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• Numpy Basic Statistics
• Numpy Summary Statistics

Over the past months, I have been gathering all the cheat sheets for Python, Machine Learning, and Data Science.I share them from time to time with teachers, friends, and colleagues, and recently I have been getting asked a lot by some of the followers on Instagram (@techtutor & @aihub), so I have managed and planned to share the entire cheat sheet collection. Python For Data Science - NumPy Library Cheat Sheet by Padma (padma-it) via cheatography.com/121896/cs/22473/ NumPy Multi- Dim ens ional Arrays (cont).

Numpy Basic Statistics

1. The topics are not only limited to. Metrics, Classification, Regression, Model selection, and; Diagnostics. A must-read for upcoming data scientists. Probabilities and Statistics (Afshine Amidi). The fifth part of the cheat sheet series of the Stanford Machine Learning Class gives you a quick start (they call it a “refresher”) in the crucial area of probability theory and statistics.
2. Cheat sheet for using NumPy in Python. “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 Summary Statistics

“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.”

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

Go to Content: NumPy Cheat Sheet – Python for Data Science