--- language: python contributors: - ["Louie Dinh", "http://ldinh.ca"] - ["Amin Bandali", "http://aminbandali.com"] - ["Andre Polykanine", "https://github.com/Oire"] - ["evuez", "http://github.com/evuez"] translators: - ["Michael Yeh", "https://github.com/hinet60613"] filename: learnpython.py lang: zh-tw --- Python是在1990年代早期由Guido Van Rossum創建的。它是現在最流行的程式語言之一。我愛上Python是因為他極為清晰的語法，甚至可以說它就是可執行的虛擬碼。 非常歡迎各位給我們任何回饋！ 你可以在[@louiedinh](http://twitter.com/louiedinh) 或 louiedinh [at] [google's email service]聯絡到我。 註： 本篇文章適用的版本為Python 2.7，但大部分的Python 2.X版本應該都適用。 Python 2.7將會在2020年停止維護，因此建議您可以從Python 3開始學Python。 Python 3.X可以看這篇[Python 3 教學 (英文)](http://learnxinyminutes.com/docs/python3/). 讓程式碼同時支援Python 2.7和3.X是可以做到的，只要引入 [`__future__` imports](https://docs.python.org/2/library/__future__.html) 模組. `__future__` 模組允許你撰寫可以在Python 2上執行的Python 3程式碼，詳細訊息請參考Python 3 教學。 ```python # 單行註解從井字號開始 """ 多行字串可以用三個雙引號 包住，不過通常這種寫法會 被拿來當作多行註解 """ #################################################### ## 1. 原始型別與運算元 #################################################### # 你可以使用數字 3 # => 3 # 還有四則運算 1 + 1 # => 2 8 - 1 # => 7 10 * 2 # => 20 35 / 5 # => 7 # 除法比較麻煩，除以整數時會自動捨去小數位。 5 / 2 # => 2 # 要做精確的除法，我們需要浮點數 2.0 # 浮點數 11.0 / 4.0 # => 2.75 精確多了！ # 整數除法的無條件捨去對正數或負數都適用 5 // 3 # => 1 5.0 // 3.0 # => 1.0 # 浮點數的整數也適用 -5 // 3 # => -2 -5.0 // 3.0 # => -2.0 # 我們可以用除法模組(參考第六節:模組)，讓 # 單一斜線代表普通除法，而非無條件捨去 from __future__ import division 11/4 # => 2.75 ...普通除法 11//4 # => 2 ...無條件捨去 # 取餘數 7 % 3 # => 1 # 指數 (x的y次方) 2**4 # => 16 # 括號即先乘除後加減 (1 + 3) * 2 # => 8 # 布林運算 # 注意 "and" 和 "or" 的大小寫 True and False #=> False False or True #=> True # 用整數與布林值做運算 0 and 2 #=> 0 -5 or 0 #=> -5 0 == False #=> True 2 == True #=> False 1 == True #=> True # 用not取反向 not True # => False not False # => True # 等於判斷是用 == 1 == 1 # => True 2 == 1 # => False # 不等於判斷是用 != 1 != 1 # => False 2 != 1 # => True # 更多比較 1 < 10 # => True 1 > 10 # => False 2 <= 2 # => True 2 >= 2 # => True # 比較是可以串接的 1 < 2 < 3 # => True 2 < 3 < 2 # => False # 字串用單引號 ' 或雙引號 " 建立 "This is a string." 'This is also a string.' # 字串相加會被串接再一起 "Hello " + "world!" # => "Hello world!" # 不用加號也可以做字串相加 "Hello " "world!" # => "Hello world!" # ... 也可以做相乘 "Hello" * 3 # => "HelloHelloHello" # 字串可以被視為字元的陣列 "This is a string"[0] # => 'T' # 字串的格式化可以用百分之符號 % # 儘管在Python 3.1後這個功能被廢棄了，並且在 # 之後的版本會被移除，但還是可以了解一下 x = 'apple' y = 'lemon' z = "The items in the basket are %s and %s" % (x,y) # 新的格式化方式是使用format函式 # 這個方式也是較為推薦的 "{} is a {}".format("This", "placeholder") "{0} can be {1}".format("strings", "formatted") # 你也可以用關鍵字，如果你不想數你是要用第幾個變數的話 "{name} wants to eat {food}".format(name="Bob", food="lasagna") # 無(None) 是一個物件 None # => None # 不要用等於符號 "==" 對 無(None)做比較 # 用 "is" "etc" is None # => False None is None # => True # 'is' 運算元是用來識別物件的。對原始型別來說或許沒什麼用， # 但對物件來說是很有用的。 # 任何物件都可以被當作布林值使用 # 以下的值會被視為是False : # - 無(None) # - 任何型別的零 (例如: 0, 0L, 0.0, 0j) # - 空序列 (例如: '', (), []) # - 空容器 (例如: {}, set()) # - 自定義型別的實體，且滿足某些條件 # 請參考文件: https://docs.python.org/2/reference/datamodel.html#object.__nonzero__ # # 其餘的值都會被視為True (用bool()函式讓他們回傳布林值). bool(0) # => False bool("") # => False #################################################### ## 2. 變數與集合 #################################################### # Python的輸出很方便 print "I'm Python. Nice to meet you!" # => I'm Python. Nice to meet you! # 從命令列獲得值也很方便 input_string_var = raw_input("Enter some data: ") # 資料會被視為字串存進變數 input_var = input("Enter some data: ") # 輸入的資料會被當作Python程式碼執行 # 注意: 請謹慎使用input()函式 # 註: 在Python 3中，input()已被棄用，raw_input()已被更名為input() # 使用變數前不需要先宣告 some_var = 5 # 方便好用 lower_case_with_underscores some_var # => 5 # 存取沒有被賦值的變數會造成例外 # 請參考錯誤流程部分做例外處理 some_other_var # 造成 NameError # if可以當判斷式使用 # 相當於C語言中的二元判斷式 "yahoo!" if 3 > 2 else 2 # => "yahoo!" # 串列型態可以儲存集合 li = [] # 你可以預先填好串列內容 other_li = [4, 5, 6] # 