[bot] Merge master/3b046b5d into rel/dev

packages/gooddata-pandas/src/gooddata_pandas/dataframe.py

@@ -1,7 +1,7 @@
 # (C) 2021 GoodData Corporation
 from __future__ import annotations
 
-from typing import Callable, Optional, Union
+from typing import Callable, Literal, Optional, Union
 
 import pandas
 from gooddata_api_client import models
@@ -259,6 +259,7 @@ def for_created_visualization(
         on_execution_submitted: Optional[Callable[[Execution], None]] = None,
         is_cancellable: bool = False,
         optimized: bool = False,
+        grand_totals_position: Optional[Literal["pinnedBottom", "pinnedTop", "bottom", "top"]] = "bottom",
     ) -> tuple[pandas.DataFrame, DataFrameMetadata]:
         """
         Creates a data frame using a created visualization.
@@ -272,6 +273,9 @@ def for_created_visualization(
                 headers in memory as lists of dicts, which can consume a lot of memory for large results.
                 Optimized accumulator stores only unique values and story only reference to them in the list,
                 which can significantly reduce memory usage.
+            grand_totals_position (Literal["pinnedBottom", "pinnedTop", "bottom", "top"], optional):
+                Position where grand totals should be placed. "pinnedBottom" and "bottom" append totals,
+                "pinnedTop" and "top" prepend totals. Defaults to "bottom".
 
         Returns:
             pandas.DataFrame: A DataFrame instance.
@@ -283,6 +287,7 @@ def for_created_visualization(
             exec_def=execution_definition,
             on_execution_submitted=on_execution_submitted,
             optimized=optimized,
+            grand_totals_position=grand_totals_position,
         )
 
     def result_cache_metadata_for_exec_result_id(self, result_id: str) -> ResultCacheMetadata:
@@ -306,6 +311,7 @@ def for_exec_def(
         page_size: int = _DEFAULT_PAGE_SIZE,
         on_execution_submitted: Optional[Callable[[Execution], None]] = None,
         optimized: bool = False,
+        grand_totals_position: Optional[Literal["pinnedBottom", "pinnedTop", "bottom", "top"]] = "bottom",
     ) -> tuple[pandas.DataFrame, DataFrameMetadata]:
         """
         Creates a data frame using an execution definition.
@@ -342,6 +348,9 @@ def for_exec_def(
                 headers in memory as lists of dicts, which can consume a lot of memory for large results.
                 Optimized accumulator stores only unique values and story only reference to them in the list,
                 which can significantly reduce memory usage.
+            grand_totals_position (Literal["pinnedBottom", "pinnedTop", "bottom", "top"], optional):
+                Position where grand totals should be placed. "pinnedBottom" and "bottom" append totals,
+                "pinnedTop" and "top" prepend totals. Defaults to "bottom".
 
         Returns:
             Tuple[pandas.DataFrame, DataFrameMetadata]: Tuple holding DataFrame and DataFrame metadata.
@@ -363,6 +372,7 @@ def for_exec_def(
             result_size_bytes_limit=result_size_bytes_limit,
             page_size=page_size,
             optimized=optimized,
+            grand_totals_position=grand_totals_position,
         )
 
     def for_exec_result_id(
@@ -376,6 +386,7 @@ def for_exec_result_id(
         use_primary_labels_in_attributes: bool = False,
         page_size: int = _DEFAULT_PAGE_SIZE,
         optimized: bool = False,
+        grand_totals_position: Optional[Literal["pinnedBottom", "pinnedTop", "bottom", "top"]] = "bottom",
     ) -> tuple[pandas.DataFrame, DataFrameMetadata]:
         """
             Retrieves a DataFrame and DataFrame metadata for a given execution result identifier.
@@ -410,6 +421,9 @@ def for_exec_result_id(
                 headers in memory as lists of dicts, which can consume a lot of memory for large results.
                 Optimized accumulator stores only unique values and story only reference to them in the list,
                 which can significantly reduce memory usage.
+            grand_totals_position (Literal["pinnedBottom", "pinnedTop", "bottom", "top"], optional):
+                Position where grand totals should be placed. "pinnedBottom" and "bottom" append totals,
+                "pinnedTop" and "top" prepend totals. Defaults to "bottom".
 
         Returns:
             Tuple[pandas.DataFrame, DataFrameMetadata]: Tuple holding DataFrame and DataFrame metadata.
@@ -436,4 +450,5 @@ def for_exec_result_id(
             use_primary_labels_in_attributes=use_primary_labels_in_attributes,
             page_size=page_size,
             optimized=optimized,
+            grand_totals_position=grand_totals_position,
         )

packages/gooddata-pandas/src/gooddata_pandas/result_convertor.py

@@ -2,7 +2,7 @@
 from abc import ABC, abstractmethod
 from collections.abc import Iterator
 from functools import cached_property
-from typing import Any, Callable, Optional, Union, cast
+from typing import Any, Callable, Literal, Optional, Union, cast
 
 import pandas
 from attrs import define, field, frozen
@@ -698,31 +698,46 @@ def _headers_to_index(
     ), primary_attribute_labels_mapping
 
 
-def _merge_grand_totals_into_data(extract: _DataWithHeaders) -> Union[_DataArray, list[_DataArray]]:
+def _merge_grand_totals_into_data(
+    extract: _DataWithHeaders,
+    grand_totals_position: Optional[Literal["pinnedBottom", "pinnedTop", "bottom", "top"]] = "bottom",
+) -> Union[_DataArray, list[_DataArray]]:
     """
     Merges grand totals into the extracted data. This function will mutate the extracted data,
     extending the rows and columns with grand totals. Going with mutation here so as not to copy arrays around.
 
