Seaborn Library for Data Visualization in Python: Part 2
In the previous article Seaborn Library for Data Visualization in Python: Part 1, we looked at how the Seaborn Library is used to plot distributional and categorial plots. In this article we will continue our discussion and will see some of the other functionalities offered by Seaborn to draw different types of plots. We will start our discussion with Matrix Plots.
Matrix Plots
Matrix plots are the type of plots that show data in the form of rows and columns. Heat maps are the prime examples of matrix plots.
Heat Maps
Heat maps are normally used to plot correlation between numeric columns in the form of a matrix. It is important to mention here that to draw matrix plots, you need to have meaningful information on rows as well as columns. Continuing with the theme from teh last article, let’s plot the first five rows of the Titanic dataset to see if both the rows and column headers have meaningful information. Execute the following script:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
import seaborn as sns
dataset = sns.load_dataset('titanic')
dataset.head()
In the output, you will