Question

我需要智能地组合数据框中的三列值，如下所示。该代码需要选择为True的第一种类型的预测，即使另一个后续预测也为True，也只能选择第一种。如果所有预测都不是True，则返回的值应为NaN。

index    name       t1        t1_check  t2       t2_check  t3       t3_check
----------------------------------------------------------------------------
0        cow        animal    True      phone    False     fruit    False
1        apple      animal    False     fruit    True      food     True
2        carrot     vehicle   False     veg      True      animal   False
3        dog        pet       True      animal   True      object   False
4        horse      window    False     object   False     animal   True
5        car        pet       False     food     False     fruit    False

这是我尝试过的：

首先，我将两个相关的列合并，并删除了旧列。

In:
df['t1_comb'] = str(df['t1']) + str(df['t1_check'])
df['t2_comb'] = str(df['t2']) + str(df['t2_check'])
df['t3_comb'] = str(df['t3']) + str(df['t3_check'])

df.drop(columns=['t1', 't1_check', 't2', 't2_check', 't3', 't3_check'], inplace=True)

Out:
index    name       t1_comb         t2_comb        t3_comb
---------------------------------------------------------------
0        cow        animalTrue      phoneFalse     fruitFalse
1        apple      animalFalse     fruitTrue      foodTrue
2        carrot     vehicleFalse    vegTrue        animalFalse
3        dog        petTrue         animalTrue     objectFalse
4        horse      windowFalse     objectFalse    animalTrue
5        car        petFalse        foodFalse      fruitFalse

然后，我尝试用False替换所有包含NaN的条目，并从每个条目中删除True字符串。

In:
df.loc[df['t1_comb'].str.contains('False'), 't1_comb'] = np.nan
df.loc[df['t2_comb'].str.contains('False'), 't2_comb'] = np.nan
df.loc[df['t3_comb'].str.contains('False'), 't3_comb'] = np.nan

df.t1_comb = df.t1_comb.str.replace('True', '')
df.t2_comb = df.t2_comb.str.replace('True', '')
df.t3_comb = df.t3_comb.str.replace('True', '')

Out:
index    name       t1_comb         t2_comb        t3_comb
---------------------------------------------------------------
0        cow        animal          NaN            NaN
1        apple      NaN             fruit          food
2        carrot     NaN             veg            NaN
3        dog        pet             animal         NaN
4        horse      NaN             NaN            animal
5        car        NaN             NaN            NaN

下一步是我遇到一些困难，这是仅考虑第一个值的部分。

我需要的结果应如下所示：

index    name       type
----------------------------
0        cow        animal
1        apple      fruit
2        carrot     veg
3        dog        pet
4        horse      animal
5        car        NaN

Answer 1

我确信有更好的解决方案，但是您可以为每行使用apply

def myfunc(row):
    if row['t1_check']:
        return row['t1']
    elif row['t2_check']:
        return row['t2']
    elif row['t3_check']:
        return row['t3']
    return np.nan

df['type']=df.apply(myfunc,axis=1)
df[['name','type']]

输出

index    name       type
----------------------------
0        cow        animal
1        apple      fruit
2        carrot     veg
3        dog        pet
4        horse      animal
5        car        NaN

如何从多个列中选择一个值？

1 个答案: