Update python tutorial

1_numpy_matplotlib_scipy_sympy/3-ipython_notebook.ipynb → 0_python/0-ipython_notebook.ipynb

@@ -4,7 +4,52 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# 介绍IPython和Jupyter笔记本"
+    "# 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，让使用者一目了然。Jupyter notebook特别适合做数据处理，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。其具有以下一些特点:\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",
+    "```"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## 常用操作\n",
+    "\n",
+    "![shortcut](images/jupyter_shortcuts.png)"
    ]
   },
   {
@@ -70,24 +115,20 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "1-numpy_tutorial_EN.ipynb\texample.png\r\n",
-      "1-numpy_tutorial.ipynb\t\tmatplotlib_ani.ipynb\r\n",
-      "2-matplotlib_tutorial_EN.ipynb\tmatplotlib_full.ipynb\r\n",
-      "2-matplotlib_tutorial.ipynb\trandom-matrix.csv\r\n",
-      "3-ipython_notebook_EN.ipynb\trandom-matrix.npy\r\n",
-      "3-ipython_notebook.ipynb\tREADME.md\r\n",
-      "4-scipy_tutorial_EN.ipynb\tstockholm_td_adj.dat\r\n",
-      "4-scipy_tutorial.ipynb\t\tutils_git_advanced.ipynb\r\n",
-      "5-sympy_tutorial_EN.ipynb\tutils_git.ipynb\r\n",
-      "5-sympy_tutorial.ipynb\t\tutils_shell.ipynb\r\n",
-      "bokeh_tutorial.ipynb\r\n"
+      "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_EN.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"
      ]
     }
    ],
@@ -272,7 +313,9 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "metadata": {},
+   "metadata": {
+    "scrolled": true
+   },
    "outputs": [
     {
      "name": "stdout",
@@ -301,145 +344,6 @@
    "source": [
     "%run?"
    ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 11,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "from IPython.display import HTML, SVG, YouTubeVideo"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 12,
-   "metadata": {
-    "podoc": {
-     "output_text": "<IPython.core.display.HTML object>"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/html": [
-       "\n",
-       "<table style=\"border: 2px solid black;\">\n",
-       "<tr><td>0,0</td><td>0,1</td><td>0,2</td><td>0,3</td><td>0,4</td></tr><tr><td>1,0</td><td>1,1</td><td>1,2</td><td>1,3</td><td>1,4</td></tr><tr><td>2,0</td><td>2,1</td><td>2,2</td><td>2,3</td><td>2,4</td></tr><tr><td>3,0</td><td>3,1</td><td>3,2</td><td>3,3</td><td>3,4</td></tr><tr><td>4,0</td><td>4,1</td><td>4,2</td><td>4,3</td><td>4,4</td></tr>\n",
-       "</table>\n"
-      ],
-      "text/plain": [
-       "<IPython.core.display.HTML object>"
-      ]
-     },
-     "execution_count": 12,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "HTML('''\n",
-    "<table style=\"border: 2px solid black;\">\n",
-    "''' +\n",
-    "     ''.join(['<tr>' +\n",
-    "              ''.join([f'<td>{row},{col}</td>'\n",
-    "                       for col in range(5)]) +\n",
-    "              '</tr>' for row in range(5)]) +\n",
-    "     '''\n",
-    "</table>\n",
-    "''')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 13,
