learn python in y minutes-Learn Python in Y Minutes

# Single line comments start with a number symbol.

""" Multiline strings can be written

using three "s, and are often used

as documentation.

"""

####################################################

## 1. Primitive Datatypes and Operators

####################################################

# You have numbers

3 # => 3

# Math is what you would expect

1 + 1 # => 2

8 - 1 # => 7

10 * 2 # => 20

35 / 5 # => 7.0

# Integer division rounds down for both positive and negative numbers.

5 // 3 # => 1

-5 // 3 # => -2

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

-5.0 // 3.0 # => -2.0

# The result of division is always a float

10.0 / 3 # => 3.3333333333333335

# Modulo operation

7 % 3 # => 1

# i % j have the same sign as j, unlike C

-7 % 3 # => 2

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

2**3 # => 8

# Enforce precedence with parentheses

1 + 3 * 2 # => 7

(1 + 3) * 2 # => 8

# Boolean values are primitives (Note: the capitalization)

True # => True

False # => False

# 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

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

True + True # => 2

True * 8 # => 8

False - 5 # => -5

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

0 == False # => True

1 == True # => True

2 == True # => False

-5 != False # => True

# Using boolean logical operators on ints casts them to booleans for evaluation, but their non-cast value is returned

# Don"t mix up with bool(ints) and bitwise and/or (&,|)

bool(0) # => False

bool(4) # => True

bool(-6) # => True

0 and 2 # => 0

-5 or 0 # => -5

# 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

# 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

# (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 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

# Strings are created with " or "

"This is a string."

"This is also a string."

# Strings can be added too

"Hello " + "world!" # => "Hello world!"

# String literals (but not variables) can be concatenated without using "+"

"Hello " "world!" # => "Hello world!"

# A string can be treated like a list of characters

"Hello world!"[0] # => "H"

# You can find the length of a string

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

# 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 expression inside the braces and it will be output in the string.

f"{name} is {len(name)} characters long." # => "Reiko is 5 characters long."

# 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

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

# All other values are True

bool(0) # => False

bool("") # => False

bool([]) # => False

bool({}) # => False

bool(()) # => False

####################################################

## 2. Variables and Collections

####################################################

# 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!

# Simple way to get input data from console

input_string_var = input("Enter some data: ") # Returns the data as a string

# 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

# if can be used as an expression

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

"yay!" if 0 > 1 else "nay!" # => "nay!"

# Lists store sequences

li = []

# You can start with a prefilled list

other_li = [4, 5, 6]

# 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.

# 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

# 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]

# 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 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

# 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] # => 1

tup[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 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

# 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

# Dictionaries store mappings from keys to values

empty_dict = {}

# Here is a prefilled dictionary

filled_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 - 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+

# 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

# "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

# 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

# Remove keys from a dictionary with del

del 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 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}

# 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}

# 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}

# 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

# 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

####################################################

## 3. Control Flow and Iterables

####################################################

# 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.")

"""

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))

"""

"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)

"""

"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)

"""

"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)

"""

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 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

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)

# Writing to a file

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

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}

# 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

# 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

####################################################

## 4. Functions

####################################################

# 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.

# You can define functions that take a variable number of

# positional arguments

def varargs(*args):

return args

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

# 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"}

# 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 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)

# 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.

# 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

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]

# 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 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) # => 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

# are defined in a module.

import math

dir(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" statement to create a class

class 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 special 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.

# There"s no need to write trivial getters and setters in Python, though.

@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*"

# Static methods can be called by instances too

print(i.grunt()) # => "*grunt*"

# 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 # => this would raise an AttributeError

####################################################

## 6.1 Inheritance

####################################################

# Inheritance allows new child classes to be defined that inherit methods and

# variables from their parent class.

# Using the Human class defined above as the base or parent class, we can

# define a child class, Superhero, which inherits the class variables like

# "species", "name", and "age", as well as methods, like "sing" and "grunt"

# from the Human class, but can also have its own unique properties.

# To take advantage of modularization by file you could place the classes above in their own files,

# say, human.py

# To import functions from other files use the following format

# from "filename-without-extension" import "function-or-class"

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

# 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:

def __init__(self, name, movie=False,

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

# add additional class attributes:

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:

super().__init__(name)

# override the sing method

def sing(self):

return "Dun, dun, DUN!"

# add an additional instance method

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

if isinstance(sup, Human):

print("I am human")

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

print(Superhero.__mro__) # => (,

# => , )

# Calls parent method but uses its own class attribute

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

# Calls overridden method

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

# Calls method from Human

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

# Call method that exists only in Superhero

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))

####################################################

## 6.2 Multiple Inheritance

####################################################

# 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

def sonar(self):

return "))) ..