Numpy Fromiter With Generator Of List

import numpy as np def gen_c(): c = np.ones(5, dtype=int) j = 0 t = 10 while j < t: c[0] = j yield c.tolist() j += 1 # What I did: # re

Solution 1:

You can only use numpy.fromiter() to create 1-dimensional arrays (not 2-D arrays) as given in the documentation of numpy.fromiter -

numpy.fromiter(iterable, dtype, count=-1)

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

One thing you can do is convert your generator function to give out single values from c and then create a 1D array from it and then reshape it to (-1,5) . Example -

import numpy as np
def gen_c():
    c = np.ones(5, dtype=int)
    j = 0
    t = 10
    while j < t:
        c[0] = j
        for i in c:
            yield i
        j += 1

np.fromiter(gen_c(),dtype=int).reshape((-1,5))

Demo -

In [5]: %paste
import numpy as np
def gen_c():
    c = np.ones(5, dtype=int)
    j = 0
    t = 10
    while j < t:
        c[0] = j
        for i in c:
            yield i
        j += 1

np.fromiter(gen_c(),dtype=int).reshape((-1,5))

## -- End pasted text --
Out[5]:
array([[0, 1, 1, 1, 1],
       [1, 1, 1, 1, 1],
       [2, 1, 1, 1, 1],
       [3, 1, 1, 1, 1],
       [4, 1, 1, 1, 1],
       [5, 1, 1, 1, 1],
       [6, 1, 1, 1, 1],
       [7, 1, 1, 1, 1],
       [8, 1, 1, 1, 1],
       [9, 1, 1, 1, 1]])

Solution 2:

As the docs suggested, np.fromiter() only accepts 1-dimensional iterables. You can use itertools.chain.from_iterable() to flatten the iterable first, and np.reshape() it back later:

import itertools
import numpy as np

def fromiter2d(it, dtype):

    # clone the iterator to get its length
    it, it2 = itertools.tee(it)
    length = sum(1 for _ in it2)

    flattened = itertools.chain.from_iterable(it)
    array_1d = np.fromiter(flattened, dtype)
    array_2d = np.reshape(array_1d, (length, -1))
    return array_2d

Demo:

>>> iter2d = (range(i, i + 4) for i in range(0, 12, 4))

>>> from_2d_iter(iter2d, int)
array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11]])

Only tested on Python 3.6, but should also work with Python 2.