23. Common pandas Mistakes and Fixes

23.2 Wrong dtypes and Silent NaNs

By Ravindra Bagale · Last updated: 23 October 2026

Why. Amount column looks numeric on screen (1,299) but dtype is object. Mean fails or skips. errors="coerce" quietly turns junk into NaN — good if you count NaNs, bad if you ignore them.

What.

• object columns often hold messy text numbers.
• pd.to_numeric(..., errors="coerce") → bad strings become NaN.
• Date strings without dayfirst=True can swap day/month (05-10-2026 vs 10-05-2026).

How.

Worked example A — Comma + N/A.

import numpy as np
import pandas as pd

raw = pd.DataFrame({
    "order_id": ["BLK-10", "BLK-11", "AMN-12", "BLK-13"],
    "city": ["Pune", "Solapur", "Nagpur", "Nashik"],
    "amount": ["64", "1,299", "N/A", "210"],
    "customer": ["Shraddha Bagale", "Salman", "Rani", "Shahrukh"],
})
before_na = raw["amount"].isna().sum()
amt = (
    raw["amount"].astype(str)
      .str.replace(",", "", regex=False)
      .replace({"N/A": np.nan, "nan": np.nan, "": np.nan})
)
raw["amount"] = pd.to_numeric(amt, errors="coerce")
print(raw.dtypes)
print("NaNs introduced", raw["amount"].isna().sum() - before_na)
print(raw)

What you should see. One NaN for AMN-12. GMV must decide: drop, fill, or exclude with an explicit filter — and log the count.

Worked example B — Silent mean on object (trap).

messy = pd.Series(["100", "200", "x"])
# pandas may error or coerce depending on version/path — always clean first
clean = pd.to_numeric(messy, errors="coerce")
print(clean.tolist(), "mean skipping NaN", clean.mean())

Worked example C — Date swap risk.

dates = pd.Series(["05-10-2026", "13-10-2026"])  # 5 Oct, 13 Oct
print(pd.to_datetime(dates, dayfirst=True))
# Without dayfirst, 05-10 may become 10 May in some settings

Zoya’s Diwali window filter breaks if 05-10 becomes May.