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[长文] 学Python不用培训班,一篇文章带你入门

来源:网络整理 浏览:309次 时间:2021-09-18


最近有许多小伙伴后台联系我,说目前想要学习Python,但是没有一份很好的资料入门。一方面的确现在市面上Python的资料过多,导致新手会不知如何选择,另一个问题很多资料内容也很杂,从1+1到深度学习都包括,纯粹关注Python本身语法的优质教材并不太多。
刚好我最近看到一份不错的英文Python入门资料,我将它做了一些整理和翻译写下了本文。这份资料非常纯粹,只有Python的基础语法,专门针对想要学习Python的小白。想要查看英文原文的同学,请点击查看原文。

注释

Python中用#表示单行注释,#之后的同行的内容都会被注释掉。

# Python中单行注释用#表示,#之后同行字符全部认为被注释。

使用三个连续的双引号表示多行注释,两个多行注释标识之间内容会被视作是注释。

""" 与之对应的是多行注释    用三个双引号表示,这两段双引号当中的内容都会被视作是注释"""

基础变量类型与操作符
Python当中的数字定义和其他语言一样:

#获得一个整数3# 获得一个浮点数10.0

我们分别使用+, -, *, /表示加减乘除四则运算符。

1 + 1   # => 28 - 1   # => 710 * 2  # => 2035 / 5  # => 7.0

这里要注意的是,在Python2当中,10/3这个操作会得到3,而不是3.33333。因为除数和被除数都是整数,所以Python会自动执行整数的计算,帮我们把得到的商取整。如果是10.0 / 3,就会得到3.33333。目前Python2已经不再维护了,可以不用关心其中的细节。
但问题是Python是一个弱类型的语言,如果我们在一个函数当中得到两个变量,是无法直接判断它们的类型的。这就导致了同样的计算符可能会得到不同的结果,这非常蛋疼。以至于程序员在运算除法的时候,往往都需要手工加上类型转化符,将被除数转成浮点数。
在Python3当中拨乱反正,修正了这个问题,即使是两个整数相除,并且可以整除的情况下,得到的结果也一定是浮点数。
如果我们想要得到整数,我们可以这么操作:

5 // 3       # => 1-5 // 3      # => -25.0 // 3.0   # => 1.0 # works on floats too-5.0 // 3.0  # => -2.0

两个除号表示取整除,Python会为我们保留去除余数的结果。
除了取整除操作之外还有取余数操作,数学上称为取模,Python中用%表示。

# Modulo operation7 % 3  # => 1

Python中支持乘方运算,我们可以不用调用额外的函数,而使用**符号来完成:

# Exponentiation (x**y, x to the yth power)2**3  # => 8

当运算比较复杂的时候,我们可以用括号来强制改变运算顺序。

# Enforce precedence with parentheses1 + 3 * 2  # => 7(1 + 3) * 2  # => 8

逻辑运算

Python中用首字母大写的True和False表示真和假。

True  # => TrueFalse  # => False

用and表示与操作,or表示或操作,not表示非操作。而不是C++或者是Java当中的&&, || 和!。

# negate with notnot True   # => Falsenot False  # => True# Boolean Operators# Note "and" and "or" are case-sensitiveTrue and False  # => FalseFalse or True   # => True

在Python底层,True和False其实是1和0,所以如果我们执行以下操作,是不会报错的,但是在逻辑上毫无意义。

# True and False are actually 1 and 0 but with different keywordsTrue + True # => 2True * 8    # => 8False - 5   # => -5

我们用==判断相等的操作,可以看出来True==1, False == 0.

# Comparison operators look at the numerical value of True and False0 == False  # => True1 == True   # => True2 == True   # => False-5 != False # => True

我们要小心Python当中的bool()这个函数,它并不是转成bool类型的意思。如果我们执行这个函数,那么只有0会被视作是False,其他所有数值都是True:

bool(0)     # => Falsebool(4)     # => Truebool(-6)    # => True0 and 2     # => 0-5 or 0     # => -5

Python中用==判断相等,>表示大于,>=表示大于等于, <表示小于,<=表示小于等于,!=表示不等。

# Equality is ==1 == 1  # => True2 == 1  # => False# Inequality is !=1 != 1  # => False2 != 1  # => True# More comparisons1 < 10  # => True1 > 10  # => False2 <= 2  # => True2 >= 2  # => True

我们可以用and和or拼装各个比较操作:

# Seeing whether a value is in a range1 < 2 and 2 < 3  # => True2 < 3 and 3 < 2  # => False# Chaining makes this look nicer1 < 2 < 3  # => True2 < 3 < 2  # => False

注意not,and,or之间的优先级,其中not > and > or。如果分不清楚的话,可以用括号强行改变运行顺序。

list和字符串

关于list的判断,我们常用的判断有两种,一种是刚才介绍的==,还有一种是is。我们有时候也会简单使用is来判断,那么这两者有什么区别呢?我们来看下面的例子:

a = [1, 2, 3, 4]  # Point a at a new list, [1, 2, 3, 4]b = a             # Point b at what a is pointing tob is a            # => True, a and b refer to the same objectb == a            # => True, a's and b's objects are equalb = [1, 2, 3, 4]  # Point b at a new list, [1, 2, 3, 4]b is a            # => False, a and b do not refer to the same objectb == a            # => True, a's and b's objects are equal

