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pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
pandas is a NumFOCUS sponsored project. This will help ensure the success of development of pandas as a world-class open-source project, and makes it possible to donate to the project.
v0.23.4 Final (August 3, 2018)This is a minor bug-fix release in the 0.23.x series and includes some regression fixes, bug fixes, and performance improvements. We recommend that all users upgrade to this version.
The release can be installed with conda from conda-forge or the default channel:
conda install pandasOr via PyPI:
python3 -m pip install --upgrade pandasSee the full whatsnew for a list of all the changes.
v0.23.0 Final (May 15, 2018)
This is a major release from 0.22.0 and includes a number of API changes, new features, enhancements, and performance improvements along with a large number of bug fixes.
Highlights include:
- Round-trippable JSON format with ‘table’ orient.
- Instantiation from dicts respects order for Python 3.6+.
- Dependent column arguments for assign.
- Merging / sorting on a combination of columns and index levels.
- Extending Pandas with custom types.
- Excluding unobserved categories from groupby.
The release candidate can be installed with conda from our development channel (builds for osx-64, linux-64 and win-64 for Python 2.7, Python 3.5, and Python 3.6 are all available):
conda install pandasor conda forge:
conda install -c conda-forge pandasOr via PyPI:
python3 -m pip install --upgrade pandas==0.23.0See the full whatsnew for a list of all the changes.
Best way to Install
The best way to get pandas is via conda
conda install pandas
Packages are available for all supported python versions on Windows, Linux, and MacOS.
Wheels are also uploaded to PyPI and can be installed with
pip install pandasQuick vignette
10-minute tour of pandas from Wes McKinney on Vimeo.
What problem does pandas solve?
Python has long been great for data munging and preparation, but less so for data analysis and modeling. pandas helps fill this gap, enabling you to carry out your entire data analysis workflow in Python without having to switch to a more domain specific language like R.
Combined with the excellent IPython toolkit and other libraries, the environment for doing data analysis in Python excels in performance, productivity, and the ability to collaborate.
pandas does not implement significant modeling functionality outside of linear and panel regression; for this, look to statsmodels and scikit-learn. More work is still needed to make Python a first class statistical modeling environment, but we are well on our way toward that goal.
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