Visualize the normalized variables
Great work! Now you will plot the normalized and unskewed variables to see the difference in the distribution as well as the range of the values. The datamart_normalized dataset from the previous exercise is loaded.
The plt.subplot(...) call before the seaborn function call allows you to plot several subplots in one chart, you do not have to change it.
Libraries seaborn and matplotlib.pyplot have been loaded as sns and plt respectively. Feel free to explore the datamart_normalized in the console.
Cet exercice fait partie du cours
Customer Segmentation in Python
Instructions
- Plot the distribution of normalized
Recency. - Plot the distribution of normalized
Frequency. - Plot the distribution of normalized
MonetaryValue. - Show the plot.
Exercice interactif pratique
Essayez cet exercice en complétant cet exemple de code.
# Plot recency distribution
plt.subplot(3, 1, 1); ____.distplot(____['Recency'])
# Plot frequency distribution
plt.subplot(3, 1, 2); ____.____(____['Frequency'])
# Plot monetary value distribution
plt.subplot(3, 1, 3); ____.____(____['MonetaryValue'])
# Show the plot
plt.____()