mirror of
https://github.com/adambard/learnxinyminutes-docs.git
synced 2024-12-26 02:38:52 +00:00
ae848c481f
Before renaming, Python 3 filenames were 'learnpython3*.py'.
This commit removes the '3' part from the filename.
To verify that the filenames were named consistently across
translations prior to this commit, and to change this:
```
ack -H 'filename:' python.html.markdown
find . -name "python-*.markdown" -exec ack -H 'filename:' {} \;
sed -i 's/^filename: learnpython3/filename: learnpython/' \
python.html.markdown
find . -name "python-*.markdown" -exec \
sed -i 's/^filename: learnpython3/filename: learnpython/' {} \;
```
633 lines
16 KiB
Markdown
633 lines
16 KiB
Markdown
---
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language: Python
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contributors:
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- ["Louie Dinh", "http://pythonpracticeprojects.com"]
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- ["Steven Basart", "http://github.com/xksteven"]
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- ["Andre Polykanine", "https://github.com/Oire"]
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translators:
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- ["Geoff Liu", "http://geoffliu.me"]
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filename: learnpython-cn.py
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lang: zh-cn
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---
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Python 是由吉多·范罗苏姆(Guido Van Rossum)在 90 年代早期设计。
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它是如今最常用的编程语言之一。它的语法简洁且优美，几乎就是可执行的伪代码。
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欢迎大家斧正。英文版原作 Louie Dinh [@louiedinh](http://twitter.com/louiedinh)
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邮箱 louiedinh [at] [谷歌的信箱服务]。中文翻译 Geoff Liu。
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注意：这篇教程是基于 Python 3 写的。如果你想学旧版 Python 2，我们特别有[另一篇教程](http://learnxinyminutes.com/docs/pythonlegacy/)。
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```python
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# 用井字符开头的是单行注释
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""" 多行字符串用三个引号
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包裹，也常被用来做多
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行注释
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"""
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####################################################
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## 1. 原始数据类型和运算符
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####################################################
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# 整数
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3 # => 3
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# 算术没有什么出乎意料的
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1 + 1 # => 2
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8 - 1 # => 7
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10 * 2 # => 20
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# 但是除法例外，会自动转换成浮点数
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35 / 5 # => 7.0
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5 / 3 # => 1.6666666666666667
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# 整数除法的结果都是向下取整
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5 // 3 # => 1
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5.0 // 3.0 # => 1.0 # 浮点数也可以
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-5 // 3 # => -2
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-5.0 // 3.0 # => -2.0
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# 浮点数的运算结果也是浮点数
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3 * 2.0 # => 6.0
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# 模除
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7 % 3 # => 1
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# x的y次方
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2**4 # => 16
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# 用括号决定优先级
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(1 + 3) * 2 # => 8
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# 布尔值
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True
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False
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# 用not取非
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not True # => False
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not False # => True
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# 逻辑运算符，注意and和or都是小写
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True and False # => False
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False or True # => True
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# 整数也可以当作布尔值
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0 and 2 # => 0
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-5 or 0 # => -5
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0 == False # => True
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2 == True # => False
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1 == True # => True
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# 用==判断相等
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1 == 1 # => True
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2 == 1 # => False
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# 用!=判断不等
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1 != 1 # => False
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2 != 1 # => True
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# 比较大小
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1 < 10 # => True
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1 > 10 # => False
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2 <= 2 # => True
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2 >= 2 # => True
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# 大小比较可以连起来！
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1 < 2 < 3 # => True
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2 < 3 < 2 # => False
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# 字符串用单引双引都可以
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"这是个字符串"
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'这也是个字符串'
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# 用加号连接字符串
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"Hello " + "world!" # => "Hello world!"
