最近有許多小伙伴后臺聯系我，說目前想要學習Python，但是沒有一份很好的資料入門。一方面的確現在市面上Python的資料過多，導致新手會不知如何選擇，另一個問題很多資料內容也很雜，從1+1到深度學習都包括，純粹關注Python本身語法的優質教材并不太多。

剛好我最近看到一份不錯的英文Python入門資料，我將它做了一些整理和翻譯寫下了本文。這份資料非常純粹，只有Python的基礎語法，專門針對想要學習Python的小白。

注釋

Python中用#表示單行注釋，#之后的同行的內容都會被注釋掉。

# Python中單行注釋用#表示，#之后同行字符全部認為被注釋。

使用三個連續的雙引號表示多行注釋，兩個多行注釋標識之間內容會被視作是注釋。

""" 與之對應的是多行注釋

用三個雙引號表示，這兩段雙引號當中的內容都會被視作是注釋

"""

基礎變量類型與操作符

Python當中的數字定義和其他語言一樣：

#獲得一個整數

3

# 獲得一個浮點數

10.0

我們分別使用+, -, *, /表示加減乘除四則運算符。

1 + 1 # => 2

8 - 1 # => 7

10 * 2 # => 20

35 / 5 # => 7.0

這里要注意的是，在Python2當中，10/3這個操作會得到3，而不是3.33333。因為除數和被除數都是整數，所以Python會自動執行整數的計算，幫我們把得到的商取整。如果是10.0 / 3，就會得到3.33333。目前Python2已經不再維護了，可以不用關心其中的細節。

但問題是Python是一個弱類型的語言，如果我們在一個函數當中得到兩個變量，是無法直接判斷它們的類型的。這就導致了同樣的計算符可能會得到不同的結果，這非常蛋疼。以至于程序員在運算除法的時候，往往都需要手工加上類型轉化符，將被除數轉成浮點數。

在Python3當中撥亂反正，修正了這個問題，即使是兩個整數相除，并且可以整除的情況下，得到的結果也一定是浮點數。

如果我們想要得到整數，我們可以這么操作：

5 // 3 # => 1

-5 // 3 # => -2

5.0 // 3.0 # => 1.0 # works on floats too

-5.0 // 3.0 # => -2.0

兩個除號表示取整除，Python會為我們保留去除余數的結果。

除了取整除操作之外還有取余數操作，數學上稱為取模，Python中用%表示。

# Modulo operation

7 % 3 # => 1

Python中支持乘方運算，我們可以不用調用額外的函數，而使用**符號來完成：

# Exponentiation (x**y, x to the yth power)

2**3 # => 8

當運算比較復雜的時候，我們可以用括號來強制改變運算順序。

# Enforce precedence with parentheses

1 + 3 * 2 # => 7

(1 + 3) * 2 # => 8

邏輯運算

Python中用首字母大寫的True和False表示真和假。

True # => True

False # => False

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

# negate with not

not True # => False

not False # => True

# Boolean Operators

# Note "and" and "or" are case-sensitive

True and False # => False

False or True # => True

在Python底層，True和False其實是1和0，所以如果我們執行以下操作，是不會報錯的，但是在邏輯上毫無意義。

# True and False are actually 1 and 0 but with different keywords

True + True # => 2

True * 8 # => 8

False - 5 # => -5

我們用==判斷相等的操作，可以看出來True==1， False == 0.

# Comparison operators look at the numerical value of True and False

0 == False # => True

1 == True # => True

2 == True # => False

-5 != False # => True

我們要小心Python當中的bool()這個函數，它并不是轉成bool類型的意思。如果我們執行這個函數，那么只有0會被視作是False，其他所有數值都是True：

bool(0) # => False

bool(4) # => True

bool(-6) # => True

0 and 2 # => 0

-5 or 0 # => -5

Python中用==判斷相等，>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 # => True

2 == 1 # => False

# Inequality is !=

1 != 1 # => False

2 != 1 # => True

# More comparisons

1 < 10 # => True

1 > 10 # => False

2 <= 2 # => True

2 >= 2 # => True

我們可以用and和or拼裝各個邏輯運算：

# Seeing whether a value is in a range

1 < 2 and 2 < 3 # => True

2 < 3 and 3 < 2 # => False

# Chaining makes this look nicer

1 < 2 < 3 # => True

2 < 3 < 2 # => False

注意not，and，or之間的優先級，其中not > and > or。如果分不清楚的話，可以用括號強行改變運行順序。

list和字符串

關于list的判斷，我們常用的判斷有兩種，一種是剛才介紹的==，還有一種是is。我們有時候也會簡單實用is來判斷，那么這兩者有什么區別呢？我們來看下面的例子：

a = [1, 2, 3, 4] # Point a at a new list, [1, 2, 3, 4]

