総合実験１日目.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 総合実験（１日目）"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter Notebook を使った基本統計量の計算と可視化"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Jupyter Notebook (IPython Notebook) とは\n",
    "* Python という名のプログラミング言語が使えるプログラミング環境。計算コードと計算結果を同じ場所に時系列で保存できるので、実験系における実験ノートのように、いつどんな処理を行って何を得たのか記録して再現するのに便利。\n",
    "* [当学の演習室での使い方](https://raw.githubusercontent.com/maskot1977/-/master/%E6%BC%94%E7%BF%92%E5%AE%A4.txt)\n",
    "* [個人PCでのインストールと始め方](http://www.task-notes.com/entry/20151129/1448794509)\n",
    "* [小寺研究室](https://github.com/maskot1977/-/blob/master/L1%E3%82%BC%E3%83%9F2015%E5%B0%8F%E5%AF%BA%E7%A0%94%E7%A9%B6%E5%AE%A4.pptx.pdf) では、MacOSX上で右記のようにセットアップしています。> [環境構築](https://sites.google.com/site/masaakikotera/8-python/8-1-huan-jing-gou-zhu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### まずは、意味が分からなくてもいいので使ってみましょう\n",
    "* Python にまだ慣れてない人は、[Pythonウォーミングアップ](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/Python%E3%82%A6%E3%82%A9%E3%83%BC%E3%83%9F%E3%83%B3%E3%82%AF%E3%82%99%E3%82%A2%E3%83%83%E3%83%95%E3%82%9A.ipynb) に進んでください。\n",
    "\n",
    "### 本実習スタート\n",
    "* 本実習ではまず、下のプログラムを順次実行してもらいます。各自の画面中の IPython Notebook のセルに順次入力して（コピペ可）、「Shift + Enter」してください。\n",
    "* 最後に、課題を解いてもらいます。課題の結果を、指定する方法で指定するメールアドレスまで送信してください。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# URL によるリソースへのアクセスを提供するライブラリをインポートする。\n",
    "# import urllib # Python 2 の場合\n",
    "import urllib.request # Python 3 の場合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ウェブ上のリソースを指定する\n",
    "url = 'https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/iris.txt'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "('iris.txt', <httplib.HTTPMessage instance at 0x10621c638>)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 指定したURLからリソースをダウンロードし、名前をつける。\n",
    "# urllib.urlretrieve(url, 'iris.txt') # Python 2 の場合\n",
    "urllib.request.urlretrieve(url, 'iris.txt') # Python 3 の場合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\tSepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\r\n",
      "\n",
      "1\t5.1\t3.5\t1.4\t0.2\tsetosa\r\n",
      "\n",
      "2\t4.9\t3\t1.4\t0.2\tsetosa\r\n",
      "\n",
      "3\t4.7\t3.2\t1.3\t0.2\tsetosa\r\n",
      "\n",
      "4\t4.6\t3.1\t1.5\t0.2\tsetosa\r\n",
      "\n",
      "5\t5\t3.6\t1.4\t0.2\tsetosa\r\n",
      "\n",
      "6\t5.4\t3.9\t1.7\t0.4\tsetosa\r\n",
      "\n",
      "7\t4.6\t3.4\t1.4\t0.3\tsetosa\r\n",
      "\n",
      "8\t5\t3.4\t1.5\t0.2\tsetosa\r\n",
      "\n",
      "9\t4.4\t2.9\t1.4\t0.2\tsetosa\r\n",
      "\n",
      "10\t4.9\t3.1\t1.5\t0.1\tsetosa\r\n",
      "\n",
      "11\t5.4\t3.7\t1.5\t0.2\tsetosa\r\n",
      "\n",
      "12\t4.8\t3.4\t1.6\t0.2\tsetosa\r\n",
      "\n",
      "13\t4.8\t3\t1.4\t0.1\tsetosa\r\n",
      "\n",
      "14\t4.3\t3\t1.1\t0.1\tsetosa\r\n",
      "\n",
      "15\t5.8\t4\t1.2\t0.2\tsetosa\r\n",
      "\n",
      "16\t5.7\t4.4\t1.5\t0.4\tsetosa\r\n",
      "\n",
      "17\t5.4\t3.9\t1.3\t0.4\tsetosa\r\n",
      "\n",
      "18\t5.1\t3.5\t1.4\t0.3\tsetosa\r\n",
      "\n",
      "19\t5.7\t3.8\t1.7\t0.3\tsetosa\r\n",
      "\n",
      "20\t5.1\t3.8\t1.5\t0.3\tsetosa\r\n",
      "\n",
      "21\t5.4\t3.4\t1.7\t0.2\tsetosa\r\n",
      "\n",
      "22\t5.1\t3.7\t1.5\t0.4\tsetosa\r\n",
      "\n",
      "23\t4.6\t3.6\t1\t0.2\tsetosa\r\n",
      "\n",
      "24\t5.1\t3.3\t1.7\t0.5\tsetosa\r\n",
      "\n",
      "25\t4.8\t3.4\t1.9\t0.2\tsetosa\r\n",
      "\n",
      "26\t5\t3\t1.6\t0.2\tsetosa\r\n",
      "\n",
      "27\t5\t3.4\t1.6\t0.4\tsetosa\r\n",
      "\n",
      "28\t5.2\t3.5\t1.5\t0.2\tsetosa\r\n",
      "\n",
      "29\t5.2\t3.4\t1.4\t0.2\tsetosa\r\n",
      "\n",
