From 840cb4e6dc1a3d5e9ac20874fdb2fc5dc0c5da22 Mon Sep 17 00:00:00 2001 From: Luigi Maiorano Date: Tue, 3 Feb 2026 13:48:05 +0100 Subject: [PATCH] exported marimo to script form --- 03_quant_report.script.py | 594 +++++++++++++++++++++++++++ 04_PPTX_Update_Images.py | 2 +- pyproject.toml | 1 + uv.lock | 832 ++++++++++++++++++++++++++++++++++++++ 4 files changed, 1428 insertions(+), 1 deletion(-) create mode 100644 03_quant_report.script.py diff --git a/03_quant_report.script.py b/03_quant_report.script.py new file mode 100644 index 0000000..10d4a99 --- /dev/null +++ b/03_quant_report.script.py @@ -0,0 +1,594 @@ + +__generated_with = "0.19.7" + +# %% +import marimo as mo +import polars as pl +from pathlib import Path + +from validation import check_progress, duration_validation, check_straight_liners +from utils import QualtricsSurvey, combine_exclusive_columns, calculate_weighted_ranking_scores +import utils + +from speaking_styles import SPEAKING_STYLES + +# %% + +# file_browser = mo.ui.file_browser( +# initial_path="./data/exports", multiple=False, restrict_navigation=True, filetypes=[".csv"], label="Select 'Labels' File" +# ) +# file_browser + +# # %% +# mo.stop(file_browser.path(index=0) is None, mo.md("**⚠️ Please select a `_Labels.csv` file above to proceed**")) +# RESULTS_FILE = Path(file_browser.path(index=0)) + +RESULTS_FILE = 'data/exports/2-2-26/JPMC_Chase Brand Personality_Quant Round 1_February 2, 2026_Labels.csv' +QSF_FILE = 'data/exports/OneDrive_2026-01-21/Soft Launch Data/JPMC_Chase_Brand_Personality_Quant_Round_1.qsf' + +# %% +S = QualtricsSurvey(RESULTS_FILE, QSF_FILE) +try: + data_all = S.load_data() +except NotImplementedError as e: + mo.stop(True, mo.md(f"**⚠️ {str(e)}**")) + +# %% +BEST_CHOSEN_CHARACTER = "the_coach" + +# # %% +# filter_form = mo.md(''' + + + +# {age} + +# {gender} + +# {ethnicity} + +# {income} + +# {consumer} +# ''' +# ).batch( +# age=mo.ui.multiselect(options=S.options_age, value=S.options_age, label="Select Age Group(s):"), +# gender=mo.ui.multiselect(options=S.options_gender, value=S.options_gender, label="Select Gender(s):"), +# ethnicity=mo.ui.multiselect(options=S.options_ethnicity, value=S.options_ethnicity, label="Select Ethnicities:"), +# income=mo.ui.multiselect(options=S.options_income, value=S.options_income, label="Select Income Group(s):"), +# consumer=mo.ui.multiselect(options=S.options_consumer, value=S.options_consumer, label="Select Consumer Groups:") +# ).form() +# mo.md(f''' +# --- + +# # Data Filter + +# {filter_form} +# ''') + +# %% +# mo.stop(filter_form.value is None, mo.md("**Please submit filter above to proceed**")) +_d = S.filter_data(data_validated, age=filter_form.value['age'], gender=filter_form.value['gender'], income=filter_form.value['income'], ethnicity=filter_form.value['ethnicity'], consumer=filter_form.value['consumer']) + +# Stop execution and prevent other cells from running if no data is selected +# mo.stop(len(_d.collect()) == 0, mo.md("**No Data available for current filter combination**")) +data = _d + +# data = data_validated +data.collect() + +# %% + + +# %% +# Check if all business owners are missing a 'Consumer type' in demographics +# assert all([a is None for a in data_all.filter(pl.col('QID4') == 'Yes').collect()['Consumer'].unique()]) , "Not