将所有这些二进制列组合成分类列的更简单的方法？

Question

这些是我想更改为一列的类别。每个列表中的值都是数据帧中当前的二进制列。你知道吗

housesitu = ['tipovivi1', 'tipovivi2', 'tipovivi3', 'tipovivi4', 'tipovivi5']
educlevels = ['instlevel1', 'instlevel2', 'instlevel3', 'instlevel4', 'instlevel5', 'instlevel6', 'instlevel7',
             'instlevel8', 'instlevel9']
regions = ['lugar1', 'lugar2', 'lugar3', 'lugar4', 'lugar5', 'lugar6']
relations = ['parentesco1', 'parentesco2', 'parentesco3', 'parentesco4', 'parentesco5', 'parentesco6',
            'parentesco7', 'parentesco8', 'parentesco9', 'parentesco10', 'parentesco11', 'parentesco12']

我目前有以下代码将二进制列组合成分类列：

    train['housesitu'] = train[housesitu].idxmax(axis=1)
    train.drop(train[housesitu], axis=1, inplace=True)
    train['educlevels'] = train[educlevels].idxmax(axis=1)
    train.drop(train[educlevels], axis=1, inplace=True)
    train['regions'] = train[regions].idxmax(axis=1)
    train.drop(train[regions], axis=1, inplace=True)
    train['relations'] = train[relations].idxmax(axis=1)
    train.drop(train[relations], axis=1, inplace=True)
    train['marital'] = train[marital].idxmax(axis=1)
    train.drop(train[marital], axis=1, inplace=True)
    train['rubbish'] = train[rubbish].idxmax(axis=1)
    train.drop(train[rubbish], axis=1, inplace=True)
    train['energy'] = train[energy].idxmax(axis=1)
    train.drop(train[energy], axis=1, inplace=True)
    train['toilets'] = train[toilets].idxmax(axis=1)
    train.drop(train[toilets], axis=1, inplace=True)
    train['floormat'] = train[floormat].idxmax(axis=1)
    train.drop(train[floormat], axis=1, inplace=True)
    train['roofmat'] = train[roofmat].idxmax(axis=1)
    train.drop(train[roofmat], axis=1, inplace=True)
    train['wallmat'] = train[wallmat].idxmax(axis=1)
    train.drop(train[wallmat], axis=1, inplace=True)
    train['floorqual'] = train[floorqual].idxmax(axis=1)
    train.drop(train[floorqual], axis=1, inplace=True)
    train['wallqual'] = train[wallqual].idxmax(axis=1)
    train.drop(train[wallqual], axis=1, inplace=True)
    train['roofqual'] = train[roofqual].idxmax(axis=1)
    train.drop(train[roofqual], axis=1, inplace=True)
    train['waterprov'] = train[waterprov].idxmax(axis=1)
    train.drop(train[waterprov], axis=1, inplace=True)
    train['electric'] = train[electric].idxmax(axis=1)
    train.drop(train[electric], axis=1, inplace=True)

我想知道是否有一个较短的方法来做这件事。你知道吗

Answer 1

我只能考虑一个groupby和idxmax，因为列名为XXXddd

df.groupby(df.columns.to_series().str.replace('\d+',''),axis=1).idxmax(1)
Out[1100]: 
    A   B
0  A2  B2
1  A1  B1
2  A1  B1

数据输入

df=pd.DataFrame({'A1':[1,2,3],'A2':[2,1,3],'B1':[1,2,3],'B2':[2,1,3]})