      "30\t4.7\t3.2\t1.6\t0.2\tsetosa\r\n",
      "\n",
      "31\t4.8\t3.1\t1.6\t0.2\tsetosa\r\n",
      "\n",
      "32\t5.4\t3.4\t1.5\t0.4\tsetosa\r\n",
      "\n",
      "33\t5.2\t4.1\t1.5\t0.1\tsetosa\r\n",
      "\n",
      "34\t5.5\t4.2\t1.4\t0.2\tsetosa\r\n",
      "\n",
      "35\t4.9\t3.1\t1.5\t0.2\tsetosa\r\n",
      "\n",
      "36\t5\t3.2\t1.2\t0.2\tsetosa\r\n",
      "\n",
      "37\t5.5\t3.5\t1.3\t0.2\tsetosa\r\n",
      "\n",
      "38\t4.9\t3.6\t1.4\t0.1\tsetosa\r\n",
      "\n",
      "39\t4.4\t3\t1.3\t0.2\tsetosa\r\n",
      "\n",
      "40\t5.1\t3.4\t1.5\t0.2\tsetosa\r\n",
      "\n",
      "41\t5\t3.5\t1.3\t0.3\tsetosa\r\n",
      "\n",
      "42\t4.5\t2.3\t1.3\t0.3\tsetosa\r\n",
      "\n",
      "43\t4.4\t3.2\t1.3\t0.2\tsetosa\r\n",
      "\n",
      "44\t5\t3.5\t1.6\t0.6\tsetosa\r\n",
      "\n",
      "45\t5.1\t3.8\t1.9\t0.4\tsetosa\r\n",
      "\n",
      "46\t4.8\t3\t1.4\t0.3\tsetosa\r\n",
      "\n",
      "47\t5.1\t3.8\t1.6\t0.2\tsetosa\r\n",
      "\n",
      "48\t4.6\t3.2\t1.4\t0.2\tsetosa\r\n",
      "\n",
      "49\t5.3\t3.7\t1.5\t0.2\tsetosa\r\n",
      "\n",
      "50\t5\t3.3\t1.4\t0.2\tsetosa\r\n",
      "\n",
      "51\t7\t3.2\t4.7\t1.4\tversicolor\r\n",
      "\n",
      "52\t6.4\t3.2\t4.5\t1.5\tversicolor\r\n",
      "\n",
      "53\t6.9\t3.1\t4.9\t1.5\tversicolor\r\n",
      "\n",
      "54\t5.5\t2.3\t4\t1.3\tversicolor\r\n",
      "\n",
      "55\t6.5\t2.8\t4.6\t1.5\tversicolor\r\n",
      "\n",
      "56\t5.7\t2.8\t4.5\t1.3\tversicolor\r\n",
      "\n",
      "57\t6.3\t3.3\t4.7\t1.6\tversicolor\r\n",
      "\n",
      "58\t4.9\t2.4\t3.3\t1\tversicolor\r\n",
      "\n",
      "59\t6.6\t2.9\t4.6\t1.3\tversicolor\r\n",
      "\n",
      "60\t5.2\t2.7\t3.9\t1.4\tversicolor\r\n",
      "\n",
      "61\t5\t2\t3.5\t1\tversicolor\r\n",
      "\n",
      "62\t5.9\t3\t4.2\t1.5\tversicolor\r\n",
      "\n",
      "63\t6\t2.2\t4\t1\tversicolor\r\n",
      "\n",
      "64\t6.1\t2.9\t4.7\t1.4\tversicolor\r\n",
      "\n",
      "65\t5.6\t2.9\t3.6\t1.3\tversicolor\r\n",
      "\n",
      "66\t6.7\t3.1\t4.4\t1.4\tversicolor\r\n",
      "\n",
      "67\t5.6\t3\t4.5\t1.5\tversicolor\r\n",
      "\n",
      "68\t5.8\t2.7\t4.1\t1\tversicolor\r\n",
      "\n",
      "69\t6.2\t2.2\t4.5\t1.5\tversicolor\r\n",
      "\n",
      "70\t5.6\t2.5\t3.9\t1.1\tversicolor\r\n",
      "\n",
      "71\t5.9\t3.2\t4.8\t1.8\tversicolor\r\n",
      "\n",
      "72\t6.1\t2.8\t4\t1.3\tversicolor\r\n",
      "\n",
      "73\t6.3\t2.5\t4.9\t1.5\tversicolor\r\n",
      "\n",
      "74\t6.1\t2.8\t4.7\t1.2\tversicolor\r\n",
      "\n",
      "75\t6.4\t2.9\t4.3\t1.3\tversicolor\r\n",
      "\n",
      "76\t6.6\t3\t4.4\t1.4\tversicolor\r\n",
      "\n",
      "77\t6.8\t2.8\t4.8\t1.4\tversicolor\r\n",
      "\n",
      "78\t6.7\t3\t5\t1.7\tversicolor\r\n",
      "\n",
      "79\t6\t2.9\t4.5\t1.5\tversicolor\r\n",
      "\n",
      "80\t5.7\t2.6\t3.5\t1\tversicolor\r\n",
      "\n",
      "81\t5.5\t2.4\t3.8\t1.1\tversicolor\r\n",
      "\n",
      "82\t5.5\t2.4\t3.7\t1\tversicolor\r\n",
      "\n",
      "83\t5.8\t2.7\t3.9\t1.2\tversicolor\r\n",
      "\n",
      "84\t6\t2.7\t5.1\t1.6\tversicolor\r\n",
      "\n",
      "85\t5.4\t3\t4.5\t1.5\tversicolor\r\n",
      "\n",
      "86\t6\t3.4\t4.5\t1.6\tversicolor\r\n",
      "\n",
      "87\t6.7\t3.1\t4.7\t1.5\tversicolor\r\n",
      "\n",
      "88\t6.3\t2.3\t4.4\t1.3\tversicolor\r\n",
      "\n",
      "89\t5.6\t3\t4.1\t1.3\tversicolor\r\n",
      "\n",
      "90\t5.5\t2.5\t4\t1.3\tversicolor\r\n",
      "\n",
      "91\t5.5\t2.6\t4.4\t1.2\tversicolor\r\n",
      "\n",
      "92\t6.1\t3\t4.6\t1.4\tversicolor\r\n",
      "\n",
      "93\t5.8\t2.6\t4\t1.2\tversicolor\r\n",
      "\n",
      "94\t5\t2.3\t3.3\t1\tversicolor\r\n",
