How can I validate a CSV column against a set of allowed values?

Define the permitted strings in a set and compare each parsed field value directly against it. csv.DictReader maps each data row to a dictionary keyed by the header fields, and the field values are strings. [S1]

import csv

allowed = {"open", "closed"}

with open("input.csv", newline="", encoding="utf-8") as file:
    reader = csv.DictReader(file)
    for data_row_number, row in enumerate(reader, start=1):
        original = row.get("status")
        if original not in allowed:
            print(
                f"Data row {data_row_number}: "
                f"status={original!r} is not allowed; "
                f"allowed values are {sorted(allowed)!r}"
            )

The example counts the first record after the header as data row 1. The !r display makes spaces in the original value visible. If you want spreadsheet row numbers and the file has one header line, add 1 to the displayed number. The example assumes the header is exactly status; adjust it if your column has a different name. The allowed set is your rule, not a decision made by the CSV reader. [S1]

With the direct set comparison above, matching is exact: " Open " and "OPEN" do not match "open". The CSV reader does not silently trim spaces or ignore letter case. [S1] The script flags those values and reports the original parsed string; it does not rewrite the source data.

If you want a different matching rule, make it explicit and keep the original value for reporting. For example, say whether your comparison ignores surrounding whitespace, letter case, or both. Don’t change the rule silently: people reviewing the report need to know what counted as a match.

Assume this sample has a status header and these data records:

status
open
 Open 
closed

With exact matching, the second data record is unexpected because its spaces and capitalization differ from open. The final blank field is an empty string, so it is also unexpected. The output could be:

Data row 2: status=' Open ' is not allowed; allowed values are ['closed', 'open']
Data row 4: status='' is not allowed; allowed values are ['closed', 'open']

These are data-record numbers, with the header excluded. A blank field and a missing status key are different cases. In this example, row.get("status") returns None if the key is absent, and that value also fails the set check and is reported as None.

Confirm the rule with whoever owns the data: are spaces and capitalization meaningful, and is a blank status permitted? Check that the header is status and that the allowed set contains every valid spelling. The CSV reader handles parsing; the allowed values and matching rule are application choices. [S1]

Keep validation separate from editing. Review the reported original values, decide how each should be handled, and write any changes to a separate output if you choose to transform the data. The example only reads and reports; it does not change the input.

You can make the rule clear in the report with wording like this:

Rule: exact match; no trimming or case folding.
Allowed values: ['closed', 'open'].
Finding: data row [N], original status=[VALUE].

Replace the bracketed fields with the row number and original value. If you choose a different comparison rule, state it in the report.