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# 字符串可以被当作字符列表
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"This is a string"[0] # => 'T'
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# 用.format来格式化字符串
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"{} can be {}".format("strings", "interpolated")
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# 可以重复参数以节省时间
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"{0} be nimble, {0} be quick, {0} jump over the {1}".format("Jack", "candle stick")
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# => "Jack be nimble, Jack be quick, Jack jump over the candle stick"
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# 如果不想数参数，可以用关键字
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"{name} wants to eat {food}".format(name="Bob", food="lasagna")
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# => "Bob wants to eat lasagna"
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# 如果你的Python3程序也要在Python2.5以下环境运行，也可以用老式的格式化语法
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"%s can be %s the %s way" % ("strings", "interpolated", "old")
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# None是一个对象
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None # => None
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# 当与None进行比较时不要用 ==，要用is。is是用来比较两个变量是否指向同一个对象。
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"etc" is None # => False
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None is None # => True
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# None，0，空字符串，空列表，空字典都算是False
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# 所有其他值都是True
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bool(0) # => False
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bool("") # => False
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bool([]) # => False
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bool({}) # => False
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####################################################
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## 2. 变量和集合
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####################################################
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# print是内置的打印函数
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print("I'm Python. Nice to meet you!")
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# 在给变量赋值前不用提前声明
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# 传统的变量命名是小写，用下划线分隔单词
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some_var = 5
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some_var # => 5
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# 访问未赋值的变量会抛出异常
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# 参考流程控制一段来学习异常处理
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some_unknown_var # 抛出NameError
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# 用列表(list)储存序列
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li = []
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# 创建列表时也可以同时赋给元素
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other_li = [4, 5, 6]
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# 用append在列表最后追加元素
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li.append(1) # li现在是[1]
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li.append(2) # li现在是[1, 2]
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li.append(4) # li现在是[1, 2, 4]
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li.append(3) # li现在是[1, 2, 4, 3]
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# 用pop从列表尾部删除
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li.pop() # => 3 且li现在是[1, 2, 4]
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# 把3再放回去
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li.append(3) # li变回[1, 2, 4, 3]
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# 列表存取跟数组一样
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li[0] # => 1
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# 取出最后一个元素
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li[-1] # => 3
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# 越界存取会造成IndexError
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li[4] # 抛出IndexError
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# 列表有切割语法
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li[1:3] # => [2, 4]
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# 取尾
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li[2:] # => [4, 3]
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# 取头
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li[:3] # => [1, 2, 4]
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# 隔一个取一个
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li[::2] # =>[1, 4]
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# 倒排列表
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li[::-1] # => [3, 4, 2, 1]
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# 可以用三个参数的任何组合来构建切割
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# li[始:终:步伐]
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# 用del删除任何一个元素
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del li[2] # li is now [1, 2, 3]
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# 列表可以相加
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# 注意：li和other_li的值都不变
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li + other_li # => [1, 2, 3, 4, 5, 6]
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# 用extend拼接列表
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li.extend(other_li) # li现在是[1, 2, 3, 4, 5, 6]
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# 用in测试列表是否包含值
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1 in li # => True
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# 用len取列表长度
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len(li) # => 6
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# 元组是不可改变的序列
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tup = (1, 2, 3)
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tup[0] # => 1
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tup[0] = 3 # 抛出TypeError
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# 列表允许的操作元组大都可以
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len(tup) # => 3
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tup + (4, 5, 6) # => (1, 2, 3, 4, 5, 6)
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tup[:2] # => (1, 2)
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2 in tup # => True
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# 可以把元组合列表解包，赋值给变量
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a, b, c = (1, 2, 3) # 现在a是1，b是2，c是3
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# 元组周围的括号是可以省略的
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d, e, f = 4, 5, 6
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# 交换两个变量的值就这么简单
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e, d = d, e # 现在d是5，e是4
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# 用字典表达映射关系
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empty_dict = {}
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# 初始化的字典
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filled_dict = {"one": 1, "two": 2, "three": 3}
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# 用[]取值
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filled_dict["one"] # => 1
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# 用 keys 获得所有的键。
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# 因为 keys 返回一个可迭代对象，所以在这里把结果包在 list 里。我们下面会详细介绍可迭代。
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# 注意：字典键的顺序是不定的，你得到的结果可能和以下不同。
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list(filled_dict.keys()) # => ["three", "two", "one"]
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# 用values获得所有的值。跟keys一样，要用list包起来，顺序也可能不同。
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list(filled_dict.values()) # => [3, 2, 1]
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# 用in测试一个字典是否包含一个键
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"one" in filled_dict # => True
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1 in filled_dict # => False
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# 访问不存在的键会导致KeyError
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filled_dict["four"] # KeyError
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# 用get来避免KeyError
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filled_dict.get("one") # => 1
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filled_dict.get("four") # => None
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# 当键不存在的时候get方法可以返回默认值
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filled_dict.get("one", 4) # => 1
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filled_dict.get("four", 4) # => 4
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# setdefault方法只有当键不存在的时候插入新值
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filled_dict.setdefault("five", 5) # filled_dict["five"]设为5
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filled_dict.setdefault("five", 6) # filled_dict["five"]还是5
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# 字典赋值
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filled_dict.update({"four":4}) # => {"one": 1, "two": 2, "three": 3, "four": 4}
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filled_dict["four"] = 4 # 另一种赋值方法
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# 用del删除
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del filled_dict["one"] # 从filled_dict中把one删除
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# 用set表达集合
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empty_set = set()
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# 初始化一个集合，语法跟字典相似。
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some_set = {1, 1, 2, 2, 3, 4} # some_set现在是{1, 2, 3, 4}
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# 可以把集合赋值于变量
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filled_set = some_set
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# 为集合添加元素
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filled_set.add(5) # filled_set现在是{1, 2, 3, 4, 5}
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# & 取交集
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other_set = {3, 4, 5, 6}
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filled_set & other_set # => {3, 4, 5}
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# | 取并集
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filled_set | other_set # => {1, 2, 3, 4, 5, 6}
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# - 取补集
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{1, 2, 3, 4} - {2, 3, 5} # => {1, 4}
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# in 测试集合是否包含元素
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2 in filled_set # => True
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10 in filled_set # => False
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####################################################
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## 3. 流程控制和迭代器
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####################################################
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# 先随便定义一个变量
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some_var = 5
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# 这是个if语句。注意缩进在Python里是有意义的
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# 印出"some_var比10小"
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if some_var > 10:
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print("some_var比10大")
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elif some_var < 10: # elif句是可选的
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print("some_var比10小")
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else: # else也是可选的
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print("some_var就是10")
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"""
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用for循环语句遍历列表
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打印:
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dog is a mammal
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cat is a mammal
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mouse is a mammal
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"""
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for animal in ["dog", "cat", "mouse"]:
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print("{} is a mammal".format(animal))
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"""
