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Python Cheat Sheet

Python Basics

Math Operators

From Highest to Lowest precedence:

Examples of expressions in the interactive shell:

2 + 3 * 6

(2 + 3) * 6

2 ** 8

23 // 7

23 % 7

(5 - 1) * ((7 + 1) / (3 - 1))

Data Types

String Concatenation and Replication

String concatenation:

'Alice' 'Bob'

Note: Avoid + operator for string concatenation. Prefer string formatting.

String Replication:

'Alice' * 5

Variables

You can name a variable anything as long as it obeys the following three rules:

1. It can be only one word.
2. It can use only letters, numbers, and the underscore (_) character.
3. It can't begin with a number.
4. Variable name starting with an underscore (_) are considered as "unuseful`.

Example:

spam = 'Hello'

_spam = 'Hello'

_spam should not be used again in the code.

Comments

Inline comment:

# This is a comment

Multiline comment:

# This is a# multiline comment

Code with a comment:

a = 1  # initialization

Please note the two spaces in front of the comment.

Function docstring:

def foo():"""
    This is a function docstring
    You can also use:
    ''' Function Docstring '''
    """

The print Function

print('Hello world!')

a = 1print('Hello world!', a)

The input Function

Example Code:

print('What is your name?')   # ask for their name
myName = input()print('It is good to meet you, {}'.format(myName))

The len Function

Evaluates to the integer value of the number of characters in a string:

len('hello')

Note: test of emptiness of strings, lists, dictionary, etc, should not use len, but prefer direct boolean evaluation.

a = [1, 2, 3]if a:print("the list is not empty!")

The str, int, and float Functions

Integer to String or Float:

str(29)

print('I am {} years old.'.format(str(29)))

str(-3.14)

Float to Integer:

int(7.7)

int(7.7) + 1

Flow Control

Comparison Operators

These operators evaluate to True or False depending on the values you give them.

Examples:

42 == 42

40 == 42

'hello' == 'hello'

'hello' == 'Hello'

'dog' != 'cat'

42 == 42.0

42 == '42'

Boolean evaluation

Never use == or != operator to evaluate boolean operation. Use the is or is not operators, or use implicit boolean evaluation.

NO (even if they are valid Python):

True == True

True != False

YES (even if they are valid Python):

True is True

True is not False

These statements are equivalent:

if a is True:passif a is not False:passif a:pass

And these as well:

if a is False:passif a is not True:passif not a:pass

Boolean Operators

There are three Boolean operators: and, or, and not.

The and Operator's Truth Table:

The or Operator's Truth Table:

The not Operator's Truth Table:

Mixing Boolean and Comparison Operators

(4 < 5) and (5 < 6)

(4 < 5) and (9 < 6)

(1 == 2) or (2 == 2)

You can also use multiple Boolean operators in an expression, along with the comparison operators:

2 + 2 == 4 and not 2 + 2 == 5 and 2 * 2 == 2 + 2

if Statements

if name == 'Alice':print('Hi, Alice.')

else Statements

name = 'Bob'if name == 'Alice':print('Hi, Alice.')else:print('Hello, stranger.')

elif Statements

name = 'Bob'
age = 5if name == 'Alice':print('Hi, Alice.')elif age < 12:print('You are not Alice, kiddo.')

name = 'Bob'
age = 30if name == 'Alice':print('Hi, Alice.')elif age < 12:print('You are not Alice, kiddo.')else:print('You are neither Alice nor a little kid.')

while Loop Statements

spam = 0while spam < 5:print('Hello, world.')
    spam = spam + 1

break Statements

If the execution reaches a break statement, it immediately exits the while loop's clause:

while True:print('Please type your name.')
    name = input()if name == 'your name':breakprint('Thank you!')

continue Statements

When the program execution reaches a continue statement, the program execution immediately jumps back to the start of the loop.

while True:print('Who are you?')
    name = input()if name != 'Joe':continueprint('Hello, Joe. What is the password? (It is a fish.)')
    password = input()if password == 'swordfish':breakprint('Access granted.')

for Loops and the range() Function

print('My name is')for i in range(5):print('Jimmy Five Times ({})'.format(str(i)))

The range() function can also be called with three arguments. The first two arguments will be the start and stop values, and the third will be the step argument. The step is the amount that the variable is increased by after each iteration.

for i in range(0, 10, 2):print(i)

You can even use a negative number for the step argument to make the for loop count down instead of up.

for i in range(5, -1, -1):print(i)

For else statement

This allows to specify a statement to execute in case of the full loop has been executed. Only useful when a break condition can occur in the loop:

for i in [1, 2, 3, 4, 5]:if i == 3:breakelse:print("only executed when no item of the list is equal to 3")

Importing Modules

import random

for i in range(5):print(random.randint(1, 10))

import random, sys, os, math

from random import *.

Ending a Program with sys.exit

import sys

while True:print('Type exit to exit.')
    response = input()if response == 'exit':
        sys.exit()print('You typed {}.'.format(response))

Functions

def hello(name):print('Hello {}'.format(name))

Return Values and return Statements

When creating a function using the def statement, you can specify what the return value should be with a return statement. A return statement consists of the following:

• The return keyword.
• -
• The value or expression that the function should return.

import random
def getAnswer(answerNumber):if answerNumber == 1:return 'It is certain'elif answerNumber == 2:return 'It is decidedly so'elif answerNumber == 3:return 'Yes'elif answerNumber == 4:return 'Reply hazy try again'elif answerNumber == 5:return 'Ask again later'elif answerNumber == 6:return 'Concentrate and ask again'elif answerNumber == 7:return 'My reply is no'elif answerNumber == 8:return 'Outlook not so good'elif answerNumber == 9:return 'Very doubtful'

r = random.randint(1, 9)
fortune = getAnswer(r)print(fortune)

The None Value

spam = print('Hello!')
spam is None

Note: never compare to None with the == operator. Always use is.

print Keyword Arguments

print('Hello', end='')print('World')

print('cats', 'dogs', 'mice')

print('cats', 'dogs', 'mice', sep=',')

Local and Global Scope

• Code in the global scope cannot use any local variables.
• -
• However, a local scope can access global variables.
• -
• Code in a function's local scope cannot use variables in any other local scope.
• You can use the same name for different variables if they are in different scopes. That is, there can be a local variable named spam and a global variable also named spam.

The global Statement

If you need to modify a global variable from within a function, use the global statement:

def spam():global eggs
    eggs = 'spam'

eggs = 'global'
spam()print(eggs)

There are four rules to tell whether a variable is in a local scope or global scope:

1. If a variable is being used in the global scope (that is, outside of all functions), then it is always a global variable.
2. If there is a global statement for that variable in a function, it is a global variable.
3. Otherwise, if the variable is used in an assignment statement in the function, it is a local variable.
4. But if the variable is not used in an assignment statement, it is a global variable.

Exception Handling

Basic exception handling

def spam(divideBy):try:return 42 / divideBy
    except ZeroDivisionError as e:print('Error: Invalid argument: {}'.format(e))print(spam(2))print(spam(12))print(spam(0))print(spam(1))

Final code in exception handling

Code inside the finally section is always executed, no matter if an exception has been raised or not, and even if an exception is not caught.

def spam(divideBy):try:return 42 / divideBy
    except ZeroDivisionError as e:print('Error: Invalid argument: {}'.format(e))finally:print("-- division finished --")print(spam(12))print(spam(0))

Lists

['cat', 'bat', 'rat', 'elephant']

Getting Individual Values in a List with Indexes

spam = ['cat', 'bat', 'rat', 'elephant']
spam[0]

spam[1]

spam[2]

spam[3]

Negative Indexes

spam = ['cat', 'bat', 'rat', 'elephant']
spam[-1]

spam[-3]

'The {} is afraid of the {}.'.format(spam[-1], spam[-3])

Getting Sublists with Slices

spam = ['cat', 'bat', 'rat', 'elephant']
spam[0:4]

spam[1:3]

spam[0:-1]

spam = ['cat', 'bat', 'rat', 'elephant']
spam[:2]

spam[1:]

spam[:]

Getting a list Length with len

spam = ['cat', 'dog', 'moose']len(spam)

Changing Values in a List with Indexes

spam = ['cat', 'bat', 'rat', 'elephant']
spam[1] = 'aardvark'
spam

spam[2] = spam[1]
spam

spam[-1] = 12345
spam

List Concatenation and List Replication

[1, 2, 3] + ['A', 'B', 'C']

['X', 'Y', 'Z'] * 3

spam = [1, 2, 3]
spam = spam + ['A', 'B', 'C']
spam

Removing Values from Lists with del Statements

spam = ['cat', 'bat', 'rat', 'elephant']del spam[2]
spam

del spam[2]
spam

Using for Loops with Lists

supplies = ['pens', 'staplers', 'flame-throwers', 'binders']for i, supply in enumerate(supplies):print('Index {} in supplies is: {}'.format(str(i), supply))

Looping Through Multiple Lists with zip

name = ['Pete', 'John', 'Elizabeth']
age = [6, 23, 44]for n, a in zip(name, age):print('{} is {} years old'.format(n, a))

The in and not in Operators

'howdy' in ['hello', 'hi', 'howdy', 'heyas']

spam = ['hello', 'hi', 'howdy', 'heyas']False

'howdy' not in spam

'cat' not in spam

The Multiple Assignment Trick

The multiple assignment trick is a shortcut that lets you assign multiple variables with the values in a list in one line of code. So instead of doing this:

cat = ['fat', 'orange', 'loud']
size = cat[0]
color = cat[1]
disposition = cat[2]

You could type this line of code:

cat = ['fat', 'orange', 'loud']
size, color, disposition = cat

The multiple assignment trick can also be used to swap the values in two variables:

a, b = 'Alice', 'Bob'
a, b = b, a
print(a)

print(b)

Augmented Assignment Operators

Examples:

spam = 'Hello'
spam += ' world!'
spam

bacon = ['Zophie']
bacon *= 3
bacon

Finding a Value in a List with the index Method

spam = ['Zophie', 'Pooka', 'Fat-tail', 'Pooka']
spam.index('Pooka')

Adding Values to Lists with append and insert

append():

spam = ['cat', 'dog', 'bat']
spam.append('moose')
spam

insert():

spam = ['cat', 'dog', 'bat']
spam.insert(1, 'chicken')
spam

Removing Values from Lists with remove

spam = ['cat', 'bat', 'rat', 'elephant']
spam.remove('bat')
spam

If the value appears multiple times in the list, only the first instance of the value will be removed.

Sorting the Values in a List with sort

spam = [2, 5, 3.14, 1, -7]
spam.sort()
spam

spam = ['ants', 'cats', 'dogs', 'badgers', 'elephants']
spam.sort()
spam

You can also pass True for the reverse keyword argument to have sort() sort the values in reverse order:

spam.sort(reverse=True)
spam

If you need to sort the values in regular alphabetical order, pass str. lower for the key keyword argument in the sort() method call:

spam = ['a', 'z', 'A', 'Z']
spam.sort(key=str.lower)
spam

You can use the built-in function sorted to return a new list:

spam = ['ants', 'cats', 'dogs', 'badgers', 'elephants']sorted(spam)

Tuple Data Type

eggs = ('hello', 42, 0.5)
eggs[0]

eggs[1:3]

len(eggs)

The main way that tuples are different from lists is that tuples, like strings, are immutable.

Converting Types with the list and tuple Functions

tuple(['cat', 'dog', 5])

list(('cat', 'dog', 5))

list('hello')

Dictionaries and Structuring Data

Example Dictionary:

myCat = {'size': 'fat', 'color': 'gray', 'disposition': 'loud'}

The keys, values, and items Methods

values():

spam = {'color': 'red', 'age': 42}for v in spam.values():print(v)

keys():

for k in spam.keys():print(k)

items():

for i in spam.items():print(i)

Using the keys(), values(), and items() methods, a for loop can iterate over the keys, values, or key-value pairs in a dictionary, respectively.

spam = {'color': 'red', 'age': 42}for k, v in spam.items():print('Key: {} Value: {}'.format(k, str(v)))

Checking if a Key or Value Exists in a Dictionary

spam = {'name': 'Zophie', 'age': 7}'name' in spam.keys()

'Zophie' in spam.values()

# You can omit the call to keys() when checking for a key'color' in spam

'color' not in spam

'color' in spam

The get Method

picnic_items = {'apples': 5, 'cups': 2}'I am bringing {} cups.'.format(str(picnic_items.get('cups', 0)))

'I am bringing {} eggs.'.format(str(picnic_items.get('eggs', 0)))

The setdefault Method

Let's consider this code:

spam = {'name': 'Pooka', 'age': 5}if 'color' not in spam:
    spam['color'] = 'black'

Using setdefault we could make the same code more shortly:

spam = {'name': 'Pooka', 'age': 5}
spam.setdefault('color', 'black')

spam

spam.setdefault('color', 'white')

spam

Pretty Printing

import pprint

message = 'It was a bright cold day in April, and the clocks were striking thirteen.'
count = {}for character in message:
    count.setdefault(character, 0)
    count[character] = count[character] + 1

pprint.pprint(count)

Merge two dictionaries

# in Python 3.5+:
x = {'a': 1, 'b': 2}
y = {'b': 3, 'c': 4}
z = {**x, **y}
z

sets

From the Python 3 documentation

A set is an unordered collection with no duplicate elements. Basic uses include membership testing and eliminating duplicate entries. Set objects also support mathematical operations like union, intersection, difference, and symmetric difference.

Initializing a set

There are two ways to create sets: using curly braces {} and the bult-in function set()

s = {1, 2, 3}
s = set([1, 2, 3])

When creating an empty set, be sure to not use the curly braces {} or you will get an empty dictionary instead.

s = {}type(s)

sets: unordered collections of unique elements

A set automatically remove all the duplicate values.

s = {1, 2, 3, 2, 3, 4}
s

And as an unordered data type, they can't be indexed.

s = {1, 2, 3}
s(0)

set add and update

Using the add() method we can add a single element to the set.

s = {1, 2, 3}
s.add(4)
s

And with update(), multiple ones .

s = {1, 2, 3}
s.update([2, 3, 4, 5, 6])
s  # remember, sets automatically remove duplicates

set remove and discard

Both methods will remove an element from the set, but remove() will raise a key error if the value doesn't exist.

s = {1, 2, 3}
s.remove(3)
s

s.remove(3)

discard() won't raise any errors.

s = {1, 2, 3}
s.discard(3)
s

s.discard(3)

set union

union() or | will create a new set that contains all the elements from the sets provided.

s1 = {1, 2, 3}
s2 = {3, 4, 5}
s1.union(s2)  # or 's1 | s2'

set intersection

intersection or & will return a set containing only the elements that are common to all of them.

s1 = {1, 2, 3}
s2 = {2, 3, 4}
s3 = {3, 4, 5}
s1.intersection(s2, s3)  # or 's1 & s2 & s3'

set difference

difference or - will return only the elements that are in one of the sets.

s1 = {1, 2, 3}
s2 = {2, 3, 4}
s1.difference(s2)  # or 's1 - s2'

set symetric_difference

symetric_difference or ^ will return all the elements that are not common between them.

s1 = {1, 2, 3}
s2 = {2, 3, 4}
s1.symmetric_difference(s2)  # or 's1 ^ s2'

itertools Module

The itertools module is a collection of tools intented to be fast and use memory efficiently when handling iterators (like lists or dictionaries).

From the official Python 3.x documentation:

The module standardizes a core set of fast, memory efficient tools that are useful by themselves or in combination. Together, they form an "iterator algebra" making it possible to construct specialized tools succinctly and efficiently in pure Python.

The itertools module comes in the standard library and must be imported.

