#### Overview of this book

Big data analytics are driving innovations in scientific research, digital marketing, policy-making and much more. Matplotlib offers simple but powerful plotting interface, versatile plot types and robust customization. Matplotlib 2.x By Example illustrates the methods and applications of various plot types through real world examples. It begins by giving readers the basic know-how on how to create and customize plots by Matplotlib. It further covers how to plot different types of economic data in the form of 2D and 3D graphs, which give insights from a deluge of data from public repositories, such as Quandl Finance. You will learn to visualize geographical data on maps and implement interactive charts. By the end of this book, you will become well versed with Matplotlib in your day-to-day work to perform advanced data visualization. This book will guide you to prepare high quality figures for manuscripts and presentations. You will learn to create intuitive info-graphics and reshaping your message crisply understandable.
Title Page
Credits
www.PacktPub.com
Customer Feedback
Preface
Free Chapter
Hello Plotting World!
Figure Aesthetics
Figure Layout and Annotations
Visualizing Online Data
Visualizing Multivariate Data
A Practical Guide to Scientific Plotting
Exploratory Data Analytics and Infographics

## Visualizing the trend of data

Once we have imported the two datasets, we can set out on a further visualization journey. Let's begin by plotting the world population trends from 1950 to 2017. To select rows based on the value of a column, we can use the following syntax: `df[df.variable_name == "target"]` or `df[df['variable_name'] == "target"]`, where `df` is the dataframe object. Other conditional operators, such as larger than > or smaller than <, are also supported. Multiple conditional statements can be chained together using the "and" operator &, or the "or" operator |.

To aggregate the population across all age groups within a year, we are going to rely on `df.groupby().sum()`, as shown in the following example:

```import matplotlib.pyplot as plt

# Select the aggregated population data from the world for both genders,
# during 1950 to 2017.
selected_data = data[(data.Location == 'WORLD') & (data.Sex == 'Both') & (data.Time <= 2017) ]

# Calculate aggregated population data...```