ボックスプロット.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ボックスプロットとバイオリンプロット\n",
    "\n",
    "ボックスプロットとは何か？知らない人は右記参照→ http://excelshogikan.com/qc/qc03/boxplot.html\n",
    "\n",
    "<a href=\"http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E5%AE%9F%E7%BF%92%E7%94%A8%E3%83%86%E3%82%99%E3%83%BC%E3%82%BF2017.ipynb\">「あやめのデータ」</a>を例に説明します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 「#」（シャープ）以降の文字はプログラムに影響しません。\n",
    "# 図やグラフを図示するためのライブラリをインポートする。\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 数値計算やデータフレーム操作に関するライブラリをインポートする\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "from pandas.tools import plotting # 高度なプロットを行うツールのインポート"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# URL によるリソースへのアクセスを提供するライブラリをインポートする。\n",
    "# import urllib # Python 2 の場合\n",
    "import urllib.request # Python 3 の場合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('iris.txt', <http.client.HTTPMessage at 0x107540cf8>)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ウェブ上のリソースを指定する\n",
    "url = 'https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/iris.txt'\n",
    "# 指定したURLからリソースをダウンロードし、名前をつける。\n",
    "# urllib.urlretrieve(url, 'iris.txt') # Python 2 の場合\n",
    "urllib.request.urlretrieve(url, 'iris.txt') # Python 3 の場合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# データの読み込み\n",
    "df1 = pd.read_csv('iris.txt', sep='\\t', index_col=0) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sepal.Length</th>\n",
       "      <th>Sepal.Width</th>\n",
       "      <th>Petal.Length</th>\n",
       "      <th>Petal.Width</th>\n",
       "      <th>Species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>4.4</td>\n",
       "      <td>2.9</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4.3</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.1</td>\n",
       "      <td>0.1</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>5.8</td>\n",
       "      <td>4.0</td>\n",
       "      <td>1.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>5.7</td>\n",
       "      <td>4.4</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.9</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>5.7</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.8</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.3</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>5.4</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.7</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.3</td>\n",
       "      <td>1.7</td>\n",
       "      <td>0.5</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>4.8</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.9</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.4</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.2</td>\n",
       "      <td>setosa</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>121</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>122</th>\n",
       "      <td>5.6</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.9</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>123</th>\n",
       "      <td>7.7</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.7</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>124</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.7</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>125</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>126</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.2</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>127</th>\n",
       "      <td>6.2</td>\n",
       "      <td>2.8</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>128</th>\n",
       "      <td>6.1</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.9</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>129</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>130</th>\n",
       "      <td>7.2</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.8</td>\n",
       "      <td>1.6</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>131</th>\n",
       "      <td>7.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>6.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>132</th>\n",
       "      <td>7.9</td>\n",
       "      <td>3.8</td>\n",
       "      <td>6.4</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>133</th>\n",
       "      <td>6.4</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.2</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>134</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.8</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>135</th>\n",
       "      <td>6.1</td>\n",
       "      <td>2.6</td>\n",
       "      <td>5.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>136</th>\n",
       "      <td>7.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>137</th>\n",
       "      <td>6.3</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>138</th>\n",
       "      <td>6.4</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.5</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>139</th>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.8</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>140</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.1</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>141</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.6</td>\n",
       "      <td>2.4</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>142</th>\n",
       "      <td>6.9</td>\n",
       "      <td>3.1</td>\n",
       "      <td>5.1</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>143</th>\n",
       "      <td>5.8</td>\n",
       "      <td>2.7</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>144</th>\n",
       "      <td>6.8</td>\n",
       "      <td>3.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>145</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>5.7</td>\n",
       "      <td>2.5</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>146</th>\n",
       "      <td>6.7</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>147</th>\n",
       "      <td>6.3</td>\n",
       "      <td>2.5</td>\n",
       "      <td>5.0</td>\n",
       "      <td>1.9</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>148</th>\n",
       "      <td>6.5</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.2</td>\n",
       "      <td>2.0</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>149</th>\n",
       "      <td>6.2</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>150</th>\n",
       "      <td>5.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.1</td>\n",
       "      <td>1.8</td>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>150 rows × 5 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sepal.Length  Sepal.Width  Petal.Length  Petal.Width    Species\n",
       "1             5.1          3.5           1.4          0.2     setosa\n",
       "2             4.9          3.0           1.4          0.2     setosa\n",
       "3             4.7          3.2           1.3          0.2     setosa\n",
       "4             4.6          3.1           1.5          0.2     setosa\n",
       "5             5.0          3.6           1.4          0.2     setosa\n",
       "6             5.4          3.9           1.7          0.4     setosa\n",
       "7             4.6          3.4           1.4          0.3     setosa\n",
       "8             5.0          3.4           1.5          0.2     setosa\n",
       "9             4.4          2.9           1.4          0.2     setosa\n",
       "10            4.9          3.1           1.5          0.1     setosa\n",
       "11            5.4          3.7           1.5          0.2     setosa\n",
       "12            4.8          3.4           1.6          0.2     setosa\n",
       "13            4.8          3.0           1.4          0.1     setosa\n",
       "14            4.3          3.0           1.1          0.1     setosa\n",
       "15            5.8          4.0           1.2          0.2     setosa\n",
       "16            5.7          4.4           1.5          0.4     setosa\n",
       "17            5.4          3.9           1.3          0.4     setosa\n",
       "18            5.1          3.5           1.4          0.3     setosa\n",
       "19            5.7          3.8           1.7          0.3     setosa\n",
       "20            5.1          3.8           1.5          0.3     setosa\n",
       "21            5.4          3.4           1.7          0.2     setosa\n",
       "22            5.1          3.7           1.5          0.4     setosa\n",
       "23            4.6          3.6           1.0          0.2     setosa\n",
       "24            5.1          3.3           1.7          0.5     setosa\n",
       "25            4.8          3.4           1.9          0.2     setosa\n",
       "26            5.0          3.0           1.6          0.2     setosa\n",
       "27            5.0          3.4           1.6          0.4     setosa\n",
       "28            5.2          3.5           1.5          0.2     setosa\n",
       "29            5.2          3.4           1.4          0.2     setosa\n",
       "30            4.7          3.2           1.6          0.2     setosa\n",
       "..            ...          ...           ...          ...        ...\n",
       "121           6.9          3.2           5.7          2.3  virginica\n",
       "122           5.6          2.8           4.9          2.0  virginica\n",
       "123           7.7          2.8           6.7          2.0  virginica\n",
       "124           6.3          2.7           4.9          1.8  virginica\n",
       "125           6.7          3.3           5.7          2.1  virginica\n",
       "126           7.2          3.2           6.0          1.8  virginica\n",
       "127           6.2          2.8           4.8          1.8  virginica\n",
       "128           6.1          3.0           4.9          1.8  virginica\n",
       "129           6.4          2.8           5.6          2.1  virginica\n",
       "130           7.2          3.0           5.8          1.6  virginica\n",
       "131           7.4          2.8           6.1          1.9  virginica\n",
       "132           7.9          3.8           6.4          2.0  virginica\n",
