How to convert single strings to floats in csv_reader

    with open('diamonds_testing.csv') as csv_file:
    csv_reader = csv.reader(csv_file)
    diamond = list(csv_reader)
    lines = []
    for line in diamond:
        for i in [0, 4, 5, 6, 7]:
            line[i] = float(line[i])
        lines.append(line)
    print(lines)

float () to do cast. to pass line by line use

for line in diamond:

The csv module is a lower level module (and far less resource consuming) that the huge pandas library. As a result, it never tries to interpret the data and only split rows into strings. If you know that some columns should contain integer or floating point values, you will have to convert the values yourself.

with open('diamonds_testing.csv') as csv_file:
    float_columns = (O, 4, 5, 6, 6)
    csv_reader = csv.reader(csv_file)
    diamond = [[float(v) if i in float_columns else v for i, v in enumerate(row)]
               for row in csv_reader]

Or you can have Pandas to guess the data types:

diamond = [row[1].to_list()
           for row in pd.read_csv('diamonds_testing.csv', header=None).iterrows()]

you can try

def validate(num):
    try:
        return int(num)
    except (ValueError, TypeError):
        try:
            return float(num)
        except (ValueError, TypeError):
            return num

with open('diamonds_testing.csv') as csv_file:
    csv_reader = csv.reader(csv_file)
    diamond = list(csv_reader)

    for i in diamond:
        diamond[i] = [validate(v) for v in diamond[i]]

Sample output

print(diamond[1])
print(diamond[2])

Output:

[0.23, 'Ideal', 'E', 'SI2', 55, 3.95, 3.98, 2.43]
[0.31, 'Very Good', 'J', 'SI1', 62, 4.39, 4.43, 2.62]