all business owners are missing 'Consumer type' in demographics." + +# %% +mo.md(r""" +# Demographic Distributions +""") + +# %% +demo_plot_cols = [ + 'Age', + 'Gender', + # 'Race/Ethnicity', + 'Bussiness_Owner', + 'Consumer' +] + +# %% +_content = """ + +""" +for c in demo_plot_cols: + _fig = S.plot_demographic_distribution( + data=S.get_demographics(data)[0], + column=c, + title=f"{c.replace('Bussiness', 'Business').replace('_', ' ')} Distribution of Survey Respondents" + ) + _content += f"""{mo.ui.altair_chart(_fig)}\n\n""" + +mo.md(_content) + +# %% +mo.md(r""" +--- + +# Brand Character Results +""") + +# %% +mo.md(r""" +## Best performing: Original vs Refined frankenstein +""") + +# %% +char_refine_rank = S.get_character_refine(data)[0] +# print(char_rank.collect().head()) +print(char_refine_rank.collect().head()) + +# %% +mo.md(r""" +## Character ranking points +""") + +# %% +mo.md(r""" +## Character ranking 1-2-3 +""") + +# %% +char_rank = S.get_character_ranking(data)[0] + +# %% +char_rank_weighted = calculate_weighted_ranking_scores(char_rank) +S.plot_weighted_ranking_score(char_rank_weighted, title="Most Popular Character - Weighted Popularity Score
(1st=3pts, 2nd=2pts, 3rd=1pt)", x_label='Voice') + +# %% +S.plot_top3_ranking_distribution(char_rank, x_label='Character Personality', title='Character Personality: Rankings Top 3') + +# %% +mo.md(r""" +### Statistical Significance Character Ranking +""") + +# %% +_pairwise_df, _meta = S.compute_ranking_significance(char_rank) + +# print(_pairwise_df.columns) + +mo.md(f""" + + +{mo.ui.altair_chart(S.plot_significance_heatmap(_pairwise_df, metadata=_meta))} + +{mo.ui.altair_chart(S.plot_significance_summary(_pairwise_df, metadata=_meta))} +""") + +# %% +mo.md(r""" +## Character Ranking: times 1st place +""") + +# %% +S.plot_most_ranked_1(char_rank, title="Most Popular Character
(Number of Times Ranked 1st)", x_label='Character Personality') + +# %% +mo.md(r""" +## Prominent predefined personality traits wordcloud +""") + +# %% +top8_traits = S.get_top_8_traits(data)[0] +S.plot_traits_wordcloud( + data=top8_traits, + column='Top_8_Traits', + title="Most Prominent Personality Traits", +) + +# %% +mo.md(r""" +## Trait frequency per brand character +""") + +# %% +char_df = S.get_character_refine(data)[0] + +# %% +from theme import ColorPalette + +# Assuming you already have char_df (your data from get_character_refine or similar) +characters = ['Bank Teller', 'Familiar Friend', 'The Coach', 'Personal Assistant'] +character_colors = { + 'Bank Teller': (ColorPalette.CHARACTER_BANK_TELLER, ColorPalette.CHARACTER_BANK_TELLER_HIGHLIGHT), + 'Familiar Friend': (ColorPalette.CHARACTER_FAMILIAR_FRIEND, ColorPalette.CHARACTER_FAMILIAR_FRIEND_HIGHLIGHT), + 'The Coach': (ColorPalette.CHARACTER_COACH, ColorPalette.CHARACTER_COACH_HIGHLIGHT), + 'Personal Assistant': (ColorPalette.CHARACTER_PERSONAL_ASSISTANT, ColorPalette.CHARACTER_PERSONAL_ASSISTANT_HIGHLIGHT), +} + +# Build consistent sort order (by total frequency across all characters) +all_trait_counts = {} +for char in characters: + freq_df, _ = S.transform_character_trait_frequency(char_df, char) + for row in freq_df.iter_rows(named=True): + all_trait_counts[row['trait']] = all_trait_counts.get(row['trait'], 0) + row['count'] + +consistent_sort_order = sorted(all_trait_counts.keys(), key=lambda x: -all_trait_counts[x]) + +_content = """""" +# Generate 4 plots (one per character) +for char in characters: + freq_df, _ = S.transform_character_trait_frequency(char_df, char) + main_color, highlight_color = character_colors[char] + chart = S.plot_single_character_trait_frequency( + data=freq_df, + character_name=char, + bar_color=main_color, + highlight_color=highlight_color, + trait_sort_order=consistent_sort_order, + ) + _content += f""" + {mo.ui.altair_chart(chart)} + + +""" + +mo.md(_content) + +# %% +mo.md(r""" +## Statistical significance best characters + +zie chat +> voorbeeld: als de nr 1 en 2 niet significant verschillen maar wel van de nr 3 bijvoorbeeld is dat ook top. Beetje meedenkend over hoe ik het kan presenteren weetje wat ik bedoel?:) +> +""") + +# %% + + +# %% + + +# %% +mo.md(r""" +--- + +# Spoken Voice Results +""") + +# %% +COLOR_GENDER = True + +# %% +mo.md(r""" +## Top 8 Most Chosen out of 18 +""") + +# %% +v_18_8_3 = S.get_18_8_3(data)[0] + +# %% +S.plot_voice_selection_counts(v_18_8_3, title="Top 8 Voice Selection from 18 Voices", x_label='Voice', color_gender=COLOR_GENDER) + +# %% +mo.md(r""" +## Top 3 most chosen out of 8 +""") + +# %% +S.plot_top3_selection_counts(v_18_8_3, title="Top 3 Voice Selection Counts from 8 Voices", x_label='Voice', color_gender=COLOR_GENDER) + +# %% +mo.md(r""" +## Voice Ranking Weighted Score +""") + +# %% +top3_voices = S.get_top_3_voices(data)[0] +top3_voices_weighted = calculate_weighted_ranking_scores(top3_voices) + +# %% +S.plot_weighted_ranking_score(top3_voices_weighted, title="Most Popular Voice - Weighted Popularity Score
(1st = 3pts, 2nd = 2pts, 3rd = 1pt)", color_gender=COLOR_GENDER) + +# %% +mo.md(r""" +## Which voice is ranked best in the ranking question for top 3? + +(not best 3 out of 8 question) +""") + +# %% +S.plot_ranking_distribution(top3_voices, x_label='Voice', title="Distribution of Top 3 Voice Rankings (1st, 2nd, 3rd)", color_gender=COLOR_GENDER) + +# %% +mo.md(r""" +### Statistical significance for voice ranking +""") + +# %% +# print(top3_voices.collect().head()) + +# %% + +# _pairwise_df, _metadata = S.compute_ranking_significance( +# top3_voices,alpha=0.05,correction="none") + +# # View significant pairs +# # print(pairwise_df.filter(pl.col('significant') == True)) + +# # Create heatmap visualization +# _heatmap = S.plot_significance_heatmap( +# _pairwise_df, +# metadata=_metadata, +# title="Weighted Voice Ranking Significance
(Pairwise Comparisons)" +# ) + +# # Create summary bar chart +# _summary = S.plot_significance_summary( +# _pairwise_df, +# metadata=_metadata +# ) + +# mo.md(f""" +# {mo.ui.altair_chart(_heatmap)} + +# {mo.ui.altair_chart(_summary)} +# """) + +# %% +## Voice Ranked 1st the most + +# %% +S.plot_most_ranked_1(top3_voices, title="Most Popular Voice
