python - 如何根据条件在pandas数据框中进行groupby级别[0,1]的累积划分?
问题描述
我有一个数据框,我想在其中追加行添加一些 groupby + 附加条件。寻找循环或其他任何可行的解决方案。
或者如果它更容易......
first melt
df 然后添加new ratio % col
then unmelt
。
由于计算是定制的,我认为for loop
可以找到有或没有 groupby 的解决方案。
---第6,7,8行是我的要求。---
0-14
=child and unemployed
14-50
=young and working
50+
=old and unemployed
# ref line 6,7,8
=showing which rows to (+) and (/)
目前我想在输出行 6、7、8 中放置 3 个条件:
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
需要输出:
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 # 20/10
7 2019 old vs young UK-old vs young 15.0 #300/20
8 2019 unemployed vs working UK-unemployed vs working. 15.5 #300+10 20
现在试用:
df2 = df.copy()
criteria = [df2['con'].str.contains('0-14'),
df2['con'].str.contains('14-50'),
df2['con'].str.contains('50+')]
#conditions should be according to requirements
values = ['young vs child','old vs young', 'unemployed vs working']
df2['con'] = df2['con']+'_'+np.select(criteria, values, 0)
df2['age group'] = df2['age group']+'_'+np.select(criteria, values, 0)
df.groupby(['year','age group','con']).sum().groupby(level=[0,1]).cumdiv()
pd.concat([df,df2])
#----errors. cumdiv() not found and missing conditions criteria-------
也试过:
df['population'].div(df.groupby('con')['population'].shift(1))
#but looking for customisations into this
#so it can first sum rows and then divide
#according to unemployed condition-- row 8 reference.
最近的小径
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 con_list:
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
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
也请找到最简单的方法来解决它......请......
解决方案
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