The operator module will also be used. This module is not necessary when using itertools, but needed for some of the examples below.

import itertools
import operator

accumulate

Makes an iterator that returns the results of a function.

itertools.accumulate(iterable[, func])

Example:

data = [1, 2, 3, 4, 5]
result = itertools.accumulate(data, operator.mul)for each in result:print(each)

The operator.mul takes two numbers and multiplies them:

operator.mul(1, 2)

operator.mul(2, 3)

operator.mul(6, 4)

operator.mul(24, 5)

Passing a function is optional:

data = [5, 2, 6, 4, 5, 9, 1]
result = itertools.accumulate(data)for each in result:print(each)

If no function is designated the items will be summed:

55 + 2 = 77 + 6 = 1313 + 4 = 1717 + 5 = 2222 + 9 = 3131 + 1 = 32

combinations

Takes an iterable and a integer. This will create all the unique combination that have r members.

itertools.combinations(iterable, r)

Example:

shapes = ['circle', 'triangle', 'square',]
result = itertools.combinations(shapes, 2)for each in result:print(each)

combinationswithreplacement

Just like combinations(), but allows individual elements to be repeated more than once.

itertools.combinations_with_replacement(iterable, r)

Example:

shapes = ['circle', 'triangle', 'square']
result = itertools.combinations_with_replacement(shapes, 2)for each in result:print(each)

count

Makes an iterator that returns evenly spaced values starting with number start.

itertools.count(start=0, step=1)

Example:

for i in itertools.count(10,3):print(i)if i > 20:break

cycle

This function cycles through an iterator endlessly.

itertools.cycle(iterable)

Example:

colors = ['red', 'orange', 'yellow', 'green', 'blue', 'violet']for color in itertools.cycle(colors):print(color)

When reached the end of the iterable it start over again from the beginning.

chain

Take a series of iterables and return them as one long iterable.

itertools.chain(*iterables)

Example:

colors = ['red', 'orange', 'yellow', 'green', 'blue']
shapes = ['circle', 'triangle', 'square', 'pentagon']
result = itertools.chain(colors, shapes)for each in result:print(each)

compress

Filters one iterable with another.

itertools.compress(data, selectors)

Example:

shapes = ['circle', 'triangle', 'square', 'pentagon']
selections = [True, False, True, False]
result = itertools.compress(shapes, selections)for each in result:print(each)

dropwhile

Make an iterator that drops elements from the iterable as long as the predicate is true; afterwards, returns every element.

itertools.dropwhile(predicate, iterable)

Example:

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1]
result = itertools.dropwhile(lambda x: x<5, data)for each in result:print(each)

filterfalse

Makes an iterator that filters elements from iterable returning only those for which the predicate is False.

itertools.filterfalse(predicate, iterable)

Example:

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
result = itertools.filterfalse(lambda x: x<5, data)for each in result:print(each)

groupby

Simply put, this function groups things together.

itertools.groupby(iterable, key=None)

Example:

robots = [{'name': 'blaster','faction': 'autobot'}, {'name': 'galvatron','faction': 'decepticon'}, {'name': 'jazz','faction': 'autobot'}, {'name': 'metroplex','faction': 'autobot'}, {'name': 'megatron','faction': 'decepticon'}, {'name': 'starcream','faction': 'decepticon'}]for key, group in itertools.groupby(robots, key=lambda x: x['faction']):print(key)print(list(group))

islice

This function is very much like slices. This allows you to cut out a piece of an iterable.

itertools.islice(iterable, start, stop[, step])

Example:

colors = ['red', 'orange', 'yellow', 'green', 'blue',]
few_colors = itertools.islice(colors, 2)for each in few_colors:print(each)

permutations

itertools.permutations(iterable, r=None)

Example:

alpha_data = ['a', 'b', 'c']
result = itertools.permutations(alpha_data)for each in result:print(each)

product

Creates the cartesian products from a series of iterables.

num_data = [1, 2, 3]
alpha_data = ['a', 'b', 'c']
result = itertools.product(num_data, alpha_data)for each in result:print(each)

repeat

This function will repeat an object over and over again. Unless, there is a times argument.

itertools.repeat(object[, times])

Example:

for i in itertools.repeat("spam", 3):print(i)

starmap

Makes an iterator that computes the function using arguments obtained from the iterable.

itertools.starmap(function, iterable)

Example:

data = [(2, 6), (8, 4), (7, 3)]
result = itertools.starmap(operator.mul, data)for each in result:print(each)

takewhile

The opposite of dropwhile(). Makes an iterator and returns elements from the iterable as long as the predicate is true.

itertools.takewhile(predicate, iterable)

Example:

data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 1]
result = itertools.takewhile(lambda x: x<5, data)for each in result:print(each)

tee

Return n independent iterators from a single iterable.

itertools.tee(iterable, n=2)

Example:

colors = ['red', 'orange', 'yellow', 'green', 'blue']
alpha_colors, beta_colors = itertools.tee(colors)for each in alpha_colors:print(each)

colors = ['red', 'orange', 'yellow', 'green', 'blue']
alpha_colors, beta_colors = itertools.tee(colors)for each in beta_colors:print(each)

zip_longest

Makes an iterator that aggregates elements from each of the iterables. If the iterables are of uneven length, missing values are filled-in with fillvalue. Iteration continues until the longest iterable is exhausted.

itertools.zip_longest(*iterables, fillvalue=None)

Example:

colors = ['red', 'orange', 'yellow', 'green', 'blue',]
data = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10,]for each in itertools.zip_longest(colors, data, fillvalue=None):print(each)

Comprehensions

List comprehension

a = [1, 3, 5, 7, 9, 11][i - 1 for i in a]

Set comprehension

b = {"abc", "def"}{s.upper() for s in b}

Dict comprehension

c = {'name': 'Pooka', 'age': 5}{v, k for k, v in c.items()}

A List comprehension can be generated from a dictionary:

c = {'name': 'Pooka', 'first_name': 'Oooka'}["{}:{}".format(k.upper(), v.upper()) for k, v in c.items()]

Manipulating Strings

Escape Characters

Example:

print("Hello there!\nHow are you?\nI\'m doing fine.")
Hello there!
How are you?

Raw Strings

A raw string completely ignores all escape characters and prints any backslash that appears in the string.

print(r'That is Carol\'s cat.')

Note: mostly used for regular expression definition (see re package)

Multiline Strings with Triple Quotes

print('''Dear Alice,

Eve's cat has been arrested for catnapping, cat burglary, and extortion.

Sincerely,
Bob''')

To keep a nicer flow in your code, you can use the dedent function from the textwrap standard package.

from textwrap import dedent

def my_function():print('''
        Dear Alice,

        Eve's cat has been arrested for catnapping, cat burglary, and extortion.

        Sincerely,
        Bob
        ''').strip()

This generates the same string than before.

Indexing and Slicing Strings

H   e   l   l   o       w   o   r   l   d    !
0   1   2   3   4   5   6   7   8   9   10   11

spam = 'Hello world!'
spam[0]

spam[4]

spam[-1]

Slicing:

spam[0:5]

spam[:5]

spam[6:]

spam[6:-1]

spam[:-1]

spam[::-1]

spam = 'Hello world!'
fizz = spam[0:5]
fizz

The in and not in Operators with Strings

'Hello' in 'Hello World'

'Hello' in 'Hello'

'HELLO' in 'Hello World'

'' in 'spam'

'cats' not in 'cats and dogs'

The in and not in Operators with list

a = [1, 2, 3, 4]5 in a

2 in a

The upper, lower, isupper, and islower String Methods

upper() and lower():

spam = 'Hello world!'
spam = spam.upper()
spam

spam = spam.lower()
spam

isupper() and islower():

spam = 'Hello world!'
spam.islower()

spam.isupper()

'HELLO'.isupper()

'abc12345'.islower()

'12345'.islower()

'12345'.isupper()

The isX String Methods

• isalpha() returns True if the string consists only of letters and is not blank.
• isalnum() returns True if the string consists only of lettersand numbers and is not blank.
• isdecimal() returns True if the string consists only ofnumeric characters and is not blank.
• isspace() returns True if the string consists only of spaces,tabs, and new-lines and is not blank.
• istitle() returns True if the string consists only of wordsthat begin with an uppercase letter followed by onlylowercase letters.

The startswith and endswith String Methods

'Hello world!'.startswith('Hello')

'Hello world!'.endswith('world!')

'abc123'.startswith('abcdef')

'abc123'.endswith('12')

'Hello world!'.startswith('Hello world!')

'Hello world!'.endswith('Hello world!')

