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Character personality ranking complete

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This commit is contained in:
Luigi Maiorano
2026-01-23 10:50:26 +01:00
parent 5327b50ab0
commit 424355f4a1
5 changed files with 421 additions and 7 deletions
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8
01_ingest_qualtrics_export.py
View File

@@ -44,6 +44,14 @@ def _(survey):
return
app._unparsable_cell(
r"""
data.
""",
name="_"
)
@app.cell
def _(mo):
mo.md(r"""

63
02_quant_analysis.py
View File

@@ -11,15 +11,20 @@ def _():
from pathlib import Path
from validation import check_progress, duration_validation
from utils import JPMCSurvey, combine_exclusive_columns
from plots import plot_average_scores_with_counts, plot_top3_ranking_distribution
from utils import JPMCSurvey, combine_exclusive_columns, calculate_weighted_ranking_scores
from plots import plot_average_scores_with_counts, plot_top3_ranking_distribution, plot_character_ranking_distribution, plot_most_ranked_1_character, plot_weighted_ranking_score
return (
JPMCSurvey,
Path,
calculate_weighted_ranking_scores,
check_progress,
duration_validation,
mo,
plot_average_scores_with_counts,
plot_character_ranking_distribution,
plot_most_ranked_1_character,
plot_top3_ranking_distribution,
plot_weighted_ranking_score,
)
@@ -108,12 +113,49 @@ def _(mo):
mo.md(r"""
## Character personality ranking
1. Which character personality is ranked best?
2. Which character personality is ranked number 1 the most?
### 1. Which character personality is ranked best?
""")
return
@app.cell
def _(data, survey):
char_rank = survey.get_character_ranking(data)[0].collect()
return (char_rank,)
@app.cell
def _(char_rank, plot_character_ranking_distribution):
plot_character_ranking_distribution(char_rank, x_label='Character Personality', width=1000)
return
@app.cell
def _(mo):
mo.md(r"""
### 2. Which character personality is ranked number 1 the most?
""")
return
@app.cell
def _(
calculate_weighted_ranking_scores,
char_rank,
plot_weighted_ranking_score,
):
char_rank_weighted = calculate_weighted_ranking_scores(char_rank)
plot_weighted_ranking_score(char_rank_weighted, x_label='Voice', width=1000)
return
@app.cell
def _(char_rank, plot_most_ranked_1_character):
plot_most_ranked_1_character(char_rank, x_label='Character Personality', width=1000)
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
@@ -122,6 +164,13 @@ def _(mo):
return
@app.cell
def _(data, survey):
v_18_8_3 = survey.get_18_8_3(data)[0].collect()
print(v_18_8_3.head())
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""
@@ -147,6 +196,12 @@ def _(mo):
return
@app.cell
def _(plot_top3_ranking_distribution, top3_voices):
plot_top3_ranking_distribution(top3_voices, x_label='Voice', width=1000)
return
@app.cell(hide_code=True)
def _(mo):
mo.md(r"""

