Pandas VS Dask

Compare Pandas vs Dask and see what are their differences.

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 (by pandas-dev)
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Pandas Dask
393 32
41,678 11,906
1.6% 1.6%
10.0 9.7
4 days ago 6 days ago
Python Python
BSD 3-clause "New" or "Revised" License 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.

Pandas

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 2024-03-04.

Dask

Posts with mentions or reviews of Dask. We have used some of these posts to build our list of alternatives and similar projects. The last one was on 2023-06-15.

What are some alternatives?

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

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

tensorflow - An Open Source Machine Learning Framework for Everyone

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

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

Keras - Deep Learning for humans

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

Numba - NumPy aware dynamic Python compiler using LLVM

pyexcel - Single API for reading, manipulating and writing data in csv, ods, xls, xlsx and xlsm files

Kedro - Kedro is a toolbox for production-ready data science. It uses software engineering best practices to help you create data engineering and data science pipelines that are reproducible, maintainable, and modular.

NetworkX - Network Analysis in Python

SymPy - A computer algebra system written in pure Python