Add key/values to dictionary using condition

Question

I have a list of items that I'm adding to dictionary below:

COLS = ['CUST', 'MODEL', 'SN', 'DATE', 'CHARGE', 'QTY', 'TOTAL']
with open('UserfeeInvoicing.csv', 'r') as infile:
    ranpak_dict = {
        row[2]: dict(zip(COLS, row)) for row in csv.reader(infile)
    }

Is there anyway to only add the records that have CHARGE =/= 0 or CHARGE > 0

Answer 1

Rather than use csv.reader() , use csv.DictReader() object . That object makes it a lot easier to both create your dictionaries and to filter the rows; your code, refactored to use DictReader() , looks like this:

COLS = ['CUST', 'MODEL', 'SN', 'DATE', 'CHARGE', 'QTY', 'TOTAL']
with open('UserfeeInvoicing.csv', 'r') as infile:
    reader = csv.DictReader(infile, fieldnames=COLS)
    ranpak_dict = {row['SN']: row for row in reader}

The csv.DictReader() object does exactly what your dict(zip(COLS, row)) call does; build a dictionary from each row, given a sequence of fieldnames.

Filtering in a list, dict or set comprehension works just like adding additional loops; just add if <condition> to the loop:

ranpak_dict = {row['SN']: row for row in reader if int(row['CHARGE']) > 0}

Note the int() call; I am assuming that the CHARGE column always contains digits.

If your textual fields are all quoted, you could also set quoting=csv.QUOTE_NONNUMERIC , at which point all columns without quotes are automatically converted to float for you. That'd reduce the code to:

reader = csv.DictReader(infile, fieldnames=COLS, quoting=csv.QUOTE_NONNUMERIC)
ranpak_dict = {row['SN']: row for row in reader if row['CHARGE'] > 0}

Answer 2

You could do the following:

ranpak_dict = {}
COLS = ['CUST', 'MODEL', 'SN', 'DATE', 'CHARGE', 'QTY', 'TOTAL']

with open('UserfeeInvoicing.csv', 'r') as infile:      

    reader = csv.reader(infile)
    for row in reader:

        if float(row[COLS.index('CHARGE')]) >= 0:
            ranpak_dict[row[2]] = dict(zip(COLS, row))