4-pandas-datahandling.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# pandasでデータの読み込み・書き込み\n",
    "pandasでのデータの取扱から、データの読み込み、書き込みを行います。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 内容\n",
    "\n",
    "- シリーズとは\n",
    "- データフレームとは\n",
    "- 概要を見る\n",
    "- インデックス名、カラム名\n",
    "- データのピックアップ\n",
    "- データの読み込み (CSV / Excel / HTML / SQL)\n",
    "- データの書き込み (CSV / Excel)\n",
    "- データの再利用 (pickle)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "pandasライブラリのインポート。\n",
    "as pdとし、pdで呼び出せるようにしています。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "後で、NumPyライブラリを使って、データク作成などを行うのでインポートしておく。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## シリーズとは"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "ser = pd.Series([10, 20, 30, 40])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    10\n",
       "1    20\n",
       "2    30\n",
       "3    40\n",
       "dtype: int64"
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     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ser"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4要素のシリーズオブジェクトを作って、内容を表示。\n",
    "\n",
    "シリーズオブジェクトは、1次元のデータ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## データフレームとは"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[10, \"a\", True], [20, \"b\", False,], [30, \"c\", False], [40, \"d\", True]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
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       "    0  1      2\n",
       "0  10  a   True\n",
       "1  20  b  False\n",
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       "3  40  d   True"
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   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "4行3列のデータフレームを作って、内容を表示。\n",
    "\n",
    "データフレームは、2次元データ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 概要を見る"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.arange(100).reshape(25, 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "NumPyのarangeを使って25行4列のデータを生成し、データフレームを作った"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <th>13</th>\n",
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       "     0   1   2   3\n",
       "0    0   1   2   3\n",
       "1    4   5   6   7\n",
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       "8   32  33  34  35\n",
       "9   36  37  38  39\n",
       "10  40  41  42  43\n",
       "11  44  45  46  47\n",
       "12  48  49  50  51\n",
       "13  52  53  54  55\n",
       "14  56  57  58  59\n",
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    "df"
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  {
   "cell_type": "code",
   "execution_count": 9,
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       "    0   1   2   3\n",
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    "df.head()"
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       "     0   1   2   3\n",
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       "24  96  97  98  99"
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   "source": [
    "df.tail()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 4)"
      ]
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     "execution_count": 11,
     "metadata": {},
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   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## インデックス名、カラム名\n",
    "\n",
    "pandasでは、わかりやすいインデックス名(行に名前付け)やカラム名(列に名前付け)ができます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.arange(6).reshape([3, 2]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "scrolled": true
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   "outputs": [
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       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   0  1\n",
       "0  0  1\n",
       "1  2  3\n",
       "2  4  5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.columns = [\"A\", \"B\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.index = [\"01\", \"02\", \"03\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>03</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
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       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "    A  B\n",
       "01  0  1\n",
       "02  2  3\n",
       "03  4  5"
      ]
     },
     "execution_count": 16,
     "metadata": {},
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   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [],
   "source": [
    "named_df = pd.DataFrame(np.arange(6).reshape([3, 2]), columns=[\"A列\", \"B列\"], index=[\"1行目\", \"2行目\", \"3行目\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <th>B列</th>\n",
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       "  <tbody>\n",
