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1256 wiersze
140 KiB
1256 wiersze
140 KiB
{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"# 实践2 Python基本语法 \n",
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"\n",
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"## 学习目标 \n",
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"\n",
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"1. 值具有类型，了解值的类型很重要； \n",
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"2. 理解变量是值的抽象，可以帮助我们处理用户输入的任何值； \n",
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"3. 掌握赋值语句，赋值语句是值与变量、变量与变量之间的桥梁。 \n",
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"4. 掌握基本运算，操作符是对数据进行操作的一些符号，最常用的有算术运算、大小比较、赋值、逻辑运算等； \n",
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"5. 了解代码注释的语法和意义； \n",
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"6. 了解浮点表示误差和规避方法； \n",
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"7. 了解 Python 中字符串的表示方法以及转义符；\n",
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"8. 了解如何使用 f-string；\n",
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"9. 掌握输入输出语句，输入输出语句提供了程序与用户的交互渠道，程序可以根据用户的输入，输出对应的结果。"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 1.关键字\n",
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"在Python中，具有特殊功能的标识符称为关键字。Python的关键字随版本不同有一些差异，可以在Python 交互方式中按如下所示的方法查阅。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import keyword\n",
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"keyword.kwlist"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 38,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"\n",
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"You are now leaving help and returning to the Python interpreter.\n",
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"If you want to ask for help on a particular object directly from the\n",
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"interpreter, you can type \"help(object)\". Executing \"help('string')\"\n",
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"has the same effect as typing a particular string at the help> prompt.\n"
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]
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}
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],
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"source": [
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"help()"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 2. 常量和变量 \n",
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"数据是计算机处理的对象，在计算机中存储和处理数据，是区分数据类型的，不同的数据类型的表示方式和运算机制都是不同的。 \n",
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"数据在程序里的存在有两种形式，一种是“值（value）”，一种是“变量（variable）”。按接近人的习惯设计并加以不同数据类型的区别，称为数据的“值（value）”，是数据的“书写形式”。这些数据是不会改变的，也称为常量。 \n",
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"\n",
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"下面是一些值的例子： \n",
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"\n",
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"```python\n",
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"42\n",
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"3.14\n",
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"'a'\n",
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"'abracadabra'\n",
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"True\n",
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"False\n",
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"```\n",
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"\n",
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"值是有类型的，Python 提供了一个函数叫做 `type()`，可以告诉我们某个值是什么类型，我们可以试一下： "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 39,
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"metadata": {},
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"outputs": [],
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"source": [
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"type(1)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 40,
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"metadata": {},
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"outputs": [],
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"source": [
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"type(1.0)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 41,
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"metadata": {},
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"outputs": [],
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"source": [
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"type(2+5j)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"type(True)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"type(\"Python\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"type([1,2,3,4,5])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"type((1,2,3,4,5))"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"type({1,2,2,3,4})"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"type({\"a\":94,\"c\":96,\"A\":65,\"C\":67})"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"有了值，我们就可以做计算，但是写不出真正的程序，为什么呢？因为程序写出来是希望能被很多人反复使用的——这些人叫做用户（user）——这样才比较划算，每次使用，用户输入一些数据，程序进行处理，然后给出用户想要的结果，每次的输入可能是同一类型，但具体的值是多少在我们写程序时是不知道的，所以我们需要一种东西能够来代替值写在程序中，而在程序运行时这些东西才根据用户的输入变成实际的“值”，这些“东西”就是变量。 \n",
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"\n",
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"变量使得我们可以编写一个程序来处理一类值，而不管具体值是多少。我们学习代数的时候用 a + b = b + a 就表达了“加法交换律”这样高度抽象的普适规律，而不管 a 和 b 到底是多少，程序里的变量也一样，让我们能够进行“数据的抽象”，这个概念很重要，后面我们还会不断深化它。 \n",
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"\n",
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"所有编程语言都支持值和变量，也支持把特定的值赋予某个变量，这样的操作叫做“赋值（assignment）”，下面是一些例子： "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = 12\n",
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"b = 30\n",
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"f = 3.14\n",
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"s = 'abracadabra'\n",
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"l = [1, 2, 3]\n",
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"t = True\n",
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"f = False"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"赋值之后变量就具有了相应的值，**同时也具有了该值对应的类型和对象ID**，所以变量一经赋值就有了类型和对象ID： "
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"s"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"id(s)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"type(s)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"如果我们想消灭一个变量不再使用，可以用 del 命令："
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"del s\n",
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"s"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 3.可变对象和不可变对象\n",
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"Python3的对象可以分为不可变对象（immutable）和可变对象（mutable）。不可变对象，一旦创建其值就不能被改变，可变对象的值可以被修改。\n",
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"Python大部分对象都是不可变对象，例如int、float、str、tuple等。变量是指向某个对象的引用，多个变量可以指向同一个对象，给变量重新赋值，并不改变原始对象的值，只是创建一个新对象，该变量指向新的对象。\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"s=\"Python\"\n",
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"s[0]='p'"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"s=\"python\"\n",
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"s"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"对象本身的值可以改变的对象称为可变对象，例如list、dict、set等。"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"l"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"l[0]=10\n",
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"l"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"## 4.运算符与表达式\n",
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"Python语言提供了丰富的运算符，例如算术运算符、关系运算符、逻辑运算符等。 \n",
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"表达式是数据对象和运算符按照一定的规则写出的式子，描述计算过程。与上述运算符对应的表达式可以分为算术表达式、关系表达式、逻辑表达式等。 \n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### （1）算术操作符\n",
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"算术操作符就是加减乘除之类的，专门针对整数和小数。"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"相同类型运算"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"a = 6\n",
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"b = 30\n",
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"f = 3.14\n",
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"g = 0.7"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"a + b"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"a - b"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"a * b"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#除法，int/int=>float\n",
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"b / a"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#整除，int//int=>int\n",
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"b // a"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#幂运算\n",
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"a**2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#求余数\n",
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"a%4"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"混合运算"
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]
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},
|
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
|
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"# int*float =>float\n",
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"a * g"
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]
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},
|
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
|
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"outputs": [],
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"source": [
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"#幂可以是小数，下例相当于求根号2\n",
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"a ** 0.5"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"输入以下表达式并查看结果。 \n",
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"①23+3 ②23>3 ③'23'+'3' ④23/3 ⑤ 23//3⑥23%3⑦23**3 \n",
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"问题： \n",
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"表达式结果为整数类型有哪些？ \n",
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"表达式结果为浮点数类型有哪些？ \n",
|
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"表达式结果为字符串类型有哪些？ \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### （2）关系运算符 \n",
|
|
"比较大小的一组操作符，`> < >= <= == !=` 分别是大于、小于、大于或等于、小于或等于、等于和不等于关系。这几个操作符比较它左右的数据的大小关系，如果符合操作符的含义就返回布尔值真（`True`），否则返回布尔值（`False`）。在 Python 里这些操作符不仅可以比较数字，还可以比较其他很多类型的数据，下面有一些简单的例子。"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"a < b"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"a >= a"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"a <= f"
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|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"a + b == 36"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"a + b != 36"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"'aaa' < 'aab'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"a * g == 4.2"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"上面的比较有没有什么问题？前面都没啥，但是注意最后一个，a 的值是 6，而 g 的值是 0.7，他们乘起来就是 4.2 啊，为啥最后一个比较返回是 `False` 呢？我们来看看计算机算出来的 `a * g` 是多少："
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"a * g"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"这是什么鬼啊？不要惊慌，这叫浮点误差，下面来简单解释下"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### 插播：浮点误差"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"目前我们用的计算机叫“电子计算机”，用电平的高低表示数，电平高的是 1，低的是 0，只有这两种状态，也就是说计算机只认识 0 和 1。要用 0 和 1 表示我们要用到的各种数字，就要采用[二进制](https://zh.wikipedia.org/zh-cn/%E4%BA%8C%E8%BF%9B%E5%88%B6)计数法。\n",
|
|
"\n",
|
|
"二进制计数法对于整数来说没啥问题，每一个十进制整数都对应唯一的二进制表示，但对于小数来说问题就来了，**绝大部分十进制小数是没法用有限位数表示为二进制小数的**。这个结论决定了在计算机里小数是以一种“近似值”的方式表示的，0.7 在计算机里是个近似等于 0.7 的二进制小数，这个小数乘上 6 之后的结果也是有微小的误差的，转换回十进制小数给我们看结果时就会出现上面那样的奇怪现象。"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"那么我们怎么解决这个问题呢？分两种情况：\n",
|
|
"1. 如果需要绝对精确的结果，那么不要用小数，只用整数进行运算，然后自己计算小数点的位置，绝大部分计算器就是这么做的；\n",
|
|
"2. 如果不需要绝对精确，那么只要结果的误差足够小就可以了，这时候可以用浮点数进行运算，但比较时不要直接比较结果是不是相等（`==`），而用误差范围来比较："
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"s = 4.2 # 这是预期结果\n",
|
|
"r = 0.001 # 这是可容忍误差\n",
|
|
"\n",
|
|
"abs(a * g - s) < r # 如果 (a * g - s) 的绝对值比 r 小，就算结果合格\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"`abs` 函数的值等于后面括号里的值的绝对值。"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### （3）逻辑运算操作符 \n",
|
|
"逻辑运算操作符是布尔值之间的运算，结果也是布尔值。我们知道布尔值是逻辑上的真和假，而逻辑运算操作符就对应了逻辑代数（也叫布尔代数）里的各种运算。\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"基本的逻辑运算就三个：`and`、`or`、`not`，即 **与**、**或**、**否**。\n",
|
|
"* 两个布尔值如果都是“真”，他们的 **与** 运算结果也为“真”，其他情况下结果都是“假”；\n",
|
|
"* 两个布尔值中只要有一个是“真”，他们的 **或** 运算结果就为“真”，如果两个都是“假”，那么 **或** 运算结果为“假”；\n",
|
|
"* 布尔值“真”的 **否** 运算结果为“假”，布尔值“假”的 **否** 运算结果为“真”。\n",
|
|
"\n",
|
|
"通过这三个操作符我们可以组合复杂的逻辑运算："
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"t = True\n",
|
|