(Number of Times Ranked 1st)", x_label='Voice', color_gender=COLOR_GENDER) + +# %% +mo.md(r""" +## Voice Scale 1-10 +""") + +# %% +# Get your voice scale data (from notebook) +voice_1_10, _ = S.get_voice_scale_1_10(data) +S.plot_average_scores_with_counts(voice_1_10, x_label='Voice', domain=[1,10], title="Voice General Impression (Scale 1-10)", color_gender=COLOR_GENDER) + +# %% +mo.md(r""" +### Statistical Significance (Scale 1-10) +""") + +# %% +# Compute pairwise significance tests +pairwise_df, metadata = S.compute_pairwise_significance( + voice_1_10, + test_type="mannwhitney", # or "ttest", "chi2", "auto" + alpha=0.05, + correction="bonferroni" # or "holm", "none" +) + +# View significant pairs +# print(pairwise_df.filter(pl.col('significant') == True)) + +# Create heatmap visualization +_heatmap = S.plot_significance_heatmap( + pairwise_df, + metadata=metadata, + title="Voice Rating Significance
(Pairwise Comparisons)" +) + +# Create summary bar chart +_summary = S.plot_significance_summary( + pairwise_df, + metadata=metadata +) + +mo.md(f""" +{mo.ui.altair_chart(_heatmap)} + +{mo.ui.altair_chart(_summary)} +""") + +# %% + + +# %% +mo.md(r""" +## Ranking points for Voice per Chosen Brand Character + +**missing mapping** +""") + +# %% +mo.md(r""" +## Correlation Speaking Styles +""") + +# %% +ss_or, choice_map_or = S.get_ss_orange_red(data) +ss_gb, choice_map_gb = S.get_ss_green_blue(data) + +# Combine the data +ss_all = ss_or.join(ss_gb, on='_recordId') +_d = ss_all.collect() + +choice_map = {**choice_map_or, **choice_map_gb} +# print(_d.head()) +# print(choice_map) +ss_long = utils.process_speaking_style_data(ss_all, choice_map) + +df_style = utils.process_speaking_style_data(ss_all, choice_map) + +vscales = S.get_voice_scale_1_10(data)[0] +df_scale_long = utils.process_voice_scale_data(vscales) + +joined_scale = df_style.join(df_scale_long, on=["_recordId", "Voice"], how="inner") + +df_ranking = utils.process_voice_ranking_data(top3_voices) +joined_ranking = df_style.join(df_ranking, on=['_recordId', 'Voice'], how='inner') + +# %% +joined_ranking.head() + +# %% +mo.md(r""" +### Colors vs Scale 1-10 +""") + +# %% +# Transform to get one row per color with average correlation +color_corr_scale, _ = utils.transform_speaking_style_color_correlation(joined_scale, SPEAKING_STYLES) +S.plot_speaking_style_color_correlation( + data=color_corr_scale, + title="Correlation: Speaking Style Colors and Voice Scale 1-10" +) + +# %% +mo.md(r""" +### Colors vs Ranking Points +""") + +# %% +color_corr_ranking, _ = utils.transform_speaking_style_color_correlation( + joined_ranking, + SPEAKING_STYLES, + target_column="Ranking_Points" +) +S.plot_speaking_style_color_correlation( + data=color_corr_ranking, + title="Correlation: Speaking Style Colors and Voice Ranking Points" +) + +# %% +mo.md(r""" +### Individual Traits vs Scale 1-10 +""") + +# %% +_content = """""" + +for _style, _traits in SPEAKING_STYLES.items(): + # print(f"Correlation plot for {style}...") + _fig = S.plot_speaking_style_correlation( + data=joined_scale, + style_color=_style, + style_traits=_traits, + title=f"Correlation: Speaking Style {_style} and Voice Scale 1-10", + ) + _content += f""" +#### Speaking Style **{_style}**: + +{mo.ui.altair_chart(_fig)} + +""" +mo.md(_content) + +# %% +mo.md(r""" +### Individual Traits vs Ranking Points +""") + +# %% +_content = """""" + +for _style, _traits in SPEAKING_STYLES.items(): + # print(f"Correlation plot for {style}...") + _fig = S.plot_speaking_style_ranking_correlation( + data=joined_ranking, + style_color=_style, + style_traits=_traits, + title=f"Correlation: Speaking Style {_style} and Voice