Pandas: Calculate Skewness with skew()

Compute unbiased skewness for values across Series or DataFrames using the Pandas skew() method. Measure distribution asymmetry, identify positive (right-tailed) or negative (left-tailed) bias, and configure calculation axes.

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Introduction to skew() 🔝

The skew() method calculates the degree of asymmetry of a data distribution around its mean. A value of 0.0 indicates a symmetric distribution, positive values indicate a right-skewed distribution (tail extends right), and negative values indicate a left-skewed distribution.

Create numerical sample dataset 🔝

We start with a Series containing right-skewed numerical data (income/price distributions).

import pandas as pd

s = pd.Series([10, 12, 15, 18, 100], name="VAL")
print(s)
assert s.shape == (5,)

Expected output

0     10
1     12
2     15
3     18
4    100
Name: VAL, dtype: int64

Basic skewness calculation 🔝

Call skew() on the Series to measure its directional asymmetry.

s_skew = s.skew()
print(f"Skewness: {s_skew:.4f}")
assert s_skew > 2.0

Expected output

Skewness: 2.1554

Understanding positive vs negative skewness 🔝

Evaluate skewness across multiple DataFrame columns with contrasting distribution patterns.

df = pd.DataFrame({
    "RIGHT_SKEW": [1, 2, 2, 3, 10],
    "LEFT_SKEW": [1, 8, 9, 9, 10]
})
df_skew = df.skew()
print(df_skew)
assert df_skew["RIGHT_SKEW"] > 0 and df_skew["LEFT_SKEW"] < 0

Expected output

RIGHT_SKEW    1.879442
LEFT_SKEW    -1.879442
dtype: float64

Exercise: evaluate sales skewness 🔝

Given a Series scores = pd.Series([100, 95, 90, 85, 20], name="SCORES"), compute its skewness using skew() and verify that it returns a negative value (left-skewed due to low score outlier).

Exercise solution 🔝

Solution creating the Series and calculating skew().

scores = pd.Series([100, 95, 90, 85, 20], name="SCORES")
answer = scores.skew()
print(answer)
assert answer < 0

Expected output

-2.0076231908861947

Practice in Google Colab 🔝

Interactive Colab Notebook

Practice skew() examples interactively directly in your browser:

View on GitHub

Reference: Pandas skew() 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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