The fundamental package for scientific computing with Python.

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


[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](
https://numfocus.org)
[![PyPI Downloads](https://img.shields.io/pypi/dm/numpy.svg?label=PyPI%20downloads)](
https://pypi.org/project/numpy/)
[![Conda Downloads](https://img.shields.io/conda/dn/conda-forge/numpy.svg?label=Conda%20downloads)](
https://anaconda.org/conda-forge/numpy)
[![Stack Overflow](https://img.shields.io/badge/stackoverflow-Ask%20questions-blue.svg)](
https://stackoverflow.com/questions/tagged/numpy)
[![Nature Paper](https://img.shields.io/badge/DOI-10.1038%2Fs41592--019--0686--2-blue)](
https://doi.org/10.1038/s41586-020-2649-2)
[![OpenSSF Scorecard](https://api.securityscorecards.dev/projects/github.com/numpy/numpy/badge)](https://api.securityscorecards.dev/projects/github.com/numpy/numpy)


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

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
[![PyPI Latest Release](https://img.shields.io/pypi/v/pandas.svg)](https://pypi.org/project/pandas/)
[![Conda Latest Release](https://anaconda.org/conda-forge/pandas/badges/version.svg)](https://anaconda.org/anaconda/pandas/)
[![DOI](https://zenodo.org/badge/DOI/10.5281/zenodo.3509134.svg)](https://doi.org/10.5281/zenodo.3509134)
[![Package Status](https://img.shields.io/pypi/status/pandas.svg)](https://pypi.org/project/pandas/)
[![License](https://img.shields.io/pypi/l/pandas.svg)](https://github.com/pandas-dev/pandas/blob/main/LICENSE)
[![Coverage](https://codecov.io/github/pandas-dev/pandas/coverage.svg?branch=main)](https://codecov.io/gh/pandas-dev/pandas)
[![Downloads](https://static.pepy.tech/personalized-badge/pandas?period=month&units=international_system&left_color=black&right_color=orange&left_text=PyPI%20downloads%20per%20month)](https://pepy.tech/project/pandas)
[![Slack](https://img.shields.io/badge/join_Slack-information-brightgreen.svg?logo=slack)](https://pandas.pydata.org/docs/dev/development/community.html?highlight=slack#community-slack)
[![Powered by NumFOCUS](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org)
[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
[![Imports: isort](https://img.shields.io/badge/%20imports-isort-%231674b1?style=flat&labelColor=ef8336)](https://pycqa.github.io/isort/)

## 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

[![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)
[![NUMFocus](https://img.shields.io/badge/powered%20by-NumFOCUS-orange.svg?style=flat&colorA=E1523D&colorB=007D8A)](https://numfocus.org)

[![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)
[![AzurePipelines](https://dev.azure.com/matplotlib/matplotlib/_apis/build/status/matplotlib.matplotlib?branchName=main)](https://dev.azure.com/matplotlib/matplotlib/_build/latest?definitionId=1&branchName=main)
[![AppVeyor](https://ci.appveyor.com/api/projects/status/github/matplotlib/matplotlib?branch=main&svg=true)](https://ci.appveyor.com/project/matplotlib/matplotlib)
[![Codecov](https://codecov.io/github/matplotlib/matplotlib/badge.svg?branch=main&service=github)](https://codecov.io/github/matplotlib/matplotlib?branch=main)

![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

Label Studio is a multi-type data labeling and annotation tool with standardized output format

<img src="https://user-images.githubusercontent.com/12534576/192582340-4c9e4401-1fe6-4dbb-95bb-fdbba5493f61.png"/>

![GitHub](https://img.shields.io/github/license/heartexlabs/label-studio?logo=heartex) ![label-studio:build](https://github.com/heartexlabs/label-studio/workflows/label-studio:build/badge.svg) ![GitHub release](https://img.shields.io/github/v/release/heartexlabs/label-studio?include_prereleases)

[Website](https://labelstud.io/) • [Docs](https://labelstud.io/guide/) • [Twitter](https://twitter.com/labelstudiohq) • [Join Slack Community <img src="https://app.heartex.ai/docs/images/slack-mini.png" width="18px"/>](https://slack.labelstudio.heartex.com/?source=github-1)


## What is Label Studio?



Label Studio is an open source data labeling tool. It lets you label data types like audio, text, images, videos, and time series with a simple and straightforward UI and export to various model formats. It can be used to prepare raw data or improve existing training data to get more accurate ML models.

