feat(plotly): implement parallel-categories-basic (#2528)

## Implementation: `parallel-categories-basic` - plotly

Implements the **plotly** version of `parallel-categories-basic`.

**File:** `plots/parallel-categories-basic/implementations/plotly.py`

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20585400189)*

---------

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

Author: github-actions[bot]

plots/parallel-categories-basic/implementations/plotly.py

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+""" pyplots.ai
+parallel-categories-basic: Basic Parallel Categories Plot
+Library: plotly 6.5.0 | Python 3.13.11
+Quality: 92/100 | Created: 2025-12-30
+"""
+
+import plotly.graph_objects as go
+import seaborn as sns
+
+
+# Data - Titanic survival data with multiple categorical dimensions
+df = sns.load_dataset("titanic")
+
+# Prepare data: select key categorical variables
+df = df[["class", "sex", "embarked", "survived"]].dropna()
+
+# Map survived to readable labels
+df["outcome"] = df["survived"].map({0: "Did Not Survive", 1: "Survived"})
+
+# Create dimension specifications for parallel categories
+dimensions = [
+    {
+        "label": "Passenger Class",
+        "values": df["class"].astype(str),
+        "categoryorder": "array",
+        "categoryarray": ["First", "Second", "Third"],
+    },
+    {
+        "label": "Sex",
+        "values": df["sex"].str.capitalize(),
+        "categoryorder": "array",
+        "categoryarray": ["Female", "Male"],
+    },
+    {
+        "label": "Embarked",
+        "values": df["embarked"].map({"C": "Cherbourg", "Q": "Queenstown", "S": "Southampton"}),
+        "categoryorder": "array",
+        "categoryarray": ["Cherbourg", "Queenstown", "Southampton"],
+    },
+    {
+        "label": "Outcome",
+        "values": df["outcome"],
+        "categoryorder": "array",
+        "categoryarray": ["Survived", "Did Not Survive"],
+    },
+]
+
+# Create color scale based on survival outcome (last dimension)
+color_values = df["survived"].values
+
+# Create parallel categories plot
+fig = go.Figure(
+    go.Parcats(
+        dimensions=dimensions,
+        line={
+            "color": color_values,
+            "colorscale": [[0, "#306998"], [1, "#FFD43B"]],  # Python Blue for not survived, Yellow for survived
+            "shape": "hspline",
+        },
+        hoveron="color",
+        hoverinfo="count+probability",
+        arrangement="freeform",
+    )
+)
+
+# Update layout for 4800x2700 canvas
+fig.update_layout(
+    title={
+        "text": "Titanic Passengers · parallel-categories-basic · plotly · pyplots.ai",
+        "font": {"size": 32, "color": "#333333"},
+        "x": 0.5,
+        "xanchor": "center",
+    },
+    font={"size": 20},
+    paper_bgcolor="white",
+    plot_bgcolor="white",
+    margin={"l": 80, "r": 80, "t": 120, "b": 80},
+    annotations=[
+        {
+            "text": "Color: Yellow = Survived, Blue = Did Not Survive",
+            "x": 0.5,
+            "y": -0.08,
+            "xref": "paper",
+            "yref": "paper",
+            "showarrow": False,
+            "font": {"size": 18, "color": "#666666"},
+            "xanchor": "center",
+        }
+    ],
+)
+
+# Save as PNG (4800 x 2700 px)
+fig.write_image("plot.png", width=1600, height=900, scale=3)
+
+# Save interactive HTML
+fig.write_html("plot.html", include_plotlyjs=True, full_html=True)

plots/parallel-categories-basic/metadata/plotly.yaml

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+library: plotly
+specification_id: parallel-categories-basic
+created: '2025-12-30T00:05:08Z'
+updated: '2025-12-30T00:13:23Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 20585400189
+issue: 0
+python_version: 3.13.11
+library_version: 6.5.0
+preview_url: https://storage.googleapis.com/pyplots-images/plots/parallel-categories-basic/plotly/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/parallel-categories-basic/plotly/plot_thumb.png
+preview_html: https://storage.googleapis.com/pyplots-images/plots/parallel-categories-basic/plotly/plot.html
+quality_score: 92
+review:
+  strengths:
+  - Excellent use of Titanic dataset providing realistic, well-known categorical data
+    with meaningful dimensions
+  - Clear color scheme with blue/yellow contrast that is colorblind-friendly
+  - Good use of Plotly Parcats with smooth hspline ribbons and interactive hover features
+  - Title follows correct format with spec-id, library, and pyplots.ai branding
+  - Helpful annotation explaining color meaning at the bottom of the plot
+  weaknesses:
+  - The Did Not Survive label on the right side is slightly cut off - consider adjusting
+    margins or abbreviating