NumPy strings.isnumeric() – Checks if Each Element Contains only Numeric

NumPy strings.isnumeric()

The numpy.strings.isnumeric() function checks if each element in an input array consists only of numeric characters. It returns a boolean array indicating whether each string contains only numeric characters.

Syntax

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

Parameters

Parameter	Type	Description
x	array_like, with StringDType or str_ dtype	Array of strings to check for numeric characters.
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 specifying which elements to check. Elements where where=False retain their original value.
casting	str, optional	Defines the casting behavior when processing the function.
order	str, optional	Memory layout order of the output array.
dtype	data-type, optional	Specifies 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 of boolean values indicating whether each string consists only of numeric characters.

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Examples

1. Checking If Strings Contain Only Numeric Characters

In this example, we check whether each element in a string array contains only numeric characters.

import numpy as np

# Define an array of strings
string_array = np.array(["123", "abc", "⅕", "3.14", "０１２３"])  # Includes Unicode numeric characters

# Check if each string is numeric
result = np.strings.isnumeric(string_array)

# Print the results
print("Input Strings:", string_array)
print("Are Numeric:", result)

Output:

Input Strings: ['123' 'abc' '⅕' '3.14' '０１２３']
Are Numeric: [ True False  True False  True]

2. Using the out Parameter

Storing the output in a predefined array instead of creating a new one.

import numpy as np

# Define an array of strings
string_array = np.array(["123", "hello", "４５６"])

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

# Check for numeric strings and store in output_array
np.strings.isnumeric(string_array, out=output_array)

# Print the results
print("Stored results:", output_array)

Output:

Stored results: [ True False  True]

3. Using the where Parameter

Checking numeric strings only in specific positions using a mask.

import numpy as np

# Define an array of strings
string_array = np.array(["123", "45a", "⅞", "789"])

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

# Apply the mask
result = np.strings.isnumeric(string_array, where=mask)

# Print the results
print("Checked values:", result)

Output:

Checked values: [ True False  True False]

The function checks only elements where mask=True, while other values remain unchanged.