Function trim_zeros

numpy/lib/_function_base_impl.py:1952–2047

Remove values along a dimension which are zero along all other. Parameters ---------- filt : array_like Input array. trim : {"fb", "f", "b"}, optional A string with 'f' representing trim from front and 'b' to trim from back. By default, zeros are trimmed on b

(filt, trim='fb', axis=None)

@array_function_dispatch(_trim_zeros)
def trim_zeros(filt, trim='fb', axis=None):
    """Remove values along a dimension which are zero along all other.

    Parameters
    ----------
    filt : array_like
        Input array.
    trim : {"fb", "f", "b"}, optional
        A string with 'f' representing trim from front and 'b' to trim from
        back. By default, zeros are trimmed on both sides.
        Front and back refer to the edges of a dimension, with "front" referring
        to the side with the lowest index 0, and "back" referring to the highest
        index (or index -1).
    axis : int or sequence, optional
        If None, `filt` is cropped such that the smallest bounding box is
        returned that still contains all values which are not zero.
        If an axis is specified, `filt` will be sliced in that dimension only
        on the sides specified by `trim`. The remaining area will be the
        smallest that still contains all values which are not zero.

        .. versionadded:: 2.2.0

    Returns
    -------
    trimmed : ndarray or sequence
        The result of trimming the input. The number of dimensions and the
        input data type are preserved.

    Notes
    -----
    For all-zero arrays, the first axis is trimmed first.

    Examples
    --------
    >>> import numpy as np
    >>> a = np.array((0, 0, 0, 1, 2, 3, 0, 2, 1, 0))
    >>> np.trim_zeros(a)
    array([1, 2, 3, 0, 2, 1])

    >>> np.trim_zeros(a, trim='b')
    array([0, 0, 0, ..., 0, 2, 1])

    Multiple dimensions are supported.

    >>> b = np.array([[0, 0, 2, 3, 0, 0],
    ...               [0, 1, 0, 3, 0, 0],
    ...               [0, 0, 0, 0, 0, 0]])
    >>> np.trim_zeros(b)
    array([[0, 2, 3],
           [1, 0, 3]])

    >>> np.trim_zeros(b, axis=-1)
    array([[0, 2, 3],
           [1, 0, 3],
           [0, 0, 0]])

    The input data type is preserved, list/tuple in means list/tuple out.