如何在python中使用条件填充空行中的值
我想在条件wrt现有表的情况下将值放入空/NaN中 请查看附件 现有数据如何在python中使用条件填充空行中的值,python,pandas,Python,Pandas,我想在条件wrt现有表的情况下将值放入空/NaN中 请查看附件 现有数据 import pandas as pd col_names = ['Date', 'ID', 'Individual','Category','Age','DW','Gender'] my_df = pd.DataFrame(columns = col_names) my_df['Date']=2112019,2112019,2112019,2112019,2112019,2112019,2112019,211
import pandas as pd
col_names = ['Date', 'ID', 'Individual','Category','Age','DW','Gender']
my_df = pd.DataFrame(columns = col_names)
my_df['Date']=2112019,2112019,2112019,2112019,2112019,2112019,2112019,2112019,2112019,2112019,3112019,3112019,3112019,3112019,
3112019,3112019,3112019,3112019,3112019,3112019,'...',8112019,8112019,8112019,8112019,8112019,8112019,8112019,
8112019,8112019,8112019]
my_df['ID']=[1,1,1,2,2,2,2,3,3,3,1,1,1,2,2,2,2,3,3,3,'...',1,1,1,2,2,2,2,3,3,3]
my_df['Individual']=[1,2,3,1,2,3,4,1,2,3,1,2,3,1,2,3,4,1,2,3,'...',1,2,3,1,2,3,4,1,2,3]
my_df['Category']=['DE','DE','DE','C','C','C','C','A','A','A','DE','DE','DE','C','C','C','C','A','A','A','...','DE',
'DE','DE','C','C','C','C','A','A','A']
my_df['Age']=['51-60','02-14','31-40','02-14','31-40','15-21','22-30','60+','22-30','02-14','51-60','02-14','31-40',
'02-14','31-40','15-21','22-30','60+','22-30','02-14','...','51-60','02-14','31-40','02-14','31-40',
'15-21','22-30','60+','22-30','02-14']
my_df['DW']=[6554,7875,10063,5661,7851,10063,6552,2365,8569,7875,6554,7875,10063,5661,7875,'...',
6554,7875,10063,5661,7851,10063,6552,2365,8569,7875,6554,7875,10063,5661,7875]
my_df['Gender']=['M','F','F','M','M','F','M','F','F','M','M','F','F','M','M','F','M','F','F','M',
'...','M','F','F','M','M','F','M','F','F','M']
Date ID Individual Category Age DW Gender
0 2112019 1 1 DE 51-60 6554 M
1 2112019 1 2 DE 02-14 7875 F
2 2112019 1 3 DE 31-40 10063 F
3 2112019 2 1 C 02-14 5661 M
4 2112019 2 2 C 31-40 7851 M
5 2112019 2 3 C 15-21 10063 F
6 2112019 2 4 C 22-30 6552 M
7 2112019 3 1 A 60+ 2365 F
8 2112019 3 2 A 22-30 8569 F
9 2112019 3 3 A 02-14 7875 M
10 3112019 1 1 DE 51-60 6554 M
11 3112019 1 2 DE 02-14 7875 F
12 3112019 1 3 DE 31-40 10063 F
13 3112019 2 1 C 02-14 5661 M
14 3112019 2 2 C 31-40 7875 M
15 3112019 2 3 C 15-21 10063 F
16 3112019 2 4 C 22-30 5661 M
17 3112019 3 1 A 60+ 2365 F
18 3112019 3 2 A 22-30 8569 F
19 3112019 3 3 A 02-14 7875 M
20 ... ... ... ... ... ... ...
21 8112019 1 1 DE 51-60 6554 M
22 8112019 1 2 DE 02-14 7875 F
23 8112019 1 3 DE 31-40 10063 F
24 8112019 2 1 C 02-14 5661 M
25 8112019 2 2 C 31-40 7851 M
26 8112019 2 3 C 15-21 10063 F
27 8112019 2 4 C 22-30 6552 M
28 8112019 3 1 A 60+ 2365 F
29 8112019 3 2 A 22-30 8569 F
30 8112019 3 3 A 02-14 7875 M
col = ['Target', 'Day1', 'Day2','Day3','Day4','Day5','Day6','Day7']
new_df = pd.DataFrame(columns = col)
new_df['Target']=['A-Category & Age 22+','F-Female & ABC-Category & Age <21','M & Age 22-30','...']
