Pandas: Calculate Variance with var()

Compute unbiased sample variance for values across Series or DataFrames using the Pandas var() method. Measure data dispersion, configure delta degrees of freedom (ddof), and handle missing observations effectively.

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Introduction to var() 🔝

The var() method calculates sample variance for requested numerical columns. By default, Pandas uses sample variance with $ ext{ddof}=1$ (delta degrees of freedom) and excludes missing values (skipna=True).

Create numerical sample DataFrame 🔝

We start with a multi-column numerical DataFrame representing sample measurements.

import pandas as pd

df = pd.DataFrame({
    "COL_X": [2, 4, 6, 8, 10],
    "COL_Y": [5, 10, 15, 20, 25]
})
print(df)
assert df.shape == (5, 2)

Expected output

   COL_X  COL_Y
0      2      5
1      4     10
2      6     15
3      8     20
4     10     25

Basic variance calculation 🔝

Call var() on the DataFrame to compute the sample variance across numerical columns.

var_res = df.var()
print(var_res)
assert var_res["COL_X"] == 10.0

Expected output

COL_X     10.0
COL_Y    62.5
dtype: float64

Variance on a single Series 🔝

Apply var() directly to a single column or standalone Pandas Series.

s_var = df["COL_X"].var()
print(f"Variance: {s_var}")
assert s_var == 10.0

Expected output

Variance: 10.0

Exercise: compute sample measurement variance 🔝

Given a Series vals = pd.Series([1, 3, 5, 7, 9], name="VALS"), compute its sample variance using var() and verify that it equals 10.0.

Exercise solution 🔝

Solution creating the Series and calculating var().

vals = pd.Series([1, 3, 5, 7, 9], name="VALS")
answer = vals.var()
print(answer)
assert answer == 10.0

Expected output

10.0

Practice in Google Colab 🔝

Interactive Colab Notebook

Practice var() examples interactively directly in your browser:

View on GitHub

Reference: Pandas var() documentation.

Subhendu Mohapatra

Author & Instructor at plus2net

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