ENH: Support for non-nanosecond resolution in read_csv
and to_datetime
#54048
Labels
Dtype Conversions
Unexpected or buggy dtype conversions
Enhancement
IO CSV
read_csv, to_csv
Non-Nano
datetime64/timedelta64 with non-nanosecond resolution
Uh oh!
There was an error while loading. Please reload this page.
Feature Type
Adding new functionality to pandas
Changing existing functionality in pandas
Removing existing functionality in pandas
Problem Description
I wish I could use the newly added support for
datetime64[ms]
directly in pandas when opening a file.Feature Description
which would return:
Or alternatively, it could also be more explicit:
Alternative Solutions
Currently, the best way I have found to read in a CSV that has entries outside the 1677-2242 range is:
Thus, I am letting numpy to the actual date parsing.
fillna
is needed because the empty cell indata
gets translated tonp.nan
byread_csv
and numpy can't cast that as adatetime64
. (I expected aNaT
but I guess that's another issue anyway).This solution requires that the dates be in ISO8601 format, which is much stricter than
to_datetime
.Additional Context
See my S/O question for more alternative solutions: https://stackoverflow.com/questions/76608166/how-do-i-parse-a-list-of-datetimes-with-a-s-resolution-in-pandas-2
Thanks to ignoring-gravity for the answer, which I reused here.
The text was updated successfully, but these errors were encountered: