【萬字長文】別再報班了，一篇文章帶你入門Python
本文始發於個人公眾號：TechFlow，原創不易，求個關注
最近有許多小夥伴後台聯繫我，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並不太多。
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，#之後的同行的內容都會被注釋掉。
# Python中單行注釋用#表示，#之後同行字元全部認為被注釋。
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
"""
基礎變數類型與操作符
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
這裡要注意的是，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數，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。以至於程式設計師在運算除法的時候，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，並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，我們可以這麼操作：
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會為我們保留去除餘數的結果。
除了取整除操作之外還有取餘數操作，數學上稱為取模，Python中用%表示。
# Modulo operation
7 % 3 # => 1
Python中支援乘方運算，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
當運算比較複雜的時候，我們可以用括弧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，它並不是轉成bool類型的意思。如果我們執行這個函數，那麼只有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，>表示大於，>=表示大於等於， <表示小於，<=表示小於等於，!=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之間的優先順序，其中not > and > or。如果分不清楚的話，可以用括弧強行改變運行順序。
list和字元串
關於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，那麼這兩者有什麼區別呢？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就是判斷兩個變數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，那麼a == b一定成立，反之則不然。
Python當中對字元串的限制比較松，雙引號和單引號都可以表示字元串，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。
字元串也支援+操作，表示兩個字元串相連。除此之外，我們把兩個字元串寫在一起，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[]來查找字元串當中某個位置的字元，用len來計算字元串的長度。
# 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
我們可以在字元串前面加上f表示格式操作，並且在格式操作當中也支援運算，比如可以嵌套上len函數等。不過要注意，只有Python3.6以上的版本支援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."
最後是None的判斷，在Python當中None也是一個對象，所有為None的變數都會指向這個對象。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數是否是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之後，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。
變數與集合
輸入輸出
Python當中的標準輸入輸出是input和print。
print會輸出一個字元串，如果傳入的不是字元串會自動調用__str__方法轉成字元串進行輸出。默認輸出會自動換行，如果想要以不同的字元結尾代替換行，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元串作為輸入。可以在input當中傳入字元串，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()
變數
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數則會出發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支援三元表達式，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!"
上段程式碼等價於：
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表示最後一個元素，-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支援切片操作，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判斷拷貝前後的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當中的元素合併。等價於使用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
由於小括弧是有改變優先順序的含義，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)) # => <class 'int'>
type((1,)) # => <class 'tuple'>
type(()) # => <class 'tuple'>
tuple支援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
我們可以用多個變數來解壓一個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
解釋一下這行程式碼：
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會在將其他變數對應上值的情況下，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，這並不會觸發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，它等價於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，等價於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)。對於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以上的版本支援使用**來解壓一個dict：
{'a': 1, **{'b': 2}} # => {'a': 1, 'b': 2}
{'a': 1, **{'a': 2}} # => {'a': 2}
set
set是用來存儲不重複元素的容器，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，所以它還支援一些集合交叉並補的操作。
# 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 |
# 計算並集
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，也就是去掉重複元素剩下的內容
{1, 2, 3, 4} ^ {2, 3, 5} # => {1, 4, 5}
set還支援超集和子集的判斷，我們可以用大於等於和小於等於號判斷一個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，並且Python不支援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範圍，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，表示每次循環變數自增的步長。
"""
"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。並且判斷條件不需要加上括弧：
"""
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後面限制異常的類型。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。並且如果文件異常，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。這裡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：
Python——五分鐘帶你弄懂迭代器與生成器，夯實程式碼能力
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.
函數內部定義的變數即使和全局變數重名，也不會覆蓋全局變數的值。想要在函數內部使用全局變數，需要加上global關鍵字，表示這是一個全局變數：
# 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支援函數式編程，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，:後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。關於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}
模組
使用import語句引入一個Python模組，我們可以用.來訪問模組中的函數或者是類。
# You can import modules
import math
print(math.sqrt(16)) # => 4.0
我們也可以使用from import的語句，單獨引入模組內的函數或者是類，而不再需要寫出完整路徑。使用from import *可以引入模組內所有內容（不推薦這麼干）
# 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給模組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查看我們用的模組的路徑：
# You can find out which functions and attributes
# are defined in a module.
import math
dir(math)
這麼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，那麼會覆蓋系統自帶的math的模組。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數表示不應該被用戶使用，其中雙下劃線的函數或者是變數將不會被子類覆蓋
# 前後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
# 加上了註解，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註解，類似於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函數也是整個程式入口的慣用寫法
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
這裡解釋一下，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後得到的對象。
繼承
繼承可以讓子類繼承父類的變數以及方法，並且我們還可以在子類當中指定一些屬於自己的特性，並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，只屬於子類
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
# 檢查繼承關係
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，然後沿著繼承順序往上，最後到object
print(Superhero.__mro__) # => (<class '__main__.Superhero'>,
# => <class 'human.Human'>, <class 'object'>)
# 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，然後才是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
# 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()小括弧來生成一個生成器：
# 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]
關於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之後，可以創建一個裝飾器。裝飾器可以在不修改函數內部程式碼的前提下，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
不知道有多少小夥伴可以看到結束，原作者的確非常厲害，把Python的基本操作基本上都囊括在裡面了。如果都能讀懂並且理解的話，那麼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程式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。
今天的文章就到這裡，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。
