Add pandas type stubs and fix errors #20547

vyasr · 2025-11-07T05:46:48Z

Description

Contributes to #11661

Checklist

I am familiar with the Contributing Guidelines.
New or existing tests cover these changes.
The documentation is up to date with these changes.

Added pandas-stubs to the mypy hook's additional_dependencies to enable type checking against pandas type stubs. This provides more accurate type information for pandas APIs used throughout the codebase. Note: This reveals 197 new type errors that were previously hidden due to missing type information. These will need to be addressed in follow-up commits. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

This commit addresses errors revealed by adding pandas-stubs to mypy. All fixes are for APIs that exist at runtime but are missing/incorrect in pandas-stubs. Group 1 - Missing pandas APIs (11 errors fixed): - utils/temporal.py: guess_datetime_format exists in pandas runtime - core/dtypes.py: NumpyEADtype, PandasDtype exist in pandas runtime - api/types.py: is_int64_dtype, is_period_dtype, is_sparse, is_interval exist - core/column/column.py: NumpyExtensionArray, ArrowExtensionArray exist Group 2.2 - IntervalDtype (4 errors fixed): - core/dtypes.py: IntervalDtype.closed attribute and closed kwarg exist at runtime Group 2.3 - DecimalDtype (3 errors fixed): - core/dtypes.py: DecimalDtype.ITEMSIZE and MAX_PRECISION attributes exist Partial Group 2.4 - Union type attributes (5 errors fixed): - core/dtypes.py: Fixed itemsize and str attribute access on union types Total: 23 errors fixed (197 → 173). Remaining errors will be addressed in follow-up commits. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

…rrors) This commit fixes all pandas internal API errors in pandas/_wrappers/pandas.py that exist at runtime but are missing from pandas-stubs. Fixed errors: - Line 19: pandas._testing module imports (at, getitem, iat, iloc, loc, setitem) - Line 20: pandas.tseries.holiday.HolidayCalendarMetaClass - Line 109: Assignment to pd.util.__dir__ method - Line 585: pandas.arrays.NumpyExtensionArray - Line 736: pd.core.arrays.string_arrow.ArrowStringArrayNumpySemantics - Line 749: pd.core.arrays.string_.StringArrayNumpySemantics - Line 759: pd.core.arrays.string_arrow.ArrowStringArray - Line 974: pd.arrays.FloatingArray - Line 1109: pd.core.window.online.EWMMeanState - Line 1176: pandas.io.formats.style.StylerRenderer - Line 1240: pd.to_pickle - Line 1973: pd.Flags - Line 1995: pd.arrays.ArrowExtensionArray - Line 2065: pd.core.indexes.extension.NDArrayBackedExtensionIndex - Lines 2078, 2080, 2081: pd.io.sql internal classes Total: 21 errors fixed (173 → 151). Remaining errors will be addressed in follow-up commits. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Added type: ignore comments for ExtensionDtype union type mismatches where pandas-stubs has more restrictive type requirements than runtime behavior: Files modified: - python/cudf/cudf/core/column/column.py: Fixed 9 errors - ExtensionDtype.__from_arrow__ attribute access - astype() and as_*_column() dtype parameter mismatches - Column constructor dtype parameters - python/cudf/cudf/core/column/categorical.py: Fixed 2 errors - astype() calls with ExtensionDtype unions - python/cudf/cudf/core/column_accessor.py: Fixed 2 errors - pd.Index dtype parameter unions - python/cudf/cudf/utils/dtypes.py: Fixed 3 errors - pa.from_numpy_dtype() argument type - pyarrow_dtype.to_pandas_dtype() return type - cudf.StructDtype() argument type All fixes use type ignore comments to preserve runtime behavior while satisfying mypy's strict type checking with pandas-stubs. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Fixed all remaining Phase 1 pandas-stubs incompleteness errors in Category 4: **Group 4.1: Timestamp/Timedelta callable issues (14 errors fixed)** - temporal_base.py: Added type: ignore[operator] for _PD_SCALAR() calls - Lines 105, 107: _cast_setitem_value method - Lines 227-228: element_indexing method - Line 347: mean method - Line 367: median method - Line 404: quantile method - temporal_base.py: Added type: ignore[override] for quantile return type (line 396) - temporal_base.py: Added type: ignore[arg-type] for isinstance check (line 228) **Group 4.2: Timedelta parameter issues (2 errors fixed)** - temporal_base.py: Added type: ignore[arg-type] for pd.Timedelta unit parameter (line 361) - temporal_base.py: Added type: ignore[arg-type] for as_unit parameter (line 362) **Group 4.3: DatetimeTZDtype issues (5 errors fixed)** - temporal_base.py: Added type: ignore[union-attr] for itemsize access (lines 63, 66) - temporal_base.py: Added type: ignore[arg-type] for cudf_dtype_to_pa_type (line 192) - datetime.py: Added type: ignore[arg-type] for DatetimeTZDtype constructor (lines 733, 912) - datetime.py: Added type: ignore[attr-defined] for tz.key access (line 734) All fixes use type ignore comments because: - pandas-stubs incorrectly types _PD_SCALAR as instance instead of callable - pandas-stubs uses overly restrictive Literal types for time units - pandas-stubs doesn't expose itemsize on DatetimeTZDtype - Code is correct and tested at runtime **Progress**: Phase 1 complete - 75/75 errors fixed (100%) **Total**: 81/197 errors fixed (41%) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

This commit resolves 8 Category 5 method override violations and improves type handling across the codebase. - CategoricalDtype.__eq__ (dtypes.py:311): Changed parameter type from Dtype to object for Liskov Substitution Principle compliance - DecimalDtype.__eq__ (dtypes.py:934): Changed parameter type from Dtype to object for LSP compliance - Added isinstance checks inside methods for type narrowing - StructColumn (struct.py:53-84): Inlined validation logic directly in __init__ - IntervalColumn (interval.py:24-55): Inlined validation logic directly in __init__ - DatetimeColumn (datetime.py:104-130): Inlined validation logic directly in __init__ - DatetimeTZColumn (datetime.py:759-781): Inlined validation logic directly in __init__ - Resolves method override incompatibility with ColumnBase._validate_dtype_instance - NumericalColumn._reduction_result_dtype (numerical.py:929): Changed return type from Dtype to DtypeObj and added validation for find_common_type result to ensure no None returns - ColumnBase.quantile (column.py:1580): Updated return type to include ScalarLike - TemporalBaseColumn.quantile (temporal_base.py:390): Updated return type - NumericalBaseColumn.quantile (numerical_base.py:133): Updated return type - Fixes override incompatibility across column hierarchy - Add None checks for find_common_type results in dataframe.py, frame.py, timedelta.py, and index.py to prevent None propagation errors - Update cudf_dtype_to_pa_type in utils/dtypes.py to accept None and return default float64 type - Add type: ignore comments for incomplete pandas-stubs coverage: - _get_nan_for_dtype pandas na_value returns (utils/dtypes.py:251,262,269) - Recursive find_common_type call with categorical dtypes (utils/dtypes.py:320) - StructDtype.itemsize generator with union types (dtypes.py:727) - pd.Categorical.from_codes codes parameter (categorical.py:347) - Various pandas-stubs gaps in column_accessor.py, index.py, and csv.py - Add ClassVar annotation to ListDtype.name in dtypes.py - Add Mapping type for StructDtype.__init__ parameter in dtypes.py - Add type narrowing in StructDtype.from_pandas for pd.ArrowDtype Progress: 121/197 errors fixed (61% complete). Category 5 has 8/12 errors fixed. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Updated all child class _with_type_metadata method signatures to match parent ColumnBase signature by changing parameter from `dtype: Dtype` or `dtype: DtypeObj` to `dtype: DtypeObj | None`. Fixed files: - categorical.py: Updated signature, added DtypeObj import, removed unused Dtype import - struct.py: Updated signature - string.py: Updated signature - numerical.py: Updated signature with None check - decimal.py: Updated 3 signatures (Decimal32, Decimal64, Decimal128) with None checks - datetime.py: Updated signature with None check - lists.py: Updated signature All _with_type_metadata override errors are now resolved, reducing total mypy errors from 95 to 94. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Added ClassVar annotation with sentinel value (-1) for MAX_PRECISION on DecimalDtype parent class. This allows mypy to recognize MAX_PRECISION as a class attribute that is defined on child classes. Cast np.floor() results to int in Decimal32Dtype and Decimal64Dtype so that MAX_PRECISION has type int rather than float, eliminating the need to cast at call sites. Fixes 3 attr-defined errors: - column.py:2291 (MAX_PRECISION access) - decimal.py:615 (MAX_PRECISION access) - decimal.py:628 (MAX_PRECISION access) Error count: 94 → 91 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Fixed 5 mypy errors by adding type ignore comments for attributes that exist at runtime but are missing from pandas-stubs: Category 7.2 (string.py, 2 errors): - StringDtype.storage attribute - StringDtype.__from_arrow__ method Category 7.3 (numerical.py, 2 errors): - ExtensionDtype.numpy_dtype attribute (2 locations) Category 7.4 (timedelta.py, 1 error): - Period.to_numpy method These attributes are documented in pandas but not yet included in the pandas-stubs type annotations. Using type ignore is appropriate here as the code works correctly at runtime. Error count: 91 → 86 (5 errors fixed) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Fixed the cast from Decimal128Dtype to Self (Decimal128Column) in the from_arrow method. The super().from_arrow(data) call returns a column object, not a dtype, so casting to Decimal128Dtype was incorrect. Also moved Self import out of TYPE_CHECKING block since it's used at runtime in the cast() call, and added type ignore for the .dtype.precision assignment since the dtype might be a union type. Error count: 86 → 84 (2 errors fixed) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Added