NumPy divmod() – Find Quotient and Remainder of Array Element-wise

NumPy divmod()

The numpy.divmod() function returns the quotient and remainder of element-wise division simultaneously. It is equivalent to (x // y, x % y) but optimized for performance.

Syntax

numpy.divmod(x1, x2, [out1, out2], /, [out=(None, None)], *, where=True, casting='same_kind', order='K', dtype=None, subok=True)

Parameters

Parameter	Type	Description
x1	array_like	Dividend array.
x2	array_like	Divisor array. If x1.shape != x2.shape, they must be broadcastable to a common shape.
out	ndarray, None, or tuple of ndarray and None, optional	Optional output arrays where the results are stored. If None, new arrays are created.
where	array_like, optional	Boolean mask specifying which elements to compute. Elements where where=False retain their original values.
casting	str, optional	Defines the casting behavior when computing the quotient and remainder.
order	str, optional	Memory layout order of the output array.
dtype	data-type, optional	Defines the data type of the output array.
subok	bool, optional	Determines if subclasses of ndarray are preserved in the output.

Return Value

Returns a tuple (quotient, remainder). Both are arrays or scalars depending on input type.

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Examples

1. Computing Quotient and Remainder for Single Values

Here, we compute the quotient and remainder of two scalar values.

import numpy as np

# Define dividend and divisor
x = 10
y = 3

# Compute quotient and remainder
quotient, remainder = np.divmod(x, y)

# Print results
print("Quotient:", quotient)
print("Remainder:", remainder)

Output:

Quotient: 3
Remainder: 1

2. Using NumPy divmod with Arrays

We compute element-wise quotient and remainder for two NumPy arrays.

import numpy as np

# Define two arrays (dividends and divisors)
x = np.array([10, 20, 30, 40])
y = np.array([3, 4, 5, 6])

# Compute quotient and remainder
quotient, remainder = np.divmod(x, y)

# Print results
print("Quotient:", quotient)
print("Remainder:", remainder)

Output:

Quotient: [3 5 6 6]
Remainder: [1 0 0 4]

3. Using the out Parameter

Using output arrays to store results instead of creating new ones.

import numpy as np

# Define two arrays (dividends and divisors)
x = np.array([15, 25, 35])
y = np.array([4, 6, 7])

# Create output arrays
quot_out = np.empty_like(x)
rem_out = np.empty_like(x)

# Compute quotient and remainder with out parameter
np.divmod(x, y, out=(quot_out, rem_out))

# Print results
print("Quotient (stored in out):", quot_out)
print("Remainder (stored in out):", rem_out)

Output:

Quotient (stored in out): [3 4 5]
Remainder (stored in out): [3 1 0]

4. Using the where Parameter

Computing quotient and remainder only for selected elements using a condition.

import numpy as np

# Define two arrays
x = np.array([15, 20, 25, 30])
y = np.array([3, 4, 5, 6])

# Define a mask (compute only for selected values)
mask = np.array([True, False, True, False])

# Compute quotient and remainder where mask is True
quotient, remainder = np.divmod(x, y, where=mask)

# Print results
print("Quotient with mask:", quotient)
print("Remainder with mask:", remainder)

Output:

Quotient with mask: [5 0 5 0]
Remainder with mask: [0 0 0 0]

Here, divmod is computed only for elements where mask=True, while the rest remain unchanged.