High-level | Cross-Platform | Huge Community | Large Ecosystem 💙💛
Python's simple, easy to learn syntax emphasizes readability and therefore reduces the cost of program maintenance.
- Official Websites
- YouTube, Groups and Communities
- Certificates and Exams
- Setup and Configuration
- IDE
- Implementation
- Integration
- Python Web Framework
- Articles, Videos and Tutorials
- Learning
- Datasets
- Libraries
- ML and Algorithms
- R vs Python
- Python - Official Website
- Python Documentation - Documentation as PDF
- OpenEDG Python Institute - The Python Institute is an independent non-profit project set up by the Open Education and Development Group (OpenEDG).
- PYPL - PYPL PopularitY of Programming Language
- PEPS - Index of Python Enhancement Proposals
- Real Python
- Python Data Structures - Joe James
- PyData - PyData is an educational program of NumFOCUS
- Python for Beginners (Full Course) - Programming with Mosh
- Python Community - LinkedIn Group
- PyCoder's Weekly - A free, weekly e-mail newsletter
- Introduction to Programming Using Python (98-381) - Microsoft
- OpenEDG Python Institute Certifications
- Python
- PIP
- How to Install PIP on Windows - PIP is the standard package manager for Python. It allows you to install and manage additional packages that are not part of the Python standard library. If you’re using Python 3.4 (or greater), then PIP comes installed with Python by default.
- How to Upgrade PIP in Windows - X:>python -m pip install --upgrade pip
- Third-party Dist.
- Anaconda Distribution - The World's Most Popular Python/R Data Science Platform
- PyPI - The Python Package Index
- PyPI - The Python Package Index (PyPI) is a repository of software for the Python programming language.
- Top Python IDEs for 2019 - DataCamp
- Visual Studio Code
- Python Development in Visual Studio Code
- Python in Visual Studio Code (January 2021 Release) -
- Getting Started with Python in Visual Studio Code (Video) - James Q Quick (March 2019)
- Getting Started with Python in Visual Studio Code (Video) - Dec 2019 (Microsoft)
- Code Runner Ext. - Code Runner for Visual Studio Code
- Linting Python in Visual Studio Code
- Jupyter Notebooks - Working with Jupyter Notebooks in Visual Studio Code
- Visual Studio Community 2019 - Install Python support in Visual Studio
- Spyder - Spyder is a powerful scientific environment written in Python, for Python, and designed by and for scientists, engineers and data analysts.
- Jupyter - Try Jupyter without installing anything
- IPython - IPython provides a rich architecture for interactive computing
- Azure Notebook (Microsoft)
- Colaboratory (Google)
- IDLE - Included in the Python setup
- Atom
- Sublime
- REPL.it (Online) - Repl.it gives you an instant IDE to learn, build, collaborate, and host all in one place.
- CPython - CPython is the reference implementation of the Python programming language. Written in C and Python, CPython is the default and most widely-used implementation of the language.
- Jython - Java
- IronPython - C#
- PyPy - RPython
- MicroPython - MicroPython is a lean and efficient implementation of the Python 3 programming language that includes a small subset of the Python standard library and is optimised to run on microcontrollers and in constrained environments.
- Stackless - Stackless Python, or Stackless, is a Python programming language interpreter, so named because it avoids depending on the C call stack for its own stack.
- Create Power BI visuals using Python (Power BI)
- How to use Python in SQL Server 2017 to obtain advanced data analytics (SQL Server)
- Django - Django makes it easier to build better Web apps more quickly and with less code.
- Flask - Flask is easy to get started with as a beginner because there is little boilerplate code for getting a simple app up and running.
- Bottle
- Pyramid
- Benchmarking
- Templating
- Heroku - Cloud platform for building Pythonic apps and APIs
- PythonAnywhere - Host, run, and code Python in the cloud!
- Docker for data scientists — Part 1 - Adam Sroka (December 2020)
- Future of Python in the industry
- Top 10 Python libraries of 2018
- Everything I know about Python... - Jeff Knupp
- 100 Days of ML Code - Avik Jain ⭐
- Top 10 Websites Using Python - Chris Hawkes
- Graph Theory Tutorial from a Google Engineer - freeCodeCamp.org
- Python Tutorial - Python for Beginners - Programming with Mosh
- KidsCanCode - Every kid should learn to code! KidsCanCode is producing a series of YouTube coding lessons based on its proven, in-class curriculum - and you can help!
- Tynker - Learn Python as you play.
- micro:bit - Power your imagination with code
- Why Is Data Literacy Important For Any Business? - Bernard Marr
- It's Time to Stop Being “Data-Driven” (And Start Being Data-Informed) - Interana Blog Staff
- "data is never going to tell you the full story"
- 9 Common Mistakes That Lead To Data Bias
- Biased and unbiased data and why they matter (IBM)
- How data and analytics can add more value to your business - PwC
- CRISP-DM - Cross-Industry Standard Process for Data Mining
- Hindsight, Insight and Foresight - Gartner
- From Hindsight to Insight to Foresight (Whitepaper) - Vertica
- Basic Data Types - Michael Castello
- Data Types in Statistics - Niklas Donges
- Interval scale vs Ratio scale
- Nominal Ordinal Interval Ratio & Cardinal - Statistics How To
- What Are Variables? - stattrek.com
- The 5 Basic Statistics Concepts Data Scientists Need to Know - George Seif
- Online Statistics Education - Developed by Rice University (Lead Developer), University of Houston Clear Lake, and Tufts University
- Computational Statistics in Python - Cliburn Chan and Janice McCarthy
- Electronic Statistics Textbook - StatSoft ⭐
- What is a REPL?
