numpy.log — NumPy v1.15 Manual (original) (raw)
numpy. log(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, _subok=True_[, signature, _extobj_]) = <ufunc 'log'>¶
Natural logarithm, element-wise.
The natural logarithm log is the inverse of the exponential function, so that log(exp(x)) = x. The natural logarithm is logarithm in basee.
| Parameters: | x : array_like Input value. out : ndarray, None, or tuple of ndarray and None, optional A location into which the result is stored. If provided, it must have a shape that the inputs broadcast to. If not provided or None, a freshly-allocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs. where : array_like, optional Values of True indicate to calculate the ufunc at that position, values of False indicate to leave the value in the output alone. **kwargs For other keyword-only arguments, see theufunc docs. |
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| Returns: | y : ndarray The natural logarithm of x, element-wise. This is a scalar if x is a scalar. |
Notes
Logarithm is a multivalued function: for each x there is an infinite number of z such that exp(z) = x. The convention is to return the_z_ whose imaginary part lies in [-pi, pi].
For real-valued input data types, log always returns real output. For each value that cannot be expressed as a real number or infinity, it yields nan and sets the invalid floating point error flag.
For complex-valued input, log is a complex analytical function that has a branch cut [-inf, 0] and is continuous from above on it. loghandles the floating-point negative zero as an infinitesimal negative number, conforming to the C99 standard.
References
| [1] | M. Abramowitz and I.A. Stegun, “Handbook of Mathematical Functions”, 10th printing, 1964, pp. 67. http://www.math.sfu.ca/~cbm/aands/ |
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| [2] | Wikipedia, “Logarithm”. http://en.wikipedia.org/wiki/Logarithm |
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Examples
np.log([1, np.e, np.e**2, 0]) array([ 0., 1., 2., -Inf])