Pandas: Upgrade to 3.0 and fix compatibility issues #4838
+257
−209
Conversation
This file contains hidden or bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
Pandas 3.0 introduces several breaking changes (https://pandas.pydata.org/docs/whatsnew/v3.0.0.html#other-api-changes) that required fixes across the codebase: 1. StringDtype inference: Pandas 3.0 infers string columns to use StringDtype instead of object dtype. This breaks DataFrame comparisons since our Data class expects object dtype (defined in COLUMN_DATA_TYPES). Fixed by setting `pd.options.future.infer_string = False` in modules that construct DataFrames. 2. Deprecated inplace on replace(): The `inplace=True` parameter on Series.replace() is deprecated. Changed to assignment pattern: `df["col"] = df["col"].replace(...)`. 3. read_json() no longer accepts strings: pd.read_json() now requires file paths or file-like objects, not raw JSON strings. Wrapped JSON strings with StringIO(). 4. Read-only arrays from DataFrame.to_numpy(): Arrays returned by to_numpy() are now read-only views. Added .copy() before passing to torch.from_numpy() which requires writable arrays. 5. Test dtype mismatches: Tests comparing DataFrames failed because manually constructed expected DataFrames had different dtypes than production code. Fixed by wrapping expected DataFrames with Data(df=...).df to ensure consistent dtype casting.
meta-cla
bot
added
the
CLA Signed
|
@CristianLara has imported this pull request. If you are a Meta employee, you can view this in D91825190. |
Codecov Report✅ All modified and coverable lines are covered by tests. Additional details and impacted files@@ Coverage Diff @@
## main #4838 +/- ##
==========================================
- Coverage 96.74% 96.74% -0.01%
==========================================
Files 588 588
Lines 61526 61532 +6
==========================================
+ Hits 59524 59527 +3
- Misses 2002 2005 +3 ☔ View full report in Codecov by Sentry. 🚀 New features to boost your workflow:
|
Sign up for free
to join this conversation on GitHub.
Already have an account?
Sign in to comment
Add this suggestion to a batch that can be applied as a single commit.
This suggestion is invalid because no changes were made to the code.
Suggestions cannot be applied while the pull request is closed.
Suggestions cannot be applied while viewing a subset of changes.
Only one suggestion per line can be applied in a batch.
Add this suggestion to a batch that can be applied as a single commit.
Applying suggestions on deleted lines is not supported.
You must change the existing code in this line in order to create a valid suggestion.
Outdated suggestions cannot be applied.
This suggestion has been applied or marked resolved.
Suggestions cannot be applied from pending reviews.
Suggestions cannot be applied on multi-line comments.
Suggestions cannot be applied while the pull request is queued to merge.
Suggestion cannot be applied right now. Please check back later.
Pandas 3.0 introduces several breaking changes (https://pandas.pydata.org/docs/whatsnew/v3.0.0.html#other-api-changes) that required fixes across the codebase:
StringDtype inference: Pandas 3.0 infers string columns to use StringDtype instead of object dtype. This breaks DataFrame comparisons since our Data class expects object dtype (defined in COLUMN_DATA_TYPES). Fixed by setting
pd.options.future.infer_string = Falsein modules that construct DataFrames.Deprecated inplace on replace(): The
inplace=Trueparameter on Series.replace() is deprecated. Changed to assignment pattern:df["col"] = df["col"].replace(...).read_json() no longer accepts strings: pd.read_json() now requires file paths or file-like objects, not raw JSON strings. Wrapped JSON strings with StringIO().
Read-only arrays from DataFrame.to_numpy(): Arrays returned by to_numpy() are now read-only views. Added .copy() before passing to torch.from_numpy() which requires writable arrays.
Test dtype mismatches: Tests comparing DataFrames failed because manually constructed expected DataFrames had different dtypes than production code. Fixed by wrapping expected DataFrames with Data(df=...).df to ensure consistent dtype casting.