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Increasingly, packages are being built on top of pandas to address specific needsin data preparation, analysis and visualization.This is encouraging because it means pandas is not only helping users to handletheir data tasks but also that it provides a better starting point for developers tobuild powerful and more focused data tools.The creation of libraries that complement pandas’ functionality also allows pandasdevelopment to remain focused around it’s original requirements.
This is an in-exhaustive list of projects that build on pandas in order to providetools in the PyData space.
We’d like to make it easier for users to find these project, if you know of othersubstantial projects that you feel should be on this list, please let us know.
Statsmodels is the prominent python “statistics and econometrics library” and it hasa long-standing special relationship with pandas. Statsmodels provides powerful statistics,econometrics, analysis and modeling functionality that is out of pandas’ scope.Statsmodels leverages pandas objects as the underlying data container for computation.
Use pandas DataFrames in yourscikit-learnML pipeline.
Bokeh is a Python interactive visualization library for large datasets that natively usesthe latest web technologies. Its goal is to provide elegant, concise construction of novelgraphics in the style of Protovis/D3, while delivering high-performance interactivity overlarge data to thin clients.
Hadley Wickham’sggplot2 is a foundational exploratory visualization package for the R language.Based on“The Grammar of Graphics” itprovides a powerful, declarative and extremely general way to generate bespoke plots of any kind of data.It’s really quite incredible. Various implementations to other languages are available,but a faithful implementation for python users has long been missing. Although still young(as of Jan-2014), theyhat/ggplot project has beenprogressing quickly in that direction.
Although pandas has quite a bit of “just plot it” functionality built-in, visualization andin particular statistical graphics is a vast field with a long tradition and lots of groundto cover. TheSeaborn project builds on top of pandasandmatplotlib to provide easy plotting of data which extends tomore advanced types of plots then those offered by pandas.
TheVincent project leveragesVega(that in turn, leveragesd3) to createplots. Although functional, as of Summer 2016 the Vincent project has not been updatedin over two years and isunlikely to receive further updates.
Like Vincent, theIPython Vega project leveragesVega to create plots, but primarilytargets the IPython Notebook environment.
Plotly’sPython API enables interactive figures and web shareability. Maps, 2D, 3D, and live-streaming graphs are rendered with WebGL andD3.js. The library supports plotting directly from a pandas DataFrame and cloud-based collaboration. Users ofmatplotlib, ggplot for Python, and Seaborn can convert figures into interactive web-based plots. Plots can be drawn inIPython Notebooks , edited with R or MATLAB, modified in a GUI, or embedded in apps and dashboards. Plotly is free for unlimited sharing, and hascloud,offline, oron-premise accounts for private use.
IPython is an interactive command shell and distributed computingenvironment.IPython Notebook is a web application for creating IPython notebooks.An IPython notebook is a JSON document containing an ordered listof input/output cells which can contain code, text, mathematics, plotsand rich media.IPython notebooks can be converted to a number of open standard output formats(HTML, HTML presentation slides, LaTeX, PDF, ReStructuredText, Markdown,Python) through ‘Download As’ in the web interface andipythonnbconvertin a shell.
Pandas DataFrames implement_repr_html_ methodswhich are utilized by IPython Notebook for displaying(abbreviated) HTML tables. (Note: HTML tables may or may not becompatible with non-HTML IPython output formats.)
qgrid is “an interactive grid for sorting and filteringDataFrames in IPython Notebook” built with SlickGrid.
pandas-datareader is a remote data access library for pandas.pandas.io from pandas < 0.17.0 is now refactored/split-off to and importable frompandas_datareader (PyPI:pandas-datareader). Many/most of the supported APIs have at least a documentation paragraph in thepandas-datareader docs:
The following data feeds are available:
- Yahoo! Finance
- Google Finance
- FRED
- Fama/French
- World Bank
- OECD
- Eurostat
- EDGAR Index
Quandl API for Python wraps the Quandl REST API to returnPandas DataFrames with timeseries indexes.
PyDatastream is a Python interface to theThomson Dataworks Enterprise (DWE/Datastream)SOAP API to return indexed Pandas DataFrames or Panels with financial data.This package requires valid credentials for this API (non free).
pandaSDMX is an extensible library to retrieve and acquire statistical dataand metadata disseminated inSDMX 2.1. This standard is currently supported bythe European statistics office (Eurostat)and the European Central Bank (ECB). Datasets may be returned as pandas Seriesor multi-indexed DataFrames.
fredapi is a Python interface to theFederal Reserve Economic Data (FRED)provided by the Federal Reserve Bank of St. Louis. It works with both the FRED database and ALFRED database thatcontains point-in-time data (i.e. historic data revisions). fredapi provides a wrapper in python to the FREDHTTP API, and also provides several convenient methods for parsing and analyzing point-in-time data from ALFRED.fredapi makes use of pandas and returns data in a Series or DataFrame. This module requires a FRED API key thatyou can obtain for free on the FRED website.
Geopandas extends pandas data objects to include geographic information which supportgeometric operations. If your work entails maps and geographical coordinates, andyou love pandas, you should take a close look at Geopandas.
xarray brings the labeled data power of pandas to the physical sciences byproviding N-dimensional variants of the core pandas data structures. It aims toprovide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels.
Dask is a flexible parallel computing library for analytics. Daskallow a familiarDataFrame interface to out-of-core, parallel and distributed computing.
Blaze provides a standard API for doing computations with variousin-memory and on-disk backends: NumPy, Pandas, SQLAlchemy, MongoDB, PyTables,PySpark.
Odo provides a uniform API for moving data between different formats. It usespandas ownread_csv for CSV IO and leverages many existing packages such asPyTables, h5py, and pymongo to move data between non pandas formats. Its graphbased approach is also extensible by end users for custom formats that may betoo specific for the core of odo.