Parallel coordinates

Parallel coordinates

When you are trying to find the relationship between multiple variables, Parallel coordinates is a good choice.

Parallel coordinates can clearly observe the distribution of data. For example, we can run Parallel coordinates on the iris data set.

iris dataset with parallel_coordinates

import seaborn
iris_data = seaborn.load_dataset('iris')
 
from pandas.plotting import parallel_coordinates
parallel_coordinates( iris_data, 'species',
                      color = ('#55627080', '#4ECDC480', '#C7F46480')
                      )

import matplotlib.pyplot as plt
plt.title('iris dataset with parallel_coordinates')
# Mandatory run display interface
plt.show()

After running Parallel coordinates on the iris data set, you can clearly observe the relationship between sepal_length, sepal_width, petal_length, petal_width and species.

For example, the petal_length of virginica is larger than the other two varieties. Then when you encounter an iris plant in the future, if its petal_length is larger, then it is likely to be a virginica.

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Start time of this page: December 20, 2021

Completion time of this page: December 20, 2021