Consolidate import and usage of pandas

airflow/providers/amazon/aws/transfers/sql_to_s3.py

@@ -29,7 +29,7 @@
 from airflow.providers.common.sql.hooks.sql import DbApiHook
 
 if TYPE_CHECKING:
-    from pandas import DataFrame
+    import pandas as pd
 
     from airflow.utils.context import Context
 
@@ -134,15 +134,15 @@ def __init__(
             raise AirflowException(f"The argument file_format doesn't support {file_format} value.")
 
     @staticmethod
-    def _fix_dtypes(df: DataFrame, file_format: FILE_FORMAT) -> None:
+    def _fix_dtypes(df: pd.DataFrame, file_format: FILE_FORMAT) -> None:
         """
         Mutate DataFrame to set dtypes for float columns containing NaN values.
 
         Set dtype of object to str to allow for downstream transformations.
         """
         try:
             import numpy as np
-            from pandas import Float64Dtype, Int64Dtype
+            import pandas as pd
         except ImportError as e:
             from airflow.exceptions import AirflowOptionalProviderFeatureException
 
@@ -163,13 +163,13 @@ def _fix_dtypes(df: DataFrame, file_format: FILE_FORMAT) -> None:
                     # The type ignore can be removed here if https://github.com/numpy/numpy/pull/23690
                     # is merged and released as currently NumPy does not consider None as valid for x/y.
                     df[col] = np.where(df[col].isnull(), None, df[col])  # type: ignore[call-overload]
-                    df[col] = df[col].astype(Int64Dtype())
+                    df[col] = df[col].astype(pd.Int64Dtype())
                 elif np.isclose(notna_series, notna_series.astype(int)).all():
                     # set to float dtype that retains floats and supports NaNs
                     # The type ignore can be removed here if https://github.com/numpy/numpy/pull/23690
                     # is merged and released
                     df[col] = np.where(df[col].isnull(), None, df[col])  # type: ignore[call-overload]
-                    df[col] = df[col].astype(Float64Dtype())
+                    df[col] = df[col].astype(pd.Float64Dtype())
 
     def execute(self, context: Context) -> None:
         sql_hook = self._get_hook()
@@ -192,7 +192,7 @@ def execute(self, context: Context) -> None:
                     filename=tmp_file.name, key=object_key, bucket_name=self.s3_bucket, replace=self.replace
                 )
 
-    def _partition_dataframe(self, df: DataFrame) -> Iterable[tuple[str, DataFrame]]:
+    def _partition_dataframe(self, df: pd.DataFrame) -> Iterable[tuple[str, pd.DataFrame]]:
         """Partition dataframe using pandas groupby() method."""
         if not self.groupby_kwargs:
             yield "", df

airflow/providers/apache/hive/hooks/hive.py

@@ -31,7 +31,7 @@
 from airflow.exceptions import AirflowProviderDeprecationWarning
 
 try:
-    import pandas
+    import pandas as pd
 except ImportError as e:
     from airflow.exceptions import AirflowOptionalProviderFeatureException
 
@@ -336,7 +336,7 @@ def test_hql(self, hql: str) -> None:
 
     def load_df(
         self,
-        df: pandas.DataFrame,
+        df: pd.DataFrame,
         table: str,
         field_dict: dict[Any, Any] | None = None,
         delimiter: str = ",",
@@ -361,7 +361,7 @@ def load_df(
         :param kwargs: passed to self.load_file
         """
 
-        def _infer_field_types_from_df(df: pandas.DataFrame) -> dict[Any, Any]:
+        def _infer_field_types_from_df(df: pd.DataFrame) -> dict[Any, Any]:
             dtype_kind_hive_type = {
                 "b": "BOOLEAN",  # boolean
                 "i": "BIGINT",  # signed integer
@@ -1037,7 +1037,7 @@ def get_pandas_df(  # type: ignore
         schema: str = "default",
         hive_conf: dict[Any, Any] | None = None,
         **kwargs,
-    ) -> pandas.DataFrame:
+    ) -> pd.DataFrame:
         """
         Get a pandas dataframe from a Hive query.
 
@@ -1056,5 +1056,5 @@ def get_pandas_df(  # type: ignore
         :return: pandas.DateFrame
         """
         res = self.get_results(sql, schema=schema, hive_conf=hive_conf)
-        df = pandas.DataFrame(res["data"], columns=[c[0] for c in res["header"]], **kwargs)
+        df = pd.DataFrame(res["data"], columns=[c[0] for c in res["header"]], **kwargs)
         return df

airflow/providers/google/cloud/hooks/bigquery.py

@@ -30,6 +30,7 @@
 from datetime import datetime, timedelta
 from typing import Any, Iterable, Mapping, NoReturn, Sequence, Union, cast
 
