Python:用 pandas 逐列缩放数字 [英] Python: Scaling numbers column by column with pandas

问题描述

我有一个熊猫数据框'df'，我想在其中逐列执行一些缩放.

I have a Pandas data frame 'df' in which I'd like to perform some scalings column by column.

• 在"a"列中，我需要将最大值设为1，将最小值设为0，并相应地分配所有其他值.
• 但是，在"b"列中，我需要最小数为1 ，最大数为0 ，并相应地分配所有其他值.

• In column 'a', I need the maximum number to be 1, the minimum number to be 0, and all other to be spread accordingly.
• In column 'b', however, I need the minimum number to be 1, the maximum number to be 0, and all other to be spread accordingly.

是否有熊猫函数来执行这两项操作?如果没有的话，numpy当然可以.

Is there a Pandas function to perform these two operations? If not, numpy would certainly do.

    a    b
A   14   103
B   90   107
C   90   110
D   96   114
E   91   114

推荐答案

您可以减去最小值，然后除以最大值(请注意0/0).请注意，减去最小值后，新的最大值就是原始最大值-最小值.

You could subtract by the min, then divide by the max (beware 0/0). Note that after subtracting the min, the new max is the original max - min.

In [11]: df
Out[11]:
    a    b
A  14  103
B  90  107
C  90  110
D  96  114
E  91  114

In [12]: df -= df.min()  # equivalent to df = df - df.min()

In [13]: df /= df.max()  # equivalent to df = df / df.max()

In [14]: df
Out[14]:
          a         b
A  0.000000  0.000000
B  0.926829  0.363636
C  0.926829  0.636364
D  1.000000  1.000000
E  0.939024  1.000000

要切换列的顺序(从1到0，而不是从0到1):

To switch the order of a column (from 1 to 0 rather than 0 to 1):

In [15]: df['b'] = 1 - df['b']

另一种方法是对 first (df['b'] = -df['b'])的b列取反.

An alternative method is to negate the b columns first (df['b'] = -df['b']).

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