-   "metadata": {
-    "podoc": {
-     "output_text": "<IPython.core.display.SVG object>"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/svg+xml": [
-       "<svg height=\"80\" width=\"600\"><circle cx=\"300\" cy=\"30\" fill=\"red\" r=\"0.0\" stroke=\"black\" stroke-width=\"2\">\n",
-       "        </circle><circle cx=\"297\" cy=\"30\" fill=\"red\" r=\"3.0\" stroke=\"black\" stroke-width=\"2\">\n",
-       "        </circle><circle cx=\"288\" cy=\"30\" fill=\"red\" r=\"6.0\" stroke=\"black\" stroke-width=\"2\">\n",
-       "        </circle><circle cx=\"273\" cy=\"30\" fill=\"red\" r=\"9.0\" stroke=\"black\" stroke-width=\"2\">\n",
-       "        </circle><circle cx=\"252\" cy=\"30\" fill=\"red\" r=\"12.0\" stroke=\"black\" stroke-width=\"2\">\n",
-       "        </circle><circle cx=\"225\" cy=\"30\" fill=\"red\" r=\"15.0\" stroke=\"black\" stroke-width=\"2\">\n",
-       "        </circle><circle cx=\"192\" cy=\"30\" fill=\"red\" r=\"18.0\" stroke=\"black\" stroke-width=\"2\">\n",
-       "        </circle><circle cx=\"153\" cy=\"30\" fill=\"red\" r=\"21.0\" stroke=\"black\" stroke-width=\"2\">\n",
-       "        </circle><circle cx=\"108\" cy=\"30\" fill=\"red\" r=\"24.0\" stroke=\"black\" stroke-width=\"2\">\n",
-       "        </circle><circle cx=\"57\" cy=\"30\" fill=\"red\" r=\"27.0\" stroke=\"black\" stroke-width=\"2\">\n",
-       "        </circle></svg>"
-      ],
-      "text/plain": [
-       "<IPython.core.display.SVG object>"
-      ]
-     },
-     "execution_count": 13,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "SVG('''<svg width=\"600\" height=\"80\">''' +\n",
-    "    ''.join([f'''<circle\n",
-    "              cx=\"{(30 + 3*i) * (10 - i)}\"\n",
-    "              cy=\"30\"\n",
-    "              r=\"{3. * float(i)}\"\n",
-    "              fill=\"red\"\n",
-    "              stroke-width=\"2\"\n",
-    "              stroke=\"black\">\n",
-    "        </circle>''' for i in range(10)]) +\n",
-    "    '''</svg>''')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {
-    "podoc": {
-     "output_text": "<IPython.lib.display.YouTubeVideo at 0x7fc0000b64a8>"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "YouTubeVideo('VQBZ2MqWBZI')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "```json\n",
-    "{\n",
-    " \"cells\": [\n",
-    "  {\n",
-    "   \"cell_type\": \"code\",\n",
-    "   \"execution_count\": 1,\n",
-    "   \"metadata\": {},\n",
-    "   \"outputs\": [\n",
-    "    {\n",
-    "     \"name\": \"stdout\",\n",
-    "     \"output_type\": \"stream\",\n",
-    "     \"text\": [\n",
-    "      \"Hello world!\\n\"\n",
-    "     ]\n",
-    "    }\n",
-    "   ],\n",
-    "   \"source\": [\n",
-    "    \"print(\\\"Hello world!\\\")\"\n",
-    "   ]\n",
-    "  }\n",
-    " ],\n",
-    " \"metadata\": {},\n",
-    " \"nbformat\": 4,\n",
-    " \"nbformat_minor\": 2\n",
-    "}\n",
-    "```"
-   ]
   }
  ],
  "metadata": {
@@ -458,7 +362,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

