pandas enables users to carry out their entire data analysis workflow in Python without having to switch to a more domain-specific language like R. Combined with IPython (also a NumFOCUS fiscally sponsored project) and other libraries, the environment for doing data analysis in Python excels in performance, productivity, and the ability to collaborate. 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.
- 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
With the addition of pandas, NumFOCUS now fiscally sponsors ten different open source data science projects. To make a donation to support pandas or any of NumFOCUS’ other projects, click here.