在多列上使用 Sum() 和 Groupby

Question

Even though the code works and bring me back the needed results but I believe there is a easier way.

dfg = df.groupby('County')['Total N, 1985 (Kg.)' , 'Total N, 2007 (Kg.)' ,'Total N, 2009 (Kg.)','Total 
N, 2010 (Kg.)','Total N, 2011 (Kg.)','Total N, 2012 (Kg.)','Total N, 2013 (Kg.)','Total N, 2014 
(Kg.)','Total N, 2015 (Kg.)','Total N, 2016 (Kg.)','Total N, 2017 (Kg.)','Total N target, 2025 
(Kg.)'].agg('sum') 

The columns from 'Total N, 2011 (Kg.)' to ''Total N target, 2025 (Kg.)'' can be sliced using loc (maybe) however, I am stuck here for hours.

( column numbers in the data set are from 6 to 12)

Answer 1

This can be done by limiting the data by loc or other means after grouping and aggregating. The sample data has been appropriated from the official plotly website.

import plotly.express as px
import pandas as pd
df = px.data.gapminder()
df['year'] = df['year'].apply(lambda x:str(x)+'y')
df = df.pivot(index=['continent','country'], columns='year', values='gdpPercap')
df.columns
Index(['1952y', '1957y', '1962y', '1967y', '1972y', '1977y', '1982y', '1987y',
       '1992y', '1997y', '2002y', '2007y'],
      dtype='object', name='year')

dfg = df.groupby('continent').sum().loc[:,slice('1997y','2007y')]
dfg
     year   1997y   2002y   2007y
continent           
Africa  123695.496865   135168.028262   160629.695446
Americas    222232.521564   232191.927683   275075.790634
Asia    324525.078743   335744.983087   411609.886714
Europe  572303.454048   651351.972673   751634.449078
Oceania     48048.350340    53877.556080    59620.376550

Answer 2

df.iloc[:, 6:13].groupby('County').sum()

I believe this is what you are looking for.