     Args:
         extract (_DataWithHeaders): Extracted data with headers and grand totals.
+        grand_totals_position (Literal["pinnedBottom", "pinnedTop", "bottom", "top"], optional):
+            Position where grand totals should be placed. "pinnedBottom" and "bottom" append totals,
+            "pinnedTop" and "top" prepend totals. Defaults to "bottom".
 
     Returns:
         Union[_DataArray, List[_DataArray]]: Mutated data with rows and columns extended with grand totals.
     """
     data: list[_DataArray] = extract.data
+    # Treat None as "bottom" as a fallback
+    if grand_totals_position is None:
+        grand_totals_position = "bottom"
+    # Determine if grand totals should be prepended or appended
+    should_prepend = grand_totals_position in ("pinnedTop", "top")
 
     if extract.grand_totals[0] is not None:
         # column totals are computed into extra rows, one row per column total
-        # add those rows at the end of the data rows
-        data.extend(extract.grand_totals[0])
+        # add those rows at the beginning or end of the data rows based on position
+        if should_prepend:
+            data[:0] = extract.grand_totals[0]
+        else:
+            data.extend(extract.grand_totals[0])
 
     if extract.grand_totals[1] is not None:
         # row totals are computed into extra columns that should be appended to
-        # existing data rows
+        # existing data rows (column position doesn't change for row totals)
         for row_idx, cols_to_append in enumerate(extract.grand_totals[1]):
             data[row_idx].extend(cols_to_append)
 
     return data
+    return data
 
 
 def _merge_grand_total_headers_into_headers(extract: _DataWithHeaders) -> _HeadersByAxis:
@@ -757,6 +772,7 @@ def convert_execution_response_to_dataframe(
     use_primary_labels_in_attributes: bool = False,
     page_size: int = _DEFAULT_PAGE_SIZE,
     optimized: bool = False,
+    grand_totals_position: Optional[Literal["pinnedBottom", "pinnedTop", "bottom", "top"]] = "bottom",
 ) -> tuple[pandas.DataFrame, DataFrameMetadata]:
     """
     Converts execution result to a pandas dataframe, maintaining the dimensionality of the result.
@@ -776,6 +792,9 @@ def convert_execution_response_to_dataframe(
             headers in memory as lists of dicts, which can consume a lot of memory for large results.
             Optimized accumulator stores only unique values and story only reference to them in the list,
             which can significantly reduce memory usage.
+        grand_totals_position (Literal["pinnedBottom", "pinnedTop", "bottom", "top"], optional):
+            Position where grand totals should be placed. "pinnedBottom" and "bottom" append totals,
+            "pinnedTop" and "top" prepend totals. Defaults to "bottom".
 
     Returns:
         Tuple[pandas.DataFrame, DataFrameMetadata]: A tuple containing the created dataframe and its metadata.
@@ -789,7 +808,7 @@ def convert_execution_response_to_dataframe(
         optimized=optimized,
     )
 
-    full_data = _merge_grand_totals_into_data(extract)
+    full_data = _merge_grand_totals_into_data(extract=extract, grand_totals_position=grand_totals_position)
     full_headers = _merge_grand_total_headers_into_headers(extract)
 
     index, primary_labels_from_index = _headers_to_index(

packages/gooddata-pandas/tests/dataframe/test_dataframe_for_exec_def.py

@@ -1,6 +1,6 @@
 # (C) 2022 GoodData Corporation
 from pathlib import Path
-from typing import Optional
+from typing import Literal, Optional
 
 import pytest
 from gooddata_pandas import DataFrameFactory
@@ -31,9 +31,12 @@ def _run_and_validate_results(
     expected_column_totals: Optional[list[list[int]]] = None,
     page_size: int = 100,
     optimized: bool = False,
+    grand_totals_position: Optional[Literal["pinnedBottom", "pinnedTop", "bottom", "top"]] = "bottom",
 ) -> str:
     # generate dataframe from exec_def
-    result, result_metadata = gdf.for_exec_def(exec_def=exec_def, page_size=page_size)
+    result, result_metadata = gdf.for_exec_def(
+        exec_def=exec_def, page_size=page_size, grand_totals_position=grand_totals_position
+    )
     assert result.values.shape == expected
 
     # use result ID from computation above and generate dataframe just from it