Hands-On Exploratory Data Analysis with Python

Univariate analysis

We have already discussed univariate, bivariate, and multivariate analysis in Chapter 7, Correlation. Let's revise and see how much you remember.

The simplest way to visualize the numeric data and their distribution is by using a histogram. Let's plot the histogram here; we start by importing the required matplotlib.pyplot library:

import matplotlib.pyplot as pltfrom mpl_toolkits.mplot3d import Axes3D%matplotlib inline

Next, we draw the histogram, as shown:

fig = df_wines.hist(bins=15, color='fuchsia', edgecolor='darkmagenta', linewidth=1.0, xlabelsize=10, ylabelsize=10, xrot=45, yrot=0, figsize=(10,9), grid=False)plt.tight_layout(rect=(0, 0, 1.5, 1.5)) 

Note that we have used the tight_layout() method to keep the graph ...