Started as a weekend hobby project by Guido van Rossum in 1989, Python is today on of the most used high-level programming languages. It has replaced Java as 2nd most popular language on Github. YouTube, Google, Dropbox, Quora, Instagram and many other popular websites are built on Python. Most coders prefer using Python for developing artificial intelligence and machine learning apps.
Rather than explaining you the importance of cheat sheets, why not just begin with the most useful Python resources available on internet (for free) in the form of cheat sheet. Here we go.
21. Plain Cheat Sheet
This is a simple and plain cheat sheet containing basis Python logic, strings, tuples, directories, class and function definition.
20. Python Bokeh Cheat Sheet
For those who don’t know, Bokeh is an interactive visualization library in Python. It is especially useful with big datasets. The cheat sheet (created by DataCamp), provides you the basic steps for plotting, renderers, visual customization and statistical charts.
19. Python Cheat Sheet by DaveChild
Yet another simple and small cheat sheet consisting of Python sys variables, list methods, datetime methods, class special methods, sys.argv, indexes and slices.
18. Just the Basics
As the name suggests, it contains all basic elements that are essential in day-to-day routine of Python programmers. It consists of data structures, functions, exception handling, control and flow, common string operation, list, set and dict comprehensions.
17. Cheat Sheet by CodeConquest
It contains common syntaxes and data properties that you will find useful whenever you get stuck at common error.
16. Python for Dummies
Python for Dummies cheat sheet is divided into two parts – First is the ‘String Method’ that shows you how to perform common string methods or action on a string, and the second is ‘Builtin Function’ along with their pattern and corresponding action.
15. Python 3 Cheat Sheet
This memento of Python 3 focuses on elements that make it possible to start in algorithm/programming. It teaches you about conversions, variable assignments, statement block, boolean logic, loop control, exception and errors, integer sequence, formatting and more.
14. Python Seaborn Cheat Sheet
The Python Seaborn cheat sheet with code samples guides you through the data visualization library based on Matplotlib. It’ll tell you how to load data, set the figure aesthetics, customize and show your plot with Seaborn.
13. Language and Syntax Cheat Sheet
This one includes variable assignment, frequently used builtin types, comparison, basic arithmetic, control flow, classes, exception, file and path manipulation.
12. Python-LiveCode Cheat Sheet
A simple yet beautiful cheat sheet that teaches you how to write Python comments, variable, process string, control structures, sort data, use operator, and more.
11. Exploratory Data Analysis in Python
To build a healthy model, you should aware of the essential steps of data exploration. This cheat sheet will help you with different code and steps while performing exploratory data analysis in Python.
10. Data Visualization in Python
No matter if you are non-techie or data scientist, visualization is easily interpreted by both. It is absolutely true that visually presented data speak for itself. This cheat sheet guides you how to perform data visualization in Python and explore the various ways to plot data into histogram, line graph, bar charts, scatter plot, heatmap and more.
9. Scikit-Learn Cheat Sheet
If your work revolves around data science, the scikit-learn (open source Python library) cheat sheet will kickstart your project. It will help you with code examples, preprocessing data, evaluating model’s performance and tuning your model.
8. Learn Python for Data Science: Infographics
One of the biggest challenges beginners face is ‘from where do I start?’. The infographics is designed to solve this problem for those who want to learn Python for Data Science.
7. Learn X in Y minutes
Here, X stands for Python and the webpage shows you how to write Python syntax. Although the article applies to Python 2.7 specifically, it is possible to write Python code which is compatible with version 2.7 and 3.x at the same time, using _future_ imports.
6. Text Data Cleaning in Python
Text cleaning could be a tedious process, and knowing the correct approach is the key to get the designed outcome. This cheat sheet presents the steps of cleaning data related to tweets before mining them. Although examples shown here are associated with Twitter, you can apply these techniques to any text mining problem.
5. OverAPI Python
The webpage contains tons of Python elements of string, file, array, system variable, data, time, classes and random functions. Clicking on each element will take you to the new page that describes the element (you have clicked) with examples.
4. Python for Data Science
This is a single-page cheat sheet that contains basic elements essential for data science, such as variable and data types, strings, lists, libraries, numpy arrays, and more.
3. Basic and Intermediate Cheat Sheet for Data Science
DataQuest provides basic and intermediate Python cheat sheet for Data Science. They have presented the information in a pretty nice way and it’s easy to understand and learn each element. Moreover, there is an interactive Python editor at the end, where you can run and test your script.
2. Common Machine Learning Algorithms in Python and R
Considering the growth in usage of machine learning techniques in building models, this cheat sheet is good to act as a code guide to help you bring these machine learning algorithms to use. It includes code for linear regression, logistic regression, SVM, decision tree, random forest, k-means, gradient boosting and AdaBoost, Naive Bayes, and more.
1. Python Crash Course
This is a set of cheat sheets that remind you syntax rules and important concepts. It includes an overview of basic Python functions, classes, variables, dictionaries, files and exceptions. Furthermore, there are cheat sheets that focus on unit tests and test cases, developing games with Pygame, creating visualizations with matplotlib and Pygal, and building web apps with Django.