我试图在我的Databricks笔记本中将列表为[599086.9706961295, 4503107.843920314]的列分成两列（“x”和“y”）。
在我的Jupyter笔记本中，列是这样分开的：

# code from my jupter notebook

# column with list in it is: xy

# Method 1

complete[['x', 'y']] = pd.Series(np.stack(complete['xy'].values).T.tolist())

# column is also getting separated using this method

# Method 2

def sepXY(xy):
    return xy[0],xy[1]

complete['x'],complete['y'] = zip(*complete['xy'].apply(sepXY))

在我的Databricks记事本中，出现错误：
两种方法我都试过了

import pyspark.pandas as ps

# Method 1

complete[['x', 'y']] = ps.Series(np.stack(complete['xy'].values).T.tolist())

判断提示错误：
如果我只运行ps.Series(np.stack(complete['xy'].values).T.tolist())，我将得到包含x和y的两个列表的输出

0    [599086.9706961295, 599079.1456765212, 599059....
1    [4503107.843920314, 4503083.465809557, 4503024...

但是当我把它赋值给complete[['x','y']]时，它抛出了错误。

# Method 2

def sepXY(xy):
    return xy[0],xy[1]

complete['x'],complete['y'] = zip(*complete['xy'].apply(sepXY))

箭头无效：无法使用类型元组转换（599086.9706961295，4503107.843920314）：在推断Arrow数据类型时无法识别Python值类型
我检查了数据类型，它不是元组
我也试过

complete[['x','y']] = pd.DataFrame(complete.xy.tolist(), index= complete.index)

如果我使用这个，我的核心会重新启动

# This is the column for sample

xy
[599086.9706961295, 4503107.843920314]
[599088.5389507986, 4503112.7796745915]
[599072.8088083105, 4503064.139248001]
[599090.0996424126, 4503117.721156018]
[599074.3909188313, 4503068.925677084]