[bar-basic] seaborn implementation

[github-actions]

[bar-basic] seaborn Implementation

Parent Issue: #117
Spec: specs/bar-basic.md
Library: seaborn
Feature Branch: plot/bar-basic

---

Attempt History

Attempts will be documented below as comments.

[github-actions]

Attempt 1/3

Technical Approach

• Imports: import matplotlib.pyplot as plt, import pandas as pd, import seaborn as sns
• Plot function: sns.barplot
• Parameters: data, category, value, figsize

Status

• PR: feat(seaborn): implement bar-basic #127
• File: plots/seaborn/barplot/bar-basic/default.py
• Workflow: 19877121454

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[claude]

AI Review - Attempt 0/3

Quality Evaluation

Evaluator	Score	Verdict
Claude	92/100	approve

Criteria Checklist

Visual Quality

• VQ-001: Axes labeled correctly (+10) - X-axis shows "Product", Y-axis shows "Sales ($)"
• VQ-002: Grid is subtle (+5) - Y-axis grid with alpha=0.3
• VQ-003: Elements clear (+8) - Bars clearly visible with good alpha=0.8
• VQ-004: No overlap (+9) - Category labels are clear, no overlap
• [N/A] VQ-005: Legend present - Not required for single-series bar chart
• VQ-006: Colorblind safe (+6) - Uses steelblue, a safe color choice
• VQ-007: Figure size (+4) - 10x6 default, reasonable aspect ratio
• VQ-008: Title (+3) - "Sales by Product" displayed correctly

Code Quality

• CQ-001: Type hints (+7) - All parameters and return type properly typed
• CQ-002: Docstring (+8) - Complete Google-style docstring with Args, Returns, Raises, Example
• CQ-003: Input validation (+10) - Validates empty data and column existence
• CQ-004: Clear error messages (+6) - Error messages include available columns

Correctness

• CR-001: Data accurate (+10) - Correct columns mapped to x/y axes
• CR-002: Spec compliance (+10) - All optional parameters implemented with correct defaults
• CR-003: Edge cases (+5) - Empty data and missing columns handled

Issues Found

1. CQ-005 Minor: Some magic numbers present (alpha=0.3 for grid) but acceptable since it is a visualization constant

Visual Analysis

The generated plot image shows:

• Clean vertical bar chart with 5 product categories
• Y-axis starts at zero as required by spec
• Subtle horizontal grid lines for easy value reading
• Professional appearance with proper margins
• Title "Sales by Product" centered at top
• Clear axis labels: "Product" on x-axis, "Sales ($)" on y-axis
• Bars are evenly spaced with consistent width
• steelblue color provides good contrast against white background

AI Feedback for Next Attempt

Implementation is excellent. No changes needed.

Generated Code

"""
bar-basic: Basic Bar Chart
Library: seaborn
"""

from typing import TYPE_CHECKING

import matplotlib.pyplot as plt
import pandas as pd
import seaborn as sns


if TYPE_CHECKING:
    from matplotlib.figure import Figure


def create_plot(
    data: pd.DataFrame,
    category: str,
    value: str,
    figsize: tuple[float, float] = (10, 6),
    color: str = "steelblue",
    edgecolor: str = "black",
    alpha: float = 0.8,
    title: str | None = None,
    xlabel: str | None = None,
    ylabel: str | None = None,
    rotation: int = 0,
    **kwargs,
) -> "Figure":
    """
    Create a basic vertical bar chart for categorical data comparison.

    A fundamental bar chart displaying rectangular bars with heights proportional
    to their numeric values, ideal for comparing quantities across categories.

    Args:
        data: Input DataFrame containing the data to plot
        category: Column name for category labels (x-axis)
        value: Column name for numeric values (bar heights)
        figsize: Figure size as (width, height) in inches
        color: Bar fill color
        edgecolor: Bar edge color
        alpha: Transparency level for bars (0-1)
        title: Plot title (optional)
        xlabel: X-axis label (defaults to column name if None)
        ylabel: Y-axis label (defaults to column name if None)
        rotation: Rotation angle for x-axis labels in degrees
        **kwargs: Additional parameters passed to seaborn.barplot

    Returns:
        Matplotlib Figure object

    Raises:
        ValueError: If data is empty
        KeyError: If required columns not found in data

    Example:
        >>> data = pd.DataFrame({
        ...     'category': ['A', 'B', 'C'],
        ...     'value': [10, 20, 15]
        ... })
        >>> fig = create_plot(data, 'category', 'value', title='Sample Chart')
    """
    # Input validation
    if data.empty:
        raise ValueError("Data cannot be empty")

    for col in [category, value]:
        if col not in data.columns:
            available = ", ".join(data.columns)
            raise KeyError(f"Column '{{col}}' not found. Available: {{available}}")

    # Create figure
    fig, ax = plt.subplots(figsize=figsize)

    # Plot data using seaborn barplot
    sns.barplot(data=data, x=category, y=value, color=color, edgecolor=edgecolor, alpha=alpha, ax=ax, **kwargs)

    # Set y-axis to start at zero for accurate visual comparison
    ax.set_ylim(bottom=0)

    # Add subtle grid on y-axis only
    ax.yaxis.grid(True, alpha=0.3)
    ax.set_axisbelow(True)

    # Labels
    ax.set_xlabel(xlabel if xlabel is not None else category)
    ax.set_ylabel(ylabel if ylabel is not None else value)

    # Title
    if title is not None:
        ax.set_title(title)

    # Rotate x-axis labels if specified
    if rotation \!= 0:
        plt.xticks(rotation=rotation, ha="right")

    # Layout
    plt.tight_layout()

    return fig

Verdict: APPROVED