Best 10 Scipy Cheat Sheets

4/5 - (1 vote)

Hey Finxters! Another 10 of the best cheat sheets is here for you to peruse and hang on your wall with your other Python cheat sheets on the wall! Today, we are going to browse cheat sheets for Scipy!! For a quick explanation, SciPy is a scientific computation library that uses NumPy underneath. SciPy stands for Scientific Python. It provides more utility functions for optimization, stats and signal processing. Now that we have a brief explanation on what it is, let us dive right into these cheat sheets that can be kept handy when learning to implement Scipy in Python!

Cheat Sheet 1: DataCamp

The first cheat sheet is from and is chock full of information for you to consume. You will learn to interact with Numpy and know which functions and methods to use for linear algebra and of course a help section. This is one I would hang behind my monitor behind the wall!

Pros: Rated β€˜E’ for everyone.

Cons: None that I can see.

Cheat Sheet 2: Quandl

This cheat sheet covers the three main data science libraries used in Python: Pandas, Numpy, and Scipy. It goes over the functions call but has explanations on each one. Near the end it shows how to import data sets for you to use! Great for a beginner project!

Pros: Rated β€˜E’ for everyone. Bonus Python project included!

Cons: None that I can see.

Cheat Sheet 3: Elite Data Science

This cheat sheet will walk you through some of the most common and useful functionality from these libraries. From importing data to a taste of Machine learning you can get a feel of what Python can do of the code examples.

Pros: Rated β€˜E’ for everyone.

Cons: None that I can see.

Cheat Sheet 4: Cheatography

If you ever needed help understanding how to test a hypothesis in Scipy using code examples and clear explanations on what is happening when you write the code.

Pros: Rated β€˜E’ for everyone.

Cons: None that I can see.

Cheat Sheet 5: Intellipaat

This cheat sheet is more a tutorial from It has full explanations with code examples to work. It has sufficient information about the scientific and technical library in Python, that is, Scipy. Nonetheless, it is more than worth your time to investigate and learn Scipy.

Pros: Rated β€˜E’ for everyone.

Cons: It is more a tutorial than a cheat sheet.

Cheat Sheet 6:

Scipy Cheat Sheet:

From the mouth of Scipy, this cheat sheet will show you all of the methods needed to perform different functions in Scipy and Python with explanations. This Comprehensive list has everything sorted neatly into the different functions to make it easy to look up as you are working in Scipy. This is one you will want in your notebook on the desk as an easy reference guide.

Pros: Rated β€˜E’ for everyone. Recommended for the wall or notebook for daily use!

Cons: None that I can see.

Cheat Sheet 7: Packt>

This is more a book than it is a cheat sheet. It focuses hard on mastering scipy giving you a project to work through so you can really get a grasp on Scipy and how it is implemented in Python. I recommend subscribing to the website for all of the information you will receive.

Pros: Rated β€˜E’ for everyone.

Cons: It is an ebook not a cheat sheet, but worth your time.

Cheat Sheet 8:

This is another ebook that I recommend keeping on hand to learn Scipy from beginner levels to advanced. This book contains code for you to work on in order to learn scipy in python building your skills. This is important for you to learn the skill you need for your data science career. I suggest reading the book, highlight the parts you don’t understand and print the code example to pin to the wall for help and minimize searching.

Pros: Rated β€˜E’ for everyone.

Cons: This is an ebook, but one of the best ways to learn.

Cheat Sheet 9: Packt>

This one is also a ebook from packt>. This ebook will teach you numerical and scientific computing in Python. You will also learn how to use Scipy in signal processing and how applications of Scipy can be used to collect, organize, analyze,a dn interpret data. By the end of the book, you will have fast, accurate, and easy-to-code solutions for numerical and scientific computing applications.

Pros: Rated β€˜E’ for everyone.

Cons: This is an ebook so you will be spending time reading and coding.

Cheat Sheet 10: Packt>

Recipes are great in that you can find the exact one you are looking for without having to wade through all the other code snippets you do not need. In this ebook, you can play around with each one of these codes and gain a hands-on understanding of Scipy and its real-world problem applications.

Pros: Rated β€˜E’ for everyone. The independent nature of the recipes allows you to hop around from each example making this book very versatile.

Cons: It is an ebook but a great one if you want to practice the different stacks of Scipy in Python.

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