How to Calculate Standard Deviation with NumPy in Python

Use numpy.std() to calculate the standard deviation of an array. Pass your data array to numpy.std(), and it returns the spread of values around the mean as a single number.

Syntax

The basic syntax to calculate standard deviation with NumPy is:

• numpy.std(a, axis=None, ddof=0, keepdims=False)

Where:

• a: Input array or data.
• axis: Axis along which to compute the std. Default is None (compute over all data).
• ddof: Delta degrees of freedom. The divisor used in calculation is N - ddof. Default is 0 for population std.
• keepdims: If True, keeps reduced dimensions for broadcasting.

python

numpy.std(a, axis=None, ddof=0, keepdims=False)

Example

This example shows how to calculate the standard deviation of a list of numbers using NumPy.

python

import numpy as np

data = np.array([10, 12, 23, 23, 16, 23, 21, 16])
std_dev = np.std(data)
print(f"Standard Deviation: {std_dev:.2f}")

Output

Standard Deviation: 5.14

Common Pitfalls

One common mistake is confusing population standard deviation with sample standard deviation. By default, numpy.std() calculates population std (dividing by N). For sample std, set ddof=1 to divide by N-1.

Another pitfall is forgetting to specify the axis when working with multi-dimensional arrays, which can lead to unexpected results.

python

import numpy as np

data = np.array([10, 12, 23, 23, 16, 23, 21, 16])

# Wrong: default is population std
pop_std = np.std(data)

# Correct: sample std with ddof=1
sample_std = np.std(data, ddof=1)

print(f"Population std: {pop_std:.2f}")
print(f"Sample std: {sample_std:.2f}")

Output

Population std: 5.14
Sample std: 5.44

Quick Reference

Parameter	Description	Default
a	Input array or data	Required
axis	Axis to compute std along	None (all data)
ddof	Delta degrees of freedom (N - ddof divisor)	0 (population std)
keepdims	Keep reduced dimensions	False

Key Takeaways

Use numpy.std() to calculate standard deviation of data arrays easily.

Set ddof=1 to get sample standard deviation instead of population standard deviation.

Specify axis parameter when working with multi-dimensional arrays to control calculation direction.

By default, numpy.std() calculates population standard deviation dividing by N.

Always check if you need sample or population std to avoid incorrect results.