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       "      <th>1行目</th>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>2行目</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>3行目</th>\n",
       "      <td>4</td>\n",
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       "</table>\n",
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      "text/plain": [
       "     A列  B列\n",
       "1行目   0   1\n",
       "2行目   2   3\n",
       "3行目   4   5"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "named_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "Index does not support mutable operations",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-19-23ace5d3e9ab>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mnamed_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\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\"11\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;32m~/dev/ipython/env-datahandling/lib/python3.6/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36m__setitem__\u001b[0;34m(self, key, value)\u001b[0m\n\u001b[1;32m   1722\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1723\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1724\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Index does not support mutable operations\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1725\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1726\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__getitem__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: Index does not support mutable operations"
     ]
    }
   ],
   "source": [
    "named_df.index[0] = \"11\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [],
   "source": [
    "named_df.index = ['11', '12', '13']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "      <th>B列</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
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       "    A列  B列\n",
       "11   0   1\n",
       "12   2   3\n",
       "13   4   5"
      ]
     },
     "execution_count": 21,
     "metadata": {},
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   ],
   "source": [
    "named_df"
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  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## データのピックアップ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "01    0\n",
       "02    2\n",
       "03    4\n",
       "Name: A, dtype: int64"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"A\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>03</th>\n",
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      "text/plain": [
       "    A  B\n",
       "01  0  1\n",
       "02  2  3\n",
       "03  4  5"
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     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "df[[\"A\", \"B\"]]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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      "text/plain": [
       "    A  B\n",
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     "execution_count": 24,
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   ],
   "source": [
    "df[:2]"
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  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    A  B\n",
       "01  0  1\n",
       "02  2  3\n",
       "03  4  5"
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     "execution_count": 25,
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   "source": [
    "df.loc[:, :]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "01    0\n",
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   ],
   "source": [
    "df.loc[:, \"A\"]"
   ]
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   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    A  B\n",
       "01  0  1\n",
       "02  2  3\n",
       "03  4  5"
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     "execution_count": 27,
     "metadata": {},
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   "source": [
    "df.loc[:, [\"A\", \"B\"]]"
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  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "A    0\n",
       "B    1\n",
       "Name: 01, dtype: int64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
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    }
   ],
   "source": [
    "df.loc[\"01\", :]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "      <th>03</th>\n",
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      "text/plain": [
       "    A  B\n",
       "01  0  1\n",
       "03  4  5"
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     "execution_count": 29,
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   ],
   "source": [
    "df.loc[[\"01\", \"03\"], :]"
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  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 30,