      "\n",
      "95\t5.6\t2.7\t4.2\t1.3\tversicolor\r\n",
      "\n",
      "96\t5.7\t3\t4.2\t1.2\tversicolor\r\n",
      "\n",
      "97\t5.7\t2.9\t4.2\t1.3\tversicolor\r\n",
      "\n",
      "98\t6.2\t2.9\t4.3\t1.3\tversicolor\r\n",
      "\n",
      "99\t5.1\t2.5\t3\t1.1\tversicolor\r\n",
      "\n",
      "100\t5.7\t2.8\t4.1\t1.3\tversicolor\r\n",
      "\n",
      "101\t6.3\t3.3\t6\t2.5\tvirginica\r\n",
      "\n",
      "102\t5.8\t2.7\t5.1\t1.9\tvirginica\r\n",
      "\n",
      "103\t7.1\t3\t5.9\t2.1\tvirginica\r\n",
      "\n",
      "104\t6.3\t2.9\t5.6\t1.8\tvirginica\r\n",
      "\n",
      "105\t6.5\t3\t5.8\t2.2\tvirginica\r\n",
      "\n",
      "106\t7.6\t3\t6.6\t2.1\tvirginica\r\n",
      "\n",
      "107\t4.9\t2.5\t4.5\t1.7\tvirginica\r\n",
      "\n",
      "108\t7.3\t2.9\t6.3\t1.8\tvirginica\r\n",
      "\n",
      "109\t6.7\t2.5\t5.8\t1.8\tvirginica\r\n",
      "\n",
      "110\t7.2\t3.6\t6.1\t2.5\tvirginica\r\n",
      "\n",
      "111\t6.5\t3.2\t5.1\t2\tvirginica\r\n",
      "\n",
      "112\t6.4\t2.7\t5.3\t1.9\tvirginica\r\n",
      "\n",
      "113\t6.8\t3\t5.5\t2.1\tvirginica\r\n",
      "\n",
      "114\t5.7\t2.5\t5\t2\tvirginica\r\n",
      "\n",
      "115\t5.8\t2.8\t5.1\t2.4\tvirginica\r\n",
      "\n",
      "116\t6.4\t3.2\t5.3\t2.3\tvirginica\r\n",
      "\n",
      "117\t6.5\t3\t5.5\t1.8\tvirginica\r\n",
      "\n",
      "118\t7.7\t3.8\t6.7\t2.2\tvirginica\r\n",
      "\n",
      "119\t7.7\t2.6\t6.9\t2.3\tvirginica\r\n",
      "\n",
      "120\t6\t2.2\t5\t1.5\tvirginica\r\n",
      "\n",
      "121\t6.9\t3.2\t5.7\t2.3\tvirginica\r\n",
      "\n",
      "122\t5.6\t2.8\t4.9\t2\tvirginica\r\n",
      "\n",
      "123\t7.7\t2.8\t6.7\t2\tvirginica\r\n",
      "\n",
      "124\t6.3\t2.7\t4.9\t1.8\tvirginica\r\n",
      "\n",
      "125\t6.7\t3.3\t5.7\t2.1\tvirginica\r\n",
      "\n",
      "126\t7.2\t3.2\t6\t1.8\tvirginica\r\n",
      "\n",
      "127\t6.2\t2.8\t4.8\t1.8\tvirginica\r\n",
      "\n",
      "128\t6.1\t3\t4.9\t1.8\tvirginica\r\n",
      "\n",
      "129\t6.4\t2.8\t5.6\t2.1\tvirginica\r\n",
      "\n",
      "130\t7.2\t3\t5.8\t1.6\tvirginica\r\n",
      "\n",
      "131\t7.4\t2.8\t6.1\t1.9\tvirginica\r\n",
      "\n",
      "132\t7.9\t3.8\t6.4\t2\tvirginica\r\n",
      "\n",
      "133\t6.4\t2.8\t5.6\t2.2\tvirginica\r\n",
      "\n",
      "134\t6.3\t2.8\t5.1\t1.5\tvirginica\r\n",
      "\n",
      "135\t6.1\t2.6\t5.6\t1.4\tvirginica\r\n",
      "\n",
      "136\t7.7\t3\t6.1\t2.3\tvirginica\r\n",
      "\n",
      "137\t6.3\t3.4\t5.6\t2.4\tvirginica\r\n",
      "\n",
      "138\t6.4\t3.1\t5.5\t1.8\tvirginica\r\n",
      "\n",
      "139\t6\t3\t4.8\t1.8\tvirginica\r\n",
      "\n",
      "140\t6.9\t3.1\t5.4\t2.1\tvirginica\r\n",
      "\n",
      "141\t6.7\t3.1\t5.6\t2.4\tvirginica\r\n",
      "\n",
      "142\t6.9\t3.1\t5.1\t2.3\tvirginica\r\n",
      "\n",
      "143\t5.8\t2.7\t5.1\t1.9\tvirginica\r\n",
      "\n",
      "144\t6.8\t3.2\t5.9\t2.3\tvirginica\r\n",
      "\n",
      "145\t6.7\t3.3\t5.7\t2.5\tvirginica\r\n",
      "\n",
      "146\t6.7\t3\t5.2\t2.3\tvirginica\r\n",
      "\n",
      "147\t6.3\t2.5\t5\t1.9\tvirginica\r\n",
      "\n",
      "148\t6.5\t3\t5.2\t2\tvirginica\r\n",
      "\n",
      "149\t6.2\t3.4\t5.4\t2.3\tvirginica\r\n",
      "\n",
      "150\t5.9\t3\t5.1\t1.8\tvirginica\r\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ダウンロードしたファイルの中身を確認する。\n",
    "for line in open('iris.txt'):\n",
    "    print (line)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0 \tSepal.Length\tSepal.Width\tPetal.Length\tPetal.Width\tSpecies\r\n",
      "\n",
      "1 1\t5.1\t3.5\t1.4\t0.2\tsetosa\r\n",
      "\n",
      "2 2\t4.9\t3\t1.4\t0.2\tsetosa\r\n",
      "\n",
      "3 3\t4.7\t3.2\t1.3\t0.2\tsetosa\r\n",
