# 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 metric
df_test['Average Earnings per Click'] = df_test['Earning'] / df_test['Click']
df_control['Average Earnings per Click'] = df_control['Earning'] / df_control['Click']
# Ploting hist
sns.histplot(df_control['Average Earnings per Click'], color="#1e2635", label="AEC of Control Group")
sns.histplot(df_test['Average Earnings per Click'], color="#ff8a00", label="AEC of Test Group")
# Add mean line
plt.axvline(df_control['Average Earnings per Click'].mean(), color="#1e2635", linestyle='dashed', linewidth=1, label='Mean Control Group')
plt.axvline(df_test['Average Earnings per Click'].mean(), color="#ff8a00", linestyle='dashed', linewidth=1, label='Mean Test Group')
# Sign the axes
plt.xlabel('Average Earnings per Click')
plt.ylabel('Frequency')
plt.legend()
plt.title('Histogram of Average Earnings per Click')
# Show the result
plt.show()