b = a # Point b at what a is pointing to

b is a # => True, a and b refer to the same object

b == a # => True, a's and b's objects are equal

b = [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 object

b == a # => True, a's and b's objects are equal

Python是全引用的語言，其中的對象都使用引用來表示。is判斷的就是兩個引用是否指向同一個對象，而==則是判斷兩個引用指向的具體內容是否相等。舉個例子，如果我們把引用比喻成地址的話，is就是判斷兩個變量的是否指向同一個地址，比如說都是沿河東路XX號。而==則是判斷這兩個地址的收件人是否都叫張三。

顯然，住在同一個地址的人一定都叫張三，但是住在不同地址的兩個人也可以都叫張三，也可以叫不同的名字。所以如果a is b，那么a == b一定成立，反之則不然。

Python當中對字符串的限制比較松，雙引號和單引號都可以表示字符串，看個人喜好使用單引號或者是雙引號。我個人比較喜歡單引號，因為寫起來方便。

字符串也支持+操作，表示兩個字符串相連。除此之外，我們把兩個字符串寫在一起，即使沒有+，Python也會為我們拼接：

# 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!"

我們可以使用[]來查找字符串當中某個位置的字符，用len來計算字符串的長度。

# A string can be treated like a list of characters

"This is a string"[0] # => 'T'

# You can find the length of a string

len("This is a string") # => 16

我們可以在字符串前面加上f表示格式操作，并且在格式操作當中也支持運算，比如可以嵌套上len函數等。不過要注意，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的判斷，在Python當中None也是一個對象，所有為None的變量都會指向這個對象。根據我們前面所說的，既然所有的None都指向同一個地址，我們需要判斷一個變量是否是None的時候，可以使用is來進行判斷，當然用==也是可以的，不過我們通常使用is。

# None is an object

None # => 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 # => False

None is None # => True

理解了None之后，我們再回到之前介紹過的bool()函數，它的用途其實就是判斷值是否是空。所有類型的默認空值會被返回False，否則都是True。比如0，""，[], {}, ()等。

# None, 0, and empty strings/lists/dicts/tuples all evaluate to False.

# All other values are True

bool(None)# => False

bool(0) # => False

bool("") # => False

bool([]) # => False

bool({}) # => False

bool(()) # => False

除了上面這些值以外的所有值傳入都會得到True。

變量與集合

輸入輸出

Python當中的標準輸入輸出是input和print。

print會輸出一個字符串，如果傳入的不是字符串會自動調用__str__方法轉成字符串進行輸出。默認輸出會自動換行，如果想要以不同的字符結尾代替換行，可以傳入end參數：

# Python has a print function

print("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時，Python會在命令行接收一行字符串作為輸入。可以在input當中傳入字符串，會被當成提示輸出：

# Simple way to get input data from console

input_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()

變量

Python中聲明對象不需要帶上類型，直接賦值即可，Python會自動關聯類型，如果我們使用之前沒有聲明過的變量則會出發NameError異常。

# There are no declarations, only assignments.

# Convention is to use lower_case_with_underscores

some_var = 5

some_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支持三元表達式，但是語法和C++不同，使用if else結構，寫成：

# if can be used as an expression

# Equivalent of C's '?:' ternary operator

"yahoo!" if 3 > 2 else 2 # => "yahoo!"

