pandas_dataframe_basic.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# pandas.DataFrame の作成方法\n",
    "# 1) 二次元配列を引数にする\n",
    "# 一次元目は縦方向、二次元目は横方向に並ぶ\n",
    "df = pd.DataFrame([[1, 11, 111], [2, 22, 222], [3, 33, 333], [4, 44, 444]])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "<div>\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",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([[1, 11, 111]])\n",
    "df2 = pd.DataFrame([[2, 22, 222]])\n",
    "df3 = pd.DataFrame([[3, 33, 333]])\n",
    "df4 = pd.DataFrame([[4, 44, 444]])\n",
    "df = pd.concat([df, df2, df3, df4])\n",
    "\n",
    "# append でもOK\n",
    "# df = df.append(df2).append(df3).append(df4)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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>one</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\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>one</th>\n",
       "      <td>1</td>\n",
       "      <td>11</td>\n",
       "      <td>111</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>two</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>three</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>four</th>\n",
       "      <td>4</td>\n",
       "      <td>44</td>\n",
       "      <td>444</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# index（縦方向のラベル）や columns（横方向のラベル）を指定することが可能\n",
    "df = pd.DataFrame([[1, 11, 111], [2, 22, 222], [3, 33, 333], [4, 44, 444]],\n",
    "                  index=['one', 'two', 'three', 'four'],\n",
    "                  columns=['A', 'B', 'C'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>18818.580078</td>\n",
       "      <td>136000</td>\n",
       "      <td>18450.980469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>18398.759766</td>\n",
       "      <td>128300</td>\n",
       "      <td>18374.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>18410.570312</td>\n",
       "      <td>142200</td>\n",
       "      <td>18191.320312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>18139.769531</td>\n",
       "      <td>163000</td>\n",
       "      <td>17767.339844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>18818.580078</td>\n",
       "      <td>136000</td>\n",
       "      <td>18450.980469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>18398.759766</td>\n",
       "      <td>128300</td>\n",
       "      <td>18374.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>18410.570312</td>\n",
       "      <td>142200</td>\n",
       "      <td>18191.320312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>18139.769531</td>\n",
       "      <td>163000</td>\n",
       "      <td>17767.339844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 時系列っぽいデータを作成\n",
    "df = pd.DataFrame([[18818.580078, 136000, 18450.980469],\n",
    "                   [18398.759766, 128300, 18374.00],\n",
    "                   [18410.570312, 142200, 18191.320312],\n",
    "                   [18139.769531, 163000, 17767.339844]],\n",
    "                  index=pd.to_datetime(['2016-01-04', '2016-01-05', '2016-01-06', '2016-01-07']),\n",
    "                  columns=['Open', 'Volume', 'Adj Close'])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\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</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111</td>\n",
       "      <td>222</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\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</td>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111</td>\n",
       "      <td>222</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2) 辞書を引数にする\n",
    "# 辞書の値は、リストまたは pandas.Series\n",
    "df = pd.DataFrame({'A': [1, 11, 111],\n",
    "                   'B': pd.Series([2, 22, 222]),\n",
    "                   'C': pd.Series({0: 3, 1: 33, 2: 333})})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "array length 2 does not match index length 3",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mValueError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-8-bcf3b97f2efd>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m df = pd.DataFrame({'A': [1, 11],\n\u001b[1;32m      3\u001b[0m                    \u001b[0;34m'B'\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mSeries\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m22\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m222\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m                    'C': pd.Series({0: 3, 1: 33, 2: 333})})\n\u001b[0m\u001b[1;32m      5\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/akiyoko/.pyenv/versions/anaconda2-4.2.0/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[1;32m    222\u001b[0m                                  dtype=dtype, copy=copy)\n\u001b[1;32m    223\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 224\u001b[0;31m             \u001b[0mmgr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_init_dict\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    225\u001b[0m         \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    226\u001b[0m             \u001b[0;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mma\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/akiyoko/.pyenv/versions/anaconda2-4.2.0/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_init_dict\u001b[0;34m(self, data, index, columns, dtype)\u001b[0m\n\u001b[1;32m    358\u001b[0m             \u001b[0marrays\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    359\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 