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 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 pandas Or via PyPI: python3 - m pip install -- upgrade pandas See 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 pandas

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.

What do our users have to say?¶ Roni Israelov, PhD Portfolio Manager AQR Capital Management “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.” David Himrod Director of Optimization & Analytics AppNexus “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.” Olivier Pomel CEO Datadog “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.”