定义用于更改列值和创建新数据集的函数 [英] Defining a function for changing column values and creating new datasets

问题描述

我正在尝试定义一个函数，该函数将采用一个数据框并更改列中的值以创建多个新数据框.

I am trying to define a function where it will take a dataframe and change values in a column to create multiple new dataframes.

以df1为例，如下所示:

As an example, from df1 looking like:

df1:

  class    colB    colC
0   1      1b      1c
1   2      2b      2c
2   3      3b      3c
3   4      4b      4c
4   5      5b      5c

我正在尝试创建多个二进制类来实现一对一分类.因此该函数将创建...

I am trying to create multiple binary classes to implement one-vs-all classification. So the function would create...

df2:
  class    colB    colC
0   1      1b      1c
1   -1      2b      2c
2   -1      3b      3c
3   -1      4b      4c
4   -1      5b      5c

df3:
  class    colB    colC
0   -1      1b      1c
1    1      2b      2c
2   -1      3b      3c
3   -1      4b      4c
4   -1      5b      5c

df4:
  class    colB    colC
0   -1      1b      1c
1   -1      2b      2c
2    1      3b      3c
3   -1      4b      4c
4   -1      5b      5c

，依此类推.所有唯一值都是1到120之间的增量值.

and so on. All the unique values are an incremental value ranging from 1 to 120.

有什么想法吗?

谢谢

推荐答案

使用np.identity(我将列名更改为class_，因此它没有使用受保护的关键字):

Using np.identity (I changed your column name to class_ so it's not using a protected keyword):

arr = np.identity(len(df1))
arr[arr==0] = -1

dfs = [df1.assign(class_=arr[:, i]) for i in range(len(df1))]

for d in dfs:
    print(d, end='\n\n')

   class_ colB colC
0     1.0   1b   1c
1    -1.0   2b   2c
2    -1.0   3b   3c
3    -1.0   4b   4c
4    -1.0   5b   5c

   class_ colB colC
0    -1.0   1b   1c
1     1.0   2b   2c
2    -1.0   3b   3c
3    -1.0   4b   4c
4    -1.0   5b   5c

   class_ colB colC
0    -1.0   1b   1c
1    -1.0   2b   2c
2     1.0   3b   3c
3    -1.0   4b   4c
4    -1.0   5b   5c

   class_ colB colC
0    -1.0   1b   1c
1    -1.0   2b   2c
2    -1.0   3b   3c
3     1.0   4b   4c
4    -1.0   5b   5c

   class_ colB colC
0    -1.0   1b   1c
1    -1.0   2b   2c
2    -1.0   3b   3c
3    -1.0   4b   4c
4     1.0   5b   5c

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