- [Try out Label Studio](#try-out-label-studio)
- [What you get from Label Studio](#what-you-get-from-label-studio)
- [Included templates for labeling data in Label Studio](#included-templates-for-labeling-data-in-label-studio)
- [Set up machine learning models with Label Studio](#set-up-machine-learning-models-with-Label-Studio)
- [Integrate Label Studio with your existing tools](#integrate-label-studio-with-your-existing-tools)

![Gif of Label Studio annotating different types of data](https://raw.githubusercontent.com/heartexlabs/label-studio/master/images/annotation_examples.gif)

Have a custom dataset? You can customize Label Studio to fit your needs. Read an [introductory blog post](https://towardsdatascience.com/introducing-label-studio-a-swiss-army-knife-of-data-labeling-140c1be92881) to learn more. 

## Try out Label Studio

Install Label Studio locally, or deploy it in a cloud instance. [Or, sign up for a free trial of our Enterprise edition.](https://heartex.com/free-trial).

- [Install locally with Docker](#install-locally-with-docker)
- [Run with Docker Compose (Label Studio + Nginx + PostgreSQL)](#run-with-docker-compose)
- [Install locally with pip](#install-locally-with-pip)
- [Install locally with Anaconda](#install-locally-with-anaconda)
- [Install for local development](#install-for-local-development)
- [Deploy in a cloud instance](#deploy-in-a-cloud-instance)

### Install locally with Docker
Official Label Studio docker image is [here](https://hub.docker.com/r/heartexlabs/label-studio) and it can be downloaded with `docker pull`. 
Run Label Studio in a Docker container and access it at `http://localhost:8080`.


```bash
docker pull heartexlabs/label-studio:latest
docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest
```
You can find all the generated assets, including SQLite3 database storage `label_studio.sqlite3` and uploaded files, in the `./mydata` directory.

#### Override default Docker install
You can override the default launch command by appending the new arguments:
```bash
docker run -it -p 8080:8080 -v $(pwd)/mydata:/label-studio/data heartexlabs/label-studio:latest label-studio --log-level DEBUG
```

#### Build a local image with Docker
If you want to build a local image, run:
```bash
docker build -t heartexlabs/label-studio:latest .
```

### Run with Docker Compose
Docker Compose script provides production-ready stack consisting of the following components:

- Label Studio
- [Nginx](https://www.nginx.com/) - proxy web server used to load various static data, including uploaded audio, images, etc.
- [PostgreSQL](https://www.postgresql.org/) - production-ready database that replaces less performant SQLite3.

To start using the app from `http://localhost` run this command:
```bash
docker-compose up
```

### Install locally with pip

```bash
# Requires Python >=3.7 <=3.9
pip install label-studio

# Start the server at http://localhost:8080
label-studio
```

### Install locally with Anaconda

```bash
conda create --name label-studio
conda activate label-studio
pip install label-studio
```

### Install for local development

You can run the latest Label Studio version locally without installing the package with pip. 

```bash
# Install all package dependencies
pip install -e .
# Run database migrations
python label_studio/manage.py migrate
python label_studio/manage.py collectstatic
# Start the server in development mode at http://localhost:8080
python label_studio/manage.py runserver
```

### Deploy in a cloud instance

You can deploy Label Studio with one click in Heroku, Microsoft Azure, or Google Cloud Platform: 

[<img src="https://www.herokucdn.com/deploy/button.svg" height="30px">](https://heroku.com/deploy?template=https://github.com/heartexlabs/label-studio/tree/heroku-persistent-pg)
[<img src="https://aka.ms/deploytoazurebutton" height="30px">](https://portal.azure.com/#create/Microsoft.Template/uri/https%3A%2F%2Fraw.githubusercontent.com%2Fheartexlabs%2Flabel-studio%2Fmaster%2Fazuredeploy.json)
[<img src="https://deploy.cloud.run/button.svg" height="30px">](https://deploy.cloud.run)


