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

Chapter 6. Visualization, Insights, and Results

Finally, yet importantly, we are going to illustrate how to create visualizations with Python to enrich your data science project. Visualization plays an important role in helping you communicate the results and insights derived from data and the learning process.

In this chapter, you will learn how to:

  • Use the basic pyplot functions from the matplotlib package

  • Leverage pandas DataFrame for Explorative Data Analysis (EDA)

  • Create beautiful and interactive charts with Seaborn and Bokeh

  • Visualize the machine learning and optimization processes we discussed in Chapter 3, The Data Pipeline, and Chapter 4, Machine Learning

  • Understand and visually communicate the importance of variables and their relationship with the target outcome

  • Set up a prediction server that uses HTTP to accept and provide predictions as-a-service.