Visualization Tools
In this lesson, some visualization tools are explored.
We'll cover the following
This lesson reviews some of the tools and libraries that are used in this chapter to visualize various forms of data.
Matplotlib
Matplotlib is an interactive python graphplotting library that helps us visualize data in various 2D plots. The following is an example of how matplotlib
can be used to create a visualization of the Sin wave.
import numpy as npimport matplotlib.pyplot as plt# Get x values of the sine wavepoints = np.arange(0, 10, 0.1);# Plot a sine waveplt.plot(points, np.sin(points))# Give a title for the sine wave plotplt.title('Sine wave')# Give x axis label for the sine wave plotplt.xlabel('Time')# Give y axis label for the sine wave plotplt.ylabel('Amplitude')plt.show()
Seaborn
Seaborn is an extension of the matplotlib
package and builds on top of the already provided matplotlib
functions. It adds more interactivity in the already present plots providing more concise information. The following code is inspired by an example from seaborn
documentation that plots a joint plot. It can also be found here.
import numpy as npimport pandas as pdimport seaborn as sns# Generate a random correlated bivariate datasetrs = np.random.RandomState(5)mean = [0, 0]cov = [(.5, 1), (1, .5)]var1, var2 = rs.multivariate_normal(mean, cov, 500).Tvar1 = pd.Series(var1, name="Xaxis")var2 = pd.Series(var2, name="Yaxis")# Show the joint distribution using kernel density estimationsns1 = sns.jointplot(var1, var2, kind="scatter")
Types of plots

Histogram

Box

Regression

Heatmaps

Scatter

KDE
More information on plots can be found on Matplotlib and Seaborn.
We start to explain and use Histogram plots in the next lesson.