tomohiko38/ch01.ipynb

## ch01.ipynb
{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 第1章 Python 入門\n",
    "Python の基本的な構文と `Numpy` や `Matplotlib` の使い方の確認。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 結果表示の Out[n] の有無\n",
    "以降の Jupyter Notebook を使ったプログラムの実行結果において、結果が `Out[1]` のような形で表示されているものと、単純に `In[1]` のプログラムの下に表示されているものの違いは、`print()` を使っているかどうかによる。Jupyter Notebook のプログラムの実行は Python において `*.py` ファイルを実行するというよりはインタプリタにより対話型の実行に近いため `1 - 2` だけを実行すれば戻り値が `Out[1]` に表示される。しかし、`print()` は標準出力への値の表示なので `Out[1]` が表示されない（のだと思う）。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 算術計算\n",
    "ごく基本的な数値の計算。`1 - 2` のように書けば結果が出るのが Python や Ruby などのいいところ。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 加算（足し算）\n",
    "特に説明の必要なし。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 + 4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-2 + 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 減算（引き算）\n",
    "こちらも説明の必要なし。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 - 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10 - 8"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 積算（掛け算）\n",
    "こちらも特に説明の必要なし。一般的な掛け算。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "20"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "4 * 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "18"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "-2 * (-9)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-21"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 * (-7)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 除算（割り算）\n",
    "割り算だけは要注意。Python 3 系は整数同士の計算であっても、浮動小数点になる。2 系は実質的にもう終わっているので使われないと考えれば、Python という言語は基本的に整数同士の除算は浮動小数点で結果が出ると考えた方がよい。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "7 / 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.0"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "10 / 5"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "このように割り切れる場合でも `2.0` という結果になる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 累乗\n",
    "`**` は累乗の計算になる。以下の例は 3 の 2 乗の例。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "3 ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 ** 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## データ型\n",
    "データ型は一般的なプログラミング言語と同様。主に以下の 3 種類を理解しておく。型を判定するには `type()` を使用する。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "int"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(2.718)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "str"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(\"hello\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 変数\n",
    "変数については動的型付けなので変数の宣言時に型を指定する必要はない。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "x = 10\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "100\n"
     ]
    }
   ],
   "source": [
    "x = 100\n",
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "314.0"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y = 3.14\n",
    "x * y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "float"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(x * y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## リスト\n",
    "基本的には配列。使い方に特筆すべき点があるのは後半から。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "a = [1, 2, 3, 4, 5]\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 99]\n"
     ]
    }
   ],
   "source": [
    "a[4] = 99\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 2, 3, 4, 99]\n"
     ]
    }
   ],
   "source": [
    "print(a)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2]"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[0:2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Javaの `substring()` と同様で、終わりのインデックスの要素は結果に含まれない。上の例ではインデックスが 0, 1, 2 が指定されているが、実際に返却されるのは 0, 1 の要素のみである。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[2, 3, 4, 99]"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1:]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "これも `substring()` と同じである。先頭のインデックスを指定するとそれから最後までを返す。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "逆パターン。終わりのみを指定する。指定したインデックスの要素が含まれないのは同じ。`:` をつけることを忘れないようにする。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3, 4]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:-1]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "終了のインデックスに `-1` を指定すると「ひとつ手前まで」の意味になる。この場合は5つの要素があるので4番目までが出力される。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1, 2, 3]"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:-2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "その応用。2つ前までを出力する。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ディクショナリ\n",
    "Java でいうところの `Map` である。key と value のペアで値を格納する点は全く同じ。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "180"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "me = {'height':180}\n",
    "me['height']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'height': 180, 'weight': 70}\n"
     ]
    }
   ],
   "source": [
    "me['weight'] = 70\n",
    "print(me)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "{'height': 180, 'weight': 70, 'test': 90}\n",
      "180\n",
      "180\n"
     ]
    }
   ],
   "source": [
    "me[\"test\"] = 90\n",
    "print(me)\n",
    "print(me[\"height\"])\n",
    "print(me['height'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "key の設定の仕方は上の例を見て分かる通り、`'` と `\"` のいずれでも同じ結果が得られることがわかる。調べてみたらPython の言語仕様的には違いはないとのこと。エスケープの関係で使い分けるのがいいらしい。\n",
    "\n",
