Leer Dive Deeper into Visualization

Let's imagine that it is essential for you to sort out the user continued subscription after the trial period. Let's move to the dataset that we use, for example:

Look at the code:


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import pandas as pd
import matplotlib.pyplot as plt
import seaborn as sns

df = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/783d7288-e86b-4b89-9966-a2fe97995277/section_2_dataset_upd.csv', index_col = 0)

df = df.groupby(['plan', 'trial']).sum().reset_index()

sns.barplot(data = df, x = 'plan', y = 'price', hue = 'trial')
plt.show()

As you can see, we just add the hue parameter, which helps you sort out data by categories. For instance, here, hue = 'trial', the column 'trial' has two categories: True and False.

And here is the output:

barplot

Taak

Swipe to start coding

Visualize the sum of money you receive from users depending on their subscription plan. Take into account if the user continued the subscription after the trial period.

Import the seaborn with the sns alias.
Import the matplotlib.pyplot with the plt alias.
Prepare data for visualization using the .groupby() function:

Extract columns 'plan', 'price', 'trial' for grouping
Group by column 'plan' and then by 'trial'.
Calculate the sum of all prices for each plan.
Reset indices.

Create the barplot using the seaborn:

Use df as the data argument.
Use the 'plan' column for x-axis.
Use the 'price' column for y-axis.
Use the 'trial' column for hue variable.

Display the plot.

Oplossing

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