NumPy nanprod() – Product of Array Elements contains NaN

NumPy nanprod()

The numpy.nanprod() function calculates the product of array elements while treating NaN (Not a Number) values as 1. This ensures that NaN values do not affect the final product.

Syntax

numpy.nanprod(a, axis=None, dtype=None, out=None, keepdims=False, initial=<no value>, where=True)

Parameters

Parameter	Type	Description
a	array_like	Input array whose product is computed. If not already an array, it is converted.
axis	int, tuple of int, None, optional	Specifies the axis along which to compute the product. If None, the product of all elements is computed.
dtype	data-type, optional	Specifies the data type of the result and the accumulator used in computation.
out	ndarray, optional	Alternative array where results are stored. It must have the same shape as the expected output.
keepdims	bool, optional	If True, reduced dimensions are kept as size one, ensuring correct broadcasting.
initial	scalar, optional	Starting value for the product computation.
where	array_like of bool, optional	Determines which elements to include in the computation.

Return Value

Returns an array containing the product of the input elements while treating NaN values as 1. If out is provided, it is returned instead.

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Examples

1. Computing Product While Ignoring NaN Values

In this example, we calculate the product of an array containing NaN values.

import numpy as np

# Define an array with NaN values
arr = np.array([2, np.nan, 5, 3])

# Compute the product while treating NaN as 1
result = np.nanprod(arr)

# Print the result
print("Product ignoring NaN values:", result)

Output:

Product ignoring NaN values: 30.0

2. Using the axis Parameter in nanprod()

We calculate the product along different axes of a 2D array while ignoring NaN values.

import numpy as np

# Define a 2D array with NaN values
arr = np.array([[1, 2, np.nan],
                [4, np.nan, 6]])

# Compute product along axis 0 (columns)
result_axis0 = np.nanprod(arr, axis=0)
print("Product along axis 0 (columns):", result_axis0)

# Compute product along axis 1 (rows)
result_axis1 = np.nanprod(arr, axis=1)
print("Product along axis 1 (rows):", result_axis1)

Output:

Product along axis 0 (columns): [ 4.  2.  6.]
Product along axis 1 (rows): [ 2. 24.]

3. Using keepdims=True to Preserve Dimensions

We use keepdims=True to maintain the dimensions of the original array.

import numpy as np

# Define a 2D array with NaN values
arr = np.array([[3, np.nan],
                [2, 4]])

# Compute the product along axis 1 while keeping dimensions
result = np.nanprod(arr, axis=1, keepdims=True)

# Print the result
print("Product along axis 1 with keepdims=True:\n", result)

Output:

Product along axis 1 with keepdims=True:
 [[ 3.]
 [ 8.]]

4. Using the initial Parameter in nanprod()

The initial parameter sets a starting value for the product computation.

import numpy as np

# Define an array with NaN values
arr = np.array([np.nan, 2, 3])

# Compute product with an initial value of 5
result = np.nanprod(arr, initial=5)

# Print the result
print("Product with initial=5:", result)

Output:

Product with initial=5: 30.0

5. Using the where Parameter in nanprod()

The where parameter allows selective computation by choosing which elements to include.

import numpy as np

# Define an array with NaN values
arr = np.array([1, 2, np.nan, 4])

# Define a condition mask
mask = np.array([True, False, True, True])

# Compute product only where mask is True
result = np.nanprod(arr, where=mask)

# Print the result
print("Product with mask:", result)

Output:

Product with mask: 4.0