NumPy nanmean()

import numpy as np

# create an array
array1 = np.array([0, np.nan, np.nan, 1, 2, 3, 4, 5, 6, 7])

# calculate the mean of the array
mean1 = np.nanmean(array1)

print(mean1)

# Output: 3.5

nanmean() Syntax

The syntax of the numpy.nanmean() method is:

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

nanmean() Arguments

The numpy.nanmean() method takes the following arguments:

• array - array containing numbers whose mean is desired (can be array_like)
• axis (optional) - axis or axes along which the means are computed (int or tuple of int)
• dtype (optional) - the datatype to use in calculation of mean (datatype)
• out (optional) - output array in which to place the result (ndarray)
• keepdims (optional) - specifies whether to preserve the shape of the original array (bool)
• where (optional) - elements to include in the mean (array of bool)

nanmean() Return Value

The numpy.nanmean() method returns the arithmetic mean of the array, ignoring NaNs.

Example 1: Find the Mean of a ndArray

import numpy as np

# create an array
array1 = np.array([[[0, 1], 
                    [2, np.NaN]], 
                    
                    [[4, 5], 
                    [6, 7]]])

# find the mean of entire array
mean1 = np.nanmean(array1)

# find the mean across axis 0
mean2 = np.nanmean(array1, 0)

# find the mean across axis 0 and 1
mean3 = np.nanmean(array1, (0, 1))

print('\nMean of the entire array:',mean1)
print('\nMean across axis 0:\n',mean2)
print('\nMean across axis 0 and 1:',mean3)

Output

Mean of the entire array: 3.5714285714285716

Mean across axis 0:
[[2. 3.]
 [4. 7.]]

Mean across axis 0 and 1: [3.         4.33333333]

Example 2: Specifying Datatype of Mean of a ndArray

The dtype parameter can be used to control the data type of the output array.

import numpy as np

array1 = np.array([[1, np.nan, 3],
                [4, 5, 6]])

# by default int is converted to float
result1 = np.nanmean(array1)

# get float32 mean
result2 = np.nanmean(array1, dtype = np.float32)

print('Float64 mean:', result1, 'with type', result1.dtype)
print('Float32 mean:', result2, 'with type', result2.dtype)

Output

Float64 mean: 3.8 with type float64
Float32 mean: 3.8 with type float32

Note: Using a lower precision dtype can lead to a loss of accuracy.

Example 3: Using Optional keepdims Argument

If keepdims is set to True, the resultant mean array is of the same number of dimensions as the original array.

import numpy as np

array1 = np.array([[1, np.nan, 3],
                [4, 5, np.nan]])

# keepdims defaults to False
result1 = np.nanmean(array1, axis = 0)

# pass keepdims as True
result2 = np.nanmean(array1, axis = 0, keepdims = True)

print('Dimensions in original array:', array1.ndim)
print('Without keepdims:', result1, 'with dimensions', result1.ndim)
print('With keepdims:', result2, 'with dimensions', result2.ndim)

Output

Dimensions in original array: 2
Without keepdims: [2.5 5.  3. ] with dimensions 1
With keepdims: [[2.5 5.  3. ]] with dimensions 2

Example 4: Using Optional where Argument

The optional argument where specifies which elements to include in the mean.

import numpy as np

array1 = np.array([[1, 2, 3, np.nan],
                [np.nan, 4, 5, 6]])

# take mean of entire array
result1 = np.nanmean(array1)

# mean of only even elements
result2 = np.nanmean(array1, where = (array1 % 2 == 0))

# mean of  numbers greater than 3
result3 = np.nanmean(array1, where = (array1 > 3))

print('Mean of entire array:', result1)
print('Mean of only even elements:', result2)
print('Mean of  numbers greater than 3:', result3)

Output

Mean of entire array: 3.5
Mean of only even elements: 4.0
Mean of  numbers greater than 3: 5.0

Example 5: Using Optional out Argument

The out parameter allows to specify an output array where the result will be stored.

import numpy as np

array1 = np.array([[1, 2, 3, np.nan],
                [4, 5, 6, np.nan]])

# create an output array
output = np.zeros(4)

# compute mean and store the result in the output array
np.mean(array1, out = output, axis = 0)

print('Mean:', output)

Output

Mean: [2.5 3.5 4.5 nan]

Note: nanmean() returns nan as output only if all elements are nan.