feat(plotly): implement facet-grid

plots/facet-grid/implementations/plotly.py

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+""" pyplots.ai
+facet-grid: Faceted Grid Plot
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 91/100 | Created: 2025-12-30
+"""
+
+import numpy as np
+import pandas as pd
+import plotly.express as px
+
+
+# Data - Create realistic dataset for faceted visualization
+np.random.seed(42)
+
+# Product performance data across regions and quarters
+regions = ["North", "South", "East", "West"]
+quarters = ["Q1", "Q2", "Q3", "Q4"]
+
+data = []
+for region in regions:
+    for quarter in quarters:
+        # Generate 15 data points per region-quarter combination
+        n = 15
+        # Base values vary by region and quarter
+        base_sales = {"North": 80, "South": 60, "East": 70, "West": 75}
+        quarter_effect = {"Q1": 0, "Q2": 10, "Q3": 5, "Q4": 15}
+
+        marketing_spend = np.random.uniform(10, 50, n)
+        sales = (
+            base_sales[region]
+            + quarter_effect[quarter]
+            + marketing_spend * (0.8 + np.random.uniform(0, 0.4, n))
+            + np.random.normal(0, 5, n)
+        )
+
+        for i in range(n):
+            data.append(
+                {
+                    "Marketing Spend ($K)": marketing_spend[i],
+                    "Sales ($K)": sales[i],
+                    "Region": region,
+                    "Quarter": quarter,
+                }
+            )
+
+df = pd.DataFrame(data)
+
+# Create faceted scatter plot
+# Using single color since Region is already encoded by facet rows
+fig = px.scatter(df, x="Marketing Spend ($K)", y="Sales ($K)", facet_row="Region", facet_col="Quarter")
+
+# Update layout for large canvas
+fig.update_layout(
+    title=dict(text="facet-grid · plotly · pyplots.ai", font=dict(size=32), x=0.5, xanchor="center"),
+    font=dict(size=16),
+    template="plotly_white",
+    showlegend=False,
+    margin=dict(l=80, r=80, t=100, b=80),
+)
+
+# Update axes for all facets
+fig.update_xaxes(title_font=dict(size=20), tickfont=dict(size=16), gridcolor="rgba(0,0,0,0.1)", gridwidth=1)
+
+fig.update_yaxes(title_font=dict(size=20), tickfont=dict(size=16), gridcolor="rgba(0,0,0,0.1)", gridwidth=1)
+
+# Update facet labels (annotations)
+fig.for_each_annotation(lambda a: a.update(font=dict(size=18)))
+
+# Update markers with consistent color for visibility
+fig.update_traces(marker=dict(size=12, opacity=0.8, color="#306998", line=dict(width=1, color="white")))
+
+# Save as PNG and HTML
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+fig.write_html("plot.html", include_plotlyjs="cdn")

plots/facet-grid/metadata/plotly.yaml

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+library: plotly
+specification_id: facet-grid
+created: '2025-12-30T16:30:15Z'
+updated: '2025-12-30T16:47:28Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20601057130
+issue: 0
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/facet-grid/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/facet-grid/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/facet-grid/plotly/plot.html
+quality_score: 91
+review:
+  strengths:
+  - Clean implementation using Plotly Express native faceting capability (facet_row,
+    facet_col)
+  - Excellent data generation with realistic business scenario (marketing spend vs
+    sales by region and quarter)
+  - Good visual hierarchy with clear title, readable axis labels with units, and subtle
+    grid
+  - Produces both PNG and HTML output leveraging Plotly interactivity
+  - Proper use of template=plotly_white for clean appearance
+  weaknesses:
+  - Legend showing regions is redundant since region is already encoded by facet rows
+  - Markers could be slightly larger (15-18 instead of 12) for better visibility with
+    ~15 points per facet
+  - Row facet labels are displayed vertically which reduces readability compared to
+    horizontal text