numpy.lib.function_base.vectorize

Parameters

pyfunc : callable

A python function or method.

otypes : str or list of dtypes, optional

The output data type. It must be specified as either a string of typecode characters or a list of data type specifiers. There should be one data type specifier for each output.

doc : str, optional

The docstring for the function. If None, the docstring will be the pyfunc.__doc__.

excluded : set, optional

Set of strings or integers representing the positional or keyword arguments for which the function will not be vectorized. These will be passed directly to pyfunc unmodified.

New in version 1.7.0.

cache : bool, optional

If True, then cache the first function call that determines the number of outputs if otypes is not provided.

New in version 1.7.0.

signature : string, optional

Generalized universal function signature, e.g., (m,n),(n)->(m) for vectorized matrix-vector multiplication. If provided, pyfunc will be called with (and expected to return) arrays with shapes given by the size of corresponding core dimensions. By default, pyfunc is assumed to take scalars as input and output.

New in version 1.12.0.

Returns

vectorized : callable

Vectorized function.

See Also

frompyfunc : Takes an arbitrary Python function and returns a ufunc

Notes

The vectorize function is provided primarily for convenience, not for performance. The implementation is essentially a for loop.

If otypes is not specified, then a call to the function with the first argument will be used to determine the number of outputs. The results of this call will be cached if cache is True to prevent calling the function twice. However, to implement the cache, the original function must be wrapped which will slow down subsequent calls, so only do this if your function is expensive.

The new keyword argument interface and excluded argument support further degrades performance.

References

Examples

>>> def myfunc(a, b):
...     "Return a-b if a>b, otherwise return a+b"
...     if a > b:
...         return a - b
...     else:
...         return a + b

>>> vfunc = np.vectorize(myfunc)
>>> vfunc([1, 2, 3, 4], 2)
array([3, 4, 1, 2])

The docstring is taken from the input function to vectorize unless it is specified:

>>> vfunc.__doc__
'Return a-b if a>b, otherwise return a+b'
>>> vfunc = np.vectorize(myfunc, doc='Vectorized `myfunc`')
>>> vfunc.__doc__
'Vectorized `myfunc`'

The output type is determined by evaluating the first element of the input, unless it is specified:

>>> out = vfunc([1, 2, 3, 4], 2)
>>> type(out[0])
<class 'numpy.int64'>
>>> vfunc = np.vectorize(myfunc, otypes=[float])
>>> out = vfunc([1, 2, 3, 4], 2)
>>> type(out[0])
<class 'numpy.float64'>

The excluded argument can be used to prevent vectorizing over certain arguments. This can be useful for array-like arguments of a fixed length such as the coefficients for a polynomial as in polyval:

>>> def mypolyval(p, x):
...     _p = list(p)
...     res = _p.pop(0)
...     while _p:
...         res = res*x + _p.pop(0)
...     return res
>>> vpolyval = np.vectorize(mypolyval, excluded=['p'])
>>> vpolyval(p=[1, 2, 3], x=[0, 1])
array([3, 6])

Positional arguments may also be excluded by specifying their position:

>>> vpolyval.excluded.add(0)
>>> vpolyval([1, 2, 3], x=[0, 1])
array([3, 6])

The signature argument allows for vectorizing functions that act on non-scalar arrays of fixed length. For example, you can use it for a vectorized calculation of Pearson correlation coefficient and its p-value:

>>> import scipy.stats
>>> pearsonr = np.vectorize(scipy.stats.pearsonr,
...                 signature='(n),(n)->(),()')
>>> pearsonr([[0, 1, 2, 3]], [[1, 2, 3, 4], [4, 3, 2, 1]])
(array([ 1., -1.]), array([ 0.,  0.]))

Or for a vectorized convolution:

>>> convolve = np.vectorize(np.convolve, signature='(n),(m)->(k)')
>>> convolve(np.eye(4), [1, 2, 1])
array([[1., 2., 1., 0., 0., 0.],
       [0., 1., 2., 1., 0., 0.],
       [0., 0., 1., 2., 1., 0.],
       [0., 0., 0., 1., 2., 1.]])