21-Pandas_box_plot.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## box plot \n",
    "\n",
    "* https://pandas.pydata.org/pandas-docs/stable/user_guide/visualization.html\n",
    "* box-and-whisker plot, box-and-whisker diagram\n",
    "\n",
    "* [상자 수염 그림 - 위키백과, 우리 모두의 백과사전](https://ko.wikipedia.org/wiki/%EC%83%81%EC%9E%90_%EC%88%98%EC%97%BC_%EA%B7%B8%EB%A6%BC)\n",
    "* 가공하지 않은 자료 그대로를 이용하여 그린 것이 아니라, 자료로부터 얻어낸 통계량인 5가지 요약 수치로 그린다.\n",
    "* 5가지 요약 수치란 기술통계학에서 자료의 정보를 알려주는 아래의 다섯 가지 수치를 의미한다.\n",
    "\n",
    "\n",
    "1. 최솟값\n",
    "1. 제 1사분위수\n",
    "1. 제 2사분위수( ), 즉 중앙값\n",
    "1. 제 3 사분위 수( )\n",
    "1. 최댓값\n"
   ]
  },
  {
   "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": [],
   "source": [
    "df = pd.DataFrame(np.random.rand(10, 5), columns=['A', 'B', 'C', 'D', 'E'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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",
       "      <th>D</th>\n",
       "      <th>E</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>10.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.285508</td>\n",
       "      <td>0.669915</td>\n",
       "      <td>0.479978</td>\n",
       "      <td>0.525089</td>\n",
       "      <td>0.322505</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.267777</td>\n",
       "      <td>0.270813</td>\n",
       "      <td>0.307008</td>\n",
       "      <td>0.323119</td>\n",
       "      <td>0.280142</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.003511</td>\n",
       "      <td>0.225511</td>\n",
       "      <td>0.030795</td>\n",
       "      <td>0.035597</td>\n",
       "      <td>0.085881</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.054128</td>\n",
       "      <td>0.545291</td>\n",
       "      <td>0.262102</td>\n",
       "      <td>0.249346</td>\n",
       "      <td>0.132547</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.220433</td>\n",
       "      <td>0.742196</td>\n",
       "      <td>0.371047</td>\n",
       "      <td>0.554549</td>\n",
       "      <td>0.232500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.450866</td>\n",
       "      <td>0.856192</td>\n",
       "      <td>0.777085</td>\n",
       "      <td>0.810332</td>\n",
       "      <td>0.319350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.753958</td>\n",
       "      <td>0.996697</td>\n",
       "      <td>0.934036</td>\n",
       "      <td>0.938871</td>\n",
       "      <td>0.879754</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               A          B          C          D          E\n",
       "count  10.000000  10.000000  10.000000  10.000000  10.000000\n",
       "mean    0.285508   0.669915   0.479978   0.525089   0.322505\n",
       "std     0.267777   0.270813   0.307008   0.323119   0.280142\n",
       "min     0.003511   0.225511   0.030795   0.035597   0.085881\n",
       "25%     0.054128   0.545291   0.262102   0.249346   0.132547\n",
       "50%     0.220433   0.742196   0.371047   0.554549   0.232500\n",
       "75%     0.450866   0.856192   0.777085   0.810332   0.319350\n",
       "max     0.753958   0.996697   0.934036   0.938871   0.879754"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11212da58>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7z8WdwLMR8T+Z+Vy9ZdXi9XOxXPTiUMy7aE7l93ngkZprWTKiOZfgTwETdddSg2Hg6alQ\nB8jMs5n5nRprWjIy8+vAKPBbddeianox2D8AfDEzXwXWRMSP1V1Qze6JiOeBr9Cc/epkveXU4q3A\nv9RdxBL3deAn6i6iJvdExPNtX++su6BOeuqjZms6v3cCn2xNdv0W4D3AX9ZZV83aP3I/QPOTzLtq\nrWjx/Svw03UXscTdDfxb3UXUxKGYJe43gT/OzIcz82FgC/DemmtaSr5F8+Jhr/kssCMi3ja1IiK2\nRMQdNda0ZETEz9AcitlTdy2qpqd67MD7gQ9OLWTmdyLiuxFxd2b26kfxqaGYpPmH/rfrLWfxZeZ/\nRMQjwKci4keA12gOzfxuvZXV6p6IOEHz5+I/gQ9k5g9MUt8jpn5Hpiz5u2J8QEmSCtNrQzGSVDyD\nXZIKY7BLUmEMdkkqjMEuSYUx2CWpMAa7JBXGYJekwhjsklSY/wcinrl8m9TjVwAAAABJRU5ErkJg\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11212d470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.box()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "color = {'boxes': 'DarkGreen', 'whiskers': 'DarkOrange',\n",