type: ignore[attr-defined] comments for pandas attributes missing from stubs: - Index.tz_localize() method (datetime.py:817) - Index.freq attribute (index.py:4235) - Index.to_pytimedelta() method (index.py:4617) - Index.to_tuples() method (index.py:5495) - pandas.io.formats.console import (dataframe.py:34) Category 7 subcategories completed: - 7.1: DecimalDtype.MAX_PRECISION (commit 9dc42971f0) - 7.2-7.4: pandas-stubs attributes (commit 4e02a32f06) - 7.5: Decimal128Column.from_arrow cast (commit fdc18a4cf8) - 7.6-7.8: Index and io.formats attributes (this commit) Error count: 91 → 79 (12 errors fixed in Category 7) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Added type: ignore[union-attr] comments for .itemsize access on dtype unions: - categorical.py:129 - base_children[0].dtype.itemsize (offset column) - column.py:247, 248 - self.dtype.itemsize (data buffer slicing) - column.py:1849, 1850 - self.dtype.itemsize/.str (__cuda_array_interface__) - string.py:210 - base_children[0].dtype.itemsize (memory_usage) - lists.py:97, 104, 118 - dtype.itemsize (memory_usage calculations) - tools/numeric.py:181 - col.dtype.itemsize (downcast comparison) All cases are safe at runtime: - Offset columns are always numpy dtypes (have itemsize) - Data buffer operations only run for non-ExtensionDtype columns - Downcast logic only runs for numeric dtypes Error count: 79 → 69 (10 errors fixed) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Added type: ignore[union-attr] for .str access in device_serialize: - column.py:1940 - self.dtype.str in serialization fallback path This is safe at runtime - the code path is only reached when dtype is a numpy dtype (after catching AttributeError and checking for pandas nullable extension dtypes), which has the .str attribute. Note: column.py:1850 was already fixed in Category 8 commit. Error count: 69 → 68 (1 error fixed) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Updated type ignore comment to include union-attr error code: - accessors/struct.py:112 - self._column.dtype.fields access The line already had type: ignore[arg-type] but was missing union-attr error code. This is safe at runtime - the struct accessor is only instantiated when dtype is StructDtype, which has the .fields attribute. Error count: 68 → 67 (1 error fixed) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Removed redundant initialization of _mask, _base_mask, _data, and _children from the constructor. The set_base_* methods already initialize these values, making the explicit None assignments wasteful. Before: self._mask = None # Redundant self._base_mask = None # Redundant self._data = None # Redundant self._children = None # Redundant self.set_base_children(children) # Already sets _children = None self.set_base_data(data) # Already sets _data = None self.set_base_mask(mask) # Already sets _mask = None, _children = None After: # Let the set_base_* methods handle initialization and cache management self.set_base_children(children) self.set_base_data(data) self.set_base_mask(mask) Benefits: - Eliminates 4 redundant assignments - DRYer code - initialization logic stays in set_base_* methods - Clearer separation of concerns - setters own cache management The set_base_* methods are designed to clear cached computed properties when base values change (for post-construction mutations). In the constructor, there's no need to pre-initialize these to None since the setters handle it. No functional change - mypy errors remain at 67. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Fixed type assignment and inference errors across temporal, index, and column modules: Category 11.1 (_PD_SCALAR type inference, 3-4 errors): - Changed _PD_SCALAR from instance type to ClassVar[type[...]] in TemporalBaseColumn - Added type: ignore[arg-type] for pandas-stubs strict literal string requirements in temporal_base.py mean(), median(), quantile() methods - Added type: ignore[arg-type] for as_unit() calls in datetime.py and timedelta.py Category 11.2 (Index/MultiIndex assignments, 2 errors): - Changed to_pandas_index return type to pd.Index | pd.MultiIndex in column_accessor.py - Added type: ignore[assignment] for MultiIndex.get_level_values() in dataframe.py Category 11.3 (Dtype assignments with None, 6 errors): - Added explicit DtypeObj | None type annotations for variables assigned from find_common_type() in column.py, indexed_frame.py, and frame.py - Added type: ignore[assignment] for dtype assignments in numerical.py and index.py All changes preserve runtime behavior and only add type annotations or suppressions for pandas-stubs limitations. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

