I was playing with pandas groupby function, and there is something I can't manage to achieve.
My data is like :
data = ({
'Color1':["Blue", "Red", "Green", "Blue", "Red", "Green", "Blue", "Red", "Green"],
'Color2':["Purple", "Pink", "Yellow", "Purple", "Pink", "Yellow", "Brown", "White", "Grey"],
'Value':[20, 20, 20, 25, 25, 25, 5, 55, 30]
})
df = pd.DataFrame(data)
I used the groupby to do some sorting (the idea behind is to extract some top N from larger datasets)
df2 = df.groupby(['Color1'], sort=True).sum()[['Value']].reset_index()
df2 = df2.sort_values(by=['Value'], ascending=False)
print(df2)
Color1 Value 2 Red 100 1 Green 75 0 Blue 50
But my biggest concern is how to groupby and sort adding Color2 while preserving the sort on Color 1 i.e. a result such as :
Color1 Color2 Value
0 Red White 55
1 Red Pink 45
2 Green Yellow 45
3 Green Grey 30
4 Blue Purple 45
5 Blue Brown 5
Thanks a lot for your help
CodePudding user response:
Try:
>>> df.groupby(['Color1', 'Color2']).sum() \
.sort_values(['Color1', 'Value'], ascending=False).reset_index()
Color1 Color2 Value
0 Red White 55
1 Red Pink 45
2 Green Yellow 45
3 Green Grey 30
4 Blue Purple 45
5 Blue Brown 5
CodePudding user response:
Problem is values are strings, so sum
join values instead summing.
Need convert column to numeric:
df = pd.DataFrame(data)
df['Value'] = df['Value'].astype(int)
df2 = df.groupby(['Color1','Color2'], sort=False)['Value'].sum().reset_index()
df2 = df2.sort_values(by=['Value'], ascending=False)
If need sorting by Color1, Color2
with original order in Color1
use ordered Categoricals:
vals = df2['Color1'].unique()
df2['Color1'] = pd.Categorical(df2['Color1'], ordered=True, categories=vals)
df2 = df2.sort_values(['Color1','Color2'])
print(df2)
Color1 Color2 Value
1 Red Pink 45
4 Red White 55
3 Blue Brown 5
0 Blue Purple 45
5 Green Grey 30
2 Green Yellow 45