How to get sum of specific column values of a list based on a condition in one column

Question

I have lists like below of length 8 which contains datewise data

['2019-07-04', 'default_default', 'FB', 'ios', '0', '0', '0', '0']
['2019-07-04', 'default_default', 'FB', 'default', '1', '1', '0', '1']
['2019-07-04', 'default_default', 'FB', 'android', '0', '0', '0', '0']
['2019-07-04', 'default_default', 'NON-FB', 'android', '2', '1', '1', '1']
['2019-07-04', 'default_default', 'NON-FB', 'default', '1', '2', '1', '1']
['2019-07-04', 'default_default', 'NON-FB', 'ios', '1', '1', '1', '2']

I want to get the count of the 5th, the 6th column for FB and NON-FB so on for the date 2019-07-04

so the final list will be

date|FB|NON-FB|total

[2019-07-04,1,4,5]
[2019-07-05,1,2,4]
... 
.. so on

Below is my code

 dic={}
 data=[str(item) for item in datewise]
 data[0]="date"
 dic[data[0]]={"FB":0,"NON-FB":0}
 dic[data[0]]["FB"]=sum(row[5] for row in data if row=="FB")

Right now I'm doing with loops and dictionary, is there any simple way to get the desired output?

Answer 1

You could use loops, but data that looks like this and needs to be manipulated like you're describing is a good use case for pandas:

import pandas as pd

df = (pd.DataFrame(datewise, 
                   columns=['col0','col1','col2','col3','col4','col5','col6','col7'])
 .assign(col5= lambda d: d.col5.astype(int))
 .pivot_table(index='col0', columns='col2', values='col5', aggfunc=sum)
 .assign(total= lambda d: d.sum(axis=1))
)

print(df)