      "\n",
      "4 4\t4.6\t3.1\t1.5\t0.2\tsetosa\r\n",
      "\n",
      "5 5\t5\t3.6\t1.4\t0.2\tsetosa\r\n",
      "\n",
      "6 6\t5.4\t3.9\t1.7\t0.4\tsetosa\r\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# ダウンロードしたファイルの中身を確認する（行番号付き）\n",
    "for i, line in enumerate(open('iris.txt')):\n",
    "    print (i, line)\n",
    "    if i > 5:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['1', '5.1', '3.5', '1.4', '0.2', 'setosa']\n",
      "['2', '4.9', '3', '1.4', '0.2', 'setosa']\n",
      "['3', '4.7', '3.2', '1.3', '0.2', 'setosa']\n",
      "['4', '4.6', '3.1', '1.5', '0.2', 'setosa']\n",
      "['5', '5', '3.6', '1.4', '0.2', 'setosa']\n",
      "['6', '5.4', '3.9', '1.7', '0.4', 'setosa']\n"
     ]
    }
   ],
   "source": [
    "# ダウンロードしたファイルの中身を確認する（先頭行を無視して、空白でデータ区切りをする）\n",
    "for i, line in enumerate(open('iris.txt')):\n",
    "    if i == 0:\n",
    "        continue\n",
    "    else:\n",
    "        a = line.split()\n",
    "        print (a)\n",
    "    if i > 5:\n",
    "        break"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ダウンロードしたファイルから、指定した列の数字をリストに入れる。\n",
    "x1 = []\n",
    "for i, line in enumerate(open('iris.txt')):\n",
    "    if i == 0:\n",
    "        continue\n",
    "    else:\n",
    "        a = line.split()\n",
    "        x1.append(float(a[1]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]\n"
     ]
    }
   ],
   "source": [
    "# リスト x1 の中身を確認する。\n",
    "print (x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# ダウンロードしたファイルから、４つの列の数字をそれぞれ４つのリストに入れる。\n",
    "x1 = []\n",
    "x2 = []\n",
    "x3 = []\n",
    "x4 = []\n",
    "for i, line in enumerate(open('iris.txt')):\n",
    "    if i == 0:\n",
    "        continue\n",
    "    else:\n",
    "        a = line.split()\n",
    "        x1.append(float(a[1]))\n",
    "        x2.append(float(a[2]))\n",
    "        x3.append(float(a[3]))\n",
    "        x4.append(float(a[4]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5.1, 4.9, 4.7, 4.6, 5.0, 5.4, 4.6, 5.0, 4.4, 4.9, 5.4, 4.8, 4.8, 4.3, 5.8, 5.7, 5.4, 5.1, 5.7, 5.1, 5.4, 5.1, 4.6, 5.1, 4.8, 5.0, 5.0, 5.2, 5.2, 4.7, 4.8, 5.4, 5.2, 5.5, 4.9, 5.0, 5.5, 4.9, 4.4, 5.1, 5.0, 4.5, 4.4, 5.0, 5.1, 4.8, 5.1, 4.6, 5.3, 5.0, 7.0, 6.4, 6.9, 5.5, 6.5, 5.7, 6.3, 4.9, 6.6, 5.2, 5.0, 5.9, 6.0, 6.1, 5.6, 6.7, 5.6, 5.8, 6.2, 5.6, 5.9, 6.1, 6.3, 6.1, 6.4, 6.6, 6.8, 6.7, 6.0, 5.7, 5.5, 5.5, 5.8, 6.0, 5.4, 6.0, 6.7, 6.3, 5.6, 5.5, 5.5, 6.1, 5.8, 5.0, 5.6, 5.7, 5.7, 6.2, 5.1, 5.7, 6.3, 5.8, 7.1, 6.3, 6.5, 7.6, 4.9, 7.3, 6.7, 7.2, 6.5, 6.4, 6.8, 5.7, 5.8, 6.4, 6.5, 7.7, 7.7, 6.0, 6.9, 5.6, 7.7, 6.3, 6.7, 7.2, 6.2, 6.1, 6.4, 7.2, 7.4, 7.9, 6.4, 6.3, 6.1, 7.7, 6.3, 6.4, 6.0, 6.9, 6.7, 6.9, 5.8, 6.8, 6.7, 6.7, 6.3, 6.5, 6.2, 5.9]\n"
     ]
    }
   ],
   "source": [
    "# リスト x1 の中身を確認する。\n",
    "print (x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3.5, 3.0, 3.2, 3.1, 3.6, 3.9, 3.4, 3.4, 2.9, 3.1, 3.7, 3.4, 3.0, 3.0, 4.0, 4.4, 3.9, 3.5, 3.8, 3.8, 3.4, 3.7, 3.6, 3.3, 3.4, 3.0, 3.4, 3.5, 3.4, 3.2, 3.1, 3.4, 4.1, 4.2, 3.1, 3.2, 3.5, 3.6, 3.0, 3.4, 3.5, 2.3, 3.2, 3.5, 3.8, 3.0, 3.8, 3.2, 3.7, 3.3, 3.2, 3.2, 3.1, 2.3, 2.8, 2.8, 3.3, 2.4, 2.9, 2.7, 2.0, 3.0, 2.2, 2.9, 2.9, 3.1, 3.0, 2.7, 2.2, 2.5, 3.2, 2.8, 2.5, 2.8, 2.9, 3.0, 2.8, 3.0, 2.9, 2.6, 2.4, 2.4, 2.7, 2.7, 3.0, 3.4, 3.1, 2.3, 3.0, 2.5, 2.6, 3.0, 2.6, 2.3, 2.7, 3.0, 2.9, 2.9, 2.5, 2.8, 3.3, 2.7, 3.0, 2.9, 3.0, 3.0, 2.5, 2.9, 2.5, 3.6, 3.2, 2.7, 3.0, 2.5, 2.8, 3.2, 3.0, 3.8, 2.6, 2.2, 3.2, 2.8, 2.8, 2.7, 3.3, 3.2, 2.8, 3.0, 2.8, 3.0, 2.8, 3.8, 2.8, 2.8, 2.6, 3.0, 3.4, 3.1, 3.0, 3.1, 3.1, 3.1, 2.7, 3.2, 3.3, 3.0, 2.5, 3.0, 3.4, 3.0]\n"
     ]
    }
   ],
   "source": [
    "# リスト x2 の中身を確認する。\n",
