgfZgAQMSLO5IiEEIWsSdzFtRBct+ROKTUaGA3QsGHDcobl3l4b2IpAP28yz+eaHUq5JD00nsyoaFpnpBHbI8rscIQQl1mTuA8CDYr8Pgw4fO1BhmFMAaYAREdHu/DwDserVsmPf8W1MTsM+7i/g9kRCCGuYU2Nex3QVCnVWCnlB9wLLHRsWEIIIUpS6hW3YRh5SqmngJ8Ab+ALwzC2OTwyIYQQxbKmVIJhGEuBpQ6ORQghhBVk5aQQQrgZSdxCCOFmJHELIYSbkcQthBBuRhK3EEK4GeWIjW6VUieA/WV8ek0go9Sj3IOnfC+e8n2AfC+uyFO+Dyjf99LIMIxQaw50SOIuD6XUesMwos2Owx485XvxlO8D5HtxRZ7yfYDzvhcplQghhJuRxC2EEG7GFRP3FLMDsCNP+V485fsA+V5ckad8H+Ck78XlatxCCCFuzBWvuIUQQtyAJG4hhHAzkriFEMLNSOIWQgg3I4lbCCHcjCRuIYRwM5K4hRDCzUjiFm5LKRWulNqulPpUKbVNKfWzUipQKfWYUmqdUmqzUmqOUiro8vHTlFIfKaUSlFJ7lVI9lVJfXH6NaUVet69SapVSaqNS6julVGXTvkkhiiGJW7i7psBkwzBaA5nAMGCuYRgdDcNoC2wHHi1yfDWgN/AssAj4D9AaaKOUilJK1QReAW4zDKM9sB54zmnfjRBWsGqzYCFc2D7DMDZd/vUGIByIUEr9E6gKVAZ+KnL8IsMwDKXUVuCYYRhbAZRS2y4/NwxoBSQqpQD8gFVO+D6EsJokbuHuLhb5dT4QCEwDhhqGsVkpNRKILeb4gmueW4B+P+QDvxiGMdxB8QpRblIqEZ4oGDiilPIF7rfxuauBbkqpJgBKqSClVDN7ByhEeUjiFp7oVWAN8Auww5YnGoZxAhgJfK2U2oJO5C3sHaAQ5SHTAYUQws3IFbcQQrgZSdxCCOFmJHELIYSbkcQthBBuRhK3EEK4GUncQgjhZiRxCyGEm5HELYQQbub/Ae/SxreStEOFAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "# gca stands for 'get current axis'\n",
    "ax = plt.gca()\n",
    "\n",
    "df.plot(kind='line',x='name',y='num_children',ax=ax)\n",
    "df.plot(kind='line',x='name',y='num_pets', color='red', ax=ax)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f31b03b6320>"
      ]
     },
     "execution_count": 6,
     "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": [
    "df.groupby('state')['name'].count().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f31b037f940>"
      ]
     },
     "execution_count": 7,
     "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": [
    "df.groupby('state').size().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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MGu4AVXUNcM2wZR8aMv0XwF80W5okaWt5haoktZDhLkktZLhLUgsZ7pLUQoa7JLWQ4S5JLWS4S1ILGe6S1EKGuyS1kOEuSS1kuEtSCxnuktRChrsktZDhLkktZLhLUgsZ7pLUQoa7JLWQ4S5JLWS4S1ILGe6S1EKGuyS1kOEuSS1kuEtSCxnuktRChrsktVBP4Z7k2CT3Jbk/yQdGWL9rksu6629OMqvpQiVJvRs13JNMAj4HHAfMBRYnmTus2TuAX1XV84C/BT7ZdKGSpN710nN/MXB/Vf2sqn4HXAqcOKzNicAXu9PLgFclSXNlSpK2xM49tNkf+MWQ+UHg8M21qaoNSR4D9gZ+ObRRkiXAku7s2iT3bU3RO4h9GPb9x9RH/be0YeP6+8W/dZvU9t/uOb006iXcR0qN2oo2