Challenge: Violin and Swarm Plots (Part 2)

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This task is similar to the previous one. You need to plot the graphs for the 'Earning' columns and conclude: are the variances different?

Concatenate the dataframes.
Build the violin plots.
Build the swarm plots.

Lösning

#Import libraries

import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

#Read .csv files

df_control = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c3b98ad3-420d-403f-908d-6ab8facc3e28/ab_control.csv', delimiter=';')

df_test = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c3b98ad3-420d-403f-908d-6ab8facc3e28/ab_test.csv', delimiter=';')

#Define colors for graphs

colors_list = ['#ff8a00', '#33435c']

#Add to the dataframes columns-labels, which mean belonging to either the control or the test group

df_control['group'] = 'Contol group'

df_test['group'] = 'Test group'

#Concat control and test dataframes

df_combined = pd.concat([df_control, df_test])

#Plotting violin plots

sns.violinplot(data=df_combined, x='group', y='Earning', color="r", alpha=0.8)

#Plotting swarm plots

sns.swarmplot(data=df_combined, x='group', y='Earning', palette=colors_list)

#Sign the axes

plt.xlabel('')

plt.ylabel('Earning')

plt.title('Comparison of Earning')

#Show the results

plt.show()

Var allt tydligt?

Tack för dina kommentarer!

Avsnitt 3. Kapitel 3

# Import libraries

import matplotlib.pyplot as plt

import pandas as pd

import seaborn as sns

# Read .csv files

df_control = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c3b98ad3-420d-403f-908d-6ab8facc3e28/ab_control.csv', delimiter=';')

df_test = pd.read_csv('https://codefinity-content-media.s3.eu-west-1.amazonaws.com/c3b98ad3-420d-403f-908d-6ab8facc3e28/ab_test.csv', delimiter=';')

# Define colors for graphs

colors_list = ['#ff8a00', '#33435c']

# Add to the dataframes columns-labels, which mean belonging to either the control or the test group

df_control['group'] = 'Contol group'

df_test['group'] = 'Test group'

# Concat control and test dataframes

df_combined = pd.___([df_control, df_test])

# Plotting violin plots