{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python 入门演示" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[i for i in range(10) if i%3==0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "[i for i in range(10)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# a= []\n", "for i in range(100):\n", " if(i%3 == 0):\n", " print(i,end=' ')\n", "# a.append(i)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 简单的数学运算" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "整数相加，得到整数：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "2 + 2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "浮点数相加，得到浮点数：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "2.0 + 2.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "整数和浮点数相加，得到浮点数：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "2 + 2.5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 变量赋值" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**Python**使用`<变量名>=<表达式>`的方式对变量进行赋值" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = 0.2" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "type(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = 'sss'\n", "type(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 字符串 String" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "字符串的生成，单引号与双引号是等价的：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = \"hello world\"\n", "s" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = 'hello ggggggggggggggggg\\\n", "world'\n", "s,_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "三引号用来输入包含多行文字的字符串：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = \"\"\"hello\\ \n", "world\"\"\"\n", "print(s)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "_" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = \"hello \\\n", "world\"\n", "print(s)\n", "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "字符串的加法：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = \"hello\" + \" world\"\n", "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "字符串索引：\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s[0]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s[-1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s[0:5],s[0:6]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 字符串的分割：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = \"hello world\"\n", "s.split()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s = \"hello,world\"\n", "s.split(',')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "查看字符串的长度：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "len(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 列表 List" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python用`[]`来生成列表" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2.0, 'hello', 6.0, [1, 2], {2, 3}]" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = [1, 2.0, 'hello', 5 + 1.0, [1,2], {2,3}]\n", "a" ] }, { "cell_type": "code", "execution_count": 2, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2]" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[4]" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[4][0]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "列表加法：" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "1" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2.0, 'hello', 6.0, [1, 2], {2, 3}, 1, 2.0, 'hello', 6.0, [1, 2], {2, 3}]" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a + a" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2.0, 'hello', 6.0, [1, 2], {2, 3}, 1, 2.0, 'hello', 6.0, [1, 2], {2, 3}]" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "_" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "列表索引：" ] }, { "cell_type": "code", "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2.0" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[1]" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[1, 2.0, 'hello', 6.0, [1, 2], {2, 3}]" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "列表长度：" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(a)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "向列表中添加元素：" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[2.0, 'hello', 6.0, [1, 2], {2, 3}, 'world', 'world']" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.append(\"world\")\n", "a" ] }, { "cell_type": "code", "execution_count": 17, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[6.0, [1, 2], {2, 3}]" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.pop(0)\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 集合 Set" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Python用{}来生成集合，集合中不含有相同元素。" ] }, { "cell_type": "code", "execution_count": 18, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{2, 3, 4}" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = {2, 3, 4, 2, 3}\n", "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "集合的长度：" ] }, { "cell_type": "code", "execution_count": 19, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "向集合中添加元素：" ] }, { "cell_type": "code", "execution_count": 20, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{1, 2, 3, 4}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.add(1)\n", "s" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "s.add(15)\n", "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "集合的交：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = {1, 2, 3, 4}\n", "b = {2, 3, 4, 5}\n", "a & b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "并：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a | b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "差：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a - b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对称差：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a ^ b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 字典 Dictionary " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python用`{key:value}`来生成Dictionary。" ] }, { "cell_type": "code", "execution_count": 21, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'dogs': 4}" ] }, "execution_count": 21, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = {'dogs':5, 'dogs':4}\n", "d" ] }, { "cell_type": "code", "execution_count": 22, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'dogs': 5, 'cats': 4}" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d = {'dogs':5, 'cats':4}\n", "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "字典的大小" ] }, { "cell_type": "code", "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "len(d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "查看字典某个键对应的值：" ] }, { "cell_type": "code", "execution_count": 24, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d[\"dogs\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "修改键值：" ] }, { "cell_type": "code", "execution_count": 25, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'dogs': 2, 'cats': 4}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d[\"dogs\"] = 2\n", "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "插入键值：" ] }, { "cell_type": "code", "execution_count": 26, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "{'dogs': 2, 'cats': 4, 'pigs': 7}" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d[\"pigs\"] = 7\n", "d" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "所有的键：" ] }, { "cell_type": "code", "execution_count": 27, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_keys(['dogs', 'cats', 'pigs'])" ] }, "execution_count": 27, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "所有的值：" ] }, { "cell_type": "code", "execution_count": 28, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_values([2, 4, 7])" ] }, "execution_count": 28, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.values()\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "所有的键值对：" ] }, { "cell_type": "code", "execution_count": 29, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "dict_items([('dogs', 2), ('cats', 4), ('pigs', 7)])" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.items()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 数组 Numpy Arrays" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "需要先导入需要的包，Numpy数组可以进行很多列表不能进行的运算。" ] }, { "cell_type": "code", "execution_count": 8, "metadata": {}, "outputs": [ { "ename": "TypeError", "evalue": "can only concatenate list (not \"int\") to list", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m+\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[1;31mTypeError\u001b[0m: can only concatenate list (not \"int\") to list" ] } ], "source": [ "[1,2,3,4] + 2" ] }, { "cell_type": "code", "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3, 4, 5, 6]" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = []\n", "for i in [1,2,3,4]:\n", " a.append(i+2)\n", "a" ] }, { "cell_type": "code", "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[3, 4, 5, 6]" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "[i+2 for i in [1,2,3,4]]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1. , 2. , 3. , 4.5])" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from numpy import array\n", "a = array([1, 2, 3, 4.5])\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "加法：" ] }, { "cell_type": "code", "execution_count": 12, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([3. , 4. , 5. , 6.5])" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a + 2" ] }, { "cell_type": "code", "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([2., 4., 6., 9.])" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a + a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 画图 Plot" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python提供了一个很像MATLAB的绘图接口。" ] }, { "cell_type": "code", "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([1. , 2. , 3. , 4.5])" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a" ] }, { "cell_type": "code", "execution_count": 15, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "# import matplotlib as plt\n", "from matplotlib.pyplot import plot\n", "\n", "plot(a, a**3)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 循环 Loop" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "line = '1 2 3 4 5'\n", "fields = line.split() #分割字符串\n", "fields" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "total = 0\n", "for field in fields:\n", " total += int(field) #字符转数字\n", "total" ] }, { "cell_type": "raw", "metadata": {}, "source": [ "\n", "Python中有一种叫做列表推导式(List comprehension)的用法：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "numbers = [int(field) for field in fields]\n", "numbers" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sum(numbers)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "写在一行：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "sum([int(field) for field in line.split()])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 文件操作 File IO" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "cd ~" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "写文件：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "f = open('data.txt', 'w')\n", "f.write('1 2 3 4\\n')\n", "f.write('2 3 4 5\\n')\n", "f.close()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "读文件：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "f = open('data.txt')\n", "data = []\n", "for line in f:\n", " data.append([int(field) for field in line.split()])\n", "f.close()\n", "data #内存数据\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "for row in data:\n", " print (row)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "删除文件：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.remove('data.txt')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os\n", "os.getcwd()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "foo = 'C:\\\\Users\\\\tanghong\\\\'\n", "f = open(foo + 'data.txt', 'w')\n", "f.write('1 2 3 4\\n')\n", "f.write('2 3 4 5\\n')\n", "f.close()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fr = open(foo + 'data.txt')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "fr = open(foo + 'data.txt')\n", "data = []\n", "for line in fr:\n", " #data.append([int(field) for field in line.split()])\n", " data.append(line.split(','))\n", "f.close()\n", "data #内存数据" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 函数 Function" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python用关键词`def`来定义函数。" ] }, { "cell_type": "code", "execution_count": 3, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "source": [ "def poly(x, a, b, c):\n", " y = a * x ** 2 + b * x + c\n", " return y\n", "\n", "x = 1\n", "poly(x, 1, 2, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "用Numpy数组做参数x：" ] }, { "cell_type": "code", "execution_count": 4, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'array' 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\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m x = array([[1, 2, 3],\n\u001b[0m\u001b[0;32m 2\u001b[0m [4, 5, 6]])\n\u001b[0;32m 3\u001b[0m \u001b[0mpoly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\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 'array' is not defined" ] } ], "source": [ "x = array([[1, 2, 3],\n", " [4, 5, 6]])\n", "poly(x, 1, 2, 3)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "可以在定义时指定参数的默认值：" ] }, { "cell_type": "code", "execution_count": 5, "metadata": {}, "outputs": [ { "ename": "NameError", "evalue": "name 'array' 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\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[0mx\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0marange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m10\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 8\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 9\u001b[1;33m \u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m3\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m5\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m6\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m7\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m8\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m9\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[1;31mNameError\u001b[0m: name 'array' is not defined" ] } ], "source": [ "from numpy import arange\n", "\n", "def poly(x, a = 1, b = 2, c = 3):\n", " y = a*x**2 + b*x + c\n", " return y\n", "\n", "x = arange(10)\n", "x\n", "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, { "cell_type": "code", "execution_count": 6, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "array([ 3, 6, 11, 18, 27, 38, 51, 66, 83, 102])" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "poly(x)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "poly(x, b = 1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 模块 Module" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python中使用`import`关键词来导入模块。" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import os" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "当前进程号：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "os.getpid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "系统分隔符：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "os.sep" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "os.getcwd()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## - 类 Class" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "用`class`来定义一个类。\n", "`Person(object)`表示继承自`object`类；\n", "`__init__`函数用来初始化对象；\n", "`self`表示对象自身，类似于`C` `Java`里面`this`。" ] }, { "cell_type": "code", "execution_count": 30, "metadata": {}, "outputs": [], "source": [ "# Person(object)表示继承自object类\n", "class Person(object):\n", " def __init__(self, first, last, age):\n", " self.first = first\n", " self.last = last\n", " self.age = age\n", " def full_name(self):\n", " return self.first + '. ' + self.last" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "构建新对象：" ] }, { "cell_type": "code", "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "per2 = Person('迪娜','哈哈','18')" ] }, { "cell_type": "code", "execution_count": 32, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "('迪娜', '哈哈', '18', '迪娜. 哈哈')" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "per2.first,per2.last,per2.age,per2.full_name()" ] }, { "cell_type": "code", "execution_count": 33, "metadata": {}, "outputs": [], "source": [ "person = Person('Mertle', 'Sedgewick', 52)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "调用对象的属性：" ] }, { "cell_type": "code", "execution_count": 34, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Mertle'" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "person.first" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "调用对象的方法：" ] }, { "cell_type": "code", "execution_count": 35, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Mertle. Sedgewick'" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "person.full_name()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "修改对象的属性：" ] }, { "cell_type": "code", "execution_count": 36, "metadata": {}, "outputs": [ { "data": { "text/plain": [ "'Mertle. Smith'" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "person.last = 'Smith'\n", "person.full_name()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "添加新属性，d是之前定义的字典：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "d" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "per2.critters = d" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "per2.critters" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "person.critters = d\n", "person.critters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 网络数据 Data from Web" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "url = 'http://ichart.finance.yahoo.com/table.csv?s=GE&d=10&e=5&f=2013&g=d&a=0&b=2&c=1962&ignore=.csv'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "处理后就相当于一个可读文件：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import urllib2\n", "ge_csv = urllib2.urlopen(url) #从网络读取数据\n", "data = [] #读取数据存入data中\n", "for line in ge_csv:\n", " data.append(line.split(',')) #按行读取。用 , 分开\n", "data[:4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "\n", "# 使用`pandas`处理数据：" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "scrolled": true }, "outputs": [], "source": [ "ge_csv = urllib2.urlopen(url)\n", "import pandas\n", "ge = pandas.read_csv(ge_csv, index_col=0, parse_dates=True)\n", "ge.plot(y='Adj Close')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# 使用turtle画图：" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# PythonDraw.py\n", "import turtle\n", "turtle.setup(650, 350, 200, 200)\n", "turtle.penup()\n", "turtle.fd(-250)\n", "turtle.pendown()\n", "turtle.pensize(25)\n", "turtle.pencolor(\"purple\")\n", "turtle.seth(-40)\n", "for i in range(4):\n", " turtle.circle(40, 80)\n", " turtle.circle(-40, 80)\n", "turtle.circle(40, 80/2)\n", "turtle.fd(40)\n", "turtle.circle(16, 180)\n", "turtle.fd(40 * 2/3)\n", "turtle.done()\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python [conda env:py_36] *", "language": "python", "name": "conda-env-py_36-py" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.8.0" }, "toc": { "base_numbering": 1, "nav_menu": {}, "number_sections": true, "sideBar": true, "skip_h1_title": false, "title_cell": "Table of Contents", "title_sidebar": "Contents", "toc_cell": false, "toc_position": { "height": "calc(100% - 180px)", "left": "10px", "top": "150px", "width": "299.837px" }, "toc_section_display": true, "toc_window_display": true } }, "nbformat": 4, "nbformat_minor": 1 }