NumPy nanmean() Function

---

---

The NumPy nanmean() function computes the arithmetic mean (average) of the elements in an array along a specified axis, ignoring NaN (Not a Number) values. The mean is the sum of the data values divided by the number of valid (non-NaN) data points. By default, the function computes the mean over the flattened array, or over the specified axis if provided.

If all elements in the specified slice are NaN, the function returns NaN and raises a RuntimeWarning. When the array contains integers, the function converts these integers into a 64-bit floating-point format (float64) before performing the calculations. This ensures higher precision in the computation of the average. After the calculation, the returned result is also in the float64 format, even if the original array was composed of integers.

In statistics, the mean (also known as the average) is the sum of the valid data values divided by the number of valid data points. The formula is mean = (sum of all valid elements) / (number of valid elements).

For a one-dimensional array, the mean is computed over all non-NaN elements. For multi-dimensional arrays, the mean is computed along the specified axis while ignoring NaN values.

Syntax

Following is the syntax of the NumPy nanmean() function −

numpy.nanmean(a, axis=None, dtype=None, out=None, keepdims=<no value>,where=<no value>)

Parameters

Following are the parameters of the NumPy nanmean() function −

• a: Input array, which can be a NumPy array, list, or a scalar value. NaN values are ignored.
• axis (optional): Axis along which to compute the mean. Default is None, which means the mean is computed over the entire array.
• dtype (optional): The data type used to compute the mean. Default is None, meaning the data type of the input array is used.
• out (optional): A location into which the result is stored. If provided, it must have the same shape as the expected output.
• keepdims (optional): If True, the reduced dimensions are retained as dimensions of size one in the output. Default is False.
• where (optional): A boolean array specifying the elements to include in the calculation.

Return Values

This function returns the mean of the input array, ignoring NaN values. If the axis parameter is specified, the mean is computed along that axis. The result is a scalar for one-dimensional input and an array for multi-dimensional input.

Example

Following is a basic example to compute the mean of an array while ignoring NaN values using the NumPy nanmean() function −

import numpy as np
# input array with NaN values
x = np.array([1, 2, np.nan, 4, 5])
# applying nanmean
result = np.nanmean(x)
print("Mean Result (ignoring NaN):", result)

Output

Following is the output of the above code −

Mean Result (ignoring NaN): 3.0

Example: Specifying an Axis

The nanmean() function can compute the mean along a specific axis of a multi-dimensional array, ignoring NaN values. In the following example, we compute the mean along axis 0 (columns) and axis 1 (rows) of a 2D array −

import numpy as np
# 2D array with NaN values
x = np.array([[1, 2, np.nan], [4, np.nan, 6], [7, 8, 9]])
# applying nanmean along axis 0 (columns)
result_axis0 = np.nanmean(x, axis=0)
# applying nanmean along axis 1 (rows)
result_axis1 = np.nanmean(x, axis=1)
print("Mean along axis 0 (ignoring NaN):", result_axis0)
print("Mean along axis 1 (ignoring NaN):", result_axis1)

Output

Following is the output of the above code −

Mean along axis 0 (ignoring NaN): [4. 5. 7.5]
Mean along axis 1 (ignoring NaN): [1.5 5.  8. ]

Example: Usage of 'keepdims' Parameter

The keepdims parameter allows the result to retain the reduced dimensions as size one. This is useful for broadcasting the result back to the original shape. In the following example, we have computed the mean along axis 0 and retain the reduced dimensions −

import numpy as np
# 2D array with NaN values
x = np.array([[1, 2, np.nan], [4, np.nan, 6], [7, 8, 9]])
result = np.nanmean(x, axis=0, keepdims=True)
print("Mean with keepdims=True (ignoring NaN):", result)

Output

Following is the output of the above code −

Mean with keepdims=True (ignoring NaN): [[4.  5.  7.5]]

Example: Plotting 'nanmean()' Function

In the following example, we have plotte the behavior of the nanmean() function, calculating and visualizing the mean for different input arrays with some NaN values using numpy and matplotlib.pyplot −

import numpy as np
import matplotlib.pyplot as plt
# Input array with some NaN values
x = np.linspace(0, 10, 100)
y = np.full_like(x, np.nanmean(x))
plt.plot(x, y, label="Mean (ignoring NaN)")
plt.title("Nanmean Function")
plt.xlabel("Input")
plt.ylabel("Mean Value")
plt.legend()
plt.grid()
plt.show()

Output

The plot demonstrates the constant nature of the mean value across the input range, while ignoring NaN values −