Often, dates in a dataset are stored as plain text, like '2023-10-26'. While this looks like a date to us, the computer just sees it as a string of characters. This limits what we can do with it.
Engagement Message
What kind of date-related questions would be hard to answer if dates were just text?
When dates are stored as text, sorting may appear to work but is unreliable. It only matches true chronological order if every value uses the same zero-padded ISO format (YYYY-MM-DD). Real datasets often mix formats or lack padding, which can misorder values (e.g., 2023-2-10 may sort after 2023-11-02). Converting to datetime ensures reliable sorting and enables date arithmetic.
Engagement Message
With me so far?
The solution is to convert these strings into a special "datetime" object. This is a data type that Pandas understands as an actual date and time, not just text. This unlocks powerful time-based analysis capabilities.
Engagement Message
What types of time-based analysis become possible with proper datetime objects?
Pandas has a powerful function for this: pd.to_datetime(). You apply it to a column of date strings, and it intelligently converts them into proper datetime objects.
For example:
df['date_col'] = pd.to_datetime(df['date_col']).
Engagement Message
Why is it helpful that this function can often guess the date format automatically?