     "metadata": {},
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    "df.iloc[1, 1]"
   ]
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  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "02    3\n",
       "03    5\n",
       "Name: B, dtype: int64"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>A</th>\n",
       "      <th>B</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>02</th>\n",
       "      <td>2</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>03</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    A  B\n",
       "02  2  3\n",
       "03  4  5"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1:, :2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## データの読み込み (CSV / Excel / HTML / SQL)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"data/201704health.csv\", encoding=\"utf-8\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\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>日付</th>\n",
       "      <th>歩数</th>\n",
       "      <th>摂取カロリー</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>5439</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>2510</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>10238</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>8209</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>9434</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>7593</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>9320</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>4873</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>12045</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-04-10</td>\n",
       "      <td>7493</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-04-11</td>\n",
       "      <td>7289</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-04-12</td>\n",
       "      <td>6481</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-04-13</td>\n",
       "      <td>10287</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017-04-14</td>\n",
       "      <td>8043</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>7435</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017-04-16</td>\n",
       "      <td>7529</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017-04-17</td>\n",
       "      <td>8031</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-04-18</td>\n",
       "      <td>8475</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>8132</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>15328</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>12849</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2017-04-22</td>\n",
       "      <td>4029</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2017-04-23</td>\n",
       "      <td>3890</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>8093</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>7823</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2017-04-26</td>\n",
       "      <td>7492</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>7203</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>7302</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017-04-29</td>\n",
       "      <td>6033</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>4093</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            日付     歩数  摂取カロリー\n",
       "0   2017-04-01   5439    2500\n",
       "1   2017-04-02   2510    2300\n",
       "2   2017-04-03  10238    1950\n",
       "3   2017-04-04   8209    1850\n",
       "4   2017-04-05   9434    1930\n",
       "5   2017-04-06   7593    1800\n",
       "6   2017-04-07   9320    1940\n",
       "7   2017-04-08   4873    2300\n",
       "8   2017-04-09  12045    1950\n",
       "9   2017-04-10   7493    1850\n",
       "10  2017-04-11   7289    1930\n",
       "11  2017-04-12   6481    2300\n",
       "12  2017-04-13  10287    1800\n",
       "13  2017-04-14   8043    1940\n",
       "14  2017-04-15   7435    2300\n",
       "15  2017-04-16   7529    2300\n",
       "16  2017-04-17   8031    1940\n",
       "17  2017-04-18   8475    2300\n",
       "18  2017-04-19   8132    1950\n",
       "19  2017-04-20  15328    1800\n",
       "20  2017-04-21  12849    1940\n",
       "21  2017-04-22   4029    2300\n",
       "22  2017-04-23   3890    1950\n",
       "23  2017-04-24   8093    1850\n",
       "24  2017-04-25   7823    1950\n",
       "25  2017-04-26   7492    1850\n",
       "26  2017-04-27   7203    1930\n",
       "27  2017-04-28   7302    1850\n",
       "28  2017-04-29   6033    2300\n",
       "29  2017-04-30   4093    1950"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Excleデータの読み込み\n",
    "\n",
    "xlrd ライブラリが必要\n",
    "\n",
    "```\n",
    "pip install xlrd\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_excel(\"data/201704health.xlsx\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>日付</th>\n",