Python是全引用的语言,其中的对象都使用引用来表示。is判断的就是两个引用是否指向同一个对象,而==则是判断两个引用指向的具体内容是否相等。举个例子,如果我们把引用比喻成地址的话,is就是判断两个变量的是否指向同一个地址,比如说都是沿河东路XX号。而==则是判断这两个地址的收件人是否都叫张三。
显然,住在同一个地址的人一定都叫张三,但是住在不同地址的两个人也可以都叫张三,也可以叫不同的名字。所以如果a is b,那么a == b一定成立,反之则不然。
Python当中对字符串的限制比较松,双引号和单引号都可以表示字符串,看个人喜好使用单引号或者是双引号。我个人比较喜欢单引号,因为写起来方便。
字符串也支持+操作,表示两个字符串相连。除此之外,我们把两个字符串写在一起,即使没有+,Python也会为我们拼接:

# Strings are created with " or '"This is a string."'This is also a string.'# Strings can be added too! But try not to do this."Hello " + "world!"  # => "Hello world!"# String literals (but not variables) can be concatenated without using '+'"Hello " "world!"    # => "Hello world!"

我们可以使用[]来查找字符串当中某个位置的字符,用len来计算字符串的长度。

# A string can be treated like a list of characters"This is a string"[0]  # => 'T'# You can find the length of a stringlen("This is a string")  # => 16

我们可以在字符串前面加上f表示格式操作,并且在格式操作当中也支持运算。不过要注意,只有Python3.6以上的版本支持f操作。

# You can also format using f-strings or formatted string literals (in Python 3.6+)name = "Reiko"f"She said her name is {name}." # => "She said her name is Reiko"# You can basically put any Python statement inside the braces and it will be output in the string.f"{name} is {len(name)} characters long." # => "Reiko is 5 characters long."

最后是None的判断,在Python当中None也是一个对象,所有为None的变量都会指向这个对象。根据我们前面所说的,既然所有的None都指向同一个地址,我们需要判断一个变量是否是None的时候,可以使用is来进行判断,当然用==也是可以的,不过我们通常使用is。

# None is an objectNone  # => None# Don't use the equality "==" symbol to compare objects to None# Use "is" instead. This checks for equality of object identity."etc" is None  # => FalseNone is None   # => True

理解了None之后,我们再回到之前介绍过的bool()函数,它的用途其实就是判断值是否是空。所有类型的默认空值会被返回False,否则都是True。比如0,“”,[], {}, ()等。

# None, 0, and empty strings/lists/dicts/tuples all evaluate to False.# All other values are Truebool(None)# => Falsebool(0)   # => Falsebool("")  # => Falsebool([])  # => Falsebool({})  # => Falsebool(())  # => False

除了上面这些值以外的所有值传入都会得到True。

变量与集合

输入输出

Python当中的标准输入输出是input和print。
print会输出一个字符串,如果传入的不是字符串会自动调用str方法转成字符串进行输出。默认输出会自动换行,如果想要以不同的字符结尾代替换行,可以传入end参数:

# Python has a print functionprint("I'm Python. Nice to meet you!")  # => I'm Python. Nice to meet you!# By default the print function also prints out a newline at the end.# Use the optional argument end to change the end string.print("Hello, World", end="!")  # => Hello, World!

使用input时,Python会在命令行接收一行字符串作为输入。可以在input当中传入字符串,会被当成提示输出:

# Simple way to get input data from consoleinput_string_var = input("Enter some data: ") # Returns the data as a string# Note: In earlier versions of Python, input() method was named as raw_input()

变量

Python中声明对象不需要带上类型,直接赋值即可,Python会自动关联类型,如果我们使用之前没有声明过的变量则会出发NameError异常。

# There are no declarations, only assignments.# Convention is to use lower_case_with_underscoressome_var = 5some_var  # => 5# Accessing a previously unassigned variable is an exception.# See Control Flow to learn more about exception handling.some_unknown_var  # Raises a NameError

Python支持三元表达式,但是语法和C++不同,使用if else结构,写成:

# if can be used as an expression# Equivalent of C's '?:' ternary operator"yahoo!" if 3 > 2 else 2  # => "yahoo!"

上段代码等价于:

if 3 > 2:    return 'yahoo'else:    return 2


list

Python中用[]表示空的list,我们也可以直接在其中填充元素进行初始化:

# Lists store sequencesli = []# You can start with a prefilled listother_li = [4, 5, 6]

使用append和pop可以在list的末尾插入或者删除元素:

# Add stuff to the end of a list with appendli.append(1)    # li is now [1]li.append(2)    # li is now [1, 2]li.append(4)    # li is now [1, 2, 4]li.append(3)    # li is now [1, 2, 4, 3]# Remove from the end with popli.pop()        # => 3 and li is now [1, 2, 4]# Let's put it backli.append(3)    # li is now [1, 2, 4, 3] again.

list可以通过[]加上下标访问指定位置的元素,如果是负数,则表示倒序访问。-1表示最后一个元素,-2表示导数第二个,以此类推。如果访问的元素超过数组长度,则会触发IndexError的错误。

# Access a list like you would any arrayli[0]   # => 1# Look at the last elementli[-1]  # => 3# Looking out of bounds is an IndexErrorli[4]  # Raises an IndexError

list支持切片操作,所谓的切片则是从原list当中拷贝出指定的一段。我们用start: end的格式来获取切片,注意,这是一个左闭右开区间。如果留空表示全部获取,我们也可以额外再加入一个参数表示步长,比如[1:5:2]表示从1号位置开始,步长为2获取元素。得到的结果为[1, 3]。如果步长设置成-1则代表反向遍历。

# You can look at ranges with slice syntax.# The start index is included, the end index is not# (It's a closed/open range for you mathy types.)li[1:3]   # Return list from index 1 to 3 => [2, 4]li[2:]    # Return list starting from index 2 => [4, 3]li[:3]    # Return list from beginning until index 3  => [1, 2, 4]li[::2]   # Return list selecting every second entry => [1, 4]li[::-1]  # Return list in reverse order => [3, 4, 2, 1]# Use any combination of these to make advanced slices# li[start:end:step]

如果我们要指定一段区间倒序,则前面的start和end也需要反过来,例如我想要获取[3: 6]区间的倒叙,应该写成[6:3:-1]。
只写一个:,表示全部获取,可以使用del删除指定位置的元素,或者可以使用remove方法。

# Make a one layer deep copy using slicesli2 = li[:]  # => li2 = [1, 2, 4, 3] but (li2 is li) will result in false.# Remove arbitrary elements from a list with "del"del li[2]  # li is now [1, 2, 3]# Remove first occurrence of a valueli.remove(2)  # li is now [1, 3]li.remove(2)  # Raises a ValueError as 2 is not in the list

insert方法可以执行指定位置插入元素,index方法可以查询某个元素第一次出现的下标。

# Insert an element at a specific indexli.insert(1, 2)  # li is now [1, 2, 3] again# Get the index of the first item found matching the argumentli.index(2)  # => 1li.index(4)  # Raises a ValueError as 4 is not in the list

list可以进行加法运算,两个list相加表示list当中的元素合并。等价于使用extend方法:

# You can add lists# Note: values for li and for other_li are not modified.li + other_li  # => [1, 2, 3, 4, 5, 6]# Concatenate lists with "extend()"li.extend(other_li)  # Now li is [1, 2, 3, 4, 5, 6]

我们想要判断元素是否在list中出现,可以使用in关键字,通过使用len计算list的长度:

# Check for existence in a list with "in"1 in li  # => True# Examine the length with "len()"len(li)  # => 6


tuple

tuple和list非常接近,tuple通过()初始化。和list不同,tuple是不可变对象。也就是说tuple一旦生成不可以改变。如果我们修改tuple,会引发TypeError异常。

# Tuples are like lists but are immutable.tup = (1, 2, 3)tup[0]      # => 1tup[0] = 3  # Raises a TypeError

由于小括号是有改变优先级的含义,所以我们定义单个元素的tuple,末尾必须加上逗号,否则会被当成是单个元素:

# Note that a tuple of length one has to have a comma after the last element but# tuples of other lengths, even zero, do not.type((1))   # => <class 'int'>type((1,))  # => <class 'tuple'>type(())    # => <class 'tuple'>

tuple支持list当中绝大部分操作:

# You can do most of the list operations on tuples toolen(tup)         # => 3tup + (4, 5, 6)  # => (1, 2, 3, 4, 5, 6)tup[:2]          # => (1, 2)2 in tup         # => True

我们可以用多个变量来解压一个tuple:

# You can unpack tuples (or lists) into variablesa, b, c = (1, 2, 3)  # a is now 1, b is now 2 and c is now 3# You can also do extended unpackinga, *b, c = (1, 2, 3, 4)  # a is now 1, b is now [2, 3] and c is now 4# Tuples are created by default if you leave out the parenthesesd, e, f = 4, 5, 6  # tuple 4, 5, 6 is unpacked into variables d, e and f# respectively such that d = 4, e = 5 and f = 6# Now look how easy it is to swap two valuese, d = d, e  # d is now 5 and e is now 4

解释一下这行代码:

a, *b, c = (1, 2, 3, 4)  # a is now 1, b is now [2, 3] and c is now 4

我们在b的前面加上了星号,表示这是一个list。所以Python会在将其他变量对应上值的情况下,将剩下的元素都赋值给b。
补充一点,tuple本身虽然是不可变的,但是tuple当中的可变元素是可以改变的。比如我们有这样一个tuple:

a = (3, [4])

我们虽然不能往a当中添加或者删除元素,但是a当中含有一个list,我们可以改变这个list类型的元素,这并不会触发tuple的异常:

a[1].append(0) # 这是合法的

dict

dict也是Python当中经常使用的容器,它等价于C++当中的map,即存储key和value的键值对。我们用{}表示一个dict,用:分隔key和value。

# Dictionaries store mappings from keys to valuesempty_dict = {}# Here is a prefilled dictionaryfilled_dict = {"one": 1, "two": 2, "three": 3}

dict的key必须为不可变对象,所以list和dict不可以作为另一个dict的key,否则会抛出异常:

# Note keys for dictionaries have to be immutable types. This is to ensure that# the key can be converted to a constant hash value for quick look-ups.# Immutable types include ints, floats, strings, tuples.invalid_dict = {[1,2,3]: "123"}  # => Raises a TypeError: unhashable type: 'list'valid_dict = {(1,2,3):[1,2,3]}   # Values can be of any type, however.