VNVSYGkP+9zhJVleVQP9rkNbx99vx+Vv19HLsMwgcMCQ+RnAg5trk2RnYBrwSBMFSpK2XC/h/hNgTpLZSXYBTgGuGtbmKuDU7vRJwPer6mk9d0nS+Bh1WKY7hn4m8G1gEnBhVd2V5GPA8qq6CrgAuCjJ/XR67KeMZdE7iAkx/NRi/n47Ln87IHawJal9vEJVklrIcJekFjLcJamFDHdJaiHDXQK6p/pOGTK/mzfA2zEkOTnJ1O70Xyb5WpL5/a6r3zxbpkHdcHgHMA/YFBRVdXrfilJPkiwHXta9fxLdazp+VFWH9bcyjSbJyqo6KMkRwCeA84Bzq2r4bVImFHvuzboIeDbwGuA6OlfzPt7XitSrnTcGO0B3epc+1qPePdV9PwH4fFVdib+d4d6w51XVfwN+U1VfpPM/24F9rkm9WZ1k0caZJCcynjd+07b4P0n+HngzcE2SXTHberpxmHq3vvv+aJI/Bv4vMKt/5WgL/Gfg4iSf7c4PAm/rYz3q3ZuBY4HzqurRJPsC/7XPNfWdY+4NSvLnwOXAQcA/AXsCH6qq8/tamDYryX8Ztmh3Onc5/Q1AVX1q3IvSVknyR/z/x7r+dx/L6Tt77g2qqn/sTl4HPLeftahnU7vvLwAOA66kE+5vA67vV1HqXXc47W+A/YCHgZnAvXRObJiw7Lk3IMlbq+rLI/QCAXt/O4Ik/wK8qaoe785PBb5aVcf2tzKNJsntwNHAd6vq0CSvBBZX1ZJRPtpqE/6gQ0P26L5P3cxL27+ZwO+GzP8Oj5fsKNZX1RpgpyQ7VdW1wCH9LqrfHJZpQFX9fff9o/2uRVvtIuBfk1xB5ylib+A/ngus7dujSfakM4x2cZKHgQ19rqnvHJZpUJLpwDvp9Pg2/cPpRUw7hu5Vja/ozl5fVbf2sx71JskewDo6x0r+lM6T4C7u9uYnLMO9QUluBH4IrOA/Lqygqi7vW1FSyyWZW1V3D1u2sKp+0KeStguGe4OS3FZVE36sTxpPSe6kM6z213ROhfxrYKCqXtrXwvrMA6rN+maS4/tdhDTBHA4cANxI55nPDwIv72tF2wHDvVln0wn4J5P8OsnjSX7d76KkllsPPAnsRqfn/u9V9fv+ltR/hntDkgSYV1U7VdVuVfWsqppaVc/qd21Sy/2ETrgfBhwBLE6yrL8l9Z9j7g1KsqKqFvS7DmkiSTJQVcuHLXtbVV3Ur5q2B/bcm/XjJN7/WxpHVbU8yRFJ3g6QZB/ghj6X1Xf23BuU5G7g+cDP6dx4KkBV1UF9LUxqsSQfBgaAF1TV85PsR+fWERP6oKpXqDbruH4XIE1AbwAOBW4BqKoHNz52byJzWKZBVfVzYC/gdd3XXt1lksbO76ozBFGw6YrVCc9wb1CSs4GLgT/qvr6c5D39rUpqvX/uPolpryTvBL4L/EOfa+o7x9wblGQl8NKq+k13fg/gJsfcpbGT5JN0Av1P6Bzn+jbw6qo6p6+F9Znh3qAkdwCHVdW67vwU4CdV5XNUpTGS5Jaqmj9s2cqJ3qnygGqz/gm4uXvbWIDXAxf0sR6ptZKcAbwLeG73r+aNpgI/6k9V2w977g1IMruq/r07PZ/OVXLB28ZKYybJNOAPgE8AHxiy6vGqeqQ/VW0/DPcGbLwyNcn3qupV/a5HkhyWacZO3Qspnj/Sc1R9hqqk8eapkM04hc6TYHbGZ6hK2g44LNOgJMdV1bf6XYckOSzTgCRvraovA3OTvGj4eodlJI03w70ZGy933rOvVUhSl8MyktRC9twbkOQzz7S+qs4ar1okCQz3pqzodwGSNJTDMpLUQvbcG5RkOnAOMJfOU9gBqKqj+1aUpAnJi5iadTFwDzAb+CjwAJ0ns0vSuHJYpkFD7jGz6XajSa6rqqP6XZukicVhmWat776vSnIC8CAwo4/1SJqgDPdm/VX3NqTvA/4OeBbw3v6WJGkicsy9WSfTGeq6s6peCRxD58nskjSuDPdmHVRVj26c6T4w4NA+1iNpgjLcm7VTkj/YOJPkD3HoS1IfGDzN+hvgxiTLgALeDPz3/pYkaSLyVMiGJZkLHE3nGarfq6q7+1ySpAnIcJekFnLMXZJayHCXpBYy3DVhJXlvkt2baidtTxxz14SV5AFgoKp+2UQ7aXtiz10TQpI9klyd5PYkdyb5MLAfcG2Sa7ttPp9keZK7kny0u+ysEdr9SZKbktyS5KtJfHautjv23DUhJHkTcGxVvbM