上段代碼等價于：

if 3 > 2:

return 'yahoo'

else:

return 2

list

Python中用[]表示空的list，我們也可以直接在其中填充元素進行初始化：

# Lists store sequences

li = []

# You can start with a prefilled list

other_li = [4, 5, 6]

使用append和pop可以在list的末尾插入或者刪除元素：

# Add stuff to the end of a list with append

li.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 pop

li.pop() # => 3 and li is now [1, 2, 4]

# Let's put it back

li.append(3) # li is now [1, 2, 4, 3] again.

list可以通過[]加上下標訪問指定位置的元素，如果是負數，則表示倒序訪問。-1表示最后一個元素，-2表示倒數第二個，以此類推。如果訪問的元素超過數組長度，則會出發IndexError的錯誤。

# Access a list like you would any array

li[0] # => 1

# Look at the last element

li[-1] # => 3

# Looking out of bounds is an IndexError

li[4] # Raises an IndexError

list支持切片操作，所謂的切片則是從原list當中拷貝出指定的一段。我們用start: end的格式來獲取切片，注意，這是一個左閉右開區間。如果留空表示全部獲取，我們也可以額外再加入一個參數表示步長，比如[1:5:2]表示從1號位置開始，步長為2獲取元素。得到的結果為[1, 3]。如果步長設置成-1則代表反向遍歷。

# 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]

如果我們要指定一段區間倒序，則前面的start和end也需要反過來，例如我想要獲取[3: 6]區間的倒序，應該寫成[6:3:-1]。

只寫一個:，表示全部拷貝，如果用is判斷拷貝前后的list會得到False。可以使用del刪除指定位置的元素，或者可以使用remove方法。

# Make a one layer deep copy using slices

li2 = 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 value

li.remove(2) # li is now [1, 3]

li.remove(2) # Raises a ValueError as 2 is not in the list

insert方法可以指定位置插入元素，index方法可以查詢某個元素第一次出現的下標。

# Insert an element at a specific index

li.insert(1, 2) # li is now [1, 2, 3] again

# Get the index of the first item found matching the argument

li.index(2) # => 1

li.index(4) # Raises a ValueError as 4 is not in the list

list可以進行加法運算，兩個list相加表示list當中的元素合并。等價于使用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]

我們想要判斷元素是否在list中出現，可以使用in關鍵字，通過使用len計算list的長度：

# 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通過()初始化。和list不同，tuple是不可變對象。也就是說tuple一旦生成不可以改變。如果我們修改tuple，會引發TypeError異常。

# Tuples are like lists but are immutable.

tup = (1, 2, 3)

tup[0] # => 1

tup[0] = 3 # Raises a TypeError

由于小括號是有改變優先級的含義，所以我們定義單個元素的tuple，末尾必須加上逗號，否則會被當成是單個元素：

# 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)) # =>

type((1,)) # =>

type(()) # =>

tuple支持list當中絕大部分操作：

# You can do most of the list operations on tuples too

len(tup) # => 3

tup + (4, 5, 6) # => (1, 2, 3, 4, 5, 6)

tup[:2] # => (1, 2)

2 in tup # => True

我們可以用多個變量來解壓一個tuple：

# You can unpack tuples (or lists) into variables

a, b, c = (1, 2, 3) # a is now 1, b is now 2 and c is now 3

# You can also do extended unpacking

a, *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 parentheses

d, 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 values

e, d = d, e # d is now 5 and e is now 4

解釋一下這行代碼：

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

我們在b的前面加上了星號，表示這是一個list。所以Python會在將其他變量對應上值的情況下，將剩下的元素都賦值給b。

補充一點，tuple本身雖然是不可變的，但是tuple當中的可變元素是可以改變的。比如我們有這樣一個tuple：

a = (3, [4])

我們雖然不能往a當中添加或者刪除元素，但是a當中含有一個list，我們可以改變這個list類型的元素，這并不會觸發tuple的異常：

a[1].append(0) # 這是合法的

dict

dict也是Python當中經常使用的容器，它等價于C++當中的map，即存儲key和value的鍵值對。我們用{}表示一個dict，用:分隔key和value。

# Dictionaries store mappings from keys to values

empty_dict = {}

# Here is a prefilled dictionary

filled_dict = {"one": 1, "two": 2, "three": 3}

dict的key必須為不可變對象，所以list、set和dict不可以作為另一個dict的key，否則會拋出異常：

# 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.

我們同樣用[]查找dict當中的元素，我們傳入key，獲得value，等價于get方法。

# Look up values with []

filled_dict["one"] # => 1

filled_dict.get('one') #=> 1

我們可以call dict當中的keys和values方法，獲取dict當中的所有key和value的集合，會得到一個list。在Python3.7以下版本當中，返回的結果的順序可能和插入順序不同，在Python3.7及以上版本中，Python會保證返回的順序和插入順序一致：