我们同样用[]查找dict当中的元素,我们传入key,获得value,等价于get方法。

# Look up values with []filled_dict["one"]  # => 1filled_dict.get('one') #=> 1

我们可以call dict当中的keys和values方法,获取dict当中的所有key和value的集合,会得到一个list。在Python3.7以下版本当中,返回的结果的顺序可能和插入顺序不同,在Python3.7及以上版本中,Python会保证返回的顺序和插入顺序一致:

 Get all keys as an iterable with "keys()". We need to wrap the call in list()# to turn it into a list. We'll talk about those later.  Note - for Python# versions <3.7, dictionary key ordering is not guaranteed. Your results might# not match the example below exactly. However, as of Python 3.7, dictionary# items maintain the order at which they are inserted into the dictionary.list(filled_dict.keys())  # => ["three", "two", "one"] in Python <3.7list(filled_dict.keys())  # => ["one", "two", "three"] in Python 3.7+# Get all values as an iterable with "values()". Once again we need to wrap it# in list() to get it out of the iterable. Note - Same as above regarding key# ordering.list(filled_dict.values())  # => [3, 2, 1]  in Python <3.7list(filled_dict.values())  # => [1, 2, 3] in Python 3.7+

我们也可以用in判断一个key是否在dict当中,注意只能判断key。

# Check for existence of keys in a dictionary with "in""one" in filled_dict  # => True1 in filled_dict      # => False

如果使用[]查找不存在的key,会引发KeyError的异常。如果使用get方法则不会引起异常,只会得到一个None:

# Looking up a non-existing key is a KeyErrorfilled_dict["four"]  # KeyError# Use "get()" method to avoid the KeyErrorfilled_dict.get("one")      # => 1filled_dict.get("four")     # => None# The get method supports a default argument when the value is missingfilled_dict.get("one", 4)   # => 1filled_dict.get("four", 4)  # => 4

setdefault方法可以为不存在的key插入一个value,如果key已经存在,则不会覆盖它:

# "setdefault()" inserts into a dictionary only if the given key isn't presentfilled_dict.setdefault("five", 5)  # filled_dict["five"] is set to 5filled_dict.setdefault("five", 6)  # filled_dict["five"] is still 5

我们可以使用update方法用另外一个dict来更新当前dict,比如a.update(b)。对于a和b交集的key会被b覆盖,a当中不存在的key会被插入进来:

# Adding to a dictionaryfilled_dict.update({"four":4})  # => {"one": 1, "two": 2, "three": 3, "four": 4}filled_dict["four"] = 4         # another way to add to dict

我们一样可以使用del删除dict当中的元素,同样只能传入key。
Python3.5以上的版本支持使用**来解压一个dict:

{'a': 1, **{'b': 2}}  # => {'a': 1, 'b': 2}{'a': 1, **{'a': 2}}  # => {'a': 2}


set

set是用来存储不重复元素的容器,当中的元素都是不同的,相同的元素会被删除。我们可以通过set(),或者通过{}来进行初始化。注意当我们使用{}的时候,必须要传入数据,否则Python会将它和dict弄混。

# Sets store ... well setsempty_set = set()# Initialize a set with a bunch of values. Yeah, it looks a bit like a dict. Sorry.some_set = {1, 1, 2, 2, 3, 4}  # some_set is now {1, 2, 3, 4}

set当中的元素也必须是不可变对象,因此list不能传入set。

# Similar to keys of a dictionary, elements of a set have to be immutable.invalid_set = {[1], 1}  # => Raises a TypeError: unhashable type: 'list'valid_set = {(1,), 1}

可以调用add方法为set插入元素:

# Add one more item to the setfilled_set = some_setfilled_set.add(5)  # filled_set is now {1, 2, 3, 4, 5}# Sets do not have duplicate elementsfilled_set.add(5)  # it remains as before {1, 2, 3, 4, 5}

set还可以被认为是集合,所以它还支持一些集合交叉并补的操作。

# Do set intersection with &# 计算交集other_set = {3, 4, 5, 6}filled_set & other_set  # => {3, 4, 5}# Do set union with |# 计算并集filled_set | other_set  # => {1, 2, 3, 4, 5, 6}# Do set difference with -# 计算差集{1, 2, 3, 4} - {2, 3, 5}  # => {1, 4}# Do set symmetric difference with ^# 这个有点特殊,计算对称集,也就是去掉重复元素剩下的内容{1, 2, 3, 4} ^ {2, 3, 5}  # => {1, 4, 5}

set还支持超集和子集的判断,我们可以用大于等于和小于等于号判断一个set是不是另一个的超集或子集:

# Check if set on the left is a superset of set on the right{1, 2} >= {1, 2, 3} # => False# Check if set on the left is a subset of set on the right{1, 2} <= {1, 2, 3} # => True

和dict一样,我们可以使用in判断元素在不在set当中。用copy可以拷贝一个set。

# Check for existence in a set with in2 in filled_set   # => True10 in filled_set  # => False# Make a one layer deep copyfilled_set = some_set.copy()  # filled_set is {1, 2, 3, 4, 5}filled_set is some_set        # => False

控制流和迭代

判断语句

Python当中的判断语句非常简单,并且Python不支持switch,所以即使是多个条件,我们也只能罗列if-else。

# Let's just make a variablesome_var = 5# Here is an if statement. Indentation is significant in Python!# Convention is to use four spaces, not tabs.# This prints "some_var is smaller than 10"if some_var > 10:    print("some_var is totally bigger than 10.")elif some_var < 10:    # This elif clause is optional.    print("some_var is smaller than 10.")else:                  # This is optional too.    print("some_var is indeed 10.")

