@@ -26,10 +26,6 @@
 
 import pytest
 
-try:
-import fastparquet
-except (ImportError, AttributeError):
-fastparquet = None
 try:
 import pandas
 except (ImportError, AttributeError):

@@ -38,7 +38,6 @@
 from google.cloud.bigquery.dataset import DatasetReference
 from google.cloud.bigquery import enums
 from google.cloud.bigquery.enums import StandardSqlDataTypes
-from google.cloud.bigquery.exceptions import PyarrowMissingWarning
 from google.cloud.bigquery.external_config import ExternalConfig
 from google.cloud.bigquery.external_config import BigtableOptions
 from google.cloud.bigquery.external_config import BigtableColumnFamily
@@ -143,8 +142,6 @@
 "WriteDisposition",
 # EncryptionConfiguration
 "EncryptionConfiguration",
-# Errors and warnings
-"PyarrowMissingWarning",
 ]
 
 

@@ -58,7 +58,6 @@
 from google.cloud.bigquery.dataset import Dataset
 from google.cloud.bigquery.dataset import DatasetListItem
 from google.cloud.bigquery.dataset import DatasetReference
-from google.cloud.bigquery.exceptions import PyarrowMissingWarning
 from google.cloud.bigquery.opentelemetry_tracing import create_span
 from google.cloud.bigquery import job
 from google.cloud.bigquery.model import Model
@@ -2135,29 +2134,31 @@ def load_table_from_dataframe(
 [Beta] The compression method to use if intermittently
 serializing ``dataframe`` to a parquet file.
 
-If ``pyarrow`` and job config schema are used, the argument
-is directly passed as the ``compression`` argument to the
-underlying ``pyarrow.parquet.write_table()`` method (the
-default value "snappy" gets converted to uppercase).
+The argument is directly passed as the ``compression``
+argument to the underlying ``pyarrow.parquet.write_table()``
+method (the default value "snappy" gets converted to uppercase).
 https://arrow.apache.org/docs/python/generated/pyarrow.parquet.write_table.html#pyarrow-parquet-write-table
 
-If either ``pyarrow`` or job config schema are missing, the
-argument is directly passed as the ``compression`` argument
-to the underlying ``DataFrame.to_parquet()`` method.
+If the job config schema is missing, the argument is directly
+passed as the ``compression`` argument to the underlying
+``DataFrame.to_parquet()`` method.
 https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.to_parquet.html#pandas.DataFrame.to_parquet
 
 Returns:
 google.cloud.bigquery.job.LoadJob: A new load job.
 
 Raises:
-ImportError:
+ValueError:
 If a usable parquet engine cannot be found. This method
-requires :mod:`pyarrow` or :mod:`fastparquet` to be
-installed.
+requires :mod:`pyarrow` to be installed.
 TypeError:
 If ``job_config`` is not an instance of :class:`~google.cloud.bigquery.job.LoadJobConfig`
 class.
 """
+if pyarrow is None:
+# pyarrow is now the only supported parquet engine.
+raise ValueError("This method requires pyarrow to be installed")
+
 job_id = _make_job_id(job_id, job_id_prefix)
 
 if job_config:
@@ -2222,7 +2223,7 @@ def load_table_from_dataframe(
 os.close(tmpfd)
 
 try:
-if pyarrow and job_config.schema:
+if job_config.schema:
 if parquet_compression == "snappy": # adjust the default value
 parquet_compression = parquet_compression.upper()
 
@@ -2233,24 +2234,6 @@ def load_table_from_dataframe(
 parquet_compression=parquet_compression,
 )
 else:
-if not pyarrow:
-warnings.warn(
-"Loading dataframe data without pyarrow installed is "
-"deprecated and will become unsupported in the future. "
-"Please install the pyarrow package.",
-PyarrowMissingWarning,
-stacklevel=2,
-)
-
-if job_config.schema:
-warnings.warn(
-"job_config.schema is set, but not used to assist in "
-"identifying correct types for data serialization. "
-"Please install the pyarrow package.",
-PendingDeprecationWarning,
-stacklevel=2,
-)
-
 dataframe.to_parquet(tmppath, compression=parquet_compression)
 
 with open(tmppath, "rb") as parquet_file:

@@ -50,7 +50,6 @@
 from google.cloud.bigquery.schema import _build_schema_resource
 from google.cloud.bigquery.schema import _parse_schema_resource
 from google.cloud.bigquery.schema import _to_schema_fields
-from google.cloud.bigquery.exceptions import PyarrowMissingWarning
 from google.cloud.bigquery.external_config import ExternalConfig
 from google.cloud.bigquery.encryption_configuration import EncryptionConfiguration
 
@@ -1679,75 +1678,38 @@ def to_dataframe(
 create_bqstorage_client = False
 bqstorage_client = None
 
-if pyarrow is not None:
-# If pyarrow is available, calling to_arrow, then converting to a
-# pandas dataframe is about 2x faster. This is because pandas.concat is
-# rarely no-copy, whereas pyarrow.Table.from_batches + to_pandas is
-# usually no-copy.
-record_batch = self.to_arrow(
-progress_bar_type=progress_bar_type,
-bqstorage_client=bqstorage_client,
-create_bqstorage_client=create_bqstorage_client,
-)
+record_batch = self.to_arrow(
+progress_bar_type=progress_bar_type,
+bqstorage_client=bqstorage_client,
+create_bqstorage_client=create_bqstorage_client,
+)
+
+# When converting timestamp values to nanosecond precision, the result
+# can be out of pyarrow bounds. To avoid the error when converting to
+# Pandas, we set the timestamp_as_object parameter to True, if necessary.
+types_to_check = {
+pyarrow.timestamp("us"),
+pyarrow.timestamp("us", tz=pytz.UTC),
+}
 
