Prepare data for the snake plot

Now you will prepare data for the snake plot. You will use the 3-cluster RFM segmentation solution you have built previously. You will transform the normalized RFM data into a long format by "melting" the metric columns into two columns - one for the name of the metric, and another for the actual numeric value.

We have loaded the normalized RFM data with the cluster labels already assigned. It is loaded as apandas DataFrame named datamart_normalized. Also, pandas is imported as pd.

Explore the datamart_normalized in the console before you begin the exercise to get a good sense of its structure!

Este exercício faz parte do curso

Customer Segmentation in Python

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Instruções do exercício

Transform the dataset into long format by applying melt function on the normalized dataset with a reset index.
Assign CustomerID and Cluster as ID variables.
Assign RFM values as value variables.
Name the variable as Metric and the value as Value.

Exercício interativo prático

Experimente este exercício completando este código de exemplo.

# Melt the normalized dataset and reset the index
datamart_melt = pd.____(
  					____.____(), 
                        
# Assign CustomerID and Cluster as ID variables
                    ____=['____', '____'],

# Assign RFM values as value variables
                    ____=['____', '____', '____'], 
                        
# Name the variable and value
                    ____='____', ____='____'
					)

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