对于有间隙的网格数据，有没有np.reshape的替代方案？

import numpy as np

# 假设有一个有间隙的网格数据
grid_data_with_gaps = np.array([[1, 2, 3],
                               [4, np.nan, np.nan],
                               [7, 8, 9]])

# 统计网格数据的行数和列数
num_rows, num_cols = grid_data_with_gaps.shape

# 将网格数据转置
transposed_data = np.transpose(grid_data_with_gaps)

# 初始化一个新的数组来存储转置后的数据，并填充间隙
reshaped_data = np.zeros((num_rows, num_cols))
reshaped_data.fill(np.nan)

# 填充转置后的数据到新数组中
for i in range(num_cols):
    reshaped_data[:, i] = np.vstack(transposed_data[i])

# 打印转换后的结果
print(reshaped_data)