Page 77 - Informatics_Practices_Fliipbook_Class12
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output:
Category
Food 450
Household 80
Hygiene 370
Name: Total Price, dtype: int64
Execution of
groceryGroupedDF['Total Price'].sum()
in the Pandas tutor yields the following visualization:
groceryGroupedDF['Total Price'].sum()
Series Series
0 40 Category
1 300 Food 450
2 40 Household 80
3 70 Hygiene 370
4 80
5 160
6 60
7 150
Sometimes, it is convinient to examine the results of applying more than one operations. This is achieved by applying
the function agg and passing the list of operations to be carried out on the groups of data as an argument to the
function agg. Below, we apply the sum and count operations by applying the function agg(['sum', 'count'])
to groceryGroupedDF['Total Price'].
>>> groceryGroupedDF['Total Price'].agg(['sum', 'count'])
output:
sum count
Category
Food 450 4
Household 80 1
Hygiene 370 3
Execution of
groceryGroupedDF['Total Price'].agg(['sum', 'count'])
in the Pandas tutor yields the following visualization:
groceryGroupedDF['Total Price'].agg(['sum', 'count'])
Series Sum count
0 40 Category
1 300 Food 450 4
2 40 Household 80 1
3 70 Hygiene 370 3
4 80
5 160
6 60
7 150
Data Handling using Pandas DataFrame 63

