19-20-Pandas_hist_plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Histograms\n",
    "### 도수분포표 vs 히스토그램\n",
    "* 도수분포표 : 특정 구간에 속하는 자료의 개수를 표현 ([도수 분포 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EB%8F%84%EC%88%98_%EB%B6%84%ED%8F%AC))\n",
    "* 히스토그램 : 도수분포표를 시각화하여 막대그래프로 표현, 하지만 bar plot과는 다름 ([히스토그램 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%ED%9E%88%EC%8A%A4%ED%86%A0%EA%B7%B8%EB%9E%A8))\n",
    "\n",
    "### 막대그래프 vs 히스토그램 (bar plot vs hist plot)\n",
    "* bar plot : 표현 값에 비례하여 높이와 길이를 지닌 직사각형 막대로 범주형 데이터를 표현하는 차트나 그래프, 합계, 평균 등의 수치를 시각화 ([막대 그래프 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EB%A7%89%EB%8C%80_%EA%B7%B8%EB%9E%98%ED%94%84))\n",
    "* hist plot : 구간별 빈도수를 표현\n",
    "\n",
    "### 히스토그램 vs 정규분포 (hist plot vs density plot)\n",
    "* hist plot : 구간별 빈도수\n",
    "* density plot : 확률 밀도 함수 - 확률 변수의 밀도를 표현 ([정규 분포 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EC%A0%95%EA%B7%9C_%EB%B6%84%ED%8F%AC))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# 한글폰트 사용시 그래프에서 마이너스 폰트 깨지는 문제에 대한 대처\n",
    "mpl.rcParams['axes.unicode_minus'] = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "      <th>c</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.205492</td>\n",
       "      <td>0.324118</td>\n",
       "      <td>1.570607</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.325603</td>\n",
       "      <td>0.488799</td>\n",
       "      <td>-1.347123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.173909</td>\n",
       "      <td>0.584763</td>\n",
       "      <td>-1.227976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.222517</td>\n",
       "      <td>-1.250168</td>\n",
       "      <td>-1.548146</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.404945</td>\n",
       "      <td>0.289920</td>\n",
       "      <td>-1.367176</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a         b         c\n",
       "0  1.205492  0.324118  1.570607\n",
       "1  0.325603  0.488799 -1.347123\n",
       "2  2.173909  0.584763 -1.227976\n",
       "3  1.222517 -1.250168 -1.548146\n",
       "4  0.404945  0.289920 -1.367176"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4 = pd.DataFrame({'a': np.random.randn(1000) + 1, \n",
    "                    'b': np.random.randn(1000),\n",
    "                    'c': np.random.randn(1000) - 1}, \n",
    "                   columns=['a', 'b', 'c'])\n",
    "df4.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112002cc0>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120026a0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# bin\n",
    "df4.plot.hist(alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11209bd30>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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HJOuraiu9QDgTuLRZdcyb7UmS5r4T9RyuArZW1TuT3FNVF85EUZKk4TpROOQYryV1ZMXh\nL0x538cXeYsyDcaJJqTrGK8lSfPYiXoOv9rcXjvAPxv3uqrKZzxL0jx1onB4xYxUIUmaVY4bDlW1\n53jrJUnzU78XwUmSFhDDQZLU0u8V0pOWZCXwZ8Deqnpzc1fXbwB/3Wzyo6p6Y7PtNcAF9Ca7r6yq\nbV3VJc1n0zoNdnBlaB7oLByAVwHXARePa7urqjaO3yjJhcC5VbU2yRnAPUlWVdWRDmuTJB1HZ8NK\nVXUz8P2jmtcnuS/JPUk2PN8G3N7s8wSwB1jZVV2SpBPrsudwtD3AWFVVkjHg7iS76T2feue47Q40\nbS1JNgGbAMbGxjouV5qa6QztSLPFjE1IV6N5vZfeHV7PBp4GlozbdAlw6BjH2FJVa6pqzejohPkh\nSRqAGQuHJC9PclrzeilwPvAAsBXY0LQvpzektHum6pIktc3ksNIZwGeT/AQYAa6qqu8k2Qdc1Nya\n4yRgc1UdnsG6JElH6TQcmlNSt417/doJtingii7rkCRNjhfBSZJaZnJYSdIstvqm1VPed9c7dg2w\nEs0GhoMkAHY9tnfYJWgWcVhJktRiz2GBmM6QgaSFx56DJKnFcJAktRgOkqQWw0GS1OKEtDQB76yq\nhc6egySpxZ6DOuWFVdLcZM9BktRiOEiSWhxWkgRMbxL+8cGVoVnCnoMkqcVwkCS1GA6SpJZOwyHJ\nyiQ7ktwyru2apm1nknVN20iSLUnuTbI9yaou65IkHV/XPYdXAdc