How to Calculate Mean Using NumPy in Python

To calculate the mean of an array in NumPy, use the numpy.mean() function. Pass your data array as the first argument, and optionally specify the axis to calculate the mean along rows or columns.

Syntax

The basic syntax of the numpy.mean() function is:

• numpy.mean(a, axis=None, dtype=None, out=None, keepdims=False)

Where:

• a: Input array containing numbers.
• axis: Axis or axes along which to compute the mean. Default is None, which computes mean of the flattened array.
• dtype: Data type for the computation, optional.
• out: Alternative output array to place the result, optional.
• keepdims: If True, retains reduced dimensions with size 1.

python

numpy.mean(a, axis=None, dtype=None, out=None, keepdims=False)

Example

This example shows how to calculate the mean of a 1D and 2D NumPy array. It demonstrates the default behavior and how to use the axis parameter.

python

import numpy as np

# 1D array
arr1 = np.array([10, 20, 30, 40, 50])
mean1 = np.mean(arr1)

# 2D array
arr2 = np.array([[1, 2, 3], [4, 5, 6]])
mean_all = np.mean(arr2)  # mean of all elements
mean_axis0 = np.mean(arr2, axis=0)  # mean of each column
mean_axis1 = np.mean(arr2, axis=1)  # mean of each row

print(f"Mean of 1D array: {mean1}")
print(f"Mean of all elements in 2D array: {mean_all}")
print(f"Mean along axis 0 (columns): {mean_axis0}")
print(f"Mean along axis 1 (rows): {mean_axis1}")

Output

Mean of 1D array: 30.0
Mean of all elements in 2D array: 3.5
Mean along axis 0 (columns): [2.5 3.5 4.5]
Mean along axis 1 (rows): [2. 5.]

Common Pitfalls

Common mistakes when calculating mean with NumPy include:

• Forgetting to specify axis when working with multi-dimensional arrays, which results in the mean of the entire array instead of along a specific axis.
• Passing a list instead of a NumPy array, which still works but is less efficient.
• Not handling empty arrays, which raises a warning and returns nan.

python

import numpy as np

# Wrong: mean of entire array instead of axis
arr = np.array([[1, 2], [3, 4]])
mean_wrong = np.mean(arr)  # mean of all elements

# Right: mean along axis 0 (columns)
mean_right = np.mean(arr, axis=0)

print(f"Wrong mean (all elements): {mean_wrong}")
print(f"Right mean (axis=0): {mean_right}")

Output

Wrong mean (all elements): 2.5
Right mean (axis=0): [2. 3.]

Quick Reference

Here is a quick summary of how to use numpy.mean():

Parameter	Description	Default
a	Input array	Required
axis	Axis to compute mean along; None for all elements	None
dtype	Data type for computation	None
out	Alternative output array	None
keepdims	Keep reduced dimensions as size 1	False

Key Takeaways

Use numpy.mean() to calculate the average value of array elements easily.

Specify the axis parameter to calculate mean along rows or columns in multi-dimensional arrays.

Passing a list works but converting to a NumPy array is more efficient.

Empty arrays cause warnings and return nan; always check your data before computing mean.

Keepdims=True helps maintain array dimensions after mean calculation when needed.