Fixed: add missing warning when max_rows is exceeded

src/ipyvizzu/data/converters/df/converter.py

@@ -4,6 +4,7 @@
 
 from abc import abstractmethod
 from typing import List
+import warnings
 
 from ipyvizzu.data.converters.converter import ToSeriesListConverter
 from ipyvizzu.data.converters.df.type_alias import DataFrame
@@ -49,6 +50,17 @@ def _get_series_from_column(self, column_name: str) -> Series:
         values, infer_type = self._convert_to_series_values_and_type(column_name)
         return self._convert_to_series(column_name, values, infer_type)
 
+    def _is_max_rows_exceeded(self, row_number: int) -> bool:
+        if row_number > self._max_rows:
+            warnings.warn(
+                "The number of rows of the dataframe exceeds the set `max_rows`, "
+                f"the dataframe is randomly sampled to the set value ({self._max_rows}).",
+                UserWarning,
+                stacklevel=2,
+            )
+            return True
+        return False
+
     @abstractmethod
     def _get_sampled_df(self, df: DataFrame) -> DataFrame:
         """

src/ipyvizzu/data/converters/pandas/converter.py

@@ -110,7 +110,7 @@ def _convert_to_df(self, series: "pandas.Series") -> "pandas.Dataframe":  # type
 
     def _get_sampled_df(self, df: "pandas.DataFrame") -> "pandas.DataFrame":  # type: ignore
         row_number = len(df)
-        if row_number > self._max_rows:
+        if self._is_max_rows_exceeded(row_number):
             frac = self._max_rows / row_number
             sampled_df = df.sample(
                 replace=False,

src/ipyvizzu/data/converters/spark/converter.py

@@ -68,7 +68,7 @@ def _get_sampled_df(
         self, df: "pyspark.sql.DataFrame"  # type: ignore
     ) -> "pyspark.sql.DataFrame":  # type: ignore
         row_number = df.count()
-        if row_number > self._max_rows:
+        if self._is_max_rows_exceeded(row_number):
             fraction = self._max_rows / row_number
             sample_df = df.sample(withReplacement=False, fraction=fraction, seed=42)
             return sample_df.limit(self._max_rows)