       "      <th>歩数</th>\n",
       "      <th>摂取カロリー</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2017-04-01</td>\n",
       "      <td>5439</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2017-04-02</td>\n",
       "      <td>2510</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2017-04-03</td>\n",
       "      <td>10238</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2017-04-04</td>\n",
       "      <td>8209</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2017-04-05</td>\n",
       "      <td>9434</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2017-04-06</td>\n",
       "      <td>7593</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2017-04-07</td>\n",
       "      <td>9320</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2017-04-08</td>\n",
       "      <td>4873</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2017-04-09</td>\n",
       "      <td>12045</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>2017-04-10</td>\n",
       "      <td>7493</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>2017-04-11</td>\n",
       "      <td>7289</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2017-04-12</td>\n",
       "      <td>6481</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>2017-04-13</td>\n",
       "      <td>10287</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2017-04-14</td>\n",
       "      <td>8043</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>2017-04-15</td>\n",
       "      <td>7435</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>2017-04-16</td>\n",
       "      <td>7529</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>2017-04-17</td>\n",
       "      <td>8031</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>2017-04-18</td>\n",
       "      <td>8475</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>2017-04-19</td>\n",
       "      <td>8132</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>2017-04-20</td>\n",
       "      <td>15328</td>\n",
       "      <td>1800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2017-04-21</td>\n",
       "      <td>12849</td>\n",
       "      <td>1940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>2017-04-22</td>\n",
       "      <td>4029</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>2017-04-23</td>\n",
       "      <td>3890</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>2017-04-24</td>\n",
       "      <td>8093</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>2017-04-25</td>\n",
       "      <td>7823</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>2017-04-26</td>\n",
       "      <td>7492</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>2017-04-27</td>\n",
       "      <td>7203</td>\n",
       "      <td>1930</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2017-04-28</td>\n",
       "      <td>7302</td>\n",
       "      <td>1850</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>2017-04-29</td>\n",
       "      <td>6033</td>\n",
       "      <td>2300</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>2017-04-30</td>\n",
       "      <td>4093</td>\n",
       "      <td>1950</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            日付     歩数  摂取カロリー\n",
       "0   2017-04-01   5439    2500\n",
       "1   2017-04-02   2510    2300\n",
       "2   2017-04-03  10238    1950\n",
       "3   2017-04-04   8209    1850\n",
       "4   2017-04-05   9434    1930\n",
       "5   2017-04-06   7593    1800\n",
       "6   2017-04-07   9320    1940\n",
       "7   2017-04-08   4873    2300\n",
       "8   2017-04-09  12045    1950\n",
       "9   2017-04-10   7493    1850\n",
       "10  2017-04-11   7289    1930\n",
       "11  2017-04-12   6481    2300\n",
       "12  2017-04-13  10287    1800\n",
       "13  2017-04-14   8043    1940\n",
       "14  2017-04-15   7435    2300\n",
       "15  2017-04-16   7529    2300\n",
       "16  2017-04-17   8031    1940\n",
       "17  2017-04-18   8475    2300\n",
       "18  2017-04-19   8132    1950\n",
       "19  2017-04-20  15328    1800\n",
       "20  2017-04-21  12849    1940\n",
       "21  2017-04-22   4029    2300\n",
       "22  2017-04-23   3890    1950\n",
       "23  2017-04-24   8093    1850\n",
       "24  2017-04-25   7823    1950\n",
       "25  2017-04-26   7492    1850\n",
       "26  2017-04-27   7203    1930\n",
       "27  2017-04-28   7302    1850\n",
       "28  2017-04-29   6033    2300\n",
       "29  2017-04-30   4093    1950"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### HTML読み込み\n",
    "PyCampの開催場所データを読み込む\n",
    "https://www.pycon.jp/support/bootcamp.html\n",
    "\n",
    "lxml と BeautifulSoup4ライブラリのインストールが必要\n",
    "\n",
    "```\n",
