Ecosystem #

Table of Contents

This is a community-maintained list of projects that build on pandas in order to provide tools in the PyData space. The pandas core development team does not necessarily endorse any particular project on this list or have any knowledge of the maintenance status of any particular library.

Extensions #

pandas has different ways to allow third-party packages to enhance its functionality. This section contains a list of known projects that extend pandas functionality.

Developers who want to extend pandas can find more information in the Extending pandas page in our documentation.

Accessors #

A directory of projects providing extension accessors. This is for users to discover new accessors and for library authors to coordinate on the namespace.

Library Accessor Classes
awkward-pandas ak Series
pdvega vgplot Series, DataFrame
pandas-genomics genomics Series, DataFrame
pint-pandas pint Series, DataFrame
physipandas physipy Series, DataFrame
composeml slice DataFrame
gurobipy-pandas gppd Series, DataFrame
staircase sc Series, DataFrame
woodwork slice Series, DataFrame

Data types #

Pandas provides an interface for defining extension types to extend NumPy's type system. The following libraries implement that interface to provide types not found in NumPy or pandas, which work well with pandas' data containers.

awkward-pandas #

Awkward-pandas provides an extension type for storing Awkward Arrays inside pandas' Series and DataFrame. It also provides an accessor for using awkward functions on Series that are of awkward type.

db-dtypes #

db-dtypes provides an extension types for working with types like DATE, TIME, and JSON from database systems. This package is used by pandas-gbq to provide natural dtypes for BigQuery data types without a natural numpy type.

Pandas-Genomics #

Pandas-Genomics provides an extension type and extension array for working with genomics data. It also includes genomics accessors for many useful properties and methods related to QC and analysis of genomics data.

Physipandas #

Physipandas provides an extension for manipulating physical quantities (like scalar and numpy.ndarray) in association with a physical unit (like meter or joule) and additional features for integration of physipy accessors with pandas Series and Dataframe.

Pint-Pandas #

Pint-Pandas provides an extension type for storing numeric arrays with units. These arrays can be stored inside pandas' Series and DataFrame. Operations between Series and DataFrame columns which use pint's extension array are then units aware.

Text Extensions #

Text Extensions for Pandas provides extension types to cover common data structures for representing natural language data, plus library integrations that convert the outputs of popular natural language processing libraries into pandas DataFrames.

Plotting backends #

pandas uses Matplotlib by default for plotting. This can be changed with the with plotting.backend option:

pd.set_option("plotting.backend", "<plotting-backend-name>")

This is the list of known plotting backends:

Altair #

Altair is a declarative statistical visualization library for Python. With Altair, you can spend more time understanding your data and its meaning. Altair's API is simple, friendly and consistent and built on top of the powerful Vega-Lite JSON specification. This elegant simplicity produces beautiful and effective visualizations with a minimal amount of code. Altair works with Pandas DataFrames.

altair-pandas provides the pandas Altair backend via:

pd.set_option("plotting.backend", "altair")

Bokeh #

Bokeh is a Python interactive visualization library for large datasets that natively uses the latest web technologies. Its goal is to provide elegant, concise construction of novel graphics in the style of Protovis/D3, while delivering high-performance interactivity over large data to thin clients.

Pandas-Bokeh provides a high level API for Bokeh that can be loaded as a native Pandas plotting backend via:

pd.set_option("plotting.backend", "pandas_bokeh")

It is very similar to the matplotlib plotting backend, but provides interactive web-based charts and maps.

hvplot #

hvPlot is a high-level plotting API for the PyData ecosystem built on HoloViews. It can be loaded as a native pandas plotting backend via:

pd.set_option("plotting.backend", "hvplot")

Plotly #

Plotly's Python API enables interactive figures and web shareability. Maps, 2D, 3D, and live-streaming graphs are rendered with WebGL and D3.js. The library supports plotting directly from a pandas DataFrame and cloud-based collaboration. Users of matplotlib, ggplot for Python, and Seaborn can convert figures into interactive web-based plots. Plots can be drawn in IPython Notebooks , edited with R or MATLAB, modified in a GUI, or embedded in apps and dashboards. Plotly is free for unlimited sharing, and has cloud, offline, or on-premise accounts for private use.

Plotly can be used as a pandas plotting backend via:

pd.set_option("plotting.backend", "plotly")

Domain specific pandas extensions #

Geopandas #

Geopandas extends pandas data objects to include geographic information which support geometric operations. If your work entails maps and geographical coordinates, and you love pandas, you should take a close look at Geopandas.

gurobipy-pandas #

gurobipy-pandas provides a convenient accessor API to connect pandas with gurobipy. It enables users to more easily and efficiently build mathematical optimization models from data stored in DataFrames and Series, and to read solutions back directly as pandas objects.

Hail Query #

An out-of-core, preemptible-safe, distributed, dataframe library serving the genetics community. Hail Query ships with on-disk data formats, in-memory data formats, an expression compiler, a query planner, and a distributed sort algorithm all designed to accelerate queries on large matrices of genome sequencing data.

It is often easiest to use pandas to manipulate the summary statistics or other small aggregates produced by Hail. For this reason, Hail provides native import to and export from pandas DataFrames:

staircase #

staircase is a data analysis package, built upon pandas and numpy, for modelling and manipulation of mathematical step functions. It provides a rich variety of arithmetic operations, relational operations, logical operations, statistical operations and aggregations for step functions defined over real numbers, datetime and timedelta domains.

xarray #

xarray brings the labeled data power of pandas to the physical sciences by providing N-dimensional variants of the core pandas data structures. It aims to provide a pandas-like and pandas-compatible toolkit for analytics on multi-dimensional arrays, rather than the tabular data for which pandas excels.

Data IO for pandas #

ArcticDB #

ArcticDB is a serverless DataFrame database engine designed for the Python Data Science ecosystem. ArcticDB enables you to store, retrieve, and process pandas DataFrames at scale. It is a storage engine designed for object storage and also supports local-disk storage using LMDB. ArcticDB requires zero additional infrastructure beyond a running Python environment and access to object storage and can be installed in seconds.

Please find full documentation here.

BCPandas #

BCPandas provides high performance writes from pandas to Microsoft SQL Server, far exceeding the performance of the native df.to_sql method. Internally, it uses Microsoft's BCP utility, but the complexity is fully abstracted away from the end user. Rigorously tested, it is a complete replacement for df.to_sql.

Deltalake #

Deltalake python package lets you access tables stored in Delta Lake natively in Python without the need to use Spark or JVM. It provides the delta_table.to_pyarrow_table().to_pandas() method to convert any Delta table into Pandas dataframe.

fredapi #

fredapi is a Python interface to the Federal Reserve Economic Data (FRED) provided by the Federal Reserve Bank of St. Louis. It works with both the FRED database and ALFRED database that contains point-in-time data (i.e. historic data revisions). fredapi provides a wrapper in Python to the FRED HTTP 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 that you can obtain for free on the FRED website.

Hugging Face #

The Hugging Face Dataset Hub provides a large collection of ready-to-use datasets for machine learning shared by the community. The platform offers a user-friendly interface to explore, discover and visualize datasets, and provides tools to easily load and work with these datasets in Python thanks to the huggingface_hub library.

You can access datasets on Hugging Face using hf:// paths in pandas, in the form hf://datasets/username/dataset_name/....

For example, here is how to load the stanfordnlp/imdb dataset:

import pandas as pd

# Load the IMDB dataset
df = pd.read_parquet("hf://datasets/stanfordnlp/imdb/plain_text/train-00000-of-00001.parquet")

Tip: on a dataset page, click on "Use this dataset" to get the code to load it in pandas.

To save a dataset on Hugging Face you need to create a public or private dataset and login, and then you can use df.to_csv/to_json/to_parquet:

# Save the dataset to my Hugging Face account
df.to_parquet("hf://datasets/username/dataset_name/train.parquet")

You can find more information about the Hugging Face Dataset Hub in the documentation.

NTV-pandas #

NTV-pandas provides a JSON converter with more data types than the ones supported by pandas directly.

It supports the following data types:

The interface is always reversible (conversion round trip) with two formats (JSON-NTV and JSON-TableSchema).

