Think of pd.to_datetime as a translator for date and time values: it turns strings, Series, or other supported inputs into pandas Timestamp values or datetime-like Series. A single value returns one Timestamp; a Series returns Timestamp values with a datetime64 dtype. section 10.0
Use it when date data needs a consistent form for inspection or time-series analysis:
- Convert an existing string Series: pd.to_datetime(s). section 10.0
- Parse several recognizable formats, including numeric dates, month names, day-first dates, and date-time strings with 12- or 24-hour times. section 10.0
- Handle ambiguous numeric dates with the month-first default and the dayfirst argument. section 10.0
- Build dates from separate year, month, and day columns by passing a DataFrame containing those columns. section 10.0
- Convert a parsed date column into a time-series index, enabling date-based row selection and resampling. section 10.8
Normalize a taxi timestamp column
Load tpep_pickup_datetime as a datetime column with read_csv(parse_dates=[...]). When a nonstandard exported format must be read separately, load it as strings and apply pd.to_datetime to the column after parsing or formatting it appropriately. Compare the two parsing approaches as a practical follow-up. section 10.5 section 10.8
Create a searchable mission timeline
Clean Apollo launch-date strings, convert them with pd.to_datetime, and set the resulting Date column as the index. Then select a specific date, month, year, or date range, and resample the time series into six-month groups. section 10.8