9v5DkQuDcqloL/BZwQ5IXAG8D\njlTV+fSeJ72l47okScfRaThU1c3A98c1rQdub9Y9AewBVjbttzXtDwHLkizusjZJ0rHN9JzDKHBg\n3PKBpu1Y7T8nyaYkDyZ58Mknn+y0UElayGY6HJ4GloxbXgIcOk77z6mqLVW1pqrWjI62skOSNCAz\nHQ5bgQ0ASZbTG1LafVT7SnrzD0/NcG2SpMZMXyH9FeCiJDvoBdPmqjqc5EbgxiT3AgHePcN1SZLG\n6TwcqmobsK15XfTORjp6m2eAy7quRZLUHy+CkyS1GA6SpBbDQZLUYjhIkloMB0lSiw/70bw1nYfX\naJKuXnLibY65r5c0zUb2HCRJLYaDJKnFcJAktRgOkqQWw0GS1OLZSnPI6ptWD7sEaULTOTPs8cGV\noQGy5yBJajEcJEkthoMkqcU5B53Qrsf2DrsESTPMnoMkqWUoPYckPwAeGtf0RmAj8FZ6jwn9w6q6\ndQilSZIY3rDSQ1W17vmFJL8MvAt4NXAqcH+Sr1bVoSHVJ0kL2rDC4ewk25vXN9Eb3rqjqp4Fnm3W\nrQW+MqT6JGlBG1Y4/KOq+mmSZfQC4M+BA+PWHwBGj94pySZgE8DY2NhM1ClJC9JQJqSr6qfNz4PA\nl+iF1Pgbwi8BWkNKVbWlqtZU1ZrR0VZ2SJIGZMZ7Dkl+CXiqqn6Q5DTgXwJXAdcmuRY4BVgHfHSm\na5M081Z8YHqjx49f+5sDqkTjDWNY6ReAzyU5GRgB/rSq7k1yJ7ADKOAPqur7Q6hNksQQwqGqdgEX\nTND+ceDjM12PJKnNi+AkSS2GgySpxXsrzTCfyTA503lOgKSps+cgSWoxHCRJLYaDJKnFcJAktRgO\nkqQWw0GS1OKprJM0V09FHdajPj0VVZqb7DlIkloMB0lSi+EgSWoxHCRJLU5IzyHDmlSWtPAYDpLm\ntOk8Sc6nyB2bw0qSpBZ7DpIWLHsdxzarwiHJ7wJvBQL8YVXdOuSSBm4uzht4IZu08MyacEjyy8C7\ngFcDpwL3J/lqVR0a9HvN1aucJc0e873XMWvCAbgQuKOqngWeTbIdWAtM/b9AB4b5L3//BS/ND9MJ\nFpiZcJlN4TAKHBi3fKBp+5kkm4BNzeKPkuyewvssP+p9JiVT3XEg3tjVgaf1ncxjfi8T83uZ2Ix9\nL/nP09r9l/rZaDaFw9PAknHLS4CfG1Kqqi3Alum8SZIHq2rNdI4x3/idTMzvZWJ+LxObb9/LbDqV\ndSvwhiQnJzkNWAf85XBLkqSFadb0HKrq20nuBHYABfxBVX1/yGVJ0oI0a8IBoKo+Dny847eZ1rDU\nPOV3MjG/l4n5vUxsXn0vqaph1yBJmmVm05yDJGmWWLDhkGRRkl1Jrh52LcOWZDTJ55P8ZZIHmyvV\nF7Qkv5tkZ5JvJLl02PXMBkkWJ7k+yf1JHkjysWHXNFuk5+4knxt2LYOyYIeVkvw+MAI8VVVXD7mc\noUpyNr2/C99uzhR7DPjFWqB/OZqr9W9n3NX6wNourtafS5K8FDirqu5LchLwCPDrnjgCSX4H+BVg\naVVtHHI5A7Egew5JXgX8Q+B/DLuW2aCq/ndVfbtZXAbsW6jB0PjZ1fpV9XfA81frL2hV9d2quq9Z\nXAw8C/xgiCXNCklWAG8ArhtuJYM1q85WGrQkvwL86VHNrwc+BrwJOGfGixqyY3wn66vqJ0kWAzcD\nl898ZbPKCa/WX8iSnEzv78n7qurwsOsZpiShFwpX0DsFf96Y1+FQVX9L72K6n0lyLb1rKA71/rsu\nLBN9JwBJXgzcBvynqnpopuuaZU54tf5ClWSEXjDcUlV3DbueWeA9wF9U1aNND2LeWHBzDkm+Avxd\ns/hSYCnwJ1X1qeFVNVxJlgBfBj5SVduGXM7QJVkFfBr4NeAU4AHgNxb62HqSU4AvAv+9qr447Hpm\ngySfpTfEVsDpwErgz6rqw0MtbAAWXDiMl2QjsMIJ6fw+vedo/J9xzW+tqu8OqaShS3IlcDG9/+m3\nVNVnh1zS0CV5L/ARYNe45t+rqm8OqaRZJck6YON8mZBe0OEgSZrYgjxbSZJ0fIaDJKnFcJAktRgO\nkqQWw0GS1GI4SJJaDAdJUovhIElqMRwkSS3/HzpARDXQGzTlAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10659e048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df4.plot.hist(stacked=True, bins=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112157b70>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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a8EfALXTXFqefmW/tH/9f9CGd5FT+f3GAPcDFVbWtf51zgGv7++5e1G/6PtcmeUz/OmuS\nnEK3GvsL6Fai2THaSKUjcGauJlRVJXkp8A9Jim6m/UbgncDO/qp1t9PtmqGqPp/k3/qVgb5Mt8wX\nwF8Db+0ffxD4T7olAA/X5wuAd/d93glsr6pb+u2tVfXKlRqztJhXTdRM6XfNHFpAZCX7eRnwA1X1\nhpXsRzrEmbk0sn4ZvefQLSMnTYX7zKURJXki3UpaH62qvatdj2aHu1kkqQHOzCWpAYa5JDXAMJek\nBhjmktQAw1ySGmCYS1ID/g+1I3cNgUD+nwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1120f7ac8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df4['a'].plot.hist(orientation='horizontal', cumulative=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11239bb00>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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BwSXcW96mC/mq+i8GN1j9QpLbGFyPf2LwNW7bSnMAkORXgG8Af15VT210XVO06l3WW0WS\nbQwC/mBV3TfteqbgT4B/q6r/7s7ot7wm1uSTHAJ+1m2+BbgY+Ouq+pvpVbWxkswA/wJ8tqr6Uy5n\nQyW5Avg74LeBC4HHgd/damvRSS4Evgb8U1V9bdr1TEOSLzNYqirgIgafjfUPVfUXUy1sipoI+WFJ\n9gNzW/AXr58D9gE/GGreV1XPTqmkDZXkU8AHGLy4F6vqy1MuacMl+TjwWeDIUPNCVT05pZKmKkkP\n2L/Vf/HaXMhLkl7TzNU1kqQ3MuQlqWGGvCQ1zJCXpIYZ8pLUMENekhpmyEtSwwx5SWqYIS9JDft/\n0+gVxd1hUNUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11233f390>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df4['a'].diff().hist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>a</th>\n",