    "pip install lxml\n",
    "pip install BeautifulSoup4\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "macOSの場合\n",
    "OS側の変更で、OpenSSLを使用しないようになったようで、SSL errorが出る場合があるようです。\n",
    "Applications/Python 3.6 フォルダにある、`Install Certificates.command` をダブルクリックする必要があるようです。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [],
   "source": [
    "tables = pd.read_html(\"https://www.pycon.jp/support/bootcamp.html\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(tables)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = tables[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "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>No.</th>\n",
       "      <th>イベント</th>\n",
       "      <th>開催日</th>\n",
       "      <th>会場</th>\n",
       "      <th>講師</th>\n",
       "      <th>参加者</th>\n",
       "      <th>TA</th>\n",
       "      <th>Staff</th>\n",
       "      <th>開催レポート</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2016年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.0</td>\n",
       "      <td>Python Boot Camp in 京都</td>\n",
       "      <td>6月18日(土)</td>\n",
       "      <td>CAMPHOR- HOUSE</td>\n",
       "      <td>谷口 英</td>\n",
       "      <td>5名</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2.0</td>\n",
       "      <td>Python Boot Camp in 愛媛</td>\n",
       "      <td>7月30日(土)</td>\n",
       "      <td>サイボウズ松山オフィス</td>\n",
       "      <td>寺田 学</td>\n",
       "      <td>12名</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3.0</td>\n",
       "      <td>Python Boot Camp in 熊本</td>\n",
       "      <td>8月28日(日)</td>\n",
       "      <td>未来会議室</td>\n",
       "      <td>寺田 学</td>\n",
       "      <td>8名</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4.0</td>\n",
       "      <td>Python Boot Camp in 札幌</td>\n",
       "      <td>11月19日(土)</td>\n",
       "      <td>株式会社インフィニットループ</td>\n",
       "      <td>村岡 友介</td>\n",
       "      <td>17名</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2017年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.0</td>\n",
       "      <td>Python Boot Camp in 栃木小山</td>\n",
       "      <td>2月11日(土)</td>\n",
       "      <td>小山市立生涯学習センター</td>\n",
       "      <td>寺田 学</td>\n",
       "      <td>10名</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6.0</td>\n",
       "      <td>Python Boot Camp in 広島</td>\n",
       "      <td>3月11日(土)</td>\n",
       "      <td>中四国マネジメントシステム推進機構</td>\n",
       "      <td>鈴木 たかのり</td>\n",
       "      <td>15名</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7.0</td>\n",
       "      <td>Python Boot Camp in 大阪</td>\n",
       "      <td>4月8日(土)</td>\n",
       "      <td>株式会社ソウ</td>\n",
       "      <td>寺田 学</td>\n",
       "      <td>15名</td>\n",
       "      <td>3.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>8.0</td>\n",
       "      <td>Python Boot Camp in 神戸</td>\n",
       "      <td>5月20日(土)</td>\n",
       "      <td>株式会社神戸デジタル・ラボ</td>\n",
       "      <td>清水川 貴之</td>\n",
       "      <td>21名</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>9.0</td>\n",
       "      <td>Python Boot Camp in 長野</td>\n",
       "      <td>6月10日(土)</td>\n",
       "      <td>GEEKLAB.NAGANO</td>\n",
       "      <td>寺田 学</td>\n",
       "      <td>27名</td>\n",
       "      <td>3.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>10.0</td>\n",
       "      <td>Python Boot Camp in 香川</td>\n",
       "      <td>6月24日(土)</td>\n",
       "      <td>e-とぴあ・かがわ</td>\n",
       "      <td>鈴木 たかのり</td>\n",
       "      <td>20名</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>11.0</td>\n",
       "      <td>Python Boot Camp in 愛知</td>\n",
       "      <td>7月29日(土)</td>\n",
       "      <td>MYCAFE CLASSIC</td>\n",
       "      <td>鈴木 たかのり</td>\n",
       "      <td>37名</td>\n",
       "      <td>7.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>12.0</td>\n",
       "      <td>Python Boot Camp in 福岡</td>\n",
       "      <td>9月30日(土)</td>\n",
       "      <td>ヌーラボ 福岡本社</td>\n",
       "      <td>清水川 貴之</td>\n",
       "      <td>32名</td>\n",
       "      <td>5.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>13.0</td>\n",
       "      <td>Python Boot Camp in 長野八ヶ岳</td>\n",
       "      <td>10月28日(土)</td>\n",
       "      <td>富士見森のオフィス</td>\n",
       "      <td>鈴木 たかのり</td>\n",
       "      <td>15名</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>14.0</td>\n",
       "      <td>Python Boot Camp in 鹿児島</td>\n",
       "      <td>11月4日(土)</td>\n",
       "      <td>リコーITソリューションズ</td>\n",
       "      <td>清水川 貴之</td>\n",
       "      <td>27名</td>\n",
       "      <td>3.0</td>\n",
       "      <td>5.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>15.0</td>\n",
       "      <td>Python Boot Camp in 静岡</td>\n",
       "      <td>11月18日(土)</td>\n",
