fix(linting): code formatting

src/ydata_profiling/profile_report.py

@@ -199,7 +199,8 @@ def __initialize_dataframe(
     ) -> Optional[Union[pd.DataFrame, sDataFrame]]:
 
         logger.info_def_report(
-            df=df, timeseries=report_config.vars.timeseries.active,
+            df=df,
+            timeseries=report_config.vars.timeseries.active,
         )
 
         if (

src/ydata_profiling/utils/common.py

@@ -99,12 +99,9 @@ def convert_timestamp_to_datetime(timestamp: int) -> datetime:
         return datetime(1970, 1, 1) + timedelta(seconds=int(timestamp))
 
 
-def analytics_features(dataframe: str,
-                       datatype: str,
-                       report_type: str,
-                       ncols: int,
-                       nrows:int,
-                       dbx: str) -> None:
+def analytics_features(
+    dataframe: str, datatype: str, report_type: str, ncols: int, nrows: int, dbx: str
+) -> None:
     endpoint = "https://packages.ydata.ai/ydata-profiling?"
     package_version = __version__
 
@@ -136,13 +133,15 @@ def analytics_features(dataframe: str,
 
             requests.get(request_message)
 
+
 def is_running_in_databricks():
-    mask = 'DATABRICKS_RUNTIME_VERSION' in os.environ
-    if 'DATABRICKS_RUNTIME_VERSION' in os.environ:
-        return os.environ['DATABRICKS_RUNTIME_VERSION']
+    mask = "DATABRICKS_RUNTIME_VERSION" in os.environ
+    if "DATABRICKS_RUNTIME_VERSION" in os.environ:
+        return os.environ["DATABRICKS_RUNTIME_VERSION"]
     else:
         return str(mask)
 
+
 def calculate_nrows(df):
     """
     Calculates the approx. number of rows spark dataframes
@@ -152,10 +151,15 @@ def calculate_nrows(df):
     try:
         n_partitions = df.rdd.getNumPartitions()
 
-        nrows = df.rdd.mapPartitionsWithIndex(
-            lambda idx, partition: [sum(1 for _ in partition)] if idx == 0 else [0]
-        ).collect()[0] * n_partitions
+        nrows = (
+            df.rdd.mapPartitionsWithIndex(
+                lambda idx, partition: [sum(1 for _ in partition)] if idx == 0 else [0]
+            ).collect()[0]
+            * n_partitions
+        )
     except:
-        nrows = 0  # returns 0 in case it was not possible to compute it from the partition
+        nrows = (
+            0  # returns 0 in case it was not possible to compute it from the partition
+        )
 
     return nrows

src/ydata_profiling/utils/logger.py

@@ -6,9 +6,11 @@
 
 import pandas as pd
 
-from ydata_profiling.utils.common import (calculate_nrows,
-                                          analytics_features,
-                                          is_running_in_databricks)
+from ydata_profiling.utils.common import (
+    analytics_features,
+    calculate_nrows,
+    is_running_in_databricks,
+)
 
 
 class ProfilingLogger(logging.Logger):
@@ -20,22 +22,26 @@ def info_def_report(self, df, timeseries: bool) -> None:  # noqa: ANN001
         if isinstance(df, pd.DataFrame):
             dataframe = "pandas"
             report_type = "regular"
-            nrows=len(df)
+            nrows = len(df)
         elif df is None:
             dataframe = "pandas"
             report_type = "compare"
-            nrows=len(df)
+            nrows = len(df)
         else:
             dataframe = "spark"
             report_type = "regular"
-            nrows=calculate_nrows(df)
+            nrows = calculate_nrows(df)
 
-        dbx=is_running_in_databricks()
+        dbx = is_running_in_databricks()
         datatype = "timeseries" if timeseries else "tabular"
 
         analytics_features(
-            dataframe=dataframe, datatype=datatype, report_type=report_type,
-            nrows=nrows, ncols=ncols, dbx=dbx
+            dataframe=dataframe,
+            datatype=datatype,
+            report_type=report_type,
+            nrows=nrows,
+            ncols=ncols,
+            dbx=dbx,
         )
 
         super().info(