comment out 'per subgroup' since these just take too long to create

03_quant_report.py

@@ -400,165 +400,183 @@ def _():
     return
 
 
-@app.cell(hide_code=True)
+# @app.cell(hide_code=True)
+# def _():
+#     mo.md(r"""
+#     # BC per Consumer
+#     """)
+#     return
+
+
+# @app.cell
+# def _():
+#     split_group = 'Consumer'
+#     return (split_group,)
+
+
+# @app.cell
+# def _(split_group):
+#     mo.md(rf"""
+#     ## Character Ranking Points (per {split_group} segment)
+#     """)
+#     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]
+#         _char_rank_weighted = calculate_weighted_ranking_scores(_char_rank)
+
+#         _plot = S.plot_weighted_ranking_score(
+#             _char_rank_weighted, 
+#             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 _(split_group):
+#     mo.md(rf"""
+#     ## Character Ranking Place 1-2-3 in one (per {split_group})
+#     """)
+#     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 _(split_group):
+#     mo.md(rf"""
+#     ## Character Ranking times 1st place (per {split_group})
+#     """)
+#     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 _(split_group):
+#     mo.md(rf"""
+#     ## Predefined personality traits WordClouds per {split_group}
+#     """)
+#     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 _(split_group):
+#     mo.md(rf"""
+#     ## Frequency traits chosen - per {split_group} 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
+# def _():
+#     mo.md(r"""
+#     # BC per Gender
+#     """)
+#     return
+
+
+@app.cell
 def _():
-    mo.md(r"""
-    # BC per Consumer
-    """)
     return
 
 
 @app.cell
 def _():
-    split_group = 'Consumer'
-    return (split_group,)
-
-
-@app.cell
-def _(split_group):
-    mo.md(rf"""
-    ## Character Ranking Points (per {split_group} segment)
-    """)
-    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]
-        _char_rank_weighted = calculate_weighted_ranking_scores(_char_rank)
-
-        _plot = S.plot_weighted_ranking_score(
-            _char_rank_weighted, 
-            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 _(split_group):
-    mo.md(rf"""
-    ## Character Ranking Place 1-2-3 in one (per {split_group})
-    """)
-    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 _(split_group):
-    mo.md(rf"""
-    ## Character Ranking times 1st place (per {split_group})
-    """)
-    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 _(split_group):
-    mo.md(rf"""
-    ## Predefined personality traits WordClouds per {split_group}
-    """)
-    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 _(split_group):
-    mo.md(rf"""
-    ## Frequency traits chosen - per {split_group} 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