numpy.isinf() test element-wise whether a value is positive or negative infinity. It returns a Boolean array with True where the input is either +inf or -inf and False otherwise. Example:
Python import numpy as np a = np.array([1, np.inf, -np.inf, 0, np.nan]) res = np.isinf(a) print(res)
Output[False True True False False]
Explanation: np.isinf() checks each element in array a for positive or negative infinity, returning True for infinite values and False for all others, including numbers and np.nan.
Syntax
numpy.isinf(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj])
Parameter:
Parameter | Description |
|---|
x | Input array or scalar |
|---|
out | A location where the result is stored (optional) |
|---|
where | Condition over input array for performing the check (optional) |
|---|
Returns: A Boolean array of the same shape as x, with True at positions where the element is infinite (inf or -inf) and False elsewhere.
Examples
Example 1: In this example, we check for positive (np.inf) and negative infinity (-np.inf) and return True for both and False for all other values, including nan.
Python import numpy as np a = np.array([1, np.inf, -np.inf, 0, np.nan]) res = np.isinf(a) print(res)
Output[False True True False False]
Explanation: np.isinf() check each element in array a for positive or negative infinity. It returns True for infinite values and False for others, including numbers and np.nan.
Example 2: In this example, we divide 1.0 by each element of the array. Division by zero gives np.inf, and np.isinf() returns True only for that value.
Python import numpy as np a = np.array([1.0, 0.0, -1.0]) b = 1.0 / a res = np.isinf(b) print(b) print(res)
Output
[ 1. inf -1.]
[False True False]
Explanation: Division by 0.0 produces inf. np.isinf() detects that and marks only that value as True.
Example 3: In this example, we filter out positive and negative infinity using ~np.isinf() and return only the finite values.
Python import numpy as np a = np.array([10, np.inf, -np.inf, 20]) res = a[~np.isinf(a)] print(res)
Explanation: We use the bitwise NOT (~) to reverse the Boolean mask from np.isinf(), keeping only non-infinite values.
Example 4: In this example, we use the out and where parameters to store the result in a separate array and check for infinity only where the values are not zero.
Python import numpy as np a = np.array([np.inf, 2, 0, -np.inf]) out_arr = np.zeros_like(a, dtype=bool) np.isinf(a, out=out_arr, where=(a != 0)) print(out_arr)
Output[ True False False True]
Explanation: We store the result in out_arr and apply the check only where the condition a != 0 is True. This avoids checking for inf on zero values.
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