@overload

…llable_pd_obj Added @overload signatures to _maybe_return_nullable_pd_obj() helper function to preserve type relationships between input and output: - DataFrame input → pd.DataFrame output - Series input → pd.Series output This allows mypy to correctly narrow the return type based on the input type, eliminating the assignment error in to_json() at line 444. Also added explicit type annotation for pd_value variable and type: ignore for pandas-stubs' strict overload definitions on to_json() call. Progress: 60 of 61 original Category 11 errors remaining (1 fixed). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

…teOffset Changed line 870 to use pd_offset intermediate variable to avoid type mismatch between pandas.tseries.offsets.DateOffset and cudf.DateOffset. The pandas-typed offset from pd.tseries.frequencies.to_offset() is now stored in pd_offset, then converted to cudf DateOffset via _from_pandas_ticks_or_weeks() before assignment to offset variable. Progress: 59 errors remaining (down from 60). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Added explicit type annotation `interval_labels: Any` because this variable can hold multiple different types throughout the function: - pd.IntervalIndex - cudf.IntervalIndex (from interval_range or from_breaks) - cudf.CategoricalIndex - labels (list-like) or None Using Any is the pragmatic solution for this complex multi-type variable. Progress: 58 errors remaining (down from 59). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Added TODO comment in SingleColumnFrame.dtype to investigate whether this property can ever actually return a string at runtime, or if the return type should be changed from Dtype (includes str) to DtypeObj (excludes str). This is causing mypy errors in Category 12.1 where functions expecting DtypeObj receive Dtype from Index.dtype -> categories.dtype chain. The type currently uses Dtype to match pandas API convention (which accepts string dtype specifications in constructors), but at runtime the property likely only returns actual dtype objects (ExtensionDtype | np.dtype). No functional changes, just documentation for future investigation. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Fixed three distinct sources of type errors related to Dtype/DtypeObj: **Source 1: Dtype (includes str) vs DtypeObj (no str)** - Changed SingleColumnFrame.dtype return type from Dtype to DtypeObj - Normalized dtype in column_empty() using cudf.dtype() - Normalized dtype in as_numerical_column() using cudf.dtype() **Source 2: find_common_type() returns DtypeObj | None** - Added None checks in column.py (2 locations) - Added assertion in numerical.py **Source 3: Dict key/value type mismatch** - Added type:ignore for np_dtypes_to_pandas_dtypes.get() Progress: Fixed approximately 12 errors (58→46) 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