#### Apply frontend changes

The frontend part of Label Studio app lies in the `frontend/` folder and written in React JSX. In case you've made some changes there, the following commands should be run before building / starting the instance:

```
cd label_studio/frontend/
npm ci
npx webpack
cd ../..
python label_studio/manage.py collectstatic --no-input
```

### Troubleshoot installation
If you see any errors during installation, try to rerun the installation

```bash
pip install --ignore-installed label-studio
```

#### Install dependencies on Windows 
To run Label Studio on Windows, download and install the following wheel packages from [Gohlke builds](https://www.lfd.uci.edu/~gohlke/pythonlibs) to ensure you're using the correct version of Python:
- [lxml](https://www.lfd.uci.edu/~gohlke/pythonlibs/#lxml)

```bash
# Upgrade pip 
pip install -U pip

# If you're running Win64 with Python 3.8, install the packages downloaded from Gohlke:
pip install lxml‑4.5.0‑cp38‑cp38‑win_amd64.whl

# Install label studio
pip install label-studio
```

#### Run test suite
```bash
pip install -r deploy/requirements-test.txt
cd label_studio

# postgres (assumes default postgres user,db,pass)
DJANGO_DB=default DJANGO_SETTINGS_MODULE=core.settings.label_studio python -m pytest -vv -n auto

# sqlite3
DJANGO_DB=sqlite DJANGO_SETTINGS_MODULE=core.settings.label_studio python -m pytest -vv -n auto
```


## What you get from Label Studio

![Screenshot of Label Studio data manager grid view with images](https://raw.githubusercontent.com/heartexlabs/label-studio/master/images/labelstudio-ui.gif)

- **Multi-user labeling** sign up and login, when you create an annotation it's tied to your account.
- **Multiple projects** to work on all your datasets in one instance.
- **Streamlined design** helps you focus on your task, not how to use the software.
- **Configurable label formats** let you customize the visual interface to meet your specific labeling needs.
- **Support for multiple data types** including images, audio, text, HTML, time-series, and video. 
- **Import from files or from cloud storage** in Amazon AWS S3, Google Cloud Storage, or JSON, CSV, TSV, RAR, and ZIP archives. 
- **Integration with machine learning models** so that you can visualize and compare predictions from different models and perform pre-labeling.
- **Embed it in your data pipeline** REST API makes it easy to make it a part of your pipeline

## Included templates for labeling data in Label Studio 

Label Studio includes a variety of templates to help you label your data, or you can create your own using specifically designed configuration language. The most common templates and use cases for labeling include the following cases:

<img src="https://raw.githubusercontent.com/heartexlabs/label-studio/master/images/templates-categories.jpg" />

## Set up machine learning models with Label Studio

Connect your favorite machine learning model using the Label Studio Machine Learning SDK. Follow these steps:

1. Start your own machine learning backend server. See [more detailed instructions](https://github.com/heartexlabs/label-studio-ml-backend).
2. Connect Label Studio to the server on the model page found in project settings.

This lets you:

- **Pre-label** your data using model predictions. 
- Do **online learning** and retrain your model while new annotations are being created. 
- Do **active learning** by labeling only the most complex examples in your data.

## Integrate Label Studio with your existing tools

You can use Label Studio as an independent part of your machine learning workflow or integrate the frontend or backend into your existing tools.  

* Use the [Label Studio Frontend](https://github.com/heartexlabs/label-studio-frontend) as a separate React library. See more in the [Frontend Library documentation](https://labelstud.io/guide/frontend.html). 

