Update numpy-tooltip-images.rst

doc/case_studies/numpy-tooltip-images.rst

@@ -40,7 +40,7 @@ in order to have a quantitative measurement to plot.
         'group': rng.choice(['a', 'b', 'c'], size=n_rows)
     })
     # Compute the area as the proportion of pixels above a threshold
-    df[['image1_area', 'image2_area']] = df[['image1', 'image2']].applymap(lambda x: (x > 0.4).mean())
+    df[['image1_area', 'image2_area']] = df[['image1', 'image2']].map(lambda x: (x > 0.4).mean())
     df
 
 Next, we define the function
@@ -87,7 +87,7 @@ is in the form of an image and render it appropriately.
         return f"data:image/png;base64,{img_str}"
 
     # The column with the base64 image string must be called "image" in order for it to trigger the image rendering in the tooltip
-    df['image'] = df[['image1', 'image2']].apply(create_tooltip_image, axis=1)
+    df['image'] = df[['image1', 'image2']].map(create_tooltip_image, axis=1)
 
     # Dropping the image arrays since they are large an no longer needed
     df_plot = df.drop(columns=['image1', 'image2'])
@@ -150,7 +150,7 @@ instead of both the images concatenated together.
         return f"data:image/png;base64,{img_str}"
 
     # The column with the base64 image string must be called "image" in order for it to trigger the image rendering in the tooltip
-    df[['image1_base64', 'image2_base64']] = df[['image1', 'image2']].applymap(create_tooltip_image)
+    df[['image1_base64', 'image2_base64']] = df[['image1', 'image2']].map(create_tooltip_image)
     # Dropping the image arrays since they are large an no longer needed
     # Also drop the previous tooltip image for clarity
     df_plot = df.drop(columns=['image1', 'image2', 'image'])