Hands-On Exploratory Data Analysis with Python

3-D visualization

Generally, we start with one-dimensional visualization and move to further dimensions. Having seen 2-D visualization earlier, let's add one more dimension and plot 3-D charts. We will use the matplotlib.pyplot method to do so:

1. Let's start first by creating the axes:

fig = plt.figure(figsize=(16, 12))ax = fig.add_subplot(111, projection='3d')

2. Then, add the columns to the axes:

xscale = df_wines['residual sugar']yscale = df_wines['free sulfur dioxide']zscale = df_wines['total sulfur dioxide']ax.scatter(xscale, yscale, zscale, s=50, alpha=0.6, edgecolors='w')

Here, we are interested in looking into the residual sugar, free sulfur dioxide, and total sulfur dioxide columns.

3. Finally, let's add the labels to all of the axes: ...