Pandas alternatives and similar packages
Based on the "Science and Data Analysis" category.
Alternatively, view Pandas alternatives based on common mentions on social networks and blogs.
9.1 9.4 L3 Pandas VS statsmodelsStatsmodels: statistical modeling and econometrics in Python
8.8 9.2 L4 Pandas VS PyMCProbabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara
8.1 9.4 L2 Pandas VS BiopythonOfficial git repository for Biopython (originally converted from CVS)
Interactive Parallel Computing in Python
6.2 0.0 L3 Pandas VS CubesLight-weight Python OLAP framework for multi-dimensional data analysis
5.5 8.8 L3 Pandas VS bcbio-nextgenValidated, scalable, community developed variant calling, RNA-seq and small RNA analysis
1.6 0.2 Pandas VS PatZillaPatZilla is a modular patent information research platform and data integration toolkit with a modern user interface and access to multiple data sources.
1.5 7.0 Pandas VS KotoriA flexible data historian based on InfluxDB, Grafana, MQTT and more. Free, open, simple.
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pandas: powerful Python data analysis toolkit
What is it?
pandas is a Python package that provides fast, flexible, and expressive data structures designed to make working with "relational" or "labeled" data both easy and intuitive. It aims to be the fundamental high-level building block for doing practical, real world data analysis in Python. Additionally, it has the broader goal of becoming the most powerful and flexible open source data analysis / manipulation tool available in any language. It is already well on its way towards this goal.
Here are just a few of the things that pandas does well:
- Easy handling of missing data (represented as
NaT) in floating point as well as non-floating point data
- Size mutability: columns can be inserted and deleted from DataFrame and higher dimensional objects
- Automatic and explicit data alignment: objects can
be explicitly aligned to a set of labels, or the user can simply
ignore the labels and let
DataFrame, etc. automatically align the data for you in computations
- Powerful, flexible group by functionality to perform split-apply-combine operations on data sets, for both aggregating and transforming data
- Make it easy to convert ragged, differently-indexed data in other Python and NumPy data structures into DataFrame objects
- Intelligent label-based slicing, fancy indexing, and subsetting of large data sets
- Intuitive merging and joining data sets
- Flexible reshaping and pivoting of data sets
- Hierarchical labeling of axes (possible to have multiple labels per tick)
- Robust IO tools for loading data from flat files (CSV and delimited), Excel files, databases, and saving/loading data from the ultrafast HDF5 format
- Time series-specific functionality: date range generation and frequency conversion, moving window statistics, date shifting and lagging
Where to get it
The source code is currently hosted on GitHub at: https://github.com/pandas-dev/pandas
# conda conda install pandas
# or PyPI pip install pandas
- NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays
- python-dateutil - Provides powerful extensions to the standard datetime module
- pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations
See the full installation instructions for minimum supported versions of required, recommended and optional dependencies.
Installation from sources
To install pandas from source you need Cython in addition to the normal dependencies above. Cython can be installed from PyPI:
pip install cython
pandas directory (same one where you found this file after
cloning the git repo), execute:
python setup.py install
or for installing in development mode:
python -m pip install -e . --no-build-isolation --no-use-pep517
If you have
make, you can also use
make develop to run the same command.
python setup.py develop
See the full instructions for installing from source.
The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable
pandas started at AQR (a quantitative hedge fund) in 2008 and
has been under active development since then.
Discussion and Development
Most development discussions take place on GitHub in this repo. Further, the pandas-dev mailing list can also be used for specialized discussions or design issues, and a Gitter channel is available for quick development related questions.
All contributions, bug reports, bug fixes, documentation improvements, enhancements, and ideas are welcome.
A detailed overview on how to contribute can be found in the contributing guide. There is also an [overview](.github/CONTRIBUTING.md) on GitHub.
If you are simply looking to start working with the pandas codebase, navigate to the GitHub "issues" tab and start looking through interesting issues. There are a number of issues listed under Docs and good first issue where you could start out.
You can also triage issues which may include reproducing bug reports, or asking for vital information such as version numbers or reproduction instructions. If you would like to start triaging issues, one easy way to get started is to subscribe to pandas on CodeTriage.
Or maybe through using pandas you have an idea of your own or are looking for something in the documentation and thinking ‘this can be improved’...you can do something about it!
As contributors and maintainers to this project, you are expected to abide by pandas' code of conduct. More information can be found at: Contributor Code of Conduct
*Note that all licence references and agreements mentioned in the Pandas README section above are relevant to that project's source code only.