Versions

Release

0.16.0 - March 2015
download // docs // pdf

Development

0.16.1 - April 2015
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Previous Releases

0.15.2 - download // docs // pdf
0.15.1 - download // docs // pdf
0.15.0 - download // docs // pdf
0.14.1 - download // docs // pdf
0.14.0 - download // docs // pdf
0.13.1 - download // docs // pdf
0.13.0 - download // docs // pdf
0.12.0 - download // docs // pdf
0.11.0 - download // docs // pdf
0.10.1 - download // docs // pdf
0.10.0 - download // docs // pdf
0.9.1 - download // docs // pdf
0.9.0 - download // docs // pdf

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Python for Data Analysis

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.

0.16.0 final (March 22, 2015)

This is a major release from 0.15.2 and includes a small number of API changes, several new features, enhancements, and performance improvements along with a large number of bug fixes. We recommend that all users upgrade to this version.

Highlights include:

  • DataFrame.assign method, see here
  • Series.to_coo/from_coo methods to interact with scipy.sparse, see here
  • Backwards incompatible change to Timedelta to conform the .seconds attribute with datetime.timedelta, see here
  • Changes to the .loc slicing API to conform with the behavior of .ix see here
  • Changes to the default for ordering in the Categorical constructor, see here
  • Enhancement to the .str accessor to make string operations easier, see here
  • The pandas.tools.rplot, pandas.sandbox.qtpandas and pandas.rpy modules are deprecated. We refer users to external packages like seaborn, pandas-qt and rpy2 for similar or equivalent functionality, see here

See the Whatsnew for an extensive list of all API changes, enhancements and bugs that have been fixed in 0.16.0.

For binaries and source archives of v0.16.0 final, see the GitHub Releases.

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?

AQR Capital Management Logo
Roni Israelov, PhD
Portfolio Manager

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.”

AppNexus Logo
David Himrod
Director of Optimization & Analytics

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.”

Datadog Logo
Olivier Pomel
CEO
“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.”

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.
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