Data Visualization with Matplotlib and Seaborn

Data visualization is a critical skill for any data scientist. It allows us to understand complex data sets and convey that understanding to others. In this unit, we will explore two powerful Python libraries for data visualization: Matplotlib and Seaborn.

Introduction to Data Visualization

Data visualization is the graphical representation of data. It involves producing images that communicate relationships among the represented data to viewers of the images. This communication is achieved through the use of a systematic mapping between graphic marks and data values in the creation of the visualization.

Basics of Matplotlib

Matplotlib is a plotting library for the Python programming language. It provides an object-oriented API for embedding plots into applications using general-purpose GUI toolkits like Tkinter, wxPython, Qt, or GTK.

Creating Plots

Creating a basic plot in Matplotlib is straightforward. Here's an example:

import matplotlib.pyplot as plt
plt.plot([1, 2, 3, 4])
plt.ylabel('some numbers')
plt.show()

Customizing Plots

Matplotlib allows for a large amount of customization, including colors, labels, and linewidths. Here's an example of a customized plot:

plt.plot([1, 2, 3, 4], [1, 4, 9, 16], 'ro')
plt.axis([0, 6, 0, 20])
plt.show()

Saving Plots

To save a plot to a file, we can use the savefig() function:

plt.savefig('my_figure.png')

Introduction to Seaborn

Seaborn is a Python data visualization library based on Matplotlib. It provides a high-level interface for drawing attractive and informative statistical graphics.

Creating Different Types of Plots

Seaborn supports several types of plots, including:

• Bar plots
• Histograms
• Scatter plots
• Line plots

Here's an example of creating a scatter plot with Seaborn:

import seaborn as sns
sns.scatterplot(x='total_bill', y='tip', data=tips)

By the end of this unit, you should be comfortable creating and customizing plots with Matplotlib, as well as creating various types of plots with Seaborn. These skills will be invaluable as you continue your journey in data science.