循环

我们可以用in来循环迭代一个list当中的内容,这也是Python当中基本的循环方式。

"""For loops iterate over listsprints:    dog is a mammal    cat is a mammal    mouse is a mammal"""for animal in ["dog", "cat", "mouse"]:    # You can use format() to interpolate formatted strings    print("{} is a mammal".format(animal))

如果我们要循环一个范围,可以使用range。range加上一个参数表示从0开始的序列,比如range(10),表示[0, 10)区间内的所有整数:

""""range(number)" returns an iterable of numbersfrom zero to the given numberprints:    0    1    2    3"""for i in range(4):    print(i)

如果我们传入两个参数,则代表迭代区间的首尾。

""""range(lower, upper)" returns an iterable of numbersfrom the lower number to the upper numberprints:    4    5    6    7"""for i in range(4, 8):    print(i)

如果我们传入第三个元素,表示每次循环变量自增的步长。

""""range(lower, upper, step)" returns an iterable of numbersfrom the lower number to the upper number, while incrementingby step. If step is not indicated, the default value is 1.prints:    4    6"""for i in range(4, 8, 2):    print(i)

如果使用enumerate函数,可以同时迭代一个list的下标和元素:

"""To loop over a list, and retrieve both the index and the value of each item in the listprints:    0 dog    1 cat    2 mouse"""animals = ["dog", "cat", "mouse"]for i, value in enumerate(animals):    print(i, value)

while循环和C++类似,当条件为True时执行,为false时退出。并且判断条件不需要加上括号:

"""While loops go until a condition is no longer met.prints:    0    1    2    3"""x = 0while x < 4:    print(x)    x += 1  # Shorthand for x = x + 1

捕获异常

Python当中使用try和except捕获异常,我们可以在except后面限制异常的类型。如果有多个类型可以写多个except,还可以使用else语句表示其他所有的类型。finally语句内的语法无论是否会触发异常都必定执行:

# Handle exceptions with a try/except blocktry:    # Use "raise" to raise an error    raise IndexError("This is an index error")except IndexError as e:    pass                 # Pass is just a no-op. Usually you would do recovery here.except (TypeError, NameError):    pass                 # Multiple exceptions can be handled together, if required.else:                    # Optional clause to the try/except block. Must follow all except blocks    print("All good!")   # Runs only if the code in try raises no exceptionsfinally:                 #  Execute under all circumstances    print("We can clean up resources here")

with操作

在Python当中我们经常会使用资源,最常见的就是open打开一个文件。我们打开了文件句柄就一定要关闭,但是如果我们手动来编码,经常会忘记执行close操作。并且如果文件异常,还会触发异常。这个时候我们可以使用with语句来代替这部分处理,使用with会自动在with块执行结束或者是触发异常时关闭打开的资源。
以下是with的几种用法和功能:

# Instead of try/finally to cleanup resources you can use a with statement# 代替使用try/finally语句来关闭资源with open("myfile.txt") as f:    for line in f:        print(line)# Writing to a file# 使用with写入文件contents = {"aa": 12, "bb": 21}with open("myfile1.txt", "w+") as file:    file.write(str(contents))        # writes a string to a filewith open("myfile2.txt", "w+") as file:    file.write(json.dumps(contents)) # writes an object to a file# Reading from a file# 使用with读取文件with open('myfile1.txt', "r+") as file:    contents = file.read()           # reads a string from a fileprint(contents)# print: {"aa": 12, "bb": 21}with open('myfile2.txt', "r+") as file:    contents = json.load(file)       # reads a json object from a fileprint(contents)     # print: {"aa": 12, "bb": 21}

可迭代对象

凡是可以使用in语句来迭代的对象都叫做可迭代对象,它和迭代器不是一个含义。这里只有可迭代对象的介绍,想要了解迭代器的具体内容,请移步传送门:
Python——五分钟带你弄懂迭代器与生成器,夯实代码能力
当我们调用dict当中的keys方法的时候,返回的结果就是一个可迭代对象。

# Python offers a fundamental abstraction called the Iterable.# An iterable is an object that can be treated as a sequence.# The object returned by the range function, is an iterable.filled_dict = {"one": 1, "two": 2, "three": 3}our_iterable = filled_dict.keys()print(our_iterable)  # => dict_keys(['one', 'two', 'three']). This is an object that implements our Iterable interface.# We can loop over it.for i in our_iterable:    print(i)  # Prints one, two, three

我们不能使用下标来访问可迭代对象,但我们可以用iter将它转化成迭代器,使用next关键字来获取下一个元素。也可以将它转化成list类型,变成一个list。

# However we cannot address elements by index.our_iterable[1]  # Raises a TypeError# An iterable is an object that knows how to create an iterator.our_iterator = iter(our_iterable)# Our iterator is an object that can remember the state as we traverse through it.# We get the next object with "next()".next(our_iterator)  # => "one"# It maintains state as we iterate.next(our_iterator)  # => "two"next(our_iterator)  # => "three"# After the iterator has returned all of its data, it raises a StopIteration exceptionnext(our_iterator)  # Raises StopIteration# We can also loop over it, in fact, "for" does this implicitly!our_iterator = iter(our_iterable)for i in our_iterator:    print(i)  # Prints one, two, three# You can grab all the elements of an iterable or iterator by calling list() on it.list(our_iterable)  # => Returns ["one", "two", "three"]list(our_iterator)  # => Returns [] because state is saved

函数

使用def关键字来定义函数,我们在传参的时候如果指定函数内的参数名,可以不按照函数定义的顺序传参:

# Use "def" to create new functionsdef add(x, y):    print("x is {} and y is {}".format(x, y))    return x + y  # Return values with a return statement# Calling functions with parametersadd(5, 6)  # => prints out "x is 5 and y is 6" and returns 11# Another way to call functions is with keyword argumentsadd(y=6, x=5)  # Keyword arguments can arrive in any order.

