Calculate Standard Deviation in Excel & Python | Standard Deviation Calculator

Excel: Overview

Microsoft Excel provides built-in functions for calculating both sample and population standard deviation. These functions are available in all modern versions of Excel.

Excel Functions

Function	Type	Description
`STDEV.S()`	Sample	Sample standard deviation (divides by n-1)
`STDEV.P()`	Population	Population standard deviation (divides by N)
`STDEV()`	Sample	Legacy function, same as STDEV.S
`STDEVP()`	Population	Legacy function, same as STDEV.P

Excel Examples

Excel Formulas

// Data in cells A1:A10
=STDEV.S(A1:A10)     // Sample SD
=STDEV.P(A1:A10)     // Population SD

// For specific values
=STDEV.S(4, 8, 6, 5, 3)    // Returns 1.924

// Ignoring text and logical values
=STDEV.S(A1:A10)    // Ignores text
=STDEVA(A1:A10)     // Includes text as 0

Pro Tip

Use STDEV.S for most real-world analyses. Only use STDEV.P when you're certain you have the complete population.

Python: Overview

Python offers multiple ways to calculate standard deviation. The most common libraries are NumPy, Pandas, and the built-in statistics module.

Using NumPy

Python (NumPy)

import numpy as np

data = [4, 8, 6, 5, 3]

# Population standard deviation (default)
pop_sd = np.std(data)
print(f"Population SD: {pop_sd}")  # 1.720

# Sample standard deviation
sample_sd = np.std(data, ddof=1)
print(f"Sample SD: {sample_sd}")  # 1.924

What is ddof?

ddof stands for "Delta Degrees of Freedom". Setting ddof=1 tells NumPy to divide by (n-1) for sample SD. The default ddof=0 gives population SD.

Using Pandas

Python (Pandas)

import pandas as pd

# Create a DataFrame
df = pd.DataFrame({'scores': [85, 90, 78, 92, 88]})

# Sample SD (default in pandas)
sample_sd = df['scores'].std()
print(f"Sample SD: {sample_sd}")

# Population SD
pop_sd = df['scores'].std(ddof=0)
print(f"Population SD: {pop_sd}")

# Multiple columns at once
df.std()  # Returns SD for all numeric columns

Quick Comparison

Tool	Sample SD	Population SD
Excel	`STDEV.S()`	`STDEV.P()`
NumPy	`np.std(data, ddof=1)`	`np.std(data)`
Pandas	`df.std()`	`df.std(ddof=0)`
Python statistics	`stdev()`	`pstdev()`

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