Skip to main content
`Ctrl`+`K`

# numpy.fromiter

numpy.fromiter(iter, dtype, count=-1, *, like=None)

  Create a new 1-dimensional array from an iterable object.

  Parameters:
    iter iterable object

      An iterable object providing data for the array.

    dtype data-type

      The data-type of the returned array.

      Changed in version 1.23: Object and subarray dtypes are now supported (note that the final result is not 1-D for a subarray dtype).

    count int, optional

      The number of items to read from iterable. The default is -1, which means all data is read.

    like array_like, optional

      Reference object to allow the creation of arrays which are not NumPy arrays. If an array-like passed in as `like` supports the `__array_function__` protocol, the result will be defined by it. In this case, it ensures the creation of an array object compatible with that passed in via this argument.

      New in version 1.20.0.

  Returns:
    out ndarray

      The output array.

  Notes

  Specify count to improve performance. It allows `fromiter` to pre-allocate the output array, instead of resizing it on demand.

  Examples

      >>> import numpy as np
      >>> iterable = (x*x for x in range(5))
      >>> np.fromiter(iterable, float)
      array([  0.,   1.,   4.,   9.,  16.])

  A carefully constructed subarray dtype will lead to higher dimensional results:

      >>> iterable = ((x+1, x+2) for x in range(5))
      >>> np.fromiter(iterable, dtype=np.dtype((int, 2)))
      array([[1, 2],
             [2, 3],
             [3, 4],
             [4, 5],
             [5, 6]])