可以在参数名之前加上*表示任意长度的参数,参数会被转化成list:

# You can define functions that take a variable number of# positional argumentsdef varargs(*args):    return argsvarargs(1, 2, 3)  # => (1, 2, 3)

也可以指定任意长度的关键字参数,在参数前加上**表示接受一个dict:

# You can define functions that take a variable number of# keyword arguments, as welldef keyword_args(**kwargs):    return kwargs# Let's call it to see what happenskeyword_args(big="foot", loch="ness")  # => {"big": "foot", "loch": "ness"}

当然我们也可以两个都用上,这样可以接受任何参数:

# You can do both at once, if you likedef all_the_args(*args, **kwargs):    print(args)    print(kwargs)"""all_the_args(1, 2, a=3, b=4) prints:    (1, 2)    {"a": 3, "b": 4}"""

传入参数的时候我们也可以使用*和**来解压list或者是dict:

# When calling functions, you can do the opposite of args/kwargs!# Use * to expand tuples and use ** to expand kwargs.args = (1, 2, 3, 4)kwargs = {"a": 3, "b": 4}all_the_args(*args)            # equivalent to all_the_args(1, 2, 3, 4)all_the_args(**kwargs)         # equivalent to all_the_args(a=3, b=4)all_the_args(*args, **kwargs)  # equivalent to all_the_args(1, 2, 3, 4, a=3, b=4)

Python中的参数可以返回多个值:

# Returning multiple values (with tuple assignments)def swap(x, y):    return y, x  # Return multiple values as a tuple without the parenthesis.                 # (Note: parenthesis have been excluded but can be included)x = 1y = 2x, y = swap(x, y)     # => x = 2, y = 1# (x, y) = swap(x,y)  # Again parenthesis have been excluded but can be included.

函数内部定义的变量即使和全局变量重名,也不会覆盖全局变量的值。想要在函数内部使用全局变量,需要加上global关键字,表示这是一个全局变量:

# Function Scopex = 5def set_x(num):    # Local var x not the same as global variable x    x = num    # => 43    print(x)   # => 43def set_global_x(num):    global x    print(x)   # => 5    x = num    # global var x is now set to 6    print(x)   # => 6set_x(43)set_global_x(6)

Python支持函数式编程,我们可以在一个函数内部返回一个函数:

# Python has first class functionsdef create_adder(x):    def adder(y):        return x + y    return adderadd_10 = create_adder(10)add_10(3)   # => 13

Python中可以使用lambda表示匿名函数,使用:作为分隔,:前面表示匿名函数的参数,:后面的是函数的返回值:

# There are also anonymous functions(lambda x: x > 2)(3)                  # => True(lambda x, y: x ** 2 + y ** 2)(2, 1)  # => 5

我们还可以将函数作为参数使用map和filter,实现元素的批量处理和过滤。关于Python中map、reduce和filter的使用,具体可以查看之前的文章:
Python专题——五分钟带你了解map、reduce和filter

# There are built-in higher order functionslist(map(add_10, [1, 2, 3]))          # => [11, 12, 13]list(map(max, [1, 2, 3], [4, 2, 1]))  # => [4, 2, 3]list(filter(lambda x: x > 5, [3, 4, 5, 6, 7]))  # => [6, 7]

我们还可以结合循环和判断语来给list或者是dict进行初始化:

# We can use list comprehensions for nice maps and filters# List comprehension stores the output as a list which can itself be a nested list[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]# You can construct set and dict comprehensions as well.{x for x in 'abcddeef' if x not in 'abc'}  # => {'d', 'e', 'f'}{x: x**2 for x in range(5)}  # => {0: 0, 1: 1, 2: 4, 3: 9, 4: 16}

模块
使用import语句引入一个Python模块,我们可以用.来访问模块中的函数或者是类。

# You can import modulesimport mathprint(math.sqrt(16))  # => 4.0

我们也可以使用from import的语句,单独引入模块内的函数或者是类,而不再需要写出完整路径。使用from import *可以引入模块内所有内容(不推荐这么干)

# You can get specific functions from a modulefrom math import ceil, floorprint(ceil(3.7))   # => 4.0print(floor(3.7))  # => 3.0# You can import all functions from a module.# Warning: this is not recommendedfrom math import *

可以使用as给模块内的方法或者类起别名:

# You can shorten module namesimport math as mmath.sqrt(16) == m.sqrt(16)  # => True

我们可以使用dir查看我们用的模块的路径:

# You can find out which functions and attributes# are defined in a module.import mathdir(math)

这么做的原因是如果我们当前的路径下也有一个叫做math的Python文件,那么会覆盖系统自带的math的模块。这是尤其需要注意的,不小心会导致很多奇怪的bug。

我们来看一个完整的类,相关的介绍都在注释当中

# We use the "class" statement to create a classclass Human:    # A class attribute. It is shared by all instances of this class    # 类属性,可以直接通过Human.species调用,而不需要通过实例    species = "H. sapiens"    # Basic initializer, this is called when this class is instantiated.    # Note that the double leading and trailing underscores denote objects    # or attributes that are used by Python but that live in user-controlled    # namespaces. Methods(or objects or attributes) like: __init__, __str__,    # __repr__ etc. are called special methods (or sometimes called dunder methods)    # You should not invent such names on your own.    # 最基础的构造函数    # 加了下划线的函数和变量表示不应该被用户使用,其中双下划线的函数或者是变量将不会被子类覆盖    # 前后都有双下划线的函数和属性是类当中的特殊属性    def __init__(self, name):        # Assign the argument to the instance's name attribute        self.name = name        # Initialize property        self._age = 0    # An instance method. All methods take "self" as the first argument    # 类中的函数,所有实例可以调用,第一个参数必须是self    # self表示实例的引用    def say(self, msg):        print("{name}: {message}".format(name=self.name, message=msg))    # Another instance method    def sing(self):        return 'yo... yo... microphone check... one two... one two...'    # A class method is shared among all instances    # They are called with the calling class as the first argument    @classmethod    # 加上了注解,表示是类函数    # 通过Human.get_species来调用,所有实例共享    def get_species(cls):        return cls.species    # A static method is called without a class or instance reference    @staticmethod    # 静态函数,通过类名或者是实例都可以调用    def grunt():        return "*grunt*"    # A property is just like a getter.    # It turns the method age() into an read-only attribute of the same name.    # There's no need to write trivial getters and setters in Python, though.    @property    # property注解,类似于get,set方法    # 效率很低,除非必要,不要使用    def age(self):        return self._age    # This allows the property to be set    @age.setter    def age(self, age):        self._age = age    # This allows the property to be deleted    @age.deleter    def age(self):        del self._age

