Justifying non-zero elements to a particular side (often useful in searches) is a common operation, which can be done directly for a NumPy array. Here's how -
import numpy as np def justify(a, invalid_val=0, axis=1, side='left'): """ Justifies a 2D array Parameters ---------- A : ndarray Input array to be justified axis : int Axis along which justification is to be made side : str Direction of justification. It could be 'left', 'right', 'up', 'down' It should be 'left' or 'right' for axis=1 and 'up' or 'down' for axis=0. """ if invalid_val is np.nan: mask = ~np.isnan(a) else: mask = a!=invalid_val justified_mask = np.sort(mask,axis=axis) if (side=='up') | (side=='left'): justified_mask = np.flip(justified_mask,axis=axis) out = np.full(a.shape, invalid_val) if axis==1: out[justified_mask] = a[mask] else: out.T[justified_mask.T] = a.T[mask.T] return out
The snippet above can justify a 2D array along a chosen axis in any of the four possible directions -
# sample input array a = np.array([[1, 0, 2, 0], [3, 0, 4, 0], [5, 0, 6, 0], [0, 7, 0, 8]]) # shift to left print(justify(a, axis=0, side='up')) # shift to down print(justify(a, axis=0, side='down')) # shift to left print(justify(a, axis=1, side='left')) # shift to right print(justify(a, axis=1, side='right'))
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