511 lines
14 KiB
Python
511 lines
14 KiB
Python
"""Plotting functions for Voice Branding analysis."""
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import plotly.graph_objects as go
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import polars as pl
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from theme import ColorPalette
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def plot_average_scores_with_counts(
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df: pl.DataFrame,
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title: str = "General Impression (1-10)<br>Per Voice with Number of Participants Who Rated It",
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x_label: str = "Stimuli",
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y_label: str = "Average General Impression Rating (1-10)",
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color: str = ColorPalette.PRIMARY,
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height: int = 500,
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width: int = 1000,
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) -> go.Figure:
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"""
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Create a bar plot showing average scores and count of non-null values for each column.
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Parameters
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----------
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df : pl.DataFrame
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DataFrame containing numeric columns to analyze.
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title : str, optional
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Plot title.
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x_label : str, optional
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X-axis label.
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y_label : str, optional
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Y-axis label.
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color : str, optional
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Bar color (hex code or named color).
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height : int, optional
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Plot height in pixels.
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width : int, optional
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Plot width in pixels.
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Returns
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-------
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go.Figure
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Plotly figure object.
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"""
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# Calculate average and count of non-null values for each column
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# Exclude _recordId column
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stats = []
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for col in [c for c in df.columns if c != '_recordId']:
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avg_score = df[col].mean()
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non_null_count = df[col].drop_nulls().len()
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stats.append({
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'column': col,
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'average': avg_score,
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'count': non_null_count
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})
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# Sort by average score in descending order
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stats_df = pl.DataFrame(stats).sort('average', descending=True)
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# Extract voice identifiers from column names (e.g., "V14" from "Voice_Scale_1_10__V14")
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labels = [col.split('__')[-1] if '__' in col else col for col in stats_df['column']]
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# Create the plot
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fig = go.Figure()
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fig.add_trace(go.Bar(
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x=labels,
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y=stats_df['average'],
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text=stats_df['count'],
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textposition='inside',
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textfont=dict(size=10, color='black'),
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marker_color=color,
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hovertemplate='<b>%{x}</b><br>Average: %{y:.2f}<br>Count: %{text}<extra></extra>'
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))
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fig.update_layout(
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title=title,
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xaxis_title=x_label,
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yaxis_title=y_label,
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height=height,
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width=width,
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plot_bgcolor=ColorPalette.BACKGROUND,
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xaxis=dict(
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showgrid=True,
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gridcolor=ColorPalette.GRID,
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tickangle=-45
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),
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yaxis=dict(
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range=[0, 10],
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showgrid=True,
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gridcolor=ColorPalette.GRID
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),
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font=dict(size=11)
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)
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return fig
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def plot_top3_ranking_distribution(
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df: pl.DataFrame,
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title: str = "Top 3 Rankings Distribution<br>Count of 1st, 2nd, and 3rd Place Votes per Voice",
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x_label: str = "Voices",
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y_label: str = "Number of Mentions in Top 3",
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height: int = 500,
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width: int = 1000,
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) -> go.Figure:
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"""
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Create a stacked bar chart showing how often each voice was ranked 1st, 2nd, or 3rd.
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The total height of the bar represents the popularity (frequency of being in Top 3),
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while the segments show the quality of those rankings.
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Parameters
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----------
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df : pl.DataFrame
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DataFrame containing ranking columns (values 1, 2, 3).
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title : str, optional
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Plot title.
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x_label : str, optional
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X-axis label.
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y_label : str, optional
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Y-axis label.
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height : int, optional
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Plot height in pixels.
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width : int, optional
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Plot width in pixels.
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Returns
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-------
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go.Figure
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Plotly figure object.
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"""
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# Exclude _recordId column
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stats = []
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for col in [c for c in df.columns if c != '_recordId']:
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# Count occurrences of each rank (1, 2, 3)
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# We ensure we're just counting the specific integer values
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rank1 = df.filter(pl.col(col) == 1).height
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rank2 = df.filter(pl.col(col) == 2).height
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rank3 = df.filter(pl.col(col) == 3).height
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total = rank1 + rank2 + rank3
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# Only include if it received at least one vote (optional, but keeps chart clean)
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if total > 0:
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stats.append({
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'column': col,
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'Rank 1': rank1,
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'Rank 2': rank2,
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'Rank 3': rank3,
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'Total': total
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})
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# Sort by Total count descending (Most popular overall)
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# Tie-break with Rank 1 count
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stats_df = pl.DataFrame(stats).sort(['Total', 'Rank 1'], descending=[True, True])
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# Extract voice identifiers from column names
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labels = [col.split('__')[-1] if '__' in col else col for col in stats_df['column']]
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fig = go.Figure()
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# Add traces for Rank 1, 2, and 3.
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# Stack order: Rank 1 at bottom (Base) -> Rank 2 -> Rank 3
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# This makes it easy to compare the "First Choice" volume across bars.