"f = False"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"t and f"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"t or f"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"not t"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"t and (not f)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"(not t) or f"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 5.赋值语句\n",
|
|
"Python的赋值语句通过赋值符“=”实现，用赋值符将右边数据对象与左边的变量名建立了引用关系，其一般使用方法如下，尖括号的内容表示具体使用时需要替代：\n",
|
|
" ` <变量> =<表达式>`\n",
|
|
"Python还支持连续赋值、同步赋值和复合赋值。\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### （1）连续赋值"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"a=b=c=0\n",
|
|
"a,b,c"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### （2）同步赋值"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#输入x，y\n",
|
|
"x,y=int(input(\"x=\")),int(input(\"y=\"))\n",
|
|
"x,y"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#交换x,y\n",
|
|
"x,y=y,x\n",
|
|
"x,y"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### (3)复合赋值 \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"i=1\n",
|
|
"i=i+1\n",
|
|
"i"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"i+=1\n",
|
|
"i"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"类似的还有 -= *= /= %= **= //= 这一组赋值操作符的变体，与 += 同理。"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 练习"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"1.执行完下面数值表达式语句后，变量a~k的值分别是多少？\n",
|
|
"```python \n",
|
|
"a=5\n",
|
|
"b=2\n",
|
|
"a*=b\n",
|
|
"b+=a\n",
|
|
"a,b=b,a\n",
|
|
"c=6\n",
|
|
"d= c%2+(c+1)%2\n",
|
|
"e =2.5\n",
|
|
"f=3.5\n",
|
|
"g=(a+b)%3+int(f)//int(e)\n",
|
|
"h=float(a+b)%3+int(f)//int(e)\n",
|
|
"i=(a+b)/3+f%e\n",
|
|
"j=a<b and c<d\n",
|
|
"k=not j and True\n",
|
|
"```"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": []
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"2.条件表达式 \n",
|
|
"假设执行了如下语句 \n",
|
|
"```python \n",
|
|
"x=384 \n",
|
|
"a,b=2.56769, 2.56789\n",
|
|
"```\n",
|
|
"写出下面条件判断语句，并测试结果。 \n",
|
|
"（1）判断x是否是奇数； \n",
|
|
"（2）判断x是否能被3和5整除； \n",
|
|
"（3）判断x是否能被3或5整除； \n",
|
|
"（4）判断b与a的差值不超过0.0001。 \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 6.字符串 \n",
|
|
"在Python语言程序设计中，字符串是最常用的数据类型之一。 \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### （1）字符串值\n",
|
|
"Python 里字符串值（*string literal*）可以用单引号、双引号、三个单引号或三个双引号来表示，主要区别是：\n",
|
|
"* 单引号里面可以包含双引号字符；\n",
|
|
"* 双引号里面可以包含单引号字符；\n",
|
|
"* 三个引号括起来的可以包含多行文字。"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"'用单引号括起来的字符串里不能直接包含单引号，但可以有\"双引号\"'"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\"用双引号括起来的字符串里不能直接包含双引号，但可以有'单引号'\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"\"中文的引号其实是另外的字符，总是可以包含的”“‘’\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"data": {
|
|
"text/plain": [
|
|
"'三个单或者双引号可以包含多行文字\\n就像这样'"
|
|
]
|
|
},
|
|
"execution_count": 1,
|
|
"metadata": {},
|
|
"output_type": "execute_result"
|
|
}
|
|
],
|
|
"source": [
|
|
"'''三个单或者双引号可以包含多行文字\n",
|
|
"就像这样'''"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"转义符 \\n 代表一个特殊字符，也就是 ASCII 里的换行（LF, linefeed）符；"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 2,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"三个单或者双引号可以包含多行文字\n",
|
|
"就像这样\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print('''三个单或者双引号可以包含多行文字\n",
|
|
"就像这样''')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 3,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"其实多行文字并没有什么特别\n",
|
|
"像这样插入一个换行字符也能实现\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"print('其实多行文字并没有什么特别\\n像这样插入一个换行字符也能实现')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## （2）组装字符串：f-string"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"我们写程序的时候经常要干的一件事是动态的攒一个字符串出来，里面有固定的文字，但是特定位置上要出现某些我们算出来的值。比如我们计算了一个题目，最后输出一句话，通常想写成这样：\n",
|
|
"\n",
|
|
"`计算完毕，获胜概率为 x%，败北概率为 y%，平局概率为 (100-x-y)%。`\n",
|
|
"\n",
|
|
"我们当然可以用很笨的拼装办法比如："
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"x = 62\n",
|
|
"y = 29\n",
|
|
"print('计算完毕，获胜概率为 ' + str(x) + '%，败北概率为 ' + str(y) + '%，平局概率为 ' + str(100-x-y) + '%。')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"这样的代码又难看又难维护，所以很多编程语言都提供了“参数化字符串”的方案，在 Python 3 里是所谓的 *f-string*，就是在字符串前面加一个小写的 `f`，下面是例子："
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 4,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"计算完毕，获胜概率为 62%，败北概率为 29%，平局概率为 9%。\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"x = 62\n",
|
|
"y = 29\n",
|
|
