{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Python 入门演示" ] }, { "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": 1, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 1, "metadata": {}, "output_type": "execute_result" } ], "source": [ "2 + 2" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "浮点数相加，得到浮点数：" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4.5" ] }, "execution_count": 2, "metadata": {}, "output_type": "execute_result" } ], "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": 3, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4.5" ] }, "execution_count": 3, "metadata": {}, "output_type": "execute_result" } ], "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": 4, "metadata": { "collapsed": true }, "outputs": [], "source": [ "a = 0.2" ] }, { "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": 5, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'hello world'" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = \"hello world\"\n", "s" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'hello world'" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = 'hello 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": 7, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello\n", "world\n" ] } ], "source": [ "s = \"\"\"hello\n", "world\"\"\"\n", "print s" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "hello\n", "world\n" ] } ], "source": [ "s = '''hello\n", "world'''\n", "print s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "字符串的加法：" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'hello world'" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = \"hello\" + \" world\"\n", "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "字符串索引：\n" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'h'" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[0]" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'d'" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[-1]" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'hello'" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s[0:5]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "字符串的分割：" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['hello', 'world']" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "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": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "11" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "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": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2.0, 'hello', 6.0]" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a = [1, 2.0, 'hello', 5 + 1.0]\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "列表加法：" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2.0, 'hello', 6.0, 1, 2.0, 'hello', 6.0]" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a + a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "列表索引：" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2.0" ] }, "execution_count": 17, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a[1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "列表长度：" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4" ] }, "execution_count": 18, "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": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2.0, 'hello', 6.0, 'world']" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a.append(\"world\")\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": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{2, 3, 4}" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s = {2, 3, 4, 2}\n", "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "集合的长度：" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "3" ] }, "execution_count": 21, "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": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1, 2, 3, 4}" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "s.add(1)\n", "s" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "集合的交：" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{2, 3, 4}" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "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": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1, 2, 3, 4, 5}" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a | b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "差：" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1}" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a - b" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "对称差：" ] }, { "cell_type": "code", "execution_count": 26, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{1, 5}" ] }, "execution_count": 26, "metadata": {}, "output_type": "execute_result" } ], "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": 27, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'cats': 4, 'dogs': 5}" ] }, "execution_count": 27, "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": 28, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "2" ] }, "execution_count": 28, "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": 29, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "5" ] }, "execution_count": 29, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d[\"dogs\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "修改键值：" ] }, { "cell_type": "code", "execution_count": 30, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'cats': 4, 'dogs': 2}" ] }, "execution_count": 30, "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": 31, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'cats': 4, 'dogs': 2, 'pigs': 7}" ] }, "execution_count": 31, "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": 32, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['cats', 'dogs', 'pigs']" ] }, "execution_count": 32, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.keys()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "所有的值：" ] }, { "cell_type": "code", "execution_count": 33, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[4, 2, 7]" ] }, "execution_count": 33, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.values()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "所有的键值对：" ] }, { "cell_type": "code", "execution_count": 34, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[('cats', 4), ('dogs', 2), ('pigs', 7)]" ] }, "execution_count": 34, "metadata": {}, "output_type": "execute_result" } ], "source": [ "d.items()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 数组 Numpy Arrays" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "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": 35, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([1, 2, 3, 4])" ] }, "execution_count": 35, "metadata": {}, "output_type": "execute_result" } ], "source": [ "from numpy import array\n", "a = array([1, 2, 3, 4])\n", "a" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "加法：" ] }, { "cell_type": "code", "execution_count": 36, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([3, 4, 5, 6])" ] }, "execution_count": 36, "metadata": {}, "output_type": "execute_result" } ], "source": [ "a + 2" ] }, { "cell_type": "code", "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([2, 4, 6, 8])" ] }, "execution_count": 37, "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": 38, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[]" ] }, "execution_count": 38, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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vzM7cNIqkspj4zjwzDwGH2sc/iYgDwLOAA0WvOWnd0ijbtplGkVQ9I+lmiYg1wAuBPaO4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\nSaoBg7kk1YDBXJJqwGAuSTVgMJekGvhf3kAwE/Ra4D0AAAAASUVORK5CYII=\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "%matplotlib inline\n", "from matplotlib.pyplot import plot\n", "plot(a, a**2)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 循环 Loop" ] }, { "cell_type": "code", "execution_count": 39, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "['1', '2', '3', '4', '5']" ] }, "execution_count": 39, "metadata": {}, "output_type": "execute_result" } ], "source": [ "line = '1 2 3 4 5'\n", "fields = line.split()\n", "fields" ] }, { "cell_type": "code", "execution_count": 40, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 40, "metadata": {}, "output_type": "execute_result" } ], "source": [ "total = 0\n", "for field in fields:\n", " total += int(field)\n", "total" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Python中有一种叫做列表推导式(List comprehension)的用法：" ] }, { "cell_type": "code", "execution_count": 41, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[1, 2, 3, 4, 5]" ] }, "execution_count": 41, "metadata": {}, "output_type": "execute_result" } ], "source": [ "numbers = [int(field) for field in fields]\n", "numbers" ] }, { "cell_type": "code", "execution_count": 42, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 42, "metadata": {}, "output_type": "execute_result" } ], "source": [ "sum(numbers)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "写在一行：" ] }, { "cell_type": "code", "execution_count": 43, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "15" ] }, "execution_count": 43, "metadata": {}, "output_type": "execute_result" } ], "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": 44, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "d:\\Users\\lijin\n" ] } ], "source": [ "cd ~" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "写文件：" ] }, { "cell_type": "code", "execution_count": 45, "metadata": { "collapsed": true }, "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": 46, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "[[1, 2, 3, 4], [2, 3, 4, 5]]" ] }, "execution_count": 46, "metadata": {}, "output_type": "execute_result" } ], "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\n" ] }, { "cell_type": "code", "execution_count": 47, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "[1, 2, 3, 4]\n", "[2, 3, 4, 5]\n" ] } ], "source": [ "for row in data:\n", " print row" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "删除文件：" ] }, { "cell_type": "code", "execution_count": 48, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import os\n", "os.remove('data.txt')" ] }, { "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": 49, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "6" ] }, "execution_count": 49, "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": 50, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 6, 11, 18])" ] }, "execution_count": 50, "metadata": {}, "output_type": "execute_result" } ], "source": [ "x = array([1, 2, 3])\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": 51, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])" ] }, "execution_count": 51, "metadata": {}, "output_type": "execute_result" } ], "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": 52, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3, 6, 11, 18, 27, 38, 51, 66, 83, 102])" ] }, "execution_count": 52, "metadata": {}, "output_type": "execute_result" } ], "source": [ "poly(x)" ] }, { "cell_type": "code", "execution_count": 53, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "array([ 3, 5, 9, 15, 23, 33, 45, 59, 75, 93])" ] }, "execution_count": 53, "metadata": {}, "output_type": "execute_result" } ], "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": 54, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import os" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "当前进程号：" ] }, { "cell_type": "code", "execution_count": 55, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "4400" ] }, "execution_count": 55, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.getpid()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "系统分隔符：" ] }, { "cell_type": "code", "execution_count": 56, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'\\\\'" ] }, "execution_count": 56, "metadata": {}, "output_type": "execute_result" } ], "source": [ "os.sep" ] }, { "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": 57, "metadata": { "collapsed": true }, "outputs": [], "source": [ "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": 58, "metadata": { "collapsed": true }, "outputs": [], "source": [ "person = Person('Mertle', 'Sedgewick', 52)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "调用对象的属性：" ] }, { "cell_type": "code", "execution_count": 59, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Mertle'" ] }, "execution_count": 59, "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": 60, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "'Mertle Sedgewick'" ] }, "execution_count": 60, "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": 61, "metadata": { "collapsed": true }, "outputs": [], "source": [ "person.last = 'Smith'" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "添加新属性，d是之前定义的字典：" ] }, { "cell_type": "code", "execution_count": 62, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "{'cats': 4, 'dogs': 2, 'pigs': 7}" ] }, "execution_count": 62, "metadata": {}, "output_type": "execute_result" } ], "source": [ "person.critters = d\n", "person.critters" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## 网络数据 Data from Web" ] }, { "cell_type": "code", "execution_count": 63, "metadata": { "collapsed": true }, "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": 64, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/plain": [ "[['Date', 'Open', 'High', 'Low', 'Close', 'Volume', 'Adj Close\\n'],\n", " ['2013-11-05', '26.32', '26.52', '26.26', '26.42', '24897500', '24.872115\\n'],\n", " ['2013-11-04',\n", " '26.59',\n", " '26.59',\n", " '26.309999',\n", " '26.43',\n", " '28166100',\n", " '24.88153\\n'],\n", " ['2013-11-01',\n", " '26.049999',\n", " '26.639999',\n", " '26.030001',\n", " '26.540001',\n", " '55634500',\n", " '24.985086\\n']]" ] }, "execution_count": 64, "metadata": {}, "output_type": "execute_result" } ], "source": [ "import urllib2\n", "ge_csv = urllib2.urlopen(url)\n", "data = []\n", "for line in ge_csv:\n", " data.append(line.split(','))\n", "data[:4]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "使用`pandas`处理数据：" ] }, { "cell_type": "code", "execution_count": 65, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/plain": [ "" ] }, "execution_count": 65, "metadata": {}, "output_type": "execute_result" }, { "data": { "image/png": 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APffrr0PjxvAf/5GONNhDR2OK1ogRmemJV0j8CR1Wr3bL4cNDH0qed17m02QB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\nG8sUQyi3yAAAAABJRU5ErkJggg==\n", "text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "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')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.6" } }, "nbformat": 4, "nbformat_minor": 0 }