fix: to_gbq allows strings for DATE and floats for NUMERIC, require pandas 0.24+ and db-dtypes

.circleci/config.yml

@@ -10,7 +10,7 @@ jobs:
       - image: continuumio/miniconda3
     environment:
       PYTHON: "3.7"
-      PANDAS: "0.23.2"
+      PANDAS: "0.24.2"
     steps:
       - checkout
       - run: ci/config_auth.sh

.coveragerc

@@ -22,7 +22,7 @@ omit =
   google/cloud/__init__.py
 
 [report]
-fail_under = 86
+fail_under = 88
 show_missing = True
 exclude_lines =
     # Re-enable the standard pragma

ci/requirements-3.7-0.23.2.conda → ci/requirements-3.7-0.24.2.conda

@@ -1,5 +1,6 @@
 codecov
 coverage
+db-dtypes==0.3.0
 fastavro
 flake8
 numpy==1.16.6

ci/requirements-3.9-NIGHTLY.conda

@@ -1,3 +1,4 @@
+db-dtypes
 pydata-google-auth
 google-cloud-bigquery
 google-cloud-bigquery-storage

noxfile.py

@@ -146,11 +146,7 @@ def system(session):
     # Install all test dependencies, then install this package into the
     # virtualenv's dist-packages.
     session.install("mock", "pytest", "google-cloud-testutils", "-c", constraints_path)
-    if session.python == "3.9":
-        extras = "[tqdm,db-dtypes]"
-    else:
-        extras = "[tqdm]"
-    session.install("-e", f".{extras}", "-c", constraints_path)
+    session.install("-e", ".[tqdm]", "-c", constraints_path)
 
     # Run py.test against the system tests.
     if system_test_exists:
@@ -179,7 +175,7 @@ def cover(session):
     test runs (not system test runs), and then erases coverage data.
     """
     session.install("coverage", "pytest-cov")
-    session.run("coverage", "report", "--show-missing", "--fail-under=86")
+    session.run("coverage", "report", "--show-missing", "--fail-under=88")
 
     session.run("coverage", "erase")
 

owlbot.py

@@ -29,16 +29,12 @@
 # ----------------------------------------------------------------------------
 
 extras = ["tqdm"]
-extras_by_python = {
-    "3.9": ["tqdm", "db-dtypes"],
-}
 templated_files = common.py_library(
     unit_test_python_versions=["3.7", "3.8", "3.9", "3.10"],
     system_test_python_versions=["3.7", "3.8", "3.9", "3.10"],
-    cov_level=86,
+    cov_level=88,
     unit_test_extras=extras,
     system_test_extras=extras,
-    system_test_extras_by_python=extras_by_python,
     intersphinx_dependencies={
         "pandas": "https://pandas.pydata.org/pandas-docs/stable/",
         "pydata-google-auth": "https://pydata-google-auth.readthedocs.io/en/latest/",

pandas_gbq/load.py

@@ -4,9 +4,11 @@
 
 """Helper methods for loading data into BigQuery"""
 
+import decimal
 import io
 from typing import Any, Callable, Dict, List, Optional
 
+import db_dtypes
 import pandas
 import pyarrow.lib
 from google.cloud import bigquery
@@ -56,6 +58,55 @@ def split_dataframe(dataframe, chunksize=None):
         yield remaining_rows, chunk
 
 
+def cast_dataframe_for_parquet(
+    dataframe: pandas.DataFrame, schema: Optional[Dict[str, Any]],
+) -> pandas.DataFrame:
+    """Cast columns to needed dtype when writing parquet files.
+
+    See: https://github.com/googleapis/python-bigquery-pandas/issues/421
+    """
+
+    columns = schema.get("fields", [])
+
+    # Protect against an explicit None in the dictionary.
+    columns = columns if columns is not None else []
+
+    for column in columns:
+        # Schema can be a superset of the columns in the dataframe, so ignore
+        # columns that aren't present.
+        column_name = column.get("name")
+        if column_name not in dataframe.columns:
+            continue
+
+        # Skip array columns for now. Potentially casting the elements of the
+        # array would be possible, but not worth the effort until there is
+        # demand for it.
+        if column.get("mode", "NULLABLE").upper() == "REPEATED":
+            continue
+
+        column_type = column.get("type", "").upper()
+        if (
+            column_type == "DATE"
+            # Use extension dtype first so that it uses the correct equality operator.
+            and db_dtypes.DateDtype() != dataframe[column_name].dtype
+        ):
+            # Construct converted column manually, because I can't use
+            # .astype() with DateDtype. With .astype(), I get the error:
+            #
+            # TypeError: Cannot interpret '<db_dtypes.DateDtype ...>' as a data type
+            cast_column = pandas.Series(
+                dataframe[column_name], dtype=db_dtypes.DateDtype()
+            )
+        elif column_type in {"NUMERIC", "DECIMAL", "BIGNUMERIC", "BIGDECIMAL"}:
+            cast_column = dataframe[column_name].map(decimal.Decimal)
+        else:
+            cast_column = None
+
+        if cast_column is not None:
+            dataframe = dataframe.assign(**{column_name: cast_column})
+    return dataframe
+
+
 def load_parquet(
     client: bigquery.Client,
     dataframe: pandas.DataFrame,
@@ -70,6 +121,7 @@ def load_parquet(
     if schema is not None:
         schema = pandas_gbq.schema.remove_policy_tags(schema)
         job_config.schema = pandas_gbq.schema.to_google_cloud_bigquery(schema)
+        dataframe = cast_dataframe_for_parquet(dataframe, schema)
 