Example:

import ntv_pandas as npd

jsn = df.npd.to_json(table=False)  # save df as a JSON-value (format Table Schema if table is True else format NTV )
df  = npd.read_json(jsn)  # load a JSON-value as a `DataFrame`

df.equals(npd.read_json(df.npd.to_json(df)))  # `True` in any case, whether `table=True` or not

pandas-datareader #

pandas-datareader is a remote data access library for pandas (PyPI:pandas-datareader). It is based on functionality that was located in pandas.io.data and pandas.io.wb but was split off in v0.19. See more in the pandas-datareader docs:

The following data feeds are available:

pandas-gbq #

pandas-gbq provides high performance reads and writes to and from Google BigQuery. Previously (before version 2.2.0), these methods were exposed as pandas.read_gbq and DataFrame.to_gbq. Use pandas_gbq.read_gbq and pandas_gbq.to_gbq, instead.

pandaSDMX #

pandaSDMX is a library to retrieve and acquire statistical data and metadata disseminated in SDMX 2.1, an ISO-standard widely used by institutions such as statistics offices, central banks, and international organisations. pandaSDMX can expose datasets and related structural metadata including data flows, code-lists, and data structure definitions as pandas Series or MultiIndexed DataFrames.

Scaling pandas #

Bodo #

Bodo is a high-performance compute engine for Python data processing. Using an auto-parallelizing just-in-time (JIT) compiler, Bodo simplifies scaling Pandas workloads from laptops to clusters without major code changes. Under the hood, Bodo relies on MPI-based high-performance computing (HPC) technology—making it both easier to use and often much faster than alternatives. Bodo also provides a SQL engine that can query distributed pandas dataframes efficiently.

import pandas as pd
import bodo

@bodo.jit
def process_data():
    df = pd.read_parquet("my_data.pq")
    df2 = pd.DataFrame({"A": df.apply(lambda r: 0 if r.A == 0 else (r.B // r.A), axis=1)})
    df2.to_parquet("out.pq")

process_data()

Dask #

Dask is a flexible parallel computing library for analytics. Dask provides a familiar DataFrame interface for out-of-core, parallel and distributed computing.

Ibis #

Ibis offers a standard way to write analytics code, that can be run in multiple engines. It helps in bridging the gap between local Python environments (like pandas) and remote storage and execution systems like Hadoop components (like HDFS, Impala, Hive, Spark) and SQL databases (Postgres, etc.).

Koalas #

Koalas provides a familiar pandas DataFrame interface on top of Apache Spark. It enables users to leverage multi-cores on one machine or a cluster of machines to speed up or scale their DataFrame code.

Modin #

The modin.pandas DataFrame is a parallel and distributed drop-in replacement for pandas. This means that you can use Modin with existing pandas code or write new code with the existing pandas API. Modin can leverage your entire machine or cluster to speed up and scale your pandas workloads, including traditionally time-consuming tasks like ingesting data (read_csv, read_excel, read_parquet, etc.).

# import pandas as pd
import modin.pandas as pd

df = pd.read_csv("big.csv")  # use all your cores!

Data cleaning and validation for pandas #

Pandera #

Pandera provides a flexible and expressive API for performing data validation on dataframes to make data processing pipelines more readable and robust. Dataframes contain information that pandera explicitly validates at runtime. This is useful in production-critical data pipelines or reproducible research settings.

pyjanitor #

Pyjanitor provides a clean API for cleaning data, using method chaining.

Development tools for pandas #

Hamilton #

Hamilton is a declarative dataflow framework that came out of Stitch Fix. It was designed to help one manage a Pandas code base, specifically with respect to feature engineering for machine learning models.

It prescribes an opinionated paradigm, that ensures all code is:

This helps one to scale your pandas code base, at the same time, keeping maintenance costs low.

For more information, see documentation.

IPython #

IPython is an interactive command shell and distributed computing environment. IPython tab completion works with Pandas methods and also attributes like DataFrame columns.

Jupyter Notebook / Jupyter Lab #

Jupyter Notebook is a web application for creating Jupyter notebooks. A Jupyter notebook is a JSON document containing an ordered list of input/output cells which can contain code, text, mathematics, plots and rich media. Jupyter 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 and jupyter convert in a shell.

Pandas DataFrames implement _repr_html_ and _repr_latex methods which are utilized by Jupyter Notebook for displaying (abbreviated) HTML or LaTeX tables. LaTeX output is properly escaped. (Note: HTML tables may or may not be compatible with non-HTML Jupyter output formats.)

