Python 有条件创建dataframe列：基于多个条件

我有一个df：

  col1 col2 col3
0    1    2    3
1    2    3    1
2    3    3    3
3    4    3    2

我想根据以下条件添加一个新列：

 - if   col1 > col2 > col3   ----->  2
 - elif col1 > col2          ----->  1
 - elif col1 < col2 < col3   -----> -2
 - elif col1 < col2          -----> -1
 - else                      ----->  0

我遵循的方法是，中的1大于或小于就可以了。但在我的情况下，大于1大于或小于，条件返回错误：

conditions = [
       (df['col1'] > df['col2'] > df['col3']), 
       (df['col1'] > df['col2']),
       (df['col1'] < df['col2'] < df['col3']),
       (df['col1'] < df['col2'])]
choices = [2,1,-2,-1]
df['new'] = np.select(conditions, choices, default=0)


Traceback (most recent call last):

  File "<ipython-input-43-768a4c0ecf9f>", line 2, in <module>
    (df['col1'] > df['col2'] > df['col3']),

  File "C:\ProgramData\Anaconda3\lib\site-packages\pandas\core\generic.py", line 1478, in __nonzero__
    .format(self.__class__.__name__))

ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().

条件=[
（df['col1']>df['col2']>df['col3']），
（df['col1']>df['col2']），
（df['col1']df['col2']>df['col3']），
文件“C:\ProgramData\Anaconda3\lib\site packages\pandas\core\generic.py”，第1478行，非零__
.format（self.\uuuuuu class.\uuuuuuu.\uuuuuu name.\uuuuuuuuu））
ValueError：序列的真值不明确。使用a.empty、a.bool（）、a.item（）、a.any（）或a.all（）。

我应该怎么做？

将代码更改为

conditions = [
       (df['col1'] > df['col2']) &  (df['col2'] > df['col3']), 
       (df['col1'] > df['col2']),
       (df['col1'] < df['col2']) & (df['col2'] < df['col3']),
       (df['col1'] < df['col2'])]
choices = [2,1,-2,-1]
df['new'] = np.select(conditions, choices, default=0)

条件=[
（df['col1']>df['col2']）和（df['col2']>df['col3']），
（df['col1']>df['col2']），
（df['col1']

尝试使用“.eval”方法创建单个布尔序列，例如，

df.eval（'col1>col2>col3'）

。如果一个元素接一个元素，col1大于col2，而col2大于col3，则这相当于指定True。否则，pandas会为

df['col1']>df['col2']

创建一个布尔序列，然后它不知道如何计算条件

boolean series>df['col3']

conditions = [
       (df['col1'] > df['col2']) &  (df['col2'] > df['col3']), 
       (df['col1'] > df['col2']),
       (df['col1'] < df['col2']) & (df['col2'] < df['col3']),
       (df['col1'] < df['col2'])]
choices = [2,1,-2,-1]
df['new'] = np.select(conditions, choices, default=0)