     try:
         client.load_table_from_dataframe(

setup.py

@@ -23,8 +23,9 @@
 release_status = "Development Status :: 4 - Beta"
 dependencies = [
     "setuptools",
+    "db-dtypes >=0.3.0,<2.0.0",
     "numpy>=1.16.6",
-    "pandas>=0.23.2",
+    "pandas>=0.24.2",
     "pyarrow >=3.0.0, <7.0dev",
     "pydata-google-auth",
     "google-auth",
@@ -35,7 +36,6 @@
 ]
 extras = {
     "tqdm": "tqdm>=4.23.0",
-    "db-dtypes": "db-dtypes >=0.3.0,<2.0.0",
 }
 
 # Setup boilerplate below this line.

testing/constraints-3.7.txt

@@ -5,12 +5,13 @@
 #
 # e.g., if setup.py has "foo >= 1.14.0, < 2.0.0dev",
 # Then this file should have foo==1.14.0
+db-dtypes==0.3.0
 google-auth==1.4.1
 google-auth-oauthlib==0.0.1
 google-cloud-bigquery==1.11.1
 google-cloud-bigquery-storage==1.1.0
 numpy==1.16.6
-pandas==0.23.2
+pandas==0.24.2
 pyarrow==3.0.0
 pydata-google-auth==0.1.2
 tqdm==4.23.0

tests/system/test_gbq.py

@@ -26,8 +26,6 @@
 
 TABLE_ID = "new_test"
 PANDAS_VERSION = pkg_resources.parse_version(pandas.__version__)
-NULLABLE_INT_PANDAS_VERSION = pkg_resources.parse_version("0.24.0")
-NULLABLE_INT_MESSAGE = "Require pandas 0.24+ in order to use nullable integer type."
 
 
 def test_imports():
@@ -173,9 +171,6 @@ def test_should_properly_handle_valid_integers(self, project_id):
         tm.assert_frame_equal(df, DataFrame({"valid_integer": [3]}))
 
     def test_should_properly_handle_nullable_integers(self, project_id):
-        if PANDAS_VERSION < NULLABLE_INT_PANDAS_VERSION:
-            pytest.skip(msg=NULLABLE_INT_MESSAGE)
-
         query = """SELECT * FROM
                     UNNEST([1, NULL]) AS nullable_integer
                 """
@@ -188,9 +183,7 @@ def test_should_properly_handle_nullable_integers(self, project_id):
         )
         tm.assert_frame_equal(
             df,
-            DataFrame(
-                {"nullable_integer": pandas.Series([1, pandas.NA], dtype="Int64")}
-            ),
+            DataFrame({"nullable_integer": pandas.Series([1, None], dtype="Int64")}),
         )
 
     def test_should_properly_handle_valid_longs(self, project_id):
@@ -204,9 +197,6 @@ def test_should_properly_handle_valid_longs(self, project_id):
         tm.assert_frame_equal(df, DataFrame({"valid_long": [1 << 62]}))
 
     def test_should_properly_handle_nullable_longs(self, project_id):
-        if PANDAS_VERSION < NULLABLE_INT_PANDAS_VERSION:
-            pytest.skip(msg=NULLABLE_INT_MESSAGE)
-
         query = """SELECT * FROM
                     UNNEST([1 << 62, NULL]) AS nullable_long
                 """
@@ -219,15 +209,10 @@ def test_should_properly_handle_nullable_longs(self, project_id):
         )
         tm.assert_frame_equal(
             df,
-            DataFrame(
-                {"nullable_long": pandas.Series([1 << 62, pandas.NA], dtype="Int64")}
-            ),
+            DataFrame({"nullable_long": pandas.Series([1 << 62, None], dtype="Int64")}),
         )
 
     def test_should_properly_handle_null_integers(self, project_id):
-        if PANDAS_VERSION < NULLABLE_INT_PANDAS_VERSION:
-            pytest.skip(msg=NULLABLE_INT_MESSAGE)
-
         query = "SELECT CAST(NULL AS INT64) AS null_integer"
         df = gbq.read_gbq(
             query,
@@ -237,7 +222,7 @@ def test_should_properly_handle_null_integers(self, project_id):
             dtypes={"null_integer": "Int64"},
         )
         tm.assert_frame_equal(
-            df, DataFrame({"null_integer": pandas.Series([pandas.NA], dtype="Int64")}),
+            df, DataFrame({"null_integer": pandas.Series([None], dtype="Int64")}),
         )
 
     def test_should_properly_handle_valid_floats(self, project_id):