NumPy true_divide() – Division of Two Arrays Element-wise

NumPy true_divide()

The numpy.true_divide() function performs element-wise division of two arrays, ensuring a true division (floating-point division) regardless of the input data types.

Syntax

numpy.true_divide(x1, x2, /, out=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. Must be broadcastable to the shape of x1.
out	ndarray, None, or tuple of ndarray and None, optional	Optional output array where results are stored. If not provided, a new array is allocated.
where	array_like, optional	Boolean mask specifying where to perform division. Where False, values remain unchanged.
casting	str, optional	Controls how types are cast.
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 whether to allow subclasses of ndarray.

Return Value

Returns an array with element-wise division results. If both x1 and x2 are scalars, a scalar is returned.

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Examples

1. Basic Element-wise Division

Dividing two arrays element-wise using numpy.true_divide().

import numpy as np

# Define two arrays
x1 = np.array([10, 20, 30, 40])
x2 = np.array([2, 4, 5, 8])

# Perform element-wise true division
result = np.true_divide(x1, x2)

# Print the result
print("Element-wise division:", result)

Output:

Element-wise division: [5.  5.  6.  5. ]

2. Broadcasting in Division

Dividing an array by a scalar, utilizing NumPy’s broadcasting.

import numpy as np

# Define an array
x1 = np.array([10, 20, 30, 40])

# Define a scalar divisor
x2 = 10

# Perform element-wise true division
result = np.true_divide(x1, x2)

# Print the result
print("Array divided by scalar:", result)

Output:

Array divided by scalar: [1.  2.  3.  4. ]

3. Using the out Parameter

Storing the division result in a pre-allocated output array.

import numpy as np

# Define two arrays
x1 = np.array([12, 18, 24, 36])
x2 = np.array([3, 6, 4, 9])

# Create an output array with the same shape
output_array = np.empty_like(x1, dtype=float)

# Perform true division and store result in output_array
np.true_divide(x1, x2, out=output_array)

# Print the output array
print("Stored result:", output_array)

Output:

Stored result: [4.  3.  6.  4. ]

4. Using the where Parameter

Performing division only for elements satisfying a condition.

import numpy as np

# Define two arrays
x1 = np.array([12, 18, 24, 36])
x2 = np.array([3, 6, 4, 9])

# Define a condition (only perform division where x2 is greater than 5)
mask = x2 > 5

# Compute division where condition is True
result = np.true_divide(x1, x2, where=mask)

# Print the results
print("Division result with condition:", result)

Output:

Division result with condition: [0. 3. 0. 4.]

The division is performed only where the condition x2 > 5 is met. Other values remain unchanged.