Introduction

Unlike machines, people are usually not equipped for interpreting a large amount of information from a random set of numbers and messages in each piece of data. Out of all our logical capabilities, we understand things best through the visual processing of information. When data is represented visually, the probability of understanding complex builds and numbers increases.

Python has recently emerged as a programming language that performs well for data analysis. It has applications across data science pipelines that convert data into a usable format (such as pandas), analyzes it (such as NumPy), and extract useful conclusions from the data to represent it in a visually appealing manner (such as Matplotlib or Bokeh). Python provides data visualization libraries that can help you assemble graphical representations efficiently.

In this book, you will learn how to use Python in combination with various libraries, such as NumPy, pandas, Matplotlib, seaborn, and geoplotlib...