    "print (x2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1.4, 1.4, 1.3, 1.5, 1.4, 1.7, 1.4, 1.5, 1.4, 1.5, 1.5, 1.6, 1.4, 1.1, 1.2, 1.5, 1.3, 1.4, 1.7, 1.5, 1.7, 1.5, 1.0, 1.7, 1.9, 1.6, 1.6, 1.5, 1.4, 1.6, 1.6, 1.5, 1.5, 1.4, 1.5, 1.2, 1.3, 1.4, 1.3, 1.5, 1.3, 1.3, 1.3, 1.6, 1.9, 1.4, 1.6, 1.4, 1.5, 1.4, 4.7, 4.5, 4.9, 4.0, 4.6, 4.5, 4.7, 3.3, 4.6, 3.9, 3.5, 4.2, 4.0, 4.7, 3.6, 4.4, 4.5, 4.1, 4.5, 3.9, 4.8, 4.0, 4.9, 4.7, 4.3, 4.4, 4.8, 5.0, 4.5, 3.5, 3.8, 3.7, 3.9, 5.1, 4.5, 4.5, 4.7, 4.4, 4.1, 4.0, 4.4, 4.6, 4.0, 3.3, 4.2, 4.2, 4.2, 4.3, 3.0, 4.1, 6.0, 5.1, 5.9, 5.6, 5.8, 6.6, 4.5, 6.3, 5.8, 6.1, 5.1, 5.3, 5.5, 5.0, 5.1, 5.3, 5.5, 6.7, 6.9, 5.0, 5.7, 4.9, 6.7, 4.9, 5.7, 6.0, 4.8, 4.9, 5.6, 5.8, 6.1, 6.4, 5.6, 5.1, 5.6, 6.1, 5.6, 5.5, 4.8, 5.4, 5.6, 5.1, 5.1, 5.9, 5.7, 5.2, 5.0, 5.2, 5.4, 5.1]\n"
     ]
    }
   ],
   "source": [
    "# リスト x3 の中身を確認する。\n",
    "print (x3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.2, 0.2, 0.2, 0.2, 0.2, 0.4, 0.3, 0.2, 0.2, 0.1, 0.2, 0.2, 0.1, 0.1, 0.2, 0.4, 0.4, 0.3, 0.3, 0.3, 0.2, 0.4, 0.2, 0.5, 0.2, 0.2, 0.4, 0.2, 0.2, 0.2, 0.2, 0.4, 0.1, 0.2, 0.2, 0.2, 0.2, 0.1, 0.2, 0.2, 0.3, 0.3, 0.2, 0.6, 0.4, 0.3, 0.2, 0.2, 0.2, 0.2, 1.4, 1.5, 1.5, 1.3, 1.5, 1.3, 1.6, 1.0, 1.3, 1.4, 1.0, 1.5, 1.0, 1.4, 1.3, 1.4, 1.5, 1.0, 1.5, 1.1, 1.8, 1.3, 1.5, 1.2, 1.3, 1.4, 1.4, 1.7, 1.5, 1.0, 1.1, 1.0, 1.2, 1.6, 1.5, 1.6, 1.5, 1.3, 1.3, 1.3, 1.2, 1.4, 1.2, 1.0, 1.3, 1.2, 1.3, 1.3, 1.1, 1.3, 2.5, 1.9, 2.1, 1.8, 2.2, 2.1, 1.7, 1.8, 1.8, 2.5, 2.0, 1.9, 2.1, 2.0, 2.4, 2.3, 1.8, 2.2, 2.3, 1.5, 2.3, 2.0, 2.0, 1.8, 2.1, 1.8, 1.8, 1.8, 2.1, 1.6, 1.9, 2.0, 2.2, 1.5, 1.4, 2.3, 2.4, 1.8, 1.8, 2.1, 2.4, 2.3, 1.9, 2.3, 2.5, 2.3, 1.9, 2.0, 2.3, 1.8]\n"
     ]
    }
   ],
   "source": [
    "# リスト x4 の中身を確認する。\n",
    "print (x4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 平均値を求める関数を作る\n",
    "* 同じ計算を繰り返すために、何度も同じプログラムを書くのは面倒です。関数を定義して、使いまわしましょう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 平均値を求める関数\n",
    "def average(data):\n",
    "    sum = 0.0\n",
    "    n = 0.0\n",
    "    for x in data:\n",
    "        sum += x\n",
    "        n += 1.0\n",
    "    return sum / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.843333333333335"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# リスト x1 の平均値を求める。\n",
    "average(x1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5.84333333333\n",
      "3.05733333333\n",
      "3.758\n",
      "1.19933333333\n"
     ]
    }
   ],
   "source": [
    "# すべてのリストの平均値をそれぞれ求める。\n",
    "print (average(x1))\n",
    "print (average(x2))\n",
    "print (average(x3))\n",
    "print (average(x4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 分散を求める関数を作る\n",
    "* 同じ計算を繰り返すために、何度も同じプログラムを書くのは面倒です。関数を定義して、使いまわしましょう。\n",
    "* 分散とは何か？標準偏差とは何か？忘れてしまった人は右記を参照。 http://www.cybernet.co.jp/cetol/kousa/kousa7.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 分散を求める関数\n",
    "def variance(data):\n",
    "    ave = average(data)\n",
    "    accum = 0.0\n",
    "    n = 0.0\n",
    "    for x in data:\n",
    "        accum += (x - ave) ** 2.0\n",
    "        n += 1.0\n",
    "    return accum / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.681122222222\n",
      "0.188712888889\n",
      "3.09550266667\n",
      "0.577132888889\n"
     ]
    }
   ],
   "source": [
    "# すべてのリストの分散をそれぞれ求める。\n",
    "print (variance(x1))\n",
    "print (variance(x2))\n",
    "print (variance(x3))\n",
    "print (variance(x4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 標準偏差を求める関数を作る\n",
    "* 同じ計算を繰り返すために、何度も同じプログラムを書くのは面倒です。関数を定義して、使いまわしましょう。\n",
    "* 分散とは何か？標準偏差とは何か？忘れてしまった人は右記を参照。 http://www.cybernet.co.jp/cetol/kousa/kousa7.html\n",
    "* １行で書ける関数は、上の「平均を求める関数」や「分散を求める関数」のような定義もできますが、下の例のように lambda というキーワードを使って関数を定義することができます。\n",
    "* 「import math」は、平方根（sqrt）などの数学関数を使うためのライブラリをインポートするために唱えます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 標準偏差を求める関数\n",
    "import math\n",
    "standard_deviation = lambda data: math.sqrt(variance(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.825301291785\n",
      "0.434410967735\n",
      "1.75940406578\n",
      "0.759692627902\n"
     ]
    }
   ],
   "source": [
    "# すべてのリストの標準偏差をそれぞれ求める。\n",
    "print (standard_deviation(x1))\n",
    "print (standard_deviation(x2))\n",
    "print (standard_deviation(x3))\n",
    "print (standard_deviation(x4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 共分散を求める関数を作る\n",
    "* 同じ計算を繰り返すために、何度も同じプログラムを書くのは面倒です。関数を定義して、使いまわしましょう。\n",
    "* 共分散とは何か？相関係数とは何か？忘れてしまった人は右記を参照。 http://kogures.com/hitoshi/webtext/stat-soukan/index.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 共分散（偏差積の平均）を求める関数\n",
    "def covariance(data1, data2): \n",
    "    ave1 = average(data1)\n",
    "    ave2 = average(data2)\n",
    "    accum = 0.0\n",
    "    n = 0.0\n",
    "    for d1, d2 in zip(data1, data2):\n",
    "        accum += (d1 - ave1) * (d2 - ave2)\n",
    "        n += 1.0\n",
    "    return accum / n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.0421511111111\n",
      "1.26582\n",
      "0.512828888889\n",
      "-0.327458666667\n",
      "-0.120828444444\n",
      "1.286972\n"
     ]
    }
   ],
   "source": [
    "# すべてのリスト対の共分散をそれぞれ求める。\n",
    "print (covariance(x1, x2))\n",
    "print (covariance(x1, x3))\n",
    "print (covariance(x1, x4))\n",
    "print (covariance(x2, x3))\n",
    "print (covariance(x2, x4))\n",
    "print (covariance(x3, x4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 相関係数を求める関数を作る\n",
    "* 同じ計算を繰り返すために、何度も同じプログラムを書くのは面倒です。関数を定義して、使いまわしましょう。\n",
    "* 共分散とは何か？相関係数とは何か？忘れてしまった人は右記を参照。 http://kogures.com/hitoshi/webtext/stat-soukan/index.html\n",
    "* １行で書ける関数は、上の「平均を求める関数」や「分散を求める関数」のような定義もできますが、下の例のように lambda というキーワードを使って関数を定義することができます。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 相関係数を求める関数\n",
    "correlation = lambda data1, data2: covariance(data1, data2) / (standard_deviation(data1) * standard_deviation(data2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "-0.117569784133\n",
      "0.871753775887\n",
      "0.817941126272\n",
      "-0.428440104331\n",
      "-0.366125932536\n",
      "0.962865431403\n"
     ]
    }
   ],
   "source": [
    "# すべてのリスト対の相関係数をそれぞれ求める。\n",
    "print (correlation(x1, x2))\n",
    "print (correlation(x1, x3))\n",
    "print (correlation(x1, x4))\n",
    "print (correlation(x2, x3))\n",
    "print (correlation(x2, x4))\n",
    "print (correlation(x3, x4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### データを図示する\n",
    "* 平均、分散、標準偏差、共分散、相関係数などの数字だけ見て満足せずに、データをヒストグラムで表したり、散布図にプロットしてデータの特徴を把握しましょう。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 図やグラフを図示するためのライブラリをインポートする。\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__ヒストグラム__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  9.,  23.,  14.,  27.,  22.,  20.,  18.,   6.,   5.,   6.]),\n",
       " array([ 4.3 ,  4.66,  5.02,  5.38,  5.74,  6.1 ,  6.46,  6.82,  7.18,\n",
       "         7.54,  7.9 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RcSwiXiqW/0nSG2z/7JjrVEQ8W/z5nKQ71H7OTqdJ6GfPOiekn09LOhQR/1q8v13t8Ow0\nCf3sWeeE9POED0p6sPjad5uEfp6wZJ0T0M8LJD0ZEc9HxE8l/aOk3+jap+9eDh3qEXFdRGyMiLdL\n+j1JeyLisq7ddkq6TDp5J+rRiFgYduyq6+ycq7J9rtqXfA73/IE+2V5j+9Ri+RRJH5D0SNdutfez\nTJ2T0M+iL4dsn1WsOl/SY1271d7PMnVOQj87XKKlpzRq72eHJeucgH7OS3q37Z+xbbW/5t33+vTd\ny5E9+8X2lWo/NuDGiPie7Q/Z/ndJxyVdMapx+9VZp6Tfsf2Hkl6R9N9qz8WN23pJd7j9eIXVkm6N\niN0T2M+edWoy+ilJV0u6tfhR/ElJV0xgP3vWqQnpp+01ap9l/kHHuonrZ686VXM/I+J+27dLmi1q\n2CfpxmF7yc1HAJAIv84OABIh1AEgEUIdABIh1AEgEUIdABIh1AEgEUIdABIh1AEgkf8FekZsSO9L\n3FYAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10be27f50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x1のヒストグラム\n",