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fig.add_trace(go.Bar(
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name='Rank 1 (1st Choice)',
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x=labels,
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y=stats_df['Rank 1'],
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marker_color=ColorPalette.RANK_1,
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hovertemplate='<b>%{x}</b><br>Rank 1: %{y}<extra></extra>'
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))
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fig.add_trace(go.Bar(
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name='Rank 2 (2nd Choice)',
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x=labels,
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y=stats_df['Rank 2'],
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marker_color=ColorPalette.RANK_2,
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hovertemplate='<b>%{x}</b><br>Rank 2: %{y}<extra></extra>'
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))
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fig.add_trace(go.Bar(
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name='Rank 3 (3rd Choice)',
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x=labels,
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y=stats_df['Rank 3'],
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marker_color=ColorPalette.RANK_3,
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hovertemplate='<b>%{x}</b><br>Rank 3: %{y}<extra></extra>'
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))
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fig.update_layout(
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barmode='stack',
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title=title,
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xaxis_title=x_label,
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yaxis_title=y_label,
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height=height,
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width=width,
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plot_bgcolor=ColorPalette.BACKGROUND,
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xaxis=dict(
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showgrid=True,
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gridcolor=ColorPalette.GRID,
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tickangle=-45
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),
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yaxis=dict(
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showgrid=True,
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gridcolor=ColorPalette.GRID
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),
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legend=dict(
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orientation="h",
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yanchor="bottom",
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y=1.02,
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xanchor="right",
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x=1,
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traceorder="normal"
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),
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font=dict(size=11)
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)
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return fig
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def plot_character_ranking_distribution(
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df: pl.DataFrame,
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title: str = "Character Personality Rankings<br>Distribution of Votes (1st to 4th Place)",
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x_label: str = "Character Personality",
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y_label: str = "Number of Votes",
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height: int = 500,
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width: int = 1000,
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) -> go.Figure:
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"""
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Create a stacked bar chart showing the distribution of rankings (1st to 4th) for character personalities.
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Sorted by the number of Rank 1 votes to highlight the 'Best' options.
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Parameters
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----------
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df : pl.DataFrame
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DataFrame containing character ranking columns (prefix 'Character_Ranking').
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title : str, optional
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Plot title.
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x_label : str, optional
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X-axis label.
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y_label : str, optional
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Y-axis label.
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height : int, optional
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Plot height in pixels.
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width : int, optional
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Plot width in pixels.
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Returns
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-------
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go.Figure
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Plotly figure object.
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"""
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stats = []
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# Identify columns related to Character Ranking (excluding ID)
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ranking_cols = [c for c in df.columns if 'Character_Ranking' in c]
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for col in ranking_cols:
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# Count occurrences of each rank (1, 2, 3, 4)
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# Using height/len to count rows in the filtered frame
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r1 = df.filter(pl.col(col) == 1).height
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r2 = df.filter(pl.col(col) == 2).height
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r3 = df.filter(pl.col(col) == 3).height
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r4 = df.filter(pl.col(col) == 4).height
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total = r1 + r2 + r3 + r4
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if total > 0:
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stats.append({
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'column': col,
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'Rank 1': r1,
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'Rank 2': r2,
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'Rank 3': r3,
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'Rank 4': r4
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})
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if not stats:
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return go.Figure()
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# Sort by Rank 1 (Most "Best" votes) descending to show the winner first
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# Secondary sort by Rank 2
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stats_df = pl.DataFrame(stats).sort(['Rank 1', 'Rank 2'], descending=[True, True])
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# Clean up labels: Remove prefix and underscores
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# e.g. "Character_Ranking_The_Coach" -> "The Coach"
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labels = [
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col.replace('Character_Ranking_', '').replace('_', ' ').strip()
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for col in stats_df['column']
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]
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fig = go.Figure()
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# Rank 1 (Best)
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fig.add_trace(go.Bar(
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name='Rank 1 (Best)',
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x=labels,
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y=stats_df['Rank 1'],
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marker_color=ColorPalette.RANK_1,
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hovertemplate='<b>%{x}</b><br>Rank 1: %{y}<extra></extra>'
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))
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# Rank 2
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fig.add_trace(go.Bar(
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name='Rank 2',
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x=labels,
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y=stats_df['Rank 2'],
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marker_color=ColorPalette.RANK_2,
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hovertemplate='<b>%{x}</b><br>Rank 2: %{y}<extra></extra>'
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))
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# Rank 3
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fig.add_trace(go.Bar(
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name='Rank 3',
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x=labels,
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y=stats_df['Rank 3'],
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marker_color=ColorPalette.RANK_3,
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hovertemplate='<b>%{x}</b><br>Rank 3: %{y}<extra></extra>'
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))
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# Rank 4 (Worst)
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# Using a neutral grey as a fallback for the lowest rank to keep focus on top ranks
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fig.add_trace(go.Bar(
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name='Rank 4 (Worst)',
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x=labels,
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y=stats_df['Rank 4'],
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marker_color=ColorPalette.RANK_4,
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hovertemplate='<b>%{x}</b><br>Rank 4: %{y}<extra></extra>'
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))
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fig.update_layout(
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barmode='stack',
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title=title,
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xaxis_title=x_label,
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yaxis_title=y_label,
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height=height,
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width=width,
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plot_bgcolor=ColorPalette.BACKGROUND,
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xaxis=dict(
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showgrid=True,
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gridcolor=ColorPalette.GRID,
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tickangle=-45
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),
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yaxis=dict(
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showgrid=True,
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gridcolor=ColorPalette.GRID
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),
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legend=dict(
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orientation="h",
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yanchor="bottom",
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y=1.02,
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xanchor="right",
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x=1,
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traceorder="normal"
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),
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font=dict(size=11)
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)
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return fig
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def plot_most_ranked_1_character(
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df: pl.DataFrame,
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title: str = "Most Popular Character Personality<br>(Number of Times Ranked 1st)",
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x_label: str = "Character Personality",
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y_label: str = "Count of 1st Place Rankings",
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height: int = 500,
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width: int = 1000,
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) -> go.Figure:
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"""
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Create a bar chart showing which character personality was ranked #1 the most.
