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
```

If you have `make`, you can also use `make develop` to run the same command.

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 [Gitter channel](https://gitter.im/pydata/pandas) 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 [Gitter](https://gitter.im/pydata/pandas).

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

The fundamental package for scientific computing with Python.

<h1 align="center">
<img src="/branding/logo/primary/numpylogo.svg" width="300">
</h1><br>


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NumPy is the fundamental package for scientific computing with Python.

- **Website:** https://www.numpy.org
- **Documentation:** https://numpy.org/doc
- **Mailing list:** https://mail.python.org/mailman/listinfo/numpy-discussion
- **Source code:** https://github.com/numpy/numpy
- **Contributing:** https://www.numpy.org/devdocs/dev/index.html
- **Bug reports:** https://github.com/numpy/numpy/issues
- **Report a security vulnerability:** https://tidelift.com/docs/security

It provides:

- a powerful N-dimensional array object
- sophisticated (broadcasting) functions
- tools for integrating C/C++ and Fortran code
- useful linear algebra, Fourier transform, and random number capabilities

Testing:

NumPy requires `pytest` and `hypothesis`.  Tests can then be run after installation with:

    python -c 'import numpy; numpy.test()'

Code of Conduct
----------------------

NumPy is a community-driven open source project developed by a diverse group of
[contributors](https://numpy.org/teams/). The NumPy leadership has made a strong
commitment to creating an open, inclusive, and positive community. Please read the
[NumPy Code of Conduct](https://numpy.org/code-of-conduct/) for guidance on how to interact
with others in a way that makes our community thrive.

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

The NumPy project welcomes your expertise and enthusiasm!

Small improvements or fixes are always appreciated. If you are considering larger contributions
to the source code, please contact us through the [mailing
list](https://mail.python.org/mailman/listinfo/numpy-discussion) first.

Writing code isn’t the only way to contribute to NumPy. You can also:
- review pull requests
- help us stay on top of new and old issues
- develop tutorials, presentations, and other educational materials
- maintain and improve [our website](https://github.com/numpy/numpy.org)
- develop graphic design for our brand assets and promotional materials
- translate website content
- help with outreach and onboard new contributors
- write grant proposals and help with other fundraising efforts

For more information about the ways you can contribute to NumPy, visit [our website](https://numpy.org/contribute/). 
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 opening a new issue or leaving a
comment on a relevant issue that is already open.

Our preferred channels of communication are all public, but if you’d like to
speak to us in private first, contact our community coordinators at
numpy-team@googlegroups.com or on Slack (write numpy-team@googlegroups.com for
an invitation).

We also have a biweekly community call, details of which are announced on the
mailing list. You are very welcome to join.

If you are new to contributing to open source, [this
guide](https://opensource.guide/how-to-contribute/) helps explain why, what,
and how to successfully get involved.




numpy

|PyPi|_ |Downloads|_ |NUMFocus|_

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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
=======

For installation instructions and requirements, see the `install documentation
<https://matplotlib.org/stable/users/installing/index.html>`_ or
`installing.rst <doc/users/installing/index.rst>`_ in the source.

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_ 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.

Research notice
~~~~~~~~~~~~~~~

Please note that this repository is participating in a study into
sustainability of open source projects. Data will be gathered about this
repository for approximately the next 12 months, starting from June 2021.

Data collected will include number of contributors, number of PRs, time taken
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For more information, please visit `the informational page
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matplotlib

An Open Source Machine Learning Framework for Everyone

TensorFlow for Java: A software library for machine intelligence.

tensorflow

Python library that makes it easy for data scientists to create charts.

Chartify
========

|status|  |release|  |python|  |CI|

.. |status| image:: https://img.shields.io/badge/Status-Beta-blue.svg
.. |release| image:: https://img.shields.io/badge/Release-3.0.5-blue.svg
.. |python| image:: https://img.shields.io/badge/Python-3.6-blue.svg
.. |CI| image:: https://github.com/spotify/chartify/workflows/Tox/badge.svg
        :target: https://github.com/spotify/chartify/actions

Chartify is a Python library that makes it easy for data scientists to create charts.

