Flexible and powerful data analysis / manipulation library for Python, providing labeled data structures similar to R data.frame objects, statistical functions, and much more

<div align="center">
  <img src="https://pandas.pydata.org/static/img/pandas.svg"><br>
</div>

-----------------

# pandas: powerful Python data analysis toolkit
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## 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.

## Main Features
Here are just a few of the things that pandas does well:

  - Easy handling of [**missing data**][missing-data] (represented as
    `NaN`, `NA`, or `NaT`) in floating point as well as non-floating point data
  - Size mutability: columns can be [**inserted and
    deleted**][insertion-deletion] from DataFrame and higher dimensional
    objects
  - Automatic and explicit [**data alignment**][alignment]: objects can
    be explicitly aligned to a set of labels, or the user can simply
    ignore the labels and let `Series`, `DataFrame`, etc. automatically
    align the data for you in computations
  - Powerful, flexible [**group by**][groupby] functionality to perform
    split-apply-combine operations on data sets, for both aggregating
    and transforming data
  - Make it [**easy to convert**][conversion] ragged,
    differently-indexed data in other Python and NumPy data structures
    into DataFrame objects
  - Intelligent label-based [**slicing**][slicing], [**fancy
    indexing**][fancy-indexing], and [**subsetting**][subsetting] of
    large data sets
  - Intuitive [**merging**][merging] and [**joining**][joining] data
    sets
  - Flexible [**reshaping**][reshape] and [**pivoting**][pivot-table] of
    data sets
  - [**Hierarchical**][mi] labeling of axes (possible to have multiple
    labels per tick)
  - Robust IO tools for loading data from [**flat files**][flat-files]
    (CSV and delimited), [**Excel files**][excel], [**databases**][db],
    and saving/loading data from the ultrafast [**HDF5 format**][hdfstore]
  - [**Time series**][timeseries]-specific functionality: date range
    generation and frequency conversion, moving window statistics,
    date shifting and lagging


   [missing-data]: https://pandas.pydata.org/pandas-docs/stable/user_guide/missing_data.html
   [insertion-deletion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#column-selection-addition-deletion
   [alignment]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html?highlight=alignment#intro-to-data-structures
   [groupby]: https://pandas.pydata.org/pandas-docs/stable/user_guide/groupby.html#group-by-split-apply-combine
   [conversion]: https://pandas.pydata.org/pandas-docs/stable/user_guide/dsintro.html#dataframe
   [slicing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#slicing-ranges
   [fancy-indexing]: https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#advanced
   [subsetting]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#boolean-indexing
   [merging]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#database-style-dataframe-or-named-series-joining-merging
   [joining]: https://pandas.pydata.org/pandas-docs/stable/user_guide/merging.html#joining-on-index
   [reshape]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
   [pivot-table]: https://pandas.pydata.org/pandas-docs/stable/user_guide/reshaping.html
   [mi]: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#hierarchical-indexing-multiindex
   [flat-files]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#csv-text-files
   [excel]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#excel-files
   [db]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#sql-queries
   [hdfstore]: https://pandas.pydata.org/pandas-docs/stable/user_guide/io.html#hdf5-pytables
   [timeseries]: https://pandas.pydata.org/pandas-docs/stable/user_guide/timeseries.html#time-series-date-functionality

## 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 [Python
Package Index (PyPI)](https://pypi.org/project/pandas) and on [Conda](https://docs.conda.io/en/latest/).

```sh
# conda
conda install pandas
```

```sh
# or PyPI
pip install pandas
```

## Dependencies
- [NumPy - Adds support for large, multi-dimensional arrays, matrices and high-level mathematical functions to operate on these arrays](https://www.numpy.org)
- [python-dateutil - Provides powerful extensions to the standard datetime module](https://dateutil.readthedocs.io/en/stable/index.html)
- [pytz - Brings the Olson tz database into Python which allows accurate and cross platform timezone calculations](https://github.com/stub42/pytz)

See the [full installation instructions](https://pandas.pydata.org/pandas-docs/stable/install.html#dependencies) for minimum supported versions of required, recommended and optional dependencies.

