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

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Luigi Maiorano
2026-02-02 23:22:09 +01:00
parent 611fc8d19a
commit fd14038253
1 changed files with 171 additions and 153 deletions
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324
03_quant_report.py
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@@ -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