#### 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

# Controlling Figure Aesthetics

As we mentioned previously, Matplotlib is highly customizable. But it also has the effect that it is very inconvenient, as it can take a long time to adjust all necessary parameters to get your desired visualization. In Seaborn, we can use customized themes and a high-level interface for controlling the appearance of Matplotlib figures.

The following code snippet creates a simple line plot in Matplotlib:

```%matplotlib inline
import matplotlib.pyplot as plt
plt.figure()
x1 = [10, 20, 5, 40, 8]
x2 = [30, 43, 9, 7, 20]
plt.plot(x1, label='Group A')
plt.plot(x2, label='Group B')
plt.legend()
plt.show()```

This is what the plot looks with Matplotlib's default parameters:

Figure 4.2: Matplotlib line plot

To switch to the Seaborn defaults, simply call the `set()` function:

```%matplotlib inline
import matplotlib.pyplot as plt
import seaborn as sns
sns.set()
plt.figure()
x1 = [10, 20, 5, 40, 8]
x2 = [30, 43,...```