Merge remote-tracking branch 'github/main' into new_execute_result

Author: TrevorBergeron

.gitignore

@@ -62,3 +62,4 @@ system_tests/local_test_setup
 # Make sure a generated file isn't accidentally committed.
 pylintrc
 pylintrc.test
+dummy.pkl

CHANGELOG.md

@@ -4,6 +4,50 @@
 
 [1]: https://pypi.org/project/bigframes/#history
 
+## [2.27.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.26.0...v2.27.0) (2025-10-24)
+
+
+### Features
+
+* Add __abs__ to dataframe ([#2186](https://github.com/googleapis/python-bigquery-dataframes/issues/2186)) ([c331dfe](https://github.com/googleapis/python-bigquery-dataframes/commit/c331dfed59174962fbdc8ace175dd00fcc3d5d50))
+* Add df.groupby().corr()/cov() support ([#2190](https://github.com/googleapis/python-bigquery-dataframes/issues/2190)) ([ccd7c07](https://github.com/googleapis/python-bigquery-dataframes/commit/ccd7c0774a65d09e6cf31d2b62d0bc64bd7c4248))
+* Add str accessor to index ([#2179](https://github.com/googleapis/python-bigquery-dataframes/issues/2179)) ([cd87ce0](https://github.com/googleapis/python-bigquery-dataframes/commit/cd87ce0d504747f44d1b5a55f869a2e0fca6df17))
+* Add support for `np.isnan` and `np.isfinite` ufuncs ([#2188](https://github.com/googleapis/python-bigquery-dataframes/issues/2188)) ([68723bc](https://github.com/googleapis/python-bigquery-dataframes/commit/68723bc1f08013e43a8b11752f908bf8fd6d51f5))
+* Include local data bytes in the dry run report when available ([#2185](https://github.com/googleapis/python-bigquery-dataframes/issues/2185)) ([ee2c40c](https://github.com/googleapis/python-bigquery-dataframes/commit/ee2c40c6789535e259fb6a9774831d6913d16212))
+* Support len() on Groupby objects ([#2183](https://github.com/googleapis/python-bigquery-dataframes/issues/2183)) ([4191821](https://github.com/googleapis/python-bigquery-dataframes/commit/4191821b0976281a96c8965336ef51f061b0c481))
+* Support pa.json_(pa.string()) in struct/list if available ([#2180](https://github.com/googleapis/python-bigquery-dataframes/issues/2180)) ([5ec3cc0](https://github.com/googleapis/python-bigquery-dataframes/commit/5ec3cc0298c7a6195d5bd12a08d996e7df57fc5f))
+
+
+### Documentation
+
+* Update AI operators deprecation notice ([#2182](https://github.com/googleapis/python-bigquery-dataframes/issues/2182)) ([2c50310](https://github.com/googleapis/python-bigquery-dataframes/commit/2c503107e17c59232b14b0d7bc40c350bb087d6f))
+
+## [2.26.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.25.0...v2.26.0) (2025-10-17)
+
+
+### ⚠ BREAKING CHANGES
+
+* turn Series.struct.dtypes into a property to match pandas (https://github.com/googleapis/python-bigquery-dataframes/pull/2169)
+
+### Features
+
+* Add df.sort_index(axis=1) ([#2173](https://github.com/googleapis/python-bigquery-dataframes/issues/2173)) ([ebf95e3](https://github.com/googleapis/python-bigquery-dataframes/commit/ebf95e3ef77822650f2e190df7b868011174d412))
+* Enhanced multimodal error handling with verbose mode for blob image functions ([#2024](https://github.com/googleapis/python-bigquery-dataframes/issues/2024)) ([f9e28fe](https://github.com/googleapis/python-bigquery-dataframes/commit/f9e28fe3f883cc4d486178fe241bc8b76473700f))
+* Implement cos, sin, and log operations for polars compiler ([#2170](https://github.com/googleapis/python-bigquery-dataframes/issues/2170)) ([5613e44](https://github.com/googleapis/python-bigquery-dataframes/commit/5613e4454f198691209ec28e58ce652104ac2de4))
+* Make `all` and `any` compatible with integer columns on Polars session ([#2154](https://github.com/googleapis/python-bigquery-dataframes/issues/2154)) ([6353d6e](https://github.com/googleapis/python-bigquery-dataframes/commit/6353d6ecad5139551ef68376c08f8749dd440014))
+
+
+### Bug Fixes
+
+* `blob.display()` shows <NA> for null rows ([#2158](https://github.com/googleapis/python-bigquery-dataframes/issues/2158)) ([ddb4df0](https://github.com/googleapis/python-bigquery-dataframes/commit/ddb4df0dd991bef051e2a365c5cacf502803014d))
+* Turn Series.struct.dtypes into a property to match pandas (https://github.com/googleapis/python-bigquery-dataframes/pull/2169) ([62f7e9f](https://github.com/googleapis/python-bigquery-dataframes/commit/62f7e9f38f26b6eb549219a4cbf2c9b9023c9c35))
+
+
+### Documentation
+
+* Clarify that only NULL values are handled by fillna/isna, not NaN ([#2176](https://github.com/googleapis/python-bigquery-dataframes/issues/2176)) ([8f27e73](https://github.com/googleapis/python-bigquery-dataframes/commit/8f27e737fc78a182238090025d09479fac90b326))
+* Remove import bigframes.pandas as bpd boilerplate from many samples ([#2147](https://github.com/googleapis/python-bigquery-dataframes/issues/2147)) ([1a01ab9](https://github.com/googleapis/python-bigquery-dataframes/commit/1a01ab97f103361f489f37b0af8c4b4d7806707c))
+
 ## [2.25.0](https://github.com/googleapis/python-bigquery-dataframes/compare/v2.24.0...v2.25.0) (2025-10-13)
 
 
@@ -463,7 +507,7 @@
 
 * Address `read_csv` with both `index_col` and `use_cols` behavior inconsistency with pandas ([#1785](https://github.com/googleapis/python-bigquery-dataframes/issues/1785)) ([ba7c313](https://github.com/googleapis/python-bigquery-dataframes/commit/ba7c313c8d308e3ff3f736b60978cb7a51715209))
 * Allow KMeans model init parameter as k-means++ alias ([#1790](https://github.com/googleapis/python-bigquery-dataframes/issues/1790)) ([0b59cf1](https://github.com/googleapis/python-bigquery-dataframes/commit/0b59cf1008613770fa1433c6da395e755c86fe22))
-* Replace function now can handle bpd.NA value. ([#1786](https://github.com/googleapis/python-bigquery-dataframes/issues/1786)) ([7269512](https://github.com/googleapis/python-bigquery-dataframes/commit/7269512a28eb42029447d5380c764353278a74e1))
+* Replace function now can handle pd.NA value. ([#1786](https://github.com/googleapis/python-bigquery-dataframes/issues/1786)) ([7269512](https://github.com/googleapis/python-bigquery-dataframes/commit/7269512a28eb42029447d5380c764353278a74e1))
 
 
 ### Documentation

bigframes/bigquery/_operations/ai.py

@@ -19,12 +19,15 @@
 from __future__ import annotations
 
 import json
-from typing import Any, List, Literal, Mapping, Tuple, Union
+from typing import Any, Iterable, List, Literal, Mapping, Tuple, Union
 
 import pandas as pd
 
-from bigframes import clients, dtypes, series, session
+from bigframes import clients, dataframe, dtypes
+from bigframes import pandas as bpd
+from bigframes import series, session
 from bigframes.core import convert, log_adapter
+from bigframes.ml import core as ml_core
 from bigframes.operations import ai_ops, output_schemas
 
 PROMPT_TYPE = Union[
@@ -53,7 +56,6 @@ def generate(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> country = bpd.Series(["Japan", "Canada"])
         >>> bbq.ai.generate(("What's the capital city of ", country, " one word only"))
         0    {'result': 'Tokyo\\n', 'full_response': '{"cand...
@@ -155,7 +157,6 @@ def generate_bool(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> df = bpd.DataFrame({
         ...     "col_1": ["apple", "bear", "pear"],
         ...     "col_2": ["fruit", "animal", "animal"]
@@ -240,7 +241,6 @@ def generate_int(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> animal = bpd.Series(["Kangaroo", "Rabbit", "Spider"])
         >>> bbq.ai.generate_int(("How many legs does a ", animal, " have?"))
         0    {'result': 2, 'full_response': '{"candidates":...
@@ -322,7 +322,6 @@ def generate_double(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> animal = bpd.Series(["Kangaroo", "Rabbit", "Spider"])
         >>> bbq.ai.generate_double(("How many legs does a ", animal, " have?"))
         0    {'result': 2.0, 'full_response': '{"candidates...
@@ -402,7 +401,6 @@ def if_(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> us_state = bpd.Series(["Massachusetts", "Illinois", "Hawaii"])
         >>> bbq.ai.if_((us_state, " has a city called Springfield"))
         0     True
@@ -459,7 +457,6 @@ def classify(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> df = bpd.DataFrame({'creature': ['Cat', 'Salmon']})
         >>> df['type'] = bbq.ai.classify(df['creature'], ['Mammal', 'Fish'])
         >>> df
@@ -517,7 +514,6 @@ def score(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> animal = bpd.Series(["Tiger", "Rabbit", "Blue Whale"])
         >>> bbq.ai.score(("Rank the relative weights of ", animal, " on the scale from 1 to 3")) # doctest: +SKIP
         0    2.0
@@ -555,6 +551,91 @@ def score(
     return series_list[0]._apply_nary_op(operator, series_list[1:])
 
 
+@log_adapter.method_logger(custom_base_name="bigquery_ai")
+def forecast(
+    df: dataframe.DataFrame | pd.DataFrame,
+    *,
+    data_col: str,
+    timestamp_col: str,
+    model: str = "TimesFM 2.0",
+    id_cols: Iterable[str] | None = None,
+    horizon: int = 10,
+    confidence_level: float = 0.95,
+    context_window: int | None = None,
+) -> dataframe.DataFrame:
+    """
+    Forecast time series at future horizon. Using Google Research's open source TimesFM(https://github.com/google-research/timesfm) model.
+
+    .. note::
+
+        This product or feature is subject to the "Pre-GA Offerings Terms" in the General Service Terms section of the
+        Service Specific Terms(https://cloud.google.com/terms/service-terms#1). Pre-GA products and features are available "as is"
+        and might have limited support. For more information, see the launch stage descriptions
+        (https://cloud.google.com/products#product-launch-stages).
+
+    Args:
+        df (DataFrame):
+            The dataframe that contains the data that you want to forecast. It could be either a BigFrames Dataframe or
+            a pandas DataFrame. If it's a pandas DataFrame, the global BigQuery session will be used to load the data.
+        data_col (str):
+            A str value that specifies the name of the data column. The data column contains the data to forecast.
+            The data column must use one of the following data types: INT64, NUMERIC and FLOAT64
+        timestamp_col (str):
+            A str value that specified the name of the time points column.
+            The time points column provides the time points used to generate the forecast.
+            The time points column must use one of the following data types: TIMESTAMP, DATE and DATETIME
+        model (str, default "TimesFM 2.0"):
+            A str value that specifies the name of the model. TimesFM 2.0 is the only supported value, and is the default value.
+        id_cols (Iterable[str], optional):
+            An iterable of str value that specifies the names of one or more ID columns. Each ID identifies a unique time series to forecast.
+            Specify one or more values for this argument in order to forecast multiple time series using a single query.
+            The columns that you specify must use one of the following data types: STRING, INT64, ARRAY<STRING> and ARRAY<INT64>
+        horizon (int, default 10):
+            An int value that specifies the number of time points to forecast. The default value is 10. The valid input range is [1, 10,000].
+        confidence_level (float, default 0.95):
+            A FLOAT64 value that specifies the percentage of the future values that fall in the prediction interval.
+            The default value is 0.95. The valid input range is [0, 1).
+        context_window (int, optional):
+            An int value that specifies the context window length used by BigQuery ML's built-in TimesFM model.
+            The context window length determines how many of the most recent data points from the input time series are use by the model.
+            If you don't specify a value, the AI.FORECAST function automatically chooses the smallest possible context window length to use
+            that is still large enough to cover the number of time series data points in your input data.
+
+    Returns:
+        DataFrame:
+            The forecast dataframe matches that of the BigQuery AI.FORECAST function.
+            See: https://cloud.google.com/bigquery/docs/reference/standard-sql/bigqueryml-syntax-ai-forecast
+
+    Raises:
+        ValueError: when any column ID does not exist in the dataframe.
+    """
+
+    if isinstance(df, pd.DataFrame):
+        # Load the pandas DataFrame with global session
+        df = bpd.read_pandas(df)
+
+    columns = [timestamp_col, data_col]
+    if id_cols:
+        columns += id_cols
+    for column in columns:
+        if column not in df.columns:
+            raise ValueError(f"Column `{column}` not found")
+
+    options: dict[str, Union[int, float, str, Iterable[str]]] = {
+        "data_col": data_col,
+        "timestamp_col": timestamp_col,
+        "model": model,
+        "horizon": horizon,
+        "confidence_level": confidence_level,
+    }
+    if id_cols:
+        options["id_cols"] = id_cols
+    if context_window:
+        options["context_window"] = context_window
+
+    return ml_core.BaseBqml(df._session).ai_forecast(input_data=df, options=options)
+
+
 def _separate_context_and_series(
     prompt: PROMPT_TYPE,
 ) -> Tuple[List[str | None], List[series.Series]]:

bigframes/bigquery/_operations/approx_agg.py

@@ -40,7 +40,6 @@ def approx_top_count(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
         >>> s = bpd.Series(["apple", "apple", "pear", "pear", "pear", "banana"])
         >>> bbq.approx_top_count(s, number=2)
         [{'value': 'pear', 'count': 3}, {'value': 'apple', 'count': 2}]

bigframes/bigquery/_operations/array.py

@@ -40,7 +40,6 @@ def array_length(series: series.Series) -> series.Series:
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series([[1, 2, 8, 3], [], [3, 4]])
         >>> bbq.array_length(s)
@@ -78,8 +77,6 @@ def array_agg(
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> import numpy as np
-        >>> bpd.options.display.progress_bar = None
 