## Installation from sources
To install pandas from source you need [Cython](https://cython.org/) in addition to the normal
dependencies above. Cython can be installed from PyPI:

```sh
pip install cython
```

In the `pandas` directory (same one where you found this file after
cloning the git repo), execute:

```sh
python setup.py install
```

or for installing in [development mode](https://pip.pypa.io/en/latest/cli/pip_install/#install-editable):


```sh
python -m pip install -e . --no-build-isolation --no-use-pep517
```

or alternatively

```sh
python setup.py develop
```

See the full instructions for [installing from source](https://pandas.pydata.org/pandas-docs/stable/getting_started/install.html#installing-from-source).

## License
[BSD 3](LICENSE)

## Documentation
The official documentation is hosted on PyData.org: https://pandas.pydata.org/pandas-docs/stable

## Background
Work on ``pandas`` started at [AQR](https://www.aqr.com/) (a quantitative hedge fund) in 2008 and
has been under active development since then.

## Getting Help

For usage questions, the best place to go to is [StackOverflow](https://stackoverflow.com/questions/tagged/pandas).
Further, general questions and discussions can also take place on the [pydata mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata).

## Discussion and Development
Most development discussions take place on GitHub in this repo. Further, the [pandas-dev mailing list](https://mail.python.org/mailman/listinfo/pandas-dev) can also be used for specialized discussions or design issues, and a [Slack channel](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack) is available for quick development related questions.

## Contributing to pandas [![Open Source Helpers](https://www.codetriage.com/pandas-dev/pandas/badges/users.svg)](https://www.codetriage.com/pandas-dev/pandas)

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](https://pandas.pydata.org/docs/dev/development/contributing.html)**.

If you are simply looking to start working with the pandas codebase, navigate to the [GitHub "issues" tab](https://github.com/pandas-dev/pandas/issues) and start looking through interesting issues. There are a number of issues listed under [Docs](https://github.com/pandas-dev/pandas/issues?labels=Docs&sort=updated&state=open) and [good first issue](https://github.com/pandas-dev/pandas/issues?labels=good+first+issue&sort=updated&state=open) 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](https://www.codetriage.com/pandas-dev/pandas).

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!

Feel free to ask questions on the [mailing list](https://groups.google.com/forum/?fromgroups#!forum/pydata) or on [Slack](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack).

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](https://github.com/pandas-dev/.github/blob/master/CODE_OF_CONDUCT.md)


pandas

.. image:: https://github.com/scipy/scipy/blob/main/doc/source/_static/logo.svg
  :target: https://scipy.org
  :width: 110
  :height: 110
  :align: left 

.. image:: https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A
  :target: https://numfocus.org

.. image:: https://img.shields.io/pypi/dm/scipy.svg?label=Pypi%20downloads
  :target: https://pypi.org/project/scipy/

.. image:: https://img.shields.io/conda/dn/conda-forge/scipy.svg?label=Conda%20downloads
  :target: https://anaconda.org/conda-forge/scipy

.. image:: https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg
  :target: https://stackoverflow.com/questions/tagged/scipy

.. image:: https://img.shields.io/badge/DOI-10.1038%2Fs41592--019--0686--2-blue
  :target: https://www.nature.com/articles/s41592-019-0686-2

SciPy (pronounced "Sigh Pie") is an open-source software for mathematics,
science, and engineering. It includes modules for statistics, optimization,
integration, linear algebra, Fourier transforms, signal and image processing,
ODE solvers, and more.

- **Website:** https://scipy.org
- **Documentation:** https://docs.scipy.org/doc/scipy/
- **Development version of the documentation:** https://scipy.github.io/devdocs
- **Mailing list:** https://mail.python.org/mailman3/lists/scipy-dev.python.org/
- **Source code:** https://github.com/scipy/scipy
- **Contributing:** https://scipy.github.io/devdocs/dev/index.html
- **Bug reports:** https://github.com/scipy/scipy/issues
- **Code of Conduct:** https://docs.scipy.org/doc/scipy/dev/conduct/code_of_conduct.html
- **Report a security vulnerability:** https://tidelift.com/docs/security
- **Citing in your work:** https://www.scipy.org/citing-scipy/

SciPy is built to work with
NumPy arrays, and provides many user-friendly and efficient numerical routines,
such as routines for numerical integration and optimization. Together, they
run on all popular operating systems, are quick to install, and are free of
charge. NumPy and SciPy are easy to use, but powerful enough to be depended
upon by some of the world's leading scientists and engineers. If you need to
manipulate numbers on a computer and display or publish the results, give
SciPy a try!