The join and split String Methods

join():

', '.join(['cats', 'rats', 'bats'])

' '.join(['My', 'name', 'is', 'Simon'])

'ABC'.join(['My', 'name', 'is', 'Simon'])

split():

'My name is Simon'.split()

'MyABCnameABCisABCSimon'.split('ABC')

'My name is Simon'.split('m')

Justifying Text with rjust, ljust, and center

rjust() and ljust():

'Hello'.rjust(10)

'Hello'.rjust(20)

'Hello World'.rjust(20)

'Hello'.ljust(10)

An optional second argument to rjust() and ljust() will specify a fill character other than a space character. Enter the following into the interactive shell:

'Hello'.rjust(20, '*')

'Hello'.ljust(20, '-')

center():

'Hello'.center(20)

'Hello'.center(20, '=')

Removing Whitespace with strip, rstrip, and lstrip

spam = '    Hello World     '
spam.strip()

spam.lstrip()

spam.rstrip()

spam = 'SpamSpamBaconSpamEggsSpamSpam'
spam.strip('ampS')

Copying and Pasting Strings with the pyperclip Module

First, install pypeerclip with pip:

pip install pyperclip

import pyperclip

pyperclip.copy('Hello world!')
pyperclip.paste()

String Formatting

% operator

name = 'Pete''Hello %s' % name

We can use the %x format specifier to convert an int value to a string:

num = 5'I have %x apples' % num

Note: For new code, using str.format or f-strings is strongly recommended over the % operator.

str.format

Python 3 introduced a new way to do string formatting that was later back-ported to Python 2.7. This makes the syntax for string formatting more regular.

name = 'John'
age = 20'

"Hello I'm {}, my age is {}".format(name, age)

"Hello I'm {0}, my age is {1}".format(name, age)

The official Python 3.x documentation recommend str.format over the % operator:

The formatting operations described here exhibit a variety of quirks that lead to a number of common errors (such as failing to display tuples and dictionaries correctly). Using the newer formatted string literals or the str.format() interface helps avoid these errors. These alternatives also provide more powerful, flexible and extensible approaches to formatting text.

Lazy string formatting

You would only use %s string formatting on functions that can do lazy parameters evaluation, the most common being logging:

Prefer:

name = "alice"
logging.debug("User name: %s", name)

Over:

logging.debug("User name: {}".format(name))

Or:

logging.debug("User name: " + name)

Formatted String Literals or f-strings

Python 3.6+

name = 'Elizabeth'f'Hello {name}!'

It is even possible to do inline arithmetic with it:

a = 5
b = 10f'Five plus ten is {a + b} and not {2 * (a + b)}.'

Template Strings

A simpler and less powerful mechanism, but it is recommended when handling format strings generated by users. Due to their reduced complexity template strings are a safer choice.

from string import Template

name = 'Elizabeth'
t = Template('Hey $name!')
t.substitute(name=name)

Regular Expressions

1. Import the regex module with import re.
2. Create a Regex object with the re.compile() function. (Remember to use a raw string.)
3. Pass the string you want to search into the Regex object's search() method. This returns a Match object.
4. Call the Match object's group() method to return a string of the actual matched text.

All the regex functions in Python are in the re module:

import re

Matching Regex Objects

phone_num_regex = re.compile(r'\d\d\d-\d\d\d-\d\d\d\d')
mo = phone_num_regex.search('My number is 415-555-4242.')print('Phone number found: {}'.format(mo.group()))

Grouping with Parentheses

phone_num_regex = re.compile(r'(\d\d\d)-(\d\d\d-\d\d\d\d)')
mo = phone_num_regex.search('My number is 415-555-4242.')
mo.group(1)

mo.group(2)

mo.group(0)

mo.group()

To retrieve all the groups at once: use the groups() method—note the plural form for the name.

mo.groups()
area_code, main_number = mo.groups()

print(area_code)

print(main_number)

Matching Multiple Groups with the Pipe

The | character is called a pipe. You can use it anywhere you want to match one of many expressions. For example, the regular expression r'Batman|Tina Fey' will match either 'Batman' or 'Tina Fey'.

hero_regex = re.compile (r'Batman|Tina Fey')
mo1 = hero_regex.search('Batman and Tina Fey.')
mo1.group()

mo2 = hero_regex.search('Tina Fey and Batman.')
mo2.group()

You can also use the pipe to match one of several patterns as part of your regex:

bat_regex = re.compile(r'Bat(man|mobile|copter|bat)')
mo = bat_regex.search('Batmobile lost a wheel')
mo.group()

mo.group(1)

Optional Matching with the Question Mark

The ? character flags the group that precedes it as an optional part of the pattern.

bat_regex = re.compile(r'Bat(wo)?man')
mo1 = bat_regex.search('The Adventures of Batman')
mo1.group()

mo2 = bat_regex.search('The Adventures of Batwoman')
mo2.group()

Matching Zero or More with the Star

The * (called the star or asterisk) means "match zero or more"—the group that precedes the star can occur any number of times in the text.

bat_regex = re.compile(r'Bat(wo)*man')
mo1 = bat_regex.search('The Adventures of Batman')
mo1.group()

mo2 = bat_regex.search('The Adventures of Batwoman')
mo2.group()

mo3 = bat_regex.search('The Adventures of Batwowowowoman')
mo3.group()

Matching One or More with the Plus

While * means "match zero or more," the + (or plus) means "match one or more". The group preceding a plus must appear at least once. It is not optional:

bat_regex = re.compile(r'Bat(wo)+man')
mo1 = bat_regex.search('The Adventures of Batwoman')
mo1.group()

mo2 = bat_regex.search('The Adventures of Batwowowowoman')
mo2.group()

mo3 = bat_regex.search('The Adventures of Batman')
mo3 is None

Matching Specific Repetitions with Curly Brackets

If you have a group that you want to repeat a specific number of times, follow the group in your regex with a number in curly brackets. For example, the regex (Ha){3} will match the string 'HaHaHa', but it will not match 'HaHa', since the latter has only two repeats of the (Ha) group.

Instead of one number, you can specify a range by writing a minimum, a comma, and a maximum in between the curly brackets. For example, the regex (Ha){3,5} will match 'HaHaHa', 'HaHaHaHa', and 'HaHaHaHaHa'.

ha_regex = re.compile(r'(Ha){3}')
mo1 = ha_regex.search('HaHaHa')
mo1.group()

mo2 = ha_regex.search('Ha')
mo2 is None

Greedy and Nongreedy Matching

Python's regular expressions are greedy by default, which means that in ambiguous situations they will match the longest string possible. The non-greedy version of the curly brackets, which matches the shortest string possible, has the closing curly bracket followed by a question mark.

greedy_ha_regex = re.compile(r'(Ha){3,5}')
mo1 = greedy_ha_regex.search('HaHaHaHaHa')
mo1.group()

nongreedy_ha_regex = re.compile(r'(Ha){3,5}?')
mo2 = nongreedy_ha_regex.search('HaHaHaHaHa')
mo2.group()

The findall Method

In addition to the search() method, Regex objects also have a findall() method. While search() will return a Match object of the first matched text in the searched string, the findall() method will return the strings of every match in the searched string.

phone_num_regex = re.compile(r'\d\d\d-\d\d\d-\d\d\d\d') # has no groups
phone_num_regex.findall('Cell: 415-555-9999 Work: 212-555-0000')

To summarize what the findall() method returns, remember the following:

• When called on a regex with no groups, such as \d-\d\d\d-\d\d\d\d, the method findall() returns a list of ng matches, such as ['415-555-9999', '212-555-0000'].
• -
• When called on a regex that has groups, such as (\d\d\d)-d\d)-(\d\ d\d\d), the method findall() returns a list of es of strings (one string for each group), such as [('415', ', '9999'), ('212', '555', '0000')].

Making Your Own Character Classes

There are times when you want to match a set of characters but the shorthand character classes (\d, \w, \s, and so on) are too broad. You can define your own character class using square brackets. For example, the character class [aeiouAEIOU] will match any vowel, both lowercase and uppercase.

vowel_regex = re.compile(r'[aeiouAEIOU]')
vowel_regex.findall('Robocop eats baby food. BABY FOOD.')

You can also include ranges of letters or numbers by using a hyphen. For example, the character class [a-zA-Z0-9] will match all lowercase letters, uppercase letters, and numbers.

By placing a caret character (^) just after the character class's opening bracket, you can make a negative character class. A negative character class will match all the characters that are not in the character class. For example, enter the following into the interactive shell:

consonant_regex = re.compile(r'[^aeiouAEIOU]')
consonant_regex.findall('Robocop eats baby food. BABY FOOD.')

The Caret and Dollar Sign Characters

• You can also use the caret symbol (^) at the start of a regex to indicate that a match must occur at the beginning of the searched text.
• -
• Likewise, you can put a dollar sign ($) at the end of the regex to indicate the string must end with this regex pattern.
• And you can use the ^ and $ together to indicate that the entire string must match the regex—that is, it's not enough for a match to be made on some subset of the string.