    "plt.hist(x1, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([  4.,   7.,  22.,  24.,  37.,  31.,  10.,  11.,   2.,   2.]),\n",
       " array([ 2.  ,  2.24,  2.48,  2.72,  2.96,  3.2 ,  3.44,  3.68,  3.92,\n",
       "         4.16,  4.4 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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yeuBTwBvG1P6ppG9f9Hyjqt4+hfo2TFU9k+Tiqnq69zeY+5N8paq+9fw5szIuBumLnubH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\nXZ2+BAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f05ce90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x2のヒストグラム\n",
    "plt.hist(x2, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 37.,  13.,   0.,   3.,   8.,  26.,  29.,  18.,  11.,   5.]),\n",
       " array([ 1.  ,  1.59,  2.18,  2.77,  3.36,  3.95,  4.54,  5.13,  5.72,\n",
       "         6.31,  6.9 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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8ge7X74/69zuVmXh3IdA/srJe/q/A/cBbq+qbEx9sSpL8PPAMcGtV/dy884wjyTZgW1U9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\nl6SGWeKS1DBLXJIa9n/apDYr+pI6QgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f1e54d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x3のヒストグラム\n",
    "plt.hist(x3, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(array([ 41.,   8.,   1.,   7.,   8.,  33.,   6.,  23.,   9.,  14.]),\n",
       " array([ 0.1 ,  0.34,  0.58,  0.82,  1.06,  1.3 ,  1.54,  1.78,  2.02,\n",
       "         2.26,  2.5 ]),\n",
       " <a list of 10 Patch objects>)"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9pcBjS94/3lsnSZqSsebU18OmTZuAn/PYYzdPpf+q4qSTMpW+JWlcqarRPpj8ITBbVRf0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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f352910>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x4のヒストグラム\n",
    "plt.hist(x4, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "__散布図(scatter plot)__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10f6a8390>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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xA0/yWVwuL8kkVmnEnOSccdVCodrOYgVTXJVLkphDXRdZ/P3JYkxpC57kzWwRsA/4OLDT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\nwAQNXWgAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f617850>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x1 と x2 の関係をプロットする。\n",
    "plt.scatter(x1, x2, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10fa314d0>"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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Tn35rrvTxb/OlD4BYbJhCwbFu3QcuOK5YooDzbNx4xbIlilJlngs/z+H5z1ON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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10f3f1750>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x1 と x3 の関係をプロットする。\n",
    "plt.scatter(x1, x3, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10fb8ad10>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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PU2xN3YxT6F8MJ8bMNgDu7jvc/Rtmdr2Z/RioArdnXRPwx2b2ceAUcJLaucu0\nrQCestpUGvN/aPetjMcqtiayGas7gS/XTykcAm7PwWsqsiayGaf5v+L/MPBfGp7LdKziaqLHY+Xu\n3zWzXcD36sfcB+zIcpw6qYkuxkl/LCYiUmB9O8e/iIiEUxMQESkwNQERkQJTExARKTA1ARGRAlMT\nEBEpMDUBEZECUxMQESmw/w8sXQzU8+mV+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb1b6d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x1 と x4 の関係をプロットする。\n",