7Pw24nSE98iR/WFWPJJkEfA84q6pWDu25J9kH+BpwXFX9Jsk5wK5V9bF+fC9pc+y5a6K4A3h1kk8meUVVPTZCmzcnuQW4FZhH56Erw72ku/xHSW4DTgWeM1ZFS1vLK1Q1IVTVvyVZABwPfCLJvwxdn2Q28H7gsKr6VZIvMORpWkObAt+pqsVjXbO0Ley5a0JIsh/wRFV9GTgPmA88DkztNnkW8BvgsST/CThuyMeHtvsx8PIkz+tud/ckzx+HryBtEXvumigOBP5nkt/TeajKGcBLgW8lWVVVr0xyK3AX8DPgR0M+u3RYu9OAS5Ls2l3/l8C/jdcXkXrhAVVJaiGHZSSphQx3SWohw12SWshwl6QWMtwlqYUMd0lqIcNdklro/wHBZFrEes2+0AAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.groupby(['state','gender']).size().unstack().plot(kind='bar',stacked=True)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "df.groupby(['gender','state']).size().unstack().plot(kind='bar',stacked=True)\n",
    "plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(7,4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## plot histogram for a dataframe column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f31b016f5c0>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAYUAAAD8CAYAAACYebj1AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADl0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uIDMuMC4yLCBodHRwOi8vbWF0cGxvdGxpYi5vcmcvOIA7rQAAFdFJREFUeJzt3XuQXnWd5/H3xxAJ0SwI6VkxFzuOyIgU1wZxdDWLDiIwgKvsYM2Ml1Kza0GJrlMzQG3hraZqrHJFWabMRGFFxlEU1I0K65JRvBWgASMKQU05rvTASgyYgNxs+e4fz5OzbdOdfjr06Sd5+v2qeirn8jvn+Z46nf70uf1OqgpJkgCe0u8CJEl7DkNBktQwFCRJDUNBktQwFCRJDUNBktQwFCRJDUNBktQwFCRJjX36XcBMLV26tIaHh/tdhiTtVW655ZZfVdXQdO32ulAYHh5m48aN/S5DkvYqSf5PL+08fSRJahgKkqSGoSBJaux11xQkaTb89re/ZXR0lEceeaTfpcyqRYsWsXz5chYuXLhbyxsKkual0dFRlixZwvDwMEn6Xc6sqCq2bdvG6Ogoq1at2q11tH76KMmCJN9P8uVJ5u2b5KokW5LcnGS47XokCeCRRx7hoIMOGphAAEjCQQcd9KSOfubimsJ5wOYp5r0ZuL+qngtcDHxgDuqRJICBCoSdnuw2tRoKSZYDpwIfn6LJGcAV3eGrgZdnEPeSJO0l2r6m8GHgr4ElU8xfBtwFUFVjSbYDBwG/arkuSfo9w+d/ZVbX9/O/O3VW1zdXWguFJKcB91bVLUlWT9Vskmk1ybrWAGsAVq5cOWs1ziez/QO/J9lb//NJe6I2Tx+9GDg9yc+BzwAnJvnHCW1GgRUASfYB9gfum7iiqlpXVSNVNTI0NG3XHZK01zjzzDM59thjecELXsC6desAuOyyy3je857H6tWreetb38q5554LwNatW3nNa17Dcccdx3HHHcd3vvOdWa+ntSOFqroAuACge6TwV1X1FxOarQfeANwIvBb4WlU94UhBkgbV5ZdfzoEHHsjDDz/Mcccdx6mnnsr73/9+br31VpYsWcKJJ57IkUceCcB5553HO9/5Tl7ykpfwi1/8gle+8pVs3jzVfTy7Z86fU0jyPmBjVa0HLgOuTLKFzhHC2XNdjyT10yWXXMIXvvAFAO666y6uvPJKXvayl3HggQcCcNZZZ/GTn/wEgA0bNnDHHXc0y+7YsYMHHniAJUumumw7c3MSClV1A3BDd/iicdMfAc6aixokaU9zww03sGHDBm688UYWL17M6tWrOfTQQ6f86//xxx/nxhtvZL/99mutJvs+kqQ+2b59O894xjNYvHgxd955JzfddBMPPfQQ3/jGN7j//vsZGxvjmmuuadqfdNJJXHrppc34pk2bZr0mu7mQJPpzF9vJJ5/M2rVrOeKIIzj00EM54YQTWLZsGRdeeCEvfOELedaznsVhhx3G/vvvD3RONZ1zzjkcccQRjI2N8dKXvpS1a9fOak2GgiT1yb777st11133hOkjIyOsWbOGsbExXv3qV3PSSScBsHTpUq666qpWa/L0kSTtYd7znvdw1FFHcfjhh7Nq1SrOPPPMOftujxQkaQ/zwQ9+sG/f7ZGCpHlrEB+LerLbZChImpcWLVrEtm3bBioYdr5PYdGiRbu9Dk8fSZqXli9fzujoKFu3bu13KbNq55vXdpehIGleWrhw4W6/nWyQefpIktQwFCRJDUNBktQwFCRJDUNBktQwFCRJDUNBktQwFCRJjdZCIcmiJN9N8oMktyd57yRt3phka5JN3c9b2qpHkjS9Np9ofhQ4saoeTLIQ+HaS66rqpgntrqqqc1usQ5LUo9ZCoTq9TD3YHV3Y/QxOz1OSNIBavaaQZEGSTcC9wPVVdfMkzV6T5LYkVydZ0WY9kqRdazUUqup3VXUUsBw4PsnhE5p8CRiuqiOADcAVk60nyZokG5NsHLQeDSVpTzIndx9V1a+BG4CTJ0zfVlWPdkc/Bhw7xfLrqmqkqkaGhoZarVWS5rM27z4aSnJAd3g/4BXAnRPaHDxu9HRgc1v1SJKm1+bdRwcDVyRZQCd8PltVX07yPmBjVa0H3p7kdGAMuA94Y4v1SJKm0ebdR7cBR08y/aJxwxcAF7RVgyRpZnyiWZLUMBQkSQ1DQZLUMBQkSQ1DQZLUMBQkSQ1DQZLUMBQkSQ1DQZLUMBQkSQ1DQZLUMBQkSQ1DQZLUMBQkSQ1DQZLUMBQkSQ1DQZLUaPMdzYuSfDfJD5LcnuS9k7TZN8lVSbYkuTnJcFv1SJKm1+aRwqPAiVV1JHAUcHKSEya0eTNwf1U9F7gY+ECL9UiSptFaKFTHg93Rhd1PTWh2BnBFd/hq4OVJ0lZNkqRda/WaQpIFSTYB9wLXV9XNE5osA+4CqKoxYDtw0CTrWZNkY5KNW7dubbNkSZrXWg2FqvpdVR0FLAeOT3L4hCaTHRVMPJqgqtZV1UhVjQwNDbVRqiSJObr7qKp+DdwAnDxh1iiwAiDJPsD+wH1zUZMk6YnavPtoKMkB3eH9gFcAd05oth54Q3f4tcDXquoJRwqSpLmxT4vrPhi4IskCOuHz2ar6cpL3ARuraj1wGXBlki10jhDObrEeSdI0WguFqroNOHqS6ReNG34EOKutGiRJM+MTzZKkhqEgSWoYCpKkhqEgSWoYCpKkhqEgSWoYCpKkhqEgSWoYCpKkhqEgSWoYCpKkhqEgSWoYCpKkhqEgSWoYCpKkhqEgSWoYCpKkRk+hkOTwma44yYokX0+yOcntSc6bpM3qJNuTbOp+LppsXZKkudHr6zjXJnkq8Angn6rq1z0sMwa8q6puTbIEuCXJ9VV1x4R236qq03ovWZLUlp6OFKrqJcCfAyuAjUn+KcmfTLPMPVV1a3f4AWAzsOxJ1itJalHP1xSq6qfAfwX+BngZcEmSO5P8h+mWTTIMHA3cPMnsFyX5QZLrkrxgiuXXJNmYZOPWrVt7LVmSNEO9XlM4IsnFdP7aPxH406p6fnf44mmWfTpwDfCOqtoxYfatwLOr6kjgvwNfnGwdVbWuqkaqamRoaKiXkiVJu6HXI4VL6fwCP7Kqzhl3WuhuOkcPk0qykE4gfKqqPj9xflXtqKoHu8PXAguTLJ3hNkiSZkmvF5pPAR6uqt8BJHkKsKiqHqqqKydbIEmAy4DNVfWhKdo8E/hlVVWS4+mE1LaZboQkaXb0GgobgFcAD3bHFwP/G/jjXSzzYuAvgR8m2dSddiGwEqCq1gKvBd6WZAx4GDi7qmpGWyBJmjW9hsKinad5AKrqwSSLd7VAVX0byDRtLqVzakqStAfo9ZrCb5Ics3MkybF0/rKXJA2QXo8U3gF8Lsnd3fGDgT9rpyRJUr/0FApV9b0kfwQcSueU0J1V9dtWK5MkzblejxQAjgOGu8scnYSq+mQrVUmS+qKnUEhyJfCHwCbgd93JBRgKkjRAej1SGAEO83ZRSRpsvd599CPgmW0WIknqv16PFJYCdyT5LvDozolVdXorVUmS+qLXUHhPm0VIkvYMvd6S+o0kzwYOqaoN3aeZF7RbmiRprvXadfZbgauBf+hOWsYU3VxLkvZevV5oPodOB3c7oHnhzh+0VZQkqT96DYVHq+qxnSNJ9qHznIIkaYD0GgrfSHIhsF/33cyfA77UXlmSpH7oNRTOB7YCPwT+E3Atu3jjmiRp79Tr3UePAx/rfiRJA6rXvo/+hUmuIVTVc2a9IklS38yk76OdFgFnAQfuaoEkK+h0mPdM4HFgXVV9ZEKbAB+h8w7oh4A3VtWtPdYkSZplPV1TqKpt4z7/WlUfBk6cZrEx4F1V9XzgBOCcJIdNaPMq4JDuZw3w0ZmVL0maTb2ePjpm3OhT6Bw5LNnVMlV1D3BPd/iBJJvpPPR2x7hmZwCf7Pa+elOSA5Ic3F1WkjTHej199N/GDY8BPwf+Y69fkmQYOBq4ecKsZcBd48ZHu9N+LxSSrKFzJMHKlSt7/VpJ0gz1evfRv9/dL0jydOAa4B1VtWPi7Mm+bpLvXwesAxgZGfGhOUlqSa+nj/7LruZX1YemWG4hnUD4VFV9fpImo8CKcePLgbt7qUmSNPt6fXhtBHgbnVM7y4D/DBxG57rCpNcWuncWXQZsnio0gPXA69NxArDd6wmS1D8zecnOMVX1AECS9wCfq6q37GKZFwN/CfwwyabutAuBlQBVtZbOk9GnAFvo3JL6pplugCRp9vQaCiuBx8aNPwYM72qBqvo2k18zGN+m6PTAKknaA/QaClcC303yBToXgl9N58E0SdIA6fXuo79Nch3w77qT3lRV32+vLElSP/R6oRlgMbCj21XFaJJVLdUkSeqTXl/H+W7gb4ALupMWAv/YVlGSpP7o9Ujh1cDpwG8AqupupunmQpK09+k1FB7r3ilUAEme1l5JkqR+6TUUPpvkH4ADkrwV2IAv3JGkgdPr3Ucf7L6beQdwKHBRVV3famWSpDk3bSgkWQB8tapeARgEkjTApj19VFW/Ax5Ksv8c1CNJ6qNen2h+hE4fRtfTvQMJoKre3kpVkqS+6DUUvtL9SJIG2C5DIcnKqvpFVV0xVwVJkvpnumsKX9w5kOSalmuRJPXZdKEwvuvr57RZiCSp/6YLhZpiWJI0gKYLhSOT7EjyAHBEd3hHkgeS7NjVgkkuT3Jvkh9NMX91ku1JNnU/F+3uRkiSZscuLzRX1YInse5PAJey65fxfKuqTnsS3yFJmkUzeZ/CjFTVN4H72lq/JGn2tRYKPXpRkh8kuS7JC/pciyTNe70+vNaGW4FnV9WDSU6hc/vrIZM1TLIGWAOwcuXKuatQkuaZvh0pVNWOqnqwO3wtsDDJ0inarquqkaoaGRoamtM6JWk+6VsoJHlmknSHj+/Wsq1f9UiSWjx9lOTTwGpgaZJR4N103u1MVa0FXgu8LckY8DBwdvftbpKkPmktFKrqddPMv5TOLauSpD1Ev+8+kiTtQQwFSVLDUJAkNQwFSVLDUJAkNQwFSVLDUJAkNQwFSVLDUJAkNQwFSVLDUJAkNQwFSVLDUJAkNQwFSVLDUJAkNQwFSVLDUJAkNVoLhSSXJ7k3yY+mmJ8klyTZkuS2JMe0VYskqTdtHil8Ajh5F/NfBRzS/awBPtpiLZKkHrQWClX1TeC+XTQ5A/hkddwEHJDk4LbqkSRNr5/XFJYBd40bH+1OkyT1yT59/O5MMq0mbZisoXOKiZUrV+72Fw6f/5XdXnZv8PO/O7XfJexx5us+n6/brSevn0cKo8CKcePLgbsna1hV66pqpKpGhoaG5qQ4SZqP+hkK64HXd+9COgHYXlX39LEeSZr3Wjt9lOTTwGpgaZJR4N3AQoCqWgtcC5wCbAEeAt7UVi2SpN60FgpV9bpp5hdwTlvfL0maOZ9oliQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1DAVJUsNQkCQ1Wg2FJCcn+XGSLUnOn2T+G5NsTbKp+3lLm/VIknatzXc0LwD+HvgTYBT4XpL1VXXHhKZXVdW5bdUhSepdm0cKxwNbqupnVfUY8BngjBa/T5L0JLUZCsuAu8aNj3anTfSaJLcluTrJihbrkSRNo81QyCTTasL4l4DhqjoC2ABcMemKkjVJNibZuHXr1lkuU5K0U5uhMAqM/8t/OXD3+AZVta2qHu2Ofgw4drIVVdW6qhqpqpGhoaFWipUktRsK3wMOSbIqyVOBs4H14xskOXjc6OnA5hbrkSRNo7W7j6pqLMm5wFeBBcDlVXV7kvcBG6tqPfD2JKcDY8B9wBvbqkeSNL3WQgGgqq4Frp0w7aJxwxcAF7RZgySpdz7RLElqGAqSpIahIElqGAqSpIahIElqGAqSpIahIElqGAqSpIahIElqGAqSpIahIElqGAqSpIahIElqGAqSpIahIElqGAqSpIahIElqtBoKSU5O8uMkW5KcP8n8fZNc1Z1/c5LhNuuRJO1aa6GQZAHw98CrgMOA1yU5bEKzNwP3V9VzgYuBD7RVjyRpem0eKRwPbKmqn1XVY8BngDMmtDkDuKI7fDXw8iRpsSZJ0i60GQrLgLvGjY92p03apqrGgO3AQS3WJEnahX1aXPdkf/HXbrQhyRpgTXf0wSQ/nkEdS4FfzaD9Xiu/f/Jtvm43zN9td7vnl5lu97N7adRmKIwCK8aNLwfunqLNaJJ9gP2B+yauqKrWAet2p4gkG6tqZHeW3ZvN1+2G+bvtbvf80tZ2t3n66HvAIUlWJXkqcDawfkKb9cAbusOvBb5WVU84UpAkzY3WjhSqaizJucBXgQXA5VV1e5L3ARuraj1wGXBlki10jhDObqseSdL02jx9RFVdC1w7YdpF44YfAc5qswZ287TTAJiv2w3zd9vd7vmlle2OZ2skSTvZzYUkqTHQoTBdNxuDIsmKJF9PsjnJ7UnO604/MMn1SX7a/fcZ/a61DUkWJPl+ki93x1d1u035abcblaf2u8bZluSAJFcnubO73180H/Z3knd2f8Z/lOTTSRYN6v5OcnmSe5P8aNy0SfdxOi7p/q67Lckxu/u9AxsKPXazMSjGgHdV1fOBE4Bzutt6PvDPVXUI8M/d8UF0HrB53PgHgIu7230/ne5UBs1HgP9VVX8EHEln+wd6fydZBrwdGKmqw+ncwHI2g7u/PwGcPGHaVPv4VcAh3c8a4KO7+6UDGwr01s3GQKiqe6rq1u7wA3R+QSzj97sRuQI4sz8VtifJcuBU4OPd8QAn0uk2BQZwu5P8G+CldO7eo6oeq6pfMw/2N52bY/brPte0GLiHAd3fVfVNnvjc1lT7+Azgk9VxE3BAkoN353sHORR66WZj4HR7mj0auBn4t1V1D3SCA/iD/lXWmg8Dfw083h0/CPh1t9sUGMz9/hxgK/A/uqfNPp7kaQz4/q6qfwU+CPyCThhsB25h8Pf3eFPt41n7fTfIodBTFxqDJMnTgWuAd1TVjn7X07YkpwH3VtUt4ydP0nTQ9vs+wDHAR6vqaOA3DNiposl0z5+fAawCngU8jc5pk4kGbX/3YtZ+7gc5FHrpZmNgJFlIJxA+VVWf707+5c5DyO6/9/arvpa8GDg9yc/pnB48kc6RwwHd0wswmPt9FBitqpu741fTCYlB39+vAP6lqrZW1W+BzwN/zODv7/Gm2sez9vtukEOhl242BkL3PPplwOaq+tC4WeO7EXkD8D/nurY2VdUFVbW8qobp7N+vVdWfA1+n020KDOZ2/1/griSHdie9HLiDAd/fdE4bnZBkcfdnfud2D/T+nmCqfbweeH33LqQTgO07TzPN1EA/vJbkFDp/Oe7sZuNv+1xSK5K8BPgW8EP+/7n1C+lcV/gssJLOf6izquoJHQ4OgiSrgb+qqtOSPIfOkcOBwPeBv6iqR/tZ32xLchSdi+tPBX4GvInOH3kDvb+TvBf4Mzp33H0feAudc+cDt7+TfBpYTac31F8C7wa+yCT7uBuSl9K5W+kh4E1VtXG3vneQQ0GSNDODfPpIkjRDhoIkqWEoSJIahoIkqWEoSJIahoIkqWEoSJIahoIkqfH/AN1Hiorpd/J4AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df[['age']].plot(kind='hist',bins=[0,20,40,60,80,100],rwidth=0.9,)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## percent single"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.ticker as mtick\n",
    "\n",
    "# create dummy variable them group by that\n",
    "# set the legend to false because we'll fix it later\n",
