feat(seaborn): implement slider-control-basic (#3097)

## Implementation: `slider-control-basic` - seaborn

Implements the **seaborn** version of `slider-control-basic`.

**File:** `plots/slider-control-basic/implementations/seaborn.py`

**Parent Issue:** #3071

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:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20620342647)*

---------

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Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>

Author: github-actions[bot]

plots/slider-control-basic/implementations/seaborn.py

@@ -0,0 +1,105 @@
+""" pyplots.ai
+slider-control-basic: Interactive Plot with Slider Control
+Library: seaborn 0.13.2 | Python 3.13.11
+Quality: 90/100 | Created: 2025-12-31
+"""
+
+import matplotlib.pyplot as plt
+import numpy as np
+import seaborn as sns
+from matplotlib.widgets import Slider
+
+
+# Data - Sales data across multiple years
+np.random.seed(42)
+years = np.arange(2018, 2025)
+
+# Generate sales data for each year with realistic patterns
+sales_data = {}
+for year in years:
+    base = 50000 + (year - 2018) * 5000  # Growing trend over years
+    seasonal = 10000 * np.sin(np.linspace(0, 2 * np.pi, 12))  # Seasonal pattern
+    noise = np.random.normal(0, 3000, 12)
+    sales_data[year] = base + seasonal + noise
+
+# Create figure with space for slider
+fig, ax = plt.subplots(figsize=(16, 9))
+plt.subplots_adjust(bottom=0.22)
+
+# Initial year to display
+initial_year = 2022
+
+# Style settings
+sns.set_style("whitegrid")
+
+# Month names for x-axis
+month_names = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
+
+# Initial plot using seaborn
+bars = sns.barplot(x=month_names, y=sales_data[initial_year], ax=ax, color="#306998", edgecolor="white", linewidth=2)
+
+# Styling
+ax.set_xlabel("Month", fontsize=20)
+ax.set_ylabel("Sales ($)", fontsize=20)
+ax.set_title("slider-control-basic · seaborn · pyplots.ai", fontsize=24, fontweight="bold")
+ax.tick_params(axis="both", labelsize=16)
+ax.set_ylim(0, 100000)
+
+# Format y-axis with dollar signs
+ax.yaxis.set_major_formatter(plt.FuncFormatter(lambda x, p: f"${x / 1000:.0f}K"))
+
+# Store text annotations for updating
+value_texts = []
+for bar, val in zip(ax.patches, sales_data[initial_year], strict=True):
+    txt = ax.text(
+        bar.get_x() + bar.get_width() / 2,
+        bar.get_height() + 1500,
+        f"${val / 1000:.0f}K",
+        ha="center",
+        va="bottom",
+        fontsize=12,
+        fontweight="bold",
+    )
+    value_texts.append(txt)
+
+# Create slider axis with proper positioning
+slider_ax = plt.axes([0.2, 0.06, 0.6, 0.04])
+year_slider = Slider(
+    ax=slider_ax, label="Year", valmin=2018, valmax=2024, valinit=initial_year, valstep=1, color="#306998"
+)
+year_slider.label.set_fontsize(18)
+year_slider.valtext.set_fontsize(18)
+
+
+# Slider update callback function
+def update_plot(val):
+    year = int(year_slider.val)
+    new_data = sales_data[year]
+
+    # Update bar heights
+    for bar, height in zip(ax.patches, new_data, strict=True):
+        bar.set_height(height)
+
+    # Update value labels
+    for txt, bar, val in zip(value_texts, ax.patches, new_data, strict=True):
+        txt.set_position((bar.get_x() + bar.get_width() / 2, bar.get_height() + 1500))
+        txt.set_text(f"${val / 1000:.0f}K")
+
+    fig.canvas.draw_idle()
+
+
+# Connect slider to update function
+year_slider.on_changed(update_plot)
+
+# Add instructional text for slider interaction
+fig.text(
+    0.5,
+    0.015,
+    "Drag the slider to explore monthly sales data from 2018-2024",
+    ha="center",
+    fontsize=16,
+    style="italic",
+    color="#666666",
+)
+
+plt.savefig("plot.png", dpi=300, bbox_inches="tight", facecolor="white")

plots/slider-control-basic/metadata/seaborn.yaml

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+library: seaborn
+specification_id: slider-control-basic
+created: '2025-12-31T13:53:48Z'
+updated: '2025-12-31T14:27:35Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20620342647
+issue: 3071
+python_version: 3.13.11
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/slider-control-basic/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/slider-control-basic/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 90
+review:
+  strengths:
+  - Excellent implementation of slider interactivity using matplotlib.widgets.Slider
+    with seaborn bar plot
+  - Clean data generation with realistic seasonal sales patterns and year-over-year
+    growth
+  - Value labels above each bar provide excellent data readability
+  - Proper font sizing throughout (24pt title, 20pt axis labels, 16pt ticks, 18pt
+    slider labels)
+  - Y-axis formatted with dollar signs and K notation for clarity
+  - Instructional text helps users understand the interactive functionality
+  - Good use of seaborn whitegrid style for professional appearance
+  weaknesses:
+  - Grid/legend score slightly reduced as whitegrid could be more subtle for a bar
+    chart
+  - Layout has extra bottom margin for slider which slightly reduces plot area proportion