Python Data Analysis Library

pandas is an open source, BSD-licensed library providing high-performance, easy-to-use data structures and data analysis tools for the Pythonopen in new window programming language.

pandas is a NumFOCUSopen in new window sponsored project. This will help ensure the success of development of pandas as a world-class open-source project, and makes it possible to donateopen in new window to the project.


v0.25.0 Final (July 18, 2019)

This is a major release from 0.24.2 and includes a number of API changes, new features, enhancements, and performance improvements along with a large number of bug fixes.

Highlights include:

The release can be installed with conda from conda-forge or the default channel:

$ conda install pandas

Or via PyPI:

python3 -m pip install --upgrade pandas

See the full whatsnew for a list of all the changes.

v0.24.2 Final (March 14, 2019)

This is a minor bug-fix release in the 0.24.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 Pandas

Or via PyPI:

$ python3 -m pip install --upgrade Pandas

See the full whatsnewopen in new window for a list of all the changes.

Best way to Install

The best way to get pandas is via condaopen in new window

$ conda install Pandas

Packages are available for all supported python versionsopen in new window on Windows, Linux, and MacOS.

Wheels are also uploaded to PyPIopen in new window and can be installed with

$ pip install Pandas

Quick vignette

10-minute tour of pandasopen in new window from Wes McKinneyopen in new window on Vimeoopen in new window.

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 IPythonopen in new window 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 statsmodelsopen in new window and scikit-learnopen in new window. More work is still needed to make Python a first class statistical modeling environment, but we are well on our way toward that goal.

What do our users have to say?

  • Roni Israelov,Phd(Portfolio Manager AQR Capital Managementopen in new window): “pandas allows us to focus more on research and less on programming. We have found pandas easy to learn, easy to use, and easy to maintain. The bottom line is that it has increased our productivity.”
    • AQR Capital Management
  • David Himrod(Director of Optimization & Analytics AppNexusopen in new window): “pandas is the perfect tool for bridging the gap between rapid iterations of ad-hoc analysis and production quality code. If you want one tool to be used across a multi-disciplined organization of engineers, mathematicians and analysts, look no further.”
    • AppNexus Logo
  • Olivier Pomel(CEO Datadogopen in new window): “We use pandas to process time series data on our production servers. The simplicity and elegance of its API, and its high level of performance for high-volume datasets, made it a perfect choice for us.”
    • Datadog Logo

Library Highlights

  • A fast and efficient DataFrame object for data manipulation with integrated indexing;
  • Tools for reading and writing data between in-memory data structures and different formats: CSV and text files, Microsoft Excel, SQL databases, and the fast HDF5 format;
  • Intelligent data alignment and integrated handling of missing data: gain automatic label-based alignment in computations and easily manipulate messy data into an orderly form;
  • Flexible reshaping and pivoting of data sets;
  • Intelligent label-based slicing, fancy indexing, and subsetting of large data sets;
  • Columns can be inserted and deleted from data structures for size mutability;
  • Aggregating or transforming data with a powerful group by engine allowing split-apply-combine operations on data sets;
  • High performance merging and joining of data sets;
  • Hierarchical axis indexing provides an intuitive way of working with high-dimensional data in a lower-dimensional data structure;
  • Time series-functionality: date range generation and frequency conversion, moving window statistics, moving window linear regressions, date shifting and lagging. Even create domain-specific time offsets and join time series without losing data;
  • Highly optimized for performance, with critical code paths written in Cython or C.
  • Python with pandas is in use in a wide variety of academic and commercial domains, including Finance, Neuroscience, Economics, Statistics, Advertising, Web Analytics, and more.