See Options and Settings for pandas display. settings.

marimo #

marimo is a reactive notebook for Python and SQL that enhances productivity when working with dataframes. It provides several features to make data manipulation and visualization more interactive and fun:

  1. Rich, interactive displays: marimo can display pandas dataframes in interactive tables or charts with filtering and sorting capabilities.
  2. Data selection: Users can select data in tables or pandas-backed plots, and the selections are automatically sent to Python as pandas dataframes.
  3. No-code transformations: Users can interactively transform pandas dataframes using a GUI, without writing code. The generated code can be copied and pasted into the notebook.
  4. Custom filters: marimo allows the creation of pandas-backed filters using UI elements like sliders and dropdowns.
  5. Dataset explorer: marimo automatically discovers and displays all dataframes in the notebook, allowing users to explore and visualize data interactively.
  6. SQL integration: marimo allows users to write SQL queries against any pandas dataframes existing in memory.

pandas-stubs #

While pandas repository is partially typed, the package itself doesn't expose this information for external use. Install pandas-stubs to enable basic type coverage of pandas API.

Learn more by reading through these issues 14468, 26766, 28142.

See installation and usage instructions on the GitHub page.

Spyder #

Spyder is a cross-platform PyQt-based IDE combining the editing, analysis, debugging and profiling functionality of a software development tool with the data exploration, interactive execution, deep inspection and rich visualization capabilities of a scientific environment like MATLAB or Rstudio.

Its Variable Explorer allows users to view, manipulate and edit pandas Index, Series, and DataFrame objects like a "spreadsheet", including copying and modifying values, sorting, displaying a "heatmap", converting data types and more. Pandas objects can also be renamed, duplicated, new columns added, copied/pasted to/from the clipboard (as TSV), and saved/loaded to/from a file. Spyder can also import data from a variety of plain text and binary files or the clipboard into a new pandas DataFrame via a sophisticated import wizard.

Most pandas classes, methods and data attributes can be autocompleted in Spyder's Editor and IPython Console, and Spyder's Help pane can retrieve and render Numpydoc documentation on pandas objects in rich text with Sphinx both automatically and on-demand.

Compose #

Compose is a machine learning tool for labeling data and prediction engineering. It allows you to structure the labeling process by parameterizing prediction problems and transforming time-driven relational data into target values with cutoff times that can be used for supervised learning.

D-Tale #

D-Tale is a lightweight web client for visualizing pandas data structures. It provides a rich spreadsheet-style grid which acts as a wrapper for a lot of pandas functionality (query, sort, describe, corr...) so users can quickly manipulate their data. There is also an interactive chart-builder using Plotly Dash allowing users to build nice portable visualizations. D-Tale can be invoked with the following command

import dtale

dtale.show(df)

D-Tale integrates seamlessly with Jupyter notebooks, Python terminals, Kaggle & Google Colab. Here are some demos of the grid.

Featuretools #

Featuretools is a Python library for automated feature engineering built on top of pandas. It excels at transforming temporal and relational datasets into feature matrices for machine learning using reusable feature engineering "primitives". Users can contribute their own primitives in Python and share them with the rest of the community.

IPython Vega #

IPython Vega leverages Vega to create plots within Jupyter Notebook.

plotnine #

Hadley Wickham's ggplot2 is a foundational exploratory visualization package for the R language. Based on "The Grammar of Graphics" it provides a powerful, declarative and extremely general way to generate bespoke plots of any kind of data. Various implementations to other languages are available. A good implementation for Python users is has2k1/plotnine.

pygwalker #

PyGWalker is an interactive data visualization and exploratory data analysis tool built upon Graphic Walker with support for visualization, cleaning, and annotation workflows.

pygwalker can save interactively created charts to Graphic-Walker and Vega-Lite JSON.

import pygwalker as pyg
pyg.walk(df)

seaborn #

Seaborn is a Python visualization library based on matplotlib. It provides a high-level, dataset-oriented interface for creating attractive statistical graphics. The plotting functions in seaborn understand pandas objects and leverage pandas grouping operations internally to support concise specification of complex visualizations. Seaborn also goes beyond matplotlib and pandas with the option to perform statistical estimation while plotting, aggregating across observations and visualizing the fit of statistical models to emphasize patterns in a dataset.

import seaborn as sns
sns.set_theme()

skrub #

Skrub facilitates machine learning on dataframes. It bridges pandas to scikit-learn and related. In particular it facilitates building features from dataframes.

Statsmodels #

Statsmodels is the prominent Python "statistics and econometrics library" and it has a 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.

STUMPY #

STUMPY is a powerful and scalable Python library for modern time series analysis. At its core, STUMPY efficiently computes something called a matrix profile, which can be used for a wide variety of time series data mining tasks.