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"range(number)"返回数字列表从0到给的数字
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打印:
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0
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1
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2
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3
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"""
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for i in range(4):
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print(i)
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"""
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while循环直到条件不满足
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打印:
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0
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1
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2
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3
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"""
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x = 0
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while x < 4:
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print(x)
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x += 1 # x = x + 1 的简写
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# 用try/except块处理异常状况
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try:
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# 用raise抛出异常
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raise IndexError("This is an index error")
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except IndexError as e:
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pass # pass是无操作，但是应该在这里处理错误
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except (TypeError, NameError):
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pass # 可以同时处理不同类的错误
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else: # else语句是可选的，必须在所有的except之后
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print("All good!") # 只有当try运行完没有错误的时候这句才会运行
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# Python提供一个叫做可迭代(iterable)的基本抽象。一个可迭代对象是可以被当作序列
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# 的对象。比如说上面range返回的对象就是可迭代的。
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filled_dict = {"one": 1, "two": 2, "three": 3}
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our_iterable = filled_dict.keys()
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print(our_iterable) # => dict_keys(['one', 'two', 'three'])，是一个实现可迭代接口的对象
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# 可迭代对象可以遍历
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for i in our_iterable:
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print(i) # 打印 one, two, three
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# 但是不可以随机访问
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our_iterable[1] # 抛出TypeError
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# 可迭代对象知道怎么生成迭代器
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our_iterator = iter(our_iterable)
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# 迭代器是一个可以记住遍历的位置的对象
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# 用__next__可以取得下一个元素
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our_iterator.__next__() # => "one"
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# 再一次调取__next__时会记得位置
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our_iterator.__next__() # => "two"
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our_iterator.__next__() # => "three"
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# 当迭代器所有元素都取出后，会抛出StopIteration
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our_iterator.__next__() # 抛出StopIteration
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# 可以用list一次取出迭代器所有的元素
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list(filled_dict.keys()) # => Returns ["one", "two", "three"]
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####################################################
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## 4. 函数
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####################################################
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# 用def定义新函数
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def add(x, y):
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print("x is {} and y is {}".format(x, y))
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return x + y # 用return语句返回
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# 调用函数
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add(5, 6) # => 印出"x is 5 and y is 6"并且返回11
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# 也可以用关键字参数来调用函数
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add(y=6, x=5) # 关键字参数可以用任何顺序
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# 我们可以定义一个可变参数函数
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def varargs(*args):
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return args
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varargs(1, 2, 3) # => (1, 2, 3)
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# 我们也可以定义一个关键字可变参数函数
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def keyword_args(**kwargs):
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return kwargs
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# 我们来看看结果是什么：
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keyword_args(big="foot", loch="ness") # => {"big": "foot", "loch": "ness"}
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# 这两种可变参数可以混着用
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def all_the_args(*args, **kwargs):
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print(args)
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print(kwargs)
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"""
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all_the_args(1, 2, a=3, b=4) prints:
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(1, 2)
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{"a": 3, "b": 4}
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"""
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# 调用可变参数函数时可以做跟上面相反的，用*展开序列，用**展开字典。
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args = (1, 2, 3, 4)
