SNOW-1458135 Implement DataFrame and Series initialization with lazy Index objects

CHANGELOG.md

@@ -35,6 +35,7 @@
 - Added support for `DatetimeIndex.month_name` and `DatetimeIndex.day_name`.
 - Added support for `Series.dt.weekday`, `Series.dt.time`, and `DatetimeIndex.time`.
 - Added support for subtracting two timestamps to get a Timedelta.
+- Added support for creating `Series` and `DataFrame` objects with the lazy `Index` object as `data`, `index`, and `columns` parameters.
 
 #### Bug Fixes
 

src/snowflake/snowpark/modin/pandas/dataframe.py

@@ -152,19 +152,30 @@ def __init__(
         # Siblings are other dataframes that share the same query compiler. We
         # use this list to update inplace when there is a shallow copy.
         from snowflake.snowpark.modin.pandas.utils import try_convert_index_to_native
+        from snowflake.snowpark.modin.plugin.extensions.index import Index
 
         self._siblings = []
 
+        if isinstance(index, DataFrame):  # pandas raises the same error
+            raise ValueError("Index data must be 1-dimensional")
+
         # Engine.subscribe(_update_engine)
+        if isinstance(data, Index):
+            # If the data is an Index object, we need to convert it to a DataFrame to make sure
+            # that the values are in the correct format -- as a data column, not an index column.
+            # Additionally, if an index is provided, converting it to an Index object ensures that
+            # its values are an index column.
+            query_compiler = data.to_frame(index=False, name=data.name)._query_compiler
+            if index is not None:
+                index = index if isinstance(index, Index) else Index(index)
+                query_compiler = query_compiler.create_qc_with_index_data_and_qc_index(
+                    index._query_compiler
+                )
+
         if isinstance(data, (DataFrame, Series)):
             self._query_compiler = data._query_compiler.copy()
-            if index is not None and any(i not in data.index for i in index):
-                ErrorMessage.not_implemented(
-                    "Passing non-existant columns or index values to constructor not"
-                    + " yet implemented."
-                )  # pragma: no cover
             if isinstance(data, Series):
-                # We set the column name if it is not in the provided Series
+                # We set the column name if it is not in the provided Series `data`.
                 if data.name is None:
                     self.columns = [0] if columns is None else columns
                 # If the columns provided are not in the named Series, pandas clears
@@ -174,22 +185,61 @@ def __init__(
                         self.__constructor__(columns=columns)
                     )._query_compiler
                 if index is not None:
+                    # The `index` parameter is used to select the rows from `data` that will be in the resultant
+                    # DataFrame. If a value in `index` is not present in `data`'s index, it will be filled with a
+                    # NaN value.
+                    # 1. The `index` is converted to an Index object so that the index values are in an index column.
+                    index = index if isinstance(index, Index) else Index(index)
+                    # 2. A right outer join is performed between `data` and `index` to create a Series object where
+                    #    any index values in `data`'s index that are not in `index` are filled with NaN.
+                    data = Series(
+                        query_compiler=data._query_compiler.create_qc_with_data_and_index_joined_on_index(
+                            index._query_compiler
+                        ),
+                        name=0 if data.name is None else data.name,
+                    )
+                    # 3. Perform .loc[] on `data` to select the rows that are in the `index`.
                     self._query_compiler = data.loc[index]._query_compiler
+
             elif columns is None and index is None:
                 data._add_sibling(self)
+
             else:
-                if columns is not None and any(i not in data.columns for i in columns):
-                    ErrorMessage.not_implemented(
-                        "Passing non-existant columns or index values to constructor not"
-                        + " yet implemented."
-                    )  # pragma: no cover
-                if index is None:
-                    index = slice(None)
+                # The `columns` parameter is used to select the columns from `data` that will be in the resultant
+                # DataFrame. If a value in `columns` is not present in `data`'s columns, it will be added as a
+                # new column filled with NaN values. These columns are tracked by the `extra_columns` variable.
+                extra_columns = None
                 if columns is None:
+                    # In case `columns` is not provided, `columns` is set to slice(None) to select all columns.
                     columns = slice(None)
+                else:
+                    extra_columns = [col for col in columns if col not in data.columns]
+
+                # The `index` parameter is used to select the rows from `data` that will be in the resultant DataFrame.
+                # If a value in `index` is not present in `data`'s index, it will be filled with a NaN value.
+                if index is None:
+                    # In case `index` is not provided, `index` is set to slice(None) to select all rows.
+                    index = slice(None)
+                    data = DataFrame(
+                        query_compiler=data._query_compiler.create_qc_with_data_and_index_joined_on_index(
+                            extra_columns=extra_columns
+                        )
+                    )
+                else:
+                    # The `index` is converted to an Index object so that the index values are in an index column.
+                    index = index if isinstance(index, Index) else Index(index)
+                    # A right outer join is performed between `data` and `index` to create a DataFrame object where any
+                    # index values in `data`'s index that are not in `index` are filled with NaN.
+                    data = DataFrame(
+                        query_compiler=data._query_compiler.create_qc_with_data_and_index_joined_on_index(
+                            index._query_compiler,
+                            extra_columns=extra_columns,
+                        )
+                    )
+                # 3. Perform .loc[] on `data` to select the rows and columns that are in `index` and `columns`.
                 self._query_compiler = data.loc[index, columns]._query_compiler
 
-        # Check type of data and use appropriate constructor
+        # Check the type of data and use the appropriate constructor
         elif query_compiler is None:
             distributed_frame = from_non_pandas(data, index, columns, dtype)
             if distributed_frame is not None:

src/snowflake/snowpark/modin/pandas/series.py

@@ -129,19 +129,41 @@ def __init__(
 
         # modified:
         # Engine.subscribe(_update_engine)
+        from snowflake.snowpark.modin.plugin.extensions.index import Index
 
         # Convert lazy index to Series without pulling the data to client.
-        if isinstance(data, pd.Index):
-            query_compiler = data.to_series(index=index, name=name)._query_compiler
-            query_compiler = query_compiler.reset_index(drop=True)
+        if isinstance(data, Index):
+            # If the data is an Index object, we need to convert it to a Series to make sure
+            # that the values are in the correct format -- as a data column, not an index column.
+            # Additionally, if an index is provided, converting it to an Index object ensures that
+            # its values are an index column.
+            query_compiler = (
+                data.to_series(index=None, name=name)
+                .reset_index(drop=True)
+                ._query_compiler
+            )
+
+            if index is not None:
+                index = index if isinstance(index, Index) else Index(index)
+                query_compiler = query_compiler.create_qc_with_index_data_and_qc_index(
+                    index._query_compiler
+                )
         elif isinstance(data, type(self)):
             query_compiler = data._query_compiler.copy()
             if index is not None:
-                if any(i not in data.index for i in index):
-                    ErrorMessage.not_implemented(
-                        "Passing non-existent columns or index values to constructor "
-                        + "not yet implemented."
-                    )  # pragma: no cover
+                # The `index` parameter is used to select the rows from `data` that will be in the resultant Series.
+                # If a value in `index` is not present in `data`'s index, it will be filled with a NaN value.
+                # 1. The `index` is converted to an Index object so that the index values are in an index column.
+                index = index if isinstance(index, Index) else Index(index)
+                # 2. A right outer join is performed between `data` and `index` to create a Series object where any
+                #    index values in `data`'s index that are not in `index` are filled with NaN.
+                data = Series(
+                    query_compiler=data._query_compiler.create_qc_with_data_and_index_joined_on_index(
+                        index._query_compiler
+                    ),
+                    name=data.name,
+                )
+                # 3. Perform .loc[] on `data` to select the rows that are in `index`.
                 query_compiler = data.loc[index]._query_compiler
         if query_compiler is None:
             # Defaulting to pandas

src/snowflake/snowpark/modin/plugin/compiler/snowflake_query_compiler.py

@@ -17390,3 +17390,118 @@ def tz_convert(self, *args: Any, **kwargs: Any) -> None:
 
     def tz_localize(self, *args: Any, **kwargs: Any) -> None:
         ErrorMessage.method_not_implemented_error("tz_convert", "BasePandasDataset")
+
+    def create_qc_with_index_data_and_qc_index(
+        self, index_qc: "SnowflakeQueryCompiler"
+    ) -> "SnowflakeQueryCompiler":
+        """
+        This is a helper function for creating a DataFrame/Series where the data is an Index
+        and an index is provided.
+        Before this method is called, the provided index is converted to an Index object;
+        the query compilers of the data and index are then joined.
+
+        Parameters
+        ----------
+        index_qc : SnowflakeQueryCompiler
+            The query compiler of the index to be joined with the data.
+
+        Returns
+        -------
+        SnowflakeQueryCompiler
+            A new query compiler with the data and index joined.
+        """
+        self_frame = self._modin_frame.ensure_row_position_column()
+        other_frame = index_qc._modin_frame.ensure_row_position_column()
+
+        new_internal_frame, _ = join_utils.join(
+            self_frame,
+            other_frame,
+            how="left",
+            left_on=[self_frame.row_position_snowflake_quoted_identifier],
+            right_on=[other_frame.row_position_snowflake_quoted_identifier],
+            inherit_join_index=InheritJoinIndex.FROM_RIGHT,
+        )
+
+        return SnowflakeQueryCompiler(new_internal_frame)
+
+    def create_qc_with_data_and_index_joined_on_index(
+        self,
+        index_qc: Optional["SnowflakeQueryCompiler"] = None,
+        extra_columns: Optional[List[Hashable]] = None,
+    ) -> "SnowflakeQueryCompiler":
+        """
+        This is a helper function for creating a DataFrame/Series where the data is a DataFrame/Series object.
+        This is a special case since only the values where the index value matches in the `data` and `index` provided
+        take on an actual value from the given `data`. Otherwise, they take on a NaN value.
+
+        For instance,
+
+        >>> data = pd.Series(["A", "B", "C", "D"], index=[1.1, 2.2, 3, 4], name="index series name")
+        >>> index = pd.Index([1, 2, 3, 4], name="some name")
+        >>> df = pd.DataFrame(data=data, index=index)
+        >>> df  # doctest: +SKIP
+                  index series name
+        some name
+        1                       NaN
+        2                       NaN
+        3                         C
+        4                         D
+
+        Notice how only the data for index values 3 and 4 have an actual value while 1 and 2 have a NaN value.
+        3 and 4 are values present in the index of the `data` and `index` provided. 1 and 2 are not present.
+
+        Parameters
+        ----------
+        index_qc : SnowflakeQueryCompiler, default None
+            The query compiler of the index to be joined with the data. If no query compiler is provided,
+            skip this join operation.
+        extra_columns : list of hashable, default None
+            If the DataFrame being created has new columns that are not a part of the data, they can be passed here
+            and appended as NaN columns.
+
+        Returns
+        -------
+        SnowflakeQueryCompiler
+            A new query compiler with the data and index joined.
+        """
+        self_frame = self._modin_frame
+
+        if extra_columns:
+            # Append the new columns to the data's internal frame.
+            new_snowflake_quoted_identifiers = self._modin_frame.ordered_dataframe.generate_snowflake_quoted_identifiers(
+                pandas_labels=extra_columns,
+                excluded=self_frame.data_column_snowflake_quoted_identifiers,
+            )
+            new_ordered_frame = append_columns(
+                self_frame.ordered_dataframe,
+                new_snowflake_quoted_identifiers,
+                [pandas_lit(np.nan)] * len(extra_columns),
+            )
+            self_frame = InternalFrame.create(
+                ordered_dataframe=new_ordered_frame,
+                data_column_pandas_labels=self_frame.data_column_pandas_labels
+                + extra_columns,
+                data_column_snowflake_quoted_identifiers=self_frame.data_column_snowflake_quoted_identifiers
+                + new_snowflake_quoted_identifiers,
+                data_column_pandas_index_names=self_frame.data_column_pandas_index_names,
+                index_column_pandas_labels=self_frame.index_column_pandas_labels,
+                index_column_snowflake_quoted_identifiers=self_frame.index_column_snowflake_quoted_identifiers,
+                data_column_types=None,
+                index_column_types=None,
+            )
+
+        if index_qc is None:
+            new_internal_frame = self_frame
+        else:
+            # Join the index and data internal frames.
+            other_frame = index_qc._modin_frame
+            new_internal_frame, _ = join_utils.join(
+                other_frame,
+                self_frame,
+                how="outer",
+                left_on=other_frame.index_column_snowflake_quoted_identifiers,
+                right_on=self_frame.index_column_snowflake_quoted_identifiers,
+                inherit_join_index=InheritJoinIndex.FROM_LEFT,
+            )
+
+        return SnowflakeQueryCompiler(new_internal_frame)