Up package version

df_to_azure/__init__.py

@@ -2,7 +2,7 @@
 
 from .export import df_to_azure
 
-__version__ = "0.8.1"
+__version__ = "0.9.0"
 
 logging.basicConfig(
     format="%(asctime)s.%(msecs)03d [%(levelname)-5s] [%(name)s] - %(message)s",

df_to_azure/db.py

@@ -71,14 +71,14 @@ def create_stored_procedure(self):
                 )
 
 
-def auth_azure():
+def auth_azure(driver: str = "ODBC Driver 17 for SQL Server"):
 
     connection_string = "mssql+pyodbc://{}:{}@{}:1433/{}?driver={}".format(
         os.environ.get("SQL_USER"),
         quote_plus(os.environ.get("SQL_PW")),
         os.environ.get("SQL_SERVER"),
         os.environ.get("SQL_DB"),
-        "ODBC Driver 17 for SQL Server",
+        driver,
     )
     con = create_engine(connection_string).connect()
 

df_to_azure/export.py

@@ -197,7 +197,7 @@ def upload_to_blob(self):
             blob=f"{self.table_name}/{self.table_name}.csv",
         )
 
-        data = self.df.to_csv(index=False, sep="^", quotechar='"', line_terminator="\n")
+        data = self.df.to_csv(index=False, sep="^", quotechar='"', lineterminator="\n")
         blob_client.upload_blob(data, overwrite=True)
 
     def create_schema(self):

df_to_azure/tests/test_general.py

@@ -161,14 +161,15 @@ def test_pipeline_name():
 def test_empty_dataframe():
     df = DataFrame()
 
-    with pytest.raises(SystemExit):
-        df_to_azure(
-            df=df,
-            tablename="empty_dataframe",
-            schema="test",
-            method="create",
-            wait_till_finished=True,
-        )
+    adf_client, run_response = df_to_azure(
+        df=df,
+        tablename="empty_dataframe",
+        schema="test",
+        method="create",
+        wait_till_finished=True,
+    )
+    assert adf_client is None
+    assert run_response is None
 
 
 def test_convert_bigint():
@@ -203,18 +204,3 @@ def test_double_column_names():
             schema="test",
             wait_till_finished=True,
         )
-
-
-def test_dataframe_with_no_data():
-
-    # scenario where there are 2 dataframes, first one empty and the second one with data.
-    df_list = [DataFrame(), DataFrame({"A": [1, 2, 3], "B": [10, 20, 30], "C": ["X", "Y", "Z"]})]
-
-    results = list()
-
-    for df in df_list:
-        r = df_to_azure(df, tablename=f"dataset_{df.shape[0]}_records", schema="test")
-        results.append(r)
-
-    # there should be 2 results from the 2 df_to_azure operations.
-    assert len(results) == 2

requirements.txt

@@ -1,8 +1,8 @@
-azure-identity~=1.7.1
-azure-mgmt-datafactory~=2.2.0
-azure-mgmt-resource~=20.1.0
-azure-storage-blob~=12.8.1
-pandas~=1.4.1
-pyarrow~=7.0.0
-pyodbc~=4.0.32
-sqlalchemy~=1.4.31
+azure-identity>=1.7.1
+azure-mgmt-datafactory>=2.2.0,<2.7.0
+azure-mgmt-resource>=20.1.0
+azure-storage-blob>=12.8.1
+pandas>=1.4.1
+pyarrow>=7.0.0
+pyodbc>=4.0.32
+sqlalchemy>=1.4.31,<2.0.0

setup.cfg

@@ -1,6 +1,6 @@
 [metadata]
 name = df_to_azure
-version = 0.8.1
+version = 0.9.0
 author = Melvin Folkers, Erfan Nariman
 author_email = melvin@zypp.io, erfan@zypp.io
 description = Automatically write pandas DataFrames to SQL by creating pipelines in Azure Data Factory with copy activity from blob to SQL
@@ -20,14 +20,15 @@ classifiers =
 packages = df_to_azure
 python_requires = >=3.7
 install_requires =
-    azure-identity~=1.7.1
-    azure-mgmt-datafactory~=2.2.0
-    azure-mgmt-resource~=20.1.0
-    azure-storage-blob~=12.8.1
-    pandas~=1.4.1
-    pyarrow~=7.0.0
-    pyodbc~=4.0.32
-    sqlalchemy~=1.4.31
+    azure-identity>=1.7.1
+    azure-mgmt-datafactory>=2.2.0,<2.7.0
+    azure-mgmt-resource>=20.1.0
+    azure-storage-blob>=12.8.1
+    pandas>=1.4.1
+    pyarrow>=7.0.0
+    pyodbc>=4.0.32
+    sqlalchemy>=1.4.31,<2.0.0
+
 
 
 [flake8]