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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
• Where to get it
• Dependencies
• Installation from sources
• License
• Documentation
• Background
• Getting Help
• Discussion and Development
• Contributing to pandas

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

• Easy handling of missing data (represented as NaN, NA, or NaT) in floating point as well as non-floating…

Flask is a lightweight WSGI web application framework. It is designed to make getting started quick and easy, with the ability to scale up to complex applications. It began as a simple wrapper around Werkzeug and Jinja, and has become one of the most popular Python web application frameworks.

Flask offers suggestions, but doesn't enforce any dependencies or project layout. It is up to the developer to choose the tools and libraries they want to use. There are many extensions provided by the community that make adding new functionality easy.

# save this as app.py
from flask import Flask

app = Flask(__name__)

@app.route("/")
def hello():
    return "Hello, World!"

$ flask run
  * Running on http://127.0.0.1:5000/ (Press CTRL+C to quit)

The Pallets organization develops and supports Flask and the libraries it uses. In order to…

Requests is a simple, yet elegant, HTTP library.

>>> import requests
>>> r = requests.get('https://httpbin.org/basic-auth/user/pass', auth=('user', 'pass'))
>>> r.status_code
200
>>> r.headers['content-type']
'application/json; charset=utf8'
>>> r.encoding
'utf-8'
>>> r.text
'{"authenticated": true, ...'
>>> r.json()
{'authenticated': True, ...}

Requests allows you to send HTTP/1.1 requests extremely easily. There’s no need to manually add query strings to your URLs, or to form-encode your PUT & POST data — but nowadays, just use the json method!

Requests is one of the most downloaded Python packages today, pulling in around 30M downloads / week— according to GitHub, Requests is currently depended upon by 1,000,000+ repositories. You may certainly put your trust in this code.

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

Check out our home page for more information.

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.

See the install documentation which is generated from /doc/install/index.rst

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!

Discourse 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 mailing list: matplotlib-users@python.org
• Announcement mailing list: matplotlib-announce@python.org
• Development mailing list: matplotlib-devel@python.org

Gitter is for coordinating…

Keras 3 is a multi-backend deep learning framework, with support for JAX, TensorFlow, and PyTorch Effortlessly build and train models for computer vision, natural language processing, audio processing timeseries forecasting, recommender systems, etc.

• Accelerated model development: Ship deep learning solutions faster thanks to the high-level UX of Keras and the availability of easy-to-debug runtimes like PyTorch or JAX eager execution.
• State-of-the-art performance: By picking the backend that is the fastest for your model architecture (often JAX!), leverage speedups ranging from 20% to 350% compared to other frameworks. Benchmark here.
• Datacenter-scale training: Scale confidently from your laptop to large clusters of GPUs or TPUs.

Join nearly three million developers, from burgeoning startups to global enterprises, in harnessing the power of Keras 3.

Keras 3 is available on PyPI as keras. Note that Keras 2 remains available…

Documentation

TensorFlow is an end-to-end open source platform for machine learning. It has a comprehensive, flexible ecosystem of tools libraries, and community resources that lets researchers push the state-of-the-art in ML and developers easily build and deploy ML-powered applications.

TensorFlow was originally developed by researchers and engineers working within the Machine Intelligence team at Google Brain to conduct research in machine learning and neural networks. However, the framework is versatile enough to be used in other areas as well.

TensorFlow provides stable Python and C++ APIs, as well as a non-guaranteed backward compatible API for other languages.

Keep up-to-date with release announcements and security updates by subscribing to announce@tensorflow.org See all the mailing lists.

See the TensorFlow install guide for the pip package, to enable GPU support, use a Docker container, and build from source.

To install the current release…