# 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 <3.7

list(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 <3.7

list(filled_dict.values()) # => [1, 2, 3] in Python 3.7+

我們也可以用in判斷一個key是否在dict當中，注意只能判斷key。

# Check for existence of keys in a dictionary with "in"

"one" in filled_dict # => True

1 in filled_dict # => False

如果使用[]查找不存在的key，會引發KeyError的異常。如果使用get方法則不會引起異常，只會得到一個None：

# Looking up a non-existing key is a KeyError

filled_dict["four"] # KeyError

# Use "get()" method to avoid the KeyError

filled_dict.get("one") # => 1

filled_dict.get("four") # => None

# The get method supports a default argument when the value is missing

filled_dict.get("one", 4) # => 1

filled_dict.get("four", 4) # => 4

setdefault方法可以為不存在的key插入一個value，如果key已經存在，則不會覆蓋它：

# "setdefault()" inserts into a dictionary only if the given key isn't present

filled_dict.setdefault("five", 5) # filled_dict["five"] is set to 5

filled_dict.setdefault("five", 6) # filled_dict["five"] is still 5

我們可以使用update方法用另外一個dict來更新當前dict，比如a.update(b)。對于a和b交集的key會被b覆蓋，a當中不存在的key會被插入進來：

# Adding to a dictionary

filled_dict.update({"four":4}) # => {"one": 1, "two": 2, "three": 3, "four": 4}

filled_dict["four"] = 4 # another way to add to dict

我們一樣可以使用del刪除dict當中的元素，同樣只能傳入key。

Python3.5以上的版本支持使用**來解壓一個dict：

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

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

set

set是用來存儲不重復元素的容器，當中的元素都是不同的，相同的元素會被刪除。我們可以通過set()，或者通過{}來進行初始化。注意當我們使用{}的時候，必須要傳入數據，否則Python會將它和dict弄混。

# Sets store ... well sets

empty_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當中的元素也必須是不可變對象，因此list不能傳入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}

可以調用add方法為set插入元素：

# Add one more item to the set

filled_set = some_set

filled_set.add(5) # filled_set is now {1, 2, 3, 4, 5}

# Sets do not have duplicate elements

filled_set.add(5) # it remains as before {1, 2, 3, 4, 5}

set還可以被認為是集合，所以它還支持一些集合交叉并補的操作。

# Do set intersection with &

# 計算交集

other_set = {3, 4, 5, 6}

filled_set & other_set # => {3, 4, 5}

# Do set union with |

# 計算并集

filled_set | other_set # => {1, 2, 3, 4, 5, 6}

# Do set difference with -

# 計算差集

{1, 2, 3, 4} - {2, 3, 5} # => {1, 4}

# Do set symmetric difference with ^

# 這個有點特殊，計算對稱集，也就是去掉重復元素剩下的內容

{1, 2, 3, 4} ^ {2, 3, 5} # => {1, 4, 5}

set還支持超集和子集的判斷，我們可以用大于等于和小于等于號判斷一個set是不是另一個的超集或子集：

# 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一樣，我們可以使用in判斷元素在不在set當中。用copy可以拷貝一個set。

# Check for existence in a set with in

2 in filled_set # => True

10 in filled_set # => False

# Make a one layer deep copy

filled_set = some_set.copy() # filled_set is {1, 2, 3, 4, 5}

filled_set is some_set # => False

控制流和迭代

判斷語句

Python當中的判斷語句非常簡單，并且Python不支持switch，所以即使是多個條件，我們也只能羅列if-else。

# Let's just make a variable

some_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.")

循環

我們可以用in來循環迭代一個list當中的內容，這也是Python當中基本的循環方式。

"""

For loops iterate over lists

prints:

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))

如果我們要循環一個范圍，可以使用range。range加上一個參數表示從0開始的序列，比如range(10)，表示[0, 10)區間內的所有整數：

"""

"range(number)" returns an iterable of numbers

from zero to the given number

prints:

0

1

2

3

"""

for i in range(4):

print(i)

如果我們傳入兩個參數，則代表迭代區間的首尾。

"""

"range(lower, upper)" returns an iterable of numbers

from the lower number to the upper number

prints:

4

5

6

7

"""

for i in range(4, 8):

print(i)

如果我們傳入第三個元素，表示每次循環變量自增的步長。

"""

