Detect Outliers Using Interquartile Range (IQR) in Pandas

Question

Write a function that calculates the Interquartile Range (IQR) of a numeric column and returns all rows flagged as statistical outliers.

Accepted Answer

Optimal Solution:
import pandas as pd

def find_iqr_outliers(df: pd.DataFrame) -> pd.DataFrame:
    column = 'amount'
    q1 = df[column].quantile(0.25)
    q3 = df[column].quantile(0.75)
    iqr = q3 - q1

lower_fence = q1 - (1.5 * iqr)
    upper_fence = q3 + (1.5 * iqr)

outliers = df[(df[column] < lower_fence) | (df[column] > upper_fence)]
    return outliers.rename(columns={column: f'outlier_{column}'})

Technical Explanation:
The Tukey IQR rule defines non-outliers as values within 1.5 times the IQR distance from Q1 and Q3. Values beyond these fences are flagged as statistical anomalies.

amount
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