fix: remove pytz dependency and require pyarrow>=3.0.0

docs/snippets.py

@@ -363,7 +363,6 @@ def test_update_table_expiration(client, to_delete):
 
     # [START bigquery_update_table_expiration]
     import datetime
-    import pytz
 
     # from google.cloud import bigquery
     # client = bigquery.Client()
@@ -375,7 +374,9 @@ def test_update_table_expiration(client, to_delete):
     assert table.expires is None
 
     # set table to expire 5 days from now
-    expiration = datetime.datetime.now(pytz.utc) + datetime.timedelta(days=5)
+    expiration = datetime.datetime.now(datetime.timezone.utc) + datetime.timedelta(
+        days=5
+    )
     table.expires = expiration
     table = client.update_table(table, ["expires"])  # API request
 

google/cloud/bigquery/_pandas_helpers.py

@@ -20,8 +20,6 @@
 import queue
 import warnings
 
-from packaging import version
-
 try:
     import pandas
 except ImportError:  # pragma: NO COVER
@@ -110,6 +108,7 @@ def pyarrow_timestamp():
     # This dictionary is duplicated in bigquery_storage/test/unite/test_reader.py
     # When modifying it be sure to update it there as well.
     BQ_TO_ARROW_SCALARS = {
+        "BIGNUMERIC": pyarrow_bignumeric,
         "BOOL": pyarrow.bool_,
         "BOOLEAN": pyarrow.bool_,
         "BYTES": pyarrow.binary,
@@ -146,23 +145,15 @@ def pyarrow_timestamp():
         pyarrow.date64().id: "DATETIME",  # because millisecond resolution
         pyarrow.binary().id: "BYTES",
         pyarrow.string().id: "STRING",  # also alias for pyarrow.utf8()
-        # The exact scale and precision don't matter, see below.
-        pyarrow.decimal128(38, scale=9).id: "NUMERIC",
-    }
-
-    if version.parse(pyarrow.__version__) >= version.parse("3.0.0"):
-        BQ_TO_ARROW_SCALARS["BIGNUMERIC"] = pyarrow_bignumeric
         # The exact decimal's scale and precision are not important, as only
         # the type ID matters, and it's the same for all decimal256 instances.
-        ARROW_SCALAR_IDS_TO_BQ[pyarrow.decimal256(76, scale=38).id] = "BIGNUMERIC"
-        _BIGNUMERIC_SUPPORT = True
-    else:
-        _BIGNUMERIC_SUPPORT = False
+        pyarrow.decimal128(38, scale=9).id: "NUMERIC",
+        pyarrow.decimal256(76, scale=38).id: "BIGNUMERIC",
+    }
 
 else:  # pragma: NO COVER
     BQ_TO_ARROW_SCALARS = {}  # pragma: NO COVER
     ARROW_SCALAR_IDS_TO_BQ = {}  # pragma: NO_COVER
-    _BIGNUMERIC_SUPPORT = False  # pragma: NO COVER
 
 
 def bq_to_arrow_struct_data_type(field):

google/cloud/bigquery/table.py

@@ -20,7 +20,6 @@
 import datetime
 import functools
 import operator
-import pytz
 import typing
 from typing import Any, Dict, Iterable, Iterator, Optional, Tuple
 import warnings
@@ -1969,7 +1968,7 @@ def to_dataframe(
         # Pandas, we set the timestamp_as_object parameter to True, if necessary.
         types_to_check = {
             pyarrow.timestamp("us"),
-            pyarrow.timestamp("us", tz=pytz.UTC),
+            pyarrow.timestamp("us", tz=datetime.timezone.utc),
         }
 
         for column in record_batch:

samples/client_query_w_timestamp_params.py

@@ -18,7 +18,6 @@ def client_query_w_timestamp_params():
     # [START bigquery_query_params_timestamps]
     import datetime
 
