microclaw/csv-tools

CSV Tools

Use Python's stdlib csv (always available). Reach for pandas only if it's installed and the task is heavy (python3 -c "import pandas" to check first).

Peek: headers, row count, first rows

python3 - <<'PY'
import csv
with open('data.csv', newline='') as f:
    r = list(csv.reader(f))
print('cols:', r[0])
print('rows:', len(r)-1)
for row in r[1:4]: print(row)
PY

Filter rows by a column

python3 - <<'PY'
import csv
with open('data.csv', newline='') as f:
    for row in csv.DictReader(f):
        if row['status'] == 'active':
            print(row['id'], row['name'])
PY

Group-by aggregate (sum / count)

python3 - <<'PY'
import csv, collections
sums = collections.Counter()
with open('sales.csv', newline='') as f:
    for row in csv.DictReader(f):
        sums[row['region']] += float(row['amount'])
for k,v in sums.most_common():
    print(f'{k}: {v:.2f}')
PY

Dedupe / clean

python3 - <<'PY'
import csv
seen=set(); out=[]
with open('data.csv', newline='') as f:
    rd=csv.reader(f); header=next(rd)
    for row in rd:
        key=tuple(c.strip() for c in row)
        if key not in seen:
            seen.add(key); out.append(row)
with open('clean.csv','w',newline='') as f:
    w=csv.writer(f); w.writerow(header); w.writerows(out)
print('wrote', len(out), 'unique rows')
PY

Guidance

• Always check the delimiter (, vs \t vs ;) and whether there's a header row.
• Watch for quoted fields containing commas/newlines — csv handles them; manual splitting won't.
• Report a short summary (rows in/out, columns, key numbers), not the whole file.
• If the deliverable is a polished spreadsheet with formatting/charts, use the xlsx skill instead.