我相信这可以通过一个for循环手动完成，该循环只迭代你的状态变量列表（即rho）。

# https://stackoverflow.com/questions/75278985/how-can-i-reshape-data-in-a-csv-into-a-structured-format
import time
start = time.time()

import numpy as np
import pandas as pd

df = pd.read_csv('test_data.csv')

min_x_coor = min(df['x'])
min_y_coor = min(df['y'])

x_dim = max(df['x']) - min_x_coor + 1
y_dim = max(df['y']) - min_y_coor + 1

rho_array = np.zeros((x_dim, y_dim))
for p in range(0, len(df['rho'])):
   x_coor = df['x'][p] - min_x_coor
   y_coor = df['y'][p] - min_y_coor

   rho_array[x_coor][y_coor] = df['rho'][p]

print(rho_array)
print(time.time() - start)

对于3x3数据：

x,y,rho
1,4,0.503
2,4,0.642
3,4,0.041
1,5,0.340
2,5,0.269
3,5,0.288
1,6,0.511
2,6,0.732
3,6,0.195

输出：

[[0.503 0.34  0.511]
 [0.642 0.269 0.732]
 [0.041 0.288 0.195]]
0.31889796257019043

对于4x4数据：

x,y,rho
1,4,0.503
2,4,0.642
3,4,0.041
4,4,0.964
1,5,0.340
2,5,0.269
3,5,0.288
4,5,0.702
1,6,0.511
2,6,0.732
3,6,0.195
4,6,0.226
1,7,0.957
2,7,0.032
3,7,0.304
4,7,0.607

输出：

[[0.503 0.34  0.511 0.957]
 [0.642 0.269 0.732 0.032]
 [0.041 0.288 0.195 0.304]
 [0.964 0.702 0.226 0.607]]
0.48914408683776855

下面是一个同时使用Xarray和Pandas的示例：

from io import StringIO

import pandas as pd
import xarray as xr

s = StringIO("""x,y,rho
1,4,0.503
2,4,0.642
3,4,0.041
4,4,0.964
1,5,0.340
2,5,0.269
3,5,0.288
4,5,0.702
1,6,0.511
2,6,0.732
3,6,0.195
4,6,0.226
1,7,0.957
2,7,0.032
3,7,0.304
4,7,0.607
""")

# open csv, create a MultiIndex from x/y columns
df = pd.read_csv(s, index_col=['x', 'y'])

# convert to Xarray Dataset
ds = df.to_xarray()

# copy over the multiindex
ds['points'] = df.index

# use ds.unstack to reshape
ds = ds.unstack()

这将生成如下所示的Xarray数据集：

<xarray.Dataset>
Dimensions:  (x: 4, y: 4)
Coordinates:
  * x        (x) int64 1 2 3 4
  * y        (y) int64 4 5 6 7
Data variables:
    rho      (x, y) float64 0.503 0.34 0.511 0.957 ... 0.964 0.702 0.226 0.607