Book Image

Python Data Science Essentials - Second Edition

By : Luca Massaron, Alberto Boschetti
Book Image

Python Data Science Essentials - Second Edition

By: Luca Massaron, Alberto Boschetti

Overview of this book

Fully expanded and upgraded, the second edition of Python Data Science Essentials takes you through all you need to know to suceed in data science using Python. Get modern insight into the core of Python data, including the latest versions of Jupyter notebooks, NumPy, pandas and scikit-learn. Look beyond the fundamentals with beautiful data visualizations with Seaborn and ggplot, web development with Bottle, and even the new frontiers of deep learning with Theano and TensorFlow. Dive into building your essential Python 3.5 data science toolbox, using a single-source approach that will allow to to work with Python 2.7 as well. Get to grips fast with data munging and preprocessing, and all the techniques you need to load, analyse, and process your data. Finally, get a complete overview of principal machine learning algorithms, graph analysis techniques, and all the visualization and deployment instruments that make it easier to present your results to an audience of both data science experts and business users.
Table of Contents (13 chapters)
Python Data Science Essentials - Second Edition
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface

Wrapping up matplotlib's commands


As we have seen in the previous paragraph, pandas can speed up exploring data visually since it wraps up into single commands what would have required an entire code snippet using Matplotlib. The idea behind this is that unless you need to tailor and configure a special visualization, using a wrapper can allow you to create standard graphics faster.

Apart from pandas, other packages assemble low-level instructions from matplotlib into more user-friendly commands for specific representations and uses:

  • Seaborn is package that extends your visualization capabilities by providing you with a set of statistical plots useful for finding out trends and discriminating groups.

  • ggplot is a port of a popular R library, ggplot2 (http://ggplot2.org/), based on the visualization grammar proposed in Leland Wilkinson's book, Grammar of Graphics. The R library is continuously developed and it offers much functionality, the Python porting (http://ggplot.yhathq.com/) features...