Pandas: Trim Outliers with clip()

Trim extreme numerical outliers in Pandas Series and DataFrames using the clip() method. Set minimum and maximum threshold boundaries to cap values, handle missing thresholds, and clean data distributions.

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Introduction to clip() 🔝

The clip() method trims values at specified threshold boundaries. Any values below the lower threshold are set to the lower bound, and any values above the upper threshold are capped at the upper bound.

Create numerical sample dataset with outliers 🔝

We start with a product price Series containing extreme outlier values.

import pandas as pd

prices = pd.Series([10, 45, 120, 50, 500], name="PRICE")
print(prices)
assert prices.max() == 500

Expected output

0     10
1     45
2    120
3     50
4    500
Name: PRICE, dtype: int64

Clipping with lower and upper bounds 🔝

Pass lower and upper parameters to clip() to restrict values within a defined interval.

clipped = prices.clip(lower=20, upper=100)
print(clipped)
assert clipped.max() == 100 and clipped.min() == 20

Expected output

0     20
1     45
2    100
3     50
4    100
Name: PRICE, dtype: int64

Clipping with a single boundary 🔝

You can specify only lower or only upper to apply unilateral threshold capping.

capped_max = prices.clip(upper=80)
print(capped_max)
assert capped_max.max() == 80

Expected output

0    10
1    45
2    80
3    50
4    80
Name: PRICE, dtype: int64

Exercise: cap product prices 🔝

Given a Series scores = pd.Series([25, 45, 85, 95, 110], name="SCORE"), use clip() with a lower bound of 30 and an upper bound of 100. Verify that the maximum value is capped at 100.

Exercise solution 🔝

Solution creating the Series and applying clip().

scores = pd.Series([25, 45, 85, 95, 110], name="SCORE")
answer = scores.clip(lower=30, upper=100)
print(answer)
assert answer.max() == 100 and answer.min() == 30

Expected output

0     30
1     45
2     85
3     95
4    100
Name: SCORE, dtype: int64

Practice in Google Colab 🔝

Interactive Colab Notebook

Practice clip() examples interactively directly in your browser:

View on GitHub

Reference: Pandas clip() documentation.

Subhendu Mohapatra

Author & Instructor at plus2net

I write and maintain practical tutorials on Python, PHP, SQL, JavaScript, HTML, jQuery, and web development at plus2net. The tutorials focus on clear explanations, working examples, and code that readers can test and adapt while learning.

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