"range(lower, upper, step)" returns an iterable of numbers

from the lower number to the upper number, while incrementing

by 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函數，可以同時迭代一個list的下標和元素：

"""

To loop over a list, and retrieve both the index and the value of each item in the list

prints:

0 dog

1 cat

2 mouse

"""

animals = ["dog", "cat", "mouse"]

for i, value in enumerate(animals):

print(i, value)

while循環和C++類似，當條件為True時執行，為false時退出。并且判斷條件不需要加上括號：

"""

While loops go until a condition is no longer met.

prints:

0

1

2

3

"""

x = 0

while x < 4:

print(x)

x += 1 # Shorthand for x = x + 1

捕獲異常

Python當中使用try和except捕獲異常，我們可以在except后面限制異常的類型。如果有多個類型可以寫多個except，還可以使用else語句表示其他所有的類型。finally語句內的語法無論是否會觸發異常都必定執行：

# Handle exceptions with a try/except block

try:

# 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 exceptions

finally: # Execute under all circumstances

print("We can clean up resources here")

with操作

在Python當中我們經常會使用資源，最常見的就是open打開一個文件。我們打開了文件句柄就一定要關閉，但是如果我們手動來編碼，經常會忘記執行close操作。并且如果文件異常，還會觸發異常。這個時候我們可以使用with語句來代替這部分處理，使用with會自動在with塊執行結束或者是觸發異常時關閉打開的資源。

以下是with的幾種用法和功能：

# Instead of try/finally to cleanup resources you can use a with statement

# 代替使用try/finally語句來關閉資源

with open("myfile.txt") as f:

for line in f:

print(line)

# Writing to a file

# 使用with寫入文件

contents = {"aa": 12, "bb": 21}

with open("myfile1.txt", "w+") as file:

file.write(str(contents)) # writes a string to a file

with open("myfile2.txt", "w+") as file:

file.write(json.dumps(contents)) # writes an object to a file

# Reading from a file

# 使用with讀取文件

with open('myfile1.txt', "r+") as file:

contents = file.read() # reads a string from a file

print(contents)

# print: {"aa": 12, "bb": 21}

with open('myfile2.txt', "r+") as file:

contents = json.load(file) # reads a json object from a file

print(contents)

# print: {"aa": 12, "bb": 21}

可迭代對象

凡是可以使用in語句來迭代的對象都叫做可迭代對象，它和迭代器不是一個含義。這里只有可迭代對象的介紹，想要了解迭代器的具體內容，請移步傳送門：

當我們調用dict當中的keys方法的時候，返回的結果就是一個可迭代對象。

# 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

我們不能使用下標來訪問可迭代對象，但我們可以用iter將它轉化成迭代器，使用next關鍵字來獲取下一個元素。也可以將它轉化成list類型，變成一個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 exception

next(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

函數

使用def關鍵字來定義函數，我們在傳參的時候如果指定函數內的參數名，可以不按照函數定義的順序傳參：

# Use "def" to create new functions

def add(x, y):

print("x is {} and y is {}".format(x, y))

return x + y # Return values with a return statement

# Calling functions with parameters

add(5, 6) # => prints out "x is 5 and y is 6" and returns 11

# Another way to call functions is with keyword arguments

add(y=6, x=5) # Keyword arguments can arrive in any order.

可以在參數名之前加上*表示任意長度的參數，參數會被轉化成list：

# You can define functions that take a variable number of

# positional arguments

def varargs(*args):

return args

varargs(1, 2, 3) # => (1, 2, 3)

也可以指定任意長度的關鍵字參數，在參數前加上**表示接受一個dict：

# You can define functions that take a variable number of

# keyword arguments, as well

def keyword_args(**kwargs):

return kwargs

# Let's call it to see what happens

keyword_args(big="foot", loch="ness") # => {"big": "foot", "loch": "ness"}

當然我們也可以兩個都用上，這樣可以接受任何參數：

# You can do both at once, if you like

def 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}

"""

傳入參數的時候我們也可以使用*和**來解壓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中的參數可以返回多個值：

# 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 = 1

y = 2

x, y = swap(x, y) # => x = 2, y = 1

# (x, y) = swap(x,y) # Again parenthesis have been excluded but can be included.

函數內部定義的變量即使和全局變量重名，也不會覆蓋全局變量的值。想要在函數內部使用全局變量，需要加上global關鍵字，表示這是一個全局變量：

# Function Scope

x = 5

def set_x(num):

