在 pandas 中创建条件列

Create conditional column in pandas

我正在尝试在 pandas 中创建一个条件列。这是数据框的样子。

    data = [{"owner" : "john", "dog" : 'magie', "dog_is_fluffy" : 1},
            {"owner" : "john", "dog" : 'stellar', "dog_is_fluffy" : 0}, 
            {"owner" : "lisa", "dog" : 'mollie' , "dog_is_fluffy" : 0},
            {"owner" : "lisa", "dog" : 'rex', "dog_is_fluffy" : 0},
            {"owner" : "john", "dog" : 'luns', "dog_is_fluffy" : 1}]

    df = pd.DataFrame(data)

如您所见,我的数据显示了狗及其主人。我们也知道狗是否蓬松。我想创建两列 fluffy_dogs_ownedowner_has_fluffy_dog.

我要找的结果是:

data_result = [{"owner" : "john", "dog" : 'magie', "dog_is_fluffy" : 1, "fluffy_dogs_owned" : 2, "owner_has_fluffy_dog" : 1},
        {"owner" : "john", "dog" : 'stellar', "dog_is_fluffy" : 0, "fluffy_dogs_owned" : 2, "owner_has_fluffy_dog" : 1}, 
        {"owner" : "lisa", "dog" : 'mollie' , "dog_is_fluffy" : 0, "fluffy_dogs_owned" : 0, "owner_has_fluffy_dog" : 0},
        {"owner" : "lisa", "dog" : 'rex', "dog_is_fluffy" : 0, "fluffy_dogs_owned" : 0, "owner_has_fluffy_dog" : 0},
        {"owner" : "john", "dog" : 'luns', "dog_is_fluffy" : 1, "fluffy_dogs_owned" : 2, "owner_has_fluffy_dog" : 1}]

df_result = pd.DataFrame(data_result)

我考虑过使用 df.groupby()np.where,但到目前为止我无法让它工作。有任何想法吗?

使用 GroupBy.transform for return Series with same size like original Dataframe with sum and then compare column for not equal by Series.ne 转换为整数

df['fluffy_dogs_owned'] = df.groupby('owner')['dog_is_fluffy'].transform('sum')
df['owner_has_fluffy_dog'] = df['fluffy_dogs_owned'].ne(0).astype(int)

Series.clip:

df['owner_has_fluffy_dog'] = df['fluffy_dogs_owned'].clip(upper=1)

print (df)
       dog  dog_is_fluffy owner  fluffy_dogs_owned  owner_has_fluffy_dog
0    magie              1  john                  2                     1
1  stellar              0  john                  2                     1
2   mollie              0  lisa                  0                     0
3      rex              0  lisa                  0                     0
4     luns              1  john                  2                     1