Pdf

src/mridle/extras/datasets/pandas_styler.py

@@ -10,17 +10,16 @@
 
 import fsspec
 import numpy as np
-import matplotlib.pyplot as plt
 
 from typing import Any, Dict
 
 
 class PandasStylerHtml(AbstractVersionedDataSet):
     def __init__(self, filepath: str, version: Version = None):
-        """Creates a new instance of ImageDataSet to load / save image data for given filepath.
+        """Creates a new instance to load / save image data for given filepath.
 
         Args:
-            filepath: The location of the image file to load / save data.
+            filepath: The location of the table file to load / save data.
         """
         # parse the path and protocol (e.g. file, http, s3, etc.)
         protocol, path = get_protocol_and_path(filepath)
@@ -35,22 +34,16 @@ def __init__(self, filepath: str, version: Version = None):
         )
 
     def _load(self) -> np.ndarray:
-        raise DataSetError("`load` is not supported on AltairDataSet")
+        raise DataSetError("`load` is not supported on PandasStylerHtml")
 
     def _save(self, data) -> None:
-        """Saves a plotly figure as html to the specified filepath."""
+        """Saves a pandas table as html to the specified filepath."""
         # using get_filepath_str ensures that the protocol and path are appended correctly for different filesystems
         save_path = get_filepath_str(self._get_save_path(), self._protocol)
         df = data.data  # Extract the DataFrame from the styler
-        # df_html = df.to_html(index=False)  # Convert DataFrame to HTML
-        # with open(save_path, "w") as f:
-        #    f.write(df_html)
-
-        df = data.data  # Extract the DataFrame from the styler
-        plt.figure(figsize=(10, 6))
-        plt.axis('off')
-        plt.table(cellText=df.values, colLabels=df.columns, cellLoc='center', loc='center')
-        plt.savefig(save_path, bbox_inches='tight')
+        df_html = df.to_html()  # Convert DataFrame to HTML
+        with open(save_path, "w") as f:
+            f.write(df_html)
 
     def _describe(self) -> Dict[str, Any]:
         """Returns a dict that describes the attributes of the dataset."""