# Calculate Q1, Q3, and IQR
Q1 = df['TotalRevenue'].quantile(0.25)
Q3 = df['TotalRevenue'].quantile(0.75)
IQR = Q3 - Q1
# Define bounds for outliers
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
# Identify outliers
outliers = df[(df['TotalRevenue'] < lower_bound) | (df['TotalRevenue'] > upper_bound)]
print("Number of outliers:", len(outliers))
# Remove outliers from the DataFrame
df_filtered = df[(df['TotalRevenue'] >= lower_bound) & (df['TotalRevenue'] <= upper_bound)]
print("Number of rows after removing outliers:", len(df_filtered))
Q1 = df['TotalRevenue'].quantile(0.25)
Q3 = df['TotalRevenue'].quantile(0.75)
IQR = Q3 - Q1
# Define bounds for outliers
lower_bound = Q1 - 1.5 * IQR
upper_bound = Q3 + 1.5 * IQR
# Identify outliers
outliers = df[(df['TotalRevenue'] < lower_bound) | (df['TotalRevenue'] > upper_bound)]
print("Number of outliers:", len(outliers))
# Remove outliers from the DataFrame
df_filtered = df[(df['TotalRevenue'] >= lower_bound) & (df['TotalRevenue'] <= upper_bound)]
print("Number of rows after removing outliers:", len(df_filtered))