BC results per consumer

03_quant_report.py

@@ -1,6 +1,6 @@
 import marimo
 
-__generated_with = "0.19.2"
+__generated_with = "0.19.7"
 app = marimo.App(width="full")
 
 with app.setup:
@@ -285,14 +285,22 @@ def _(S, char_rank):
     return
 
 
+@app.cell
+def _():
+    mo.md(r"""
+    ### Statistical Significance Character Ranking
+    """)
+    return
+
+
 @app.cell
 def _(S, char_rank):
     _pairwise_df, _meta = S.compute_ranking_significance(char_rank)
 
-    print(_pairwise_df.columns)
+    # print(_pairwise_df.columns)
 
     mo.md(f"""
-    ### Statistical Significance Character Ranking
+
 
     {mo.ui.altair_chart(S.plot_significance_heatmap(_pairwise_df, metadata=_meta))}
 
@@ -389,10 +397,10 @@ def _(S, char_df):
     """
 
     mo.md(_content)
-    return
+    return character_colors, consistent_sort_order
 
 
-@app.cell
+@app.cell(hide_code=True)
 def _():
     mo.md(r"""
     ## Statistical significance best characters
@@ -427,19 +435,136 @@ def _(S, data):
 
         _plot = S.plot_weighted_ranking_score(
             _char_rank_weighted, 
-            title=f'Most Popular Character - Weighted Popularity Score - CONSUMER: "{_consumer_group.replace()}"<br>(1st=3pts, 2nd=2pts, 3rd=1pt)',
+            title=f'Most Popular Character - Weighted Popularity Score - CONSUMER: "{_consumer_group.replace("_", " ").replace("Woth", 'Worth')}"<br>(1st=3pts, 2nd=2pts, 3rd=1pt)',
             x_label='Voice'
         )
 
         _content += f"""
         {mo.ui.altair_chart(_plot)}
-    
+
         """
 
     mo.md(_content)
     return
 
 
+@app.cell
+def _():
+    mo.md(r"""
+    ## Character Ranking Place 1-2-3 in one (per consumer)
+    """)
+    return
+
+
+@app.cell
+def _(S, data):
+    _content = ""
+    for _consumer_group, _consumer_df in utils.split_consumer_groups(data).items():
+
+        _char_rank = S.get_character_ranking(_consumer_df)[0]
+    
+
+        _plot = S.plot_top3_ranking_distribution(_char_rank, x_label='Character Personality', title='Character Personality: Rankings Top 3 - CONSUMER: "'+_consumer_group.replace("_", " ").replace("Woth", 'Worth')+'"')
+
+        _content += f"""
+        {mo.ui.altair_chart(_plot)}
+
+        """
+
+    mo.md(_content)
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Character Ranking times 1st place (per consumer)
+    """)
+    return
+
+
+@app.cell
+def _(S, data):
+    _content = ""
+    for _consumer_group, _consumer_df in utils.split_consumer_groups(data).items():
+
+        _char_rank = S.get_character_ranking(_consumer_df)[0]
+
+        _plot = S.plot_most_ranked_1(_char_rank, title=f'Most Popular Character - CONSUMER: "{_consumer_group.replace("_", " ").replace("Woth", 'Worth')}"<br>(Number of Times Ranked 1st)', x_label='Character Personality')
+
+        _content += f"""
+        {mo.ui.altair_chart(_plot)}
+
+        """
+
+    mo.md(_content)
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Predefined personality traits WordClouds per Consumer
+    """)
+    return
+
+
+@app.cell
+def _(S, data):
+    _content = ""
+    for _consumer_group, _consumer_df in utils.split_consumer_groups(data).items():
+        _top8_traits = S.get_top_8_traits(_consumer_df)[0]
+    
+        _plot = S.plot_traits_wordcloud(
+            data=_top8_traits,
+            column='Top_8_Traits',
+            title=f'Most Prominent Personality Traits - CONSUMER: "{_consumer_group.replace("_", " ").replace("Woth", "Worth")}"',
+        )
+
+        _content += f"""
+        {_plot}
+
+        """
+
+    mo.md(_content)
+    return
+
+
+@app.cell
+def _():
+    mo.md(r"""
+    ## Frequency traits chosen - per consumer segment
+    """)
+    return
+
+
+@app.cell
+def _(S, character_colors, consistent_sort_order, data):
+    top_char = "The Coach"
+
+    _content = ""
+    for _consumer_group, _consumer_df in utils.split_consumer_groups(data).items():
+
+        _char_df = S.get_character_refine(_consumer_df)[0]
+
+        _freq_df, _ = S.transform_character_trait_frequency(_char_df, top_char)
+        _main_color, _highlight_color = character_colors[top_char]
+        _chart = S.plot_single_character_trait_frequency(
+            data=_freq_df,
+            character_name=top_char,
+            bar_color=_main_color,
+            highlight_color=_highlight_color,
+            trait_sort_order=consistent_sort_order,
+            title=f"""Top Personality Traits for '{top_char}' - CONSUMER: "{_consumer_group.replace('_', ' ').replace("Woth", "Worth")}"""
+        )
+        _content += f"""
+        {mo.ui.altair_chart(_chart)}
+    
+        """
+    mo.md(_content)
+    return
+
+
 @app.cell(hide_code=True)
 def _():
     mo.md(r"""
@@ -614,7 +739,6 @@ def _(S, voice_1_10):
 
     {mo.ui.altair_chart(_summary)}
     """)
-
     return