format and pyspark path

Author: FBruzzesi

packages/python/plotly/plotly/express/_core.py

@@ -160,7 +160,7 @@ def _is_continuous(df: nw.DataFrame, col_name: str) -> bool:
         # fastpath for pandas: Narwhals' Series.dtype has a bit of overhead, as it
         # tries to distinguish between true "object" columns, and "string" columns
         # disguised as "object". But here, we deal with neither.
-        return df_native[col_name].dtype.kind in 'ifc'
+        return df_native[col_name].dtype.kind in "ifc"
     return df.get_column(col_name).dtype.is_numeric()
 
 
@@ -1454,6 +1454,7 @@ def build_dataframe(args, constructor):
             is_pd_like = True
 
         elif hasattr(args["data_frame"], "__dataframe__"):
+            # data_frame supports interchange protocol
             args["data_frame"] = nw.from_native(
                 nw.from_native(
                     args["data_frame"], eager_or_interchange_only=True
@@ -1463,6 +1464,15 @@ def build_dataframe(args, constructor):
             columns = args["data_frame"].columns
             is_pd_like = True
 
+        elif hasattr(args["data_frame"], "toPandas"):
+            # data_frame is PySpark: it does not support interchange and it is not
+            # integrated in narwhals just yet
+            args["data_frame"] = nw.from_native(
+                args["data_frame"].toPandas(), eager_only=True
+            )
+            columns = args["data_frame"].columns
+            is_pd_like = True
+
         else:
             try:
                 import pandas as pd

packages/python/plotly/plotly/figure_factory/_hexbin_mapbox.py

@@ -407,7 +407,11 @@ def create_hexbin_mapbox(
         center = dict(lat=lat_range.mean(), lon=lon_range.mean())
 
     if args["animation_frame"] is not None:
-        groups = dict(args["data_frame"].group_by(args["animation_frame"], drop_null_keys=True).__iter__())
+        groups = dict(
+            args["data_frame"]
+            .group_by(args["animation_frame"], drop_null_keys=True)
+            .__iter__()
+        )
     else:
         groups = {(0,): args["data_frame"]}