# Single line comments start with a number symbol. """ Multiline strings can be written using three "s, and are often used as documentation. """
Variables and Data Types
Variables
There are no declarations, only assignments. Convention is to use lower_case_with_underscores.
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some_var = 5
Data Types
Category
Type
Text Type
str
Numeric Types
int, float, complex
Sequence Types
list, tuple, range
Mapping Type
dict
Set Types
set, frozenset
Boolean Type
bool
Binary Types
bytes, bytearray, memoryview
None Type
NoneType
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> x = 5 > print(type(x)) <class'int'>
Example
Data Type
x = “Hello World”
str
x = 20
int
x = 20.5
float
x = 1j
complex
x = [“apple”, “banana”, “cherry”]
list
x = (“apple”, “banana”, “cherry”)
tuple
x = range(6)
range
x = {“name” : “John”, “age” : 36}
dict
x = {“apple”, “banana”, “cherry”}
set
x = frozenset({“apple”, “banana”, “cherry”})
frozenset
x = True
bool
x = b”Hello”
bytes
x = bytearray(5)
bytearray
x = memoryview(bytes(5))
memoryview
x = None
NoneType
String and Array
String
Strings are created with “ or ‘
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str1 = "This is a string." str2 = 'This is also a string.'
A string can be treated like a list of characters
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"Hello world!"[0] # => 'H'
Properties of Strings
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len("This is a string")
String concatenation
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"Hello " + "world!"
String formatting
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name = "Reiko" format_str = f"She said her name is {name}." format_str2 = f"{name} is {len(name)} characters long."
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format_str = "She said her name is {}.".format("Reiko") format_str = "She said her name is {name}.".format(name="Reiko")
Array
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li = [] other_li = [4, 5, 6]
Access
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# Access a list like you would any array li[0] # Look at the last element li[-1]
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# Examine the length with "len()" len(li)
Lookup
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# Check for existence in a list with "in" 1in li # => True
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# Get the index of the first item found matching the argument li.index("a")
Insert
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# Add stuff to the end of a list with append li.append(1) # Insert an element at a specific index li.insert(1, 2)
Update
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li[1] = 11
Remove
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# Remove from the end with pop li.pop() # Remove by index del li[2] # delete the 2th element # Remove by value li.remove(2) # Remove first occurrence of a value
Slice
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li[start:end:step]
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li[1:3] # Return list from index 1 to 3 li[2:] # Return list starting from index 2 li[:3] # Return list from beginning until index 3 li[::2] # Return list selecting every second entry li[::-1] # Return list in reverse order
one layer deep copy
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li2 = li[:]
Concatenate
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li + other_li li.extend(other_li)
Tuple
Tuples are like lists but are immutable. You can’t insert, update, remove elements.
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tup = (1, 2, 3) # Tuples are created by default if you leave out the parentheses tup2 = 11, 22, 33 tup[0] # => 1 tup[0] = 3# Raises a TypeError
Access
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tup[0]
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len(tup)
Lookup
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1in tup # => True
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li.index("a")
Slice
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tup[:2]
Concatenate
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tup + (4, 5, 6)
Unpack tuples (or lists) into variables
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a, b, c = (1, 2, 3) d, e, f = 4, 5, 6 # swap two values e, d = d, e
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.
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invalid_dict = {[1,2,3]: "123"} # => Yield a TypeError: unhashable type: 'list' valid_dict = {(1,2,3):[1,2,3]} # Values can be of any type, however.
Access
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filled_dict["one"] # Looking up a non-existing key is a KeyError filled_dict["four"] # KeyError # Use "get()" method to avoid the KeyError filled_dict.get("one") # The get method supports a default argument when the value is missing filled_dict.get("one", 4)
Put
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# 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
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# "setdefault()" inserts into a dictionary only if the given key isn't present filled_dict.update({"four":4}) # => {"one": 1, "two": 2, "three": 3, "four": 4} filled_dict["four"] = 4# another way to add to dict
Delete
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# Remove keys from a dictionary with del del filled_dict["one"] # Removes the key "one" from filled dict
Get all keys as an iterable with “keys()”. We need to wrap the call in list() to turn it into a list. 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.
