Python:NumPy .trunc()

In NumPy, the .trunc() function truncates the decimal part of each element in an array, returning the integer part as a float. It is used to discard the fractional part without rounding the number.

Syntax

numpy.trunc(x, /, out=None, *, where=True)

• x: The input array (or a single number) whose elements are to be truncated.
• out (Optional): A location where the result will be stored. If not provided, a new array is created and returned.
• where (Optional): A boolean array or condition that specifies which elements to compute for. The default is True, meaning the function operates on all elements of x.

It returns a new array (or a scalar if the input is a single value) with the decimal part truncated from each element, leaving the integer part as a float.

Example 1

The example demonstrates how .trunc() truncates the decimal values in a NumPy array:

import numpy as np

arr = np.array([3.14, -2.718, 1.618, -4.0])
truncated = np.trunc(arr)
print(truncated)

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The output will be as follows:

[ 3. -2.  1. -4.]

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The above example also shows that .trunc() does not round but it simply removes the fractional part.

Example 2

The example below demonstrates how to selectively apply truncation using the where parameter, allowing truncation only for elements meeting a specified condition while leaving others unchanged:

import numpy as np

# Array of numbers
numbers = np.array([3.14, -2.718, 1.618, -4.0])

# Truncate only the positive numbers
result = np.where(numbers > 0, np.trunc(numbers), numbers)

# Print the original and modified arrays
print("Original Numbers:", numbers)
print("Conditionally Truncated Numbers:", result)

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The output will be as follows:

Original Numbers: [ 3.14  -2.718  1.618  -4.   ]
Conditionally Truncated Numbers: [ 3.   -2.718  1.   -4.   ]

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Here, truncation is only applied to elements where the value is greater than 0, leaving other elements unchanged.

Codebyte Example

Run the following codebyte example to understand how the .trunc() works:

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