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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.
0.11.0 released (April 22, 2013)¶
The new major 0.11 release is out. This includes the usual slate of bug fixes, and many new important features. Some highlights among new features include:
- New precision indexing fields loc, iloc, at, and iat, to reduce occasional ambiguity in the catch-all hitherto ix method.
- Expanded support for NumPy data types in DataFrame
- NumExpr integration to accelerate various operator evaluation
- New Cookbook and 10 minutes to pandas pages in the documentation by Jeff Reback
- Improved DataFrame to CSV exporting performance
Please check out the What’s New page for much more detail.
Find binaries and source archives for v0.11.0 are available now
Note we are posting stable daily development builds of the pandas windows binaries here. You can download them via the Development Version section of the “get pandas” page.
Quick 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 statistical modeling outside of linear and panel regression; for this, look to the statsmodels project. 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?¶
“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.”
“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.”
“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;
- The library has been ruthlessly optimized for performance, with critical code paths compiled to 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.
Why not R?¶
First of all, we love open source R! It is the most widely-used open source environment for statistical modeling and graphics, and it provided some early inspiration for pandas features. R users will be pleased to find this library adopts some of the best concepts of R, like the foundational DataFrame (one user familiar with R has described pandas as “R data.frame on steroids”). But pandas also seeks to solve some frustrations common to R users:
- R has barebones data alignment and indexing functionality, leaving much work to the user. pandas makes it easy and intuitive to work with messy, irregularly indexed data, like time series data. pandas also provides rich tools, like hierarchical indexing, not found in R;
- R is not well-suited to general purpose programming and system development. pandas enables you to do large-scale data processing seamlessly when developing your production applications;
- Hybrid systems connecting R to a low-productivity systems language like Java, C++, or C# suffer from significantly reduced agility and maintainability, and you’re still stuck developing the system components in a low-productivity language;
- The “copyleft” GPL license of R can create concerns for commercial software vendors who want to distribute R with their software under another license. Python and pandas use more permissive licenses.
