{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1章 Python 入門"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.3 Python インタプリタ"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※コマンドラインではなくてインタプリタ（REPL や Jupyter notebook 等）上でバージョン確認するには `sys.version` を見れば良い。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import sys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'3.6.0 |Continuum Analytics, Inc.| (default, Dec 23 2016, 12:22:00) \\n[GCC 4.4.7 20120313 (Red Hat 4.4.7-1)]'"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sys.version"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 + 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.3.1 算術演算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 * 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7 / 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 ** 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※補足：整数除算は `//` 演算子が利用できる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7 // 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.3.2 データ型"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(2.718)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(\"hello\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.3.3 変数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x = 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y = 3.14"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "314.0"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x * y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x * y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.3.4 リスト"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "a = [1, 2, 3, 4, 5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "a[4] = 99"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4, 99]"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2]"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4, 99]"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:3]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.3.5 ディクショナリ"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "me = {'height': 180}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "me['height']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "me['weight'] = 70"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'height': 180, 'weight': 70}"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "me"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.3.6 ブーリアン"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "hungry = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "sleepy = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.3.X タプル"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※データ型の説明中にタプルが出てこなかったので、実例を挙げて説明。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 3.14)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type((x, y))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(100, 3.14, 1, 2, 3, 'a', True)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(x, y, 1, 2, 3, \"a\", True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tuple"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type((x, y, 1, 2, 3, \"a\", True))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.3.7 if 文"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "hungry = False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'm not hungry.\n"
     ]
    }
   ],
   "source": [
    "if hungry:\n",
    "    print(\"I'm hungry.\")\n",
    "else:\n",
    "    print(\"I'm not hungry.\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.3.8 for 文"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "for i in [1, 2, 3]:\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※タプルも `for` 文（の `in` 句）に渡せる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n",
      "3.14\n",
      "1\n",
      "2\n",
      "3\n",
      "a\n",
      "True\n"
     ]
    }
   ],
   "source": [
    "for i in (x, y, 1, 2, 3, \"a\", True):\n",
    "    print(i)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.3.9 関数"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def hello():\n",
    "    print(\"Hello World!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!\n"
     ]
    }
   ],
   "source": [
    "hello()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "def hello(object):\n",
    "    print(\"Hello \" + object + \"!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello cat!\n"
     ]
    }
   ],
   "source": [
    "hello(\"cat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.4 Python スクリプトファイル"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.4.1 ファイルに保存"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※Jupyter notebook を利用する場合、新たにテキストエディタを起動する必要はない。\n",
    "\n",
    "+ ディレクトリのファイルリストが表示されている画面で、[New]→[Text File] を選択すると、簡易テキストエディタ画面になる。名前を付けて新しいテキストファイルとして保存できる（もちろん `.py` ファイルも作れる）\n",
    "+ ディレクトリのファイルリストが表示されている画面で、テキストファイルを選択すると、↑と同様テキストエディタ画面が開き、編集できる。\n",
    "+ 短い内容なら、↓のようにすれば notebook 上で直接ファイルを作成（存在すれば上書き保存）することも可能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Overwriting hungry.py\n"
     ]
    }
   ],
   "source": [
    "%%file hungry.py\n",
    "print(\"I'm hungry!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※↓`.py`ファイルの内容を表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%pycat hungry.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※↓`.py`ファイルを実行し結果を表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'm hungry!\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d34a01eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%run -i hungry.py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.4.2 クラス"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※テキストでは `man.py` というファイルに保存して実行とあるが、ここでは直接 notebook にクラスを記述し、各セルで実行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "class Man:\n",
    "    def __init__(self, name):\n",
    "        self.name = name\n",
    "        print(\"Initialized!\")\n",
    "    \n",
    "    def hello(self):\n",
    "        print(\"Hello \" + self.name + \"!\")\n",
    "    \n",
    "    def goodby(self):\n",
    "        print(\"Good-bye \" + self.name + \"!\")\n",
    "        "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※↑ `class` の定義は1つのセル内に全部書く。複数のセルにまたがることは出来ない。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized!\n"
     ]
    }
   ],
   "source": [
    "m = Man(\"David\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello David!\n"
     ]
    }
   ],
   "source": [
    "m.hello()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Good-bye David!\n"
     ]
    }
   ],
   "source": [
    "m.goodby()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.5 NumPy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.5.1 NumPy のインポート"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.5.2 NumPy の配列の生成"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x = np.array([1.0, 2.0, 3.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1.  2.  3.]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※ここで質問：「`np.array`と`numpy.ndarray`の違いは？」  \n",
    "  →回答「`numpy.ndarray`は型、`np.array`は`numpy.ndarray`型のオブジェクトを生成する便利なメソッド」"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.5.3 NumPy の算術計算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x = np.array([1.0, 2.0, 3.0])\n",
    "y = np.array([2.0, 4.0, 6.0])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.,  6.,  9.])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1., -2., -3.])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x - y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  2.,   8.,  18.])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x * y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.5,  0.5,  0.5])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x / y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.5,  1. ,  1.5])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x / 2.0 # ブロードキャスト"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.5.4 NumPy の N 次元配列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "A = np.array([[1, 2], [3, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n"
