Return the cumulative product treating NaNs as one but change the type of result in Python

To return the cumulative product of array elements over a given axis treating NaNs as one, use the nancumprod() method. The cumulative product does not change when NaNs are encountered and leading NaNs are replaced by ones. Ones are returned for slices that are all-NaN or empty.

The method returns a new array holding the result unless out is specified. Cumulative works like: 5, 5*10, 5*10*15, 5*10*15*20. The dtype parameter allows you to change the type of the returned array from the original array's data type.

Syntax

numpy.nancumprod(a, axis=None, dtype=None, out=None)

Parameters

a: Input array

axis: Axis along which the cumulative product is computed. By default the input is flattened

dtype: Type of the returned array and accumulator. If not specified, defaults to the dtype of a

out: Alternative output array in which to place the result

Example

Let's create an array with NaN values and calculate the cumulative product ?

import numpy as np

# Creating a numpy array with NaN values
arr = np.array([[5, 10, 15], [20, np.nan, 30]])

# Display the array
print("Original Array:")
print(arr)

# Check the original datatype
print("\nOriginal datatype:", arr.dtype)

# Calculate cumulative product treating NaNs as 1, changing dtype to int
result = np.nancumprod(arr, axis=1, dtype=int)
print("\nCumulative Product (dtype=int):")
print(result)
print("Result datatype:", result.dtype)

Original Array:
[[ 5. 10. 15.]
 [20. nan 30.]]

Original datatype: float64

Cumulative Product (dtype=int):
[[ 5 50 750]
 [20 20 600]]
Result datatype: int32

How It Works

In the above example:

• Row 1: [5, 10, 15] ? [5, 5*10=50, 50*15=750]
• Row 2: [20, NaN, 30] ? [20, 20*1=20, 20*30=600] (NaN treated as 1)

Different Data Types

You can specify different data types for the result ?

import numpy as np

arr = np.array([2.5, 4.0, np.nan, 6.0])

# Default dtype (float)
result_default = np.nancumprod(arr)
print("Default dtype:", result_default.dtype)
print("Result:", result_default)

# Convert to integer
result_int = np.nancumprod(arr, dtype=int)
print("\nInteger dtype:", result_int.dtype) 
print("Result:", result_int)

# Convert to complex
result_complex = np.nancumprod(arr, dtype=complex)
print("\nComplex dtype:", result_complex.dtype)
print("Result:", result_complex)

Default dtype: float64
Result: [ 2.5 10.   10.   60. ]

Integer dtype: int32
Result: [ 2 10 10 60]

Complex dtype: complex128
Result: [ 2.5+0.j 10. +0.j 10. +0.j 60. +0.j]

Conclusion

The nancumprod() method treats NaN values as 1 during cumulative product calculation. Use the dtype parameter to control the data type of the returned array, which is useful for memory optimization or specific numerical requirements.