## Ecosystem

| Project | Description |
|-|-|
| label-studio | Server, distributed as a pip package |
| [label-studio-frontend](https://github.com/heartexlabs/label-studio-frontend) | React and JavaScript frontend and can run standalone in a web browser or be embedded into your application. |  
| [data-manager](https://github.com/heartexlabs/dm2) | React and JavaScript frontend for managing data. Includes the Label Studio Frontend. Relies on the label-studio server or a custom backend with the expected API methods. | 
| [label-studio-converter](https://github.com/heartexlabs/label-studio-converter) | Encode labels in the format of your favorite machine learning library | 
| [label-studio-transformers](https://github.com/heartexlabs/label-studio-transformers) | Transformers library connected and configured for use with Label Studio |


## Roadmap

Want to use **The Coolest Feature X** but Label Studio doesn't support it? Check out [our public roadmap](roadmap.md)!

## Citation

```tex
@misc{Label Studio,
  title={{Label Studio}: Data labeling software},
  url={https://github.com/heartexlabs/label-studio},
  note={Open source software available from https://github.com/heartexlabs/label-studio},
  author={
    Maxim Tkachenko and
    Mikhail Malyuk and
    Andrey Holmanyuk and
    Nikolai Liubimov},
  year={2020-2022},
}
```

## License

This software is licensed under the [Apache 2.0 LICENSE](/LICENSE) © [Heartex](https://www.heartex.com/). 2020-2022

<img src="https://user-images.githubusercontent.com/12534576/192582529-cf628f58-abc5-479b-a0d4-8a3542a4b35e.png" title="Hey everyone!" width="180" />

label-studio

An open-source, low-code machine learning library in Python

<div align="center">

<img src="docs/images/logo.png" alt="drawing" width="200"/>

## **An open-source, low-code machine learning library in Python**
## 🎉🎉🎉 **PyCaret 3.0 is now available. 🎉🎉🎉**
## `pip install --upgrade pycaret` </br>

<p align="center">
<h3>
  <a href="https://pycaret.gitbook.io/">Docs</a> •
  <a href="https://pycaret.gitbook.io/docs/get-started/tutorials">Tutorials</a> •
  <a href="https://pycaret.gitbook.io/docs/learn-pycaret/official-blog">Blog</a> •
  <a href="https://www.linkedin.com/company/pycaret/">LinkedIn</a> •
  <a href="https://www.youtube.com/channel/UCxA1YTYJ9BEeo50lxyI_B3g">YouTube</a> •
    <a href="https://join.slack.com/t/pycaret/shared_invite/zt-row9phbm-BoJdEVPYnGf7_NxNBP307w">Slack</a>
</h3>
</p>

| Overview | |
|---|---|
| **CI/CD** | ![pytest on push](https://github.com/pycaret/pycaret/workflows/pytest%20on%20push/badge.svg) [![Documentation Status](https://readthedocs.org/projects/pip/badge/?version=stable)](http://pip.pypa.io/en/stable/?badge=stable) |
| **Code** |  [![!pypi](https://img.shields.io/pypi/v/pycaret?color=orange)](https://pypi.org/project/pycaret/) [![!python-versions](https://img.shields.io/badge/Python-3.7%20%7C%203.8%20%7C%203.9%20%7C%203.10-blue)](https://badge.fury.io/py/pycaret) [![!black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black)
| **Downloads**| [![Downloads](https://static.pepy.tech/personalized-badge/pycaret?period=week&units=international_system&left_color=grey&right_color=blue&left_text=weekly%20(pypi))](https://pepy.tech/project/pycaret) [![Downloads](https://static.pepy.tech/personalized-badge/pycaret?period=month&units=international_system&left_color=grey&right_color=blue&left_text=monthly%20(pypi))](https://pepy.tech/project/pycaret) [![Downloads](https://static.pepy.tech/personalized-badge/pycaret?period=total&units=international_system&left_color=grey&right_color=blue&left_text=cumulative%20(pypi))](https://pepy.tech/project/pycaret) |
| **License** | [![License](https://img.shields.io/pypi/l/ansicolortags.svg)](https://img.shields.io/pypi/l/ansicolortags.svg)
| **Community** | [![Slack](https://img.shields.io/badge/slack-chat-green.svg?logo=slack)](https://join.slack.com/t/pycaret/shared_invite/zt-row9phbm-BoJdEVPYnGf7_NxNBP307w) |



![alt text](docs/images/quick_start.gif)

<div align="left">

# Welcome to PyCaret
PyCaret is an open-source, low-code machine learning library in Python that automates machine learning workflows. It is an end-to-end machine learning and model management tool that speeds up the experiment cycle exponentially and makes you more productive.

