bscho333 / PythonDataScienceHandbook

"Python Data Science Handbook" for MVPLAB Tutorial

Python Data Science Handbook

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

You can read the book in its entirety online at https://jakevdp.github.io/PythonDataScienceHandbook/

The book was written and tested with Python 3.5, though older 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, and related packages.

Contents

• 0.0 Preface

Chapter 1. IPython a.k.a Jupyter Notebook

• 1.0 Beyond Normal Python

Chapter 2. Numpy

• 2.0 Numpy
• 2.1 Data Types
• 2.2 Numpy Arrays
• 2.3 Universal Functions
• 2.4 Aggregations
• 2.5 Broadcasting
• 2.6 Boolean Arrays and Masks
• 2.7 Indexing
• 2.8 Sorting
• 2.9 Structured Arrays

Chapter 3. Pandas

• 3.0 Pandas
• 3.1 Pandas Objects
• 3.2 Indexing and Selection
• 3.3 Operations
• 3.4 Missing Values
• 3.5 Hierarchical Indexing
• 3.6 Concat and Append
• 3.7 Merge and Join
• 3.8 Aggregation and Grouping
• 3.9 Pivot Tables
• 3.10 Strings
• 3.11 Time Series
• 3.12 Eval and Query

Chapter 4. Matplotlib

• 4.0 Matplotlib
• 4.1 Line Plots
• 4.2 Scatter Plots
• 4.3 Errorbars
• 4.4 Density and Contour Plots
• 4.5 Histograms and Binnings
• 4.6 Legends
• 4.7 Colorbars
• 4.8 Subplots
• 4.9 Text and Annotation
• 4.10 Ticks
• 4.11 Setting and Stylesheets
• 4.12 3D Plotting
• 4.13 Geographic Data with Basemap
• 4.14 Visualization with Seaborn

Chapter 5. Scikit-Learn for Machine Learning (Optional)

• 5.0 Machine Learning
• 5.1 What is Machine Learning
• 5.2 Scikit-Learn
• 5.3 Hyperparameters and Validation
• 5.4 Feature Engineering
• 5.5 Naive Bayes
• 5.6 Linear Regression
• 5.7 Support Vector Machines
• 5.8 Random Forests
• 5.9 Priciple Component Analysis
• 5.10 Manifold Learning
• 5.11 K-means
• 5.12 Gaussian Mixtures
• 5.13 Kernel Density Estimation
• 5.14 Image Features

Chapter 6. Drawing Figures in Python (Optional)

• 6. Drawing Figures

Required Packages

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.