Character personality ranking complete

This commit is contained in:
2026-01-23 10:50:26 +01:00
parent 5327b50ab0
commit 424355f4a1
5 changed files with 421 additions and 7 deletions

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