Making A Group In Dataframe In Pandas

I have a list such as groups = [['Group1', 'A', 'B'], ['Group2', 'C', 'D']] and a dataframe such as A 100 B 200 C 300 D 400 I want to make a group sum from the list above to beco

Solution 1:

You need create dict by lists and then groupby and aggregating sum:

df = pd.DataFrame({'a': ['A', 'B', 'C', 'D'], 'b': [100, 200, 300, 400]})
print (df)
   a    b
0  A  100
1  B  200
2  C  300
3  D  400

groups = [['Group1', 'A', 'B'], ['Group2', 'C', 'D']]

#http://stackoverflow.com/q/43227103/2901002
d = {k:row[0] for row ingroupsfor k in row[1:]}
print (d)
{'B': 'Group1', 'C': 'Group2', 'D': 'Group2', 'A': 'Group1'}

print (df.set_index('a').groupby(d).sum())
          b
Group1  300
Group2  700

Is possible a bit modify solution - if where only column b is aggregate by sum. Last reset_index for convert index to column.

df1 = df.set_index('a').groupby(pd.Series(d, name='a'))['b'].sum().reset_index()
print (df1)
        a    b
0  Group1  300
1  Group2  700

df2 = df.groupby(df['a'].map(d))['b'].sum().reset_index()
print (df2)
        a    b
0  Group1  300
1  Group2  700

Solution 2:

Another option...but seems @jezrael's way is better!

import pandas as pd

groups = [['Group1', 'A', 'B'], ['Group2', 'C', 'D']]

df0 = pd.melt(pd.DataFrame(groups).set_index(0).T)
df1 = pd.read_clipboard(header=None)  # Your example datadf = df1.merge(df0, left_on=0, right_on='value')[['0_y', 1]]
df.columns = ['Group', 'Value']

print df.groupby('Group').sum()


        Value
Group        
Group1    300
Group2    700

Solution 3:

Use python 3 unpacking and comprehension to create dictionary. Use that dictionary in a map on first column. Use that mapping to group by.

Consider the list of lists groups and dataframe df

groups = [['Group1', 'A', 'B'], ['Group2', 'C', 'D']]
df = pd.DataFrame(dict(a=list('ABCD'), b=range(100, 401, 100)))

Then:

df.groupby(df.a.map({k: g for g, *c in groups for k in c})).sum()

          ba          
Group1  300
Group2  700