NumPy Cheat Sheet — Python for Data Science April 13, 2017 NumPy is the library that gives Python its ability to work with data at speed. Originally, launched in 1995 as ‘Numeric,’ NumPy is the foundation on which many important Python data science libraries are built, including Pandas, SciPy and scikit-learn.
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Link to Content:
NumPy Cheat Sheet - Python for Data Science
Created/Published/Taught by:
Dataquest
Josh Devlin
Content Found Via:
Open Data Science
Free? Yes
Tags: functions / importing data / mathematics / numpy / python / statistics
Difficulty Rating:
“It’s common when first learning NumPy to have trouble remembering all the functions and methods that you need, and while at Dataquest we advocate getting used to consulting the NumPy documentation, sometimes it’s nice to have a handy reference, so we’ve put together this cheat sheet to help you out.”
Numpy Statistics Cheat Sheet Free
This cheat sheet covers the following topics:
- Key and Imports
- Importing/Exporting
- Creating Arrays
- Inspecting Properties
- Copying/sorting/reshaping
- Adding/removing Elements
- Combining/splitting
- Indexing/slicing/subsetting
- Scalar Math
- Vector Math
- Statstics
Recommended Prerequisites: none
Numpy Statistics Cheat Sheet Printable
Numpy Descriptive Statistics
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