A Complete Python Cheat Sheet (Updated 2023)

The United States has the largest number of software developers specializing in technologies like Python. If you want to be one of them, it is best to start with the fundamentals. We have compiled a Python cheat sheet below to kickstart your learning journey!

Applications of Python

As a leading general-purpose programming language, Python is used for a wide range of industry applications.  Here are some of its popular use cases:

  • Web development is backed by frameworks like Django, Pyramid, Flask, and content management systems such as Plone.
  • Scientific and numeric computing powered by SciPy, Pandas, IPython, etc.
  • Desktop GUIs enabled by toolkits like Livy, wxWidgets, PySide, and GTK+.
  • Software development, including build, control and management, and testing.
  • Programming-related education and training, both at the introductory and advanced levels.
  • Business applications encompassing ERP and e-commerce solutions. Examples of enterprise application platforms include Odoo and Tryton. 

In terms of technical skills, Python lets you master two coding tasks at once, i.e., server-side development and machine learning. It is open-sourced, equipped with extensive libraries, and supports user-friendly data structures. Moreover, you can easily find a Python cheat sheet pdf online to clarify the basics. 

The following Python cheat sheet will familiarize you with the data types, math operators, strings, functions, lists, and tuples. We have also included Regular Expressions (Regex) information to give you a well-rounded view of the programming language. 

Getting Started with Python

The first step is to check whether your computer has pre-installed Python. You can do so via the Command Line search. After that, you can begin writing your code in any text editor and save the file in .py format. You would then be able to run the code in the Command Line prompt. 

However, this approach is suitable only for straightforward, non-data science tasks. You might want to switch to IDE or IDLE if you want to interpret your code. If you are a beginner in python and data science, upGrad’s data science online courses can definitely help you dive deeper into the world of data and analytics.

IDLE stands for Integrated Development and Learning Environment. Every installation comes with a Python IDLE that highlights relevant keywords or string functions. Shell is the default mode of operation that lets you test various code snippets via the following tasks: 

  • Read statements
  • Evaluate results
  • Print results on the screen
  • Loop to the next statement 

Data Types in Python

A Python value is termed an “object.” Every object has a particular data type. Here is a list of the most-used data types with examples:

  • Integers: Represented by keyword (int), it includes integer numbers, such as -2, -1, 0, 1, 2, etc.
  • Floating-point numbers: Non-integer fractional numbers denoted by (float). For example, -1.5, -1, -0.5, 0, 0.5, 1, 1.5
  • Strings: Sequence of characters that cannot be changed once defined. For example, “hello”, “hey”. Typically, single, double, or triple quotes are used to create a basic Python string.  Whichever option you choose, keep it consistent throughout the program. Here are some other things to keep in mind:
    • The print() function would output your string to the console window. 
    • You can apply join() or replace() to modify these strings but cannot rewrite the original. 
  • Lists: Ordered sequence of elements that keep the data together so that you can perform operations on several values at once. Each value is termed as an “item” and placed within square brackets. The items can be changed once stored. Consider the examples below.
    • one_list = [1, 2, 3, 4]
    • two_list = [“b”, “c”, “f” “g”]
    • three_list = [“4”, d, “car”, 7]
  • Tuples: Similar to lists, but the stored values cannot be changed. You can create a tuple as follows:
    • new_tuple = (5, 6, 7, 8)
    • my_tuple[0:5]
    • (2, 3, 4)
  • Dictionaries: Indexes that hold key-value pairs. It can include integers, boolean, or strings. For instance, Buyer 1= {‘username’: ‘john doe, ‘online’: true ‘friends’:150} 

You can use the any of these two options to create a dictionary:

    • my_dict = {}
    • new_dict= dict() 

Let us now look at the common practicalities of these data types. 

String Concatenation & Replication

Concatenation involves adding two strings together with the “+” operator, as demonstrated below.

    • my_string = “I love”
    • other_string = “reading books”
    • final_string = my_string + other_string

Notably, concatenation is only possible for the same data types. If you try to use “+” for a string and integer, you will encounter an error in Python. 

The replication command lets you repeat a string using the * operator. 

    • ‘Alex’ * 4 ‘AlexAlexAlexAlex’
    • print(“Alex” * 4)

However, this only holds true for string data types. When * is applied to numbers, it acts as a multiplier, not a replicator.

Math Operators

You can apply several math operations with numbers via specific operators. For reference, let’s examine this list:

  • To return an exponent, use “**” (2 ** 4 = 16)
  • To multiply numbers, use the single asterisk sign, “*” (2 * 2 = 4)
  • To get the quotient in integer division, use “//” as the operator (20 // 8 = 2) 
  • For the remainder, apply the “%” symbol (20 % 8 = 4)
  • For the floating point number, apply “/” (20 / 8 = 2.5)
  • For subtraction, “-” is the standard operator (6 -2 = 4)
  • To add numbers, use “+” (3 + 3 = 6)

Functions in Python

Functions are blocks of coded instruction capable of performing particular actions. Python has some built-in functions, namely: 

  • Input(): Prompts the user for input, which is further stored as a string. 
  • len(): Finds the length of strings, lists, tuples, dictionaries, and other data types.
  • filter(): Excludes items in iterable objects, such as lists, tuples, or dictionaries. 