    "df.assign(\n",
    " dummy = 1   \n",
    ").groupby(['dummy','state']).size().groupby(level=0).apply(\n",
    "    lambda x: 100 * x / x.sum()\n",
    ").to_frame().unstack().plot(kind='bar',stacked=True,legend=False)\n",
    "\n",
    "\n",
    "plt.title('Amount of records by State, normalized')\n",
    "\n",
    "\n",
    "# other it'll show up as 'dummy' \n",
    "plt.xlabel('state')\n",
    "\n",
    "# disable ticks in the x axis\n",
    "plt.xticks([])\n",
    "\n",
    "# fix the legend\n",
    "current_handles, _ = plt.gca().get_legend_handles_labels()\n",
    "reversed_handles = reversed(current_handles)\n",
    "\n",
    "labels = reversed(df['state'].unique())\n",
    "\n",
    "plt.legend(reversed_handles,labels,loc='lower right')\n",
    "\n",
    "plt.gcf().set_size_inches(7,4)\n",
    "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## percent, two-level group by"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 504x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.ticker as mtick\n",
    "\n",
    "df.groupby(['gender','state']).size().groupby(level=0).apply(\n",
    "    lambda x: 100 * x / x.sum()\n",
    ").unstack().plot(kind='bar',stacked=True,legend='reverse')\n",
    "\n",
    "plt.gca().yaxis.set_major_formatter(mtick.PercentFormatter())\n",
    "plt.title('Amount of records by Gender and State, normalized')\n",
    "# plt.legend(loc='lower right')\n",
    "plt.gcf().set_size_inches(7,4)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### date histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "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>name</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>john</td>\n",
       "      <td>01/21/1988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lisa</td>\n",
       "      <td>03/10/1977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>peter</td>\n",
       "      <td>07/25/1999</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>carl</td>\n",
       "      <td>01/22/1977</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>linda</td>\n",
       "      <td>09/30/1968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>betty</td>\n",
