Right now, Python is the prevalent language of AI: a de facto standard for any one who wants to work in data science. From data analysis and visualization to complex deep neural networks, you can do anything with this easy-to-learn, free, open source, understandable, versatile, and library-rich scripting language. The language is not limited to data science use cases. You can also create web and mobile applications and games in python.
One of the most distinguishing features of Python is its vast, supportive community. Which enables you to find answers, guides and resources for every question you might have about this amazing script. In this week’s post, we have gathered a few well-organized, easy-to-use python cheat sheets. You can refer to them whenever you need a quick hint.
If you know basic programming concepts but absolutely no Python; If you’re moving from another programming language, or if you feel the need to google basic constructs and methods all the time, you could use a simple cheat sheet at your hand. Our suggestions here cover everything, from basic data types, lists, dictionaries, tuples,… to functions, exception handling and object oriented programming in python. Download this cheatsheet:
https://ehmatthes.github.io/pcc_2e/cheat_sheets/cheat_sheets/ The all-in-one beginner python cheat sheet from Python Crash Course, 2nd edition. Covering everything Python 3 and some basic libraries
https://overapi.com/python : Covers all basic structures and core built-in libraries you need to start programming in python.
: I don’t think many python programmers would actually forget how to define a function, or instantiate a dictionary. How about catching exceptions or using lambda functions? maybe. A more advanced cheat sheet would be more useful to those of us who are confident in our basic python, but less so when talking about things like concurrency and inheritance.
https://github.com/gto76/python-cheatsheet: The most comprehensive short reference on python you can find in only one web page. You can use it to do nearly anything with python. It covers not only every needed language feature and trick with a code example, but also reviews the basics of many essential libraries you need to make your gui, process audio, or do simple data science. The online version is free, but you need to buy the pdf version of it.
https://www.pythonsheets.com/: This website is a very nice resource to refer to is the large collection of code snippets in pysheets, a free, open source project gathering compact, simple, well commented code snippets to demonstrate how to use every feature of the programming language.
https://github.com/dennyzhang/cheatsheet-python-A4 : is shorter than the last two and does not cover core python, or libraries. It is useful to you if you are already an advanced pythonista, and just need to find some geekly tricks and subtle commands to complete your skills.
a. Importing Data: This cheat sheet from data camp covers everything you need to load your data and get your project started.
b. Data Cleaning : A summary of every step you must take in order to get your data cleaned and ready for processing.
4.Data Visualization Libraries:
There is no way someone could overemphasize the importance of how you communicate your work and demonstrate its results. These great cheat sheets help you remember every tool and trick in your hand that you can use to make your points stand out. We have one cheat sheet for each of the most known Python visualization libraries.
Matplotlib: A compact reference to the most used plotting library for Python, used for making line graphs and scatter plots, plot customization, making multiple plots, and analysing time-based data.
Seaborn: A more simple, less syntax-oriented way to do data visualization in Python, with beautiful default plot styling. https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Seaborn_Cheat_Sheet.pdf
Plotly : Plotly is also used for creative data visualization. It is more sophisticated than matplotlib, suitable for elaborate and creative graphs. Including interactive and geographical data visualization. https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf
5.Core Python Data Science Libraries:
It is nowadays -most probably- not easy at all to complete a data science project in Python without using any of these four elemental libraries. Pandas, Numpy, SciPy and Scikit-learn are your tools to process data frames and make Machine learning models quickly without coding each one from scratch. The following cheat sheets contain all the vital points you must find out to make use of this great toolbox.
Numpy : This cheatsheet from the famous DataCamp school contains all the basic array operations you can perform using Numpy with code snippets. https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Numpy_Python_Cheat_Sheet.pdf
Pandas : Here we have a two-page cheat sheet to the most popular data wrangling library from PyData website: https://pandas.pydata.org/Pandas_Cheat_Sheet.pdf
SciPy : The data camp SciPy cheat sheet is both compact and complete. https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_SciPy_Cheat_Sheet_Linear_Algebra.pdf
Scikit-learn: We found two best options for this essential machine learning library.
i)This beautiful, clean single page cheat sheet summarizes the SKlearn methods you need just to survive. https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Scikit_Learn_Cheat_Sheet_Python.pdf
ii)An infographic containing the signature and short description of the most important methods for data pre-processing, regression, classification, clustering, dimensionality reduction, model selection, metrics, etc. https://www.analyticsvidhya.com/blog/2016/12/cheatsheet-scikit-learn-caret-package-for-python-r-respectively/?utm_source=linkedin.com&utm_medium=social