总和 DataFrame 行值按列分组在另一个 DataFrame

Question

I have two DataFrames as follows:

df2 = pd.DataFrame({ 
    'Code':['ABC','DEF','GHI','JKL','MNO'],       
    '2000': [19647.0, 1814135.0, 1864791.0,261630.0, 20758.0], 
    '2001': [1762621.0,1814135.0,1864791.0,1914573.0,1965598.0], 
    '2002': [25998340.0,26920466.0,207633.0,28813463.0,29784193.0] }) 
df2.set_index('Code')


df3 = pd.DataFrame({ 
    'Code':['ABC','DEF','GHI','JKL','MNO'],       
    'Groups': ['Group A', 'Group B', 'Group C','Group B', 'Group A']})
df3.set_index('Code')

I need to get total values for each year by the respective group. eg. the sum for the year 2000 for Group A is 40405.0.

Answer 1

For the grouper map the index of df2 to the 'Groups' and then take the sum. Also you set the index you never assign it back so you should do df2=df2.set_index('Code') , though it's not necessary to have them as the index to solve this.

#df2=df2.set_index('Code')
#df3=df3.set_index('Code')
df2.groupby(df2.index.map(df3['Groups'])).sum()

#              2000       2001        2002
#Code                                     
#Group A    40405.0  3728219.0  55782533.0
#Group B  2075765.0  3728708.0  55733929.0
#Group C  1864791.0  1864791.0    207633.0

Answer 2

I would try something like this:

df2 = pd.DataFrame({ 
'Code':['ABC','DEF','GHI','JKL','MNO'],       
'2000': [19647.0, 1814135.0, 1864791.0,261630.0, 20758.0], 
'2001': [1762621.0,1814135.0,1864791.0,1914573.0,1965598.0], 
'2002': [25998340.0,26920466.0,207633.0,28813463.0,29784193.0] }) 


df3 = pd.DataFrame({ 
'Code':['ABC','DEF','GHI','JKL','MNO'],       
'Groups': ['Group A', 'Group B', 'Group C','Group B', 'Group A']})

df3 = df3.merge(df2, on=['Code'])
df3.groupby(['Groups']).sum()