…aframe Added type narrowing to help mypy understand None checks: - max_rows: Added assertion after `if max_rows in {0, None}` check, since mypy doesn't automatically narrow when checking membership in a set - ncols: Created intermediate variable with explicit type annotation to separate the None check from the usage Mypy correctly narrows max_seq_items inside `if max_seq_items is not None`, so no assertion needed there. This fixes 8 mypy errors related to unsupported operations on potentially None values. Progress: 46→38 errors remaining 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Fixed 4 mypy errors that appeared after adding | None type annotations: - indexed_frame.py: Added None check for to_type from find_common_type() with fallback to object dtype (lines 6982-6983) - frame.py: Reordered condition to check None before calling is_dtype_equal() for proper short-circuit evaluation (lines 1435, 1445) Progress: 32→28 errors remaining 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

1. column_accessor.py:212 - Convert pd.Index.names list to tuple - Wrapped return value with tuple() to match return type annotation 2. column/decimal.py:247 - Cast type() result to clarify DecimalDtype constructor - Added cast(type[DecimalDtype], type(self.dtype)) to help mypy understand the constructor signature - Removed call-overload from type:ignore, kept union-attr Progress: 28→26 errors remaining 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

- Add explicit type annotation for codes_list in to_pandas() - Add _name class attribute type annotation Reduces mypy errors from 27 to 25. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

- Cast self.dtype to np.dtype in _min_column_type returns - Cast dtype ternary expressions to np.dtype in can_cast_safely Reduces mypy errors from 25 to 22. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Cast self.dtype to DecimalDtype before accessing MAX_PRECISION attribute. Reduces mypy errors from 22 to 20. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Cast ltype and rtype to np.dtype when calling max() to satisfy type checker. Reduces mypy errors from 20 to 18. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

- datetime.py:837,849,872: Cast self.dtype to DatetimeTZDtype for _get_base_dtype() - datetime.py:872: Cast dtype to np.dtype for as_datetime_column() - index.py:2897: Cast return value to np.dtype in RangeIndex.dtype property - interval.py:157: Move type: ignore[override] to function definition Reduces mypy errors from 18 to 13 (72% total reduction from original 61). 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

This commit resolves all remaining mypy errors from Phase 2 analysis. Phase 2A - Type annotation and casting fixes (3 errors): - column_accessor.py:322: Added union type annotation at first assignment to allow mypy to track `result: pd.Index | pd.MultiIndex` through branches - index.py:4236: Changed DatetimeIndex.to_pandas() return type from pd.DatetimeIndex to pd.Index to match actual return value - timedelta.py:130: Added type: ignore[override] for __contains__ method signature mismatch with parent class Phase 2B - Upstream pandas-stubs issues (7 errors): - accessor.py:5: Added type: ignore[attr-defined] for CachedAccessor import - parquet.py:1460: Added type: ignore[misc] for pd.read_parquet call - json.py:267: Added type: ignore[arg-type] for compression parameter - pandas/_wrappers/pandas.py:591: Added type: ignore[attr-defined] for NumpyExtensionArray import (pandas internal API) - pandas/_wrappers/pandas.py:1184: Added type: ignore[attr-defined] for StylerRenderer import (pandas internal API) - utils.py:473: Added type: ignore[arg-type] for assert_frame_equal call Phase 2C - Complex overload issues (3 errors): - series.py:1962: Added type: ignore[call-overload] for pd.Series constructor - series.py:2024: Added type: ignore[index] for dtype dictionary indexing - dataframe.py:2459: Added type: ignore[call-overload] for pd.DataFrame.from_dict Total progress: 13 errors fixed in Phase 2. All mypy checks now pass. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

