feat: Add Index constructor, copy, get_level_values, to_series

bigframes/core/blocks.py

@@ -287,15 +287,14 @@ def reset_index(self, drop: bool = True) -> Block:
             A new Block because dropping index columns can break references
             from Index classes that point to this block.
         """
-        block = self
         new_index_col_id = guid.generate_guid()
         expr = self._expr.promote_offsets(new_index_col_id)
         if drop:
             # Even though the index might be part of the ordering, keep that
             # ordering expression as reset_index shouldn't change the row
             # order.
             expr = expr.drop_columns(self.index_columns)
-            block = Block(
+            return Block(
                 expr,
                 index_columns=[new_index_col_id],
                 column_labels=self.column_labels,
@@ -321,13 +320,12 @@ def reset_index(self, drop: bool = True) -> Block:
                 # See: https://pandas.pydata.org/docs/reference/api/pandas.Index.insert.html
                 column_labels_modified = column_labels_modified.insert(level, label)
 
-            block = Block(
+            return Block(
                 expr,
                 index_columns=[new_index_col_id],
                 column_labels=column_labels_modified,
                 index_labels=[None],
             )
-        return block
 
     def set_index(
         self,

bigframes/core/indexers.py

@@ -294,7 +294,7 @@ def _loc_getitem_series_or_dataframe(
         keys_df = keys_df.set_index(temp_name, drop=True)
         return _perform_loc_list_join(series_or_dataframe, keys_df)
     elif isinstance(key, bigframes.core.indexes.Index):
-        block = key._data._get_block()
+        block = key._block
         block = block.select_columns(())
         keys_df = bigframes.dataframe.DataFrame(block)
         return _perform_loc_list_join(series_or_dataframe, keys_df)

bigframes/core/indexes/index.py

@@ -17,8 +17,9 @@
 from __future__ import annotations
 
 import typing
-from typing import Mapping, Sequence, Tuple, Union
+from typing import Hashable, Mapping, Optional, Sequence, Tuple, Union
 
+import google.cloud.bigquery as bigquery
 import numpy as np
 import pandas
 
@@ -33,16 +34,60 @@
 import bigframes.core.utils as utils
 import bigframes.dtypes
 import bigframes.dtypes as bf_dtypes
+import bigframes.formatting_helpers as formatter
 import bigframes.operations as ops
 import bigframes.operations.aggregations as agg_ops
 import third_party.bigframes_vendored.pandas.core.indexes.base as vendored_pandas_index
 
+if typing.TYPE_CHECKING:
+    import bigframes.dataframe
+    import bigframes.series
+
 
 class Index(vendored_pandas_index.Index):
     __doc__ = vendored_pandas_index.Index.__doc__
 
-    def __init__(self, data: blocks.BlockHolder):
-        self._data = data
+    def __init__(
+        self,
+        data=None,
+        dtype=None,
+        *,
+        name=None,
+    ):
+        import bigframes.dataframe as df
+        import bigframes.series as series
+
+        if isinstance(data, blocks.Block):
+            block = data.select_columns([])
+        elif isinstance(data, df.DataFrame):
+            raise ValueError("Cannot construct index from dataframe.")
+        elif isinstance(data, series.Series) or isinstance(data, Index):
+            if isinstance(data, series.Series):
+                block = data._block
+                block = block.set_index(
+                    col_ids=[data._value_column],
+                )
+            elif isinstance(data, Index):
+                block = data._block
+            index = Index(data=block)
+            name = data.name if name is None else name
+            if name is not None:
+                index.name = name
+            if dtype is not None:
+                index = index.astype(dtype)
+            block = index._block
+        else:
+            pd_index = pandas.Index(data=data, dtype=dtype, name=name)
+            pd_df = pandas.DataFrame(index=pd_index)
+            block = df.DataFrame(pd_df)._block
+        self._query_job = None
+        self._block: blocks.Block = block
+
+    @classmethod
+    def from_frame(
+        cls, frame: Union[bigframes.series.Series, bigframes.dataframe.DataFrame]
+    ) -> Index:
+        return FrameIndex(frame)
 
