这可能是一种方法-
def numpy_fillna(data): # Get lengths of each row of data lens = np.array([len(i) for i in data]) # Mask of valid places in each row mask = np.arange(lens.max()) < lens[:,None] # Setup output array and put elements from data into masked positions out = np.zeros(mask.shape, dtype=data.dtype) out[mask] = np.concatenate(data) return out
样本输入，输出-
In [222]: # Input object dtype array ...: data = np.array([[1, 2, 3, 4], ...: [2, 3, 1], ...: [5, 5, 5, 5, 8 ,9 ,5], ...: [1, 1]])In [223]: numpy_fillna(data)Out[223]: array([[1, 2, 3, 4, 0, 0, 0], [2, 3, 1, 0, 0, 0, 0], [5, 5, 5, 5, 8, 9, 5], [1, 1, 0, 0, 0, 0, 0]], dtype=object)
