Python Data Science Handbook: full text in Jupyter Notebooks

Overview

Python Data Science Handbook

Binder Colab

This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks.

cover image

How to Use this Book

About

The book was written and tested with Python 3.5, though other Python versions (including Python 2.7) should work in nearly all cases.

The book introduces the core libraries essential for working with data in Python: particularly IPython, NumPy, Pandas, Matplotlib, Scikit-Learn, and related packages. Familiarity with Python as a language is assumed; if you need a quick introduction to the language itself, see the free companion project, A Whirlwind Tour of Python: it's a fast-paced introduction to the Python language aimed at researchers and scientists.

See Index.ipynb for an index of the notebooks available to accompany the text.

Software

The code in the book was tested with Python 3.5, though most (but not all) will also work correctly with Python 2.7 and other older Python versions.

The packages I used to run the code in the book are listed in requirements.txt (Note that some of these exact version numbers may not be available on your platform: you may have to tweak them for your own use). To install the requirements using conda, run the following at the command-line:

$ conda install --file requirements.txt

To create a stand-alone environment named PDSH with Python 3.5 and all the required package versions, run the following:

$ conda create -n PDSH python=3.5 --file requirements.txt

You can read more about using conda environments in the Managing Environments section of the conda documentation.

License

Code

The code in this repository, including all code samples in the notebooks listed above, is released under the MIT license. Read more at the Open Source Initiative.

Text

The text content of the book is released under the CC-BY-NC-ND license. Read more at Creative Commons.

Owner
Jake Vanderplas
Python, Astronomy, Data Science
Jake Vanderplas
Datamol is a python library to work with molecules

Datamol is a python library to work with molecules. It's a layer built on top of RDKit and aims to be as light as possible.

datamol 276 Dec 19, 2022
Incubator for useful bioinformatics code, primarily in Python and R

Collection of useful code related to biological analysis. Much of this is discussed with examples at Blue collar bioinformatics. All code, images and

Brad Chapman 560 Dec 24, 2022
A framework for feature exploration in Data Science

Beehive A framework for feature exploration in Data Science Background What do we do when we finish one episode of feature exploration in a jupyter no

Steven IJ 1 Jan 03, 2022
Algorithms covered in the Bioinformatics Course part of the Cambridge Computer Science Tripos

Bioinformatics This is a repository of all the algorithms covered in the Bioinformatics Course part of the Cambridge Computer Science Tripos Algorithm

16 Jun 30, 2022
Python Data Science Handbook: full text in Jupyter Notebooks

Python Data Science Handbook This repository contains the entire Python Data Science Handbook, in the form of (free!) Jupyter notebooks. How to Use th

Jake Vanderplas 36.9k Dec 28, 2022
CoCalc: Collaborative Calculation in the Cloud

logo CoCalc Collaborative Calculation and Data Science CoCalc is a virtual online workspace for calculations, research, collaboration and authoring do

SageMath, Inc. 1k Dec 29, 2022
SeqLike - flexible biological sequence objects in Python

SeqLike - flexible biological sequence objects in Python Introduction A single object API that makes working with biological sequences in Python more

186 Dec 23, 2022
Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"

Graphic notes on Gilbert Strang's "Linear Algebra for Everyone"

Kenji Hiranabe 3.2k Jan 08, 2023
artisan: visual scope for coffee roasters

Artisan Visual scope for coffee roasters WARNING: pre-release builds may not work. Use at your own risk. Summary Artisan is a software that helps coff

Artisan – Visual Scope for Coffee Roasters 705 Jan 05, 2023
Data intensive science for everyone.

InVesalius InVesalius generates 3D medical imaging reconstructions based on a sequence of 2D DICOM files acquired with CT or MRI equipments. InVesaliu

Galaxy Project 1k Jan 08, 2023
🍊 :bar_chart: :bulb: Orange: Interactive data analysis

Orange Data Mining Orange is a data mining and visualization toolbox for novice and expert alike. To explore data with Orange, one requires no program

Bioinformatics Laboratory 3.9k Jan 05, 2023
A mathematica expression evaluator with PokemonTypes

A simple mathematical expression evaluator that uses Pokemon types to replace symbols.

Arnav Jindal 2 Nov 14, 2021
AnuGA for the simulation of the shallow water equation

ANUGA Contents ANUGA What is ANUGA? Installation Documentation and Help Mailing Lists Web sites Latest source code Bug reports Developer information L

Geoscience Australia 147 Dec 14, 2022
3D visualization of scientific data in Python

Mayavi: 3D visualization of scientific data in Python Mayavi docs: http://docs.enthought.com/mayavi/mayavi/ TVTK docs: http://docs.enthought.com/mayav

Enthought, Inc. 1.1k Jan 06, 2023
CS 506 - Computational Tools for Data Science

CS 506 - Computational Tools for Data Science Code, slides, and notes for Boston University CS506 Fall 2021 The Final Project Repository can be found

Lance Galletti 14 Mar 23, 2022
Probabilistic Programming in Python: Bayesian Modeling and Probabilistic Machine Learning with Aesara

PyMC3 is a Python package for Bayesian statistical modeling and Probabilistic Machine Learning focusing on advanced Markov chain Monte Carlo (MCMC) an

PyMC 7.2k Dec 30, 2022
CONCEPT (COsmological N-body CodE in PyThon) is a free and open-source simulation code for cosmological structure formation

CONCEPT (COsmological N-body CodE in PyThon) is a free and open-source simulation code for cosmological structure formation. The code should run on any Linux system, from massively parallel computer

Jeppe Dakin 62 Dec 08, 2022
Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis

Validated, scalable, community developed variant calling, RNA-seq and small RNA analysis. You write a high level configuration file specifying your in

Blue Collar Bioinformatics 915 Dec 29, 2022
Zipline, a Pythonic Algorithmic Trading Library

Zipline is a Pythonic algorithmic trading library. It is an event-driven system for backtesting. Zipline is currently used in production as the backte

Quantopian, Inc. 15.7k Jan 07, 2023
Doing bayesian data analysis - Python/PyMC3 versions of the programs described in Doing bayesian data analysis by John K. Kruschke

Doing_bayesian_data_analysis This repository contains the Python version of the R programs described in the great book Doing bayesian data analysis (f

Osvaldo Martin 851 Dec 27, 2022