diff --git a/pandas/core/arrays/interval.py b/pandas/core/arrays/interval.py index cf15f36cb03a3..c5366884fbdfe 100644 --- a/pandas/core/arrays/interval.py +++ b/pandas/core/arrays/interval.py @@ -27,7 +27,6 @@ from pandas.core.dtypes.dtypes import IntervalDtype from pandas.core.dtypes.generic import ( ABCDatetimeIndex, - ABCExtensionArray, ABCIndexClass, ABCIntervalIndex, ABCPeriodIndex, @@ -767,7 +766,7 @@ def size(self) -> int: # Avoid materializing self.values return self.left.size - def shift(self, periods: int = 1, fill_value: object = None) -> ABCExtensionArray: + def shift(self, periods: int = 1, fill_value: object = None) -> "IntervalArray": if not len(self) or periods == 0: return self.copy() diff --git a/pandas/core/dtypes/cast.py b/pandas/core/dtypes/cast.py index 0855d9335cc3d..424eb9d673df5 100644 --- a/pandas/core/dtypes/cast.py +++ b/pandas/core/dtypes/cast.py @@ -103,9 +103,9 @@ def is_nested_object(obj) -> bool: This may not be necessarily be performant. """ - if isinstance(obj, ABCSeries) and is_object_dtype(obj): + if isinstance(obj, ABCSeries) and is_object_dtype(obj.dtype): - if any(isinstance(v, ABCSeries) for v in obj.values): + if any(isinstance(v, ABCSeries) for v in obj._values): return True return False diff --git a/pandas/core/frame.py b/pandas/core/frame.py index 31015e3095e7d..4f5cbddc4eb05 100644 --- a/pandas/core/frame.py +++ b/pandas/core/frame.py @@ -1159,7 +1159,7 @@ def dot(self, other): left = self.reindex(columns=common, copy=False) right = other.reindex(index=common, copy=False) lvals = left.values - rvals = right.values + rvals = right._values else: left = self lvals = self.values @@ -1891,7 +1891,7 @@ def to_records( if index: if isinstance(self.index, MultiIndex): # array of tuples to numpy cols. copy copy copy - ix_vals = list(map(np.array, zip(*self.index.values))) + ix_vals = list(map(np.array, zip(*self.index._values))) else: ix_vals = [self.index.values] @@ -3009,7 +3009,7 @@ def _setitem_frame(self, key, value): raise ValueError("Array conditional must be same shape as self") key = self._constructor(key, **self._construct_axes_dict()) - if key.values.size and not is_bool_dtype(key.values): + if key.size and not is_bool_dtype(key.values): raise TypeError( "Must pass DataFrame or 2-d ndarray with boolean values only" ) @@ -7450,7 +7450,7 @@ def applymap(self, func) -> "DataFrame": def infer(x): if x.empty: return lib.map_infer(x, func) - return lib.map_infer(x.astype(object).values, func) + return lib.map_infer(x.astype(object)._values, func) return self.apply(infer) diff --git a/pandas/core/generic.py b/pandas/core/generic.py index 85b6a8431617a..1c7dd20945478 100644 --- a/pandas/core/generic.py +++ b/pandas/core/generic.py @@ -4980,7 +4980,7 @@ def sample( else: raise ValueError("Invalid weights: weights sum to zero") - weights = weights.values + weights = weights._values # If no frac or n, default to n=1. if n is None and frac is None: diff --git a/pandas/core/groupby/groupby.py b/pandas/core/groupby/groupby.py index 55b9c28c74cb2..e169260f087c0 100644 --- a/pandas/core/groupby/groupby.py +++ b/pandas/core/groupby/groupby.py @@ -1948,7 +1948,7 @@ def nth(self, n: Union[int, List[int]], dropna: Optional[str] = None) -> DataFra grb = dropped.groupby(grouper, as_index=self.as_index, sort=self.sort) sizes, result = grb.size(), grb.nth(n) - mask = (sizes < max_len).values + mask = (sizes < max_len)._values # set the results which don't meet the criteria if len(result) and mask.any(): diff --git a/pandas/core/indexes/base.py b/pandas/core/indexes/base.py index d9828707b6164..a97407394e7d7 100644 --- a/pandas/core/indexes/base.py +++ b/pandas/core/indexes/base.py @@ -639,13 +639,15 @@ def astype(self, dtype, copy=True): elif is_categorical_dtype(dtype): from pandas.core.indexes.category import CategoricalIndex - return CategoricalIndex(self.values, name=self.name, dtype=dtype, copy=copy) + return CategoricalIndex( + self._values, name=self.name, dtype=dtype, copy=copy + ) elif is_extension_array_dtype(dtype): return Index(np.asarray(self), name=self.name, dtype=dtype, copy=copy) try: - casted = self.values.astype(dtype, copy=copy) + casted = self._values.astype(dtype, copy=copy) except (TypeError, ValueError) as err: raise TypeError( f"Cannot cast {type(self).__name__} to dtype {dtype}" @@ -906,7 +908,7 @@ def format(self, name: bool = False, formatter=None, **kwargs): return self._format_with_header(header, **kwargs) def _format_with_header(self, header, na_rep="NaN", **kwargs): - values = self.values + values = self._values from pandas.io.formats.format import format_array diff --git a/pandas/core/indexes/datetimes.py b/pandas/core/indexes/datetimes.py index 6f1614d050cad..47c50dd2c7b14 100644 --- a/pandas/core/indexes/datetimes.py +++ b/pandas/core/indexes/datetimes.py @@ -440,7 +440,7 @@ def to_series(self, keep_tz=lib.no_default, index=None, name=None): # preserve the tz & copy values = self.copy(deep=True) else: - values = self.values.copy() + values = self._values.view("M8[ns]").copy() return Series(values, index=index, name=name) diff --git a/pandas/core/indexes/multi.py b/pandas/core/indexes/multi.py index f1e1ebcaca1c4..d1b0cdec5721c 100644 --- a/pandas/core/indexes/multi.py +++ b/pandas/core/indexes/multi.py @@ -1464,7 +1464,7 @@ def is_monotonic_increasing(self) -> bool: # reversed() because lexsort() wants the most significant key last. values = [ - self._get_level_values(i).values for i in reversed(range(len(self.levels))) + self._get_level_values(i)._values for i in reversed(range(len(self.levels))) ] try: sort_order = np.lexsort(values) @@ -2455,7 +2455,7 @@ def get_indexer(self, target, method=None, limit=None, tolerance=None): "tolerance not implemented yet for MultiIndex" ) indexer = self._engine.get_indexer( - values=self.values, target=target, method=method, limit=limit + values=self._values, target=target, method=method, limit=limit ) elif method == "nearest": raise NotImplementedError( diff --git a/pandas/core/internals/construction.py b/pandas/core/internals/construction.py index b2af149ccf14c..d49f1f154a2c1 100644 --- a/pandas/core/internals/construction.py +++ b/pandas/core/internals/construction.py @@ -347,7 +347,7 @@ def _homogenize(data, index, dtype: Optional[DtypeObj]): val = com.dict_compat(val) else: val = dict(val) - val = lib.fast_multiget(val, oindex.values, default=np.nan) + val = lib.fast_multiget(val, oindex._values, default=np.nan) val = sanitize_array( val, index, dtype=dtype, copy=False, raise_cast_failure=False ) diff --git a/pandas/core/internals/managers.py b/pandas/core/internals/managers.py index 4f6d84e52ea54..f548b479b96d2 100644 --- a/pandas/core/internals/managers.py +++ b/pandas/core/internals/managers.py @@ -475,7 +475,7 @@ def get_axe(block, qs, axes): b.mgr_locs = sb.mgr_locs else: - new_axes[axis] = Index(np.concatenate([ax.values for ax in axes])) + new_axes[axis] = Index(np.concatenate([ax._values for ax in axes])) if transposed: new_axes = new_axes[::-1] diff --git a/pandas/plotting/_matplotlib/core.py b/pandas/plotting/_matplotlib/core.py index 19a75eb151782..a049ac99f0e08 100644 --- a/pandas/plotting/_matplotlib/core.py +++ b/pandas/plotting/_matplotlib/core.py @@ -247,7 +247,7 @@ def _iter_data(self, data=None, keep_index=False, fillna=None): yield col, values.values @property - def nseries(self): + def nseries(self) -> int: if self.data.ndim == 1: return 1 else: