NumPy VS Pandas

Compare NumPy vs Pandas and see what are their differences.


The fundamental package for scientific computing with Python. (by numpy)


Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more (by pandas-dev)
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NumPy Pandas
272 399
26,510 42,159
1.4% 1.0%
10.0 10.0
5 days ago 1 day ago
Python Python
GNU General Public License v3.0 or later BSD 3-clause "New" or "Revised" License
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 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.


Posts with mentions or reviews of Pandas. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2024-05-08.

What are some alternatives?

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

SymPy - A computer algebra system written in pure Python

Cubes - [NOT MAINTAINED] Light-weight Python OLAP framework for multi-dimensional data analysis

blaze - NumPy and Pandas interface to Big Data

tensorflow - An Open Source Machine Learning Framework for Everyone

SciPy - SciPy library main repository

orange - 🍊 :bar_chart: :bulb: Orange: Interactive data analysis

Numba - NumPy aware dynamic Python compiler using LLVM

Airflow - Apache Airflow - A platform to programmatically author, schedule, and monitor workflows

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).

Keras - Deep Learning for humans

manim - Animation engine for explanatory math videos

Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration