numpy.fromiter#
- numpy.fromiter(iter, dtype, count=-1, *, like=None)#
Create a new 1-dimensional array from an iterable object.
- Parameters:
- iteriterable object
An iterable object providing data for the array.
- dtypedata-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).
- countint, optional
The number of items to read from iterable. The default is -1, which means all data is read.
- likearray_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:
- outndarray
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
>>> 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]])