    "例えば、`'dd\"ddd'` とか `\"ddf'f'ffff\"` のような時はエスケープしなくて良いので、HTML を書くときなどは `'` が使いやすいとのこと。\n",
    "\n",
    "(例) `html = '<input type=\"text\" name=\"sample\" />'`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## ブーリアン\n",
    "他の言語と違うとすれば、`True` と `False` のように先頭を大文字で書くことくらいか。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "bool"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hungry = True\n",
    "sleepy = False\n",
    "type(hungry)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "以下の記述は主に `if` 文とかで使えると思うけど、他の言語にはない興味深い書き方である。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "not hungry"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hungry and sleepy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hungry or sleepy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## if 文\n",
    "ここからのブロックが関係してくる構文の場合は、インデントが重要になる。他の言語と違い `{` や `}` で囲わないのでインデントが正確でないと間違えるばかりかエラーとなる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'm hungry\n"
     ]
    }
   ],
   "source": [
    "hungry = True\n",
    "if hungry:\n",
    "    print(\"I'm hungry\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "I'm not hungry\n",
      "I'm sleepy\n"
     ]
    }
   ],
   "source": [
    "hungry = False\n",
    "if hungry:\n",
    "    print(\"I'm hungry\")\n",
    "else:\n",
    "    print(\"I'm not hungry\")\n",
    "    print(\"I'm sleepy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "繰り返しになるが、ブロックの`{}`がない言語なのでインデントがとにかく重要。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## for 文\n",
    "Java でいうところの拡張`for`文の形式がデフォルトな様子。インデックスを使った形式もある。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "for value in [1, 2, 3]:\n",
    "    print(value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "この形式が最もシンプルで、Java の拡張`for`文に近い形である。本文中では`i`を使用しているが、この場合の要素は値なので分かりやすく`value`にしている。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "3\n",
      "4\n",
      "5\n"
     ]
    }
   ],
   "source": [
    "list = [1, 2, 3, 4, 5]\n",
    "for i in range(len(list)):\n",
    "    print(list[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`range`を使用した`for`文の書き方。`list`の要素数まで順番にインデックスを`i`に設定していく感じ。値が取れているわけではないので、自分で値を取る必要がある。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0:A\n",
      "1:B\n",
      "2:C\n"
     ]
    }
   ],
   "source": [
    "list = [\"A\", \"B\", \"C\"]\n",
    "for i, value in enumerate(list):\n",
    "    print(str(i) + \":\" + value)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`enumerate`関数を使うことで同時に２つの変数へインデックスと値を格納していくことができる。インデックスを使う場合はこの書き方が便利。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 関数\n",
    "上の `for` 文や `if` 文と同様にインデントが重要。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello World!!\n"
     ]
    }
   ],
   "source": [
    "def hello():\n",
    "    print(\"Hello World!!\")\n",
    "\n",
    "hello()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'sample' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-4-85b193cb70d0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0msample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m     \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Hello World!\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'sample' is not defined"
     ]
    }
   ],
   "source": [
    "sample()\n",
    "\n",
    "def sample():\n",
    "    print(\"Hello World!\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "インタープリタ言語なので、上の例のように呼び出し処理を先に書いてから関数の定義を書くとエラーになる。シェルスクリプトなどと同様。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hello cat\n"
     ]
    }
   ],
   "source": [
    "def hello(object):\n",
    "    print(\"hello \" + object)\n",
    "    \n",
    "hello(\"cat\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## クラス"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Initialized!\n",
      "hello tomohiko\n",
      "Good-bye tomohiko\n"
     ]
    }
   ],
   "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 goodbye(self):\n",
    "        print(\"Good-bye \" + self.name)\n",
    "\n",
    "\n",
    "m = Man(\"tomohiko\")\n",
    "m.hello()\n",
    "m.goodbye()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'Person' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-6-462b351f5ecc>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mm\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mPerson\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"tomohiko\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m      2\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mhello\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      3\u001b[0m \u001b[0mm\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgoodbye\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m      5\u001b[0m \u001b[1;32mclass\u001b[0m \u001b[0mPerson\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mNameError\u001b[0m: name 'Person' is not defined"
     ]
    }
   ],
   "source": [
    "m = Person(\"tomohiko\")\n",
    "m.hello()\n",
    "m.goodbye()\n",
    "\n",
    "class Person:\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 goodbye(self):\n",
    "        print(\"Good-bye \" + self.name)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "クラスの定義も関数と同様に呼び出しが先だとエラーになる。上から下へプログラムは解析されるので、当たり前といえば当たり前。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy\n",
    "主に機械学習などの分野でPythonを使う際の最重要ライブラリ。もはや標準に組み込めばいいのに、というほど有名であり、重要なライブラリである。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1. 2. 3.]\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "numpy.ndarray"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array([1.0, 2.0, 3.0])\n",
    "print(x)\n",
    "type(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy の算術計算"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3., 6., 9.])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array([1.0, 2.0, 3.0])\n",
    "y = np.array([2.0, 4.0, 6.0])\n",
    "x + y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1., -2., -3.])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array([1.0, 2.0, 3.0])\n",
    "y = np.array([2.0, 4.0, 6.0])\n",
    "x - y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n",
      "(2,)\n"
     ]
    },
    {
     "ename": "ValueError",
     "evalue": "operands could not be broadcast together with shapes (3,) (2,) ",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-1-48cc369a3025>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 6\u001b[0;31m \u001b[0mx\u001b[0m \u001b[0;34m*\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[0;31mValueError\u001b[0m: operands could not be broadcast together with shapes (3,) (2,) "
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array([1.0, 2.0, 3.0])\n",
    "y = np.array([2.0, 4.0])\n",
    "print(x.shape)\n",
    "print(y.shape)\n",
    "x * y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "次元数が同じか1でないとブロードキャストされないので、この場合は3と2であることからブロードキャストはされないことになる。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(3,)\n",
      "(3,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([0.5, 0.5, 0.5])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array([1.0, 2.0, 3.0])\n",
    "y = np.array([2.0, 4.0, 6.0])\n",
    "print(x.shape)\n",
    "print(y.shape)\n",
    "x / y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "この場合は次元数が同じなので計算できている。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.5, 1. , 1.5])"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "x = np.array([1.0, 2.0, 3.0])\n",
    "x / 2.0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "次元数が1の例。これも問題なくブロードキャストされる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Numpy の N 次元配列"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n",
      "(2, 2)\n",
      "int64\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "A = np.array([[1, 2], [3, 4]])\n",
    "print(A)\n",
    "print(A.shape)\n",
    "print(A.dtype)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`shape` は形状。すなわち2x2の配列であることを表す。この`shape`の値を比較したときに次元数が同じか1でないとブロードキャストされない点に注意。`dtype` は要素のデータ型。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4  2]\n",
      " [ 3 10]]\n",
      "[[ 3  0]\n",
      " [ 0 24]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "A = np.array([[1, 2], [3, 4]])\n",
    "B = np.array([[3, 0], [0, 6]])\n",
    "print(A + B)\n",
    "print(A * B)\n",
    "print(A * 10)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### ブロードキャスト"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 2)\n",
      "(2,)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([[10, 40],\n",
       "       [30, 80]])"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "A = np.array([[1, 2], [3, 4]])\n",
    "B = np.array([10, 20])\n",
    "print(A.shape)\n",
    "print(B.shape)\n",
    "A * B"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "この場合もできる模様。次元数が同じか1という条件に加えて、数が合わなくても良いことになる。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 要素へのアクセス\n",
    "ここは一般的な配列に対するアクセスと概ね同じ。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[51 55]\n",
      " [14 19]\n",
      " [ 0  4]]\n",
      "55\n",
      "[51 55]\n",
      "[14 19]\n",
      "[0 4]\n",
      "[51 55 14 19  0  4]\n",
      "[51 14  0]\n",
      "[ True  True False  True False False]\n",
      "[51 55 19]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "X = np.array([[51, 55], [14, 19], [0, 4]])\n",
    "print(X)\n",
    "print(X[0][1])\n",
    "\n",
    "for row in X:\n",
    "    print(row)\n",
    "\n",
    "X = X.flatten()\n",
    "print(X)\n",
    "print(X[np.array([0, 2, 4])])\n",
    "print(X > 15)\n",
    "print(X[X > 15])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`flatten()`を使うと多次元配列が1次元配列に変換されている。何に使うのか現時点では想像できないが。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib\n",
    "グラフの描画のためのライブラリ。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# データの作成\n",
    "x = np.arange(0, 6, 0.1)\n",
    "y = np.sin(x)\n",
    "\n",
    "# グラフの描画\n",
    "plt.plot(x, y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`%matplotlib inline`が必要なはずだが、なくてもグラフが描画されるのでいらない可能性もある。出なければ書くくらいで良いかもしれない。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### pyplot の機能\n",
    "細かいラベルや凡例を記載することが可能。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "# データの作成\n",
    "x = np.arange(0, 6, 0.1)\n",
    "y1 = np.sin(x)\n",
    "y2 = np.cos(x)\n",
    "\n",
    "# グラフの描画\n",
    "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()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### 画像の表示\n",
    "今後、Jupyter Notebookで画像を表示するときの方法になるかもしれない。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib.image import imread\n",
    "\n",
    "img = imread('../dataset/lena.png')\n",
    "plt.imshow(img)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`%matplotlib inline`の記述が必要かもしれないので念のため記載しておく。"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}