360\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_arrays_to_mgr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    361\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    362\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m_init_ndarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mNone\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/akiyoko/.pyenv/versions/anaconda2-4.2.0/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36m_arrays_to_mgr\u001b[0;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[1;32m   5229\u001b[0m     \u001b[0;31m# figure out the index, if necessary\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5230\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mindex\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5231\u001b[0;31m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mextract_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5232\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5233\u001b[0m         \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_ensure_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/akiyoko/.pyenv/versions/anaconda2-4.2.0/lib/python2.7/site-packages/pandas/core/frame.pyc\u001b[0m in \u001b[0;36mextract_index\u001b[0;34m(data)\u001b[0m\n\u001b[1;32m   5287\u001b[0m                     msg = ('array length %d does not match index length %d' %\n\u001b[1;32m   5288\u001b[0m                            (lengths[0], len(index)))\n\u001b[0;32m-> 5289\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mmsg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5290\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5291\u001b[0m                 \u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_default_index\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mlengths\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mValueError\u001b[0m: array length 2 does not match index length 3"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "# ちなみに、リストの要素数が他と合わない場合は ValueError\n",
    "df = pd.DataFrame({'A': [1, 11],\n",
    "                   'B': pd.Series([2, 22, 222]),\n",
    "                   'C': pd.Series({0: 3, 1: 33, 2: 333})})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
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       "      <th>2</th>\n",
       "      <td>111</td>\n",
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       "<div>\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",
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       "  </thead>\n",
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       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>22.0</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111</td>\n",
       "      <td>NaN</td>\n",
       "      <td>333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# ただし、Series の場合は NaN で補完されるので便利\n",
    "df = pd.DataFrame({'A': [1, 11, 111],\n",
    "                   'B': pd.Series([2, 22]),\n",
    "                   'C': pd.Series({0: 3, 1: 33, 2: 333})})\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
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       "  <tbody>\n",
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       "</table>\n",
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      "text/plain": [
       "<div>\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</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>11</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111</td>\n",
       "      <td>222</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# DataFrame を横方向に結合する\n",
    "df = pd.DataFrame({'A': [1, 11, 111]})\n",
    "df2 = pd.DataFrame({'B': [2, 22, 222]})\n",
    "\n",
    "# 横方向に結合するときは axis=1 を指定\n",
    "df = pd.concat([df, df2], axis=1)\n",
    "# あるいは、index が同じであれば、index をキーにして結合する DataFrame.join を使ってもよい\n",
    "# df = df.join(df2)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>18818.580078</td>\n",
       "      <td>136000.0</td>\n",
       "      <td>18450.980469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>18398.759766</td>\n",
       "      <td>128300.0</td>\n",
       "      <td>18374.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>18410.570312</td>\n",
       "      <td>142200.0</td>\n",
       "      <td>18191.320312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>18139.769531</td>\n",
       "      <td>163000.0</td>\n",
       "      <td>17767.339844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>18818.580078</td>\n",
       "      <td>136000.0</td>\n",
       "      <td>18450.980469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>18398.759766</td>\n",
       "      <td>128300.0</td>\n",
       "      <td>18374.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>18410.570312</td>\n",
       "      <td>142200.0</td>\n",
       "      <td>18191.320312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>18139.769531</td>\n",
       "      <td>163000.0</td>\n",
       "      <td>17767.339844</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2) の方式では時系列データは扱いづらい。。\n",
    "# もしやるなら、一旦こういう感じで作成して、 \n",
    "df = pd.DataFrame({'2016-01-04': [18818.580078, 136000, 18450.980469],\n",
    "                   '2016-01-05': [18398.759766, 128300, 18374.00],\n",
    "                   '2016-01-06': [18410.570312, 142200, 18191.320312],\n",
    "                   '2016-01-07': [18139.769531, 163000, 17767.339844]},\n",
    "                  index=['Open', 'Volume', 'Adj Close'])\n",
    "# DataFrame.T で縦横を反転させるのがいいのかな。。\n",
