Sample DataFrame context
A sales export with order_date stored as text.
Syntax or pattern
pd.to_datetime(df["order_date"], errors="coerce")5 practical examples
Convert a text date column
Turn imported date text into datetime values.
sales["order_date"] = pd.to_datetime(sales["order_date"], errors="coerce")The column can now be filtered and grouped by date.
Filter a date range
Keep orders from a reporting period.
mask = sales["order_date"].between("2026-01-01", "2026-03-31")
q1 = sales[mask]The output contains only Q1 rows.
Create month column
Create a month label for reporting.
sales["month"] = sales["order_date"].dt.to_period("M").astype(str)The month column can be used in summaries and charts.
Calculate delivery days
Find the number of days between order and ship dates.
sales["delivery_days"] = (sales["ship_date"] - sales["order_date"]).dt.daysThe result shows delivery time per order.
Monthly revenue trend
Summarize revenue by month.
monthly = sales.resample("M", on="order_date")["revenue"].sum().reset_index()The result is ready for a time-series chart.
Common mistakes to avoid
- Changing the original DataFrame before checking the result.
- Forgetting to inspect row counts before and after the operation.
- Using a pattern without confirming column names and data types.
FAQ
What is the main use of to_datetime Examples?
Convert text dates to datetime and handle mixed date formats.
Should I use this in a notebook or a script?
Both work. Use a notebook while exploring the data, then move the final workflow into a repeatable script when it is stable.
How do I avoid breaking my source data?
Create a copy of the DataFrame before transforming it and check row counts, missing values and sample rows after each important step.