numpy.log¶

numpy.
log
(x, /, out=None, *, where=True, casting='same_kind', order='K', dtype=None, subok=True[, signature, extobj]) = <ufunc 'log'>¶ Natural logarithm, elementwise.
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
 xarray_like
Input value.
 outndarray, 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 freshlyallocated array is returned. A tuple (possible only as a keyword argument) must have length equal to the number of outputs.
 wherearray_like, optional
This condition is broadcast over the input. At locations where the condition is True, the out array will be set to the ufunc result. Elsewhere, the out array will retain its original value. Note that if an uninitialized out array is created via the default
out=None
, locations within it where the condition is False will remain uninitialized. **kwargs
For other keywordonly arguments, see the ufunc docs.
 Returns
 yndarray
The natural logarithm of x, elementwise. 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 realvalued input data types,
log
always returns real output. For each value that cannot be expressed as a real number or infinity, it yieldsnan
and sets the invalid floating point error flag.For complexvalued input,
log
is a complex analytical function that has a branch cut [inf, 0] and is continuous from above on it.log
handles the floatingpoint 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/
 2
Wikipedia, “Logarithm”. https://en.wikipedia.org/wiki/Logarithm
Examples
>>> np.log([1, np.e, np.e**2, 0]) array([ 0., 1., 2., Inf])