     @property
     def name(self) -> blocks.Label:
@@ -55,15 +100,16 @@ def name(self, value: blocks.Label):
     @property
     def names(self) -> typing.Sequence[blocks.Label]:
         """Returns the names of the Index."""
-        return self._data._get_block()._index_labels
+        return self._block._index_labels
 
     @names.setter
     def names(self, values: typing.Sequence[blocks.Label]):
-        return self._data._set_block(self._block.with_index_labels(values))
+        new_block = self._block.with_index_labels(values)
+        self._block = new_block
 
     @property
     def nlevels(self) -> int:
-        return len(self._data._get_block().index_columns)
+        return len(self._block.index_columns)
 
     @property
     def values(self) -> np.ndarray:
@@ -75,7 +121,7 @@ def ndim(self) -> int:
 
     @property
     def shape(self) -> typing.Tuple[int]:
-        return (self._data._get_block().shape[0],)
+        return (self._block.shape[0],)
 
     @property
     def dtype(self):
@@ -107,9 +153,7 @@ def is_monotonic_increasing(self) -> bool:
         """
         return typing.cast(
             bool,
-            self._data._get_block().is_monotonic_increasing(
-                self._data._get_block().index_columns
-            ),
+            self._block.is_monotonic_increasing(self._block.index_columns),
         )
 
     @property
@@ -122,9 +166,7 @@ def is_monotonic_decreasing(self) -> bool:
         """
         return typing.cast(
             bool,
-            self._data._get_block().is_monotonic_decreasing(
-                self._data._get_block().index_columns
-            ),
+            self._block.is_monotonic_decreasing(self._block.index_columns),
         )
 
     @property
@@ -149,14 +191,65 @@ def has_duplicates(self) -> bool:
         duplicates_df = df.DataFrame(duplicates_block)
         return duplicates_df["is_duplicate"].any()
 
-    @property
-    def _block(self) -> blocks.Block:
-        return self._data._get_block()
-
     @property
     def T(self) -> Index:
         return self.transpose()
 
+    @property
+    def query_job(self) -> Optional[bigquery.QueryJob]:
+        """BigQuery job metadata for the most recent query.
+
+        Returns:
+            The most recent `QueryJob
+            <https://cloud.google.com/python/docs/reference/bigquery/latest/google.cloud.bigquery.job.QueryJob>`_.
+        """
+        if self._query_job is None:
+            self._query_job = self._block._compute_dry_run()
+        return self._query_job
+
+    def __repr__(self) -> str:
+        # TODO(swast): Add a timeout here? If the query is taking a long time,
+        # maybe we just print the job metadata that we have so far?
+        # TODO(swast): Avoid downloading the whole series by using job
+        # metadata, like we do with DataFrame.
+        opts = bigframes.options.display
+        max_results = opts.max_rows
+        if opts.repr_mode == "deferred":
+            return formatter.repr_query_job(self.query_job)
+
+        pandas_df, _, query_job = self._block.retrieve_repr_request_results(max_results)
+        self._query_job = query_job
+        return repr(pandas_df.index)
+
+    def copy(self, name: Optional[Hashable] = None):
+        copy_index = Index(self._block)
+        if name is not None:
+            copy_index.name = name
+        return copy_index
+
+    def to_series(
+        self, index: Optional[Index] = None, name: Optional[Hashable] = None
+    ) -> bigframes.series.Series:
+        if self.nlevels != 1:
+            NotImplementedError(
+                f"Converting multi-index to series is not yet supported. {constants.FEEDBACK_LINK}"
+            )
+
+        import bigframes.series
+
+        name = self.name if name is None else name
+        if index is None:
+            return bigframes.series.Series(data=self, index=self, name=name)
+        else:
+            return bigframes.series.Series(data=self, index=Index(index), name=name)
+
+    def get_level_values(self, level) -> Index:
+        level_n = level if isinstance(level, int) else self.names.index(level)
+        block = self._block.drop_levels(
+            [self._block.index_columns[i] for i in range(self.nlevels) if i != level_n]
+        )
+        return Index(block)
+
     def _memory_usage(self) -> int:
         (n_rows,) = self.shape
         return sum(
@@ -180,7 +273,7 @@ def sort_values(self, *, ascending: bool = True, na_position: str = "last"):
             order.OrderingColumnReference(column, direction=direction, na_last=na_last)
             for column in index_columns
         ]
-        return Index._from_block(self._block.order_by(ordering))
+        return Index(self._block.order_by(ordering))
 
