Pandas - Detecting outliers in a DataFrame using the IQR Method

Last update on April 09 2025 12:55:11 (UTC/GMT +8 hours)

8. Detecting Outliers in a DataFrame

Write a Pandas program to detect outliers in a DataFrame.

This exercise shows how to detect outliers in a column using the Interquartile Range (IQR) method.

Sample Solution :

Code :

import pandas as pd

# Create a sample DataFrame with outliers
df = pd.DataFrame({
    'Value': [10, 15, 14, 18, 90, 12, 11, 13]
})

# Calculate the IQR for the 'Value' column
Q1 = df['Value'].quantile(0.25)
Q3 = df['Value'].quantile(0.75)
IQR = Q3 - Q1

# Define outliers as values outside 1.5*IQR from Q1 and Q3
outliers = df[(df['Value'] < Q1 - 1.5 * IQR) | (df['Value'] > Q3 + 1.5 * IQR)]

# Output the result
print(outliers)

Output:

   Value
4     90

Explanation:

• Created a DataFrame with numerical values, including outliers.
• Calculated the Interquartile Range (IQR) for detecting outliers.
• Defined outliers as values outside 1.5 times the IQR and filtered the DataFrame to display them.

For more Practice: Solve these Related Problems:

• Write a Pandas program to detect outliers in a DataFrame using the IQR method and mark them in a new column.
• Write a Pandas program to identify outliers in a numerical column using Z-score and return the indices of the outlier rows.
• Write a Pandas program to detect outliers and replace them with the median value of the column.
• Write a Pandas program to create a box plot for a DataFrame column and programmatically flag the outlier values.

Python-Pandas Code Editor:

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