    "df = df.T\n",
    "df\n",
    "# うーん。やっぱり使いづらい。。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
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       "      <td>136000</td>\n",
       "      <td>18450.980469</td>\n",
       "    </tr>\n",
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       "      <th>2016-01-05</th>\n",
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       "      <td>128300</td>\n",
       "      <td>18374.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
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       "      <td>18139.769531</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>17562.230469</td>\n",
       "      <td>178800</td>\n",
       "      <td>17697.960938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>17470.929688</td>\n",
       "      <td>173000</td>\n",
       "      <td>17218.960938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>17449.119141</td>\n",
       "      <td>145900</td>\n",
       "      <td>17715.630859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>17384.929688</td>\n",
       "      <td>173000</td>\n",
       "      <td>17240.949219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>17522.460938</td>\n",
       "      <td>167800</td>\n",
       "      <td>17147.109375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>16826.929688</td>\n",
       "      <td>152500</td>\n",
       "      <td>16955.570312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Date</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>18818.580078</td>\n",
       "      <td>136000</td>\n",
       "      <td>18450.980469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>18398.759766</td>\n",
       "      <td>128300</td>\n",
       "      <td>18374.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>18410.570312</td>\n",
       "      <td>142200</td>\n",
       "      <td>18191.320312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>18139.769531</td>\n",
       "      <td>163000</td>\n",
       "      <td>17767.339844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>17562.230469</td>\n",
       "      <td>178800</td>\n",
       "      <td>17697.960938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>17470.929688</td>\n",
       "      <td>173000</td>\n",
       "      <td>17218.960938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>17449.119141</td>\n",
       "      <td>145900</td>\n",
       "      <td>17715.630859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>17384.929688</td>\n",
       "      <td>173000</td>\n",
       "      <td>17240.949219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>17522.460938</td>\n",
       "      <td>167800</td>\n",
       "      <td>17147.109375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>16826.929688</td>\n",
       "      <td>152500</td>\n",
       "      <td>16955.570312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 3) pandas.read_csv を使う\n",
    "# 日経平均のデータ（n225.csv）を読み込む\n",
    "df = pd.read_csv('n225.csv', index_col='Date', parse_dates=True, usecols=['Date', 'Open', 'Volume', 'Adj Close'])\n",
    "df = df.sort_index()\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>18818.580078</td>\n",
       "      <td>136000.0</td>\n",
       "      <td>18450.980469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>18398.759766</td>\n",
       "      <td>128300.0</td>\n",
       "      <td>18374.000000</td>\n",
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       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>18410.570312</td>\n",
       "      <td>142200.0</td>\n",
       "      <td>18191.320312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>18139.769531</td>\n",
       "      <td>163000.0</td>\n",
       "      <td>17767.339844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>17562.230469</td>\n",
       "      <td>178800.0</td>\n",
       "      <td>17697.960938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>17470.929688</td>\n",
       "      <td>173000.0</td>\n",
       "      <td>17218.960938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>17449.119141</td>\n",
       "      <td>145900.0</td>\n",
       "      <td>17715.630859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>17384.929688</td>\n",
       "      <td>173000.0</td>\n",
       "      <td>17240.949219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>17522.460938</td>\n",
       "      <td>167800.0</td>\n",
       "      <td>17147.109375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>16826.929688</td>\n",
       "      <td>152500.0</td>\n",
       "      <td>16955.570312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Open</th>\n",
       "      <th>Volume</th>\n",
       "      <th>Adj Close</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2016-01-04</th>\n",
       "      <td>18818.580078</td>\n",
       "      <td>136000.0</td>\n",
       "      <td>18450.980469</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-05</th>\n",
       "      <td>18398.759766</td>\n",
       "      <td>128300.0</td>\n",
       "      <td>18374.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-06</th>\n",
       "      <td>18410.570312</td>\n",
       "      <td>142200.0</td>\n",
       "      <td>18191.320312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-07</th>\n",
       "      <td>18139.769531</td>\n",
       "      <td>163000.0</td>\n",
       "      <td>17767.339844</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-08</th>\n",
       "      <td>17562.230469</td>\n",
