{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import matplotlib\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pandas version: 1.3.2, matplotlib version: 3.4.3\n"
     ]
    }
   ],
   "source": [
    "print(f\"Pandas version: {pd.__version__}, matplotlib version: {matplotlib.__version__}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## example 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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": 3,
     "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": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='num_children', ylabel='num_pets'>"
      ]
     },
     "execution_count": 4,
     "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.plot(kind='scatter',x='num_children',y='num_pets',color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='name'>"
      ]
     },
     "execution_count": 5,
     "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.plot(kind='bar',x='name',y='age')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "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": [
    "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": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='state'>"
      ]
     },
     "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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\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": {
    "collapsed": true
   },
   "source": [
    "## plot histogram for dataframe column"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 10,
     "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[['age']].plot(kind='hist',bins=[0,20,40,60,80,100],rwidth=0.9)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## stacked bar plot"
   ]
  },
  {
   "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",
    "\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().to_frame().unstack().plot(kind='bar',stacked=True,legend=False)\n",
    "\n",
    "plt.title('Number of records by State')\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.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Stacked bar plot percentages"
   ]
  },
  {
   "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.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": 13,
   "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": {
    "collapsed": true
   },
   "source": [
    "### date histograms"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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": 20,
     "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": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "df['date_of_birth'] = pd.to_datetime(df['date_of_birth'], infer_datetime_format=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                     object\n",
       "date_of_birth    datetime64[ns]\n",
       "dtype: object"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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bMUnSHtLZVUxVtT3JWfR+sM8CVlTV3UnOB0araiXwb5KcCmwHtgK/02y7NclH6ZUMwPlVtbWrrJKkZ+usIACqahWwaszYuX3T5wDnjLPtCmBFl/kkSeMb9klqSdI0ZUFIklpZEJKkVhaEJKmVBSFJamVBSJJaWRCSpFYWhCSplQUhSWplQUiSWlkQkqRWFoQkqZUFIUlqZUFIklpZEJKkVhaEJKmVBSFJatVpQSRZkuTeJOuTnN2y/ENJ1iW5I8n/SnJI37Knk9zevFaO3VaS1K3OHjmaZBZwIXASsBFYk2RlVa3rW+07wEhVPZHk94H/Avx2s+zJqjqqq3ySpJ3rcg/iWGB9VW2oqm3A1cBp/StU1Teq6olm9mZgfod5JEkT0GVBzAMe7Jvf2IyN533AV/rm900ymuTmJO8cb6Mky5r1Rrds2bJbgSVJP9PZIaaJSPJuYAR4Y9/wIVW1KckvAF9PcmdV/f3YbatqObAcYGRkpPZIYEmaAbrcg9gELOibn9+MPUOSNwP/ETi1qp7aMV5Vm5o/NwDfBI7uMKskaYwuC2INcGiSxUn2AZYCz7gaKcnRwMX0ymFz3/iBSV7YTM8Bjgf6T25LkjrW2SGmqtqe5CzgBmAWsKKq7k5yPjBaVSuBPwFeDHwxCcADVXUqcBhwcZKf0CuxC8Zc/SRJ6lin5yCqahWwaszYuX3Tbx5nu28DR3SZTZK0c36TWpLUyoKQJLWyICRJrSwISVIrC0KS1MqCkCS1siAkSa0sCElSKwtCktTKgpAktbIgJEmtLAhJUisLQpLUyoKQJLWyICRJrSwISVIrC0KS1KrTgkiyJMm9SdYnObtl+QuTfL5ZfkuSRX3LzmnG703yli5zSpKerbOCSDILuBA4BTgcOD3J4WNWex/wSFX9EvAJ4GPNtocDS4FfBpYAf968nyRpD+lyD+JYYH1VbaiqbcDVwGlj1jkNuLyZvgZ4U5I041dX1VNV9T1gffN+kqQ9ZK8O33se