new_df
Target Day1 Day2 Day3 Day4 Day5 Day6 Day7
0 A-Category & Age 22+ NaN NaN NaN NaN NaN NaN NaN
1 F-Female & ABC-Category & Age <21 NaN NaN NaN NaN NaN NaN NaN
2 M & Age 22-30 NaN NaN NaN NaN NaN NaN NaN
3 ... NaN NaN NaN NaN NaN NaN NaN
col=['Target'，'Day1'，'Day2'，'Day3'，'Day4'，'Day5'，'Day6'，'Day7']
新的数据帧（列=列）
新的_df['Target']=['A-Category&Age 22+'，'F-Female&ABC Category&Age您没有WT列，因此我们现在不知道它是什么。但是对于这个示例，我将使用DW列作为聚合列。您可以根据需要更改它
import pandas as pd
col_names = ['Date', 'ID', 'Individual','Category','Age','DW','Gender']
my_df = pd.DataFrame(columns = col_names)
my_df['Date']=[2112019,2112019,2112019,2112019,2112019,2112019,2112019,2112019,2112019,2112019,3112019,3112019,3112019,3112019,
3112019,3112019,3112019,3112019,3112019,3112019,8112019,8112019,8112019,8112019,8112019,8112019,8112019,
8112019,8112019,8112019]
my_df['ID']=[1,1,1,2,2,2,2,3,3,3,1,1,1,2,2,2,2,3,3,3,1,1,1,2,2,2,2,3,3,3]
my_df['Individual']=[1,2,3,1,2,3,4,1,2,3,1,2,3,1,2,3,4,1,2,3,1,2,3,1,2,3,4,1,2,3]
my_df['Category']=['DE','DE','DE','C','C','C','C','A','A','A','DE','DE','DE','C','C','C','C','A','A','A','DE',
'DE','DE','C','C','C','C','A','A','A']
my_df['Age']=['51-60','02-14','31-40','02-14','31-40','15-21','22-30','60+','22-30','02-14','51-60','02-14','31-40',
'02-14','31-40','15-21','22-30','60+','22-30','02-14','51-60','02-14','31-40','02-14','31-40',
'15-21','22-30','60+','22-30','02-14']
my_df['DW']=[6554,7875,10063,5661,7851,10063,6552,2365,8569,7875,6554,7875,10063,5661,7875,
6554,7875,10063,5661,7851,10063,6552,2365,8569,7875,6554,7875,10063,5661,7875]
my_df['Gender']=['M','F','F','M','M','F','M','F','F','M','M','F','F','M','M','F','M','F','F','M',
'M','F','F','M','M','F','M','F','F','M']
col = ['Target', 'Day1', 'Day2','Day3','Day4','Day5','Day6','Day7']
new_df = pd.DataFrame(columns = col)
new_df['Target']=['A-Category & Age 22+','F-Female & ABC-Category & Age <21','M & Age 22-30','...']
迭代条件列表，对数据帧进行切片、分组、对聚合列求和、数据透视并附加到最终数据帧
output = pd.DataFrame(columns=['Target'])
for condition in condition_list:
t = my_df[
(my_df['ID'].isin(condition[0])) &
(my_df['Individual'].isin(condition[1])) &
(my_df['Category'].isin(condition[2]) &
(my_df['Age'].apply(lambda x: int(min(x.replace('+','').split('-')))) >= condition[3][0]) &
(my_df['Age'].apply(lambda x: int(max(x.replace('+','').split('-')))) <= condition[4][0]) &
(my_df['Gender']).isin(condition[5]))
]
t['Target'] = condition[6][0]
output = output.append(t.groupby(['Target','Date'])['DW'].sum().reset_index().pivot(index='Target',columns='Date',values='DW'))
输出
2112019 3112019 8112019 Target
0 10934.0 15724.0 15724.0 A-Category & Age 22+
1 6552.0 7875.0 7875.0 M & Age 22-30
什么是“WT的合计和”？它是对条件的individual
列求和？还是对individual
列的唯一值进行计数？还是其他什么？日期中每天的DW总和我想保持列名day1、day2、day3不变，并分配DWI总和尝试在python 3.5版本中运行上述代码：getting t下面的错误类型错误-->7（my_df['Age'].apply（lambda x:int（max（x.replace（+''''，''）.split（'-'）））python 3.5中的条件列表索引正在被洗牌，这就是为什么会出现上述错误。是否有任何解决方案可以保持条件列表索引不变？您的解决方案不响应输出['Target']指数1，即ABC类合计-->F-女性和ABC类及年龄
output['Target'] = output.index
output = output.reset_index(drop=True)
2112019 3112019 8112019 Target
0 10934.0 15724.0 15724.0 A-Category & Age 22+
1 6552.0 7875.0 7875.0 M & Age 22-30