Fixed mypy error: Need type annotation for "np_dtype" at line 22.
Added explicit type annotation np.dtype[np.object_] to class attribute.

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Fixed mypy error: Need type annotation for "np_dtype" at line 33.
Added explicit type annotation np.dtype[np.object_] to class attribute.

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Co-Authored-By: Claude <noreply@anthropic.com>

Fixed mypy error: Need type annotation for "np_dtype" at line 44.
Added explicit type annotation np.dtype[np.object_] to class attribute.

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Co-Authored-By: Claude <noreply@anthropic.com>

Fixed mypy error: Need type annotation for "min_date" at line 124.
Added explicit type annotation np.datetime64 for local variable.

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Fixed mypy error: Need type annotation for "np_dtypes_to_pandas_dtypes" at line 23.
Added explicit type annotation dict[np.dtype[Any], pd.core.dtypes.base.ExtensionDtype]
and imported Any from typing.

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Fixed mypy error: Need type annotation for "dtype" at line 205.
Added explicit type annotation np.dtype[Any] for local variable in loop.

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Fixed mypy error: Need type annotation for "dtype" at line 219.
Added explicit type annotation np.dtype[Any] for local variable in loop.

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Co-Authored-By: Claude <noreply@anthropic.com>

Fixed mypy error: Need type annotation for "SUPPORTED_NUMPY_TO_PYLIBCUDF_TYPES" at line 763.
Added explicit type annotation dict[np.dtype[Any], plc.types.TypeId].

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Co-Authored-By: Claude <noreply@anthropic.com>

Fixed mypy error: Need type annotation for "_UNDERLYING_DTYPE" at line 47.
Added explicit type annotation np.dtype[np.int64] to class attribute.

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Co-Authored-By: Claude <noreply@anthropic.com>

Fixed 4 mypy errors in dtypes.py by:

1. Converting is_pandas_nullable_extension_dtype to use TypeGuard[pd.core.dtypes.base.ExtensionDtype]
   - This resolved .na_value access errors on lines 255 and 262

2. Filtering dtypes to create cat_dtypes list with explicit isinstance checks
   - Ensures mypy knows all items are cudf.CategoricalDtype

3. Adding None check when filtering categorical dtypes
   - Filters out dtypes where _categories is None

4. Using explicit loop with assertions instead of list comprehensions
   - Helps mypy understand _categories is not None after filtering
   - This resolved ._categories access errors on lines 297 and 300

The TypeGuard pattern tells mypy to narrow the dtype type when the function
returns True, making attribute access type-safe without runtime overhead.

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Co-Authored-By: Claude <noreply@anthropic.com>

Added type: ignore[call-overload] comment with detailed explanation.

The issue is that numpy's type stubs for datetime64/timedelta64 constructors
only accept literal strings for the time unit parameter (like "ns", "us", etc.)
to enable compile-time validation. However, we're passing a variable string
(to_unit) which contains a time unit that we know is valid at runtime.

This is one of 20 errors on this line from numpy's overly restrictive stubs.

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Co-Authored-By: Claude <noreply@anthropic.com>

Added type: ignore[call-overload] comment with detailed explanation.

The issue is that numpy's type stubs for timedelta64 constructors only
accept literal strings for the time unit parameter to enable compile-time
validation. However, we're passing self.time_unit which is a variable
containing a valid time unit at runtime.

This is one of 30 errors on lines 313-323 from numpy's overly restrictive stubs.

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Co-Authored-By: Claude <noreply@anthropic.com>

Added type: ignore[call-overload] comment (reusing explanation from max_dist).

The issue is that numpy's type stubs for timedelta64 constructors only
accept literal strings for the time unit parameter to enable compile-time
validation. However, we're passing self.time_unit which is a variable
containing a valid time unit at runtime.

This is one of 30 errors on lines 313-323 from numpy's overly restrictive stubs.

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Co-Authored-By: Claude <noreply@anthropic.com>

Added type: ignore[call-overload] comment (reusing explanation from max_dist).

The issue is that numpy's type stubs for timedelta64 constructors only
accept literal strings for the time unit parameter to enable compile-time
validation. However, we're passing to_res which is a variable containing
a valid time unit at runtime.

This is one of 30 errors on lines 313-323 from numpy's overly restrictive stubs.

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Co-Authored-By: Claude <noreply@anthropic.com>

Added assertion to narrow type for mypy before the constructor call.

The issue is that col_dtype has a union type (DtypeObj) which includes
many types, but at this point in the code we know it's one of the decimal
types because of the check on lines 2224-2228. The assertion tells mypy
that col_dtype is specifically a decimal dtype, so type(col_dtype) will
be a decimal dtype constructor that accepts (precision, scale) arguments.

This fixes 59 errors on this single line from numpy's strict type stubs.

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Co-Authored-By: Claude <noreply@anthropic.com>

Changed comment blocks from starting with "# type: ignore[call-overload]:"
to "# call-overload must be ignored because" to avoid mypy treating them
as malformed type: ignore directives.

Fixed 2 errors at lines 165 and 317.

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Co-Authored-By: Claude <noreply@anthropic.com>

Group 2: Changed _NP_SCALAR from instance annotation to ClassVar to allow
subclasses to assign specific numpy scalar types (datetime64 or timedelta64).

Group 3: Added type: ignore[call-overload] comments for numpy constructor
calls with variable time unit strings, which numpy stubs don't support.

Fixed 7 total errors (3 from Group 2 + 4 from Group 3).

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Co-Authored-By: Claude <noreply@anthropic.com>

Added type: ignore[arg-type] comments for as_interval_column and
as_decimal_column calls where mypy cannot narrow the dtype type from
the is_dtype_obj_* function checks (which are not TypeGuards).

Fixed 2 errors at lines 1734 and 1742.

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Co-Authored-By: Claude <noreply@anthropic.com>

Fixed 8 mypy errors related to _get_nan_for_dtype:
1. Changed return type from DtypeObj to np.generic in dtypes.py
   - Function returns numpy scalar values like np.float64('nan') or np.datetime64('NaT')
2. Added type: ignore[return-value] comments in numerical_base.py at 7 locations
   - kurtosis() lines 93, 98
   - quantile() line 184
   - median() line 228
   - cov() line 247
   - corr() lines 255, 261

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Co-Authored-By: Claude <noreply@anthropic.com>

Added explicit type annotations for 12 variables across multiple files
where mypy could not infer types after adding numpy stubs:

- dtypes.py line 694: Fixed fields dict type annotation (str not bytes)
- decimal.py lines 387, 512: Added data_buf_128 ndarray annotations
- index.py lines 2882, 5175: Added dtype and child_type annotations
- frame.py line 559: Updated to_array parameter to allow None
- groupby.py line 1558: Added high ndarray annotation
- csv.py line 38: Added _CSV_HEX_TYPE_MAP dict annotation
- numeric.py line 180: Added downcast_dtype annotation
- datetimes.py line 852: Added dtype annotation
- queryutils.py line 26: Added SUPPORTED_QUERY_TYPES set annotation
- fast_slow_proxy.py line 1166: Added transformed list annotation

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Co-Authored-By: Claude <noreply@anthropic.com>

- Line 1167: Added type: ignore for list comprehension loop variable
- Line 1174: Changed dtype=object to dtype=np.object_ for np.empty()
- Line 1389: Fixed NUMPY_TYPES annotation from set[str] to set[type[np.generic]]

These fixes address strict numpy stub type checking requirements.

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Co-Authored-By: Claude <noreply@anthropic.com>

Updated type signatures across multiple files to accept both int and numpy
unsigned integer types for seed parameters, resolving type inconsistencies
between public APIs, column methods, and pylibcudf stubs.

Changes:
1. Column-level methods (string.py, lists.py): Updated minhash, minhash64,
   hash_character_ngrams, minhash_ngrams, and minhash64_ngrams to accept
   int | np.uint32 or int | np.uint64 for seed parameters
2. Added runtime validation to convert int to appropriate numpy unsigned
   integer type with bounds checking before calling pylibcudf
3. Accessor-level methods (accessors/string.py): Updated method signatures
   to accept int | np.uint32 or int | np.uint64 for consistency
4. pylibcudf stubs: Updated minhash.pyi and generate_ngrams.pyi to accept
   int | np.unsignedinteger[Any] for seed parameters

This allows public APIs to accept convenient int literals while maintaining
type safety and proper conversion to numpy unsigned integers at runtime.

Progress: 19 of 200 original mypy errors remain (90.5% complete)

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Co-Authored-By: Claude <noreply@anthropic.com>

Fixed remaining mypy type errors across core modules:

Column modules:
- struct.py: Added type narrowing for dtype assignment in to_arrow()
- numerical.py: Added union type annotations for finfo bounds
- numerical_base.py: Corrected placement of type: ignore for return value
- decimal.py: Added None check for data_buf, fixed dtype assignment in to_arrow()

Core modules:
- series.py: Added type: ignore for is_dict_like() check
- index.py: Added type: ignore for as_column return type in RangeIndex
- frame.py: Widened dtype parameter type to Any in to_array helper
- groupby.py: Inlined to_take construction to avoid assignment conflicts

Tools modules:
- numeric.py: Added type: ignore for numpy typecodes string access
- datetimes.py: Added type: ignore for np.datetime64 with non-literal unit

Pandas modules:
- _wrappers/numpy.py: Added type: ignore for conditional flagsobj import
- fast_slow_proxy.py: Corrected placement of type annotations for list comprehension and np.empty call

Stub updates:
- quantiles.pyi: Changed parameter type from Sequence[float] to Iterable[float] to accept numpy arrays

All fixes preserve runtime behavior while satisfying mypy's strict type checking.

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Co-Authored-By: Claude <noreply@anthropic.com>

…numpy