NumPy strings.less() – Compare Two String Arrays

NumPy strings.less()

The numpy.strings.less() function compares two string arrays element-wise and returns True where elements in the first array are lexicographically less than elements in the second array.

Syntax

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

Parameters

Parameter	Type	Description
x1, x2	array_like	Input arrays containing strings. If their shapes differ, they must be broadcastable to a common shape.
out	ndarray, None, or tuple of ndarray and None, optional	Optional output array where results are stored. If None, a new array is created.
where	array_like, optional	Boolean mask that determines where comparisons occur.
casting	str, optional	Defines type casting behavior.
order	str, optional	Specifies memory layout of the output array.
dtype	data-type, optional	Defines the output data type.
subok	bool, optional	Determines if subclasses of ndarray are preserved in the output.

Return Value

Returns an array of boolean values indicating whether elements in x1 are lexicographically less than elements in x2. If both inputs are scalars, a single boolean value is returned.

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Examples

1. Comparing Two Single Strings

We compare two string values lexicographically.

import numpy as np

# Define two strings
str1 = "apple"
str2 = "banana"

# Compare the strings using numpy.strings.less()
result = np.strings.less(str1, str2)

# Print the result
print(f'Is "{str1}" less than "{str2}"? {result}')

Output:

Is "apple" less than "banana"? True

2. Comparing Two Arrays of Strings

We compare two arrays of strings element-wise.

import numpy as np

# Define two arrays of strings
arr1 = np.array(["apple", "banana", "cherry"])
arr2 = np.array(["banana", "apple", "date"])

# Compare the arrays using numpy.strings.less()
result = np.strings.less(arr1, arr2)

# Print the results
print("First array:", arr1)
print("Second array:", arr2)
print("Comparison result:", result)

Output:

First array: ['apple' 'banana' 'cherry']
Second array: ['banana' 'apple' 'date']
Comparison result: [ True False  True]

3. Using the out Parameter

Storing the comparison result in a predefined output array.

import numpy as np

# Define input arrays
arr1 = np.array(["apple", "banana", "cherry"])
arr2 = np.array(["banana", "apple", "date"])

# Create an output array of the same shape
output_array = np.empty(arr1.shape, dtype=bool)

# Compare strings and store the result in output_array
np.strings.less(arr1, arr2, out=output_array)

# Print the results
print("Comparison result stored in output array:", output_array)

Output:

Comparison result stored in output array: [ True False  True]

4. Using the where Parameter

Performing the comparison only at specific indices.

import numpy as np

# Define input arrays
arr1 = np.array(["apple", "banana", "cherry"])
arr2 = np.array(["banana", "apple", "date"])

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

# Perform comparison only where mask is True
result = np.strings.less(arr1, arr2, where=mask)

# Print the results
print("Comparison result with mask:", result)

Output:

Comparison result with mask: [ True False  True]

Elements where mask=False retain their previous values, while only mask=True positions undergo comparison.