Python was created by Guido Van Rossum in the early 90s. It is now one of the most popular
languages in existence. I fell in love with Python for its syntactic clarity. It’s basically executable pseudocode.Feedback would be highly appreciated! You can reach me at or louiedinh [at] [google’s email service]
Note: This article applies to Python 3 specifically. Check out if you want to learn the old Python 2.7
# Single line comments start with a number symbol.""" Multiline strings can be written using three "s, and are often used as comments"""###################################################### 1. Primitive Datatypes and Operators##################################################### You have numbers3 # => 3# Math is what you would expect1 + 1 # => 28 - 1 # => 710 * 2 # => 20# Except division which returns floats, real numbers, by default35 / 5 # => 7.0# Result of integer division truncated down both for positive and negative.5 // 3 # => 15.0 // 3.0 # => 1.0 # works on floats too-5 // 3 # => -2-5.0 // 3.0 # => -2.0# When you use a float, results are floats3 * 2.0 # => 6.0# Modulo operation7 % 3 # => 1# Exponentiation (x**y, x to the yth power)2**4 # => 16# Enforce precedence with parentheses(1 + 3) * 2 # => 8# Boolean values are primitives (Note: the capitalization)TrueFalse# negate with notnot True # => Falsenot False # => True# Boolean Operators# Note "and" and "or" are case-sensitiveTrue and False # => FalseFalse or True # => True# Note using Bool operators with ints0 and 2 # => 0-5 or 0 # => -50 == False # => True2 == True # => False1 == True # => True# Equality is ==1 == 1 # => True2 == 1 # => False# Inequality is !=1 != 1 # => False2 != 1 # => True# More comparisons1 < 10 # => True1 > 10 # => False2 <= 2 # => True2 >= 2 # => True# Comparisons can be chained!1 < 2 < 3 # => True2 < 3 < 2 # => False# (is vs. ==) is checks if two variables refer to the same object, but == checks# if the objects pointed to have the same values.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# 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!"# Strings can be added without using '+'"Hello " "world!" # => "Hello world!"# 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# .format can be used to format strings, like this:"{} can be {}".format("Strings", "interpolated") # => "Strings can be interpolated"# You can repeat the formatting arguments to save some typing."{0} be nimble, {0} be quick, {0} jump over the {1}".format("Jack", "candle stick")# => "Jack be nimble, Jack be quick, Jack jump over the candle stick"# You can use keywords if you don't want to count."{name} wants to eat {food}".format(name="Bob", food="lasagna") # => "Bob wants to eat lasagna"# If your Python 3 code also needs to run on Python 2.5 and below, you can also# still use the old style of formatting:"%s can be %s the %s way" % ("Strings", "interpolated", "old") # => "Strings can be interpolated the old way"# 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# None, 0, and empty strings/lists/dicts all evaluate to False.# All other values are Truebool(0) # => Falsebool("") # => Falsebool([]) # => Falsebool({}) # => False###################################################### 2. Variables and Collections##################################################### 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 character.print("Hello, World", end="!") # => Hello, World!# 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()# No need to declare variables before assigning to them.# 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# if can be used as an expression# Equivalent of C's '?:' ternary operator"yahoo!" if 3 > 2 else 2 # => "yahoo!"# Lists store sequencesli = []# You can start with a prefilled listother_li = [4, 5, 6]# 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.# 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# You can look at ranges with slice syntax.# (It's a closed/open range for you mathy types.)li[1:3] # => [2, 4]# Omit the beginningli[2:] # => [4, 3]# Omit the endli[:3] # => [1, 2, 4]# Select every second entryli[::2] # =>[1, 4]# Return a reversed copy of the listli[::-1] # => [3, 4, 2, 1]# Use any combination of these to make advanced slices# li[start:end:step]# 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 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# 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]# Check for existence in a list with "in"1 in li # => True# Examine the length with "len()"len(li) # => 6# Tuples are like lists but are immutable.tup = (1, 2, 3)tup[0] # => 1tup[0] = 3 # Raises a TypeError# 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(()) # => # 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# 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# Now look how easy it is to swap two valuese, d = d, e # d is now 5 and e is now 4# Dictionaries store mappingsempty_dict = {}# Here is a prefilled dictionaryfilled_dict = { "one": 1, "two": 2, "three": 3}# 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.# Look up values with []filled_dict["one"] # => 1# 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 - Dictionary key# ordering is not guaranteed. Your results might not match this exactly.list(filled_dict.keys()) # => ["three", "two", "one"]# 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]# Check for existence of keys in a dictionary with "in""one" in filled_dict # => True1 in filled_dict # => False# 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()" 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# 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# Remove keys from a dictionary with deldel filled_dict["one"] # Removes the key "one" from filled dict# From Python 3.5 you can also use the additional unpacking options{ 'a': 1, **{ 'b': 2}} # => {'a': 1, 'b': 2}{ 'a': 1, **{ 'a': 2}} # => {'a': 2}# 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}# 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}# Can set new variables to a setfilled_set = some_set# Add one more item to the setfilled_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 in2 in filled_set # => True10 in filled_set # => False###################################################### 3. Control Flow and Iterables##################################################### Let's just make a variablesome_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 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))""""range(number)" returns an iterable of numbersfrom zero to the given numberprints: 0 1 2 3"""for i in range(4): print(i)""""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)""""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)"""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# 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")# Instead of try/finally to cleanup resources you can use a with statementwith open("myfile.txt") as f: for line in f: print(line)# Python offers a fundamental abstraction called the Iterable.# An iterable is an object that can be treated as a sequence.# The object returned 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# 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 gives you a StopIterator Exceptionnext(our_iterator) # Raises StopIteration# You can grab all the elements of an iterator by calling list() on it.list(filled_dict.keys()) # => Returns ["one", "two", "three"]###################################################### 4. Functions##################################################### 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.# You can define functions that take a variable number of# positional argumentsdef varargs(*args): return argsvarargs(1, 2, 3) # => (1, 2, 3)# 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"}# 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}"""# 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 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)# 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.# 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 has first class functionsdef create_adder(x): def adder(y): return x + y return adderadd_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 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]# 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}###################################################### 5. Modules##################################################### You can import modulesimport mathprint(math.sqrt(16)) # => 4.0# 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 *# You can shorten module namesimport math as mmath.sqrt(16) == m.sqrt(16) # => 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 mathdir(math)# If you have a Python script named math.py in the same# folder as your current script, the file math.py will# be loaded instead of the built-in Python module.# This happens because the local folder has priority# over Python's built-in libraries.###################################################### 6. Classes##################################################### We use the "class" operator to get a classclass Human: # 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. Methods(or objects or attributes) like: __init__, __str__, # __repr__ etc. are called magic methods (or sometimes called dunder methods) # 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): 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 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# 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.if __name__ == '__main__': # Instantiate a class i = Human(name="Ian") i.say("hi") # "Ian: hi" j = Human("Joel") j.say("hello") # "Joel: hello" # i and j are instances of type Human, or in other words: they are Human objects # Call our class method 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" # Call the static method print(Human.grunt()) # => "*grunt*" print(i.grunt()) # => "*grunt*" # Update the property for this instance i.age = 42 # Get the property i.say(i.age) # => 42 j.say(j.age) # => 0 # Delete the property del i.age # i.age # => this would raise an AttributeError###################################################### 6.1 Multiple Inheritance##################################################### Another class definitionclass 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 def sonar(self): return '))) ... ((('if __name__ == '__main__': b = Bat() print(b.say('hello')) print(b.fly)# from "filename-without-extension" import "function-or-class"from human import Humanfrom bat import Bat# Batman inherits from both Human and Batclass Batman(Human, Bat): # Batman has its own value for the species class attribute species = 'Superhero' 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". Human.__init__(self, 'anonymous', *args, **kwargs) Bat.__init__(self, *args, can_fly=False, **kwargs) # override the value for the name attribute self.name = 'Sad Affleck' def sing(self): return 'nan nan nan nan nan batman!'if __name__ == '__main__': sup = Batman() # Instance type checks if isinstance(sup, Human): print('I am human') if isinstance(sup, Bat): print('I am bat') if type(sup) is Batman: print('I am Batman') # Get the Method Resolution search Order used by both getattr() and super(). # This attribute is dynamic and can be updated print(Batman.__mro__) # => ( , , , ) # Calls parent method but uses its own class attribute print(sup.get_species()) # => Superhero # Calls overloaded 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 print(sup.sonar()) # => ))) ... ((( # Inherited class attribute sup.age = 100 print(sup.age) # Inherited attribute from 2nd ancestor whose default value was overridden. print('Can I fly? ' + str(sup.fly))###################################################### 7. Advanced##################################################### 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# 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]# 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) 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 :(