Qumo/JPMC-quant
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fixed normalization functions

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This commit is contained in:
Luigi Maiorano
2026-01-29 21:53:58 +01:00
parent becc435d3c
commit 036dd911df
2 changed files with 55 additions and 32 deletions
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32
02_quant_analysis.py
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@@ -388,38 +388,48 @@ def _(S, data, mo):
return (vscales,)
@app.cell
def _(pl, vscales):
# Count non-null values per row
nn_vscale = vscales.with_columns(
non_null_count = pl.sum_horizontal(pl.all().exclude("_recordID").is_not_null())
)
nn_vscale.collect()['non_null_count'].describe()
return
@app.cell(hide_code=True)
def _(S, mo, vscales):
mo.md(f"""
### How does each voice score on a scale from 1-10?
{mo.ui.altair_chart(S.plot_average_scores_with_counts(vscales, x_label='Voice', width=1000, domain=[1,10]))}
{mo.ui.altair_chart(S.plot_average_scores_with_counts(vscales, x_label='Voice', width=1000, domain=[1,10], title="Voice General Impression (Scale 1-10)"))}
""")
return
@app.cell
def _(utils, vscales):
def _(S, mo, utils, vscales):
_target_cols=[c for c in vscales.collect().columns if c not in ['_recordId']]
vscales_row_norm = utils.normalize_row_values(vscales.collect(), target_cols=_target_cols)
vscales_row_norm
return (vscales_row_norm,)
@app.cell
def _(S, mo, vscales_row_norm):
mo.md(f"""
### Voice scale 1-10 normalized per respondent?
{mo.ui.altair_chart(S.plot_average_scores_with_counts(vscales_row_norm, x_label='Voice', width=1000))}
{mo.ui.altair_chart(S.plot_average_scores_with_counts(vscales_row_norm, x_label='Voice', width=1000, domain=[1,10], title="Voice General Impression (Scale 1-10) - Normalized per Respondent"))}
""")
return
@app.cell
def _(mo):
mo.md(r"""
def _(S, mo, utils, vscales):
_target_cols=[c for c in vscales.collect().columns if c not in ['_recordId']]
vscales_global_norm = utils.normalize_global_values(vscales.collect(), target_cols=_target_cols)
mo.md(f"""
### Voice scale 1-10 normalized per respondent?
{mo.ui.altair_chart(S.plot_average_scores_with_counts(vscales_global_norm, x_label='Voice', width=1000, domain=[1,10], title="Voice General Impression (Scale 1-10) - Normalized Across All Respondents"))}
""")
return