       "      <td>178800.0</td>\n",
       "      <td>17697.960938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-12</th>\n",
       "      <td>17470.929688</td>\n",
       "      <td>173000.0</td>\n",
       "      <td>17218.960938</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-13</th>\n",
       "      <td>17449.119141</td>\n",
       "      <td>145900.0</td>\n",
       "      <td>17715.630859</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-14</th>\n",
       "      <td>17384.929688</td>\n",
       "      <td>173000.0</td>\n",
       "      <td>17240.949219</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-15</th>\n",
       "      <td>17522.460938</td>\n",
       "      <td>167800.0</td>\n",
       "      <td>17147.109375</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2016-01-18</th>\n",
       "      <td>16826.929688</td>\n",
       "      <td>152500.0</td>\n",
       "      <td>16955.570312</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 2016年1年分のDataFrameを用意\n",
    "# （参考）http://www.madopro.net/entry/MachineLearningForTrading\n",
    "df = pd.DataFrame(index=pd.date_range('2016-01-01', '2016-12-31'))\n",
    "\n",
    "# 日経平均のデータを読み込んで join\n",
    "df = df.join(\n",
    "    pd.read_csv('n225.csv', index_col='Date', parse_dates=True, usecols=['Date', 'Open', 'Volume', 'Adj Close']))\n",
    "\n",
    "# 市場が休みの日のデータを取り除く\n",
    "df = df.dropna()\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "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>0</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>1</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>2</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>3</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>4</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "<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>0</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>1</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>2</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>3</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>4</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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 余談\n",
    "# Iris のデータを Pandas で使う方法\n",
    "# （参考）http://dragstar.hatenablog.com/entry/2016/07/24/150534\n",
    "import pandas as pd\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "df = pd.DataFrame(iris.data, columns=iris.feature_names)\n",
    "df['Species'] = iris.target_names[iris.target]\n",
    "df.rename(columns={'sepal length (cm)': 'Sepal.Length',\n",
    "                   'sepal width (cm)': 'Sepal.Width',\n",
    "                   'petal length (cm)': 'Petal.Length',\n",
    "                   'petal width (cm)': 'Petal.Width'},\n",
    "          inplace=True)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/akiyoko/.pyenv/versions/anaconda2-4.2.0/lib/python2.7/site-packages/ipykernel/__main__.py:3: FutureWarning: The pandas.rpy module is deprecated and will be removed in a future version. We refer to external packages like rpy2. \nSee here for a guide on how to port your code to rpy2: http://pandas.pydata.org/pandas-docs/stable/r_interface.html\n  app.launch_new_instance()\n"
     ]
    },
    {
     "ename": "ImportError",
     "evalue": "No module named rpy2.robjects.packages",
     "traceback": [
      "\u001b[0;31m\u001b[0m",
      "\u001b[0;31mImportError\u001b[0mTraceback (most recent call last)",
      "\u001b[0;32m<ipython-input-15-5d661ab594f2>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# rpy2 (R interface) パッケージを使ってもいいらしいが、Anaconda 4.2.0 にはデフォルトで入っていない\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# （参考）http://stackoverflow.com/a/28417338\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcommon\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mrcom\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0miris\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mrcom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mload_data\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'iris'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/Users/akiyoko/.pyenv/versions/anaconda2-4.2.0/lib/python2.7/site-packages/pandas/rpy/common.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mutil\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtesting\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0m_test\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m \u001b[0;32mfrom\u001b[0m \u001b[0mrpy2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrobjects\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpackages\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mimportr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     19\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0mrpy2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrobjects\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mrpy2\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrobjects\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mrobj\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mImportError\u001b[0m: No module named rpy2.robjects.packages"
     ],
     "output_type": "error"
    }
   ],
   "source": [
    "# rpy2 (R interface) パッケージを使ってもいいらしいが、Anaconda 4.2.0 にはデフォルトで入っていない\n",
    "# （参考）http://stackoverflow.com/a/28417338\n",
    "import pandas.rpy.common as rcom\n",
    "iris = rcom.load_data('iris')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    ""
   ]
  }
 ],
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