cheatsheets VS NumPy

Compare cheatsheets vs NumPy and see what are their differences.


The fundamental package for scientific computing with Python. (by numpy)
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cheatsheets NumPy
126 272
7,235 26,290
0.6% 1.6%
7.1 10.0
10 days ago 4 days ago
Python Python
BSD 2-clause "Simplified" License GNU General Public License v3.0 or later
The number of mentions indicates the total number of mentions that we've tracked plus the number of user suggested alternatives.
Stars - the number of stars that a project has on GitHub. Growth - month over month growth in stars.
Activity is a relative number indicating how actively a project is being developed. Recent commits have higher weight than older ones.
For example, an activity of 9.0 indicates that a project is amongst the top 10% of the most actively developed projects that we are tracking.


Posts with mentions or reviews of cheatsheets. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-12-25.


Posts with mentions or reviews of NumPy. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-03-20.

What are some alternatives?

When comparing cheatsheets and NumPy you can also consider the following projects:

finplot - Performant and effortless finance plotting for Python

SymPy - A computer algebra system written in pure Python

manim - A community-maintained Python framework for creating mathematical animations.

Pandas - Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

Fast-F1 - FastF1 is a python package for accessing and analyzing Formula 1 results, schedules, timing data and telemetry

blaze - NumPy and Pandas interface to Big Data

SciPy - SciPy library main repository

Keras - Deep Learning for humans

Numba - NumPy aware dynamic Python compiler using LLVM

geogebra - GeoGebra apps (mirror)

Nim - Nim is a statically typed compiled systems programming language. It combines successful concepts from mature languages like Python, Ada and Modula. Its design focuses on efficiency, expressiveness, and elegance (in that order of priority).