-    import pytz
     from google.cloud import bigquery
 
     # Construct a BigQuery client object.
@@ -30,7 +29,7 @@ def client_query_w_timestamp_params():
             bigquery.ScalarQueryParameter(
                 "ts_value",
                 "TIMESTAMP",
-                datetime.datetime(2016, 12, 7, 8, 0, tzinfo=pytz.UTC),
+                datetime.datetime(2016, 12, 7, 8, 0, tzinfo=datetime.timezone.utc),
             )
         ]
     )

setup.py

@@ -54,9 +54,9 @@
         # grpc.Channel.close() method isn't added until 1.32.0.
         # https://github.com/grpc/grpc/pull/15254
         "grpcio >= 1.38.1, < 2.0dev",
-        "pyarrow >= 1.0.0, < 6.0dev",
+        "pyarrow >= 3.0.0, < 6.0dev",
     ],
-    "pandas": ["pandas>=0.23.0", "pyarrow >= 1.0.0, < 6.0dev"],
+    "pandas": ["pandas>=0.23.0", "pyarrow >= 3.0.0, < 6.0dev"],
     "bignumeric_type": ["pyarrow >= 3.0.0, < 6.0dev"],
     "tqdm": ["tqdm >= 4.7.4, <5.0.0dev"],
     "opentelemetry": [

testing/constraints-3.6.txt

@@ -16,7 +16,7 @@ opentelemetry-sdk==0.11b0
 pandas==0.23.0
 proto-plus==1.10.0
 protobuf==3.12.0
-pyarrow==1.0.0
+pyarrow==3.0.0
 requests==2.18.0
 six==1.13.0
 tqdm==4.7.4

tests/system/test_client.py

@@ -30,7 +30,6 @@
 import psutil
 import pytest
 
-from google.cloud.bigquery._pandas_helpers import _BIGNUMERIC_SUPPORT
 from . import helpers
 
 try:
@@ -1972,15 +1971,12 @@ def test_query_w_query_params(self):
                 "expected": {"friends": [phred_name, bharney_name]},
                 "query_parameters": [with_friends_param],
             },
+            {
+                "sql": "SELECT @bignum_param",
+                "expected": bignum,
+                "query_parameters": [bignum_param],
+            },
         ]
-        if _BIGNUMERIC_SUPPORT:
-            examples.append(
-                {
-                    "sql": "SELECT @bignum_param",
-                    "expected": bignum,
-                    "query_parameters": [bignum_param],
-                }
-            )
 
         for example in examples:
             jconfig = QueryJobConfig()

tests/system/test_pandas.py

@@ -24,10 +24,8 @@
 import google.api_core.retry
 import pkg_resources
 import pytest
-import pytz
 
 from google.cloud import bigquery
-from google.cloud.bigquery._pandas_helpers import _BIGNUMERIC_SUPPORT
 from . import helpers
 
 
@@ -64,7 +62,7 @@ def test_load_table_from_dataframe_w_automatic_schema(bigquery_client, dataset_i
                         datetime.datetime(2012, 3, 14, 15, 16),
                     ],
                     dtype="datetime64[ns]",
-                ).dt.tz_localize(pytz.utc),
+                ).dt.tz_localize(datetime.timezone.utc),
             ),
             (
                 "dt_col",
@@ -189,12 +187,11 @@ def test_load_table_from_dataframe_w_nulls(bigquery_client, dataset_id):
         bigquery.SchemaField("geo_col", "GEOGRAPHY"),
         bigquery.SchemaField("int_col", "INTEGER"),
         bigquery.SchemaField("num_col", "NUMERIC"),
+        bigquery.SchemaField("bignum_col", "BIGNUMERIC"),
         bigquery.SchemaField("str_col", "STRING"),
         bigquery.SchemaField("time_col", "TIME"),
         bigquery.SchemaField("ts_col", "TIMESTAMP"),
     )
-    if _BIGNUMERIC_SUPPORT:
-        scalars_schema += (bigquery.SchemaField("bignum_col", "BIGNUMERIC"),)
 
     table_schema = scalars_schema + (
         # TODO: Array columns can't be read due to NULLABLE versus REPEATED
@@ -216,12 +213,11 @@ def test_load_table_from_dataframe_w_nulls(bigquery_client, dataset_id):
         ("geo_col", nulls),
         ("int_col", nulls),
         ("num_col", nulls),
+        ("bignum_col", nulls),
         ("str_col", nulls),
         ("time_col", nulls),
         ("ts_col", nulls),
     ]
-    if _BIGNUMERIC_SUPPORT:
-        df_data.append(("bignum_col", nulls))
     df_data = collections.OrderedDict(df_data)
     dataframe = pandas.DataFrame(df_data, columns=df_data.keys())
 
