pandas

Introduction to the pandas Project

Pandas is a Python package that acts as a powerful toolkit for data analysis. Known for its speed, flexibility, and ease of use, pandas makes working with "relational" or "labeled" data both simple and straightforward. It serves as a crucial component for practical, real-world data analysis in Python. Furthermore, it strives to become the most potent and adaptable open-source data manipulation tool available across any programming language, and it is already progressing towards this goal.

Main Features of Pandas

Pandas offers a plethora of features that make it a favorite among data scientists and analysts:

• Handling Missing Data: It efficiently manages missing data, whether represented as NaN, NA, or NaT.
• Size Mutability: One can easily add or delete columns from DataFrame and higher dimensional objects.
• Data Alignment: Pandas automatically aligns data, although users can opt for explicit alignment through labels.
• Group By Functionality: The package provides robust and flexible capabilities for performing split-apply-combine operations on datasets, useful for both aggregation and data transformation.
• Conversion: It simplifies converting data structures from other Python and NumPy formats into DataFrame objects.
• Advanced Indexing and Subsetting: The package allows intelligent label-based slicing, fancy indexing, and subsetting of large datasets.
• Merging and Joining: Merging and joining datasets becomes intuitive with pandas.
• Data Reshaping and Pivoting: It offers flexibility in reshaping and pivoting datasets to suit various analysis needs.
• Hierarchical Labeling: Supports having multiple labels per axis tick.
• Robust Input/Output Options: Pandas includes tools for loading data from flat files such as CSVs, Excel files, databases, and the high-speed HDF5 format.
• Time Series Functionality: It includes features specific to time series data, including date range generation, frequency conversion, and moving window statistics.

Where to Get Pandas

Pandas's source code is hosted on GitHub. The latest released version is available through the Python Package Index (PyPI) and Conda. It can be installed using the following commands:

# Installing with Conda
conda install -c conda-forge pandas

# Installing with PyPI
pip install pandas

The changes between versions are documented in detail and can be reviewed on their official release notes page.

Dependencies

Pandas relies on other Python libraries to function effectively. Its primary dependencies include:

• NumPy: Facilitates the handling of large, multi-dimensional arrays and matrices along with mathematical operations on these arrays.
• python-dateutil: Provides extensions for Python's standard datetime module for robust date/time operations.
• pytz: Brings timezone support, enabling accurate cross-platform timezone calculations.

A full list of required and optional dependencies can be found within pandas's installation documentation.

Installation from Sources

For those interested in installing pandas from its source, additional installations such as Cython are necessary. This can be achieved with:

pip install cython

After obtaining the source code, execute the following commands in the pandas directory:

pip install .

For development mode, use:

python -m pip install -ve . --no-build-isolation

Full guidelines for installing from the source are available in the pandas documentation.

License

Pandas is licensed under the BSD 3-Clause license, making it open-source and free to use.

Documentation

Comprehensive documentation for pandas can be found on PyData's website. This includes user guides, installation instructions, and feature descriptions.

Background

The development of pandas began in 2008 at AQR, a quantitative hedge fund. Since then, it has continued to evolve under active development.

Getting Help and Contributing to Pandas

For questions on usage or troubleshooting, one can visit StackOverflow or the PyData mailing list. Collaborative discussions occur on GitHub, via issue tracker, and through the pandas-dev mailing list. There are also Slack channels available for immediate queries and community meetings for broader discussions.

Pandas operates a welcoming contributor environment, encouraging anyone to report bugs, suggest enhancements, or seek ways to improve the project. Interested contributors can explore open issues on the project's GitHub site to start working with the pandas codebase. Guidance for new contributors is regularly provided through community meetings and resource materials.

Pandas prioritizes maintaining a respectful and constructive community, as indicated in its Contributor Code of Conduct. More information on contributing can be found in their official contributing guide.