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kwargs = {"a": 3, "b": 4}
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all_the_args(*args) # 相当于 foo(1, 2, 3, 4)
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all_the_args(**kwargs) # 相当于 foo(a=3, b=4)
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all_the_args(*args, **kwargs) # 相当于 foo(1, 2, 3, 4, a=3, b=4)
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# 函数作用域
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x = 5
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def setX(num):
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# 局部作用域的x和全局域的x是不同的
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x = num # => 43
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print (x) # => 43
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def setGlobalX(num):
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global x
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print (x) # => 5
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x = num # 现在全局域的x被赋值
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print (x) # => 6
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setX(43)
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setGlobalX(6)
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# 函数在Python是一等公民
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def create_adder(x):
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def adder(y):
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return x + y
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return adder
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add_10 = create_adder(10)
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add_10(3) # => 13
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# 也有匿名函数
|
||
(lambda x: x > 2)(3) # => True
|
||
|
||
# 内置的高阶函数
|
||
map(add_10, [1, 2, 3]) # => [11, 12, 13]
|
||
filter(lambda x: x > 5, [3, 4, 5, 6, 7]) # => [6, 7]
|
||
|
||
# 用列表推导式可以简化映射和过滤。列表推导式的返回值是另一个列表。
|
||
[add_10(i) for i in [1, 2, 3]] # => [11, 12, 13]
|
||
[x for x in [3, 4, 5, 6, 7] if x > 5] # => [6, 7]
|
||
|
||
####################################################
|
||
## 5. 类
|
||
####################################################
|
||
|
||
|
||
# 定义一个继承object的类
|
||
class Human(object):
|
||
|
||
# 类属性，被所有此类的实例共用。
|
||
species = "H. sapiens"
|
||
|
||
# 构造方法，当实例被初始化时被调用。注意名字前后的双下划线，这是表明这个属
|
||
# 性或方法对Python有特殊意义，但是允许用户自行定义。你自己取名时不应该用这
|
||
# 种格式。
|
||
def __init__(self, name):
|
||
# Assign the argument to the instance's name attribute
|
||
self.name = name
|
||
|
||
# 实例方法，第一个参数总是self，就是这个实例对象
|
||
def say(self, msg):
|
||
return "{name}: {message}".format(name=self.name, message=msg)
|
||
|
||
# 类方法，被所有此类的实例共用。第一个参数是这个类对象。
|
||
@classmethod
|
||
def get_species(cls):
|
||
return cls.species
|
||
|
||
# 静态方法。调用时没有实例或类的绑定。
|
||
@staticmethod
|
||
def grunt():
|
||
return "*grunt*"
|
||
|
||
|
||
# 构造一个实例
|
||
i = Human(name="Ian")
|
||
print(i.say("hi")) # 印出 "Ian: hi"
|
||
|
||
j = Human("Joel")
|
||
print(j.say("hello")) # 印出 "Joel: hello"
|
||
|
||
# 调用一个类方法
|
||
i.get_species() # => "H. sapiens"
|
||
|
||
# 改一个共用的类属性
|
||
Human.species = "H. neanderthalensis"
|
||
i.get_species() # => "H. neanderthalensis"
|
||
j.get_species() # => "H. neanderthalensis"
|
||
|
||
# 调用静态方法
|
||
Human.grunt() # => "*grunt*"
|
||
|
||
|
||
####################################################
|
||
## 6. 模块
|
||
####################################################
|
||
|
||
# 用import导入模块
|
||
import math
|
||
print(math.sqrt(16)) # => 4.0
|
||
|
||
# 也可以从模块中导入个别值
|
||
from math import ceil, floor
|
||
print(ceil(3.7)) # => 4.0
|
||
print(floor(3.7)) # => 3.0
|
||
|
||
# 可以导入一个模块中所有值
|
||
# 警告：不建议这么做
|
||
from math import *
|
||
|
||
# 如此缩写模块名字
|
||
import math as m
|
||
math.sqrt(16) == m.sqrt(16) # => True
|
||
|
||
# Python模块其实就是普通的Python文件。你可以自己写，然后导入，
|
||
# 模块的名字就是文件的名字。
|
||
|
||
# 你可以这样列出一个模块里所有的值
|
||
import math
|
||
dir(math)
|
||
|
||
|
||
####################################################
|
||
## 7. 高级用法
|
||
####################################################
|
||
|
||
# 用生成器(generators)方便地写惰性运算
|
||
def double_numbers(iterable):
|
||
for i in iterable:
|
||
yield i + i
|
||
|
||
# 生成器只有在需要时才计算下一个值。它们每一次循环只生成一个值，而不是把所有的
|
||
# 值全部算好。
|
||
#
|
||
# range的返回值也是一个生成器，不然一个1到900000000的列表会花很多时间和内存。
|
||
#
|
||
# 如果你想用一个Python的关键字当作变量名，可以加一个下划线来区分。
|
||
range_ = range(1, 900000000)
|
||
# 当找到一个 >=30 的结果就会停
|
||
# 这意味着 `double_numbers` 不会生成大于30的数。
|
||
for i in double_numbers(range_):
|
||
print(i)
|
||
if i >= 30:
|
||
break
|
||
|
||
|
||
# 装饰器(decorators)
|
||
# 这个例子中，beg装饰say
|
||
# beg会先调用say。如果返回的say_please为真，beg会改变返回的字符串。
|
||
from functools import wraps
|
||
|
||
|
||
def beg(target_function):
|
||
@wraps(target_function)
|
||
def wrapper(*args, **kwargs):
|
||
msg, say_please = target_function(*args, **kwargs)
|
||
if say_please:
|
||
return "{} {}".format(msg, "Please! I am poor :(")
|
||
return msg
|
||
|
||
return wrapper
|
||
|
||
|
||
@beg
|
||
def say(say_please=False):
|
||
msg = "Can you buy me a beer?"
|
||
return msg, say_please
|
||
|
||
|
||
print(say()) # Can you buy me a beer?
|
||
print(say(say_please=True)) # Can you buy me a beer? Please! I am poor :(
|
||
```
|
||
|
||
## 想继续学吗？
|
||
|
||
### 线上免费材料（英文）
|
||
|
||
* [Learn Python The Hard Way](http://learnpythonthehardway.org/book/)
|
||
* [Dive Into Python](http://www.diveintopython.net/)
|
||
* [Ideas for Python Projects](http://pythonpracticeprojects.com)
|
||
|
||
* [The Official Docs](http://docs.python.org/3/)
|
||
* [Hitchhiker's Guide to Python](http://docs.python-guide.org/en/latest/)
|
||
* [Python Module of the Week](http://pymotw.com/3/)
|
||
* [A Crash Course in Python for Scientists](http://nbviewer.ipython.org/5920182)
|
||
|
||
### 书籍（也是英文）
|
||
|
||
* [Programming Python](http://www.amazon.com/gp/product/0596158106/ref=as_li_qf_sp_asin_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=0596158106&linkCode=as2&tag=homebits04-20)
|
||
* [Dive Into Python](http://www.amazon.com/gp/product/1441413022/ref=as_li_tf_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=1441413022&linkCode=as2&tag=homebits04-20)
|
||
* [Python Essential Reference](http://www.amazon.com/gp/product/0672329786/ref=as_li_tf_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=0672329786&linkCode=as2&tag=homebits04-20)
|
||
|