In comparison with the other open-source machine learning libraries, PyCaret is an alternate low-code library that can be used to replace hundreds of lines of code with few lines only. This makes experiments exponentially fast and efficient. PyCaret is essentially a Python wrapper around several machine learning libraries and frameworks such as scikit-learn, XGBoost, LightGBM, CatBoost, Optuna, Hyperopt, Ray, and few more.

The design and simplicity of PyCaret are inspired by the emerging role of citizen data scientists, a term first used by Gartner. Citizen Data Scientists are power users who can perform both simple and moderately sophisticated analytical tasks that would previously have required more technical expertise. PyCaret was inspired by the caret library in R programming language.

# 🚀 Installation

## 🌐 Option 1: Install via PyPi
PyCaret is tested and supported on 64-bit systems with:
- Python 3.7, 3.8, 3.9, and 3.10
- Ubuntu 16.04 or later
- Windows 7 or later

You can install PyCaret with Python's pip package manager:

```python
# install pycaret
pip install pycaret
```

PyCaret's default installation will not install all the optional dependencies automatically. Depending on the use case, you may be interested in one or more extras:

```python
# install analysis extras
pip install pycaret[analysis]

# models extras
pip install pycaret[models]

# install tuner extras
pip install pycaret[tuner]

# install mlops extras
pip install pycaret[mlops]

# install parallel extras
pip install pycaret[parallel]

# install test extras
pip install pycaret[test]

##

# install multiple extras together
pip install pycaret[analysis,models]
```

Check out all [optional dependencies](https://github.com/pycaret/pycaret/blob/master/requirements-optional.txt). If you want to install everything including all the optional dependencies:

```python
# install full version
pip install pycaret[full]
```
## 📄 Option 2: Build from Source
Install the development version of the library directly from the source. The API may be unstable. It is not recommended for production use.

```python
pip install git+https://github.com/pycaret/pycaret.git@master --upgrade
```

## 📦 Option 3: Docker
Docker creates virtual environments with containers that keep a PyCaret installation separate from the rest of the system. PyCaret docker comes pre-installed with a Jupyter notebook. It can share resources with its host machine (access directories, use the GPU, connect to the Internet, etc.). The PyCaret Docker images are always tested for the latest major releases.

```python
# default version
docker run -p 8888:8888 pycaret/slim

# full version
docker run -p 8888:8888 pycaret/full
```

## 🏃‍♂️ Quickstart

### 1. Functional API
```python
# Classification Functional API Example

# loading sample dataset
from pycaret.datasets import get_data
data = get_data('juice')

# init setup
from pycaret.classification import *
s = setup(data, target = 'Purchase', session_id = 123)

# model training and selection
best = compare_models()

# evaluate trained model
evaluate_model(best)

# predict on hold-out/test set
pred_holdout = predict_model(best)

# predict on new data
new_data = data.copy().drop('Purchase', axis = 1)
predictions = predict_model(best, data = new_data)

# save model
save_model(best, 'best_pipeline')
```

### 2. OOP API

```python
# Classification OOP API Example

# loading sample dataset
from pycaret.datasets import get_data
data = get_data('juice')

# init setup
from pycaret.classification import ClassificationExperiment
s = ClassificationExperiment()
s.setup(data, target = 'Purchase', session_id = 123)

# model training and selection
best = s.compare_models()

# evaluate trained model
s.evaluate_model(best)

# predict on hold-out/test set
pred_holdout = s.predict_model(best)

# predict on new data
new_data = data.copy().drop('Purchase', axis = 1)
predictions = s.predict_model(best, data = new_data)

# save model
s.save_model(best, 'best_pipeline')
```