"print(f'计算完毕，获胜概率为 {x}%，败北概率为 {y}%，平局概率为 {100-x-y}%。')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"这和上面的笨办法输出完全一样。所谓 *f-string* 就是一个字符串前面加个字母 f，也就是 `f'...'` 或者 `f\"...\"` 这样的。这样的字符串里可以用一对大括号括起来任何合法的表达式，这个表达式会被计算，并用结果替换 `{表达式}`。在上面的例子中，`{x}` 会用 `x` 的值 62 替换，`{y}` 会用 `y` 的值 29 替换，`{100-x-y}` 会用 `100-x-y` 的值 9 替换，最后就得到了我们想要的结果。\n",
|
|
"\n",
|
|
"这个方案是不是漂亮多了呢？"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": 6,
|
|
"metadata": {},
|
|
"outputs": [
|
|
{
|
|
"name": "stdout",
|
|
"output_type": "stream",
|
|
"text": [
|
|
"计算完毕，获胜概率为 61.20%，败北概率为 28.51%，平局概率为 10.29%。\n"
|
|
]
|
|
}
|
|
],
|
|
"source": [
|
|
"x = 61.196\n",
|
|
"y = 28.511\n",
|
|
"print(f'计算完毕，获胜概率为 {x:.2f}%，败北概率为 {y:.2f}%，平局概率为 {100-x-y:.2f}%。')"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"在花括号中还可以设置输出的格式，{:.2f}表示小数点保留两位的float值。"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## （2）字符串运算 \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"hello = 'hello'\n",
|
|
"world = \"world\""
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"# 可以用操作符 + 来串接字符串\n",
|
|
"hw = hello + ' ' + world + '!'\n",
|
|
"print(hw)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#可以用操作符*来赋值字符串\n",
|
|
"hc = hello * 3\n",
|
|
"print(hc)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"## "
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"## 6.输入与输出"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### (1) 输入语句"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#输入字符串\n",
|
|
"s=input(\"请输入：\")\n",
|
|
"s"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#输入数值数据，要数据类型转换\n",
|
|
"n=int(input(\"请输入一个整数：\"))\n",
|
|
"n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"x=float(input(\"请输入一个浮点数：\"))\n",
|
|
"x"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"### （2）输出语句"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#输出一个字符串\n",
|
|
"print(\"Hello,Python!\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#输出一个变量的值\n",
|
|
"x,y=10,20\n",
|
|
"print(x)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#输出一个表达式的值\n",
|
|
"print(x+y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#输出多个数据值，每个数据之间以空格分隔。\n",
|
|
"print(\"x=\",x,\"y=\",y)\n",
|
|
"print(\"x+y=\",x+y)"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"#f格式串输出\n",
|
|
"x,y=74.5,13.8\n",
|
|
"print(f\"{x} / {y} = {x/y}\")\n",
|
|
"print(f\"{x:6.2f} / {y:6.2f} = {x/y:6.2f}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"# 案例：求解BMI问题(1) 计算BMI值（顺序结构）"
|
|
]
|
|
},
|
|
{
|
|
"attachments": {
|
|
"image.png": {
|
|
"image/png": 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"
|
|
}
|
|
},
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"BMI指数（即身体质量指数，英文为Body Mass Index，简称BMI），是反应体内脂肪总量的指标，BMI指数的计算方法如下图所示。\n",
|
|
" \n",
|
|
"该指标考虑了体重和身高两个因素，比单一按体重衡量人体胖瘦程度以及是否健康更加客观准确。例如：一个人的身高为1.75米,体重为70千克，他的BMI=70/(1.75*1.75)=22.86。 \n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"编写程序，根据体重和身高计算BMI值。该问题的IPO描述如下：\n",
|
|
"输入：身高和体重\n",
|
|
"处理：计算 BMI = 体重(kg) ÷ 身高2(m2)\n",
|
|
"输出：BMI值\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"本例是一个典型的IPO问题，分三步，顺序执行每一个步骤。每一个算法无论简单复杂，都是一个顺序结构的算法，体现了程序从上至下，逐条执行的执行顺序。 算法描述\n",
|
|
"1 输入身高height和体重weight\n",
|
|
"2 计算BMI = weight / (height**2)\n",
|
|
"3 输出BMI\n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "code",
|
|
"execution_count": null,
|
|
"metadata": {},
|
|
"outputs": [],
|
|
"source": [
|
|
"height = float(input(\"请输入你的身高(m):\"))\n",
|
|
"weight = float(input(\"请输入你的体重(kg):\"))\n",
|
|
"value = weight / pow(height, 2)\n",
|
|
"print(f\"你的BMI指数为{value:.2f}\")"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"说明：\n",
|
|
"input函数的作用是从控制台接收用户输入，接受的是一个字符串数据。 \n",
|
|
"float函数的作用是将接受的字符串转化为浮点数。通过赋值语句，将数据对象赋值给变量height和weight。 \n",
|
|
"input函数中的字符串是输入时的提示文字，提示用户需要输入的数据内容。 \n",
|
|
"第三条语句的右边是一个算术表达式，它通过变量weight和height计算出BMI指数。再赋值给左边的变量value。pow()函数是一个数学函数，用来求x的n次方，例如pow(height, 2)计算身高height的平方。 \n",
|
|
"print()函数输出最后的输出结果。 \n",
|
|
"\n"
|
|
]
|
|
},
|
|
{
|
|
"cell_type": "markdown",
|
|
"metadata": {},
|
|
"source": [
|
|
"请思考在哪些情况下，程序会运行出错？\n",
|
|
"以下提示可供读者参考：\n",
|
|
"1. 输入一些非数值型的数据，例如字符串。\n",
|
|
"2. 输入一些特殊的数据，例如身高是0。\n",
|
|
"3. 输入很大的数据，例如100万、1000万、1亿、10亿...（你是否发现使用Python的好处了？）\n",
|
|
"4. 不输入数据，直接按回车。\n",
|
|
"现阶段似乎还不能解决部分输入问题和数据类型不符问题，但是随着学习的深入，我们就会掌握足够的知识去处理这些异常。\n"
|
|
]
|
|
}
|
|
],
|
|
"metadata": {
|
|
"kernelspec": {
|
|
"display_name": "Python 3 (ipykernel)",
|
|
"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": "3.9.5"
|
|
}
|
|
},
|
|
"nbformat": 4,
|
|
"nbformat_minor": 4
|
|
}
|