Ranking Points", +) + _content += f""" +#### Speaking Style **{_style}**: + +{mo.ui.altair_chart(_fig)} + +""" +mo.md(_content) + +# %% +mo.md(r""" +## Correlations when "Best Brand Character" is chosen + +Select only the traits that fit with that character +""") + +# %% +from reference import ORIGINAL_CHARACTER_TRAITS +chosen_bc_traits = ORIGINAL_CHARACTER_TRAITS[BEST_CHOSEN_CHARACTER] + +# %% +STYLES_SUBSET = utils.filter_speaking_styles(SPEAKING_STYLES, chosen_bc_traits) + +# %% +mo.md(r""" +### Individual Traits vs Ranking Points +""") + +# %% +_content = "" +for _style, _traits in STYLES_SUBSET.items(): + _fig = S.plot_speaking_style_ranking_correlation( + data=joined_ranking, + style_color=_style, + style_traits=_traits, + title=f"""Brand Character "{BEST_CHOSEN_CHARACTER.replace('_', ' ').title()}" - Correlation: Speaking Style {_style} and Voice Ranking Points""" + ) + _content += f""" +{mo.ui.altair_chart(_fig)} + +""" +mo.md(_content) + +# %% +mo.md(r""" +### Individual Traits vs Scale 1-10 +""") + +# %% +_content = """""" + +for _style, _traits in STYLES_SUBSET.items(): + # print(f"Correlation plot for {style}...") + _fig = S.plot_speaking_style_correlation( + data=joined_scale, + style_color=_style, + style_traits=_traits, + title=f"""Brand Character "{BEST_CHOSEN_CHARACTER.replace('_', ' ').title()}" - Correlation: Speaking Style {_style} and Voice Scale 1-10""", + ) + _content += f""" +{mo.ui.altair_chart(_fig)} + +""" +mo.md(_content) + +# %% +mo.md(r""" +### Colors vs Scale 1-10 (Best Character) +""") + +# %% +# Transform to get one row per color with average correlation +_color_corr_scale, _ = utils.transform_speaking_style_color_correlation(joined_scale, STYLES_SUBSET) +S.plot_speaking_style_color_correlation( + data=_color_corr_scale, + title=f"""Brand Character "{BEST_CHOSEN_CHARACTER.replace('_', ' ').title()}" - Correlation: Speaking Style Colors and Voice Scale 1-10""" +) + +# %% +mo.md(r""" +### Colors vs Ranking Points (Best Character) +""") + +# %% +_color_corr_ranking, _ = utils.transform_speaking_style_color_correlation( + joined_ranking, + STYLES_SUBSET, + target_column="Ranking_Points" +) +S.plot_speaking_style_color_correlation( + data=_color_corr_ranking, + title=f"""Brand Character "{BEST_CHOSEN_CHARACTER.replace('_', ' ').title()}" - Correlation: Speaking Style Colors and Voice Ranking Points""" +) \ No newline at end of file diff --git a/04_PPTX_Update_Images.py b/04_PPTX_Update_Images.py index 6825954..b6d03d0 100644 --- a/04_PPTX_Update_Images.py +++ b/04_PPTX_Update_Images.py @@ -22,7 +22,7 @@ def _(): @app.cell def _(): TAG_SOURCE = Path('data/reports/Perception-Research-Report_2-2.pptx') - TAG_TARGET = Path('data/reports/Perception-Research-Report_2-2_tagged.pptx') + # TAG_TARGET = Path('data/reports/Perception-Research-Report_2-2_tagged.pptx') TAG_IMAGE_DIR = Path('figures/2-2-26') return TAG_IMAGE_DIR, TAG_SOURCE diff --git a/pyproject.toml b/pyproject.toml index dfdf2be..685345d 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -7,6 +7,7 @@ requires-python = ">=3.12" dependencies = [ "altair>=6.0.0", "imagehash>=4.3.1", + "jupyter>=1.1.1", "marimo>=0.18.0", "matplotlib>=3.10.8", "modin[dask]>=0.37.1", diff --git a/uv.lock 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