The source code is currently hosted on GitHub at:īinary installers for the latest released version are available at the Python Generation and frequency conversion, moving window statistics, Time series-specific functionality: date range.
(CSV and delimited), Excel files, databases,Īnd saving/loading data from the ultrafast HDF5 format
Robust IO tools for loading data from flat files. Hierarchical labeling of axes (possible to have multiple. Split-apply-combine operations on data sets, for both aggregatingĭifferently-indexed data in other Python and NumPy data structures Powerful, flexible group by functionality to perform. Ignore the labels and let Series, DataFrame, etc. Automatic and explicit data alignment: objects canīe explicitly aligned to a set of labels, or the user can simply. Size mutability: columns can be inserted andĭeleted from DataFrame and higher dimensional. NaN, NA, or NaT) in floating point as well as non-floating point data Easy handling of missing data (represented as. Here are just a few of the things that pandas does well: The broader goal of becoming the most powerful and flexible open source dataĪnalysis / manipulation tool available in any language. It aims to be the fundamental high-level building block forĭoing practical, real world data analysis in Python. Structures designed to make working with "relational" or "labeled" data bothĮasy and intuitive. Pandas is a Python package that provides fast, flexible, and expressive data Pandas: powerful Python data analysis toolkit
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