@@ -297,12 +293,11 @@ def test_load_table_from_dataframe_w_explicit_schema(bigquery_client, dataset_id
         bigquery.SchemaField("geo_col", "GEOGRAPHY"),
         bigquery.SchemaField("int_col", "INTEGER"),
         bigquery.SchemaField("num_col", "NUMERIC"),
+        bigquery.SchemaField("bignum_col", "BIGNUMERIC"),
         bigquery.SchemaField("str_col", "STRING"),
         bigquery.SchemaField("time_col", "TIME"),
         bigquery.SchemaField("ts_col", "TIMESTAMP"),
     )
-    if _BIGNUMERIC_SUPPORT:
-        scalars_schema += (bigquery.SchemaField("bignum_col", "BIGNUMERIC"),)
 
     table_schema = scalars_schema + (
         # TODO: Array columns can't be read due to NULLABLE versus REPEATED
@@ -340,6 +335,14 @@ def test_load_table_from_dataframe_w_explicit_schema(bigquery_client, dataset_id
                 decimal.Decimal("99999999999999999999999999999.999999999"),
             ],
         ),
+        (
+            "bignum_col",
+            [
+                decimal.Decimal("-{d38}.{d38}".format(d38="9" * 38)),
+                None,
+                decimal.Decimal("{d38}.{d38}".format(d38="9" * 38)),
+            ],
+        ),
         ("str_col", ["abc", None, "def"]),
         (
             "time_col",
@@ -348,23 +351,14 @@ def test_load_table_from_dataframe_w_explicit_schema(bigquery_client, dataset_id
         (
             "ts_col",
             [
-                datetime.datetime(1, 1, 1, 0, 0, 0, tzinfo=pytz.utc),
+                datetime.datetime(1, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc),
                 None,
-                datetime.datetime(9999, 12, 31, 23, 59, 59, 999999, tzinfo=pytz.utc),
+                datetime.datetime(
+                    9999, 12, 31, 23, 59, 59, 999999, tzinfo=datetime.timezone.utc
+                ),
             ],
         ),
     ]
-    if _BIGNUMERIC_SUPPORT:
-        df_data.append(
-            (
-                "bignum_col",
-                [
-                    decimal.Decimal("-{d38}.{d38}".format(d38="9" * 38)),
-                    None,
-                    decimal.Decimal("{d38}.{d38}".format(d38="9" * 38)),
-                ],
-            )
-        )
     df_data = collections.OrderedDict(df_data)
     dataframe = pandas.DataFrame(df_data, dtype="object", columns=df_data.keys())
 
@@ -484,10 +478,10 @@ def test_load_table_from_dataframe_w_explicit_schema_source_format_csv(
             (
                 "ts_col",
                 [
-                    datetime.datetime(1, 1, 1, 0, 0, 0, tzinfo=pytz.utc),
+                    datetime.datetime(1, 1, 1, 0, 0, 0, tzinfo=datetime.timezone.utc),
                     None,
                     datetime.datetime(
-                        9999, 12, 31, 23, 59, 59, 999999, tzinfo=pytz.utc
+                        9999, 12, 31, 23, 59, 59, 999999, tzinfo=datetime.timezone.utc
                     ),
                 ],
             ),

tests/unit/job/test_base.py

@@ -295,11 +295,11 @@ def test_user_email(self):
     @staticmethod
     def _datetime_and_millis():
         import datetime
-        import pytz
         from google.cloud._helpers import _millis
 
         now = datetime.datetime.utcnow().replace(
-            microsecond=123000, tzinfo=pytz.UTC  # stats timestamps have ms precision
+            microsecond=123000,
+            tzinfo=datetime.timezone.utc,  # stats timestamps have ms precision
         )
         return now, _millis(now)