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# numpy.vectorize

class `numpy.``vectorize`(pyfunc, otypes='', doc=None)

  Generalized function class.

  Define a vectorized function which takes a nested sequence of objects or numpy arrays as inputs and returns a numpy array as output. The vectorized function evaluates pyfunc over successive tuples of the input arrays like the python map function, except it uses the broadcasting rules of numpy.

  The data type of the output of vectorized is determined by calling the function with the first element of the input. This can be avoided by specifying the otypes argument.

  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 one.

  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])
      <type 'numpy.int32'>
      >>> vfunc = np.vectorize(myfunc, otypes=[np.float])
      >>> out = vfunc([1, 2, 3, 4], 2)
      >>> type(out[0])
      <type 'numpy.float64'>

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