fixed minor bugs and improved comments.

doc/python/bar-charts.md

@@ -603,26 +603,25 @@ def pictogram_bar(data, title, icon_size, max_icons_per_column=10, units_per_ico
     tick_locations = []
     #loop through each group and create a trace with its icons 
     for i, (category, value) in enumerate(data.items()):
-        # compute the number of icons represent this category
+        # compute the number of icons to use to represent this category.  Depending on your use case, you might replace round with floor or ceiling.
         icon_count = round(value / units_per_icon)
-        # compute the number of columns in which to arrange this category
-        # every category gets at least one column; we use integer division
-        # to compute the number of additional columns
-        num_columns = (icon_count // max_icons_per_column)+1
+        # compute the number of columns in which to arrange the icons for this category
+        # using a double negative sign to convert a floor(division) operation into a ceiling(division) operation
+        num_columns = -(-icon_count // max_icons_per_column)
 
-        #create lists of coordinates and populate them
+        #create and populate lists of icon coordinates
         x_coordinates, y_coordinates = [], []
         for col in range(num_columns):
             # the number of icons in this column is the lesser of the column height or
             # the number of icons remaining to place
             column_icons = min(max_icons_per_column, icon_count - col * max_icons_per_column)
 
-            # create a list element containing the x-coordinate of this column; 
-            # add column_icons copies of that coordinate to the list of icon x coordinates
-            # normalizing the width of each within group column to 1 simplifies the code
-            # we can adjust the visible space between columns by adjusting the total width below
+            # Create a one item list containing the x-coordinate of this column.
+            # Then add column_icons copies of that coordinate to the list of icon x coordinates using list multiplication.
+            # Normalizing the width of each within-category column to 1 simplifies the code.
+            # We can adjust the visible space between columns by adjusting the total width below.
             x_coordinates.extend([x_start + col] * column_icons)
-            # create a list of sequentially increasing y-coordinates for icons 
+            # Create a list of sequentially increasing y-coordinates for icons.
             y_coordinates.extend([y + icon_vertical_spacing * y for y in range(1, column_icons + 1)])
         # Add scatter plot for the category
         fig.add_trace(go.Scatter(
@@ -631,43 +630,48 @@ def pictogram_bar(data, title, icon_size, max_icons_per_column=10, units_per_ico
             mode='markers',
             marker=dict(size=icon_size, symbol="square", color= i),
             name=category,
-            #suppress the x and y coordinates in the hover text, since they are meaningless to readers
+            # Suppress the x and y coordinates in the hover text, since they are irrelevant implementation details.
             hoverinfo="text",
             text=[f"{category}: {value}" for _ in range(len(x_coordinates))]
         ))
 
-        # add an annotation above the center of each section showing its value
+        # Add an annotation above the center of each category showing its value
         fig.add_trace(go.Scatter(
-            x=[x_start + (num_columns - 1) / 2],  #center
-            y=[max_icons_per_column + 1.2],
+            x=[x_start + (num_columns - 1) / 2],  # Compute the location of the center
+            y=[max_icons_per_column* (1+icon_vertical_spacing) + 1.15],
             mode="text",
             text=[f"{value}"],
             textfont=dict(size=14, color="black"),
             showlegend=False
         ))
-        # Track locations where we will put the text for each category
+        # Track locations where we will put the text labeling each category
         tick_locations.append(x_start + (num_columns - 1) / 2)
         #compute the left edge of the next category
         x_start += num_columns + inter_group_spacing
-    # Update layout
+
     fig.update_layout(
         title=title,
         xaxis=dict(
             tickvals=tick_locations,
+            # Label ecah category
             ticktext=list(data.keys()),
             tickangle=-45,
             showgrid=False,
             title="Categories"
         ),
         yaxis=dict(
-            title=f"Units (1 icon = {units_per_icon:,g} {unit_description})",
+            title=f"Each icon represents {units_per_icon:,g} {unit_description}",
+            # The y-axis goes above the top icon to make room for the annotations.  
+            # We set tick values so the axis labeling does not go above the top icon.
+            # If you choose a value of max_icons_per_column that is not a multiple of 5, consider changing this.
+            tickvals=list(range(0,max_icons_per_column+1,5)),
             showgrid=False,
             zeroline=False,
         ),
-        # we've got all the labeling we need without a legend
+        # We have already got all the labeling we need so we suppress the legend.
         showlegend=False,  
         height=700,
-        # the x-coordinates scale to fill available space, so adjusting the width of the image is a good way to adjust spacing between columns
+        # The x-coordinates scale to fill available space, so adjusting the width of the image is a good way to adjust spacing between columns.
         width=(len(data) * 150 + 50)  
     )
     fig.show()