296
plots.py
View File

@@ -5,6 +5,7 @@ import polars as pl
from theme import ColorPalette
def plot_average_scores_with_counts(
df: pl.DataFrame,
title: str = "General Impression (1-10)<br>Per Voice with Number of Participants Who Rated It",
@@ -213,3 +214,298 @@ def plot_top3_ranking_distribution(
)
return fig
def plot_character_ranking_distribution(
df: pl.DataFrame,
title: str = "Character Personality Rankings<br>Distribution of Votes (1st to 4th Place)",
x_label: str = "Character Personality",
y_label: str = "Number of Votes",
height: int = 500,
width: int = 1000,
) -> go.Figure:
"""
Create a stacked bar chart showing the distribution of rankings (1st to 4th) for character personalities.
Sorted by the number of Rank 1 votes to highlight the 'Best' options.
Parameters
----------
df : pl.DataFrame
DataFrame containing character ranking columns (prefix 'Character_Ranking').
title : str, optional
Plot title.
x_label : str, optional
X-axis label.
y_label : str, optional
Y-axis label.
height : int, optional
Plot height in pixels.
width : int, optional
Plot width in pixels.
Returns
-------
go.Figure
Plotly figure object.
"""
stats = []
# Identify columns related to Character Ranking (excluding ID)
ranking_cols = [c for c in df.columns if 'Character_Ranking' in c]
for col in ranking_cols:
# Count occurrences of each rank (1, 2, 3, 4)
# Using height/len to count rows in the filtered frame
r1 = df.filter(pl.col(col) == 1).height
r2 = df.filter(pl.col(col) == 2).height
r3 = df.filter(pl.col(col) == 3).height
r4 = df.filter(pl.col(col) == 4).height
total = r1 + r2 + r3 + r4
if total > 0:
stats.append({
'column': col,
'Rank 1': r1,
'Rank 2': r2,
'Rank 3': r3,
'Rank 4': r4
})
if not stats:
return go.Figure()
# Sort by Rank 1 (Most "Best" votes) descending to show the winner first
# Secondary sort by Rank 2
stats_df = pl.DataFrame(stats).sort(['Rank 1', 'Rank 2'], descending=[True, True])
# Clean up labels: Remove prefix and underscores
# e.g. "Character_Ranking_The_Coach" -> "The Coach"
labels = [
col.replace('Character_Ranking_', '').replace('_', ' ').strip()
for col in stats_df['column']
]
fig = go.Figure()
# Rank 1 (Best)
fig.add_trace(go.Bar(
name='Rank 1 (Best)',
x=labels,
y=stats_df['Rank 1'],
marker_color=ColorPalette.RANK_1,
hovertemplate='<b>%{x}</b><br>Rank 1: %{y}<extra></extra>'
))
# Rank 2
fig.add_trace(go.Bar(
name='Rank 2',
x=labels,
y=stats_df['Rank 2'],
marker_color=ColorPalette.RANK_2,
hovertemplate='<b>%{x}</b><br>Rank 2: %{y}<extra></extra>'
))
# Rank 3
fig.add_trace(go.Bar(
name='Rank 3',
x=labels,
y=stats_df['Rank 3'],
marker_color=ColorPalette.RANK_3,
hovertemplate='<b>%{x}</b><br>Rank 3: %{y}<extra></extra>'
))
# Rank 4 (Worst)
# Using a neutral grey as a fallback for the lowest rank to keep focus on top ranks
fig.add_trace(go.Bar(
name='Rank 4 (Worst)',
x=labels,
y=stats_df['Rank 4'],
marker_color=ColorPalette.RANK_4,
hovertemplate='<b>%{x}</b><br>Rank 4: %{y}<extra></extra>'
))
fig.update_layout(
barmode='stack',
title=title,
xaxis_title=x_label,
yaxis_title=y_label,
height=height,
width=width,
plot_bgcolor=ColorPalette.BACKGROUND,
xaxis=dict(
showgrid=True,
gridcolor=ColorPalette.GRID,
tickangle=-45
),
yaxis=dict(
showgrid=True,
gridcolor=ColorPalette.GRID
),
legend=dict(
orientation="h",
yanchor="bottom",
y=1.02,
xanchor="right",
x=1,
traceorder="normal"
),
font=dict(size=11)
)
return fig
def plot_most_ranked_1_character(
df: pl.DataFrame,
title: str = "Most Popular Character Personality<br>(Number of Times Ranked 1st)",
x_label: str = "Character Personality",
y_label: str = "Count of 1st Place Rankings",
height: int = 500,
width: int = 1000,
) -> go.Figure:
"""
Create a bar chart showing which character personality was ranked #1 the most.
Parameters
----------
df : pl.DataFrame
DataFrame containing character ranking columns.
title : str, optional
Plot title.
x_label : str, optional
X-axis label.
y_label : str, optional
Y-axis label.
height : int, optional
Plot height in pixels.
width : int, optional
Plot width in pixels.
Returns
-------
go.Figure
Plotly figure object.
"""
stats = []
# Identify columns related to Character Ranking
ranking_cols = [c for c in df.columns if 'Character_Ranking' in c]
for col in ranking_cols:
# Count occurrences of rank 1
count_rank_1 = df.filter(pl.col(col) == 1).height
stats.append({
'column': col,
'count': count_rank_1
})
# Sort by count descending
stats_df = pl.DataFrame(stats).sort('count', descending=True)
# Clean up labels
labels = [
col.replace('Character_Ranking_', '').replace('_', ' ').strip()
for col in stats_df['column']
]
fig = go.Figure()
fig.add_trace(go.Bar(
x=labels,
y=stats_df['count'],
text=stats_df['count'],
textposition='inside',
textfont=dict(size=10, color='white'),
marker_color=ColorPalette.PRIMARY,
hovertemplate='<b>%{x}</b><br>1st Place Votes: %{y}<extra></extra>'
))
fig.update_layout(
title=title,
xaxis_title=x_label,
yaxis_title=y_label,
height=height,
width=width,
plot_bgcolor=ColorPalette.BACKGROUND,
xaxis=dict(
showgrid=True,
gridcolor=ColorPalette.GRID,
tickangle=-45
),
yaxis=dict(
showgrid=True,
gridcolor=ColorPalette.GRID
),
font=dict(size=11)
)
return fig
def plot_weighted_ranking_score(
weighted_df: pl.DataFrame,
title: str = "Character Popularity Score<br>(Weighted: 1st=3pts, 2nd=2pts, 3rd=1pt)",
x_label: str = "Character Personality",
y_label: str = "Total Weighted Score",
color: str = ColorPalette.PRIMARY,
height: int = 500,
width: int = 1000,
) -> go.Figure:
"""
Create a bar chart showing the weighted ranking score for each character.
Parameters
----------
df : pl.DataFrame
DataFrame containing ranking columns.
title : str, optional
Plot title.
x_label : str, optional
X-axis label.
y_label : str, optional
Y-axis label.
color : str, optional
Bar color.
height : int, optional
Plot height.
width : int, optional
Plot width.
Returns
-------
go.Figure
Plotly figure object.
"""
fig = go.Figure()
fig.add_trace(go.Bar(
x=weighted_df['Character'],
y=weighted_df['Weighted Score'],
text=weighted_df['Weighted Score'],
textposition='inside',
textfont=dict(size=11, color='white'),
marker_color=color,
hovertemplate='<b>%{x}</b><br>Score: %{y}<extra></extra>'
))
fig.update_layout(
title=title,
xaxis_title=x_label,
yaxis_title=y_label,
height=height,
width=width,
plot_bgcolor=ColorPalette.BACKGROUND,
xaxis=dict(
showgrid=True,
gridcolor=ColorPalette.GRID,
tickangle=-45
),
yaxis=dict(
showgrid=True,
gridcolor=ColorPalette.GRID
),
font=dict(size=11)
)
return fig