    "        'medians': 'DarkBlue', 'caps': 'Gray'}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112140780>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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30xjsbwU+U1XfAjYm2dJ3QT27KcnjwJcYzH51rN9yevFK4Jm+i1jjvgr8ZN9F9OSmJI8v\n+ff6vgsaZao+ag6n83s98MHBBSovB94E/EWfdfVs6UfuWxh8knlDrxVdef8G/HTfRaxx24F/77uI\nntgVs8b9DnCwqt5YVW8EdgFv7rmmteQbDL48nDYfA343yasXVyTZleRVPda0ZiT5GQZdMfv7rkXd\nTNUVO3AH8LbFhar6TpLvJdleVdP6UXyxK6YYvNG/o99yrryq+o8ktwMfTvJjwAsMumbu7beyXt2U\n5PMMfi/+E3hrVf3QJPVTYvFvZNGavyvGAUqS1Jhp64qRpOYZ7JLUGINdkhpjsEtSYwx2SWqMwS5J\njTHYJakxBrskNcZgl6TG/D+BC8jEHiAwxwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11215d710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.box(color=color, sym='r+')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x1122f7898>"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAW4AAAD8CAYAAABXe05zAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11222cc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot.box(vert=False, positions=[1, 4, 5, 6, 8])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\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>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.173765</td>\n",
       "      <td>0.189882</td>\n",
       "      <td>0.661878</td>\n",
       "      <td>0.429158</td>\n",
       "      <td>0.603812</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          0         1         2         3         4\n",
       "0  0.173765  0.189882  0.661878  0.429158  0.603812"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10, 5))\n",
    "df.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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t20er1aJWq9FoNIq/cFravDmesUu6wvT0NKdPn+b48eOcPn26+FCH8ubNMdglqTDFdsWU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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112507ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp = df.boxplot()"
   ]
  },
  {
   "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>Col1</th>\n",
       "      <th>Col2</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.140733</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.280750</td>\n",
       "      <td>0.718366</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2\n",
       "0  0.293464  0.140733\n",
       "1  0.280750  0.718366"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame(np.random.rand(10, 2), columns=['Col1', 'Col2'])\n",
    "df.head(2)"
   ]
  },
  {
   "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>Col1</th>\n",
       "      <th>Col2</th>\n",
       "      <th>X</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.293464</td>\n",
       "      <td>0.140733</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.280750</td>\n",
       "      <td>0.718366</td>\n",
       "      <td>A</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Col1      Col2  X\n",
       "0  0.293464  0.140733  A\n",
       "1  0.280750  0.718366  A"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['X'] = pd.Series(['A', 'A', 'A', 'A', 'A', 'B', 'B', 'B', 'B', 'B'])\n",
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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87YDdOUtbb9WjU9r7addsnXL8dO/bdPOXplu3W6NtvpHuoLuA8yJiSUQsBQaA3RFxcjV9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\nAPEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11254a6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp = df.boxplot(by='X')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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0aQYJ/qcDD6wafqAYd1xEXA5cCUxNTU2xtLQ0wOqGq8q1VcVa22hlZWXdbZfbNq1qe/dz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\nkpQZg1+SMmPwS1Jm/j+LX4zyEKBZUAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1128b2550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "np.random.seed(1234)\n",
    "df_box = pd.DataFrame(np.random.randn(50, 2))\n",
    "df_box['g'] = np.random.choice(['A', 'B'], size=50)\n",
    "df_box.loc[df_box['g'] == 'B', 1] += 3\n",
    "bp = df_box.boxplot(by='g')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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ouo5JRN9NJrZbNtPSb5nO43f3E1vvzexhSXOTGvoAEM3QF3C5+7MjqAMAMCZcwLXhmaIL\nmGD03WRiu2UzFf2W6Rg/AGBysccPAMGED34ze9TMfmhmr5vZF4uuZ1KY2SfM7DUz+07RteDgGO/Z\nTdOYDx38ZvZxSY9I+qykX5f0uJl9tNiqJsaDkp4quggcHON9aFMz5kMHv6TPSXrR3T909yuSfiDp\n0wXXNBHc/XlJ7xVdBw6F8T6EaRrz0YP/bu28Cu/SZhswjRjvkETwX5V07IblY5LeL6gWIG+Md0gi\n+F+V9JCZzZjZEW3ck+iNYksCcsN4h6Tgj15097fM7Jyk1yS5Nm48NxXH8IDdGO/YwgVcABBM9EM9\nABAOwQ8AwRD8ABAMwQ8AwRD8ABAMwQ8AwRD8ABAMwQ8AwRD8ABDM/wOw1AQh77j0hAAAAABJRU5E\nrkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11289a908>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "bp = df_box.groupby('g').boxplot()"
   ]
  },
  {
   "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
}