Does np.exp work on a Python list?

Yes. NumPy converts the list to an array before the ufunc runs and returns a float64 array, so a list of ints comes back as an array of floats with the same length.

Why does np.exp return inf instead of raising an error?

Floating-point overflow saturates at infinity rather than raising a Python exception, and NumPy reports it as a RuntimeWarning. Wrap the call in np.errstate with over set to raise when you want a FloatingPointError, or set it to ignore when the infinity is expected.

When should you use expm1 instead of exp?

Use expm1 when the input is near zero and you need e raised to x minus one. Computing exp first and subtracting one throws away the leading digits, so the single-step version keeps precision that the two-step version loses.

Does np.exp preserve the shape of its input?

It does. The ufunc applies element-wise after broadcasting, so a two by three input returns a two by three array, a higher-dimensional input keeps every axis, and a scalar returns a NumPy scalar.

How do you stop np.exp from overflowing inside a softmax?

Subtract the largest logit before exponentiating. The shift cancels between the numerator and the denominator, so the probabilities are unchanged while the largest exponent becomes exactly 1 and nothing overflows.

NumPy exp() Explained: Element-wise Exponential and Overflow

Tanvi Bugdani
September 11, 2026
NumPy

np.exp turns a large input into inf and only warns instead of raising an error. The overflow matters because it turns a softmax into NaNs, and the fix is one subtraction.

What NumPy exp computes

np.exp is a universal function, or ufunc, so it applies the same operation to every element of its input without a Python loop. Each element comes back as e raised to that value, where e is Euler’s number and equals roughly 2.718281828.