我试图在我的数据框架上循环，并寻找df.con中每个元素的额外3行，这只是在第二个元素US上循环，缺少UK。
请查收附件中的代码。
import pandas as pd
d = { 'year': [2019,2019,2019,2020,2020,2020],
'age group': ['(0-14)','(14-50)','(50+)','(0-14)','(14-50)','(50+)'],
'con': ['UK','UK','UK','US','US','US'],
'population': [10,20,300,400,1000,2000]}
df = pd.DataFrame(data=d)
df2 = df.copy()
df
year age group con population
0 2019 (0-14) UK 10
1 2019 (14-50) UK 20
2 2019 (50+) UK 300
3 2020 (0-14) US 400
4 2020 (14-50) US 1000
5 2020 (50+) US 2000
n_df_2 = df.copy()
con_list = [x for x in df.con]
year_list = [x for x in df.year]
age_list = [x for x in df['age group']]
new_list = ['young vs child','old vs young', 'unemployed vs working']
for country in df.con:
bev_child = n_df_2[(n_df_2['con'].str.contains(country)) & (n_df_2['age group'].str.contains(age_list[0]))]
bev_work = n_df_2[(n_df_2['con'].str.contains(country)) & (n_df_2['age group'].str.contains(age_list[1]))]
bev_old = n_df_2[(n_df_2['con'].str.contains(country)) & (n_df_2['age group'].str.contains(age_list[2]))]
bev_child.loc[:,'population'] = bev_work.loc[:,'population'].max() / bev_child.loc[:,'population'].max()
bev_child.loc[:,'con'] = country +'-'+new_list[0]
bev_child.loc[:,'age group'] = new_list[0]
s = n_df_2.append(bev_child, ignore_index=True)
bev_child.loc[:,'population'] = bev_child.loc[:,'population'].max() + bev_old.loc[:,'population'].max()/ bev_work.loc[:,'population'].max()
bev_child.loc[:,'con'] = country +'-'+ new_list[2]
bev_child.loc[:,'age group'] = new_list[2]
s = s.append(bev_child, ignore_index=True)
bev_child.loc[:,'population'] = bev_old.loc[:,'population'].max() / bev_work.loc[:,'population'].max()
bev_child.loc[:,'con'] = country +'-'+ new_list[1]
bev_child.loc[:,'age group'] = new_list[1]
s = s.append(bev_child, ignore_index=True)
s
输出缺少英国行…
year age group con population
0 2019 (0-14) UK 10.0
1 2019 (14-50) UK 20.0
2 2019 (50+) UK 300.0
3 2020 (0-14) US 400.0
4 2020 (14-50) US 1000.0
5 2020 (50+) US 2000.0
6 2020 young vs child US-young vs child 2.5
7 2020 unemployed vs working US-unemployed vs working 4.5
8 2020 old vs young US-old vs young 2.0
每次通过循环，s在这一行被重新初始化为一个新的数据帧:
s = n_df_2.append(bev_child, ignore_index=True)
这使得s最终成为n_df_2的原始值，加上最后一次执行循环体时附加到它的三个值。
我认为这更接近你想要的(在循环改变之前什么都没有):
for country in df.con.unique():
bev_child = n_df_2[(n_df_2['con'].str.contains(country)) & (n_df_2['age group'].str.contains(age_list[0]))]
bev_work = n_df_2[(n_df_2['con'].str.contains(country)) & (n_df_2['age group'].str.contains(age_list[1]))]
bev_old = n_df_2[(n_df_2['con'].str.contains(country)) & (n_df_2['age group'].str.contains(age_list[2]))]
bev_child.loc[:, 'population'] = bev_work.loc[:, 'population'].max() / bev_child.loc[:, 'population'].max()
bev_child.loc[:, 'con'] = country + '-' + new_list[0]
bev_child.loc[:, 'age group'] = new_list[0]
n_df_2 = n_df_2.append(bev_child, ignore_index=True)
bev_child.loc[:, 'population'] = bev_child.loc[:, 'population'].max() + bev_old.loc[:,
'population'].max() / bev_work.loc[:,
'population'].max()
bev_child.loc[:, 'con'] = country + '-' + new_list[2]
bev_child.loc[:, 'age group'] = new_list[2]
n_df_2 = n_df_2.append(bev_child, ignore_index=True)
bev_child.loc[:, 'population'] = bev_old.loc[:, 'population'].max() / bev_work.loc[:, 'population'].max()
bev_child.loc[:, 'con'] = country + '-' + new_list[1]
bev_child.loc[:, 'age group'] = new_list[1]
n_df_2 = n_df_2.append(bev_child, ignore_index=True)
print(n_df_2)
输出:
year age group con population
0 2019 (0-14) UK 10.0
1 2019 (14-50) UK 20.0
2 2019 (50+) UK 300.0
3 2020 (0-14) US 400.0
4 2020 (14-50) US 1000.0
5 2020 (50+) US 2000.0
6 2019 young vs child UK-young vs child 2.0
7 2019 unemployed vs working UK-unemployed vs working 17.0
8 2019 old vs young UK-old vs young 15.0
9 2020 young vs child US-young vs child 2.5
10 2020 unemployed vs working US-unemployed vs working 4.5
11 2020 old vs young US-old vs young 2.0
注意，这只循环df.con中的唯一值，所以循环体只运行两次。每次循环运行时，将向输出中添加三条记录。还要注意，输出被附加到n_df_2，因此不需要变量s。