01_Introduction_to_Jupyter_Notebook.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Python 3.5.1 :: Anaconda 2.5.0 (x86_64)\r\n"
     ]
    }
   ],
   "source": [
    "!python --version  # Pythonのバージョン"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "4.0.6\r\n"
     ]
    }
   ],
   "source": [
    "!jupyter --version  # Jupyterのバージョン"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Jupyter Notebookの紹介\n",
    "\n",
    "Shift + Enterで各コードを実行"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Pythonコードの実行"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "3\n"
     ]
    }
   ],
   "source": [
    "a = 1 + 2\n",
    "print(a)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "関数の前に `?` をつけるとヘルプが確認出来る"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "?print"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## マークダウンを実行\n",
    "\n",
    "右上の　`Code`　を `Markdown`　に変更することで `Markdown` モードになる"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true
   },
   "source": [
    "#### マークダウンを挿入\n",
    "\n",
    "Mathjax形式で数式を記述\n",
    "\n",
    "$$a = 1+2 $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 表形式でデータを表現\n",
    "\n",
    "pandas を利用して表形式なデータを扱う ( ※後ほど説明 )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>sin_x</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1.00</td>\n",
       "      <td>0.841471</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1.01</td>\n",
       "      <td>0.846832</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1.02</td>\n",
       "      <td>0.852108</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1.03</td>\n",
       "      <td>0.857299</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1.04</td>\n",
       "      <td>0.862404</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      x     sin_x\n",
       "0  1.00  0.841471\n",
       "1  1.01  0.846832\n",
       "2  1.02  0.852108\n",
       "3  1.03  0.857299\n",
       "4  1.04  0.862404"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import scipy as sp\n",
    "\n",
    "df = pd.DataFrame(np.arange(1, 50, 0.01), columns=['x'])\n",
    "df['sin_x'] = sp.sin(df.x)\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## データをグラフで描画\n",
    "\n",
    "matplotlib を利用してグラフを描画する　(※後ほど説明)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.text.Text at 0x11239b668>"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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dA8tL+geIRlXciQT0GBhnVMxH97hNRAZRu93AzLMU9lTYjZhy1nziDEwYTI2K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\nzsA4HA6HQwvOwDgcDodDC/8/172Ecre6ARUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x11159ca58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import scipy as sp\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "plt.plot(df.x, df.sin_x)\n",
    "\n",
    "plt.xlabel('x')\n",
    "plt.ylabel('sin(x)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## その他のリッチな機能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"http://python.org/images/python-logo.gif\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# URLで画像を指定して表示\n",
    "\n",
    "from IPython.display import Image\n",
    "i = Image(url='http://python.org/images/python-logo.gif')\n",
    "i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "\n",
       "        <iframe\n",
       "            width=\"400\"\n",
       "            height=\"300\"\n",
       "            src=\"https://www.youtube.com/embed/1j_HxD4iLn8\"\n",
       "            frameborder=\"0\"\n",
       "            allowfullscreen\n",
       "        ></iframe>\n",
       "        "
      ],
      "text/plain": [
       "<IPython.lib.display.YouTubeVideo at 0x104445c18>"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# YouTube Videoを埋め込み\n",
    "\n",
    "from IPython.display import YouTubeVideo\n",
    "YouTubeVideo('1j_HxD4iLn8')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}