       "      <td>09/15/1970</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name date_of_birth\n",
       "0   john    01/21/1988\n",
       "1   lisa    03/10/1977\n",
       "2  peter    07/25/1999\n",
       "3   carl    01/22/1977\n",
       "4  linda    09/30/1968\n",
       "5  betty    09/15/1970"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame({\n",
    "    'name':['john','lisa','peter','carl','linda','betty'],\n",
    "    'date_of_birth':[\n",
    "        '01/21/1988','03/10/1977','07/25/1999','01/22/1977','09/30/1968','09/15/1970'\n",
    "    ]\n",
    "})\n",
    "\n",
    "df"
   ]
  },
  {
   "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>name</th>\n",
       "      <th>date_of_birth</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>john</td>\n",
       "      <td>1988-01-21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>lisa</td>\n",
       "      <td>1977-03-10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>peter</td>\n",
       "      <td>1999-07-25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>carl</td>\n",
       "      <td>1977-01-22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>linda</td>\n",
       "      <td>1968-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>betty</td>\n",
       "      <td>1970-09-15</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    name date_of_birth\n",
       "0   john    1988-01-21\n",
       "1   lisa    1977-03-10\n",
       "2  peter    1999-07-25\n",
       "3   carl    1977-01-22\n",
       "4  linda    1968-09-30\n",
       "5  betty    1970-09-15"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['date_of_birth'] = pd.to_datetime(df['date_of_birth'],infer_datetime_format=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                     object\n",
       "date_of_birth    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.clf()\n",
    "df['date_of_birth'].map(lambda d: d.month).plot(kind='hist')\n",
    "plt.xlabel('Month number')\n",
    "plotting.add_grid()\n",
    "plt.show()"
   ]
  },
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   "execution_count": null,
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   "source": []
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