8GDf/EbguPHWqartSR4FXtaM3zxm23ltH5JkGbCsmX08yb2TzDsH+IdJbjtp+dguVxlKrgFMOtcAf+fd8bz779Wx512umfjvKx/brVyHjLegy4LYI6pqObB8d98nyWhVjUxBpCllrokx18SYa2JmWq4uDzFtAhb0zc9vxlrXSbIX8BLg4QG3lSR1qMuCWAMcmmRxkn3onXReOWadlcAZzfRvAV+vqmrGlzZXOS0GDgVu7TCrJGmMzg4xNecUzgJuAGYBK6rq7iTnA6NVtRK4FPhskvXAVnolQrPeF4B1wHbgzKp6uqusjd0+TNURc02MuSbGXBMzo3Kl9wu7JEnP5DepJUmtLAhJUqsZXxBJViTZnOSuYWfZIcmCJN9Isi7J3Uk+MOxMAEn2TXJrkr9tcv2nYWfql2RWku8k+Z/DztIvyf1J7kxye5LRYefZIckBSa5J8t0k9yT51WmQ6RXNf6cdr8eSfHDYuQCS/GHz7/6uJFcl2XfYmQCSfKDJdPdU/7ea8ecgkrwBeBy4oqpeNew8AEkOBg6uqtuS7A+sBd5ZVeuGnCvAflX1eJK9gb8BPlBVN+9i0z0iyYeAEeDnqurtw86zQ5L7gZGqmlZfsEpyOfDXVXVJc6Xh7Kr6xyHH+qnm9jqbgOOq6vtDzjKP3r/3w6vqyeYimlVVddmQc72K3l0qjgW2AV8F/lVVrZ+K95/xexBVdRO9K6imjap6qKpua6Z/CNzDON8k35Oq5/Fmdu/mNS1+w0gyH3gbcMmwszwXJHkJ8AZ6VxJSVdumUzk03gT8/bDLoc9ewIua72zNBn4w5DwAhwG3VNUTVbUd+BbwT6fqzWd8QUx3zR1ujwZuGXIU4KeHcW4HNgOrq2pa5AI+Cfx74CdDztGmgBuTrG1uDTMdLAa2AP+9OSx3SZL9hh1qjKXAVcMOAVBVm4D/CjwAPAQ8WlU3DjcVAHcBr0/ysiSzgbfyzC8Z7xYLYhpL8mLgWuCDVfXYsPMAVNXTVXUUvW+3H9vs4g5VkrcDm6tq7bCzjON1VXUMvTsbn9kc1hy2vYBjgM9U1dHAj4Bn3ZJ/WJpDXqcCXxx2FoAkB9K7iehi4J8A+yV593BTQVXdQ+8u2DfSO7x0OzBl3xmzIKap5hj/tcDnquq6YecZqzkc8Q16t2MftuOBU5tj/VcDv57kL4cb6Wea3z6pqs3A9UyPOxNvBDb27QFeQ68wpotTgNuq6v8NO0jjzcD3qmpLVf0YuA74tSFnAqCqLq2q11TVG4BHgL+bqve2IKah5mTwpcA9VfXxYefZIcncJAc00y8CTgK+O9RQQFWdU1Xzq2oRvcMSX6+qof92B5Bkv+ZCA5pDOCfTOywwVFX1f4EHk7yiGXoTvTsXTBenM00OLzUeAH4lyezm/8830Ts3OHRJDmr+XEjv/MOVU/Xez/m7ue6uJFcBJwBzkmwEzquqS4ebiuOB9wB3Nsf7AT5cVauGFwmAg4HLm6tLXgB8oaqm1SWl09DLget7P1PYC7iyqr463Eg/9QfA55rDORuA9w45D/DTIj0J+L1hZ9mhqm5Jcg1wG73b/3yH6XPbjWuTvAz4Mb3bEv3jVL3xjL/MVZLUzkNMkqRWFoQkqZUFIUlqZUFIklpZEJKkVhaEJKmVBSFJavX/AfeezmOXjL6fAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.clf()\n",
    "df['date_of_birth'].map(lambda d: d.month).plot(kind='hist')\n",
    "# plt.xlabel('Month number')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "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",
       "    <tr>\n",
       "      <th>date_of_birth</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1968-09-30</th>\n",
       "      <td>linda</td>\n",
       "      <td>1968-09-30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-10-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-11-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1968-12-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1969-01-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-02-28</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-03-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-04-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-05-31</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1999-06-30</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>370 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                name date_of_birth\n",
       "date_of_birth                     \n",
       "1968-09-30     linda    1968-09-30\n",
       "1968-10-31       NaN           NaT\n",
       "1968-11-30       NaN           NaT\n",
       "1968-12-31       NaN           NaT\n",
       "1969-01-31       NaN           NaT\n",
       "...              ...           ...\n",
       "1999-02-28       NaN           NaT\n",
       "1999-03-31       NaN           NaT\n",
       "1999-04-30       NaN           NaT\n",
       "1999-05-31       NaN           NaT\n",
       "1999-06-30       NaN           NaT\n",
       "\n",
       "[370 rows x 2 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.set_index(df['date_of_birth']).asfreq('M')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
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  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.10"
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   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
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   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  }
 },
 "nbformat": 4,
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}