Python - How to count specific section in a list

Question

I'm brand new to python and I'm struggling how to add certain sections of a cvs file in python. I'm not allowed to use "import cvs" I'm importing the TipJoke CVS file from https://vincentarelbundock.github.io/Rdatasets/datasets.html

This is the only code I have so far that worked and I'm at a total loss on where to go from here.

if __name__ == '__main__':
from pprint import pprint
from string import punctuation
f = open("TipJoke.csv", "r")
tipList = []
for line in f:
    #deletes the quotes
    line = line.replace('"', '')
    tipList.append(line)
pprint(tipList[])

Output:

 [',Card,Tip,Ad,Joke,None\n',
 '1,None,1,0,0,1\n',
 '2,Joke,1,0,1,0\n',
 '3,Ad,0,1,0,0\n',
 '4,None,0,0,0,1\n',
 '5,None,1,0,0,1\n',
 '6,None,0,0,0,1\n',
 '7,Ad,0,1,0,0\n',
 '8,Ad,0,1,0,0\n',
 '9,None,0,0,0,1\n',
 '10,None,0,0,0,1\n',
 '11,None,1,0,0,1\n',
 '12,Ad,0,1,0,0\n',
 '13,None,0,0,0,1\n',
 '14,Ad,1,1,0,0\n',
 '15,Joke,1,0,1,0\n',
 '16,Joke,0,0,1,0\n',
 '17,Joke,1,0,1,0\n',
 '18,None,0,0,0,1\n',
 '19,Joke,0,0,1,0\n',
 '20,None,0,0,0,1\n',
 '21,Ad,1,1,0,0\n',
 '22,Ad,1,1,0,0\n',
 '23,Ad,0,1,0,0\n',
 '24,Joke,0,0,1,0\n',
 '25,Joke,1,0,1,0\n',
 '26,Joke,0,0,1,0\n',
 '27,None,1,0,0,1\n',
 '28,Joke,1,0,1,0\n',
 '29,Joke,1,0,1,0\n',
 '30,None,1,0,0,1\n',
 '31,Joke,0,0,1,0\n',
 '32,None,1,0,0,1\n',
 '33,Joke,1,0,1,0\n',
 '34,Ad,0,1,0,0\n',
 '35,Joke,0,0,1,0\n',
 '36,Ad,1,1,0,0\n',
 '37,Joke,0,0,1,0\n',
 '38,Ad,0,1,0,0\n',
 '39,Joke,0,0,1,0\n',
 '40,Joke,0,0,1,0\n',
 '41,Joke,1,0,1,0\n',
 '42,None,0,0,0,1\n',
 '43,None,0,0,0,1\n',
 '44,Ad,0,1,0,0\n',
 '45,None,0,0,0,1\n',
 '46,None,0,0,0,1\n',
 '47,Ad,0,1,0,0\n',
 '48,Joke,0,0,1,0\n',
 '49,Joke,1,0,1,0\n',
 '50,None,1,0,0,1\n',
 '51,None,0,0,0,1\n',
 '52,Joke,1,0,1,0\n',
 '53,Joke,1,0,1,0\n',
 '54,Joke,0,0,1,0\n',
 '55,None,1,0,0,1\n',
 '56,Ad,0,1,0,0\n',
 '57,Joke,0,0,1,0\n',
 '58,None,0,0,0,1\n',
 '59,Ad,0,1,0,0\n',
 '60,Joke,1,0,1,0\n',
 '61,Ad,0,1,0,0\n',
 '62,None,1,0,0,1\n',
 '63,Joke,0,0,1,0\n',
 '64,Ad,0,1,0,0\n',
 '65,Joke,0,0,1,0\n',
 '66,Ad,0,1,0,0\n',
 '67,Ad,0,1,0,0\n',
 '68,Ad,0,1,0,0\n',
 '69,None,0,0,0,1\n',
 '70,Joke,1,0,1,0\n',
 '71,None,1,0,0,1\n',
 '72,None,0,0,0,1\n',
 '73,None,0,0,0,1\n',
 '74,Joke,0,0,1,0\n',
 '75,Ad,1,1,0,0\n',
 '76,Ad,0,1,0,0\n',
 '77,Ad,1,1,0,0\n',
 '78,Joke,0,0,1,0\n',
 '79,Joke,0,0,1,0\n',
 '80,Ad,1,1,0,0\n',
 '81,Ad,0,1,0,0\n',
 '82,None,0,0,0,1\n',
 '83,Ad,0,1,0,0\n',
 '84,Joke,0,0,1,0\n',
 '85,Joke,0,0,1,0\n',
 '86,Ad,1,1,0,0\n',
 '87,None,1,0,0,1\n',
 '88,Joke,1,0,1,0\n',
 '89,Ad,0,1,0,0\n',
 '90,None,0,0,0,1\n',
 '91,None,0,0,0,1\n',
 '92,Joke,0,0,1,0\n',
 '93,Joke,0,0,1,0\n',
 '94,Ad,0,1,0,0\n',
 '95,Ad,0,1,0,0\n',
 '96,Ad,0,1,0,0\n',
 '97,Joke,1,0,1,0\n',
 '98,None,0,0,0,1\n',
 '99,None,0,0,0,1\n',
 '100,None,1,0,0,1\n',
 '101,Joke,0,0,1,0\n',
 '102,Joke,0,0,1,0\n',
 '103,Ad,1,1,0,0\n',
 '104,Ad,0,1,0,0\n',
 '105,Ad,0,1,0,0\n',
 '106,Ad,1,1,0,0\n',
 '107,Ad,0,1,0,0\n',
 '108,None,0,0,0,1\n',
 '109,Ad,0,1,0,0\n',
 '110,Joke,1,0,1,0\n',
 '111,None,0,0,0,1\n',
 '112,Ad,0,1,0,0\n',
 '113,Ad,0,1,0,0\n',
 '114,None,0,0,0,1\n',
 '115,Ad,0,1,0,0\n',
 '116,None,0,0,0,1\n',