- Embracing the Four Python Programming Styles - By John Paul Mueller (Aug. 2018)
- Code Style - python-guide.org
- Understanding the underscore( _ ) of Python
- Garbage collection in Python: things you need to know - Artem Golubin
- Python: Mutable vs Immutable
- Tricky Python I : Memory Management for Mutable & Immutable Objects - Tanya Kryukova
- Python Exploratory Data Analysis Tutorial - DataCamp
- Analyzing the Food Culture of Bangalore - Niranjan Kumar
- Speed Up Your Exploratory Data Analysis With Pandas-Profiling - Lukas Frei (April 2019)
- Data Cleaning with Python and Pandas: Detecting Missing Values
- The Dummy’s Guide to Creating Dummy Variables - Rowan Langford
- The Ultimate Guide to 12 Dimensionality Reduction Techniques - Pulkit Sharma
- Summarising, Aggregating, and Grouping data in Python Pandas - Shane Lynn
- Pandas Tutorials - w3resource.com
- The Python Gallery - This website displays hundreds of charts, always providing the reproducible python code!
- Pyplot tutorial - matplotlib.pyplot is a collection of command style functions that make matplotlib work like MATLAB.
- Visualization with Matplotlib - Python Data Science Handbook by Jake VanderPlas
- Data Visualization with Seaborn - Jovian Lin
- The Interesting Python Graphics Libraries for Python Programmers
- Spatial Visualizations and Analysis in Python with Folium
- Top 10 Types of Data Visualization -
- Microsoft Developer (YouTube)
- Python (Microsoft) - Read the latest updates about all things Python at Microsoft
- Python Tutorial (SoloLearn) - soloLearn.com
- w3resources - Python Exercises, Practice, Solution
- Python Essentials - OpenEDG | Open Education and Development Group ⭐
- Cognitive Class (IBM)
- Google's Python Class - Google's Python Class
- Learn Python - thispointer.com
- learnPython - Essentials of Python by Stephanie Hicks
- Python by Example - By Xah Lee
- Python Tutorials (Tutorials Teacher) - These tutorials are designed for beginners and professionals who want to learn Python programming language.
- The Python Tutorial - Python.org
- Python Tutorial (W3Schools) - W3Schools.com
- Data To Fish - Python Tutorials - DataFish.com
- Data Science & Artificial Intelligence - Chris Albon
- Python Programming Language - geeksforgeeks.org
- Programiz - programiz.com
- Making a Stand Alone Executable from a Python Script using PyInstaller
- Python Tutorials (GitHub) - Yiannis Pitsillides
- scikit-learn
- Scikit-learn course - inria.github.io/scikit-learn-mooc
- Python - 10 Apps from Scratch - Michael Crump
-
IDE
- Jupyter - Install the Jupyter system, including the notebook, qtconsole, and the IPython kernel.
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Core Libraries and Statistics
- NumPy - NumPy (Numeric Python) is the fundamental package for scientific computing with Python. It contains among other things; 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.
- SciPy - The SciPy library is one of the core packages that make up the SciPy stack. It provides many user-friendly and efficient numerical routines such as routines for numerical integration, interpolation, optimization, linear algebra and statistics.
- Pandas - Pandas library providing high-performance, easy-to-use data structures and data analysis tools for the Python programming language.
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Visualization
- Matplotlib - Matplotlib is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
- Seaborn - Seaborn is a Python data visualization library based on matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.
- Plotly - Plotly's Python graphing library makes interactive, publication-quality graphs online. Examples of how to make line plots, scatter plots, area charts, bar charts, error bars, box plots, histograms, heatmaps, subplots, multiple-axes, polar charts, and bubble charts.
- chartify - Chartify is a Python library that makes it easy for data scientists to create charts.
- ggplot - ggplot is a plotting system for Python based on R's ggplot2 and the Grammar of Graphics. It is built for making profressional looking, plots quickly with minimal code.
- folium - Make beautiful maps with Leaflet.js & Python
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Machine Learning
- Scikit-learn - Simple and efficient tools for data mining and data analysis
- TensorFlow - The core open source library to help you develop and train ML models. Get started quickly by running Colab notebooks directly in your browser.
- PyTorch - An open source deep learning platform that provides a seamless path from research prototyping to production deployment.
- Keras - Keras is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano.
- NeuronBlocks - NLP DNN Toolkit - Building Your NLP DNN Models Like Playing Lego
- pythia - A modular framework for Visual Question Answering research from Facebook AI Research (FAIR) https://learnpythia.readthedocs.io/
- NimbusML - NimbusML provides state-of-the-art ML algorithms, transforms and components, aiming to make them useful for all developers, data scientists, and information workers and helpful in all products, services and devices.
- ML.NET in Python with NimbusML (Video) - A Python wrapper of ML.NET that extends scikit-learn package
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Dashboard
-
Python Datasets
- scikit-learn - The sklearn.datasets package embeds some small toy datasets.
- PyDataset - Provides instant access to many datasets right from Python (in pandas DataFrame structure).
- gapminder - A Python version of Jennifer Bryan's excellent gapminder teaching package for R.
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Apps
- Awesome ML - Awesome ML Resources and References
- R vs Scala vs Python - Google Trend
- TIOBE Index for R
- TIOBE Index for Python
- R vs Python: What’s The Difference?
- Choosing R or Python for Data Analysis? An Infographic
- R Vs Python: What’s the Difference? - R and Python are both open-source programming languages with a large community. New libraries or tools are added continuously to their respective catalog. R is mainly used for statistical analysis while Python provides a more general approach to data science.
- R versus Python (Data Science Wars)