NumPy remainder() – Remainder of Division of Array Element-wise

NumPy remainder()

The numpy.remainder() function computes the element-wise remainder of division, similar to the modulus (%) operator in Python. It complements numpy.floor_divide() and ensures that the remainder has the same sign as the divisor.

Syntax

numpy.remainder(x1, x2, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True)

Parameters

Parameter	Type	Description
x1	array_like	Dividend array (numerator values).
x2	array_like	Divisor array (denominator values). Must be broadcastable with x1.
out	ndarray, None, or tuple of ndarray and None, optional	Optional output array to store results. If not provided, a new array is created.
where	array_like, optional	Boolean condition array determining which elements are computed.
casting	str, optional	Specifies casting behavior for the output array.
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 an array containing the element-wise remainder from floor_divide(x1, x2). If both x1 and x2 are scalars, a scalar is returned.

---

Examples

1. Computing Remainder of Two Scalars

We compute the remainder of two scalar values using numpy.remainder().

import numpy as np

# Define two scalar values
x1 = 10
x2 = 3

# Compute the remainder
result = np.remainder(x1, x2)

# Print the result
print("Remainder of 10 divided by 3:", result)

Output:

Remainder of 10 divided by 3: 1

2. Computing Remainder for Arrays

We apply numpy.remainder() element-wise to an array.

import numpy as np

# Define dividend and divisor arrays
x1 = np.array([10, 20, 30, 40])
x2 = np.array([3, 4, 5, 6])

# Compute the remainder for each element
remainder_values = np.remainder(x1, x2)

# Print the results
print("Dividends:", x1)
print("Divisors:", x2)
print("Remainder values:", remainder_values)

Output:

Dividends: [10 20 30 40]
Divisors: [3 4 5 6]
Remainder values: [1 0 0 4]

3. Using the out Parameter

We use an output array to store the remainder values instead of creating a new array.

import numpy as np

# Define input arrays
x1 = np.array([15, 25, 35, 45])
x2 = np.array([4, 5, 6, 7])

# Create an empty output array
output_array = np.empty_like(x1)

# Compute remainder and store it in output_array
np.remainder(x1, x2, out=output_array)

# Print the results
print("Computed remainder values:", output_array)

Output:

Computed remainder values: [3 0 5 3]

4. Using the where Parameter

The where parameter allows computing the remainder only for selected elements.

import numpy as np

# Define input arrays
x1 = np.array([12, 22, 32, 42])
x2 = np.array([5, 4, 3, 2])

# Define a mask (compute remainder only where mask is True)
mask = np.array([True, False, True, False])

# Compute remainder values where mask is True
result = np.remainder(x1, x2, where=mask)

# Print the results
print("Computed remainder values with mask:", result)

Output:

Computed remainder values with mask: [2 0 2 0]

Only elements where mask=True are computed.