NumPy strings.greater() – Compare Two String Arrays

NumPy strings.greater()

The numpy.strings.greater() function performs an element-wise comparison of two input string arrays and returns a boolean array indicating whether the corresponding elements of x1 are lexicographically greater than those in x2.

Syntax

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

Parameters

Parameter	Type	Description
x1, x2	array_like	Input string arrays. They must either have the same shape or be broadcastable to a common shape.
out	ndarray, None, or tuple of ndarray and None, optional	Optional output array where the result is stored. If None, a new array is created.
where	array_like, optional	Boolean mask specifying which elements to compare. Unselected elements retain their original value.
casting	str, optional	Defines the casting behavior for data types.
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 boolean array or scalar indicating the result of lexicographical comparison (x1 > x2) for each element.

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Examples

1. Comparing Two Single Strings

Checking if one string is lexicographically greater than another.

import numpy as np

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

# Compare lexicographically
result = np.strings.greater(str1, str2)

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

Output:

Is "apple" greater than "banana"? : False

2. Comparing Two Arrays of Strings

Comparing corresponding elements in two arrays lexicographically.

import numpy as np

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

# Perform element-wise comparison
result = np.strings.greater(arr1, arr2)

# Print the results
print("Array 1:", arr1)
print("Array 2:", arr2)
print("Comparison Result:", result)

Output:

Array 1: ['apple' 'cherry' 'banana']
Array 2: ['banana' 'apple' 'banana']
Comparison Result: [False  True False]

3. Using Broadcasting for Comparison

Comparing a single string with an array of strings using broadcasting.

import numpy as np

# Define a single string and an array
single_str = "banana"
arr = np.array(["apple", "banana", "cherry"])

# Perform element-wise comparison
result = np.strings.greater(arr, single_str)

# Print the results
print("Array:", arr)
print(f'Comparison with "{single_str}":', result)

Output:

Array: ['apple' 'banana' 'cherry']
Comparison with "banana": [False False  True]

4. Using the where Parameter

Applying a condition to control which elements are compared.

import numpy as np

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

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

# Compare elements where mask is True
result = np.strings.greater(arr1, arr2, where=mask)

# Print the results
print("Array 1:", arr1)
print("Array 2:", arr2)
print("Comparison Result with Mask:", result)

Output:

Array 1: ['apple' 'cherry' 'banana']
Array 2: ['banana' 'apple' 'banana']
Comparison Result with Mask: [False False False]

The comparison is only performed where the mask is True. Unmasked elements retain their original value.