    "plt.scatter(x1, x4, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10fe34d90>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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t2yTbldLB6tX/nsHBLpqbP0hf3+B5x6VSKUZHlxOPr5643moGBpZPO5fPZ8zx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\nIiIhpOQtIhJCSt4iIiGk5C0iEkL/H5klDfOPZksqAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fb8acd0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x2 と x3 の関係をプロットする。\n",
    "plt.scatter(x2, x3, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x10fef72d0>"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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FL5JHSgIZFFoREyJmRUxoX7WbvgF1N33r7e3F7ABjY/sAGBvbh9kBent7Wxa/SB7pdFBG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10fcc0090>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x2 と x4 の関係をプロットする。\n",
    "plt.scatter(x2, x4, alpha=0.5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x110046b10>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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bzOwLdR5TRERqULaHb2Zbgdn5L5Er4N8osrmXGGaZu/eZ2fvIFf4ud/+Xqmcr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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x10feace50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# x3 と x4 の関係をプロットする。\n",
    "plt.scatter(x3, x4, alpha=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Scatter Matrix\n",
    "* データの全体像を眺めたいと思った時、こういう方法も便利です。本日の実習では「こんな方法もあるんだな」と知っておく程度で十分です。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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cECJmenoaAwPjWL36ixgYGJ/1KpuI5nzB2E05DAy8igsv3IqBgVdnTVGa685k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\nTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1189a0f10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 図やグラフを図示するためのライブラリをインポートする。\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import pandas # データフレームワーク処理のライブラリをインポート\n",
    "from pandas.tools import plotting # 高度なプロットを行うツールのインポート\n",
    "df = pandas.read_csv('iris.txt', sep='\\t', na_values=\".\") # データの読み込み\n",
    "plotting.scatter_matrix(df[['Sepal.Length', 'Sepal.Width', 'Petal.Length', 'Petal.Width']], figsize=(10, 10))   #データのプロット\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "-------\n",
    "# 課題\n",
    "新しいノートを開いて、以下の課題を解いてください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __課題１__：下記URLからデータをダウンロードしてください。\n",
    "\n",
    " * https://raw.githubusercontent.com/maskot1977/ipython_notebook/master/toydata/anscombe.txt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __課題２__： x1, x2, x3, x4, y1, y2, y3, y4 の平均値をそれぞれ求めてください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __課題３__： x1, x2, x3, x4, y1, y2, y3, y4 の分散をそれぞれ求めてください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __課題４__： x1, x2, x3, x4, y1, y2, y3, y4 の標準偏差をそれぞれ求めてください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __課題５__： x1とy1の共分散、x2とy2の共分散、x3とy3の共分散、x4とy4の共分散をそれぞれ求めてください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __課題６__： x1とy1の相関係数、x2とy2の相関係数、x3とy3の相関係数、x4とy4の相関係数をそれぞれ求めてください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __課題７__： x1とy1の関係、x2とy2の関係、x3とy3の関係、x4とy4の関係をそれぞれ散布図としてプロットしてください。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* __課題８__：以上の結果をふまえ、平均値・標準偏差・相関係数とデータの分布の形について考察してください。また、計算結果を [自分の氏名].ipynb ファイルとして保存し、指定したアドレスまでメールしてください。メールタイトルは「総合実験１日目」とし、メール本文に学籍番号と氏名を明記のこと。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "お疲れ様でした。もし時間が余ったら、[総合実験２日目](http://nbviewer.jupyter.org/github/maskot1977/ipython_notebook/blob/master/%E7%B7%8F%E5%90%88%E5%AE%9F%E9%A8%93%EF%BC%92%E6%97%A5%E7%9B%AE.ipynb)に進んでもらって結構です。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.12"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}