用append()在串列後新增東西 append li.append(1) # 此時 li 內容為 [1] li.append(2) # 此時 li 內容為 [1, 2] li.append(4) # 此時 li 內容為 [1, 2, 4] li.append(3) # 此時 li 內容為 [1, 2, 4, 3] # 用pop()移除串列尾端的元素 li.pop() # => 3 and li is now [1, 2, 4] # 然後再塞回去 li.append(3) # li is now [1, 2, 4, 3] again. # 你可以像存取陣列一樣的存取串列 li[0] # => 1 # 用等號 = 給串列中特定索引的元素賦值 li[0] = 42 li[0] # => 42 li[0] = 1 # 註: 將其設定回原本的值 # 用 -1 索引值查看串列最後一個元素 li[-1] # => 3 # 存取超過範圍會產生IndexError li[4] # Raises an IndexError # 你可以用切片語法來存取特定範圍的值 # (相當於數學中的左閉右開區間，即包含最左邊界，但不包含右邊界) li[1:3] # => [2, 4] # 略過開頭元素 li[2:] # => [4, 3] # 略過結尾元素 li[:3] # => [1, 2, 4] # 每隔兩個元素取值 li[::2] # =>[1, 4] # 串列反轉 li[::-1] # => [3, 4, 2, 1] # 你可以任意組合來達到你想要的效果 # li[開始索引:結束索引:間隔] # 用 "del" 從串列中移除任意元素 del li[2] # 現在 li 內容為 [1, 2, 3] # 你可以做串列相加 li + other_li # => [1, 2, 3, 4, 5, 6] # 註: li 及 other_li 沒有被更動 # 用 "extend()" 做串列串接 li.extend(other_li) # 現在 li 內容為 [1, 2, 3, 4, 5, 6] # 移除特定值的第一次出現 li.remove(2) # 現在 li 內容為 [1, 3, 4, 5, 6] li.remove(2) # 2 不在串列中，造成 ValueError # 在特定位置插入值 li.insert(1, 2) # 現在 li 內容再次回復為 [1, 2, 3, 4, 5, 6] # 取得特定值在串列中第一次出現的位置 li.index(2) # => 1 li.index(7) # 7 不在串列中，造成 ValueError # 用 "in" 檢查特定值是否出現在串列中 1 in li # => True # 用 "len()" 取得串列長度 len(li) # => 6 # Tuples are like lists but are immutable. tup = (1, 2, 3) tup[0] # => 1 tup[0] = 3 # Raises a TypeError # You can do all those list thingies on tuples too len(tup) # => 3 tup + (4, 5, 6) # => (1, 2, 3, 4, 5, 6) tup[:2] # => (1, 2) 2 in tup # => True # 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 d, e, f = 4, 5, 6 # you can leave out the parentheses # Tuples are created by default if you leave out the parentheses g = 4, 5, 6 # => (4, 5, 6) # Now look how easy it is to swap two values e, d = d, e # d is now 5 and e is now 4 # Dictionaries store mappings empty_dict = {} # Here is a prefilled dictionary filled_dict = {"one": 1, "two": 2, "three": 3} # Look up values with [] filled_dict["one"] # => 1 # Get all keys as a list with "keys()" filled_dict.keys() # => ["three", "two", "one"] # Note - Dictionary key ordering is not guaranteed. # Your results might not match this exactly. # Get all values as a list with "values()" filled_dict.values() # => [3, 2, 1] # Note - Same as above regarding key ordering. # Check for existence of keys in a dictionary with "in" "one" in filled_dict # => True 1 in filled_dict # => False # 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 # note that filled_dict.get("four") is still => None # (get doesn't set the value in the dictionary) # set the value of a key with a syntax similar to lists filled_dict["four"] = 4 # now, filled_dict["four"] => 4 # "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 # Sets store ... well sets (which are like lists but can contain no duplicates) empty_set = set() # Initialize a "set()" with a bunch of values some_set = set([1, 2, 2, 3, 4]) # some_set is now set([1, 2, 3, 4]) # order is not guaranteed, even though it may sometimes look sorted another_set = set([4, 3, 2, 2, 1]) # another_set is now set([1, 2, 3, 4]) # Since Python 2.7, {} can be used to declare a set filled_set = {1, 2, 2, 3, 4} # => {1, 2, 3, 4} # Add more items to a set filled_set.add(5) # filled_set is now {1, 2, 3, 4, 5} # Do set intersection with & other_set = {3, 4, 5, 6} filled_set & other_set # => {3, 4, 5} # Do set union with | filled_set | other_set # => {1, 2, 3, 4, 5, 6} # Do set difference with - {1, 2, 3, 4} - {2, 3, 5} # => {1, 4} # Do set symmetric difference with ^ {1, 2, 3, 4} ^ {2, 3, 5} # => {1, 4, 5} # 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 # Check for existence in a set with in 2 in filled_set # => True 10 in filled_set # => False #################################################### ## 3. Control Flow #################################################### # Let's just make