[FIXED] How to parse csv file in python to get this output?

Issue

I have a csv file which contains data like that

Sample csv

Name	Start	End
John	12:00	13:00
John	12:10	13:00
John	12:20	13:20
Tom	12:00	13:10
John	13:50	14:00
Jerry	14:00	14:30
Alice	15:00	16:00
Jerry	11:00	15:00

1. I need to find the average time taken by each people in python. How do i do that?

Sample output

Avg time taken by different people are :

John (60+50+60+10)/4 min Tom (70)/1 min Jerry (30+240)/2 min Alice (60)/1 min

I tried parsing the csv file by python csv

import datetime
import csv


with open('people.csv', 'r') as file:
    reader = csv.DictReader(file)
    for row in reader:
        print(row['Start'],row['End'])

But i am unable to parse the column with the particular row name belongs to Jerry and find the difference in their time.

2. Also Need to find which Person took maximum time

Here in case Jerry took maximum time

3. Also need to perform merge operation

ex - john [12:00,13:00],[12:10,13:00],[12:20,13:20],[13:50,14:00]

output - [12:00,13:20],[13:50,14:00]

Any help will be appreciated.

Solution

Here's another method without using pandas -

from datetime import datetime, timedelta

with open("data.csv", "r") as f:
    f = csv.DictReader(f)
    data = [row for row in f]

diffs = {list(row.values())[0]: [] for row in data}
for row in data:
    vals = list(row.values())
    diffs[vals[0]].append(datetime.strptime(vals[2], "%H:%M") - datetime.strptime(vals[1], "%H:%M"))

diffs_avg = [str(timedelta(seconds = sum(map(timedelta.total_seconds, times)) / len(times))) for times in diffs.values()]
dict(zip(diffs.keys(), diffs_avg))

Output -

{'Alice': '1:00:00', 'Jerry': '2:15:00', 'John': '0:45:00', 'Tom': '1:10:00'}

Answered By - Zero

Answer Checked By - Mildred Charles (PHPFixing Admin)