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How to compute mean and std in numpy

Q: How to compute mean and std in numpy

Use numpy.mean() to compute the average and numpy.std() to compute the standard deviation of arrays. These functions support axis parameters to calculate statistics along specific dimensions.

Quick answer

Use numpy.mean() to compute the average and numpy.std() to compute the standard deviation of arrays. These functions support axis parameters to calculate statistics along specific dimensions.

PREREQUISITES

Python 3.8+
pip install numpy>=1.23

Setup

Install numpy if not already installed using pip. Import numpy in your Python script to access its statistical functions.

bash

pip install numpy

Step by step

Compute mean and standard deviation of a numpy array with simple calls. Use the axis argument to specify dimensions.

python

import numpy as np

# Create a sample numpy array
arr = np.array([[1, 2, 3], [4, 5, 6]])

# Compute mean of all elements
mean_all = np.mean(arr)

# Compute std of all elements
std_all = np.std(arr)

# Compute mean along columns (axis=0)
mean_axis0 = np.mean(arr, axis=0)

# Compute std along rows (axis=1)
std_axis1 = np.std(arr, axis=1)

print(f"Mean (all): {mean_all}")
print(f"Std (all): {std_all}")
print(f"Mean (axis=0): {mean_axis0}")
print(f"Std (axis=1): {std_axis1}")

output

Mean (all): 3.5
Std (all): 1.707825127659933
Mean (axis=0): [2.5 3.5 4.5]
Std (axis=1): [0.81649658 0.81649658]

Common variations

You can compute weighted means using numpy.average() or use ddof=1 in numpy.std() for sample standard deviation. These variations help in different statistical contexts.

python

import numpy as np

arr = np.array([1, 2, 3, 4, 5])
weights = np.array([1, 2, 3, 4, 5])

# Weighted average
weighted_mean = np.average(arr, weights=weights)

# Sample standard deviation (ddof=1)
sample_std = np.std(arr, ddof=1)

print(f"Weighted mean: {weighted_mean}")
print(f"Sample std (ddof=1): {sample_std}")

output

Weighted mean: 3.6666666666666665
Sample std (ddof=1): 1.5811388300841898

Troubleshooting

If you get unexpected results, check that your input array is numeric and not empty. Also, verify the axis parameter is within the array's dimensions to avoid errors.

✅

Key Takeaways

Use numpy.mean() and numpy.std() for fast computation of mean and standard deviation.
Specify the axis parameter to compute statistics along specific dimensions of arrays.
Use ddof=1 in numpy.std() for sample standard deviation instead of population std.
Weighted averages can be computed with numpy.average() by passing weights.
Always validate input arrays to be numeric and non-empty to avoid runtime errors.

Verified 2026-04

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