machinelearning_notebook/0_python/0-ipython_notebook.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# IPython和Jupyter笔记本"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "本在线讲义使用Jupyter Notebook编写，因此首先介绍Jupter Notebook的使用方法。使用Notebook，可以方便的将理论描述、程序、数据可视化等集成在一个多媒体页面，方便大家的学习。\n",
    "\n",
    "Jupyter notebook 是一种 Web 应用，它能让用户将说明文本、数学方程、代码和可视化内容全部组合到一个易于共享的文档中，非常方便研究和教学，让编写、阅读变得一目了然。Jupyter notebook特别适合做科学计算、数据处理，其用途可以包括数据清理和探索、可视化、机器学习和大数据分析。其具有以下特点:\n",
    "* 编程时具有语法高亮、缩进、tab补全的功能。\n",
    "* 可直接通过浏览器运行代码，同时在代码块下方展示运行结果。\n",
    "* 以富媒体格式展示计算结果。富媒体格式包括：HTML，LaTeX，PNG，SVG等。\n",
    "* 对代码编写说明文档或语句时，支持Markdown语法。\n",
    "* 支持使用LaTeX编写数学性说明。\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Jupyter 安装\n",
    "安装Jupyter最简单的方法就是使用 Anaconda，其发行版附带了 Jupyter Notebook。在 conda 环境下安装 Jupyter Notebook 可以使用 \n",
    "\n",
    "```\n",
    "conda install jupyter\n",
    "```\n",
    "\n",
    "当然，也可以通过 `pip` 来安装 \n",
    "```\n",
    "pip install jupyter。\n",
    "```\n",
    "\n",
    "安装后便可在终端中输入以下命令启动：\n",
    "```\n",
    "# jupyter notebook\n",
    "\n",
    "或者\n",
    "# jupyter-notebook\n",
    "```"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 常用操作\n",
    "\n",
    "![shortcut](images/jupyter_shortcuts.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "podoc": {
     "output_text": "Screenshot of a Jupyter notebook"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Hello world!\n"
     ]
    }
   ],
   "source": [
    "print(\"Hello world!\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0-ipython_notebook_EN.ipynb  3_Data_Structure_1.ipynb\t  7_Class_EN.ipynb\r\n",
      "0-ipython_notebook.ipynb     4_Data_Structure_2_EN.ipynb  7_Class.ipynb\r\n",
      "1_Basics_EN.ipynb\t     4_Data_Structure_2.ipynb\t  images\r\n",
      "1_Basics.ipynb\t\t     5_Control_Flow_EN.ipynb\t  Python.pdf\r\n",
      "2_Print_Statement_EN.ipynb   5_Control_Flow.ipynb\t  README_ENG.md\r\n",
      "2_Print_Statement.ipynb      6_Function_EN.ipynb\t  README.md\r\n",
      "3_Data_Structure_1_EN.ipynb  6_Function.ipynb\t\t  test.txt\r\n"
     ]
    }
   ],
   "source": [
    "!ls"
   ]
  },
  {
   "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": [
    "# code block & drawing\n",
    "\n",
    "%matplotlib inline\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "# generate data\n",
    "data_num = 100\n",
    "X = np.random.rand(data_num, 1)*10\n",
    "Y = X * 3 + 4 + 5*np.random.randn(data_num,1)\n",
    "\n",
    "# draw original data\n",
    "plt.scatter(X, Y)\n",
    "plt.xlabel(\"X\")\n",
    "plt.ylabel(\"Y\")\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.5.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}