以上内容的详细介绍之前也有过相关文章,可以查看:
Python——slots,property和对象命名规范
下面我们来看看Python当中类的使用:

# When a Python interpreter reads a source file it executes all its code.# This __name__ check makes sure this code block is only executed when this# module is the main program.# 这个是main函数也是整个程序入口的惯用写法if __name__ == '__main__':    # Instantiate a class    # 实例化一个类,获取类的对象    i = Human(name="Ian")    # 执行say方法    i.say("hi")                     # "Ian: hi"    j = Human("Joel")    j.say("hello")                  # "Joel: hello"    # i和j都是Human的实例,都称作是Human类的对象    # i and j are instances of type Human, or in other words: they are Human objects    # Call our class method    # 类属性被所有实例共享,一旦修改全部生效    i.say(i.get_species())          # "Ian: H. sapiens"    # Change the shared attribute    Human.species = "H. neanderthalensis"    i.say(i.get_species())          # => "Ian: H. neanderthalensis"    j.say(j.get_species())          # => "Joel: H. neanderthalensis"    # 通过类名调用静态方法    # Call the static method    print(Human.grunt())            # => "*grunt*"    # Cannot call static method with instance of object     # because i.grunt() will automatically put "self" (the object i) as an argument    # 不能通过对象调用静态方法,因为对象会传入self实例,会导致不匹配    print(i.grunt())                # => TypeError: grunt() takes 0 positional arguments but 1 was given    # Update the property for this instance    # 实例级别的属性是独立的,各个对象各自拥有,修改不会影响其他对象内的值    i.age = 42    # Get the property    i.say(i.age)                    # => "Ian: 42"    j.say(j.age)                    # => "Joel: 0"    # Delete the property    del i.age    # i.age                         # => this would raise an AttributeError

这里解释一下,实例和对象可以理解成一个概念,实例的英文是instance,对象的英文是object。都是指类经过实例化之后得到的对象。
继承
继承可以让子类继承父类的变量以及方法,并且我们还可以在子类当中指定一些属于自己的特性,并且还可以重写父类的一些方法。一般我们会将不同的类放在不同的文件当中,使用import引入,一样可以实现继承。

from human import Human# Specify the parent class(es) as parameters to the class definitionclass Superhero(Human):    # If the child class should inherit all of the parent's definitions without    # any modifications, you can just use the "pass" keyword (and nothing else)    # but in this case it is commented out to allow for a unique child class:    # pass    # 如果要完全继承父类的所有的实现,我们可以使用关键字pass,表示跳过。这样不会修改父类当中的实现    # Child classes can override their parents' attributes    species = 'Superhuman'    # Children automatically inherit their parent class's constructor including    # its arguments, but can also define additional arguments or definitions    # and override its methods such as the class constructor.    # This constructor inherits the "name" argument from the "Human" class and    # adds the "superpower" and "movie" arguments:    # 子类会完全继承父类的构造方法,我们也可以进行改造,比如额外增加一些参数    def __init__(self, name, movie=False,                 superpowers=["super strength", "bulletproofing"]):        # add additional class attributes:        # 额外新增的参数        self.fictional = True        self.movie = movie        # be aware of mutable default values, since defaults are shared        self.superpowers = superpowers        # The "super" function lets you access the parent class's methods        # that are overridden by the child, in this case, the __init__ method.        # This calls the parent class constructor:        # 子类可以通过super关键字调用父类的方法        super().__init__(name)    # override the sing method    # 重写父类的sing方法    def sing(self):        return 'Dun, dun, DUN!'    # add an additional instance method    # 新增方法,只属于子类    def boast(self):        for power in self.superpowers:            print("I wield the power of {pow}!".format(pow=power))if __name__ == '__main__':    sup = Superhero(name="Tick")    # Instance type checks    # 检查继承关系    if isinstance(sup, Human):        print('I am human')    # 检查类型    if type(sup) is Superhero:        print('I am a superhero')    # Get the Method Resolution search Order used by both getattr() and super()    # This attribute is dynamic and can be updated    # 查看方法查询的顺序    # 先是自身,然后沿着继承顺序往上,最后到object    print(Superhero.__mro__)    # => (<class '__main__.Superhero'>,                                # => <class 'human.Human'>, <class 'object'>)    # 相同的属性子类覆盖了父类    # Calls parent method but uses its own class attribute    print(sup.get_species())    # => Superhuman    # Calls overridden method    # 相同的方法也覆盖了父类    print(sup.sing())           # => Dun, dun, DUN!    # Calls method from Human    # 继承了父类的方法    sup.say('Spoon')            # => Tick: Spoon    # Call method that exists only in Superhero    # 子类特有的方法    sup.boast()                 # => I wield the power of super strength!                                # => I wield the power of bulletproofing!    # Inherited class attribute    sup.age = 31    print(sup.age)              # => 31    # Attribute that only exists within Superhero    print('Am I Oscar eligible? ' + str(sup.movie))

