Python learning and data analysis resources. Please, contribute and get in touch! See MDmisc notes for other programming and genomics-related notes.
-
Advanced Jupyter Notebooks: A Tutorial - detailed and illustrated guide
-
SciPy scientific Python library development, history, algorithms (signal/image processing, plotting, integrals, ODE solvers, optimization, genetic algorithms, splines, parallel programming, many more). Started in 2001, over 100K dependent repositories. Includes 16 data packages (Box2). Documentation.
Paper
SciPy 1.0 Contributors, Pauli Virtanen, Ralf Gommers, Travis E. Oliphant, Matt Haberland, Tyler Reddy, David Cournapeau, et al. “SciPy 1.0: Fundamental Algorithms for Scientific Computing in Python.” Nature Methods, February 3, 2020. https://doi.org/10.1038/s41592-019-0686-2.
-
py_resources - Collection of Python learning resources (beginner resources, specific features, intermediate to advance resources, domain specific resources)
-
python-cheatsheet - Comprehensive Python Cheatsheet
-
awesome-jupyter - A curated list of awesome Jupyter projects, libraries and resources
-
awesome-python - A curated list of awesome Python frameworks, libraries, software and resources, GitHub
-
awesome-python-talks - An opinionated list of awesome videos related to Python, with a focus on training and gaining hands-on experience
-
matplotlib/cheatsheets GitHub repo, Official Matplotlib cheat sheets
-
python-guide - Python best practices guidebook, written for humans. "The Hitchhiker's Guide to Python" book by Kenneth Reitz and Tanya Schlusser
-
Python for Data Analysis, 2nd Edition - Materials and IPython notebooks for "Python for Data Analysis" by Wes McKinney, published by O'Reilly Media.
-
Tiny Python Projectsby Ken Youens-Clark, GitHub code and Youtube videos. new.py - automates skeleton creation for new Python programs
-
Mastering Python for Bioinformatics by Ken Youens-Clark, GitHub code part
-
How to Think Like a Computer Scientist - Learning Python programming from ground up
-
A Whirlwind Tour of Python book by Jake VanderPlas, O'Relly web wersion
-
Python Data Science Handbook: full text in Jupyter Notebooks GitHub repo and web version, by Jake VanderPlas. The corresponding print Python Data Science Handbook
-
Python 101 book, and a link to "Python 201". Blog post announcing it as free-donation available, blog post
-
cookbook-2nd - IPython Cookbook, Second Edition, by Cyrille Rossant, Packt Publishing 2018. Code and chapters, excellent resource. Compiled version
-
PY4E, Python for Everybody, Exploring Data In Python 3 - book providing an Informatics-oriented introduction to programming, with videos
-
Learn Python the Hard Way - book with exercises. Python 2 and 3 versions
-
About Learning Python, 5th Edition by Mark Lutz. Chapter samples, code
-
Pandas Exercises - Structured exercises as files: 1. Exercise instructions, 2. Solutions without code, 3. Solutions with code and comments.
-
Data 8: The Foundations of Data Science - statistically-oriented data science course with Python programming. Computational and Inferential Thinking book, lecture materials and videos.
-
Intro to Python for Data Science - free DataCamp course
-
Virgilio - Your new Mentor for Data Science E-Learning. Layered approach to Pyhton and Machine Learning. From basics to advanced deep learning
-
practical-python - Practical Python Programming, course by David Beazley
-
data-science-ipython-notebooks - Continually updated data science Python notebooks: Deep learning (TensorFlow, Theano, Caffe), scikit-learn, Kaggle, Spark, Hadoop MapReduce, HDFS, matplotlib, pandas, NumPy, SciPy, Python essentials, AWS, and various command lines
-
Scientific Python lectures - Lectures on scientific computing with python, as IPython notebooks
-
A gallery of interesting IPython Notebooks - notebooks introducing Python and statistics, machine learning and data-driven analysis
-
pytudes - Python programs, usually short, of considerable difficulty, to perfect particular skills. IPython notebooks for Colab, DeepNote, MyBinder, etc., python scripts. By Peter Norvig
-
learn-python3 - a series of notebooks with exercises covering Python3 programming language. Beginner and intermediate material
-
Intermediate Python, review with references to other e-books
-
An open access book on Python, OpenGL and Scientific Visualization, Nicolas P. Rougier, 2018. GitHub
-
Introductory Python lectures - IPython notebooks
-
Hydropythonica - Python programming basics. Lecture slides, iPython notebooks. Rus
-
Scientific_graphics_in_python - Scientific graphics in Python. Rus
-
pandas-cookbook - Recipes for using Python's pandas library. Detailed tutorials
-
Easier data analysis in Python with pandas (video series) - Pandas for beginners. Youtube
-
Intro to Pandas data structures - a gentle introduction to data analysis using pandas
-
Scientific Visualization: Python + Matplotlib by Nicolas P. Rougier. Tweet, GitHub
-
Python Visualization Landscape - Adaptation of Jake VanderPlas graphic about python visualization landscape. Web, Jake's Slides and Video
-
graph-tool - Graph-tool is an efficient Python module for manipulation and statistical analysis of graphs (a.k.a. networks).
-
Clust - Python script for gene clustering without strict requirement of all genes being assigned to clusters. Also, clustering across multiple datasets to find similar patterns. Timecourse clustering. Outperforms seven clustering techniques (cross-clustering, k-means, SOM, MCL, HC, Click, WGCNA) using seven metrics (Davies-Bouldin, BIC, silhouette, Calinski-Harabasz, Ball-Hall, Xu, within-between indices).
- Abu-Jamous, Basel, and Steven Kelly. “Clust: Automatic Extraction of Optimal Co-Expressed Gene Clusters from Gene Expression Data.” Genome Biology 19, no. 1 (December 2018)
-
70+ Python Projects with source code and tutorials. Tweet
-
Comprehensive Beginner’s Guide to Jupyter Notebooks for Data Science & Machine Learning, by Analytics Vidhya
-
Rule, Adam, Amanda Birmingham, Cristal Zuniga, Ilkay Altintas, Shih-Cheng Huang, Rob Knight, Niema Moshiri, et al. “Ten Simple Rules for Writing and Sharing Computational Analyses in Jupyter Notebooks.” PLoS Computational Biology 15, no. 7 (July 2019) - Jupyter notebook practices. ipywidgets, watermark, papermill, nbviewer, binder. Notebook examples, Guide for Reproducible Research and Data Science in Jupyter Notebooks