# 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))

for calculating iqr and outlier

a bit correlated than cte then window than repeating

Normalization Metrics stats data marts

Sql theories
Probability Bayes Theorem

Use having with groupby

statts criteria usage

normal distribution uniformal binomial

Aggregation needs group by