# Local var x not the same as global variable x

x = num # => 43

print(x) # => 43

def set_global_x(num):

global x

print(x) # => 5

x = num # global var x is now set to 6

print(x) # => 6

set_x(43)

set_global_x(6)

Python支持函數式編程，我們可以在一個函數內部返回一個函數：

# Python has first class functions

def create_adder(x):

def adder(y):

return x + y

return adder

add_10 = create_adder(10)

add_10(3) # => 13

Python中可以使用lambda表示匿名函數，使用:作為分隔，:前面表示匿名函數的參數，:后面的是函數的返回值：

# There are also anonymous functions

(lambda x: x > 2)(3) # => True

(lambda x, y: x ** 2 + y ** 2)(2, 1) # => 5

我們還可以將函數作為參數使用map和filter，實現元素的批量處理和過濾。關于Python中map、reduce和filter的使用，具體可以查看之前的文章：

# There are built-in higher order functions

list(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]

我們還可以結合循環和判斷語來給list或者是dict進行初始化：

# 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}

模塊

使用import語句引入一個Python模塊，我們可以用.來訪問模塊中的函數或者是類。

# You can import modules

import math

print(math.sqrt(16)) # => 4.0

我們也可以使用from import的語句，單獨引入模塊內的函數或者是類，而不再需要寫出完整路徑。使用from import *可以引入模塊內所有內容（不推薦這么干）

# You can get specific functions from a module

from math import ceil, floor

print(ceil(3.7)) # => 4.0

print(floor(3.7)) # => 3.0

# You can import all functions from a module.

# Warning: this is not recommended

from math import *

可以使用as給模塊內的方法或者類起別名：

# You can shorten module names

import math as m

math.sqrt(16) == m.sqrt(16) # => True

我們可以使用dir查看我們用的模塊的路徑：

# You can find out which functions and attributes

# are defined in a module.

import math

dir(math)

這么做的原因是如果我們當前的路徑下也有一個叫做math的Python文件，那么會覆蓋系統自帶的math的模塊。這是尤其需要注意的，不小心會導致很多奇怪的bug。

類

我們來看一個完整的類，相關的介紹都在注釋當中

# We use the "class" statement to create a class

class Human:

# A class attribute. It is shared by all instances of this class

# 類屬性，可以直接通過Human.species調用，而不需要通過實例

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.

# 最基礎的構造函數

# 加了下劃線的函數和變量表示不應該被用戶使用，其中雙下劃線的函數或者是變量將不會被子類覆蓋

# 前后都有雙下劃線的函數和屬性是類當中的特殊屬性

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

# 類中的函數，所有實例可以調用，第一個參數必須是self

# self表示實例的引用

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，表示是類函數

# 通過Human.get_species來調用，所有實例共享

def get_species(cls):

return cls.species

# A static method is called without a class or instance reference

@staticmethod

# 靜態函數，通過類名或者是實例都可以調用

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，類似于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

以上內容的詳細介紹之前也有過相關文章，可以查看：

下面我們來看看Python當中類的使用：

# 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.

# 這個是main函數也是整個程序入口的慣用寫法

if __name__ == '__main__':

# Instantiate a class

# 實例化一個類，獲取類的對象

i = Human(name="Ian")

# 執行say方法

i.say("hi") # "Ian: hi"

j = Human("Joel")

j.say("hello") # "Joel: hello"

# i和j都是Human的實例，都稱作是Human類的對象

# i and j are instances of type Human, or in other words: they are Human objects

# Call our class method

# 類屬性被所有實例共享，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"

# 通過類名調用靜態方法

# 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

# 不能通過對象調用靜態方法，因為對象會傳入self實例，會導致不匹配

print(i.grunt()) # => TypeError: grunt() takes 0 positional arguments but 1 was given

