Get max value of column for rows where a condition is met

Question

I have a DataFrame that looks like this:

| Age | Married | OwnsHouse |
| 23  | True    | False     |
| 35  | True    | True      |
| 14  | False   | False     |
| 27  | True    | True      |

I want to find the highest age of anyone who is married and owns a house. The answer here would be 35. My first thought was to do:

df_subset = df[df['Married'] == True and df['OwnsHouse'] == True]
max_age = df_subset.max()

However, dataset is big (50MB) and I fear this will be computationally expensive as it goes through the dataset twice.

My second thought was to do:

max_age = 0
for index, row in df.iterrows():
    if(row[index]['Married] and row['index']['OwnsHouse'] and row[index]['Age] > max_age):
    max_age = row[index]['Age']

Is there a faster way of doing this?

Answer 1

您的第一种方法是可靠的，但这是一个简单的选择：

df[df['Married'] & df['OwnsHouse']].max()

Age          35.0
Married       1.0
OwnsHouse     1.0
dtype: float64

或者，只是年龄：

df.loc[df['Married'] & df['OwnsHouse'], 'Age'].max()
# 35

如果您有多个布尔列，我建议您进行一些扩展，

df[df[['Married', 'OwnsHouse']].all(axis=1)].max()

Age          35.0
Married       1.0
OwnsHouse     1.0
dtype: float64

哪里，

df[['Married', 'OwnsHouse']].all(axis=1)

0    False
1     True
2    False
3     True
dtype: bool

一样，

df['Married'] & df['OwnsHouse']

0    False
1     True
2    False
3     True
dtype: bool

但是，.all与其手动查找N个布尔掩码的与，不如为您做。

query 是另一种选择：

df.query("Married and OwnsHouse")['Age'].max()
# 35

它不需要计算遮罩的中间步骤。

您的方法足够快，但是如果要进行微优化，可以使用numpy进行以下操作：

# <= 0.23
df[(df['Married'].values & df['OwnsHouse'].values)].max()
df[df[['Married', 'OwnsHouse']].values.all(axis=1)].max()
# 0.24+
df[(df['Married'].to_numpy() & df['OwnsHouse'].to_numpy())].max()
df[df[['Married', 'OwnsHouse']].to_numpy().all(axis=1)].max()

Age          35.0
Married       1.0
OwnsHouse     1.0
dtype: float64

虽然您可能只想年龄。做这个

df.loc[(df['Married'].to_numpy() & df['OwnsHouse'].to_numpy()), 'Age'].max()
# 35

如果您想要更多的numpy，请执行以下操作：

df.loc[(
   df['Married'].to_numpy() & df['OwnsHouse'].to_numpy()), 'Age'
].to_numpy().max()
# 35

还是更好，丢掉熊猫，

df['Age'].to_numpy()[df['Married'].to_numpy() & df['OwnsHouse'].to_numpy()].max()
# 35