…ignores After rebase, 77 mypy errors appeared: - Removed 65 unused type: ignore comments across 15 files - Added 14 targeted type: ignore comments for legitimate type issues: * Override signature differences in categorical column * Complex inheritance types in column base classes * Pandas stub limitations for temporal types * TPC-H benchmark pandas stubs issues All mypy errors now resolved. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Instead of ignoring import-untyped errors, install types-ujson stubs to enable proper type checking for ujson imports. This requires adding type: ignore[no-redef] to the fallback json imports since mypy now sees both import branches. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

copy-pr-bot · 2025-11-07T07:29:15Z

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Implemented a cleaner dtype validation pattern across DatetimeColumn, StructColumn, and IntervalColumn where subclasses only need to override the _validate_dtype_instance() static method instead of __init__. Changes: - DatetimeColumn: Added _validate_dtype_instance() that passes through DatetimeTZDtype unchanged for subclass handling - DatetimeTZColumn: Removed __init__ override, now only overrides _validate_dtype_instance() with matching parent signature - StructColumn: Moved inline validation to _validate_dtype_instance() static method - IntervalColumn: Added _validate_dtype_instance() override that validates IntervalDtype before passing to parent This design follows the Liskov substitution principle and makes the code more maintainable by centralizing validation logic in a single overridable method. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Matt711 · 2025-11-07T19:21:25Z

Did we perchance get any pandas tests passing because of these type fixes? 😄

Fixed type errors in: - column.py:2667,2676,2690,2753 - Added type ignores for type narrowing issues where mypy doesn't narrow ExtensionDtype | np.dtype to np.dtype through control flow checks - numerical.py:920 - Fixed LSP violation by adding | None to dtype parameter - struct.py:220 - Added type ignore for IntervalDtype.subtype which can be None in stubs but is guaranteed at runtime - datetime.py:859 - Added cast to pd.DatetimeTZDtype following the pattern used elsewhere in the file All mypy errors are now resolved. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

vyasr · 2025-11-07T20:14:49Z

Did we perchance get any pandas tests passing because of these type fixes? 😄

We're about to find out when CI finally runs here!

mroeschke

I'm mainly concerned that a few PRs got reverted with these changes, namely #20436 #20304 #20438 #20384 #20304 #20464 AFAICT

mroeschke · 2025-11-07T20:31:03Z

python/cudf/cudf/core/column/categorical.py

        return codes_col._with_type_metadata(CategoricalDtype(categories=cats))  # type: ignore[return-value]

-    def _with_type_metadata(self: Self, dtype: Dtype) -> Self:
+    def _with_type_metadata(self: Self, dtype: DtypeObj | None) -> Self:


Suggested change

def _with_type_metadata(self: Self, dtype: DtypeObj | None) -> Self:

def _with_type_metadata(self: Self, dtype: DtypeObj) -> Self:

This should be the ideal annotation, but I suppose we could potentially pass None somewhere?

mroeschke · 2025-11-07T20:33:55Z

python/cudf/cudf/core/column/column.py

        # The skipna argument is only used for numerical columns.
        # If all entries are null the result is True, including when the column
        # is empty.
-        if not isinstance(skipna, bool):


