It is difficult to gain insights from a multivariate data. It is also challenging to plot a multivariate data for better understanding. Even so there are techniques to visualize high-dimensional data. We can use these techniques easily via the Pandas plotting API.

What

The Pandas plotting API contains many visualization techniques. This helps us in understanding the data stored in a dataframe. When we have multivariate data, the following three plots are quite handy:

• Andrews plot
• Parallel coordinates plot
• Scatter plot matrix

Why

Understanding correlations between multidimensional data points is important. An equaly important task is to detect outliers. All this can give us a hint on how the space is divided and whether the features are discriminative enough.

How

It is very easy to produce the plots using the Pandas plotting API. We will use the Iris dataset to demonstrate this.

1
2
3
4
5
6
7
8
9
10
11
12
13
import matplotlib.pyplot as plt
import pandas as pd

data_url="https://raw.githubusercontent.com/pandas-dev/pandas/main/pandas/tests/io/data/csv/iris.csv"
df_iris = pd.read_csv(data_url)

plt.figure(figsize=(12, 7))
pd.plotting.andrews_curves(df_iris, class_column="Name")

plt.figure(figsize=(12, 7))
pd.plotting.parallel_coordinates(df_iris, class_column="Name")

pd.plotting.scatter_matrix(df_iris.drop("Name", axis=1), figsize=(8, 8), alpha=0.7)

The resulting plots are shown below:

Figure 1: Andrews plot of the Iris dataset

Figure 2: Parallel coordinates plot of the Iris dataset

Figure 3: Scatter plot matrix of the Iris dataset

The source code for this work can be found in this Jupyter Notebook. If this is something you like and would like to see similar content you could follow me on LinkedIn or Twitter. Additionally, you can subscribe to the mailing list below to get similar updates from time to time.