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feat: use ArrowDtype for STRUCT columns in to_pandas (#85)
Thank you for opening a Pull Request! Before submitting your PR, there are a few things you can do to make sure it goes smoothly: - [ ] Make sure to open an issue as a [bug/issue](https://togithub.com/googleapis/python-bigquery-dataframes/issues/new/choose) before writing your code! That way we can discuss the change, evaluate designs, and agree on the general idea - [ ] Ensure the tests and linter pass - [ ] Code coverage does not decrease (if any source code was changed) - [ ] Appropriate docs were updated (if necessary) Fixes #<issue_number_goes_here> 🦕
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bigframes/core/blocks.py

+2
Original file line numberDiff line numberDiff line change
@@ -377,7 +377,9 @@ def _to_dataframe(
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cls, result, schema: typing.Mapping[str, bigframes.dtypes.Dtype]
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) -> pd.DataFrame:
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"""Convert BigQuery data to pandas DataFrame with specific dtypes."""
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dtypes = bigframes.dtypes.to_pandas_dtypes_overrides(result.schema)
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df = result.to_dataframe(
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dtypes=dtypes,
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bool_dtype=pd.BooleanDtype(),
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int_dtype=pd.Int64Dtype(),
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float_dtype=pd.Float64Dtype(),

bigframes/dtypes.py

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Original file line numberDiff line numberDiff line change
@@ -19,6 +19,7 @@
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from typing import Any, Dict, Iterable, Literal, Tuple, Union
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import geopandas as gpd # type: ignore
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import google.cloud.bigquery as bigquery
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import ibis
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import ibis.expr.datatypes as ibis_dtypes
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import ibis.expr.types as ibis_types
@@ -27,6 +28,7 @@
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import pyarrow as pa
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import bigframes.constants as constants
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import third_party.bigframes_vendored.google_cloud_bigquery._pandas_helpers as gcb3p_pandas_helpers
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# Type hints for Pandas dtypes supported by BigQuery DataFrame
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Dtype = Union[
@@ -401,3 +403,18 @@ def cast_ibis_value(
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raise TypeError(
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f"Unsupported cast {value.type()} to {to_type}. {constants.FEEDBACK_LINK}"
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)
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def to_pandas_dtypes_overrides(schema: Iterable[bigquery.SchemaField]) -> Dict:
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"""For each STRUCT field, make sure we specify the full type to use."""
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# TODO(swast): Also override ARRAY fields.
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dtypes = {}
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for field in schema:
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if field.field_type == "RECORD" and field.mode != "REPEATED":
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# TODO(swast): We're using a private API here. Would likely be
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# better if we called `to_arrow()` and converted to a pandas
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# DataFrame ourselves from that.
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dtypes[field.name] = pd.ArrowDtype(
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gcb3p_pandas_helpers.bq_to_arrow_data_type(field)
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)
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return dtypes

noxfile.py

+2
Original file line numberDiff line numberDiff line change
@@ -185,6 +185,7 @@ def run_unit(session, install_test_extra):
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# Run py.test against the unit tests.
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tests_path = os.path.join("tests", "unit")
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third_party_tests_path = os.path.join("third_party", "bigframes_vendored")
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session.run(
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"py.test",
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"--quiet",
@@ -196,6 +197,7 @@ def run_unit(session, install_test_extra):
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"--cov-report=term-missing",
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"--cov-fail-under=0",
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tests_path,
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third_party_tests_path,
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*session.posargs,
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)
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tests/system/small/test_dataframe_io.py

+20-3
Original file line numberDiff line numberDiff line change
@@ -16,6 +16,7 @@
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import google.api_core.exceptions
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import pandas as pd
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import pyarrow as pa
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import pytest
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from tests.system.utils import (
@@ -44,7 +45,7 @@ def test_to_pandas_w_correct_dtypes(scalars_df_default_index):
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def test_to_pandas_array_struct_correct_result(session):
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"""In future, we should support arrays and structs with arrow types.
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"""In future, we should support arrays with arrow types.
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For now we fall back to the current connector behavior of converting
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to Python objects"""
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df = session.read_gbq(
@@ -59,11 +60,27 @@ def test_to_pandas_array_struct_correct_result(session):
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expected = pd.DataFrame(
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{
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"array_column": [[1, 3, 2]],
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"struct_column": [{"string_field": "a", "float_field": 1.2}],
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"struct_column": pd.Series(
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[{"string_field": "a", "float_field": 1.2}],
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dtype=pd.ArrowDtype(
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pa.struct(
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[
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("string_field", pa.string()),
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("float_field", pa.float64()),
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]
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)
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),
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),
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}
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)
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expected.index = expected.index.astype("Int64")
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pd.testing.assert_frame_equal(result, expected)
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pd.testing.assert_series_equal(result.dtypes, expected.dtypes)
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pd.testing.assert_series_equal(result["array_column"], expected["array_column"])
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# assert_series_equal not implemented for struct columns yet. Compare
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# values as Python objects, instead.
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pd.testing.assert_series_equal(
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result["struct_column"].astype("O"), expected["struct_column"].astype("O")
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)
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@pytest.mark.parametrize(
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