a variable some_var = 5 # Here is an if statement. Indentation is significant in python! # 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." """ 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 {0} to interpolate formatted strings. (See above.) print "{0} is a mammal".format(animal) """ "range(number)" returns a list of numbers from zero to the given number prints: 0 1 2 3 """ for i in range(4): print i """ "range(lower, upper)" returns a list of numbers from the lower number to the upper number prints: 4 5 6 7 """ for i in range(4, 8): print i """ 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 # Handle exceptions with a try/except block # Works on Python 2.6 and up: 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" # Instead of try/finally to cleanup resources you can use a with statement with open("myfile.txt") as f: for line in f: print line #################################################### ## 4. Functions #################################################### # Use "def" to create new functions def add(x, y): print "x is {0} and y is {1}".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. # You can define functions that take a variable number of # positional args, which will be interpreted as a tuple if you do not use the * def varargs(*args): return args varargs(1, 2, 3) # => (1, 2, 3) # You can define functions that take a variable number of # keyword args, as well, which will be interpreted as a dict if you do not use ** def keyword_args(**kwargs): return kwargs # Let's call it to see what happens keyword_args(big="foot", loch="ness") # => {"big": "foot", "loch": "ness"} # 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} """ # When calling functions, you can do the opposite of args/kwargs! # Use * to expand positional args and use ** to expand keyword args. args = (1, 2, 3, 4) kwargs = {"a": 3, "b": 4} all_the_args(*args) # equivalent to foo(1, 2, 3, 4) all_the_args(**kwargs) # equivalent to foo(a=3, b=4) all_the_args(*args, **kwargs) # equivalent to foo(1, 2, 3, 4, a=3, b=4) # you can pass args and kwargs along to other functions that take args/kwargs # by expanding them with * and ** respectively def pass_all_the_args(*args, **kwargs): all_the_args(*args, **kwargs) print varargs(*args) print keyword_args(**kwargs) # 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 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 # There are also anonymous functions (lambda x: x > 2)(3) # => True (lambda x, y: x ** 2 + y ** 2)(2, 1) # => 5 # There are built-in higher order functions map(add_10, [1, 2, 3]) # => [11, 12, 13] map(max, [1, 2, 3], [4, 2, 1]) # => [4, 2, 3] filter(lambda x: x > 5, [3, 4, 5, 6, 7]) # => [6, 7] # We can use list comprehensions for nice maps and filters [add_10(i) for i in [1, 2, 3]] # => [11, 12, 13] [x for x in [3, 4, 5, 6, 7] if x > 5] # => [6, 7] #################################################### ## 5. Classes #################################################### # We subclass from object to get a class. class Human(object): # A class attribute. It is shared by all instances of this class 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. You should not invent such names on your own. 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 def say(self, msg): return "{0}: {1}".format(self.name, msg) # A class method is shared among all instances # They are called with the calling class as the first argument @classmethod def get_species(cls): return cls.species # A static method is called without a class or instance reference @staticmethod 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. @property 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 # Instantiate a class i = Human(name="Ian") print i.say("hi") # prints out "Ian: hi" j = Human("Joel") print j.say("hello") # prints out "Joel: hello" # Call our class method i.get_species() # => "H. sapiens" # Change the shared attribute Human.species = "H. neanderthalensis" i.get_species() # => "H. neanderthalensis" j.get_species() # => "H. neanderthalensis" # Call the static method Human.grunt() # => "*grunt*" # Update the property i.age = 42 # Get the property i.age # => 42 # Delete the property del i.age i.age # => raises an AttributeError #################################################### ## 6. Modules #################################################### # You can import modules import math print math.sqrt(16) # => 4 # 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 * # You can shorten module names import math as m math.sqrt(16) == m.sqrt(16) # => True # you can also test that the functions are equivalent from math import sqrt math.sqrt == m.sqrt == sqrt # => True # Python modules are just ordinary python files. You # can write your own, and import them. The name of the # module is the same as the name of the file. # You can find out which functions and attributes # defines a module. import math dir(math) #################################################### ## 7. Advanced #################################################### # Generators help you make lazy code def double_numbers(iterable): for i in iterable: yield i + i # A generator creates values on the fly. # Instead of generating and returning all values at once it creates one in each # iteration. This means values bigger than 15 wont be processed in # double_numbers. # Note xrange is a generator that does the same thing range does. # Creating a list 1-900000000 would take lot of time and space to be made. # xrange creates an xrange generator object instead of creating the entire list # like range does. # We use a trailing underscore in variable names when we want to use a name that # would normally collide with a python keyword xrange_ = xrange(1, 900000000) # will double all numbers until a result >=30 found for i in double_numbers(xrange_): print i if i >= 30: break # Decorators # in this example beg wraps say # Beg will call say. If say_please is True then it will change the returned # message from functools import wraps def beg(target_function): @wraps(target_function) def wrapper(*args, **kwargs): msg, say_please = target_function(*args, **kwargs) if say_please: return "{} {}".format(msg, "Please! I am poor :(") return msg return wrapper @beg def say(say_please=False): msg = "Can you buy me a beer?" return msg, say_please print say() # Can you buy me a beer? print say(say_please=True) # Can you buy me a beer? Please! I am poor :( ``` ## Ready For More? ### Free Online * [Automate the Boring Stuff with Python](https://automatetheboringstuff.com) * [Learn Python The Hard Way](http://learnpythonthehardway.org/book/) * [Dive Into Python](http://www.diveintopython.net/) * [The Official Docs](http://docs.python.org/2/) * [Hitchhiker's Guide to Python](http://docs.python-guide.org/en/latest/) * [Python Module of the Week](http://pymotw.com/2/) * [A Crash Course in Python for Scientists](http://nbviewer.ipython.org/5920182) * [First Steps With Python](https://realpython.com/learn/python-first-steps/) ### Dead Tree * [Programming Python](http://www.amazon.com/gp/product/0596158106/ref=as_li_qf_sp_asin_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=0596158106&linkCode=as2&tag=homebits04-20) * [Dive Into Python](http://www.amazon.com/gp/product/1441413022/ref=as_li_tf_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=1441413022&linkCode=as2&tag=homebits04-20) * [Python Essential Reference](http://www.amazon.com/gp/product/0672329786/ref=as_li_tf_tl?ie=UTF8&camp=1789&creative=9325&creativeASIN=0672329786&linkCode=as2&tag=homebits04-20)