Catch unexpected missing-value codes in a research CSV

Before calculating a summary from a research export, decide which values your codebook treats as missing. One site might leave a field blank while another sends 999. A numeric conversion will accept 999 and carry it into a mean.

Here is a fictional symptom score with a range of 0–20. The agreed missing tokens are blank and NA. The check keeps those rows separate and flags a numeric value outside the range for review.

from csv import DictReader
from io import StringIO

source = StringIO("record,score\nA,6\nB,NA\nC,999\nD,0\n")
valid = []
missing = []
review = []

for row in DictReader(source):
    record = row["record"]
    raw = row["score"].strip()
    if raw in {"", "NA"}:
        missing.append(record)
        continue
    try:
        score = int(raw)
    except ValueError:
        review.append((record, raw))
        continue
    if 0 <= score <= 20:
        valid.append((record, score))
    else:
        review.append((record, raw))

print("Valid:", valid)
print("Missing:", missing)
print("Review:", review)

Valid: [('A', 6), ('D', 0)]
Missing: ['B']
Review: [('C', '999')]

The zero is a valid score. Keep the raw export and ask the data owner what 999 means before adding it to the missing-token list. The range and tokens belong to this example. Use the definitions from your own codebook.

Python documentation for csv.DictReader