You can also define your own function using the def keyword followed by name():. Here, the parentheses can either stay empty or contain any parameters to specify the purpose of the function. 

Performing Operations with Lists 

The list() function provides an alternate way of creating lists in Python. The statements mentioned below illustrate this option. 

  • my_list = list ((“1”, “2”, “3”))
  • print(my_list)

The append() or insert() functions are used to add new items to a list. Functions like remove() and pop() let you remove items from a list. Alternatively, you may try the del keyword to delete a specific item. The “+” operator combines two lists, and the sort () function organizes the items in your list. 

Working with ‘If Statements’

Python supports the basic logical conditions from math:  

  • Equals: a == b
  • Not Equals: a != b
  • Less than: a < b
  • Less than or equal to a <= b
  • Greater than: a > b 
  • Greater than or equal to a >= b 

You can leverage these conditions in various ways. But most likely, you’ll use them in “if statements” and loop.

The goal of a conditional statement is to check if it’s True or False.

if 5 > 1: print(“That’s True!”)

Output: That’s True!

You can know more about Nested If Statements, Elif statements, If Else Statements, and If-Not statements in any Python cheat sheet pdf

Creating Python Classes

Every element, along with its methods and properties, is an object in Python, considering it is an object-oriented programming language. Classes are blueprints for creating these objects. While a class is manifested in a program, objects are the instances of the class. Suppose you have to create a SampleClass with a property named x. You will begin with:

  • class SampleClass: 
  • z = 4

In the next step, you will create an object using your SampleClass. You can do so using p1 = SampleClass(). You can further assign attributes and methods to your object with a few simple steps. 

Python Exceptions (Errors)

Here is a list of some common errors that pop up while using Python. 

    • KeyError: When a dictionary key doesn’t feature in the set of existing keys.
    • TypeError: When an operation or function is inapplicable to an object type.
    • ValueError: When a built-in operation or function gets an argument with the correct type but of inappropriate value. 
    • IndexError: When a subscript cannot be detected, being out of range. 
  • ZeroDivision: When the second argument of a division operation is zero.
  • AttributeError: When an attribute assignment fails.
  • ImportError: When an import statement fizzles in locating the module definition.
  • OSError: A system-related error.

For troubleshooting these errors in Python, you can use exception-handling resources — try/except statements. 

Python Regex Cheat Sheet

Regex is an integral part of any programming language. It helps you search and replace specific text patterns. In other words, it is a set of characters that lets you remember syntax and how to form patterns depending on your requirements. So, let’s look at some useful regex resources for Python. 

Basic characters

  • ^ matches a string expression to its right before the line break
  • $ matches the expression to its left before the string experiences a line break
  • xy matches the xy string.
  • a|b matches the a or b expressions. b is left untried if a gets matched first. 


  • + matches an expression to its left once or more than once.
  • * matches an expression to its left 0 or multiple times.
  • ? matches an expression to its left between 0 and 1 time. 
  • {p} matches an expression to its left not less than p times.
  • {p,q} matches an expression to its left between p and q times. 
  • {p,} matches an expression to its left p times or more than p times. 
  • {,q} matches an expression to its left through q times. 

Module Functions

  • re.findall (A, B) returns a list of all instances of expression A in the B string. 
  • (A, B) returns a re-match object of the first insurance of expression A in the B string. 
  • re.sub (A, B, C) replaces A with B in the C string. 

You can find more regular expressions on character classes, sets, and groups in any Python regex cheat sheet available online. 

Summing up

In this blog, we detailed the foundational steps of working with the Python programming language. We covered everything from IDLE to integers, strings, lists, dictionaries, tuples, and math operators. We also learned how to define a function and discussed examples of different statements and errors. By no means is the above checklist complete, but it can definitely help you get the hang of Python. Once you are through with these nuts and bolts, you can increase your speed and productivity with regular practice. 

Additionally, Python’s active support community and advanced online courses can help you stay updated. Check out upGrad’s Executive PG Program in Software Development and other programs in technology, data science, and machine learning. The platform allows the flexibility to learn at your own pace, a benefit that is celebrated in over 85 countries. upGrad courses have transformed the career trajectory of more than 40,000 paid learners and 500,000 working professionals globally. Perhaps the above Python cheat sheet would fuel your curiosity to explore and upskill! 

What does a Python cheat sheet help you with?

A comprehensive cheat sheet can refresh your knowledge about the fundamental concepts and use cases of Python. It typically includes details like data types, functions, classes, common errors, and python regular expressions (regex).

Which data types are most used in Python?

Integers, Floating-point numbers, strings, lists, tuples, and dictionaries are some of the most commonly used data types in Python. Every type has its own specifications and practicalities. For example, items stored in strings and tuples cannot be changed once they are defined. However, lists are mutable, i.e., items can be changed.

How can Python training advance your career?

Python has various applications across industry sectors, such as web development, scientific computing, data science, and software development. Once you are through with the basics of this programming language, you can upskill with advanced courses and transition to high-paying roles.

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