Pandas:用于在 DataFrame 中设置值的三元条件运算符

Pandas: Ternary conditional operator for setting a value in a DataFrame(Pandas:用于在 DataFrame 中设置值的三元条件运算符)

问题描述

我有一个数据框 pd.我想更改列 irr 的值，具体取决于它是高于还是低于阈值.

I have a dataframe pd. I would like to change a value of column irr depending on whether it is above or below a thresh hold.

我怎样才能在一行中做到这一点?现在我有

How can I do this in a single line? Now I have

pd['irr'] = pd['irr'][pd['cs']*0.63 > pd['irr']] = 1.0
pd['irr'] = pd['irr'][pd['cs']*0.63 <=  pd['irr']] = 0.0

问题当然是我改了irr，在下一行再次检查.

The problem of course is that I change irr and check it again in the next line.

是否有类似 pandas 的三元条件运算符?

Is there something like a ternary conditional operator for pandas?

推荐答案

在 pandas 中没有，在 numpy 中是.

In pandas no, in numpy yes.

您可以使用 numpy.where 或将条件创建的 boolean Series 转换为 float - True 是 1.0 和 >Falses 是 0.0:

You can use numpy.where or convert boolean Series created by condition to float - Trues are 1.0 and Falses are 0.0:

pd['irr'] = np.where(pd['cs']*0.63 > pd['irr'], 1.0, 0.0)

或者:

pd['irr'] = (pd['cs']*0.63 > pd['irr']).astype(float)

示例:

pd = pd.DataFrame({'cs':[1,2,5],
                   'irr':[0,100,0.04]})

print (pd)
   cs     irr
0   1    0.00
1   2  100.00
2   5    0.04

pd['irr'] = (pd['cs']*0.63 > pd['irr']).astype(float)
print (pd)
   cs  irr
0   1  1.0
1   2  0.0
2   5  1.0

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