Released in 1991, Python has become one of the most popular general-purpose programming languages. It gained popularity because of its design philosophy, features, and ease of use.
The majority of professional developers agree that Python is the simplest and most convenient language. Even beginners can learn, write and execute Python programs much faster than other programming languages.
And since there are plenty of documentation, guides, and video tutorials available out there, both students and professionals can receive the support required to enhance their knowledge about Python.
A lot of organizations use Python to handle data processing workloads. It is extensively used in Big Data, Machine learning, Cloud Computing, and Artificial Intelligence research purposes.
According to the Emergen Research report, the global Python market size will exceed $100 million by 2030, growing at a CAGR of 44.8% from 2022 to 2030. The key factors behind this growth include rising demand for Python in end-use applications, increasing adoption of data analytics and edge computing, and growing dependency on Python over other programming languages.
Overall, the trend clearly indicates that Python developers have great career opportunities ahead of them.
Below, we have curated a list of the best Python cheat sheet that will make you more productive. Whether you are learning basics or working on a complex project, these cheat sheets will help you brush up on your skills and save time.
23. Basic Reference by wilfredinni
This Python cheat sheet includes all basic references for both newcomers and advanced programmers. It is designed to lower the entry barrier for beginners and help experts refresh old tricks. It is based on the book Automate the Boring Stuff with Python.
22. Python 3 Cheat Sheet
Designed for students, this one will help you learn and remember common Python syntax. In addition to Python basics, it also includes modules, decorators, expectations, file IO, command line arguments, and useful libraries.
21. Plain Cheat Sheet
This is a simple and plain cheat sheet containing basic Python logic, strings, tuples, directories, and 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 with 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 the basic elements that are essential in the day-to-day routine of Python programmers. It consists of data structures, functions, exception handling, control and flow, common string operation, list, and 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 errors.
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 actions 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, variables, process strings, control structures, sort data, use operators, 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 scripts and steps while performing exploratory data analysis in Python.
10. Data Visualization in Python
No matter if you are a non-techie or a data scientist, visualization is easily interpreted by both. It is absolutely true that visually presented data speak for itself. This cheat sheet guides you on how to perform data visualization in Python and explore various ways to plot data into histograms, line graphs, bar charts, scatter plots, heatmap,s 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 the model’s performance, and tuning your model.
8. Learn Python for Data Science: Infographics
One of the biggest challenges beginners face is ‘where do I start?’. The infographic is designed to solve this problem for those who want to learn Python for Data Science.
7. Learn X in Y minutes
The 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 that is compatible with version 2.7 and 3.x, 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 getting the designed outcome. This cheat sheet presents the steps of cleaning data related to tweets before mining them. Although the 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 sheets for Data Science. They have presented the information in a pretty nice way, and it’s easy to understand and learn each element. Plus, 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 increasing adoption of machine learning techniques, this cheat sheet acts as a perfect guide to help you bring 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 of syntax rules and important concepts. It includes an overview of basic Python functions, classes, variables, dictionaries, files, and exceptions. Plus, 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.
More to Know
How many libraries are available in Python?
More than 137,500 Python libraries are available for developing models and applications in various fields, including data science, data visualization, artificial intelligence, machine learning, and more.
NuPIC, Scikit-learn, Ramp, NumPy, TensorFlow, PyTorch, PyBrain, Matplotlib, SymPy, Scipy, Theano, and Dash are some of the most popular examples of Python libraries.
What companies use Python?
Several companies, from tech giants to fintech startups, rely on Python as part of their tech stack. For example, it is used by Google, IBM, Facebook, NASA, NetFlix, Spotify, Reddit, JP Morgan, Goldman Sachs, and a number of other well-established companies.
Moreover, Python is heavily used in academic research, especially in mathematics, biology, and bioinformatics. And since it has built-in support for scientific computing and task automation, it is extensively for scientific computing as well.
Following are the top Python applications in the real world
- Machine learning
- Data Science
- Web development and web scraping applications
- Game development
- Robotics and automation
- Image processing
- Business applications
- Embedded Applications
How much do Python developers make?
According to Indeed, Python developers make an average of $116,000 per year in the United States. While the average junior developer salary is about $80,000, senior developers earn $135,000 a year on average.
Selby Jennings, Stefanini IT Solution, NCS, Vaco, and Bank of America are among the highest paying companies.