       "133           6.4          2.8           5.6          2.2  virginica\n",
       "134           6.3          2.8           5.1          1.5  virginica\n",
       "135           6.1          2.6           5.6          1.4  virginica\n",
       "136           7.7          3.0           6.1          2.3  virginica\n",
       "137           6.3          3.4           5.6          2.4  virginica\n",
       "138           6.4          3.1           5.5          1.8  virginica\n",
       "139           6.0          3.0           4.8          1.8  virginica\n",
       "140           6.9          3.1           5.4          2.1  virginica\n",
       "141           6.7          3.1           5.6          2.4  virginica\n",
       "142           6.9          3.1           5.1          2.3  virginica\n",
       "143           5.8          2.7           5.1          1.9  virginica\n",
       "144           6.8          3.2           5.9          2.3  virginica\n",
       "145           6.7          3.3           5.7          2.5  virginica\n",
       "146           6.7          3.0           5.2          2.3  virginica\n",
       "147           6.3          2.5           5.0          1.9  virginica\n",
       "148           6.5          3.0           5.2          2.0  virginica\n",
       "149           6.2          3.4           5.4          2.3  virginica\n",
       "150           5.9          3.0           5.1          1.8  virginica\n",
       "\n",
       "[150 rows x 5 columns]"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/kot/miniconda3/envs/py3/lib/python3.4/site-packages/pandas/tools/plotting.py:2633: FutureWarning: \n",
      "The default value for 'return_type' will change to 'axes' in a future release.\n",
      " To use the future behavior now, set return_type='axes'.\n",
      " To keep the previous behavior and silence this warning, set return_type='dict'.\n",
      "  warnings.warn(msg, FutureWarning)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "{'boxes': [<matplotlib.lines.Line2D at 0x1097180f0>,\n",
       "  <matplotlib.lines.Line2D at 0x109759748>,\n",
       "  <matplotlib.lines.Line2D at 0x109768d68>,\n",
       "  <matplotlib.lines.Line2D at 0x10977f3c8>],\n",
       " 'caps': [<matplotlib.lines.Line2D at 0x1075e16a0>,\n",
       "  <matplotlib.lines.Line2D at 0x109740fd0>,\n",
       "  <matplotlib.lines.Line2D at 0x10975fe48>,\n",
       "  <matplotlib.lines.Line2D at 0x109763630>,\n",
       "  <matplotlib.lines.Line2D at 0x1097734a8>,\n",
       "  <matplotlib.lines.Line2D at 0x109773c50>,\n",
       "  <matplotlib.lines.Line2D at 0x109785ac8>,\n",
       "  <matplotlib.lines.Line2D at 0x1097892b0>],\n",
       " 'fliers': [<matplotlib.lines.Line2D at 0x109753f60>,\n",
       "  <matplotlib.lines.Line2D at 0x1097685c0>,\n",
       "  <matplotlib.lines.Line2D at 0x109779be0>,\n",
       "  <matplotlib.lines.Line2D at 0x10978e240>],\n",
       " 'means': [],\n",
       " 'medians': [<matplotlib.lines.Line2D at 0x1097537b8>,\n",
       "  <matplotlib.lines.Line2D at 0x109763dd8>,\n",
       "  <matplotlib.lines.Line2D at 0x109779438>,\n",
       "  <matplotlib.lines.Line2D at 0x109789a58>],\n",
       " 'whiskers': [<matplotlib.lines.Line2D at 0x1075f2080>,\n",
       "  <matplotlib.lines.Line2D at 0x1075ef710>,\n",
       "  <matplotlib.lines.Line2D at 0x109759eb8>,\n",
       "  <matplotlib.lines.Line2D at 0x10975f6a0>,\n",
       "  <matplotlib.lines.Line2D at 0x10976e518>,\n",
       "  <matplotlib.lines.Line2D at 0x10976ecc0>,\n",
       "  <matplotlib.lines.Line2D at 0x10977fb38>,\n",
       "  <matplotlib.lines.Line2D at 0x109785320>]}"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Pnj7mzKltptFrQX22Lkx2GqOxapv4D4G3A3cCfwmsBW4zs5c45x71XdxEnHIKXHJJvea2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10754eb38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ボックスプロット（箱ひげ図）を作成する\n",
    "df1.boxplot()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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dxJo1a0KHIBn60Y+Uz9iojcZF+UxN5Eh8PTALePPhVjr33HMpFAqjHmeccQaD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\n8s4u5wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1097c9ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# ボックスプロットの描き方（別法）\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.boxplot(df1.iloc[:, 0:4].as_matrix().T.tolist())\n",
    "ax.set_xticklabels(df1.columns[0:4], rotation=90)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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sFolaLE/nz0uV0fITCoWg1Uo/x4GUGa2sZFhcXJSwxfJDfTQtqyLOOf8R5/y7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\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x109a4c6d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# バイオリンプロット\n",
    "fig = plt.figure()\n",
    "ax = fig.add_subplot(111)\n",
    "ax.violinplot(df1.iloc[:, 0:4].as_matrix().T.tolist())\n",
    "ax.set_xticks([1, 2, 3, 4]) #データ範囲のどこに目盛りが入るかを指定する\n",
    "ax.set_xticklabels(df1.columns[0:4], rotation=90)\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "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": "3.4.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}