+import pandas as pd
 from aiohttp import ClientSession as ClientSession
 from gcloud.aio.bigquery import Job, Table as Table_async
 from google.api_core.page_iterator import HTTPIterator
@@ -49,7 +50,6 @@
 from google.cloud.bigquery.table import EncryptionConfiguration, Row, RowIterator, Table, TableReference
 from google.cloud.exceptions import NotFound
 from googleapiclient.discovery import Resource, build
-from pandas import DataFrame
 from pandas_gbq import read_gbq
 from pandas_gbq.gbq import GbqConnector  # noqa
 from requests import Session
@@ -244,7 +244,7 @@ def get_pandas_df(
         parameters: Iterable | Mapping[str, Any] | None = None,
         dialect: str | None = None,
         **kwargs,
-    ) -> DataFrame:
+    ) -> pd.DataFrame:
         """Get a Pandas DataFrame for the BigQuery results.
 
         The DbApiHook method must be overridden because Pandas doesn't support

airflow/providers/presto/hooks/presto.py

@@ -158,7 +158,7 @@ def get_first(
             raise PrestoException(e)
 
     def get_pandas_df(self, sql: str = "", parameters=None, **kwargs):
-        import pandas
+        import pandas as pd
 
         cursor = self.get_cursor()
         try:
@@ -168,10 +168,10 @@ def get_pandas_df(self, sql: str = "", parameters=None, **kwargs):
             raise PrestoException(e)
         column_descriptions = cursor.description
         if data:
-            df = pandas.DataFrame(data, **kwargs)
+            df = pd.DataFrame(data, **kwargs)
             df.columns = [c[0] for c in column_descriptions]
         else:
-            df = pandas.DataFrame(**kwargs)
+            df = pd.DataFrame(**kwargs)
         return df
 
     def insert_rows(

airflow/providers/slack/transfers/sql_to_slack.py

@@ -19,7 +19,7 @@
 from tempfile import NamedTemporaryFile
 from typing import TYPE_CHECKING, Any, Iterable, Mapping, Sequence
 
-from pandas import DataFrame
+import pandas as pd
 from tabulate import tabulate
 
 from airflow.exceptions import AirflowException
@@ -70,7 +70,7 @@ def _get_hook(self) -> DbApiHook:
             )
         return hook
 
-    def _get_query_results(self) -> DataFrame:
+    def _get_query_results(self) -> pd.DataFrame:
         sql_hook = self._get_hook()
 
         self.log.info("Running SQL query: %s", self.sql)

airflow/providers/trino/hooks/trino.py

@@ -178,7 +178,7 @@ def get_first(
     def get_pandas_df(
         self, sql: str = "", parameters: Iterable | Mapping[str, Any] | None = None, **kwargs
     ):  # type: ignore[override]
-        import pandas
+        import pandas as pd
 
         cursor = self.get_cursor()
         try:
@@ -188,10 +188,10 @@ def get_pandas_df(
             raise TrinoException(e)
         column_descriptions = cursor.description
         if data:
-            df = pandas.DataFrame(data, **kwargs)
+            df = pd.DataFrame(data, **kwargs)
             df.columns = [c[0] for c in column_descriptions]
         else:
-            df = pandas.DataFrame(**kwargs)
+            df = pd.DataFrame(**kwargs)
         return df
 
     def insert_rows(

airflow/serialization/serializers/pandas.py

@@ -28,19 +28,19 @@
 deserializers = serializers
 
 if TYPE_CHECKING:
-    from pandas import DataFrame
+    import pandas as pd
 
     from airflow.serialization.serde import U
 
 __version__ = 1
 
 
 def serialize(o: object) -> tuple[U, str, int, bool]:
+    import pandas as pd
     import pyarrow as pa
-    from pandas import DataFrame
     from pyarrow import parquet as pq
 
-    if not isinstance(o, DataFrame):
+    if not isinstance(o, pd.DataFrame):
         return "", "", 0, False
 
     # for now, we *always* serialize into in memory
@@ -53,7 +53,7 @@ def serialize(o: object) -> tuple[U, str, int, bool]:
     return buf.getvalue().hex().decode("utf-8"), qualname(o), __version__, True
 
 
-def deserialize(classname: str, version: int, data: object) -> DataFrame:
+def deserialize(classname: str, version: int, data: object) -> pd.DataFrame:
     if version > __version__:
         raise TypeError(f"serialized {version} of {classname} > {__version__}")
 

tests/serialization/serializers/test_serializers.py

@@ -20,7 +20,7 @@
 import decimal
 
 import numpy
-import pandas
+import pandas as pd
 import pendulum.tz
 import pytest
 from pendulum import DateTime
@@ -94,7 +94,7 @@ def test_params(self):
         assert i["x"] == d["x"]
 
     def test_pandas(self):
-        i = pandas.DataFrame(data={"col1": [1, 2], "col2": [3, 4]})
+        i = pd.DataFrame(data={"col1": [1, 2], "col2": [3, 4]})
         e = serialize(i)
         d = deserialize(e)
         assert i.equals(d)