Skip to content
Python Central Hub

Outlier Detection (IQR Method)

What is IQR?

The Interquartile Range (IQR) is:

  • Q3 (75th percentile) − Q1 (25th percentile)

A common rule flags outliers outside:

  • Lower bound = Q1 − 1.5 × IQR
  • Upper bound = Q3 + 1.5 × IQR

Example

IQR bounds
import pandas as pd
 
df = pd.DataFrame({"amount": [100, 110, 120, 115, 105, 130, 125, 3000]})
 
q1 = df["amount"].quantile(0.25)
q3 = df["amount"].quantile(0.75)
iqr = q3 - q1
 
lower = q1 - 1.5 * iqr
upper = q3 + 1.5 * iqr
 
print("Q1:", q1)
print("Q3:", q3)
print("IQR:", iqr)
print("Bounds:", lower, upper)
 
outliers = df[(df["amount"] < lower) | (df["amount"] > upper)]
print(outliers)
IQR bounds
import pandas as pd
 
df = pd.DataFrame({"amount": [100, 110, 120, 115, 105, 130, 125, 3000]})
 
q1 = df["amount"].quantile(0.25)
q3 = df["amount"].quantile(0.75)
iqr = q3 - q1
 
lower = q1 - 1.5 * iqr
upper = q3 + 1.5 * iqr
 
print("Q1:", q1)
print("Q3:", q3)
print("IQR:", iqr)
print("Bounds:", lower, upper)
 
outliers = df[(df["amount"] < lower) | (df["amount"] > upper)]
print(outliers)

Flag outliers (keep rows, add a column)

Outlier flag
df["is_outlier"] = (df["amount"] < lower) | (df["amount"] > upper)
print(df)
Outlier flag
df["is_outlier"] = (df["amount"] < lower) | (df["amount"] > upper)
print(df)

Notes

  • IQR is robust for skewed data.
  • Outlier detection is not outlier removal.
  • Always validate outliers with domain context.

If this helped you, consider buying me a coffee ☕

Buy me a coffee

Was this page helpful?

Let us know how we did