Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more
pandas: powerful Python data analysis toolkit
What is it?
pandas is a Python package providing fast, flexible, and expressive datastructures designed to make working with "relational" or "labeled" data botheasy and intuitive. It aims to be the fundamental high-level building block fordoing practical, real world data analysis in Python. Additionally, it hasthe broader goal of becoming the most powerful and flexible open source dataanalysis / manipulation tool available in any language. It is already well onits way towards this goal.
Here are just a few of the things that pandas does well:
- Easy handling of missing data (represented as
NaN) in floating point as well as non-floating point data
- Size mutability: columns can be inserted anddeleted from DataFrame and higher dimensionalobjects
- Automatic and explicit data alignment: objects canbe explicitly aligned to a set of labels, or the user can simplyignore the labels and let
DataFrame, etc. automaticallyalign the data for you in computations
- Powerful, flexible group by functionality to performsplit-apply-combine operations on data sets, for both aggregatingand transforming data
- Make it easy to convert ragged,differently-indexed data in other Python and NumPy data structuresinto DataFrame objects
- Intelligent label-based slicing, fancyindexing, and subsetting oflarge data sets
- Intuitive merging and joining datasets
- Flexible reshaping and pivoting ofdata sets
- Hierarchical labeling of axes (possible to have multiplelabels 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 rangegeneration and frequency conversion, moving window statistics,moving window linear regressions, date shifting and lagging, etc.
Where to get it
The source code is currently hosted on GitHub at:https://github.com/pandas-dev/pandas
Binary installers for the latest released version are available at the Pythonpackage index and on conda.
conda install pandas```
pip install pandas```
See the full installation instructionsfor recommended and optional dependencies.
Installation from sources
To install pandas from source you need Cython in addition to the normaldependencies above. Cython can be installed from pypi:
shpip install cython
pandas directory (same one where you found this file aftercloning the git repo), execute:
shpython setup.py install
or for installing in development mode:
shpython -m pip install --no-build-isolation -e .
If you have
make, you can also use
make develop to run the same command.
shpython 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 andhas been under active development since then.
Discussion and Development
Most development discussion is taking 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.
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
To restore the repository download the bundle
git clone pandas-dev-pandas_-_2019-10-03_07-31-46.bundle
Upload date: 2019-10-03
- 2019-10-03 09:04:09
- 2019-10-03 07:31:46
- Internet Archive Python library 1.8.1
- iagitup - v1.6.2