     def astype(
         self,
@@ -269,7 +362,7 @@ def rename(self, name: Union[str, Sequence[str]]) -> Index:
         names = [name] if isinstance(name, str) else list(name)
         if len(names) != self.nlevels:
             raise ValueError("'name' must be same length as levels")
-        return Index._from_block(self._block.with_index_labels(names))
+        return Index(self._block.with_index_labels(names))
 
     def drop(
         self,
@@ -291,17 +384,17 @@ def drop(
             )
         block = block.filter(condition_id, keep_null=True)
         block = block.drop_columns([condition_id])
-        return Index._from_block(block)
+        return Index(block)
 
     def dropna(self, how: str = "any") -> Index:
         if how not in ("any", "all"):
             raise ValueError("'how' must be one of 'any', 'all'")
         result = block_ops.dropna(self._block, self._block.index_columns, how=how)  # type: ignore
-        return Index._from_block(result)
+        return Index(result)
 
     def drop_duplicates(self, *, keep: str = "first") -> Index:
         block = block_ops.drop_duplicates(self._block, self._block.index_columns, keep)
-        return Index._from_block(block)
+        return Index(block)
 
     def isin(self, values) -> Index:
         if not utils.is_list_like(values):
@@ -330,7 +423,7 @@ def _apply_unary_expr(
             result_ids.append(result_id)
 
         block = block.set_index(result_ids, index_labels=self._block.index_labels)
-        return Index._from_block(block)
+        return Index(block)
 
     def _apply_aggregation(self, op: agg_ops.AggregateOp) -> typing.Any:
         if self.nlevels > 1:
@@ -344,7 +437,7 @@ def __getitem__(self, key: int) -> typing.Any:
                 result_pd_df, _ = self._block.slice(key, key + 1, 1).to_pandas()
             else:  # special case, want [-1:] instead of [-1:0]
                 result_pd_df, _ = self._block.slice(key).to_pandas()
-            if result_pd_df.empty:
+            if result_pd_df.index.empty:
                 raise IndexError("single positional indexer is out-of-bounds")
             return result_pd_df.index[0]
         else:
@@ -367,11 +460,36 @@ def to_numpy(self, dtype=None, **kwargs) -> np.ndarray:
     def __len__(self):
         return self.shape[0]
 
-    @classmethod
-    def _from_block(cls, block: blocks.Block) -> Index:
-        import bigframes.dataframe as df
 
-        return Index(df.DataFrame(block))
+# Index that mutates the originating dataframe/series
+class FrameIndex(Index):
+    def __init__(
+        self,
+        series_or_dataframe: typing.Union[
+            bigframes.series.Series, bigframes.dataframe.DataFrame
+        ],
+    ):
+        super().__init__(series_or_dataframe._block)
+        self._whole_frame = series_or_dataframe
+
+    @property
+    def name(self) -> blocks.Label:
+        return self.names[0]
+
+    @name.setter
+    def name(self, value: blocks.Label):
+        self.names = [value]
+
+    @property
+    def names(self) -> typing.Sequence[blocks.Label]:
+        """Returns the names of the Index."""
+        return self._block._index_labels
+
+    @names.setter
+    def names(self, values: typing.Sequence[blocks.Label]):
+        new_block = self._whole_frame._get_block().with_index_labels(values)
+        self._whole_frame._set_block(new_block)
+        self._block = new_block
 
 
 class IndexValue:
@@ -406,15 +524,6 @@ def dtypes(
     def session(self) -> core.Session:
         return self._expr.session
 
-    def __repr__(self) -> str:
-        """Converts an Index to a string."""
-        # TODO(swast): Add a timeout here? If the query is taking a long time,
-        # maybe we just print the job metadata that we have so far?
-        # TODO(swast): Avoid downloading the whole index by using job
-        # metadata, like we do with DataFrame.
-        preview = self.to_pandas()
-        return repr(preview)
-
     def to_pandas(self) -> pandas.Index:
         """Executes deferred operations and downloads the results."""
         # Project down to only the index column. So the query can be cached to visualize other data.