PDEP-4: Consistent datetime parsing

Abstract

The suggestion is that: - to_datetime becomes strict and uses the same datetime format to parse all elements in its input. The format will either be inferred from the first non-NaN element (if format is not provided by the user), or from format; - infer_datetime_format be deprecated (as a strict version of it will become the default); - an easy workaround for non-strict parsing be clearly documented.

Motivation and Scope

Pandas date parsing is very flexible, but arguably too much so - see https://github.com/pandas-dev/pandas/issues/12585 and linked issues for how much confusion this causes. Pandas can swap format midway, and though this is documented, it regularly breaks users' expectations.

Simple example:

In [1]: pd.to_datetime(['12-01-2000 00:00:00', '13-01-2000 00:00:00'])
Out[1]: DatetimeIndex(['2000-12-01', '2000-01-13'], dtype='datetime64[ns]', freq=None)

The user was almost certainly intending the data to be read as "12th of January, 13th of January". However, it's read as "1st of December, 13th of January". No warning or error is thrown.

Currently, the only way to ensure consistent parsing is by explicitly passing format=. The argument infer_datetime_format isn't strict, can be called together with format, and can still break users' expectations:

In [2]: pd.to_datetime(['12-01-2000 00:00:00', '13-01-2000 00:00:00'], infer_datetime_format=True)
Out[2]: DatetimeIndex(['2000-12-01', '2000-01-13'], dtype='datetime64[ns]', freq=None)

Detailed Description

Concretely, the suggestion is: - if no format is specified, pandas will guess the format from the first non-NaN row and parse the rest of the input according to that format. Errors will be handled according to the errors argument - there will be no silent switching of format; - infer_datetime_format will be deprecated; - dayfirst and yearfirst will continue working as they currently do; - if the format cannot be guessed from the first non-NaN row, a UserWarning will be thrown, encouraging users to explicitly pass in a format. Note that this should only happen for invalid inputs such as 'a' (which would later throw a ParserError anyway), or inputs such as '00:12:13', which would currently get converted to ''2022-09-18 00:12:13''.

If a user has dates in a mixed format, they can still use flexible parsing and accept the risks that poses, e.g.:

In [3]: pd.Series(['12-01-2000 00:00:00', '13-01-2000 00:00:00']).apply(pd.to_datetime)
Out[3]:
0   2000-12-01
1   2000-01-13
dtype: datetime64[ns]

Usage and Impact

My expectation is that the impact would be a net-positive: - potentially severe bugs in people's code will be caught early; - users who actually want mixed formats can still parse them, but now they'd be forced to be very explicit about it; - the codebase would be noticeably simplified.

As far as I can tell, there is no chance of introducing bugs.

Implementation

The whatsnew notes read

In the next major version release, 2.0, several larger API changes are being considered without a formal deprecation.

I'd suggest making this change as part of the above, because: - it would only help prevent bugs, not introduce any; - given the severity of bugs that can result from the current behaviour, waiting another 2 years until pandas 3.0.0 would potentially cause a lot of damage.

Note that this wouldn't mean getting rid of dateutil.parser, as that would still be used within guess_datetime_format. With this proposal, however, subsequent rows would be parsed with the guessed format rather than repeatedly calling dateutil.parser and risk having it silently switch format

Finally, the function from pandas._libs.tslibs.parsing import guess_datetime_format would be made public, under pandas.tools.

Out of scope

We could make guess_datetime_format smarter by using a random sample of elements to infer the format.

PDEP History