Pandas converting string to numeric - getting invalid literal for int() with base 10 error

Question

I am trying to convert data from a csv file to a numeric type so that I can find the greatest and least value in each category. This is a short view of the data I am referencing:

Course	Grades_Recieved
098321	A,B,D
324323	C,B,D,F
213323	A,B,D,F

I am trying to convert the grades_received to numeric types so that I can create new categories that list the highest grade received and the lowest grade received in each course.

This is my code so far:

import pandas as pd 
df = pd.read_csv('grades.csv')

df.astype({Grades_Recieved':'int64'}).dtypes`

I have tried the code above, I have tried using to_numeric, but I keep getting an error: invalid literal for int() with base 10: 'A,B,D' and I am not sure how to fix this. I have also tried getting rid of the ',' but the error remains the same.

int means integer. Why would pandas know how to convert e.g. A or C into integer ? — keepAlive
– keepAlive, Commented Feb 12, 2023 at 23:08

rocha-p · Accepted Answer · 2023-02-12 23:32:48Z

2

You can't convert a list of non-numeric strings into int/float, but you can get the desired result doing something like this:

df['Highest_Grade'] = df['Grades_Recieved'].str.split(',').apply(lambda x: min(x))
df['Lowest_Grade'] = df['Grades_Recieved'].str.split(',').apply(lambda x: max(x))

edited Feb 12, 2023 at 23:32

answered Feb 12, 2023 at 23:09

rocha-p

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