       "      <th>a_shift</th>\n",
       "      <th>a_minus</th>\n",
       "      <th>a_diff</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.205492</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.325603</td>\n",
       "      <td>1.205492</td>\n",
       "      <td>-0.879889</td>\n",
       "      <td>-0.879889</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.173909</td>\n",
       "      <td>0.325603</td>\n",
       "      <td>1.848306</td>\n",
       "      <td>1.848306</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.222517</td>\n",
       "      <td>2.173909</td>\n",
       "      <td>-0.951392</td>\n",
       "      <td>-0.951392</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.404945</td>\n",
       "      <td>1.222517</td>\n",
       "      <td>-0.817572</td>\n",
       "      <td>-0.817572</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a   a_shift   a_minus    a_diff\n",
       "0  1.205492       NaN       NaN       NaN\n",
       "1  0.325603  1.205492 -0.879889 -0.879889\n",
       "2  2.173909  0.325603  1.848306  1.848306\n",
       "3  1.222517  2.173909 -0.951392 -0.951392\n",
       "4  0.404945  1.222517 -0.817572 -0.817572"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4['a_diff'] = df4['a'].diff()\n",
    "df4['a_shift'] = df4['a'].shift(1)\n",
    "df4['a_minus'] = df4['a'] - df4['a_shift']\n",
    "df4[['a', 'a_shift', 'a_minus', 'a_diff']].head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x112466080>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x11258cdd8>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x11266ce48>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1126a7d68>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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kPWRF6DUNrZJeuCqPaYWkq/N1O4Dz8xjPAD4v6SWy8/PaiPhJj2Nsma+uMTNLmNs1ZmYJ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\nXR3Rd2VGWd0AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112441ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df4[['a', 'b', 'c']].diff().hist(color='k', alpha=0.5, bins=30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>a</th>\n",
       "      <th>b</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.115810</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-2.514327</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-1.197815</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.691071</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-0.371239</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          a  b\n",
       "0  1.115810  2\n",
       "1 -2.514327  1\n",
       "2 -1.197815  1\n",
       "3  0.691071  0\n",
       "4 -0.371239  1"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.Series(np.random.randn(1000))\n",
    "\n",
    "data = pd.DataFrame({'a' : np.random.randn(1000),\n",
    "                    'b': np.random.randint(0, 4, 1000) })\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[<matplotlib.axes._subplots.AxesSubplot object at 0x11286d518>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x1127e69b0>],\n",
       "       [<matplotlib.axes._subplots.AxesSubplot object at 0x1129199b0>,\n",
       "        <matplotlib.axes._subplots.AxesSubplot object at 0x112956e10>]],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x11243d5f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "data['a'].hist(by=data['b'], figsize=(6, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}