根据if-elif-else条件创建新列 [英] Creating a new column based on if-elif-else condition

问题描述

我有一个DataFrame df:

I have a DataFrame df:

    A    B
a   2    2 
b   3    1
c   1    3

我要根据以下条件创建一个新列:

I want to create a new column based on the following criteria:

如果行A == B: 0

如果行A > B: 1

如果行A < B: -1

因此，鉴于上表，它应该是:

so given the above table, it should be:

    A    B    C
a   2    2    0
b   3    1    1
c   1    3   -1 

对于典型的if else情况，我会做np.where(df.A > df.B, 1, -1)，pandas是否提供一种特殊的语法来一步解决我的问题(无需创建3个新列，然后合并结果)?

For typical if else cases I do np.where(df.A > df.B, 1, -1), does pandas provide a special syntax for solving my problem with one step (without the necessity of creating 3 new columns and then combining the result)?

推荐答案

要正式化上面列出的一些方法:

To formalize some of the approaches laid out above:

创建一个对数据框的行进行操作的函数，如下所示:

Create a function that operates on the rows of your dataframe like so:

def f(row):
    if row['A'] == row['B']:
        val = 0
    elif row['A'] > row['B']:
        val = 1
    else:
        val = -1
    return val

然后通过axis=1选项将其应用于您的数据框:

Then apply it to your dataframe passing in the axis=1 option:

In [1]: df['C'] = df.apply(f, axis=1)

In [2]: df
Out[2]:
   A  B  C
a  2  2  0
b  3  1  1
c  1  3 -1

当然，这不是矢量化的，因此在扩展到大量记录时，性能可能会不佳.不过，我认为它更具可读性.尤其是来自SAS背景.

Of course, this is not vectorized so performance may not be as good when scaled to a large number of records. Still, I think it is much more readable. Especially coming from a SAS background.

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