Why use Chartify?
-----------------

- Consistent input data format: Spend less time transforming data to get your charts to work. All plotting functions use a consistent tidy input data format.
- Smart default styles: Create pretty charts with very little customization required.
- Simple API: We've attempted to make the API as intuitive and easy to learn as possible.
- Flexibility: Chartify is built on top of `Bokeh <http://bokeh.pydata.org/en/latest/>`_, so if you do need more control you can always fall back on Bokeh's API.

Examples
--------

.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify1.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify2.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify3.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify4.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify5.png
.. image:: https://raw.githubusercontent.com/spotify/chartify/master/docs/_static/chartify6.png

`See this notebook for more examples! </examples/Examples.ipynb>`_.

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

1. Chartify can be installed via pip:

``pip3 install chartify``

2. Install chromedriver requirement (Optional. Needed for PNG output):
    - Install google chrome.
    - Download the appropriate version of chromedriver for your OS `here <https://sites.google.com/chromium.org/driver/>`_.
    - Copy the executable file to a directory within your PATH.
	- View directorys in your PATH variable: ``echo $PATH``
	- Copy chromedriver to the appropriate directory, e.g.: ``cp chromedriver /usr/local/bin``

Getting started
---------------

This `tutorial notebook <https://github.com/spotify/chartify/blob/master/examples/Chartify%20Tutorial.ipynb>`_ is the best place to get started with a guided tour of the core concepts of Chartify.

From there, check out the `example notebook <https://github.com/spotify/chartify/blob/master/examples/Examples.ipynb>`_ for a list of all the available plots.

Docs
---------------

Documentation available on `chartify.readthedocs.io <https://chartify.readthedocs.io/en/latest/>`_

Getting support
---------------

Join #chartify on spotify-foss.slack.com (`Get an invite <https://slackin.spotify.com/>`_)

Use the `chartify tag on StackOverflow <https://stackoverflow.com/questions/tagged/chartify>`_.

Resources
---------------

- Data Visualization with `Chartify <https://www.section.io/engineering-education/data-viz-chartify/>`_

Code of Conduct
---------------

This project adheres to the `Open Code of Conduct <https://github.com/spotify/code-of-conduct/blob/master/code-of-conduct.md>`_. By participating, you are expected to honor this code.

Contributing
------------

`See the contributing docs <CONTRIBUTING.rst>`_.


=======
History
=======

3.0.5 (2022-12-13)
------------------

* Fixed a few errors in example and tutorial notebooks
* Fixed a typo in requirements.txt

3.0.4 (2022-10-18)
------------------

* Updated package requirements
* Got rid of future deprecation warnings
* Bugfix related to legend for graphs with multiple groups and colors

3.0.2 (2020-10-21)
------------------

* Support pyyaml 5.2+

3.0.1 (2020-06-02)
------------------

* Reduce dependencies by switching from Jupyter to IPython.

3.0.0 (2020-05-29)
------------------

* Updated Python to 3.6+ and Pandas to 1.0+ (Thanks @tomasaschan!)
* Updated Bokeh to 2.0+
* Removed colour dependency to fix setup errors.

2.7.0 (2019-11-27)
------------------

Bugfixes:

* Updated default yaml loader to move off of
  deprecated method (Thanks @vh920!)
* Updated legend handling to adjust for deprecated methods
  in recent versions of Bokeh (Thanks for reporting @jpkoc)
* Updated license in setup.py (Thanks for reporting @jsignell)
* Bump base Pillow dependency to avoid insecure version.
* Update MANIFEST to include missing files (Thanks @toddrme2178!)

2.6.1 (2019-08-15)
------------------

Bugfixes:

* Moved package requirements and fixed bug that occured with
  latest version of Bokeh (Thanks @emschuch & @mollymzhu!)
* Fixed bug in README while generating docs (Thanks @Bharat123rox!)

