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
- Data structures: for 1, 2, and 3 dimensional labeled data sets (respectively
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
matplotlibintegration 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