This (and for any) were added to pass some pandas unit tests #20436

mroeschke · 2025-11-07T20:37:16Z

python/cudf/cudf/core/column/column.py

    def take(
        self,
        indices: ColumnBase,
-        *,


This was added in #20304 per @Matt711's review https://github.com/rapidsai/cudf/pull/20262/files#r2452571947

mroeschke · 2025-11-07T20:38:19Z

python/cudf/cudf/core/column/column.py

        self,
        op: str,
-        skipna: bool = True,
+        skipna: bool | None = None,


It appears this was reverted from #20436?

mroeschke · 2025-11-07T20:38:56Z

python/cudf/cudf/core/column/column.py


-    def _with_type_metadata(self: ColumnBase, dtype: DtypeObj) -> ColumnBase:
+    def _with_type_metadata(
+        self: ColumnBase, dtype: DtypeObj | None


Suggested change

self: ColumnBase, dtype: DtypeObj | None

self: ColumnBase, dtype: DtypeObj

Similarly, this is the ideal annotation

mroeschke · 2025-11-07T20:44:54Z

python/cudf/cudf/core/column/datetime.py

                # attr was not called yet, so ignore.
                pass

-    def _scan(self, op: str) -> ColumnBase:


This was reverted from #20464

mroeschke · 2025-11-07T20:45:14Z

python/cudf/cudf/core/column/datetime.py

            return result_col

-    def _with_type_metadata(self, dtype: DtypeObj) -> DatetimeColumn:
+    def _with_type_metadata(self, dtype: DtypeObj | None) -> DatetimeColumn:


Suggested change

def _with_type_metadata(self, dtype: DtypeObj | None) -> DatetimeColumn:

def _with_type_metadata(self, dtype: DtypeObj) -> DatetimeColumn:

Similarly, this is the ideal annotation

And I think in other files

mroeschke · 2025-11-07T20:46:25Z

python/cudf/cudf/core/column/numerical.py

+        # Normalize dtype from Dtype (which may include strings) to DtypeObj
+        dtype = cudf.dtype(dtype)


An annotation of dtype: DtypObj would be better if possible

mroeschke · 2025-11-07T20:47:56Z

python/cudf/cudf/core/indexed_frame.py

            distinct = ColumnBase.from_pylibcudf(plc_column)
        result = as_column(
-            True, length=len(self), dtype=bool
+            True, length=len(self), dtype="bool"


Suggested change

True, length=len(self), dtype="bool"

True, length=len(self), dtype=np.dtype(np.bool_)

mroeschke · 2025-11-07T20:50:34Z

python/cudf/cudf/io/orc.py


 try:
-    import ujson as json  # type: ignore[import-untyped]
+    import ujson as json


I was thinking if we should just remove this ujson import (I think parquet.py or json.py) has this too). I don't think we explicitly test if these readers/writers work with ujson

We don't, yeah. I'd also be fine with removing this too. Done in #20560

vyasr · 2025-11-08T00:20:35Z

I'm mainly concerned that a few PRs got reverted with these changes, namely #20436 #20304 #20438 #20384 #20304 #20464 AFAICT

Ugh that's no bueno. I may have screwed up the rebase, this PR changed so many bits 😕 There were also lots of conflicts in the rebase because in the meantime we removed all extra ignores and made other similar config changes. We can try to work through the errors here, but given how much you think went wrong maybe it would make sense to break up this PR into smaller chunks instead?

mroeschke · 2025-11-08T00:30:44Z

but given how much you think went wrong maybe it would make sense to break up this PR into smaller chunks instead?

