使用嵌套元组对列应用多个过滤器

Question

正在尝试向元组过滤器添加附加条件....

没有附加条件的当前工作元组过滤器（稍后讨论）：

import pandas as pd

data = [['A',23], ['D',50], ['C',32], ['D',21], ['D',24], ['B',20], ['C',68], ['A',52], ['A',41],[ 'D',44], ['B',29], ['B',70], ['B',33], ['C',56], ['A',72]]

df = pd.DataFrame(data, columns = ['group', 'age'])

group_mask = {(20, 30): 'A', (25, 30): 'B', (65, 70): 'C', (40, 50): 'D'}

df['range'] = df['group'].map({v:k for k, v in group_mask.items()})
df['in_range'] = (df['range'].str[0] <= df['age'])  &  (df['age'] <= df['range'].str[1])

#filtered
df = df[df['in_range']]
df.drop(columns=['range', 'in_range'], inplace=True)

上面的代码将数据框过滤到年龄等于的行，或者在每个相应组的 group_mask 中设置的范围之间。从而产生以下输出...

      group   age
0     A       23
1     D       50
6     C       68
9     D       44
10    B       29

但是，我需要考虑一个额外的条件（列）；列 gender。根据 gender，group 的 age 过滤器范围不同

数据现已修改为包含此附加列：

data = [['A', 'male', 23], ['D','female',50], ['C','male',32], ['D','male',21], ['D','female',24], ['B','female',20], ['C','male',68], ['A','male',52], ['A','male',41],[ 'D','male',44], ['B','female',29], ['B','female',70], ['B','female',33], ['C','female',56], ['A','female',72]]

df = pd.DataFrame(data, columns = ['group', 'gender', 'age'])

但是，将现有的 group_mask 元组过滤器调整为现在包含 'gender' 依赖范围是我遇到的问题，如下所示。

我试过从....

group_mask = {(20, 30): 'A', (25, 30): 'B', (65, 70): 'C', (40, 50): 'D'}

到....

group_mask  = {(((20, 30), 'A') , 'male' ), (((25, 30), 'B') , 'male' ), (((65, 70), 'C') , 'male' ), (((40, 50), 'D'), 'male' ), \
(((60, 80), 'A') , 'female'), (((15, 30), 'B'),  'female'), (((50, 60), 'C'), 'female'), (((30, 40), 'D'), 'female' )}

..然后重新应用 map 和过滤器....

df['range'] = df[['group', 'gender']].map({v:k for k, v in group_mask .items()})
df['in_range'] = (df['range'].str[0] <= df['age'])  &  (df['age'] <= df['range'].str[1])

但是，它会引发一条错误消息 AttributeError: 'DataFrame' object has no attribute 'map'

首先，我不确定修改后的 group_mask 格式是否正确，其次我不确定如何更正 map 功能。

需要帮助。提前谢谢你。

Answer 1

你可以让 group_mask 变成这样：

group_mask = {(30, 40): ('D', 'female'), (25, 30): ('B', 'male'), (40, 50): ('D', 'male'), (65, 70): ('C', 'male'), (60, 80): ('A', 'female'), (20, 30): ('A', 'male'), (15, 30): ('B', 'female'), (50, 60): ('C', 'female')}

要应用地图，您可以这样：

df['range']=df.apply(lambda x: [(x[0],x[1])], axis=1, result_type='expand')[0].map({v:k for k, v in group_mask .items()})
df['in_range'] = (df['range'].str[0] <= df['age'])  &  (df['age'] <= df['range'].str[1])

使用嵌套元组对列应用多个过滤器

Apply multiple filter on column using nested tuple

python

filtering

tuples

dataframe

pandas