2.6.0 (2019-03-08)
------------------

Improvements:

* Allows users to plot colors on bar charts that aren't contained in the
  categorical axis.


Bugfixes:

* Fixed bug that caused float types to break when plotted with categorical
  text plots (Thanks for finding @danela!)
* Fixed broken readme links.

2.5.0 (2019-02-17)
------------------

Improvements:

* Added Radar Chart

2.4.0 (2019-02-16)
------------------

Improvements:

* Added second Y axis plotting.
* Removed Bokeh loading notification on import (Thanks @canavandl!)
* Added support for custom Bokeh resource loading (Thanks @canavandl!)
* Added example for Chart.save() method (Thanks @david30907d!)

Bugfixes:

* Updated documentation for saving and showing svgs.
* Fixed bug that broke plots with no difference between min and max
  points. (Thanks for finding @fabioconcina!)

2.3.5 (2018-11-21)
------------------

Improvements:

* Updated docstrings (Thanks @gregorybchris @ItsPugle!)
* Added SVG output options to Chart.show() and Chart.save()
  (Thanks for the suggestion @jdmendoza!)

Bugfixes:

* Fixed bug that caused source label to overlap with xaxis labels.
* Fixed bug that prevented x axis orientation changes
  with datetime axes (Thanks for finding @simonwongwong!)
* Fixed bug that caused subtitle to disappear
  with `outside_top` legend location (Thanks for finding @simonwongwong!)
* Line segment callout properties will work
  correctly. (Thanks @gregorybchris!)

2.3.4 (2018-11-13)
------------------

* Updated Bokeh version requirements to support 1.0

2.3.3 (2018-10-24)
------------------

* Removed upper bound of Pillow dependency.

2.3.2 (2018-10-18)
------------------

* Stacked bar and area order now matches default vertical legend order.
* Added method for shifting color palettes.
* Added scatter plots with a single categorical axis.
* Fixed bug with text_stacked that occurred with multiple categorical levels.

2.3.1 (2018-09-27)
------------------

* Fix scatter plot bug that can occur due to nested data types.

2.3.0 (2018-09-26)
------------------

* Added hexbin plot type.
* More control over grouped axis label orientation.
* Added alpha control to scatter, line, and parallel plots.
* Added control over marker style to scatter plot.
* Added ability to create custom color palettes.
* Changed default accent color.
* Visual tweaks to lollipop plot.
* Bar plots with a few number of series will have better widths.


2.2.0 (2018-09-17)
------------------

* First release on PyPI.


chartify

A Django app to plot charts and pivot charts directly from the models. Uses HighCharts and jQuery JavaScript libraries to render the charts on the webpage.

A python package for animating plots build on matplotlib.

[![Documentation Status](https://readthedocs.org/projects/animatplot/badge/?version=latest)](https://animatplot.readthedocs.io/en/latest/?badge=latest)

# animatplot
A python package for making interactive as well as animated plots with matplotlib.

![image](docs/source/tutorial/images/multiblock.gif)
![image](docs/source/_static/animatplot-mouse.gif)

## Requires
- Python >= 3.5
- Matplotlib >= 2.2 (because [sliders have discrete values](https://matplotlib.org/users/whats_new.html#slider-ui-widget-can-snap-to-discrete-values))
## Installation
```bash
pip install animatplot
```

or using conda
```bash
conda install animatplot -c conda-forge
```

## Documentation
Documentation can be found [here](https://animatplot.readthedocs.io/en/latest/index.html)

## Dev Install
```bash
git clone https://github.com/t-makaro/animatplot.git
pip install -e .
```
This project uses ```pycodestyle``` for linting. For testing, ```pytest``` is used.

Student Analytics

pandasby pandas-dev

pandasby pandas-dev

numpyby numpy

numpyby numpy

matplotlibby matplotlib

matplotlibby matplotlib

tensorflowby tensorflow

tensorflowby tensorflow

chartifyby spotify

chartifyby spotify

django-chartitby chartit

django-chartitby chartit

animatplotby t-makaro

animatplotby t-makaro