-# When converting timestamp values to nanosecond precision, the result
-# can be out of pyarrow bounds. To avoid the error when converting to
-# Pandas, we set the timestamp_as_object parameter to True, if necessary.
-types_to_check = {
-pyarrow.timestamp("us"),
-pyarrow.timestamp("us", tz=pytz.UTC),
-}
-
-for column in record_batch:
-if column.type in types_to_check:
-try:
-column.cast("timestamp[ns]")
-except pyarrow.lib.ArrowInvalid:
-timestamp_as_object = True
-break
-else:
-timestamp_as_object = False
-
-extra_kwargs = {"timestamp_as_object": timestamp_as_object}
-
-df = record_batch.to_pandas(date_as_object=date_as_object, **extra_kwargs)
-
-for column in dtypes:
-df[column] = pandas.Series(df[column], dtype=dtypes[column])
-return df
+for column in record_batch:
+if column.type in types_to_check:
+try:
+column.cast("timestamp[ns]")
+except pyarrow.lib.ArrowInvalid:
+timestamp_as_object = True
+break
 else:
-warnings.warn(
-"Converting to a dataframe without pyarrow installed is "
-"often slower and will become unsupported in the future. "
-"Please install the pyarrow package.",
-PyarrowMissingWarning,
-stacklevel=2,
-)
+timestamp_as_object = False
 
-# The bqstorage_client is only used if pyarrow is available, so the
-# rest of this method only needs to account for tabledata.list.
-progress_bar = self._get_progress_bar(progress_bar_type)
+extra_kwargs = {"timestamp_as_object": timestamp_as_object}
 
-frames = []
-for frame in self.to_dataframe_iterable(dtypes=dtypes):
-frames.append(frame)
+df = record_batch.to_pandas(date_as_object=date_as_object, **extra_kwargs)
 
-if progress_bar is not None:
-# In some cases, the number of total rows is not populated
-# until the first page of rows is fetched. Update the
-# progress bar's total to keep an accurate count.
-progress_bar.total = progress_bar.total or self.total_rows
-progress_bar.update(len(frame))
-
-if progress_bar is not None:
-# Indicate that the download has finished.
-progress_bar.close()
-
-# Avoid concatting an empty list.
-if not frames:
-column_names = [field.name for field in self._schema]
-return pandas.DataFrame(columns=column_names)
-return pandas.concat(frames, ignore_index=True)
+for column in dtypes:
+df[column] = pandas.Series(df[column], dtype=dtypes[column])
+
+return df
 
 
 class _EmptyRowIterator(object):

@@ -49,10 +49,7 @@ def default(session):
 constraints_path,
 )
 
-# fastparquet is not included in .[all] because, in general, it's
-# redundant with pyarrow. We still want to run some unit tests with
-# fastparquet serialization, though.
-session.install("-e", ".[all,fastparquet]", "-c", constraints_path)
+session.install("-e", ".[all]", "-c", constraints_path)
 
 session.install("ipython", "-c", constraints_path)
 

@@ -47,13 +47,12 @@
 "grpcio >= 1.32.0, < 2.0dev",
 "pyarrow >= 1.0.0, < 2.0dev",
 ],
-"pandas": ["pandas>=0.23.0"],
-"pyarrow": [
+"pandas": [
+"pandas>=0.23.0",
 # pyarrow 1.0.0 is required for the use of timestamp_as_object keyword.
 "pyarrow >= 1.0.0, < 2.0dev",
 ],
 "tqdm": ["tqdm >= 4.7.4, <5.0.0dev"],
-"fastparquet": ["fastparquet", "python-snappy", "llvmlite>=0.34.0"],
 "opentelemetry": [
 "opentelemetry-api==0.9b0",
 "opentelemetry-sdk==0.9b0",
@@ -64,13 +63,6 @@
 all_extras = []
 
 for extra in extras:
-if extra in (
-# Skip fastparquet from "all" because it is redundant with pyarrow and
-# creates a dependency on versions of numpy. See:
-# https://.com/googleapis/google-cloud-python/issues/8549
-"fastparquet",
-):
-continue
 all_extras.extend(extras[extra])
 
 extras["all"] = all_extras

@@ -1,4 +1,3 @@
-fastparquet==0.4.1
 google-api-core==1.22.2
 google-cloud-bigquery-storage==2.0.0
 google-cloud-core==1.4.1

@@ -1329,3 +1329,11 @@ def test_download_dataframe_tabledata_list_dict_sequence_schema(module_under_tes
 )
 )
 assert result.equals(expected_result)
+
+with pytest.raises(StopIteration):
+result = next(results_gen)
+
+
+def test_table_data_listpage_to_dataframe_skips_stop_iteration(module_under_test):
+dataframe = module_under_test._tabledata_list_page_to_dataframe([], [], {})
+assert isinstance(dataframe, pandas.DataFrame)