CODEWITHSUNDEEP
pythonpython-3.xdictionary
c4ss10p314
c4ss10p314

Reputation: 5

(Python) Filling a dictionary with specific elements

I am kind of lost here and can't really find a similar question. Maybe also because I don't know how to search for it.

I want to import the names.csv file, which holds a bunch of names in the structure: ID, Name, Gender, Year, State, Count

Now I'm trying to transcribe all the names and count integers into a dictionary which I call names. I don't understand why it now continually returns me an empty dictionary.

Through the conditionals, I'm trying to say that IF the name is in the dictionary already, it is supposed to sum the count onto the existing count.

Can anyone help? Sadly I'm really a newbie and can't help myself...

with open("../data/names.csv") as file:
names = {}

for lines in file:
    data = lines.strip().split(",")
    name = data[1]
    count = data[5]
    if name == "Name":
        continue
    for name, count in names.items():
        if name in names:
            names[name] = names[name] + count
        else:
            names[name] = count

print(names)

Upvotes: 0

Views: 41

Answers (2)

Vladimir Vernidubov
Vladimir Vernidubov

Reputation: 64

Better to use defaultdict from standard python lib (for details link) and for working with csv files csv module (link for details)

from collections import defaultdict

food_list = 'spam spam spam spam spam spam eggs spam'.split()
food_count = defaultdict(int) # default value of int is 0
for food in food_list:
    food_count[food] += 1 # increment element's value by 1

food_count

in result you will have:

defaultdict(<type 'int'>, {'eggs': 1, 'spam': 7})

Upvotes: 1

soerface
soerface

Reputation: 6863

First, you should avoid parsing the CSV file yourself, it can become quite tricky when e.g. quotes are involved. There is a csv module built-in

For your case, I would use pandas. The .groupby() function together with .sum() will do exactly what you want:

import pandas as pd
df = pd.read_csv('names.csv')
print(df[['Name', 'Count']].groupby('Name').sum())

Upvotes: 1

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