Yeah that sounds like a good idea. Maybe even just 2 PRs:

A PR with better/changed annotations before adding the pandas type stubs
A PR with the pandas type stubs including all the ignores needed

vyasr · 2025-11-08T01:04:32Z

I might be able to pull that off, although the number of changes required for compatibility is enough that maybe not. I'll see what I can do.

pytest stubs are just added for completeness and didn't require any fixes. I removed ujson usage because we never test it to validate that it actually works for us. If we find that this meaningfully slows down particular use cases we can easily add it back (with proper testing), although at this point the better option would be to migrate to orjson, which is both faster and more correct. Contributes to #11661 Split out from #20547 Authors: - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - GALI PREM SAGAR (https://github.com/galipremsagar) - Matthew Roeschke (https://github.com/mroeschke) - Bradley Dice (https://github.com/bdice) URL: #20560

pytest stubs are just added for completeness and didn't require any fixes. I removed ujson usage because we never test it to validate that it actually works for us. If we find that this meaningfully slows down particular use cases we can easily add it back (with proper testing), although at this point the better option would be to migrate to orjson, which is both faster and more correct. Contributes to rapidsai#11661 Split out from rapidsai#20547 Authors: - Vyas Ramasubramani (https://github.com/vyasr) Approvers: - GALI PREM SAGAR (https://github.com/galipremsagar) - Matthew Roeschke (https://github.com/mroeschke) - Bradley Dice (https://github.com/bdice) URL: rapidsai#20560

vyasr and others added 30 commits November 7, 2025 04:36

Fix mypy errors in multiindex.py

07dc65b

- Add explicit type annotation for codes_list in to_pandas() - Add _name class attribute type annotation Reduces mypy errors from 27 to 25. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Fix mypy MAX_PRECISION error in decimal.py

a7fbfb0

Cast self.dtype to DecimalDtype before accessing MAX_PRECISION attribute. Reduces mypy errors from 22 to 20. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

Fix mypy max() type variable errors in join_helpers.py

c782dd8

Cast ltype and rtype to np.dtype when calling max() to satisfy type checker. Reduces mypy errors from 20 to 18. 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <[email protected]>

vyasr requested a review from a team as a code owner November 7, 2025 05:46

vyasr added the non-breaking Non-breaking change label Nov 7, 2025

vyasr requested review from AyodeAwe, mroeschke and wence- November 7, 2025 05:46

github-actions bot added Python Affects Python cuDF API. cudf.pandas Issues specific to cudf.pandas labels Nov 7, 2025

github-project-automation bot added this to cuDF Python Nov 7, 2025

GPUtester moved this to In Progress in cuDF Python Nov 7, 2025

vyasr force-pushed the fix/typing_pandas branch from 300a7e9 to 715f007 Compare November 7, 2025 06:08

vyasr and others added 2 commits November 7, 2025 06:09

vyasr force-pushed the fix/typing_pandas branch from 715f007 to 572c016 Compare November 7, 2025 06:10

vyasr mentioned this pull request Nov 7, 2025

[ENH] More type-stubs in the mypy pre-commit environment? #11661

Open

Also add pytest to the environment

d2162e9

vyasr marked this pull request as draft November 7, 2025 07:29

vyasr and others added 2 commits November 7, 2025 18:05

Make sure column attributes are set

fda419b

vyasr marked this pull request as ready for review November 7, 2025 19:16

mroeschke requested changes Nov 7, 2025

View reviewed changes

vyasr mentioned this pull request Nov 8, 2025

Add pytest stubs and remove ujson usage #20560

Merged

3 tasks

	def _with_type_metadata(self: Self, dtype: DtypeObj \| None) -> Self:
	def _with_type_metadata(self: Self, dtype: DtypeObj) -> Self:

	self: ColumnBase, dtype: DtypeObj \| None
	self: ColumnBase, dtype: DtypeObj

	def _with_type_metadata(self, dtype: DtypeObj \| None) -> DatetimeColumn:
	def _with_type_metadata(self, dtype: DtypeObj) -> DatetimeColumn:

		# Normalize dtype from Dtype (which may include strings) to DtypeObj
		dtype = cudf.dtype(dtype)

	True, length=len(self), dtype="bool"
	True, length=len(self), dtype=np.dtype(np.bool_)

Add pandas type stubs and fix errors #20547

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Add pandas type stubs and fix errors #20547

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