     For a SeriesGroupBy object:
 
@@ -128,8 +125,6 @@ def array_to_string(series: series.Series, delimiter: str) -> series.Series:
 
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> import numpy as np
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series([["H", "i", "!"], ["Hello", "World"], np.nan, [], ["Hi"]])
         >>> bbq.array_to_string(s, delimiter=", ")

bigframes/bigquery/_operations/datetime.py

@@ -21,10 +21,8 @@ def unix_seconds(input: series.Series) -> series.Series:
 
     **Examples:**
 
-        >>> import pandas as pd
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series([pd.Timestamp("1970-01-02", tz="UTC"), pd.Timestamp("1970-01-03", tz="UTC")])
         >>> bbq.unix_seconds(s)
@@ -48,10 +46,8 @@ def unix_millis(input: series.Series) -> series.Series:
 
     **Examples:**
 
-        >>> import pandas as pd
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series([pd.Timestamp("1970-01-02", tz="UTC"), pd.Timestamp("1970-01-03", tz="UTC")])
         >>> bbq.unix_millis(s)
@@ -75,10 +71,8 @@ def unix_micros(input: series.Series) -> series.Series:
 
     **Examples:**
 
-        >>> import pandas as pd
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
-        >>> bpd.options.display.progress_bar = None
 
         >>> s = bpd.Series([pd.Timestamp("1970-01-02", tz="UTC"), pd.Timestamp("1970-01-03", tz="UTC")])
         >>> bbq.unix_micros(s)

bigframes/bigquery/_operations/geo.py

@@ -53,7 +53,6 @@ def st_area(
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
         >>> from shapely.geometry import Polygon, LineString, Point
-        >>> bpd.options.display.progress_bar = None
 
         >>> series = bigframes.geopandas.GeoSeries(
         ...         [
@@ -125,7 +124,6 @@ def st_buffer(
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
         >>> from shapely.geometry import Point
-        >>> bpd.options.display.progress_bar = None
 
         >>> series = bigframes.geopandas.GeoSeries(
         ...         [
@@ -195,7 +193,6 @@ def st_centroid(
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
         >>> from shapely.geometry import Polygon, LineString, Point
-        >>> bpd.options.display.progress_bar = None
 
         >>> series = bigframes.geopandas.GeoSeries(
         ...         [
@@ -250,7 +247,6 @@ def st_convexhull(
         >>> import bigframes.pandas as bpd
         >>> import bigframes.bigquery as bbq
         >>> from shapely.geometry import Polygon, LineString, Point
-        >>> bpd.options.display.progress_bar = None
 
         >>> series = bigframes.geopandas.GeoSeries(
         ...         [
@@ -312,7 +308,6 @@ def st_difference(
         >>> import bigframes.bigquery as bbq
         >>> import bigframes.geopandas
         >>> from shapely.geometry import Polygon, LineString, Point
-        >>> bpd.options.display.progress_bar = None
 
     We can check two GeoSeries against each other, row by row:
 
@@ -407,7 +402,6 @@ def st_distance(
         >>> import bigframes.bigquery as bbq
         >>> import bigframes.geopandas
         >>> from shapely.geometry import Polygon, LineString, Point
-        >>> bpd.options.display.progress_bar = None
 
     We can check two GeoSeries against each other, row by row.
 
@@ -489,7 +483,6 @@ def st_intersection(
         >>> import bigframes.bigquery as bbq
         >>> import bigframes.geopandas
         >>> from shapely.geometry import Polygon, LineString, Point
-        >>> bpd.options.display.progress_bar = None
 
     We can check two GeoSeries against each other, row by row.
 
@@ -583,7 +576,6 @@ def st_isclosed(
         >>> import bigframes.bigquery as bbq
 
         >>> from shapely.geometry import Point, LineString, Polygon
-        >>> bpd.options.display.progress_bar = None
 
         >>> series = bigframes.geopandas.GeoSeries(
         ...     [
@@ -650,7 +642,6 @@ def st_length(
         >>> import bigframes.bigquery as bbq
 
         >>> from shapely.geometry import Polygon, LineString, Point, GeometryCollection
-        >>> bpd.options.display.progress_bar = None
 
         >>> series = bigframes.geopandas.GeoSeries(
         ...         [