       "      <td>遠鉄モール街ビル Any</td>\n",
       "      <td>清水川 貴之</td>\n",
       "      <td>10名</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>16.0</td>\n",
       "      <td>Python Boot Camp in 新潟南魚沼</td>\n",
       "      <td>12月9日(土)</td>\n",
       "      <td>国際大学</td>\n",
       "      <td>鈴木 たかのり</td>\n",
       "      <td>21名</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>開催レポート</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>17.0</td>\n",
       "      <td>Python Boot Camp in 埼玉</td>\n",
       "      <td>12月16日(土)</td>\n",
       "      <td>貸し会議室6F（ロクエフ）</td>\n",
       "      <td>寺田 学</td>\n",
       "      <td>35名</td>\n",
       "      <td>10.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2018年</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>18.0</td>\n",
       "      <td>Python Boot Camp in 神奈川</td>\n",
       "      <td>1月27日(土)</td>\n",
       "      <td>FG-Space</td>\n",
       "      <td>鈴木 たかのり</td>\n",
       "      <td>15名(予定)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>19.0</td>\n",
       "      <td>Python Boot Camp in 金沢</td>\n",
       "      <td>2月24日(土)</td>\n",
       "      <td>ITビジネスプラザ武蔵</td>\n",
       "      <td>鈴木 たかのり</td>\n",
       "      <td>20名(予定)</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     No.                       イベント        開催日                 会場       講師  \\\n",
       "0    NaN                      2016年        NaN                NaN      NaN   \n",
       "1    1.0     Python Boot Camp in 京都   6月18日(土)     CAMPHOR- HOUSE     谷口 英   \n",
       "2    2.0     Python Boot Camp in 愛媛   7月30日(土)        サイボウズ松山オフィス     寺田 学   \n",
       "3    3.0     Python Boot Camp in 熊本   8月28日(日)              未来会議室     寺田 学   \n",
       "4    4.0     Python Boot Camp in 札幌  11月19日(土)     株式会社インフィニットループ    村岡 友介   \n",
       "5    NaN                      2017年        NaN                NaN      NaN   \n",
       "6    5.0   Python Boot Camp in 栃木小山   2月11日(土)       小山市立生涯学習センター     寺田 学   \n",
       "7    6.0     Python Boot Camp in 広島   3月11日(土)  中四国マネジメントシステム推進機構  鈴木 たかのり   \n",
       "8    7.0     Python Boot Camp in 大阪    4月8日(土)             株式会社ソウ     寺田 学   \n",
       "9    8.0     Python Boot Camp in 神戸   5月20日(土)      株式会社神戸デジタル・ラボ   清水川 貴之   \n",
       "10   9.0     Python Boot Camp in 長野   6月10日(土)     GEEKLAB.NAGANO     寺田 学   \n",
       "11  10.0     Python Boot Camp in 香川   6月24日(土)          e-とぴあ・かがわ  鈴木 たかのり   \n",
       "12  11.0     Python Boot Camp in 愛知   7月29日(土)     MYCAFE CLASSIC  鈴木 たかのり   \n",
       "13  12.0     Python Boot Camp in 福岡   9月30日(土)          ヌーラボ 福岡本社   清水川 貴之   \n",
       "14  13.0  Python Boot Camp in 長野八ヶ岳  10月28日(土)          富士見森のオフィス  鈴木 たかのり   \n",
       "15  14.0    Python Boot Camp in 鹿児島   11月4日(土)      リコーITソリューションズ   清水川 貴之   \n",
       "16  15.0     Python Boot Camp in 静岡  11月18日(土)       遠鉄モール街ビル Any   清水川 貴之   \n",
       "17  16.0  Python Boot Camp in 新潟南魚沼   12月9日(土)               国際大学  鈴木 たかのり   \n",
       "18  17.0     Python Boot Camp in 埼玉  12月16日(土)      貸し会議室6F（ロクエフ）     寺田 学   \n",
       "19   NaN                      2018年        NaN                NaN      NaN   \n",
       "20  18.0    Python Boot Camp in 神奈川   1月27日(土)           FG-Space  鈴木 たかのり   \n",
       "21  19.0     Python Boot Camp in 金沢   2月24日(土)        ITビジネスプラザ武蔵  鈴木 たかのり   \n",
       "\n",
       "        参加者    TA  Staff  開催レポート  \n",
       "0       NaN   NaN    NaN     NaN  \n",
       "1        5名   2.0    1.0  開催レポート  \n",
       "2       12名   2.0    1.0  開催レポート  \n",
       "3        8名   2.0    1.0  開催レポート  \n",
       "4       17名   2.0    1.0  開催レポート  \n",
       "5       NaN   NaN    NaN     NaN  \n",
       "6       10名   2.0    1.0  開催レポート  \n",
       "7       15名   3.0    1.0  開催レポート  \n",
       "8       15名   3.0    4.0  開催レポート  \n",
       "9       21名   3.0    1.0  開催レポート  \n",
       "10      27名   3.0    2.0  開催レポート  \n",
       "11      20名   3.0    1.0  開催レポート  \n",
       "12      37名   7.0    1.0  開催レポート  \n",
       "13      32名   5.0    3.0  開催レポート  \n",
       "14      15名   2.0    1.0  開催レポート  \n",
       "15      27名   3.0    5.0  開催レポート  \n",
       "16      10名   2.0    1.0  開催レポート  \n",
       "17      21名   2.0    2.0  開催レポート  \n",
       "18      35名  10.0    3.0     NaN  \n",
       "19      NaN   NaN    NaN     NaN  \n",
       "20  15名(予定)   NaN    NaN     NaN  \n",
       "21  20名(予定)   NaN    NaN     NaN  "
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### SQL DBからの読み込み\n",
    "SQLAlchemy が必要。データベースドライバも必要。\n",
    "\n",
    "```\n",
    "pip install sqlalchemy\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sqlalchemy import create_engine"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [],
   "source": [