For the installation instructions, see `our install
guide <https://docs.scipy.org/doc/scipy/getting_started.html#installation>`__.


Call for Contributions
----------------------

We appreciate and welcome contributions. Small improvements or fixes are always appreciated; issues labeled as "good
first issue" may be a good starting point. Have a look at `our contributing
guide <https://scipy.github.io/devdocs/dev/index.html>`__.

Writing code isn’t the only way to contribute to SciPy. You can also:

- review pull requests
- triage issues
- develop tutorials, presentations, and other educational materials
- maintain and improve `our website <https://github.com/scipy/scipy.org>`__
- develop graphic design for our brand assets and promotional materials
- help with outreach and onboard new contributors
- write grant proposals and help with other fundraising efforts

If you’re unsure where to start or how your skills fit in, reach out! You can
ask on the mailing list or here, on GitHub, by leaving a
comment on a relevant issue that is already open.

If you are new to contributing to open source, `this
guide <https://opensource.guide/how-to-contribute/>`__ helps explain why, what,
and how to get involved.


scipy

[![PyPi](https://badge.fury.io/py/matplotlib.svg)](https://badge.fury.io/py/matplotlib)
[![Downloads](https://pepy.tech/badge/matplotlib/month)](https://pepy.tech/project/matplotlib)
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[![DiscourseBadge](https://img.shields.io/badge/help_forum-discourse-blue.svg)](https://discourse.matplotlib.org)
[![Gitter](https://badges.gitter.im/matplotlib/matplotlib.svg)](https://gitter.im/matplotlib/matplotlib)
[![GitHubIssues](https://img.shields.io/badge/issue_tracking-github-blue.svg)](https://github.com/matplotlib/matplotlib/issues)
[![GitTutorial](https://img.shields.io/badge/PR-Welcome-%23FF8300.svg?)](https://git-scm.com/book/en/v2/GitHub-Contributing-to-a-Project)

[![GitHubActions](https://github.com/matplotlib/matplotlib/workflows/Tests/badge.svg)](https://github.com/matplotlib/matplotlib/actions?query=workflow%3ATests)
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![image](https://matplotlib.org/_static/logo2.svg)

Matplotlib is a comprehensive library for creating static, animated, and
interactive visualizations in Python.

Check out our [home page](https://matplotlib.org/) for more information.

![image](https://matplotlib.org/_static/readme_preview.png)

Matplotlib produces publication-quality figures in a variety of hardcopy
formats and interactive environments across platforms. Matplotlib can be
used in Python scripts, Python/IPython shells, web application servers,
and various graphical user interface toolkits.

## Install

See the [install
documentation](https://matplotlib.org/stable/users/installing/index.html),
which is generated from `/doc/users/installing/index.rst`

## Contribute

You've discovered a bug or something else you want to change -
excellent!

You've worked out a way to fix it -- even better!

You want to tell us about it -- best of all!

Start at the [contributing
guide](https://matplotlib.org/devdocs/devel/contributing.html)!

## Contact

[Discourse](https://discourse.matplotlib.org/) is the discussion forum
for general questions and discussions and our recommended starting
point.

Our active mailing lists (which are mirrored on Discourse) are:

-   [Users](https://mail.python.org/mailman/listinfo/matplotlib-users)
    mailing list: <matplotlib-users@python.org>
-   [Announcement](https://mail.python.org/mailman/listinfo/matplotlib-announce)
    mailing list: <matplotlib-announce@python.org>
-   [Development](https://mail.python.org/mailman/listinfo/matplotlib-devel)
    mailing list: <matplotlib-devel@python.org>

[Gitter](https://gitter.im/matplotlib/matplotlib) is for coordinating
development and asking questions directly related to contributing to
matplotlib.

## Citing Matplotlib

If Matplotlib contributes to a project that leads to publication, please
acknowledge this by citing Matplotlib.