## 📁 Modules
<div align="center">

## **Classification**

  Functional API           |  OOP API
:-------------------------:|:-------------------------:
![](docs/images/classification_functional.png)  | ![](docs/images/classification_OOP.png)

## **Regression**

  Functional API           |  OOP API
:-------------------------:|:-------------------------:
![](docs/images/regression_functional.png)  | ![](docs/images/regression_OOP.png)

## **Time Series**

  Functional API           |  OOP API
:-------------------------:|:-------------------------:
![](docs/images/time_series_functional.png)  | ![](docs/images/time_series_OOP.png)

## **Clustering**

  Functional API           |  OOP API
:-------------------------:|:-------------------------:
![](docs/images/clustering_functional.png)  | ![](docs/images/clustering_OOP.png)

## **Anomaly Detection**

  Functional API           |  OOP API
:-------------------------:|:-------------------------:
![](docs/images/anomaly_functional.png)  | ![](docs/images/anomaly_OOP.png)

<div align="left">

# 👥 Who should use PyCaret?
PyCaret is an open source library that anybody can use. In our view the ideal target audience of PyCaret is: <br />

- Experienced Data Scientists who want to increase productivity.
- Citizen Data Scientists who prefer a low code machine learning solution.
- Data Science Professionals who want to build rapid prototypes.
- Data Science and Machine Learning students and enthusiasts.

# 🎮 Training on GPUs
To train models on the GPU, simply pass use_gpu = True in the setup function. There is no change in the use of the API; however, in some cases, additional libraries have to be installed. The following models can be trained on GPUs:

- Extreme Gradient Boosting
- CatBoost
- Light Gradient Boosting Machine requires [GPU installation](https://lightgbm.readthedocs.io/en/latest/GPU-Tutorial.html)
- Logistic Regression, Ridge Classifier, Random Forest, K Neighbors Classifier, K Neighbors Regressor, Support Vector Machine, Linear Regression, Ridge Regression, Lasso Regression requires [cuML >= 0.15](https://github.com/rapidsai/cuml)

# 🖥️ PyCaret Intel sklearnex support
You can apply [Intel optimizations](https://github.com/intel/scikit-learn-intelex) for machine learning algorithms and speed up your workflow. To train models with Intel optimizations use `sklearnex` engine. There is no change in the use of the API, however, installation of Intel sklearnex is required:

```python
pip install scikit-learn-intelex
```

# 🤝 Contributors
<a href="https://github.com/pycaret/pycaret/graphs/contributors">
  <img src="https://contributors-img.web.app/image?repo=pycaret/pycaret" width = 800/>
</a>

# 📝 License
PyCaret is completely free and open-source and licensed under the [MIT](https://github.com/pycaret/pycaret/blob/master/LICENSE) license.

# ℹ️ More Information

| Important Links              |            Description                                       |
| -------------------------- | -------------------------------------------------------------- |
| :star: **[Tutorials]**        | Tutorials developed and maintained by core developers       |
| :clipboard: **[Example Notebooks]** | Example notebooks created by community               |
| :orange_book: **[Blog]** | Official blog by creator of PyCaret                      |
| :books: **[Documentation]**      | API docs                              |
| :tv: **[Videos]**            | Video resources             |
| ✈️ **[Cheat sheet]**            | Community Cheat sheet            |
| :loudspeaker: **[Discussions]**        | Community Discussion board on GitHub|
| :hammer_and_wrench: **[Release Notes]**          | Release Notes          |

[tutorials]: https://pycaret.gitbook.io/docs/get-started/tutorials
[Example notebooks]: https://github.com/pycaret/examples
[Blog]: https://pycaret.gitbook.io/docs/learn-pycaret/official-blog
[Documentation]: https://pycaret.gitbook.io/docs/
[Videos]: https://pycaret.gitbook.io/docs/learn-pycaret/videos
[Cheat sheet]: https://pycaret.gitbook.io/docs/learn-pycaret/cheat-sheet
[Discussions]: https://github.com/pycaret/pycaret/discussions
[Release Notes]: https://github.com/pycaret/pycaret/releases




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