    "engine = create_engine('sqlite:///data/sample.sqlite')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_sql_table(\"names\", engine)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>emal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>寺田</td>\n",
       "      <td>47</td>\n",
       "      <td>terada@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>佐藤</td>\n",
       "      <td>40</td>\n",
       "      <td>sato@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>鈴木</td>\n",
       "      <td>29</td>\n",
       "      <td>suzuki@example.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id name  age                emal\n",
       "0   1   寺田   47  terada@example.com\n",
       "1   2   佐藤   40    sato@example.com\n",
       "2   3   鈴木   29  suzuki@example.com"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## データの書き込み (CSV / Excel)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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>id</th>\n",
       "      <th>name</th>\n",
       "      <th>age</th>\n",
       "      <th>emal</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>寺田</td>\n",
       "      <td>47</td>\n",
       "      <td>terada@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>佐藤</td>\n",
       "      <td>40</td>\n",
       "      <td>sato@example.com</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>鈴木</td>\n",
       "      <td>29</td>\n",
       "      <td>suzuki@example.com</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id name  age                emal\n",
       "0   1   寺田   47  terada@example.com\n",
       "1   2   佐藤   40    sato@example.com\n",
       "2   3   鈴木   29  suzuki@example.com"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv(\"data/write_data.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Excel書き込み\n",
    "\n",
    "openpyxl ライブラリが必要\n",
    "\n",
    "```\n",
    "pip install openpyxl\n",
    "```"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_excel(\"data/write_data.xlsx\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## データの再利用 (pickle)\n",
    "\n",
    "データフレームを再利用できるように、Pythonのシリアライズ化の仕組みを使って、ファイルシステムに保存します。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame(np.random.rand(25, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.502265</td>\n",
       "      <td>0.506615</td>\n",
       "      <td>0.073911</td>\n",
       "      <td>0.934442</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.033736</td>\n",
       "      <td>0.765823</td>\n",
       "      <td>0.962033</td>\n",
       "      <td>0.097123</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.863137</td>\n",
       "      <td>0.367517</td>\n",
       "      <td>0.526101</td>\n",
       "      <td>0.563238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.666591</td>\n",
       "      <td>0.589653</td>\n",
       "      <td>0.819424</td>\n",
       "      <td>0.779328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.128294</td>\n",
       "      <td>0.302731</td>\n",
       "      <td>0.918204</td>\n",
       "      <td>0.255819</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.866347</td>\n",
       "      <td>0.228111</td>\n",
       "      <td>0.171862</td>\n",
       "      <td>0.790774</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.414800</td>\n",
       "      <td>0.810770</td>\n",
       "      <td>0.574628</td>\n",
       "      <td>0.492051</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.107245</td>\n",
       "      <td>0.838237</td>\n",
       "      <td>0.916670</td>\n",
       "      <td>0.805333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.324012</td>\n",
       "      <td>0.227226</td>\n",
       "      <td>0.919911</td>\n",
       "      <td>0.351835</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.036433</td>\n",
       "      <td>0.426265</td>\n",
       "      <td>0.250363</td>\n",
       "      <td>0.639535</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.299983</td>\n",
       "      <td>0.828170</td>\n",
       "      <td>0.430576</td>\n",
       "      <td>0.646176</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.423773</td>\n",
       "      <td>0.996568</td>\n",
       "      <td>0.786628</td>\n",
       "      <td>0.925284</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.991740</td>\n",
       "      <td>0.644650</td>\n",
       "      <td>0.672287</td>\n",
       "      <td>0.146793</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.402158</td>\n",
       "      <td>0.405343</td>\n",
       "      <td>0.463766</td>\n",
       "      <td>0.589826</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.518243</td>\n",
       "      <td>0.723570</td>\n",
       "      <td>0.360002</td>\n",
       "      <td>0.033282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.392618</td>\n",
       "      <td>0.575910</td>\n",