[A ready-made citation
entry](https://matplotlib.org/stable/users/project/citing.html) is
available.


matplotlib

<img src="https://raw.githubusercontent.com/mwaskom/seaborn/master/doc/_static/logo-wide-lightbg.svg"><br>

--------------------------------------

seaborn: statistical data visualization
=======================================

[![PyPI Version](https://img.shields.io/pypi/v/seaborn.svg)](https://pypi.org/project/seaborn/)
[![License](https://img.shields.io/pypi/l/seaborn.svg)](https://github.com/mwaskom/seaborn/blob/master/LICENSE)
[![DOI](https://joss.theoj.org/papers/10.21105/joss.03021/status.svg)](https://doi.org/10.21105/joss.03021)
[![Tests](https://github.com/mwaskom/seaborn/workflows/CI/badge.svg)](https://github.com/mwaskom/seaborn/actions)
[![Code Coverage](https://codecov.io/gh/mwaskom/seaborn/branch/master/graph/badge.svg)](https://codecov.io/gh/mwaskom/seaborn)

Seaborn is a Python visualization library based on matplotlib. It provides a high-level interface for drawing attractive statistical graphics.


Documentation
-------------

Online documentation is available at [seaborn.pydata.org](https://seaborn.pydata.org).

The docs include a [tutorial](https://seaborn.pydata.org/tutorial.html), [example gallery](https://seaborn.pydata.org/examples/index.html), [API reference](https://seaborn.pydata.org/api.html), [FAQ](https://seaborn.pydata.org/faq), and other useful information.

To build the documentation locally, please refer to [`doc/README.md`](doc/README.md).

Dependencies
------------

Seaborn supports Python 3.7+ and no longer supports Python 2.

Installation requires [numpy](https://numpy.org/), [pandas](https://pandas.pydata.org/), and [matplotlib](https://matplotlib.org/). Some advanced statistical functionality requires [scipy](https://www.scipy.org/) and/or [statsmodels](https://www.statsmodels.org/).


Installation
------------

The latest stable release (and required dependencies) can be installed from PyPI:

    pip install seaborn

It is also possible to include optional statistical dependencies (only relevant for v0.12+):

    pip install seaborn[stats]

Seaborn can also be installed with conda:

    conda install seaborn

Note that the main anaconda repository lags PyPI in adding new releases, but conda-forge (`-c conda-forge`) typically updates quickly.

Citing
------

A paper describing seaborn has been published in the [Journal of Open Source Software](https://joss.theoj.org/papers/10.21105/joss.03021). The paper provides an introduction to the key features of the library, and it can be used as a citation if seaborn proves integral to a scientific publication.

Testing
-------

Testing seaborn requires installing additional dependencies; they can be installed with the `dev` extra (e.g., `pip install .[dev]`).

To test the code, run `make test` in the source directory. This will exercise the unit tests (using [pytest](https://docs.pytest.org/)) and generate a coverage report.

Code style is enforced with `flake8` using the settings in the [`setup.cfg`](./setup.cfg) file. Run `make lint` to check. Alternately, you can use `pre-commit` to automatically run lint checks on any files you are committing: just run `pre-commit install` to set it up, and then commit as usual going forward.

Development
-----------

Seaborn development takes place on Github: https://github.com/mwaskom/seaborn

Please submit bugs that you encounter to the [issue tracker](https://github.com/mwaskom/seaborn/issues) with a reproducible example demonstrating the problem. Questions about usage are more at home on StackOverflow, where there is a [seaborn tag](https://stackoverflow.com/questions/tagged/seaborn).


seaborn

A library for transfer learning by reusing parts of TensorFlow models.

An Open Source Machine Learning Framework for Everyone

[![Python](https://img.shields.io/pypi/pyversions/tensorflow.svg?style=plastic)](https://badge.fury.io/py/tensorflow)
[![PyPI](https://badge.fury.io/py/tensorflow.svg)](https://badge.fury.io/py/tensorflow)

TensorFlow is an open source software library for high performance numerical
computation. Its flexible architecture allows easy deployment of computation
across a variety of platforms (CPUs, GPUs, TPUs), and from desktops to clusters
of servers to mobile and edge devices.

Originally developed by researchers and engineers from the Google Brain team
within Google's AI organization, it comes with strong support for machine
learning and deep learning and the flexible numerical computation core is used
across many other scientific domains. TensorFlow is licensed under [Apache
2.0](https://github.com/tensorflow/tensorflow/blob/master/LICENSE).




Food Consumption Prediction

Open Weaver – Develop Applications Faster with Open Source

kandi

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