Book Image

Learning IPython for Interactive Computing and Data Visualization, Second Edition

By : Cyrille Rossant
Book Image

Learning IPython for Interactive Computing and Data Visualization, Second Edition

By: Cyrille Rossant

Overview of this book

Python is a user-friendly and powerful programming language. IPython offers a convenient interface to the language and its analysis libraries, while the Jupyter Notebook is a rich environment well-adapted to data science and visualization. Together, these open source tools are widely used by beginners and experts around the world, and in a huge variety of fields and endeavors. This book is a beginner-friendly guide to the Python data analysis platform. After an introduction to the Python language, IPython, and the Jupyter Notebook, you will learn how to analyze and visualize data on real-world examples, how to create graphical user interfaces for image processing in the Notebook, and how to perform fast numerical computations for scientific simulations with NumPy, Numba, Cython, and ipyparallel. By the end of this book, you will be able to perform in-depth analyses of all sorts of data.
Table of Contents (13 chapters)
Learning IPython for Interactive Computing and Data Visualization Second Edition
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Exploring a dataset in the Notebook


Here, we will explore a dataset containing the taxi trips made in New York City in 2013. Maintained by the New York City Taxi and Limousine Commission, this 50GB dataset contains the date, time, geographical coordinates of pickup and dropoff locations, fare, and other information for 170 million taxi trips.

To keep the analysis times reasonable, we will analyze a subset of this dataset containing 0.5% of all trips (about 850,000 rides). Compressed, this subset data represents a little less than 100MB. You are free to download and analyze the full dataset (or a larger subset), as explained below.

Provenance of the data

You will find the data subset we will be using in this chapter at https://github.com/ipython-books/minibook-2nd-data.

If you are interested in the original dataset containing all trips, you can refer to https://github.com/ipython-books/minibook-2nd-code/tree/master/chapter2/cleaning. This page contains the code to download the original dataset...