Set
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empty_set = set() # Initialize a set with a bunch of values. some_set = {1, 1, 2, 2, 3, 4} # some_set is now {1, 2, 3, 4}
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# 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}
Insert
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my_set.add(5)
Delete
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my_set.remove(1)
Lookup
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2in filled_set
Intersection/union/difference/subset
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filled_set = {1, 2, 3, 4, 5} other_set = {3, 4, 5, 6} # Do set intersection with & 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
Copy
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# 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
Expressions
Arithmetic Operators
+: add
-: subtract
*: multiply
/: divide
//: integer division rounds down
%: modulo
**: exponentiation
Logical Operators
and
or
not
Note “and” and “or” are case-sensitive
Comparison operators
==, !=, >, <, >=, <=
Statements
Simple statements
Assignment
Call
return
Control Flow Statements
If Conditions
if…else
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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.")
case/switch
For loop
for
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for animal in ["dog", "cat", "mouse"]: print("{} is a mammal".format(animal)) for i, value inenumerate(["dog", "cat", "mouse"]): print(i, value)
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# "range(number)" returns an iterable of numbers from zero up to (but excluding) the given number for i inrange(4): print(i) # "range(lower, upper)" returns an iterable of numbers from the lower number to the upper number for i inrange(4, 8): print(i) # "range(lower, upper, step)" for i inrange(4, 8, 2): print(i)
while
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x = 0 while x < 4: print(x) x += 1
do…while
Exception handling
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# 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# Refrain from this, provide a recovery (next example). except (TypeError, NameError): pass# Multiple exceptions can be processed jointly. 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")
Functions
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defadd(x, y): print("x is {} and y is {}".format(x, y)) return x + y
add(5, 6)
# Another way to call functions is with keyword arguments add(y=6, x=5) # Keyword arguments can arrive in any order.
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# You can define functions that take a variable number of positional arguments defvarargs(*args): return args
varargs(1, 2, 3)
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# You can define functions that take a variable number of keyword arguments, as well defkeyword_args(**kwargs): return kwargs
defset_global_x(num): # global indicates that particular var lives in the global scope global x print(x) # => 5 x = num # global var x is now set to 6 print(x)
Nested function
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defcreate_adder(x): defadder(y): return x + y return adder
add_10 = create_adder(10) add_10(3) # => 13
Anonymous functions
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# There are also anonymous functions (lambda x: x > 2)(3) # => True (lambda x, y: x ** 2 + y ** 2)(2, 1) # => 5
Modules
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.
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.
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# You can import modules import math print(math.sqrt(16)) # => 4.0
# 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)
Classes
Classes
Class members
attribute
class attribute (set by class_name.class_attribute = value)
instance attribute (initialized by initializer)
instance properties (Properties are special kind of attributes which have getter, setter and delete methods like get, set and delete methods.)
Methods
initializer
instance method (called by instances)
class method (called by instances)
static method (called by class_name.static_method())
getter
setter
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.
# We use the "class" statement to create a class classHuman:
# A class attribute. It is shared by all instances of this class species = "H. sapiens" # Basic initializer 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 defsay(self, msg): print("{name}: {message}".format(name=self.name, message=msg))
# Another instance method defsing(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 defget_species(cls): return cls.species
# A static method is called without a class or instance reference @staticmethod defgrunt(): return"*grunt*" # A property is just like a getter. @property defage(self): return self._age # This allows the property to be set @age.setter defage(self, age): self._age = age # This allows the property to be deleted @age.deleter defage(self): del self._age
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# Instantiate a class i = Human(name="Ian") # Call instance method i.say("hi") # "Ian: hi"
j = Human("Joel") j.say("hello")
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# Call our class method i.say(i.get_species()) # "Ian: H. sapiens" # Change the class attribute (shared attribute) Human.species = "H. neanderthalensis" i.say(i.get_species()) # => "Ian: H. neanderthalensis" j.say(j.get_species()) # => "Joel: H. neanderthalensis"
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# Call the static method print(Human.grunt()) # => "*grunt*"
# Static methods can be called by instances too print(i.grunt())
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# Update the property for this instance 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
Inheritance
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# Define Batman as a child that inherits from both Superhero and Bat classBatman(Superhero, Bat):
Standard Library
I/O Streams and Files
Read
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# Instead of try/finally to cleanup resources you can use a with statement withopen("myfile.txt") as f: for line in f: print(line)
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# Reading from a file withopen('myfile1.txt', "r+") as file: contents = file.read() # reads a string from a file print(contents)
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withopen('myfile2.txt', "r+") as file: contents = json.load(file) # reads a json object from a file print(contents)
Write
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# Writing to a file contents = {"aa": 12, "bb": 21} withopen("myfile1.txt", "w+") as file: file.write(str(contents))