1
theme.py
View File

@@ -14,6 +14,7 @@ class ColorPalette:
RANK_1 = "#004C6D" # Dark Blue (1st Choice)
RANK_2 = "#008493" # Teal (2nd Choice)
RANK_3 = "#5AAE95" # Sea Green (3rd Choice)
RANK_4 = "#9E9E9E" # Grey (4th Choice / Worst)
# General UI elements
TEXT = "black"

58
utils.py
View File

@@ -55,6 +55,45 @@ def combine_exclusive_columns(df: pl.DataFrame, id_col: str = "_recordId", targe
def calculate_weighted_ranking_scores(df: pl.DataFrame) -> pl.DataFrame:
"""
Calculate weighted scores for character rankings.
Points system: 1st place = 3 pts, 2nd place = 2 pts, 3rd place = 1 pt.
Parameters
----------
df : pl.DataFrame
DataFrame containing character ranking columns.
Returns
-------
pl.DataFrame
DataFrame with columns 'Character' and 'Weighted Score', sorted by score.
"""
scores = []
# Identify columns related to Character Ranking
ranking_cols = [c for c in df.columns if 'Character_Ranking' in c]
for col in ranking_cols:
# Calculate score:
# (Count of Rank 1 * 3) + (Count of Rank 2 * 2) + (Count of Rank 3 * 1)
r1_count = df.filter(pl.col(col) == 1).height
r2_count = df.filter(pl.col(col) == 2).height
r3_count = df.filter(pl.col(col) == 3).height
weighted_score = (r1_count * 3) + (r2_count * 2) + (r3_count * 1)
# Clean name
clean_name = col.replace('Character_Ranking_', '').replace('_', ' ').strip()
scores.append({
'Character': clean_name,
'Weighted Score': weighted_score
})
return pl.DataFrame(scores).sort('Weighted Score', descending=True)
class JPMCSurvey:
"""Class to handle JPMorgan Chase survey data."""
@@ -249,9 +288,19 @@ class JPMCSurvey:
rename_dict = {
'QID29': '18-8_Set-A',
'QID101': '18-8_Set-B',
'QID36_0_GROUP': '8-3_Ranked'
'QID36_0_GROUP': '3_Ranked'
}
return self._get_subset(q, QIDs, rename_cols=False).rename(rename_dict), None
subset = self._get_subset(q, QIDs, rename_cols=False).rename(rename_dict)
# Combine 18-8 Set A and Set B into single column
subset = subset.with_columns(
pl.coalesce(['18-8_Set-A', '18-8_Set-B']).alias('8_Combined')
)
# Change order of columns
subset = subset.select(['_recordId', '18-8_Set-A', '18-8_Set-B', '8_Combined', '3_Ranked'])
return subset, None
def get_voice_scale_1_10(self, q: pl.LazyFrame) -> Union[pl.LazyFrame, None]:
@@ -364,3 +413,8 @@ class JPMCSurvey:
QIDs = ['QID44', 'QID97', 'QID95', 'QID96']
return self._get_subset(q, QIDs, rename_cols=True), None