The r'^Hello' regular expression string matches strings that begin with 'Hello':

begins_with_hello = re.compile(r'^Hello')
begins_with_hello.search('Hello world!')

begins_with_hello.search('He said hello.') is None

The r'\d$' regular expression string matches strings that end with a numeric character from 0 to 9:

whole_string_is_num = re.compile(r'^\d+$')
whole_string_is_num.search('1234567890')

whole_string_is_num.search('12345xyz67890') is None

whole_string_is_num.search('12 34567890') is None

The Wildcard Character

The . (or dot) character in a regular expression is called a wildcard and will match any character except for a newline:

at_regex = re.compile(r'.at')
at_regex.findall('The cat in the hat sat on the flat mat.')

Matching Everything with Dot-Star

name_regex = re.compile(r'First Name: (.*) Last Name: (.*)')
mo = name_regex.search('First Name: Some Last Name: One')
mo.group(1)

mo.group(2)

The dot-star uses greedy mode: It will always try to match as much text as possible. To match any and all text in a nongreedy fashion, use the dot, star, and question mark (.*?). The question mark tells Python to match in a nongreedy way:

nongreedy_regex = re.compile(r'<.*?>')
mo = nongreedy_regex.search('<To serve man> for dinner.>')
mo.group()

greedy_regex = re.compile(r'<.*>')
mo = greedy_regex.search('<To serve man> for dinner.>')
mo.group()

Matching Newlines with the Dot Character

The dot-star will match everything except a newline. By passing re.DOTALL as the second argument to re.compile(), you can make the dot character match all characters, including the newline character:

no_newline_regex = re.compile('.*')
no_newline_regex.search('Serve the public trust.\nProtect the innocent.\nUphold the law.').group()

newline_regex = re.compile('.*', re.DOTALL)
newline_regex.search('Serve the public trust.\nProtect the innocent.\nUphold the law.').group()

Review of Regex Symbols

Case-Insensitive Matching

To make your regex case-insensitive, you can pass re.IGNORECASE or re.I as a second argument to re.compile():

robocop = re.compile(r'robocop', re.I)
robocop.search('Robocop is part man, part machine, all cop.').group()

robocop.search('ROBOCOP protects the innocent.').group()

robocop.search('Al, why does your programming book talk about robocop so much?').group()

Substituting Strings with the sub() Method

The sub() method for Regex objects is passed two arguments:

1. The first argument is a string to replace any matches.
2. The second is the string for the regular expression.

The sub() method returns a string with the substitutions applied:

names_regex = re.compile(r'Agent \w+')
names_regex.sub('CENSORED', 'Agent Alice gave the secret documents to Agent Bob.')

Another example:

agent_names_regex = re.compile(r'Agent (\w)\w*')
agent_names_regex.sub(r'\1****', 'Agent Alice told Agent Carol that Agent Eve knew Agent Bob was a double agent.')

Managing Complex Regexes

To tell the re.compile() function to ignore whitespace and comments inside the regular expression string, "verbose mode" can be enabled by passing the variable re.VERBOSE as the second argument to re.compile().

Now instead of a hard-to-read regular expression like this:

phone_regex = re.compile(r'((\d{3}|\(\d{3}\))?(\s|-|\.)?\d{3}(\s|-|\.)\d{4}(\s*(ext|x|ext.)\s*\d{2,5})?)')

you can spread the regular expression over multiple lines with comments like this:

phone_regex = re.compile(r'''(
    (\d{3}|\(\d{3}\))?            # area code
    (\s|-|\.)?                    # separator
    \d{3}                         # first 3 digits
    (\s|-|\.)                     # separator
    \d{4}                         # last 4 digits
    (\s*(ext|x|ext.)\s*\d{2,5})?  # extension
    )''', re.VERBOSE)

Handling File and Directory Paths

There are two main modules in Python that deals with path manipulation. One is the os.path module and the other is the pathlib module. The pathlib module was added in Python 3.4, offering an object-oriented way to handle file system paths.

Backslash on Windows and Forward Slash on OS X and Linux

On Windows, paths are written using backslashes () as the separator between folder names. On Unix based operating system such as macOS, Linux, and BSDs, the forward slash (/) is used as the path separator. Joining paths can be a headache if your code needs to work on different platforms.

Fortunately, Python provides easy ways to handle this. We will showcase how to deal with this with both os.path.join and pathlib.Path.joinpath

Using os.path.join on Windows:

import os

os.path.join('usr', 'bin', 'spam')

And using pathlib on *nix:

from pathlib import Path

print(Path('usr').joinpath('bin').joinpath('spam'))

pathlib also provides a shortcut to joinpath using the / operator:

from pathlib import Path

print(Path('usr') / 'bin' / 'spam')

Notice the path separator is different between Windows and Unix based operating system, that's why you want to use one of the above methods instead of adding strings together to join paths together.

Joining paths is helpful if you need to create different file paths under the same directory.

Using os.path.join on Windows:

my_files = ['accounts.txt', 'details.csv', 'invite.docx']for filename in my_files:print(os.path.join('C:\\Users\\asweigart', filename))

Using pathlib on *nix:

my_files = ['accounts.txt', 'details.csv', 'invite.docx']
home = Path.home()for filename in my_files:print(home / filename)

The Current Working Directory

Using os on Windows:

import os

os.getcwd()

os.chdir('C:\\Windows\\System32')
os.getcwd()

Using pathlib on *nix:

from pathlib import Path
from os import chdir

print(Path.cwd())

chdir('/usr/lib/python3.6')print(Path.cwd())

Creating New Folders

Using os on Windows:

import os

os.makedirs('C:\\delicious\\walnut\\waffles')

Using pathlib on *nix:

from pathlib import Path
cwd = Path.cwd()(cwd / 'delicious' / 'walnut' / 'waffles').mkdir()

Oh no, we got a nasty error! The reason is that the 'delicious' directory does not exist, so we cannot make the 'walnut' and the 'waffles' directories under it. To fix this, do:

from pathlib import Path
cwd = Path.cwd()(cwd / 'delicious' / 'walnut' / 'waffles').mkdir(parents=True)

And all is good :)

Absolute vs. Relative Paths

There are two ways to specify a file path.

• An absolute path, which always begins with the root folder
• A relative path, which is relative to the program's current working directory

There are also the dot (.) and dot-dot (..) folders. These are not real folders but special names that can be used in a path. A single period ("dot") for a folder name is shorthand for "this directory." Two periods ("dot-dot") means "the parent folder."

Handling Absolute and Relative Paths

To see if a path is an absolute path:

Using os.path on *nix:

import os

os.path.isabs('/')

os.path.isabs('..')

Using pathlib on *nix:

from pathlib import Path
Path('/').is_absolute()

Path('..').is_absolute()

You can extract an absolute path with both os.path and pathlib

Using os.path on *nix:

import os

os.getcwd()

os.path.abspath('..')

Using pathlib on *nix:

from pathlib import Path

print(Path.cwd())

print(Path('..').resolve())

You can get a relative path from a starting path to another path.

Using os.path on *nix:

import os

os.path.relpath('/etc/passwd', '/')

Using pathlib on *nix:

from pathlib import Path

print(Path('/etc/passwd').relative_to('/'))

Checking Path Validity

Checking if a file/directory exists:

Using os.path on *nix:

import os
os.path.exists('.')

os.path.exists('setup.py')

os.path.exists('/etc')

os.path.exists('nonexistentfile')

Using pathlib on *nix:

from pathlib import Path
Path('.').exists()

Path('setup.py').exists()

Path('/etc').exists()

Path('nonexistentfile').exists()

Checking if a path is a file:

Using os.path on *nix:

import os
os.path.isfile('setup.py')

os.path.isfile('/home')

os.path.isfile('nonexistentfile')

Using pathlib on *nix:

from pathlib import Path

Path('setup.py').is_file()

Path('/home').is_file()

Path('nonexistentfile').is_file()

Checking if a path is a directory:

Using os.path on *nix:

import os
os.path.isdir('/')

os.path.isdir('setup.py')

os.path.isdir('/spam')

Using pathlib on *nix:

from pathlib import Path

Path('/').is_dir()

Path('setup.py').is_dir()

Path('/spam').is_dir()

Finding File Sizes and Folder Contents

Getting a file's size in bytes:

Using os.path on Windows:

import os
os.path.getsize('C:\\Windows\\System32\\calc.exe')

Using pathlib on *nix:

from pathlib import Path

stat = Path('/bin/python3.6').stat()print(stat) # stat contains some other information about the file as well

print(stat.st_size) # size in bytes

Listing directory contents using os.listdir on Windows:

import os
os.listdir('C:\\Windows\\System32')

Listing directory contents using pathlib on *nix:

from pathlib import Path

for f in Path('/usr/bin').iterdir():print(f)

To find the total size of all the files in this directory:

WARNING: Directories themselves also have a size! So you might want to check for whether a path is a file or directory using the methods in the methods discussed in the above section!

