Removing outliers from the Dataset using Z-Score method in Pandas

Last update on December 21 2024 09:15:08 (UTC/GMT +8 hours)

Pandas: Machine Learning Integration Exercise-10 with Solution

Write a Pandas program that removes outliers from a Dataset.

This exercise demonstrates how to remove outliers from a dataset using the Z-score method.

Sample Solution :

Code :

import pandas as pd
import numpy as np
from scipy import stats

# Load the dataset
df = pd.read_csv('data.csv')

# Remove outliers from the 'Age' column using Z-scores
z_scores = np.abs(stats.zscore(df['Age']))
df_cleaned = df[z_scores < 3]  # Keep rows where Z-score is less than 3

# Output the cleaned dataset
print(df_cleaned)

Output:

Empty DataFrame
Columns: [ID, Name, Age, Gender, Salary, Target]
Index: []

Explanation:

• Loaded the dataset using Pandas.
• Calculated the Z-scores of the 'Age' column using stats.zscore().
• Removed rows where the Z-score was greater than 3 (indicating outliers).
• Displayed the cleaned dataset.

Python-Pandas Code Editor:

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