How do you 'remove' a numpy array from a list of numpy arrays?

Question

If I have a list of numpy arrays, then using remove method returns a value error.

For example:

import numpy as np

l = [np.array([1,1,1]),np.array([2         


        

Answer 1

Use Base Functionalities from Python and Numpy

You can run the following one-liner to get the result ...

import numpy as np

# Your inputs ...
l = [np.array([1, 1, 1]), np.array([2, 2, 2]), np.array([3, 3, 3])]
array_to_remove = np.array([2, 2, 2])

# My result ...
result = [a for a, skip in zip(l, [np.allclose(a, array_to_remove) for a in l]) if not skip]

print(result)

... or copy paste the following in a script and experiment a bit.

You need

• numpy.allclose to compare numpy arrays up to floating point representation error
• zip
• list comprehension
• the concept of a mask

Note, ...

• this solution returns a list without all occurencies of the array we searched for
• the returned list has references to the np.ndarray instances also referred from the initial list. There are no copies!

---

import numpy as np


def remove(array, arrays):
    """
    Remove the `array` from the `list` of `arrays`
    Returns list with remaining arrays by keeping the order.

    :param array: `np.ndarray`
    :param arrays: `list:np.ndarray`
    :return: `list:np.ndarray`
    """

    assert isinstance(arrays, list), f'Expected a list, got {type(arrays)} instead'
    assert isinstance(array, np.ndarray), f'Expected a numpy.ndarray, got {type(array)} instead'
    for a in arrays:
        assert isinstance(a, np.ndarray), f'Expected a numpy.ndarray instances in arrays, found {type(a)} instead'

    # We use np.allclose for comparing arrays, this will work even if there are
    # floating point representation differences.
    # The idea is to create a boolean mask of the same lenght as the input arrays.
    # Then we loop over the arrays-elements and the mask-elements and skip the
    # flagged elements
    mask = [np.allclose(a, array) for a in arrays]
    return [a for a, skip in zip(arrays, mask) if not skip]


if __name__ == '__main__':

    # Let's start with the following arrays as given in the question
    arrays = [np.array([1, 1, 1]), np.array([2, 2, 2]), np.array([3, 3, 3])]
    print(arrays)

    # And remove this array instance from it.
    # Note, this is a new instance, so the object id is
    # different. Structure and values coincide.
    _arrays = remove(np.array([2, 2, 2]), arrays)

    # Let's check the result
    print(_arrays)

    # Let's check, whether our edge case handling works.
    print(arrays)
    _arrays = remove(np.array([1, 2, 3]), arrays)
    print(_arrays)