Using os.path.getsize() and os.listdir() together on Windows:

import os

total_size = 0for filename in os.listdir('C:\\Windows\\System32'):
      total_size = total_size + os.path.getsize(os.path.join('C:\\Windows\\System32', filename))print(total_size)

Using pathlib on *nix:

from pathlib import Path
total_size = 0for sub_path in Path('/usr/bin').iterdir():
    total_size += sub_path.stat().st_size

print(total_size)

Copying Files and Folders

The shutil module provides functions for copying files, as well as entire folders.

import shutil, os

os.chdir('C:\\')

shutil.copy('C:\\spam.txt', 'C:\\delicious')

shutil.copy('eggs.txt', 'C:\\delicious\\eggs2.txt')'C:\\delicious\\eggs2.txt'

While shutil.copy() will copy a single file, shutil.copytree() will copy an entire folder and every folder and file contained in it:

import shutil, os

os.chdir('C:\\')
shutil.copytree('C:\\bacon', 'C:\\bacon_backup')

Moving and Renaming Files and Folders

import shutil

shutil.move('C:\\bacon.txt', 'C:\\eggs')

The destination path can also specify a filename. In the following example, the source file is moved and renamed:

shutil.move('C:\\bacon.txt', 'C:\\eggs\\new_bacon.txt')

If there is no eggs folder, then move() will rename bacon.txt to a file named eggs.

shutil.move('C:\\bacon.txt', 'C:\\eggs')

Permanently Deleting Files and Folders

• Calling os.unlink(path) or Path.unlink() will delete the file at path.
• -
• Calling os.rmdir(path) or Path.rmdir() will delete the folder at path. This folder must be empty of any files or folders.
• Calling shutil.rmtree(path) will remove the folder at path, and all files and folders it contains will also be deleted.

Safe Deletes with the send2trash Module

You can install this module by running pip install send2trash from a Terminal window.

import send2trash

with open('bacon.txt', 'a') as bacon_file: # creates the file
    bacon_file.write('Bacon is not a vegetable.')

send2trash.send2trash('bacon.txt')

Walking a Directory Tree

import os

for folder_name, subfolders, filenames in os.walk('C:\\delicious'):print('The current folder is {}'.format(folder_name))for subfolder in subfolders:print('SUBFOLDER OF {}: {}'.format(folder_name, subfolder))for filename in filenames:print('FILE INSIDE {}: {}'.format(folder_name, filename))print('')

pathlib provides a lot more functionality than the ones listed above, like getting file name, getting file extension, reading/writing a file without manually opening it, etc. Check out the official documentation if you want to know more!

Reading and Writing Files

The File Reading/Writing Process

To read/write to a file in Python, you will want to use the with statement, which will close the file for you after you are done.

Opening and reading files with the open function

with open('C:\\Users\\your_home_folder\\hello.txt') as hello_file:
    hello_content = hello_file.read()

hello_content

Alternatively, you can use the readlines() method to get a list of string values from the file, one string for each line of text:

with open('sonnet29.txt') as sonnet_file:
    sonnet_file.readlines()

You can also iterate through the file line by line:

with open('sonnet29.txt') as sonnet_file:for line in sonnet_file: # note the new line character will be included in the lineprint(line, end='')

Writing to Files

with open('bacon.txt', 'w') as bacon_file:
    bacon_file.write('Hello world!\n')

with open('bacon.txt', 'a') as bacon_file:
    bacon_file.write('Bacon is not a vegetable.')

with open('bacon.txt') as bacon_file:
    content = bacon_file.read()print(content)

Saving Variables with the shelve Module

To save variables:

import shelve

cats = ['Zophie', 'Pooka', 'Simon']with shelve.open('mydata') as shelf_file:
    shelf_file['cats'] = cats

To open and read variables:

with shelve.open('mydata') as shelf_file:print(type(shelf_file))print(shelf_file['cats'])

Just like dictionaries, shelf values have keys() and values() methods that will return list-like values of the keys and values in the shelf. Since these methods return list-like values instead of true lists, you should pass them to the list() function to get them in list form.

with shelve.open('mydata') as shelf_file:print(list(shelf_file.keys()))print(list(shelf_file.values()))

Saving Variables with pprint.pformat

import pprint

cats = [{'name': 'Zophie', 'desc': 'chubby'}, {'name': 'Pooka', 'desc': 'fluffy'}]
pprint.pformat(cats)

with open('myCats.py', 'w') as file_obj:
    file_obj.write('cats = {}\n'.format(pprint.pformat(cats)))

Reading ZIP Files

import zipfile, os

os.chdir('C:\\')    # move to the folder with example.zipwith zipfile.ZipFile('example.zip') as example_zip:print(example_zip.namelist())
    spam_info = example_zip.getinfo('spam.txt')print(spam_info.file_size)print(spam_info.compress_size)print('Compressed file is %sx smaller!' % (round(spam_info.file_size / spam_info.compress_size, 2)))

Extracting from ZIP Files

The extractall() method for ZipFile objects extracts all the files and folders from a ZIP file into the current working directory.

import zipfile, os

os.chdir('C:\\')    # move to the folder with example.zipwith zipfile.ZipFile('example.zip') as example_zip:
    example_zip.extractall()

The extract() method for ZipFile objects will extract a single file from the ZIP file. Continue the interactive shell example:

with zipfile.ZipFile('example.zip') as example_zip:print(example_zip.extract('spam.txt'))print(example_zip.extract('spam.txt', 'C:\\some\\new\\folders'))

Creating and Adding to ZIP Files

import zipfile

with zipfile.ZipFile('new.zip', 'w') as new_zip:
    new_zip.write('spam.txt', compress_type=zipfile.ZIP_DEFLATED)

This code will create a new ZIP file named new.zip that has the compressed contents of spam.txt.

JSON, YAML and configuration files

JSON

Open a JSON file with:

import json
with open("filename.json", "r") as f:
    content = json.loads(f.read())

Write a JSON file with:

import json

content = {"name": "Joe", "age": 20}with open("filename.json", "w") as f:
    f.write(json.dumps(content, indent=2))

YAML

Compared to JSON, YAML allows a much better humain maintainance and gives ability to add comments. It is a convinient choice for configuration files where human will have to edit.

There are two main librairies allowing to access to YAML files:

• PyYaml
• Ruamel.yaml

Install them using pip install in your virtual environment.

The first one it easier to use but the second one, Ruamel, implements much better the YAML specification, and allow for example to modify a YAML content without altering comments.

Open a YAML file with:

from ruamel.yaml import YAML

with open("filename.yaml") as f:
    yaml=YAML()
    yaml.load(f)

Anyconfig

Anyconfig is a very handy package allowing to abstract completly the underlying configuration file format. It allows to load a Python dictionary from JSON, YAML, TOML, and so on.

Install it with:

pip install anyconfig

Usage:

import anyconfig

conf1 = anyconfig.load("/path/to/foo/conf.d/a.yml")

Debugging

Raising Exceptions

Exceptions are raised with a raise statement. In code, a raise statement consists of the following:

• The raise keyword
• A call to the Exception() function
• A string with a helpful error message passed to the Exception() function

raise Exception('This is the error message.')
Traceback (most recent call last):
  File "<pyshell#191>", line 1, in <module>raise Exception('This is the error message.')
Exception: This is the error message.