0_python/1_Basics.ipynb

@@ -882,36 +882,6 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**cmp(x,y)**\n",
-    "\n",
-    "|x ? y|Output|\n",
-    "|---|---|\n",
-    "| x < y | -1 |\n",
-    "| x == y | 0 |\n",
-    "| x > y | 1 |"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "True\n"
-     ]
-    }
-   ],
-   "source": [
-    "print(1<2)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
     "**pow(x,y,z)** 可以被用来找到幂函数$x^y$也可以找到第三个指定数字的mod值，即:($x^y$ % z)。"
    ]
   },
@@ -1030,7 +1000,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

0_python/3_Data_Structure_1.ipynb

@@ -578,26 +578,6 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": 25,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "False"
-      ]
-     },
-     "execution_count": 25,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "'Rajath' in names"
-   ]
-  },
-  {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
@@ -2145,7 +2125,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

0_python/5_Control_Flow.ipynb

@@ -511,7 +511,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "就是这样!只记得用方括号括起来。"
+    "将表达方式用方括号括起来。"
    ]
   },
   {
@@ -685,7 +685,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

0_python/6_Function.ipynb

@@ -34,7 +34,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "将上面的语法理解为，定义了一个名为“funcname”的函数，它接受“arg1,arg2，…argN”的参数。函数在执行语句后返回一个“值”。"
+    "将上面的语法理解为，定义了一个名为`funcname`的函数，它接受`arg1,arg2，…argN`的参数。函数在执行语句后返回一个`<value>`。"
    ]
   },
   {
@@ -62,16 +62,16 @@
    "source": [
     "不需要每次都写上面的两个语句，可以通过定义一个函数来替换它，这个函数只需一行就能完成任务。\n",
     "\n",
-    "定义一个函数 firstfunc()."
+    "定义一个函数 `first_func()`."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def firstfunc():\n",
+    "def first_func():\n",
     "    print(\"Hey Rajath!\")\n",
     "    print(\"Rajath, How do you do?\")"
    ]
@@ -93,8 +93,8 @@
     }
    ],
    "source": [
-    "firstfunc()\n",
-    "funca=firstfunc\n",
+    "first_func()\n",
+    "funca=first_func\n",
     "funca()"
    ]
   },
@@ -102,30 +102,30 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "**firstfunc()** 每一次只打印一个人的消息。我们可以让我们的函数 **firstfunc()** 接受参数，该参数将存储名称然后打印相应地接受字符串。为了这样做我们需要像所示的那样在函数内添加一个参数。"
+    "**first_func()** 每一次只打印一个人的消息。我们可以让我们的函数 **first_func()** 接受参数，该参数将存储名称然后打印相应地接受字符串。为了这样做我们需要像所示的那样在函数内添加一个参数。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def firstfunc(username):\n",
+    "def first_func(username):\n",
     "    print(\"Hey\", username + '!')\n",
     "    print(username + ',' ,\"How do you do?\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Please enter your name : Jack\n"
+      "Please enter your name : hello\n"
      ]
     }
    ],
@@ -142,60 +142,50 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Hey Jack!\n",
-      "Jack, How do you do?\n"
+      "Hey hello!\n",
+      "hello, How do you do?\n"
      ]
     }
    ],
    "source": [
-    "firstfunc(name1)"
+    "first_func(name1)"
    ]
   },
   {
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "让我们通过定义另一个函数**secondfunc()** 来进一步简化它，该函数接受名称并将其存储在一个变量中，然后从函数本身内部调用**firstfunc()**。"
+    "让我们通过定义另一个函数**second_func()** 来进一步简化它，该函数接受名称并将其存储在一个变量中，然后从函数本身内部调用**first_func()**。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 12,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def firstfunc(username):\n",
+    "def first_func(username):\n",
     "    print(\"Hey\", username + '!')\n",
     "    print(username + ',' ,\"How do you do?\")\n",
-    "def secondfunc():\n",
+    "def second_func():\n",
     "    name = input(\"Please enter your name : \")\n",
-    "    firstfunc(name)"
+    "    first_func(name)"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 2,
+   "execution_count": null,
    "metadata": {},
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "Please enter your name : Tom\n",
-      "Hey Tom!\n",
-      "Tom, How do you do?\n"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "secondfunc()"
+    "second_func()"
    ]
   },
   {
@@ -415,16 +405,16 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "当一个函数的参数在大多数情况下是常见的或者它是“隐式的”时，使用这个概念。"
+    "当一个函数的参数在大多数情况下是常见的或者它是`隐式的`时，使用这个概念。"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": null,
    "metadata": {},
    "outputs": [],
    "source": [
-    "def implicitadd(x,addnumber=3):\n",
+    "def implicitadd(x, addnumber=3):\n",
     "    return x+addnumber"
    ]
   },
@@ -1060,7 +1050,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

0_python/7_Class.ipynb

@@ -11,7 +11,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Python中的变量、列表、字典等其实都是对象。不涉及面向对象编程的理论部分，在本教程中对概念进行解释。"
+    "Python中的变量、列表、字典等其实都是对象。"
    ]
   },
   {
@@ -1275,9 +1275,9 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "找各个方面的练习题，并独立完成能帮助你掌握Python的窍门，例如给自己一个问题并解决它们，你还可以在任何编程竞赛平台上提交问题求解。你编写的代码越多，你发现的越多，你就越开始欣赏这门语言。强烈建议把[Python作业](https://gitee.com/pi-lab/machinelearning_homework/blob/master/homework_01_python/README.md)完成，并在[其他编程练习](https://gitee.com/pi-lab/machinelearning_homework/blob/master/homework_01_python/README.md#references)里面找一些练习题或者项目做一下。\n",
+    "找各个方面的练习题，并独立完成能帮助你掌握Python的窍门，例如给自己一个问题并解决它们，你还可以在任何编程竞赛平台上提交问题求解。你编写的代码越多，你发现的越多，你就越开始欣赏这门语言。强烈建议把[《Python作业》](https://gitee.com/pi-lab/machinelearning_homework/blob/master/homework_01_python/README.md)完成，并在[《其他编程练习》](https://gitee.com/pi-lab/machinelearning_homework/blob/master/homework_01_python/README.md#references)里面找一些练习题或者项目做一下。\n",
     "\n",
-    "现在已经向你介绍了Python，您可以尝试您感兴趣的领域中的不同Python库。我强烈建议您查看这个Python框架、库和软件列表 http://awesome-python.com\n",
+    "现在已经介绍了Python，可以尝试感兴趣的领域中的不同Python库。强烈建议查看这个Python框架、库和软件列表 http://awesome-python.com\n",
     "\n",
     "\n",
     "Python 教程:\n",
@@ -1286,17 +1286,15 @@
     "* [Python官方教程（中文版)](https://docs.python.org/zh-cn/3/tutorial/index.html)\n",
     "* Python官方文档: https://docs.python.org/3/\n",
     "* 本教程来源于：https://github.com/rajathkumarmp/Python-Lectures \n",
-    "\n",
-    "\n",
-    "**最后，享受解决问题的快乐！因为生命短暂，你需要Python!**"
+    "\n"
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "**最后，享受解决问题的快乐！因为生命短暂，你需要Python!**"
+   ]
   }
  ],
  "metadata": {
@@ -1315,7 +1313,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.6.9"
+   "version": "3.7.9"
   }
  },
  "nbformat": 4,

0_python/README.md

@@ -9,22 +9,23 @@ Python 是一门上手简单、功能强大、通用型的脚本编程语言。P
 
 ## 内容
 0. [Install Python](../references_tips/InstallPython.md)
-1. [Basics](1_Basics.ipynb)
+1. [IPython & Jupyter Notebook](0-ipython_notebook.ipynb)
+2. [Basics](1_Basics.ipynb)
     - Why Python, Zen of Python
     - Variables, Operators, Built-in functions
-2. [Print statement](2_Print_Statement.ipynb)
+3. [Print statement](2_Print_Statement.ipynb)
     - Tips of print
-3. [Data structure - 1](3_Data_Structure_1.ipynb)
+4. [Data structure - 1](3_Data_Structure_1.ipynb)
     - Lists, Tuples, Sets
-4. [Data structure - 2](4_Data_Structure_2.ipynb)
+5. [Data structure - 2](4_Data_Structure_2.ipynb)
     - Strings, Dictionaries
-5. [Control flow](5_Control_Flow.ipynb)
+6. [Control flow](5_Control_Flow.ipynb)
     - if, else, elif, for, while, break, continue
-6. [Functions](6_Function.ipynb)
+7. [Functions](6_Function.ipynb)
     - Function define, return, arguments
     - Gloabl and local variables
     - Lambda functions
-7. [Class](7_Class.ipynb)
+8. [Class](7_Class.ipynb)
     - Class define
     - Inheritance