多继承

我们创建一个蝙蝠类:

# Another class definition# bat.pyclass Bat:    species = 'Baty'    def __init__(self, can_fly=True):        self.fly = can_fly    # This class also has a say method    def say(self, msg):        msg = '... ... ...'        return msg    # And its own method as well    # 蝙蝠独有的声呐方法    def sonar(self):        return '))) ... ((('if __name__ == '__main__':    b = Bat()    print(b.say('hello'))    print(b.fly)

我们再创建一个蝙蝠侠的类,同时继承Superhero和Bat:

# And yet another class definition that inherits from Superhero and Bat# superhero.pyfrom superhero import Superherofrom bat import Bat# Define Batman as a child that inherits from both Superhero and Batclass Batman(Superhero, Bat):    def __init__(self, *args, **kwargs):        # Typically to inherit attributes you have to call super:        # super(Batman, self).__init__(*args, **kwargs)              # However we are dealing with multiple inheritance here, and super()        # only works with the next base class in the MRO list.        # So instead we explicitly call __init__ for all ancestors.        # The use of *args and **kwargs allows for a clean way to pass arguments,        # with each parent "peeling a layer of the onion".        # 通过类名调用两个父类各自的构造方法        Superhero.__init__(self, 'anonymous', movie=True,                            superpowers=['Wealthy'], *args, **kwargs)        Bat.__init__(self, *args, can_fly=False, **kwargs)        # override the value for the name attribute        self.name = 'Sad Affleck'    # 重写父类的sing方法    def sing(self):        return 'nan nan nan nan nan batman!'

执行这个类:

if __name__ == '__main__':    sup = Batman()    # Get the Method Resolution search Order used by both getattr() and super().    # This attribute is dynamic and can be updated    # 可以看到方法查询的顺序是先沿着superhero这条线到human,然后才是bat    print(Batman.__mro__)       # => (<class '__main__.Batman'>,                                 # => <class 'superhero.Superhero'>,                                 # => <class 'human.Human'>,                                 # => <class 'bat.Bat'>, <class 'object'>)    # Calls parent method but uses its own class attribute    # 只有superhero有get_species方法    print(sup.get_species())    # => Superhuman    # Calls overridden method    print(sup.sing())           # => nan nan nan nan nan batman!    # Calls method from Human, because inheritance order matters    sup.say('I agree')          # => Sad Affleck: I agree    # Call method that exists only in 2nd ancestor    # 调用蝙蝠类的声呐方法    print(sup.sonar())          # => ))) ... (((    # Inherited class attribute    sup.age = 100    print(sup.age)              # => 100    # Inherited attribute from 2nd ancestor whose default value was overridden.    print('Can I fly? ' + str(sup.fly)) # => Can I fly? False

进阶

生成器

我们可以通过yield关键字创建一个生成器,每次我们调用的时候执行到yield关键字处则停止。下次再次调用则还是从yield处开始往下执行:

# Generators help you make lazy code.def double_numbers(iterable):    for i in iterable:        yield i + i# Generators are memory-efficient because they only load the data needed to# process the next value in the iterable. This allows them to perform# operations on otherwise prohibitively large value ranges.# NOTE: `range` replaces `xrange` in Python 3.for i in double_numbers(range(1, 900000000)):  # `range` is a generator.    print(i)    if i >= 30:        break

除了yield之外,我们还可以使用()小括号来生成一个生成器:

# Just as you can create a list comprehension, you can create generator# comprehensions as well.values = (-x for x in [1,2,3,4,5])for x in values:    print(x)  # prints -1 -2 -3 -4 -5 to console/terminal# You can also cast a generator comprehension directly to a list.values = (-x for x in [1,2,3,4,5])gen_to_list = list(values)print(gen_to_list)  # => [-1, -2, -3, -4, -5]

关于生成器和迭代器更多的内容,可以查看下面这篇文章:
Python——五分钟带你弄懂迭代器与生成器,夯实代码能力

装饰器

我们引入functools当中的wraps之后,可以创建一个装饰器。装饰器可以在不修改函数内部代码的前提下,在外面包装一层其他的逻辑:

# Decorators# In this example `beg` wraps `say`. If say_please is True then it# will change the returned message.from functools import wrapsdef beg(target_function):    @wraps(target_function)    # 如果please为True,额外输出一句Please! I am poor :(    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@begdef say(say_please=False):    msg = "Can you buy me a beer?"    return msg, say_pleaseprint(say())                 # Can you buy me a beer?print(say(say_please=True))  # Can you buy me a beer? Please! I am poor :(

装饰器之前也有专门的文章详细介绍,可以移步下面的传送门:
一文搞定Python装饰器,看完面试不再慌

结尾

不知道有多少小伙伴可以看到结束,原作者的确非常厉害,把Python的基本操作基本上都囊括在里面了。如果都能读懂并且理解的话,那么Python这门语言就算是入门了。
原作者写的是一个Python文件,所有的内容都在Python的注释当中。我在它的基础上做了修补和额外的描述。如果想要获得原文,可以点击查看原文,或者是在公众号内回复learnpython获取。
如果你之前就有其他语言的语言基础,我想本文读完应该不用30分钟。当然在30分钟内学会一门语言是不可能的,也不是我所提倡的。但至少通过本文我们可以做到熟悉Python的语法,知道大概有哪些操作,剩下的就要我们亲自去写代码的时候去体会和运用了。
根据我的经验,在学习一门新语言的前期,不停地查阅资料是免不了的。希望本文可以作为你在使用Python时候的查阅文档。

推荐站点

  • 腾讯腾讯

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