# Update the property for this instance

# 實例級別的屬性是獨立的，各個對象各自擁有，修改不會影響其他對象內的值

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

這里解釋一下，實例和對象可以理解成一個概念，實例的英文是instance，對象的英文是object。都是指類經過實例化之后得到的對象。

繼承

繼承可以讓子類繼承父類的變量以及方法，并且我們還可以在子類當中指定一些屬于自己的特性，并且還可以重寫父類的一些方法。一般我們會將不同的類放在不同的文件當中，使用import引入，一樣可以實現繼承。

from human import Human

# Specify the parent class(es) as parameters to the class definition

class 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

# 如果要完全繼承父類的所有的實現，我們可以使用關鍵字pass，表示跳過。這樣不會修改父類當中的實現

# 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:

# 子類會完全繼承父類的構造方法，我們也可以進行改造，比如額外增加一些參數

def __init__(self, name, movie=False,

superpowers=["super strength", "bulletproofing"]):

# add additional class attributes:

# 額外新增的參數

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:

# 子類可以通過super關鍵字調用父類的方法

super().__init__(name)

# override the sing method

# 重寫父類的sing方法

def sing(self):

return 'Dun, dun, DUN!'

# add an additional instance method

# 新增方法，只屬于子類

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

# 檢查繼承關系

if isinstance(sup, Human):

print('I am human')

# 檢查類型

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

# 查看方法查詢的順序

# 先是自身，然后沿著繼承順序往上，最后到object

print(Superhero.__mro__) # => (,

# => , )

# 相同的屬性子類覆蓋了父類

# Calls parent method but uses its own class attribute

print(sup.get_species()) # => Superhuman

# Calls overridden method

# 相同的方法也覆蓋了父類

print(sup.sing()) # => Dun, dun, DUN!

# Calls method from Human

# 繼承了父類的方法

sup.say('Spoon') # => Tick: Spoon

# Call method that exists only in Superhero

# 子類特有的方法

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))

多繼承

我們創建一個蝙蝠類：

# Another class definition

# bat.py

class 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

# 蝙蝠獨有的聲吶方法

def sonar(self):

return '))) ... ((('

if __name__ == '__main__':

b = Bat()

print(b.say('hello'))

print(b.fly)

我們再創建一個蝙蝠俠的類，同時繼承Superhero和Bat：

# And yet another class definition that inherits from Superhero and Bat

# superhero.py

from superhero import Superhero

from bat import Bat

# Define Batman as a child that inherits from both Superhero and Bat

class 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".

# 通過類名調用兩個父類各自的構造方法

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'

# 重寫父類的sing方法

def sing(self):

return 'nan nan nan nan nan batman!'

執行這個類：

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

# 可以看到方法查詢的順序是先沿著superhero這條線到human，然后才是bat

print(Batman.__mro__) # => (,

# => ,

# => ,

# => , )

# 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

# 調用蝙蝠類的聲吶方法

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

進階

生成器

我們可以通過yield關鍵字創建一個生成器，每次我們調用的時候執行到yield關鍵字處則停止。下次再次調用則還是從yield處開始往下執行：

# 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，我們還可以使用()小括號來生成一個生成器：

# 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]

關于生成器和迭代器更多的內容，可以查看下面這篇文章：

裝飾器

我們引入functools當中的wraps之后，可以創建一個裝飾器。裝飾器可以在不修改函數內部代碼的前提下，在外面包裝一層其他的邏輯:

# Decorators

# In this example `beg` wraps `say`. If say_please is True then it

# will change the returned message.

from functools import wraps

def beg(target_function):

@wraps(target_function)

# 如果please為True，額外輸出一句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

@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 :(

裝飾器之前也有專門的文章詳細介紹，可以移步下面的傳送門：

結尾

不知道有多少小伙伴可以看到結束，原作者的確非常厲害，把Python的基本操作基本上都囊括在里面了。如果都能讀懂并且理解的話，那么Python這門語言就算是入門了。

原作者寫的是一個Python文件，所有的內容都在Python的注釋當中。我在它的基礎上做了修補和額外的描述。如果想要獲得原文，可以點擊查看原文，或者是在公眾號內回復learnpython獲取。

如果你之前就有其他語言的語言基礎，我想本文讀完應該不用30分鐘。當然在30分鐘內學會一門語言是不可能的，也不是我所提倡的。但至少通過本文我們可以做到熟悉Python的語法，知道大概有哪些操作，剩下的就要我們親自去寫代碼的時候去體會和運用了。

根據我的經驗，在學習一門新語言的前期，不停地查閱資料是免不了的。希望本文可以作為你在使用Python時候的查閱文檔。

今天的文章就到這里，原創不易，需要你的一個關注，你的舉手之勞對我來說很重要。

總結

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