Python Cheat Sheet Table of Contents

This cheat sheet provides a quick reference for essential Python

concepts, focusing on practical use cases for data analysis and
Basics Control Flow
comments, arithmetical IF, ELSE, ELIF,

programming. It covers fundamental topics like variables,

operations, variables, DATA FOR LOOPS
arithmetic, data types, and expands into key areas such as lists, TYPES
dictionaries, functions, and control flow.

Examples throughout the cheat sheet are drawn from the

Data Structures OOP Basics
Mobile App Store Dataset and illustrate common operations, Lists, dictionaries, CLASS, Instantiate, init,
frequency tables method
from basic string manipulation to building frequency tables
and working with dates and times.

Each section is designed to give you a concise, actionable Functions Dates and Times
overview of Python’s core functionality in the context of real- DEFINITION, arguments, RETURN DATETIME, DATE, TIME,

STATEMENTS, PARAMETERS STRFTIME, STRPTIME

world data.

Strings
FORMATTING, TRANSFORMING,
CLEANING

Python Cheat Sheet Free resources at dataquest.io/guide/introduction-to-python-tutorial

Basics
Syntax for How to use Explained Syntax for How to use Explained

Comments # print(1 + 2)
We call the sequence of Operation
x += 2 # Addition
Augmented assignment
characters that follows the
Shortcuts operators: used to update a
print(5 * 10)
x -= 2 # Subtraction

# This program will only print 50

# a code comment; any code x *= 2 # Multiplication
variable in place without
that follows # will not be repeating the variable name;
x /= 2 # Division

executed for example, instead of

x **= 2 # Exponentiation
writing: x = x + 2 , you can
Arithmetical
use: x += 2
1 + 2 # output: 3 Addition
Operations
Data Types x = [1, 2, 3]
Use the type() command to
4 - 5 # output: -1 Substraction
determine the data type of a
y = 4

value or variable.
Multiplication
print(type(x)) # list

30 * 2 # output: 60
print(type(y)) # integer

print(type('4')) # string
20 / 3 # output: 6.666666666666667 Division

4 ** 3 # output: 64 Exponentiation Converting

int('4') # casting a string to an integer
Converting between data
Data Types types is also referred to as
str(4) # casting an integer as a string

(4 * 18) ** 2 / 10 # output: 518.4 Use parentheses to control

float('4.3') # casting a string as a float
casting
the order of operations
str(4.3) # casting a float as a string

Initializing
cost = 20
Variable names can only
Variables total_cost = 20 + 2**5
contain letters, numbers, and
underscores―they cannot
currency = 'USD'

begin with a number

1_app = 'Facebook' # this will cause an error

Updating
x = 30
To update a variable, use the
Variables print(x) # 30 is printed

= assignment operator to set

a new value
x = 50

print(x) # 50 is printed

Python Cheat Sheet Free resources at dataquest.io/guide/introduction-to-python-tutorial

Data Structures

Syntax for How to use Explained Syntax for How to use Explained

Lists Creating a list and appending List of Lists Opening a dataset file and
a_list = [1, 2]
from csv import reader

a value to it using it to create a list of lists

a_list.append(3)
opened_file = open('AppleStore.csv')

print(a_list) # output: [1, 2, 3] read_file = reader(opened_file)

apps_data = list(read_file)
Creating a list of data points;
row_1 = ['Facebook', 0.0, 'USD', 2974676]

lists can store multiple data

row_2 = ['Instagram', 4.5, 'USD', 2161558]
types at the same time Creating a list of lists by
row_1 = ['Facebook', 'USD', 2974676, 3.5]

initializing a new list whose

Indexing row_2 = ['Instagram', 'USD', 2161558, 8.2]

Retrieving an element from a elements are themselves lists

print(row_1[0]) # output: 'Facebook'

list using each item’s index row_3 = ['Clash', 0.0, 'USD', 2130805, 4.5]

print(row_2[1]) # output: 4.5

row_4 = ['Fruit', 1.99, 'USD', 698516, 9.1]

number; note that list

print(row_1[3]) # output: 2974676

indexing begins at 0

print(row_2[3]) # output: 2161558 lists = [row_1, row_2, row_3, row_4]