       "      <td>0.130106</td>\n",
       "      <td>0.528645</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.096264</td>\n",
       "      <td>0.030194</td>\n",
       "      <td>0.214438</td>\n",
       "      <td>0.051166</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.855383</td>\n",
       "      <td>0.214913</td>\n",
       "      <td>0.514782</td>\n",
       "      <td>0.818940</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.487411</td>\n",
       "      <td>0.137878</td>\n",
       "      <td>0.811166</td>\n",
       "      <td>0.603647</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.300247</td>\n",
       "      <td>0.408401</td>\n",
       "      <td>0.459504</td>\n",
       "      <td>0.024867</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.046584</td>\n",
       "      <td>0.261426</td>\n",
       "      <td>0.525490</td>\n",
       "      <td>0.554100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.703849</td>\n",
       "      <td>0.923588</td>\n",
       "      <td>0.714248</td>\n",
       "      <td>0.784633</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.345314</td>\n",
       "      <td>0.297163</td>\n",
       "      <td>0.331774</td>\n",
       "      <td>0.458114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.963648</td>\n",
       "      <td>0.106859</td>\n",
       "      <td>0.351487</td>\n",
       "      <td>0.191750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.607376</td>\n",
       "      <td>0.079341</td>\n",
       "      <td>0.684015</td>\n",
       "      <td>0.853392</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
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       "           0         1         2         3\n",
       "0   0.502265  0.506615  0.073911  0.934442\n",
       "1   0.033736  0.765823  0.962033  0.097123\n",
       "2   0.863137  0.367517  0.526101  0.563238\n",
       "3   0.666591  0.589653  0.819424  0.779328\n",
       "4   0.128294  0.302731  0.918204  0.255819\n",
       "5   0.866347  0.228111  0.171862  0.790774\n",
       "6   0.414800  0.810770  0.574628  0.492051\n",
       "7   0.107245  0.838237  0.916670  0.805333\n",
       "8   0.324012  0.227226  0.919911  0.351835\n",
       "9   0.036433  0.426265  0.250363  0.639535\n",
       "10  0.299983  0.828170  0.430576  0.646176\n",
       "11  0.423773  0.996568  0.786628  0.925284\n",
       "12  0.991740  0.644650  0.672287  0.146793\n",
       "13  0.402158  0.405343  0.463766  0.589826\n",
       "14  0.518243  0.723570  0.360002  0.033282\n",
       "15  0.392618  0.575910  0.130106  0.528645\n",
       "16  0.096264  0.030194  0.214438  0.051166\n",
       "17  0.855383  0.214913  0.514782  0.818940\n",
       "18  0.487411  0.137878  0.811166  0.603647\n",
       "19  0.300247  0.408401  0.459504  0.024867\n",
       "20  0.046584  0.261426  0.525490  0.554100\n",
       "21  0.703849  0.923588  0.714248  0.784633\n",
       "22  0.345314  0.297163  0.331774  0.458114\n",
       "23  0.963648  0.106859  0.351487  0.191750\n",
       "24  0.607376  0.079341  0.684015  0.853392"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_pickle(\"data/write_df.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.DataFrame([[1, 2, 3, 4], [4, 5, 7, 8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "      <td>5</td>\n",
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       "      <td>8</td>\n",
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       "  </tbody>\n",
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      "text/plain": [
       "   0  1  2  3\n",
       "0  1  2  3  4\n",
       "1  4  5  7  8"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_pickle(\"data/write_df.pickle\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>0.863137</td>\n",
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       "      <td>0.526101</td>\n",
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       "      <td>0.819424</td>\n",
       "      <td>0.779328</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.128294</td>\n",
       "      <td>0.302731</td>\n",
       "      <td>0.918204</td>\n",
       "      <td>0.255819</td>\n",
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       "          0         1         2         3\n",
       "0  0.502265  0.506615  0.073911  0.934442\n",
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       "2  0.863137  0.367517  0.526101  0.563238\n",
       "3  0.666591  0.589653  0.819424  0.779328\n",
       "4  0.128294  0.302731  0.918204  0.255819"
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     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(25, 4)"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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