我有一个包含多列的 pd 数据框,如下所示(为了便于阅读而进行了简化) - 每行由一个 id (uuid)、索引和一个或多个功能组成:
uuid index Atrium Ventricle
di-abc 0 20.73 26.21
di-abc 1 18.92 25.14
di-efg 7 19.02 0.30
di-efg 9 1.23 0.51
di-efg 6 21.24 26.02
di-hjk 3 22.10 25.16
di-hjk 6 19.16 25.57
我愿意:
- 查找每个特征的异常值(即“Atrium”和“Ventricle”列)
- 按以下格式导出异常值:
outliers = {
'Atrium' : [
{'uuid' : 'di-efg', 'index' : 9, 'value' : 1.23},
],
'Ventricle' : [
{'uuid' : 'di-efg', 'index' : 7, 'value' : 0.30},
{'uuid' : 'di-efg', 'index' : 9, 'value' : 0.53},
]
}
注意事项(提交此内容可获得奖励积分):
- 特征(以及列)的数量是动态的
- 单行可以包含零个、一个、两个或多个异常值
我在双 for 循环之外的两个步骤都遇到困难。 有没有一种有效的方法来计算此数据帧中的异常值?
这是一种有效但效率不高的方法来捕获我想要完成的任务:
# initialize variables:
outliers = {}
features = ['Atrium', 'Ventricle']
# iterate over each feature:
for feature in features:
# set feature on outlier to empty list:
outliers[feature] = []
# create a dataframe of outliers for that specific feature:
outlier_df = df[df[feature] > (df[feature].mean() + df[feature].std())] # can mess with this if needed
outlier_df = outlier_df[['dicom', 'frame', 'index', feature]]
# iterate through the data frame and find the uuid, index, and feature:
for index, row in outlier_df.iterrows():
# append each outlier to the outlier dictionary:
outliers[feature].append({
'uuid' : row['uuid'],
'index' : row['index'],
'value' : row[feature],
})
最佳答案
下面是解决该问题的一种方法,即定义一个函数,该函数将输入参数作为列名,并以所需的格式返回当前列中的所有异常值:
def detect_outliers(col):
# Define your outlier detection condition here
mask = (df[col] - df[col].mean()).abs() > df[col].std()
return df.loc[mask, ['uuid', 'index', col]]\
.rename(columns={col: 'value'}).to_dict('records')
outliers = {col: detect_outliers(col) for col in features}
替代方法更多地涉及 pandas 操作,例如堆叠
、分组
和聚合
:
# Select only feature columns
feature_df = df.set_index(['uuid', 'index'])[features]
# Define your outlier detection condition
mask = (feature_df - feature_df.mean()).abs() > feature_df.std()
# Prepare outlier dataframe
outlier_df = feature_df[mask].stack().reset_index(level=[0, 1], name='value')
outlier_df['records'] = outlier_df.to_dict('r')
# Get the outliers in the desired format
outliers = outlier_df.groupby(level=0).agg(list)['records'].to_dict()
>>> outliers
{
'Atrium': [
{'uuid': 'di-efg', 'index': 9, 'value': 1.23}
],
'Ventricle': [
{'uuid': 'di-efg', 'index': 7, 'value': 0.3},
{'uuid': 'di-efg', 'index': 9, 'value': 0.51}
]
}
关于Python Pandas - 查找并分组异常值,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/66518757/