-
Notifications
You must be signed in to change notification settings - Fork 11
New issue
Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.
By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.
Already on GitHub? Sign in to your account
Support pandas 2 #138
Support pandas 2 #138
Conversation
.github/workflows/test.yml
Outdated
@@ -15,12 +15,16 @@ jobs: | |||
matrix: | |||
os: [ubuntu-latest, windows-latest] | |||
python-version: ["3.8", "3.9", "3.10", "3.11"] | |||
pandas-version: ["1.3.5", "1.4.4", "1.5.3", "2.2.2"] | |||
pandas-version: ["1.3.5", "1.4.4", "1.5.3", "2.0.3", "2.1.4", "2.2.2"] |
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
I think testing over two versions would be enough, i.e., 1.5.3 and 2.2.2. Is there any specific reason to test for 1.3.5, 1.4.4, 2.0.3, and 2.1.4?
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
No specific reason. I'm just following current convention (1.3, 1.4, 1.5)
If you think it's uneccessary, I'll test major versions only. I think testing minor version has limited benefit as well (unless there is some bug in one of pandas versions). Testing only 2 versions will drastically improve test time (now close to 1 hour 😬 )
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Okay, then let's test 2 major versions only.
There was a problem hiding this comment.
Choose a reason for hiding this comment
The reason will be displayed to describe this comment to others. Learn more.
Updated in cad4317
Co-authored-by: Aki Ariga <[email protected]>
Couple of sanity tests I've done for pandas 2 support:
Setup
Upload to TD table
Results: table is successfully imported to TD
Query TD table
Result:
Read through pandas 2.0.0 release notes
https://pandas.pydata.org/docs/dev/whatsnew/v2.0.0.html
Seems like the only change that can potentially break backward compatibility is Construction with datetime64 or timedelta64 dtype with unsupported resolution
Currently, we only support datetime64[ns] for time column. In pandas 1, if pytd users specify a wrong resolution for datetime64, for example, datetime64[s], they can still convert and import the column correctly. But in pandas 2, they won't be able to convert and import the time column