Convert a column's type in Python (pandas)

When to use it

Every column that will be compared, summed or sorted as something other than text.

How

Use explicit conversions: astype("Int64") for nullable integers, decimal.Decimal for money, pd.to_datetime(..., utc=True, format=...) for timestamps.

df["amount"] = df["amount"].map(lambda s: Decimal(s).quantize(Decimal("0.01")))

• pd.to_numeric(errors="coerce") turns bad values into NaN silently; count them before accepting that.
• astype(float) is wrong for money.

Pitfalls

• Letting a parser guess formats.
• Silently turning bad values into missing ones.
• Floating point for money.