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": 1,
   "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": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "4"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "2 + 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "12"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "_ * 3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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": 8,
   "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": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}