Negative 

print(row_1[-1]) # output: 2974676

Negative list indexing works Indexing Retrieving an element from a

Indexing by counting backwards from first_row_first_element = lists[0][0]

print(row_2[-1]) # output: 2161558

list of lists by first selecting
the last element, beginning # output: 'Facebook'

print(row_1[-3]) # output: 0.0

the row, then the element
with -1 second_row_third_element = lists[1][2]

print(row_2[-4]) # output: 'Instagram' within that row

# output: 2161558

third_row_last_element = lists[-2][4]

Retrieving multiple list

num_ratings = [row_1[-1], row_2[-1]]
# output: 4.5

elements to create a new list

print(name_and_ratings)
last_row_last_element = lists[-1][-1]

# output: [2974676, 2161558] # output: 9.1

List Slicing List slicing includes the start
row_3 = ['Clash of Clans', 0.0,
Slicing

first_two_rows = lists[:2]

Slicing lists of lists allows

index but excludes the end
'USD', 2130805, 4.5]
List of Lists extracting full rows or
index; when the start is last_two_rows = lists[-2:]

print(row_3[:2])
specific elements from a
omitted, the slice begins at all_but_first_row = lists[1:]

# output: ['Clash of Clans', 0.0]

single row; positive indices
the start of the list; when the second_row_partial = lists[1][:3]

print(row_3[1:4])
select from the start, and
end is omitted, it continues # output: ['Instagram', 'USD', 2161558]

# output: [0.0, 'USD', 2130805]

negative indices select from
to the end of the list last_row_partial = lists[-1][1:-2]

print(row_3[3:])
the end
# output: [1.99, 'USD']
# output: [2130805, 4.5]

Python Cheat Sheet Free resources at dataquest.io/guide/introduction-to-python-tutorial

Data Structures
Syntax for How to use Explained Syntax for How to use Explained

Dictionaries
# First way

Creating a dictionary by Frequency

frequency_table = {}

Builds a frequency table by

dictionary = {'key_1': 1, 'key_2': 2}

defining key:value pairs at Tables

for row in a_data_set:

counting occurrences of
# Second way

time of initialization (first values in the 6th column

a_data_point = row[5]

way) or by creating an empty (row[5]) of a_data_set ,

dictionary = {}
if a_data_point in frequency_table:

dictionary and setting the incrementing the count if the

dictionary['key_1'] = 1
frequency_table[a_data_point] += 1

value for each key (second value exists, or adding it if

dictionary['key_2'] = 2 else:

way) not
frequency_table[a_data_point] = 1

dictionary = {'key_1': 100, 'key_2': 200}

Retrieve individual dictionary

values by specifying the key; Defined
Categorizes app sizes from
dictionary['key_1'] # outputs 100
data_sizes = {'0 - 10 MB': 0,

keys can be strings, numbers, Intervals apps_data into predefined

dictionary['key_2'] # outputs 200 '10 - 50 MB': 0,

or tuples, but not lists or sets ranges (e.g., '0 - 10 MB')

'50 - 100 MB': 0,

and increments the

Use the in operator to check '100 - 500 MB': 0,

dictionary = {'key_1': 100, 'key_2': 200}

corresponding count based
for dictionary key '500 MB +': 0}
on each app's size inside the
'key_1' in dictionary # outputs True

membership for row in apps_data[1:]:

data_sizes dictionary
'key_5' in dictionary # outputs False

data_size = float(row[2])

100 in dictionary # outputs False

if data_size <= 10000000:

data_sizes['0 - 10 MB'] += 1

dictionary = {'key_1': 100, 'key_2': 200}

Update dictionary values by elif 10000000 < data_size <= 50000000:

dictionary['key_1'] += 600

specifying the key and data_sizes['10 - 50 MB'] += 1

dictionary['key_2'] = 400

assigning a new value elif 50000000 < data_size <= 100000000:

data_sizes['50 - 100 MB'] += 1

print(dictionary)

elif 100000000 < data_size <= 500000000:

# output: {'key_1': 700, 'key_2': 400}

data_sizes['100 - 500 MB'] += 1

elif data_size > 500000000:

data_sizes['500 MB +'] += 1

Python Cheat Sheet Free resources at dataquest.io/guide/introduction-to-python-tutorial

Functions

Syntax for How to use Explained Syntax for How to use Explained

Basic

def square(number):

Create a function with a Arguments

def subtract(a, b):

Use named arguments and

Functions single parameter: number positional arguments
return number**2

return a - b

print(square(5)) # output: 25 print(subtract(a=10, b=7)) # output: 3

print(subtract(b=7, a=10)) # output: 3

Create a function with more print(subtract(10, 7)) # output: 3

def add(x, y):

than one parameter x and y

return x + y

Helper

Functions
def find_sum(lst):
Define helper functions to
print(add(3 + 14)) # output: 17 a_sum = 0
find the sum and length of a
for element in lst:
list; the mean function reuses
This function creates a a_sum += float(element)
these to calculate the
def freq_table(list_of_lists, index):
average by dividing the sum
frequency table for any given return a_sum

frequency_table = {}
by the length
column index of the
for row in list_of_lists:

provided list_of_lists def find_length(lst):

value = row[index]

length = 0

if value in frequency_table:

for element in lst:

frequency_table[value] += 1

length += 1

else:

return length

frequency_table[value] = 1

return frequency_table

def mean(lst):

return find_sum(lst) / find_length(lst)

print(mean([1, 2, 4, 6, 2]) # output: 3

Python Cheat Sheet Free resources at dataquest.io/guide/introduction-to-python-tutorial

Functions

Syntax for How to use Explained Syntax for How to use Explained

Multiple
Define a function that Multiple
This function uses multiple
def price(item, cost):
def sum_or_difference(a, b, return_sum=True):

Arguments accepts multiple arguments Return

return statements to either
return "The " + item + " costs $" +
if return_sum:

and returns a formatted Statements return the sum or the

str(cost) + "."

return a + b

string combining both inputs difference of two values,

else:

depending on the
print(price("chair", 40.99))
return a - b

return_sum argument,
# output: 'The chair costs $40.99.'
which defaults to True
print(sum_or_difference(10, 7))

# output: 17

Similar to the previous

def price (item, cost):

function, but uses print() print(sum_or_difference(10, 7, False))

print("The + item + costs $" +

to display the string

" "
# output: 3
str(cost) + .")

immediately rather than

"

returning it for further use This function is similar to the

price("chair", )

40.99 def sum_or_difference(a, b, return_sum=True):

previous one but omits the

# utput:
o 'The chair costs 0.99.'
$4 if return_sum:

else clause, returning the

return a + b

difference directly when

Default
Define a function with a return a - b

def add_value(x, constant=3.14):

return_sum is False,
Arguments
return x + constant

default argument; if no simplifying the logic

second argument is print(sum_or_difference(10, 7))

provided, the default value is # output: 17

print(add_value(6, 3)) # output: 9

used in the calculation print(sum_or_difference(10, 7, False))

print(add_value(6)) # output: 9.14

# output: 3

Returning
This function returns
def sum_and_difference(a, b):

Multiple
 multiple values (sum and

a_sum = a + b

Values difference) at once by

difference = a - b

separating them with

return a_sum, difference

commas, allowing them to

be unpacked into separate

sum_1, diff_1 = sum_and_difference(15, 10)
variables when called

P ython Cheat Sheet F ree resources at dataquest.io/guide/introduction-to-python-tutorial

