PYTHON FOR DATA SCIENCE CHEAT SHEET - created by Tomi Mester - print it in color and keep it next to you

Oct 13, 2026

Upload by Grenda

Python for Data Science Cheat Sheet by Tomi Mester provides a structured learning path from absolute basics to intermediate concepts for data-focused Python usage. It covers core syntax and variable assignment, fundamental data types (integers, floats, booleans, strings), arithmetic operators, and essential data structures such as lists, dictionaries, plus nested combinations. It also introduces key functions and methods, including practical built-ins like print, len, and type, supporting beginner workflows through concise examples.

LanguageEnglish

CategoryPresentations

FormatPDF

Pages23

Reading Time8 min

AccessFree to download

VARIABLES IN PYTHON

BASIC DATA TYPES

ARITHMETIC OPERATORS

DATA STRUCTURES

#1: LISTS

#2: DICTIONARY

NESTED LISTS AND/OR DICTIONARY

FUNCTIONS AND METHODS

THE MOST IMPORTANT BUILT-IN FUNCTIONS

THE MOST IMPORTANT METHODS FOR PYTHON STRINGS

PYTHON FOR DATA SCIENCE CHEAT SHEET created by Tomi Mester I originally created this cheat sheet for my Python course and workshop participants.* But I have decided to open-source it and make it available for everyone who wants to learn Python for data science. It's designed to give you a meaningful structure but also to let you add your own notes (that's why the empty boxes are there) . It starts from the absolute basicsprint(‘Hello World!’) -and guides you to the intermediate level (for loops, if statements, importing advanced libraries) . It also contains a few important functions of advanced libraries like pandas. I added everything that you will need to get started as an absolute beginner — and I’ll continuously update and extend it to make it a full comprehensive cheat sheet for junior data analysts/scientists. The ideal use case of this cheat sheet is that you print it in color and keep it next to you while you are learning and practicing Python on your computer. Enjoy! Cheers, Tomi Mester *The workshops and courses I mentioned: Online Python and Pandas tutorial (free): [data36.com/python-tutorial](data36.com/python-tutorial)[ ](data36.com/python-tutorial)Python workshop for companies: [data36.com/python-workshop](data36.com/python-workshop) 6-week Data Science course: [data36.com/jds](data36.com/jds) PYTHON FOR DATA SCIENCE CHEAT SHEET VARIABLES IN PYTHON In Python, you' ll work with variables a lot. You can assign a value to a variable as simply as: variable_name = variable_value If you assign a new value to a variable that you have used before, it will overwrite your previous value. Examples: a = 100 b = 'some_random_text' c = True d = 0.75 [your notes] BASIC DATA TYPES In Python, we have quite a few different data types. But these four are the most important ones (for now): 1. Integer. A whole number without a fractional part. E.g. 100 , 156, 2012412 2. Float. A number with a fractional part. E.g. 0.75, 3.1415, 961.1241250, 7/8 3. Boolean. A binary value. It can be either True or False. 4. String. It's a sequence of Unicode characters (e.g. numbers, letters, punctuation) . It can be alphabetical letters only—or a mix of letters, numbers and other characters. In Python, it's easy to identify a string since it has to be between apostrophes (or quotation marks) . E.g. 'hello', 'R2D2', 'Tom i',' [124.56.128.41](124.56.128.41) ' CREATED BY TOMI MESTER | DATA36.COM 1 PYTHON FOR DATA SCIENCE CHEAT SHEET ARITHMETIC OPERATORS Let's assign two values! a = 3 b = 4 The arithmetic operations you can do with them: Artihmetic operator What does it do? Result in our example a + b adds a to b 7 a-b subtracts b from a -1 | a * b | multiplies a | by b | 12 | | --- | --- | --- | --- | | a / b | divides a by | b | 0.75 | b % a divides b by a and returns remainder 1 a ** b raises a to the the power of b 81 DATA STRUCTURES Data structures exist to organize your data and store related/similar data points in one "place. " There are four data structure types. The two most important in data science are: lists and dictionaries. \#1: LISTS A list is a sequence of values. It can store integers, strings, booleans, anythingeven a mix of these. Example: sample_list = ['value1 ', 'value2 ', 'value3 ', 'value4 ', 1, 2, 3, 4, True, False] Querying an element of a list: sample_list[3] IMPORTANT! Python works with zero-based indexing. E.g. Example: sample_list[3]—(This returns 'value4'.) CREATED BY TOMI MESTER | DATA36.COM 2 PYTHON FOR DATA SCIENCE CHEAT SHEET \#2: DICTIONARY A dictionary is a collection of key-value pairs. (Key is a unique identifier, value is the actual data.) Example: sample_dict = {'apple': 3, 'tomato': 4, 'orange': 1, 'banana': 14, 'is_store_open': True} Querying an element of a dictionary: sample_dict['banana'] —(This returns 14.) NESTED LISTS AND/OR DICTIONARY You can create nested lists and

FAQs

What does the cheat sheet help you learn in Python for data science?

It guides learners from basic Python concepts to intermediate topics and includes important advanced-library functionality such as pandas functions. It also encourages adding personal notes.

Which basic Python data types are covered?

The sheet focuses on integers, floats, booleans, and strings, with examples and notes on how strings are represented using quotes.

How do lists and dictionaries work, and how do you access elements?

Lists store sequences of values and use zero-based indexing for element access, while dictionaries store key-value pairs and are queried by key. It also shows how to use nested lists and nested dictionaries.

Oct 13, 2026

Upload by Grenda