See the full instructions for installing from source. If you have make, you can also use make develop to run the same command. Or for installing in development mode: python -m pip install -e. In the pandas directory (same one where you found this file afterĬloning the git repo), execute: python setup.py install
Cython can be installed from PyPI: pip install cython To install pandas from source you need Cython in addition to the normalĭependencies above. See the full installation instructions for minimum supported versions of required, recommended and optional dependencies. pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations.python-dateutil - Provides powerful extensions to the standard datetime module.NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays.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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