 '117,None,0,0,0,1\n',
 '118,Ad,0,1,0,0\n',
 '119,None,1,0,0,1\n',
 '120,Ad,1,1,0,0\n',
 '121,Ad,0,1,0,0\n',
 '122,Ad,1,1,0,0\n',
 '123,None,0,0,0,1\n',
 '124,None,0,0,0,1\n',
 '125,Joke,1,0,1,0\n',
 '126,Joke,1,0,1,0\n',
 '127,Ad,0,1,0,0\n',
 '128,Joke,0,0,1,0\n',
 '129,Joke,0,0,1,0\n',
 '130,Ad,0,1,0,0\n',
 '131,None,0,0,0,1\n',
 '132,None,0,0,0,1\n',
 '133,None,0,0,0,1\n',
 '134,Joke,1,0,1,0\n',
 '135,Ad,0,1,0,0\n',
 '136,None,0,0,0,1\n',
 '137,Joke,0,0,1,0\n',
 '138,Ad,0,1,0,0\n',
 '139,Ad,0,1,0,0\n',
 '140,None,0,0,0,1\n',
 '141,Joke,0,0,1,0\n',
 '142,None,0,0,0,1\n',
 '143,Ad,0,1,0,0\n',
 '144,None,1,0,0,1\n',
 '145,Joke,0,0,1,0\n',
 '146,Ad,0,1,0,0\n',
 '147,Ad,0,1,0,0\n',
 '148,Ad,0,1,0,0\n',
 '149,Joke,1,0,1,0\n',
 '150,Ad,1,1,0,0\n',
 '151,Joke,1,0,1,0\n',
 '152,None,0,0,0,1\n',
 '153,Ad,0,1,0,0\n',
 '154,None,0,0,0,1\n',
 '155,None,0,0,0,1\n',
 '156,Ad,0,1,0,0\n',
 '157,Ad,0,1,0,0\n',
 '158,Joke,0,0,1,0\n',
 '159,None,0,0,0,1\n',
 '160,Joke,1,0,1,0\n',
 '161,None,1,0,0,1\n',
 '162,Ad,1,1,0,0\n',
 '163,Joke,0,0,1,0\n',
 '164,Joke,0,0,1,0\n',
 '165,Ad,0,1,0,0\n',
 '166,Joke,1,0,1,0\n',
 '167,Joke,1,0,1,0\n',
 '168,Ad,0,1,0,0\n',
 '169,Joke,1,0,1,0\n',
 '170,Joke,0,0,1,0\n',
 '171,Ad,0,1,0,0\n',
 '172,Joke,0,0,1,0\n',
 '173,Joke,0,0,1,0\n',
 '174,Ad,0,1,0,0\n',
 '175,None,0,0,0,1\n',
 '176,Joke,1,0,1,0\n',
 '177,Ad,0,1,0,0\n',
 '178,Joke,0,0,1,0\n',
 '179,Joke,0,0,1,0\n',
 '180,None,0,0,0,1\n',
 '181,None,0,0,0,1\n',
 '182,Ad,0,1,0,0\n',
 '183,None,0,0,0,1\n',
 '184,None,0,0,0,1\n',
 '185,None,0,0,0,1\n',
 '186,None,0,0,0,1\n',
 '187,Ad,0,1,0,0\n',
 '188,None,1,0,0,1\n',
 '189,Ad,0,1,0,0\n',
 '190,Ad,0,1,0,0\n',
 '191,Ad,0,1,0,0\n',
 '192,Joke,1,0,1,0\n',
 '193,Joke,0,0,1,0\n',
 '194,Ad,0,1,0,0\n',
 '195,None,0,0,0,1\n',
 '196,Joke,1,0,1,0\n',
 '197,Joke,0,0,1,0\n',
 '198,Joke,1,0,1,0\n',
 '199,Ad,0,1,0,0\n',
 '200,None,0,0,0,1\n',
 '201,Joke,1,0,1,0\n',
 '202,Joke,0,0,1,0\n',
 '203,Joke,0,0,1,0\n',
 '204,Ad,0,1,0,0\n',
 '205,None,0,0,0,1\n',
 '206,Ad,0,1,0,0\n',
 '207,Ad,0,1,0,0\n',
 '208,Joke,0,0,1,0\n',
 '209,Ad,0,1,0,0\n',
 '210,Joke,0,0,1,0\n',
 '211,None,0,0,0,1\n']

I'm currently trying to find the Total number of entries of the specified card type and the Percentage of tips given for the specified card type with two decimal places of precision. The tip column is the 0 or 1 right after the card type (None, Ad, Joke).

Answer 1

This works

from pprint import pprint
from string import punctuation

counts = {"Joke": 0, "Ad": 0, "None": 0}
with open("TipJoke.csv", "r") as f:
    for line in f:
        line_clean = line.replace('"', "").replace("\n", "").split(",")
        try:
            counts[line_clean[1]] += int(line_clean[2])
        except:
            pass


print(counts)

Answer 2

if you are allowed with pandas library then

import pandas as pd

df = pd.read_csv("TipJoke.csv")

df is a pandas dataframe object in which you can perform multiple filtering task according to your need.

for example if you want to get data for Joke you can filter like this:

print(df[df["Card"] == "Joke"])

Though, i'm just providing you the direction , not whole logic for your question.