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Parameters
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----------
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df : pl.DataFrame
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DataFrame containing character ranking columns.
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title : str, optional
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Plot title.
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x_label : str, optional
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X-axis label.
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y_label : str, optional
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Y-axis label.
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height : int, optional
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Plot height in pixels.
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width : int, optional
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Plot width in pixels.
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Returns
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-------
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go.Figure
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Plotly figure object.
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"""
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stats = []
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# Identify columns related to Character Ranking
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ranking_cols = [c for c in df.columns if 'Character_Ranking' in c]
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for col in ranking_cols:
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# Count occurrences of rank 1
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count_rank_1 = df.filter(pl.col(col) == 1).height
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stats.append({
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'column': col,
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'count': count_rank_1
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})
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# Sort by count descending
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stats_df = pl.DataFrame(stats).sort('count', descending=True)
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# Clean up labels
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labels = [
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col.replace('Character_Ranking_', '').replace('_', ' ').strip()
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for col in stats_df['column']
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]
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fig = go.Figure()
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fig.add_trace(go.Bar(
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x=labels,
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y=stats_df['count'],
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text=stats_df['count'],
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textposition='inside',
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textfont=dict(size=10, color='white'),
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marker_color=ColorPalette.PRIMARY,
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hovertemplate='<b>%{x}</b><br>1st Place Votes: %{y}<extra></extra>'
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))
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fig.update_layout(
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title=title,
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xaxis_title=x_label,
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yaxis_title=y_label,
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height=height,
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width=width,
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plot_bgcolor=ColorPalette.BACKGROUND,
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xaxis=dict(
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showgrid=True,
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gridcolor=ColorPalette.GRID,
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tickangle=-45
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),
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yaxis=dict(
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showgrid=True,
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gridcolor=ColorPalette.GRID
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),
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font=dict(size=11)
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)
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return fig
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def plot_weighted_ranking_score(
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weighted_df: pl.DataFrame,
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title: str = "Character Popularity Score<br>(Weighted: 1st=3pts, 2nd=2pts, 3rd=1pt)",
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x_label: str = "Character Personality",
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y_label: str = "Total Weighted Score",
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color: str = ColorPalette.PRIMARY,
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height: int = 500,
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width: int = 1000,
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) -> go.Figure:
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"""
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Create a bar chart showing the weighted ranking score for each character.
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Parameters
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----------
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df : pl.DataFrame
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DataFrame containing ranking columns.
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title : str, optional
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Plot title.
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x_label : str, optional
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X-axis label.
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y_label : str, optional
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Y-axis label.
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color : str, optional
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Bar color.
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height : int, optional
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Plot height.
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width : int, optional
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Plot width.
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Returns
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-------
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go.Figure
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Plotly figure object.
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"""
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fig = go.Figure()
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fig.add_trace(go.Bar(
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x=weighted_df['Character'],
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y=weighted_df['Weighted Score'],
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text=weighted_df['Weighted Score'],
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textposition='inside',
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textfont=dict(size=11, color='white'),
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marker_color=color,
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hovertemplate='<b>%{x}</b><br>Score: %{y}<extra></extra>'
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))
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fig.update_layout(
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title=title,
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xaxis_title=x_label,
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yaxis_title=y_label,
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height=height,
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width=width,
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plot_bgcolor=ColorPalette.BACKGROUND,
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xaxis=dict(
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showgrid=True,
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gridcolor=ColorPalette.GRID,
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tickangle=-45
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),
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yaxis=dict(
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showgrid=True,
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gridcolor=ColorPalette.GRID
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),
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font=dict(size=11)
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)
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return fig |