#### Overview of this book

Do you want to transform data into captivating images? Do you want to make it easy for your audience to process and understand the patterns, trends, and relationships hidden within your data? The Data Visualization Workshop will guide you through the world of data visualization and help you to unlock simple secrets for transforming data into meaningful visuals with the help of exciting exercises and activities. Starting with an introduction to data visualization, this book shows you how to first prepare raw data for visualization using NumPy and pandas operations. As you progress, you’ll use plotting techniques, such as comparison and distribution, to identify relationships and similarities between datasets. You’ll then work through practical exercises to simplify the process of creating visualizations using Python plotting libraries such as Matplotlib and Seaborn. If you’ve ever wondered how popular companies like Uber and Airbnb use geoplotlib for geographical visualizations, this book has got you covered, helping you analyze and understand the process effectively. Finally, you’ll use the Bokeh library to create dynamic visualizations that can be integrated into any web page. By the end of this workshop, you’ll have learned how to present engaging mission-critical insights by creating impactful visualizations with real-world data.
Preface
1. The Importance of Data Visualization and Data Exploration
Free Chapter
2. All You Need to Know about Plots
3. A Deep Dive into Matplotlib
4. Simplifying Visualizations Using Seaborn
5. Plotting Geospatial Data
6. Making Things Interactive with Bokeh
7. Combining What We Have Learned

In the previous chapter, we discussed various plots in Matplotlib, but there are still a few visualizations left that we want to discuss. First, we will revise bar plots since Seaborn offers some neat additional features for them. Moreover, we will cover kernel density estimation, correlograms, and violin plots.

## Bar Plots

In the last chapter, we already explained how to create bar plots with Matplotlib. Creating bar plots with subgroups was quite tedious, but Seaborn offers a very convenient way to create various bar plots. They can also be used in Seaborn to represent estimates of central tendency with the height of each bar, while uncertainty is indicated by error bars at the top of the bar.

The following example gives you a good idea of how this works:

```import pandas as pd
import seaborn as sns