#### 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 univariate distribution

Seaborn makes the task of visualizing the distribution of a dataset much easier. Starting with the population data as discussed before, let's see how it distributes among different countries in 2017 by plotting a bar plot:

```import seaborn as sns
import matplotlib.pyplot as plt

# Extract USA population data in 2017
current_population = population_df[(population_df.Location
== 'United States of America') &
(population_df.Time == 2017) &
(population_df.Sex != 'Both')]

# Population Bar chart
sns.barplot(x="AgeGrp",y="Value", hue="Sex", data = current_population)

# Use Matplotlib functions to label axes rotate tick labels
ax = plt.gca()
ax.set(xlabel="Age Group", ylabel="Population (thousands)")
ax.set_xticklabels(ax.xaxis.get_majorticklabels(), rotation=45)
plt.title("Population Barchart (USA)")

# Show the figure
plt.show()```