Strings

Syntax for How to use Explained Syntax for How to use Explained

Formatting
continents = "France is in {} and China is

Insert values by order into String

green_ball = "red ball".replace("red",

Replace parts of a string by
placeholders for simple Cleaning specifying the old and new
in {}".format("Europe", "Asia")
"green")

string formatting values

# France is in Europe and China is in Asia # green ball

Use indexed placeholders to friend_removed = "hello there

Remove a specified substring
squares = "{0} times {0} equals

repeat or position values from a string by replacing it

{1}".format(3,9)
friend!".replace(" friend", "")

with an empty string

# 3 times 3 equals 9 # hello there!

Use a loop to remove

Assign values to named bad_chars = ["'", ",", ".", "!"]

population = "{name}'s population is {pop}

multiple specified characters
placeholders using variable string = "We'll remove apostrophes, commas,

million".format(name="Brazil", pop=209)
from a string by replacing
names periods, and exclamation marks!"

# Brazil's population is 209 million

them with an empty string
for char in bad_chars:

Insert values into a string by name

string = string.replace(char, "")

two_decimal_places = "I own

{:.2f}% of the
Format a float to two decimal # Well remove apostrophes commas periods and
places for precise output
company".format(32.5548651132)
# exclamation marks
# I own 32.55% of the company
print("hello, my friend".title())

Capitalize the first letter of

each word in the string
Insert a number with # Hello, My Friend
india_pop = "The approx pop of {} is

commas as a thousand
{:,}".format("India", 1324000000)
Split a string into a list of
separator by position split_on_dash = "1980-12-08".split("-")

# The approx pop of India is 1,324,000,000 substrings based on the

# ['1980', '12', '08']
specified delimiter

Format a number with first_four_chars = "This is a long

Slice the string to return the

balance_string = "Your bank balance is first four characters; missing
commas and two decimal string."[:4]

${:,.2f}".format(12345.678)
indices default to the start or
places for currency formatting # This
# Your bank balance is $12,345.68 end of the string

superman = "Clark" + " " + "Kent"

Concatenate strings using

the + operator to join them
# Clark Kent
with a space

Python Cheat Sheet Free resources at dataquest.io/guide/introduction-to-python-tutorial

Control Flow

Syntax for How to use Explained Syntax for How to use Explained

For Loops row_1 = ['Facebook', 0.0, 'USD', 2974676]

With each iteration, this loop If
if True:
The condition True always
for element in row_1:
will print an element Statements print('This will always be printed.') executes the code inside the
print(element) from row_1, in order if block

rating_sum = 0
Convert a column of strings if True:
Both conditions evaluate to
for row in apps_data[1:]:
(row[7]) in a list of lists print(1)
True, so all print statements
rating = float(row[7])
(apps_data) to a float and if 1 == 1:
are executed
rating_sum = rating_sum + rating keep a running sum of print(2)

ratings print(3)

apps_names = []
Append values with each
for row in apps_data[1:]:
iteration of a for loop if True:
Only the blocks with True
name = row[1]
print('First Output')
conditions are executed, so
apps_names.append(name) if False:
the second print statement is
print('Second Output')
skipped
if True:

Conditional
price = 0
Use comparison operators to print('Third Output')
Statements print(price == 0) # Outputs True
check if a value equals
print(price == 2) # Outputs False another, returning True or
False Else
if False:
The code in the else clause
Statements print(1)
is always executed when
print('Games' == 'Music') # Outputs False
Compare strings and lists else:
the if statement is False
print('Games' != 'Music') # Outputs True
using == for equality and != print('The condition above was false.')
print([1,2,3] == [1,2,3]) # Outputs True
for inequality, returning True
print([1,2,3] == [1,2,3,4]) # Outputs False or False

Python Cheat Sheet Free resources at dataquest.io/guide/introduction-to-python-tutorial

Control Flow Object-Oriented Programming Basics

Syntax for How to use Explained Syntax for How to use Explained

Else

if "car" in "carpet":

The in operator checks if a Defining

class MyClass:

Define an empty class

Statements substring exists in a string,
print("The substring was found.")
Classes pass
executing the corresponding
else:

if or else block
print("The substring was not found.") Instantiate an object from
Instantiating
class MyClass:

the class by calling the class

Class Objects pass

name followed by
Elif
The elif statement allows mc_1 = MyClass()
if 3 == 1:
parentheses
Statements for multiple conditions to be
print('3 does not equal 1.')

tested; if the if condition is Setting

Use the __init__ method to
elif 3 < 1:
class MyClass:

False, the elif condition is Class

initialize an object's
print('3 is not less than 1.')
def __init__(self, param_1):

checked, and if both are Attributes attributes during

else:
self.attribute = param_1

False, the else block is instantiation by passing

print('Both conditions above are mc_2 = MyClass("arg_1")

executed arguments
false.') # mc_2.attribute is set to "arg_1"

Defining
Define a method within the
class MyClass:

Multiple
if 3 > 1 and 'data' == 'data':

Use and to require both Class

class to modify an attribute;
conditions to be True and or
def __init__(self, param_1):

Conditions print('Both conditions are true!')

Methods add_20 increases the value
self.attribute = param_1

if 10 < 20 or 4 >= 5:

to require at least one of attribute by 20 when

condition to be True def add_20(self):

print('At least one condition is true.') called

self.attribute += 20

mc_3 = MyClass(10) # mc_3.attribute is 10

if (20 > 3 and 2 != 1) or 'Games' == 'Game':

Use parentheses to group mc_3.add_20() # mc_3.attribute is now 30
print('At least one condition is true.') conditions and control the
order of evaluation in
complex logical expressions

Python Cheat Sheet Free resources at dataquest.io/guide/introduction-to-python-tutorial

Dates and Time

Syntax for How to use Explained

Importing
Import the module, requiring Creating
Convert a datetime object into
import datetime
eg_2_str = eg_2.strftime(

Datetime
the full path to access Datetime
a formatted string; the "f "
current_time = datetime.datetime.now() "%B %d, %Y at %I:%M %p")

Examples functions or classes Objects stands for formatting
# "August 15, 1990 at 08:45 AM"
Import the module with alias
import datetime as dt

Create a time object that includes
current_time = dt.datetime.now()
dt for shorter references, a eg_3 = dt.time(hour=5, minute=23,

common practice microseconds

second=45, microsecond=123456)

# 05:23:45.123456
Import only the datetime class,
from datetime import datetime

enabling direct access without
current_time = datetime.now()
the module name prefix eg_4 = dt.timedelta(weeks=3)
 Add a timedelta object
representing 3 weeks to
future_date = eg_1 + eg_4

Import multiple classes from a datetime object to calculate a
from datetime import datetime, date
 # 1985-04-03 14:30:45
the module, allowing direct future date
current_time = datetime.now()

use of their respective
current_date = date.today()
methods Accessing

eg_1.year # returns 1985
 Access specific components

Datetime
directly from datetime and
Import all classes and functions, eg_1.month # returns 3

from datetime import *
 Attributes time objects using their built-in
making every definition and all eg_2.day # returns 15

current_time = datetime.now()
 attributes
constants accessible without eg_2.hour # returns 8

current_date = date.today()

using a prefix; this is not advised eg_3.minute # returns 23

min_year = MINYEAR

for this module
eg_3.microsecond # returns 123456
max_year = MAXYEAR

Creating
Create a datetime object with eg_2_time = eg_2.time()

Extract the time component
import datetime as dt

Datetime
both date (March 13, 1985) and from a datetime object that
eg_1 = dt.datetime(1985, 3, 13, 14, 30, 45) # 08:45:30
Objects time (14:30:45) components contains both date and time
using the .time() method
from datetime import datetime as dt
Convert a formatted string
into a datetime object; the
eg_2 = dt.strptime("15/08/1990 08:45:30",

"p" stands for parsing

"%d/%m/%Y %H:%M:%S")

Python Cheat Sheet Free resources at dataquest.io/guide/introduction-to-python-tutorial