     ]
    }
   ],
   "source": [
    "print(A)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 2)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int64')"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "B = np.array([[3, 0], [0, 6]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  2],\n",
       "       [ 3, 10]])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A + B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3,  0],\n",
       "       [ 0, 24]])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A * B"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 20],\n",
       "       [30, 40]])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A * 10"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.5.5 ブロードキャスト"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "A = np.array([[1, 2], [3, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "B = np.array([10, 20])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 40],\n",
       "       [30, 80]])"
      ]
     },
     "execution_count": 87,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A * B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※↑は仮想的に↓と同じ意味、というコード（テキストに載っていないものを説明のために記述）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "B2 = np.array([[10, 20], [10, 20]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 20],\n",
       "       [10, 20]])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true,
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 40],\n",
       "       [30, 80]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A * B2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.5.6 要素へのアクセス"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X = np.array([[51, 55], [14, 19], [0, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[51, 55],\n",
       "       [14, 19],\n",
       "       [ 0,  4]])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51, 55])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[51 55]\n",
      "[14 19]\n",
      "[0 4]\n"
     ]
    }
   ],
   "source": [
    "for row in X:\n",
    "    print(row)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "X = X.flatten()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51, 55, 14, 19,  0,  4])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51, 14,  0])"
      ]
     },
     "execution_count": 101,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[np.array([0, 2, 4])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51, 14])"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[0:4:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True,  True, False,  True, False, False], dtype=bool)"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X > 15"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([51, 55, 19])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X[X>15]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.6 Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.6.1 単純なグラフの描画"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x = np.arange(0, 6, 0.1)\n",
    "y = np.sin(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7f5d34b08198>]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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l5U7HMR4w9bs8CvYe5MFzUgkMkBZ7XysGY3yEiHD/2ansL6uwORt8wPbiMl75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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d5f3658d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y)\n",
    "# plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※↑テキストには `%matplotlib inline` の行がなく、最後の `plt.show()` がコメントアウトされていない。  \n",
    "これは Jupyter の機能で、`%matplotlib inline` を記述しておくとグラフがセル単位でまとまり、`plt.show()` を書かなくてもそのセルで定義したグラフがそのセルの出力として描画される。便利。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.6.2 pyplot の機能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7f5d349a3e10>"
      ]
     },
     "execution_count": 111,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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LqurvzfDOdZm95Tjbj6XojmNcTr1u0O5+3Slcgs6Csh6IEJG6IuIFDAFm5V1B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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d642cd278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x, y1, label=\"sin\")\n",
    "plt.plot(x, y2, linestyle=\"--\", label=\"cos\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.title('sin & cos')\n",
    "plt.legend()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "※↑一連の `plt.xxx()` を1つのセルにまとめて記述している点に注意。  \n",
    "これを複数のセルに分割してしまうと、それぞれのセルごとにグラフを描画してしまう。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "### 1.6.3 画像の表示"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "from matplotlib.image import imread"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "img = imread('lena.png')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.image.AxesImage at 0x7f5d345d0b38>"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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O1MS1JVpxWjPMBlommh/knaiP6EMmSkFyEH5YNs2CnibudaaNnTSEdQghg3k4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SjnaLSIVmNGJEpKgrENaDs2GlHjVLBDGvvxVVBXIc7jcxhblNsOiyKFjGd2wmj4aiHnDnWG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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f5d34b536d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(img)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※「jpeg が表示できない（`imread()`でエラーになる）」という意見が出ました。  \n",
    "調査した結果、標準では jpeg は読み込めず、別ライブラリ [Pillow](https://pillow.readthedocs.io/en/4.1.x/) が必要なようです。  \n",
    "用意した Dockerイメージ には含めていないのでそれを利用されている方は表示できません。実案件ではその環境も用意する必要があります。  \n",
    "この勉強会では特に重要ではない（おそらくこの先 `.jpg` ファイルを読み込むことはない）ので、このまま進めます。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1.99 補足"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 20],\n",
       "       [10, 20]])"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "B2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[10, 40],\n",
       "       [30, 80]])"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A * B2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※↑これがテキストに出てきた積（要素ごとの積、elementwise multiplication）。  \n",
    "↓（通常の）行列積は、こう書く。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 30,  60],\n",
       "       [ 70, 140]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.dot(B2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "※ベクトルで `.dot()` すると、内積（所謂「ドット積」）になる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "x = np.array([1, 2])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": [
    "y = np.array([3, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "11"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dot(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "deletable": true,
    "editable": true
   },
   "source": [
    "※↓こんな計算も出来る（ドット積のブロードキャストになっている）"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false,
    "deletable": true,
    "editable": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5, 11])"
      ]
     },
     "execution_count": 123,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "A.dot(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true,
    "deletable": true,
    "editable": true
   },
   "outputs": [],
   "source": []
  }
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