Pandas

Description

Pandas is a Python package providing fast, powerful, and easy-to-use data structures and data analysis tools designed to make it easy and intuitive to work with "relational" or "labeled" data. It is expected to become the fundamental high-level building block for doing practical, real-world data analysis in Python. It also has the ambitious goal of becoming the most powerful and flexible open source data analysis/manipulation tool available in any language. It is well on its way in this direction already.

What is Pandas?

Pandas is a free Python library focused on data manipulation and analysis. It provides high-performance, flexible, and expressive data structures such as DataFrame, making it easy to manage structured data like spreadsheets or SQL tables. NumPy-based, it efficiently handles large data sets, as well as merging, reshaping and grouping data. Its versatility is unparalleled, making it a staple for data analysis in Python.

How alive is this project?

The project started in 2008, and is still growing. It currently has 44.465 stars on GitHub and it has over 10.000 contributors, 22 of them actively commmitting in the last 6 months. In the last month 42 authors have pushed 55 commits to main and 58 commits to all branches (excluding merges). To date the project has over 23.000 closed issues and over 26.000 merged pull requests.

How important is this?

Pandas is an essential tool in the Python universe for data manipulation and analysis. It simplifies complex data operations, making it a go-to choice for analysts and data scientists. With Pandas, you can analyse large datasets and draw conclusions based on statistical models. Its ability to handle various data types, including CSV, Excel, SQL and JSON, is a clear indication of its importance in simplifying data workflow. Its robust functionality and ease of use make it a must-have tool for efficient and effective data analysis.

What is it good for?

Pandas is an essential Python component for data manipulation and analysis. It provides powerful tools for preprocessing, transforming, and visualising data. Its usefulness is clear across industries: financial analysts use it to predict stock trends, healthcare professionals interpret patient records with it, and marketers measure campaign impact. Its simplicity and broad functionality make it the de facto library for extracting insights and driving decisions.

What are the technologies involved?

Pandas is based on Python (mainly), Cython, HTML and C. The project integrates NumPy.