What problem it solves

Working with relational or labeled data in Python requires flexible structures and tools for handling missing values, reshaping, indexing, combining, and loading data. pandas provides these capabilities through data structures intended for practical data analysis workflows.

Key features

• Labeled data structures — pandas provides flexible, expressive structures for working with relational or labeled data and supports automatic or explicit label alignment.

  Source: README

• Data manipulation and aggregation — Users can insert or delete columns, group and transform data, merge and join datasets, and reshape or pivot tables.

  Source: README

• Indexing and missing data — Label-based slicing, fancy indexing, and subsetting support analysis of large datasets, while missing values can be handled across floating-point and non-floating-point data.

  Source: README

• Data input and output — pandas includes tools for loading data from flat files, Excel files, and databases, as well as saving and loading HDF5 data.

  Source: README

• Time series support — Time series workflows include date range generation, frequency conversion, moving window statistics, and date shifting and lagging.

  Source: README

Quick start

Copied verbatim from the project's README.

# conda
conda install -c conda-forge pandas

# or PyPI
pip install pandas

Who it may suit

This project may suit Python developers and data practitioners who need to analyze, transform, or load relational and labeled data. It may also suit contributors interested in open-source data analysis tooling.

Topics to discuss

• Which data-analysis workflows should receive more examples or documentation?

• Which data formats or optional integrations need stronger support?

• How can the contributor community improve onboarding for new contributors?

Written by ReadmeX AI from the project's README, source and public GitHub data; it may contain mistakes.

Read the original README https://github.com/pandas-dev/pandas#readme

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