Often it's the code that calls the function, not the function itself, that knows how to handle an expection. So you will commonly see a raise statement inside a function and the try and except statements in the code calling the function.

def box_print(symbol, width, height):if len(symbol) != 1:raise Exception('Symbol must be a single character string.')if width <= 2:raise Exception('Width must be greater than 2.')if height <= 2:raise Exception('Height must be greater than 2.')print(symbol * width)for i in range(height - 2):print(symbol + (' ' * (width - 2)) + symbol)print(symbol * width)for sym, w, h in (('*', 4, 4), ('O', 20, 5), ('x', 1, 3), ('ZZ', 3, 3)):try:
        box_print(sym, w, h)except Exception as err:print('An exception happened: ' + str(err))

Getting the Traceback as a String

The traceback is displayed by Python whenever a raised exception goes unhandled. But can also obtain it as a string by calling traceback.format_exc(). This function is useful if you want the information from an exception's traceback but also want an except statement to gracefully handle the exception. You will need to import Python's traceback module before calling this function.

import traceback

try:raise Exception('This is the error message.')except:with open('errorInfo.txt', 'w') as error_file:
         error_file.write(traceback.format_exc())print('The traceback info was written to errorInfo.txt.')

The 116 is the return value from the write() method, since 116 characters were written to the file. The traceback text was written to errorInfo.txt.

Traceback (most recent call last):
  File "<pyshell#28>", line 2, in <module>
Exception: This is the error message.

Assertions

An assertion is a sanity check to make sure your code isn't doing something obviously wrong. These sanity checks are performed by assert statements. If the sanity check fails, then an AssertionError exception is raised. In code, an assert statement consists of the following:

• The assert keyword
• A condition (that is, an expression that evaluates to True or False)
• A comma
• A string to display when the condition is False

pod_bay_door_status = 'open'assert pod_bay_door_status == 'open', 'The pod bay doors need to be "open".'

pod_bay_door_status = 'I\'m sorry, Dave. I\'m afraid I can\'t do that.'assert pod_bay_door_status == 'open', 'The pod bay doors need to be "open".'

In plain English, an assert statement says, "I assert that this condition holds true, and if not, there is a bug somewhere in the program." Unlike exceptions, your code should not handle assert statements with try and except; if an assert fails, your program should crash. By failing fast like this, you shorten the time between the original cause of the bug and when you first notice the bug. This will reduce the amount of code you will have to check before finding the code that's causing the bug.

Disabling Assertions

Assertions can be disabled by passing the -O option when running Python.

Logging

To enable the logging module to display log messages on your screen as your program runs, copy the following to the top of your program (but under the #! python shebang line):

import logging

logging.basicConfig(level=logging.DEBUG, format=' %(asctime)s - %(levelname)s- %(message)s')

Say you wrote a function to calculate the factorial of a number. In mathematics, factorial 4 is 1 × 2 × 3 × 4, or 24. Factorial 7 is 1 × 2 × 3 × 4 × 5 × 6 × 7, or 5,040. Open a new file editor window and enter the following code. It has a bug in it, but you will also enter several log messages to help yourself figure out what is going wrong. Save the program as factorialLog.py.

import logging

logging.basicConfig(level=logging.DEBUG, format=' %(asctime)s - %(levelname)s- %(message)s')

logging.debug('Start of program')def factorial(n):

    logging.debug('Start of factorial(%s)' % (n))
    total = 1for i in range(1, n + 1):
        total *= i
        logging.debug('i is ' + str(i) + ', total is ' + str(total))

    logging.debug('End of factorial(%s)' % (n))return total

print(factorial(5))
logging.debug('End of program')

Logging Levels

Logging levels provide a way to categorize your log messages by importance. There are five logging levels, described in Table 10-1 from least to most important. Messages can be logged at each level using a different logging function.

Disabling Logging

After you've debugged your program, you probably don't want all these log messages cluttering the screen. The logging.disable() function disables these so that you don't have to go into your program and remove all the logging calls by hand.

import logging

logging.basicConfig(level=logging.INFO, format=' %(asctime)s -%(levelname)s - %(message)s')
logging.critical('Critical error! Critical error!')
logging.disable(logging.CRITICAL)
logging.critical('Critical error! Critical error!')
logging.error('Error! Error!')

Logging to a File

Instead of displaying the log messages to the screen, you can write them to a text file. The logging.basicConfig() function takes a filename keyword argument, like so:

import logging

logging.basicConfig(filename='myProgramLog.txt', level=logging.DEBUG, format='%(asctime)s - %(levelname)s - %(message)s')

Lambda Functions

This function:

def add(x, y):return x + y

add(5, 3)

Is equivalent to the lambda function:

add = lambda x, y: x + y
add(5, 3)

It's not even need to bind it to a name like add before:

(lambda x, y: x + y)(5, 3)

Like regular nested functions, lambdas also work as lexical closures:

def make_adder(n):return lambda x: x + n

plus_3 = make_adder(3)
plus_5(4)

Note: lambda can only evaluate an expression, like a single line of code.

Ternary Conditional Operator

Many programming languages have a ternary operator, which define a conditional expression. The most common usage is to make a terse simple conditional assignment statement. In other words, it offers one-line code to evaluate the first expression if the condition is true, otherwise it evaluates the second expression.

<expression1> if <condition> else <expression2>

Example:

age = 15print('kid' if age < 18 else 'adult')

Ternary operators can be changed:

age = 15print('kid' if age < 13 else 'teenager' if age < 18 else 'adult')

The code above is equivalent to:

if age < 18:if age < 12:print('kid')else:print('teenager')else:print('adult')

args and kwargs

The names args and kwargs are arbitrary - the important thing are the * and ** operators. They can mean:

1. In a function declaration, * means "pack all remaining positional arguments into a tuple named <name>", while ** is the same for keyword arguments (except it uses a dictionary, not a tuple).
2. In a function call, * means "unpack tuple or list named <name> to positional arguments at this position", while ** is the same for keyword arguments.

For example you can make a function that you can use to call any other function, no matter what parameters it has:

def forward(f, *args, **kwargs):return f(*args, **kwargs)

Inside forward, args is a tuple (of all positional arguments except the first one, because we specified it - the f), kwargs is a dict. Then we call f and unpack them so they become normal arguments to f.

You use *args when you have an indefinite amount of positional arguments.

def fruits(*args):for fruit in args:print(fruit)

fruits("apples", "bananas", "grapes")

Similarly, you use **kwargs when you have an indefinite number of keyword arguments.

def fruit(**kwargs):for key, value in kwargs.items():print("{0}: {1}".format(key, value))

fruit(name = "apple", color = "red")

def show(arg1, arg2, *args, kwarg1=None, kwarg2=None, **kwargs):print(arg1)print(arg2)print(args)print(kwarg1)print(kwarg2)print(kwargs)

data1 = [1,2,3]
data2 = [4,5,6]
data3 = {'a':7,'b':8,'c':9}

show(*data1,*data2, kwarg1="python",kwarg2="cheatsheet",**data3)

show(*data1, *data2, **data3)

If you do not specify ** for kwargs

show(*data1, *data2, *data3)

Thinks to Remember(args)

1. Functions can accept a variable number of positional arguments by using *args in the def statement.
2. You can use the items from a sequence as the positional arguments for a function with the * operator.
3. Using the * operator with a generator may cause your program to run out of memory and crash.
4. Adding new positional parameters to functions that accept *args can introduce hard-to-find bugs.

Thinks to remember(kwargs)

1. Function arguments can be specified by position or by keyword.
2. Keywords make it clear what the purpose of each argument is when it would be confusing with only positional arguments.
3. Keyword arguments with default values make it easy to add new behaviors to a function, especially when the function has existing callers.
4. Optional keyword arguments should always be passed by keyword instead of by position.

Context Manager

While Python's context managers are widely used, few understand the purpose behind their use. These statements, commonly used with reading and writing files, assist the application in conserving system memory and improve resource management by ensuring specific resources are only in use for certain processes.

with statement

A context manager is an object that is notified when a context (a block of code) starts and ends. You commonly use one with the with statement. It takes care of the notifying.

For example, file objects are context managers. When a context ends, the file object is closed automatically:

with open(filename) as f:
    file_contents = f.read()# the open_file object has automatically been closed.

Anything that ends execution of the block causes the context manager's exit method to be called. This includes exceptions, and can be useful when an error causes you to prematurely exit from an open file or connection. Exiting a script without properly closing files/connections is a bad idea, that may cause data loss or other problems. By using a context manager you can ensure that precautions are always taken to prevent damage or loss in this way.

Writing your own contextmanager using generator syntax

It is also possible to write a context manager using generator syntax thanks to the contextlib.contextmanager decorator:

import contextlib

@contextlib.contextmanagerdef context_manager(num):print('Enter')yield num + 1print('Exit')with context_manager(2) as cm:# the following instructions are run when the 'yield' point of the context# manager is reached.# 'cm' will have the value that was yieldedprint('Right in the middle with cm = {}'.format(cm))

__main__ Top-level script environment

__main__ is the name of the scope in which top-level code executes. A module's name is set equal to __main__ when read from standard input, a script, or from an interactive prompt.

A module can discover whether or not it is running in the main scope by checking its own __name__, which allows a common idiom for conditionally executing code in a module when it is run as a script or with python -m but not when it is imported:

if __name__ == "__main__":# execute only if run as a script
    main()

For a package, the same effect can be achieved by including a main.py module, the contents of which will be executed when the module is run with -m.

For example we are developing script which is designed to be used as module, we should do:

# Python program to execute function directlydef add(a, b):return a+b

add(10, 20) # we can test it by calling the function save it as calculate.py

# Now if we want to use that module by importing we have to comment out our call,# Instead we can write like this in calculate.pyif __name__ == "__main__":
    add(3, 5)

import calculate

calculate.add(3, 5)

Advantages

1. Every Python module has it's __name__ defined and if this is __main__, it implies that the module is being run standalone by the user and we can do corresponding appropriate actions.
2. If you import this script as a module in another script, the name is set to the name of the script/module.
3. Python files can act as either reusable modules, or as standalone programs.
4. if __name__ == "main": is used to execute some code only if the file was run directly, and not imported.

setup.py

The setup script is the centre of all activity in building, distributing, and installing modules using the Distutils. The main purpose of the setup script is to describe your module distribution to the Distutils, so that the various commands that operate on your modules do the right thing.

The setup.py file is at the heart of a Python project. It describes all of the metadata about your project. There a quite a few fields you can add to a project to give it a rich set of metadata describing the project. However, there are only three required fields: name, version, and packages. The name field must be unique if you wish to publish your package on the Python Package Index (PyPI). The version field keeps track of different releases of the project. The packages field describes where you've put the Python source code within your project.

This allows you to easily install Python packages. Often it's enough to write:

python setup.py install

and module will install itself.

Our initial setup.py will also include information about the license and will re-use the README.txt file for the long_description field. This will look like:

from distutils.core import setup
setup(
   name='pythonCheatsheet',
   version='0.1',
   packages=['pipenv',],
   license='MIT',
   long_description=open('README.txt').read(),)

Find more information visit http://docs.python.org/install/index.html.

Dataclasses

Dataclasses are python classes but are suited for storing data objects. This module provides a decorator and functions for automatically adding generated special methods such as __init__() and __repr__() to user-defined classes.

Features

1. They store data and represent a certain data type. Ex: A number. For people familiar with ORMs, a model instance is a data object. It represents a specific kind of entity. It holds attributes that define or represent the entity.
2. They can be compared to other objects of the same type. Ex: A number can be greater than, less than, or equal to another number.

Python 3.7 provides a decorator dataclass that is used to convert a class into a dataclass.

python 2.7

class Number:def __init__(self, val):
        self.val = val

obj = Number(2)
obj.val

with dataclass

from dataclasses import dataclass

@dataclassclass Number:
    val: int

obj = Number(2)
obj.val

Default values

It is easy to add default values to the fields of your data class.

from dataclasses import dataclass

@dataclassclass Product:
    name: str
    count: int = 0
    price: float = 0.0

obj = Product("Python")
obj.name

obj.count

obj.price

Type hints

It is mandatory to define the data type in dataclass. However, If you don't want specify the datatype then, use typing.Any.

from dataclasses import dataclass
from typing import Any

@dataclassclass WithoutExplicitTypes:
   name: Any
   value: Any = 42

PART 2:

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

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

2**3 # => 8

Enforce precedence with parentheses

(1 + 3) * 2 # => 8

Boolean values are primitives (Note: the capitalization)

True 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! But try not to do this.

"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

"This is a string"[0] # => 'T'

You can find the length of a string

len("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"

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."

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

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

Note: In earlier versions of Python, input() method was named as raw_input()

There are no declarations, only assignments.

Convention is to use lowercasewith_underscores

somevar = 5 somevar # => 5

Accessing a previously unassigned variable is an exception.

See Control Flow to learn more about exception handling.

someunknownvar # 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 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)) # => <class 'int'> type((1,)) # => <class 'tuple'> type(()) # => <class 'tuple'>

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.

invaliddict = {[1,2,3]: "123"} # => Raises a TypeError: unhashable type: 'list' validdict = {(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(filleddict.keys()) # => ["three", "two", "one"] in Python <3.7 list(filleddict.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(filleddict.values()) # => [3, 2, 1] in Python <3.7 list(filleddict.values()) # => [1, 2, 3] in Python 3.7+

Check for existence of keys in a dictionary with "in"

"one" in filleddict # => True 1 in filleddict # => False

Looking up a non-existing key is a KeyError

filled_dict["four"] # KeyError

Use "get()" method to avoid the KeyError

filleddict.get("one") # => 1 filleddict.get("four") # => None

The get method supports a default argument when the value is missing

filleddict.get("one", 4) # => 1 filleddict.get("four", 4) # => 4

"setdefault()" inserts into a dictionary only if the given key isn't present

filleddict.setdefault("five", 5) # filleddict["five"] is set to 5 filleddict.setdefault("five", 6) # filleddict["five"] is still 5

Adding to a dictionary

filleddict.update({"four":4}) # => {"one": 1, "two": 2, "three": 3, "four": 4} filleddict["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.

someset = {1, 1, 2, 2, 3, 4} # someset is now {1, 2, 3, 4}

Similar to keys of a dictionary, elements of a set have to be immutable.

invalidset = {[1], 1} # => Raises a TypeError: unhashable type: 'list' validset = {(1,), 1}

Add one more item to the set

filledset = someset filledset.add(5) # filledset 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 &

otherset = {3, 4, 5, 6} filledset & other_set # => {3, 4, 5}

Do set union with |

filledset | otherset # => {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 filledset # => True 10 in filledset # => 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 somevar > 10: print("somevar is totally bigger than 10.") elif somevar < 10: # This elif clause is optional. print("somevar 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 """ list = ["dog", "cat", "mouse"] for i, value in enumerate(list): 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.

filleddict = {"one": 1, "two": 2, "three": 3} ouriterable = filleddict.keys() print(ouriterable) # => 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.

ouriterator = iter(ouriterable)

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(ouriterator) # => "two" next(ouriterator) # => "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!

ouriterator = iter(ouriterable) 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(ouriterable) # => Returns ["one", "two", "three"] list(ouriterator) # => Returns [] because state is saved

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4. Functions

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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 alltheargs(*args, **kwargs): print(args) print(kwargs) """ alltheargs(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} alltheargs(args) # equivalent to alltheargs(1, 2, 3, 4) alltheargs(\*kwargs) # equivalent to alltheargs(a=3, b=4) alltheargs(args, \*kwargs) # equivalent to alltheargs(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 setglobalx(num): global x print(x) # => 5 x = num # global var x is now set to 6 print(x) # => 6

setx(43) setglobal_x(6)

Python has first class functions

def create_adder(x): def adder(y): return x + y return adder

add10 = createadder(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}

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5. Modules

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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) # => 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.

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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*"

# Cannot call static method with instance of object
# because i.grunt() will automatically put "self" (the object i) as an argument
print(i.grunt())                # => TypeError: grunt() takes 0 positional arguments but 1 was given

# 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

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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__)    # => (<class '__main__.Superhero'>,
                            # => <class 'human.Human'>, <class 'object'>)

# 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 '))) ... ((('

if name == 'main': b = Bat() print(b.say('hello')) print(b.fly)

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".
    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'

def sing(self):
    return 'nan nan nan nan nan batman!'

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
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
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
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

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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]) gentolist = list(values) print(gentolist) # => [-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 wraps

def beg(targetfunction): @wraps(targetfunction) def wrapper(args, \*kwargs): msg, sayplease = targetfunction(args, \*kwargs) if say_please: return "{} {}".format(msg, "Please! I am poor :(") return msg

return wrapper

@beg def say(sayplease=False): msg = "Can you buy me a beer?" return msg, sayplease

print(say()) # Can you buy me a beer? print(say(say_please=True)) # Can you buy me a beer? Please! I am poor :(