pandas is a python library for Panel Data manipulation and analysis, e.g. multidimensional time series and cross-sectional data sets commonly found in statistics, experimental science results, econometrics, or finance.

pandas is a python library for PAN-el DA-ta manipulation and analysis, i.e. multidimensional time series and cross-sectional data sets commonly found in statistics, experimental science results, econometrics, or finance. pandas is implemented primarily using numpy and Cython; it is intended to be able to integrate very easily with other numpy-based scientific libraries, such as statsmodels.

Main Features:

  • Data structures: for 1, 2, and 3 dimensional labeled data sets (respectively Series, DataFrames and Panels). Some of their main features include:
    • Automatically aligning data and interpolation
    • Handling missing observations in calculations
    • Convenient slicing and reshaping ("reindexing") functions
    • Categorical data types
    • Provide 'group by' aggregation or transformation functionality
    • Tools for merging / joining together data sets
    • Simple matplotlib integration for plotting
  • Date tools: objects for expressing date offsets or generating date ranges; some functionality similar to scikits.timeseries. Dates can be aligned to a specific timezone and converted / compared at-will
  • Statistical models: convenient ordinary least squares and panel OLS implementations for in-sample or rolling time series / cross-sectional regressions. These will hopefully be the starting point for implementing other models
  • Intelligent Cython offloading; complex computations are performed rapidly due to these optimizations.
  • Static and moving statistical tools: mean, standard deviation, correlation, covariance
  • Rich User Documentation, using Sphinx

Resources:

Books:

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