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feat(plotnine): implement wordcloud-basic (#1031)
## Implementation: `wordcloud-basic` - plotnine Implements the **plotnine** version of `wordcloud-basic`. **File:** `plots/wordcloud-basic/implementations/plotnine.py` **Parent Issue:** #1012 --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20257492021)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/wordcloud-basic/implementations/plotnine.py‎

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"""
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wordcloud-basic: Basic Word Cloud
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Library: plotnine
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"""
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import numpy as np
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import pandas as pd
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from plotnine import (
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aes,
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element_blank,
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element_rect,
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element_text,
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geom_text,
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ggplot,
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labs,
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scale_color_identity,
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scale_size_identity,
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theme,
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)
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# Word frequency data - technology survey responses
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np.random.seed(42)
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words_data = {
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"word": [
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"Python",
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"Data",
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"Machine",
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"Learning",
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"AI",
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"Cloud",
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"API",
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"Database",
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"Security",
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"DevOps",
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"Analytics",
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"Automation",
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"Software",
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"Code",
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"Development",
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"Integration",
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"Platform",
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"Infrastructure",
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"Performance",
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"Scalability",
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"Testing",
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"Deployment",
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"Monitoring",
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"Architecture",
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"Framework",
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"Microservices",
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"Container",
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"Kubernetes",
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"Docker",
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"AWS",
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"Azure",
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"Innovation",
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"Digital",
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"Transform",
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"Agile",
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],
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"frequency": [
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95,
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88,
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82,
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78,
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75,
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70,
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65,
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62,
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58,
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42,
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],
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}
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df = pd.DataFrame(words_data)
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# Calculate font sizes scaled by frequency (range 10-36 for readability)
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min_freq, max_freq = df["frequency"].min(), df["frequency"].max()
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df["size"] = 10 + (df["frequency"] - min_freq) / (max_freq - min_freq) * 26
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# Sort by frequency descending for placement (largest words first)
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df = df.sort_values("frequency", ascending=False).reset_index(drop=True)
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# Fixed positions using concentric rings to guarantee no overlaps
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np.random.seed(42)
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width, height = 100, 56.25
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center_x, center_y = width / 2, height / 2
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# Define rings with word counts: inner ring has fewer, larger words
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rings = [
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{"count": 5, "radius": 0, "y_offset": 0}, # Center - 5 largest words
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{"count": 8, "radius": 16, "y_offset": 0}, # Ring 1
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{"count": 10, "radius": 28, "y_offset": 0}, # Ring 2
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{"count": 12, "radius": 40, "y_offset": 0}, # Ring 3 (outer)
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]
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positions_x = []
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positions_y = []
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word_idx = 0
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# Place center words in a horizontal line with spacing
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center_words = 5
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center_spacing = 14
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center_start_x = center_x - (center_words - 1) * center_spacing / 2
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for i in range(center_words):
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x = center_start_x + i * center_spacing
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y = center_y
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positions_x.append(x)
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positions_y.append(y)
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word_idx = center_words
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# Place remaining words in concentric rings
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for ring in rings[1:]:
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ring_count = min(ring["count"], len(df) - word_idx)
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if ring_count <= 0:
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break
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for i in range(ring_count):
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angle = (2 * np.pi * i / ring_count) + np.random.uniform(-0.1, 0.1)
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# Adjust radius based on 16:9 aspect ratio
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x = center_x + ring["radius"] * np.cos(angle) * 1.1
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y = center_y + ring["radius"] * np.sin(angle) * 0.6
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# Keep within bounds
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x = np.clip(x, 12, width - 12)
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y = np.clip(y, 6, height - 6)
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positions_x.append(x)
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positions_y.append(y)
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word_idx += ring_count
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df = df.head(len(positions_x))
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df["x"] = positions_x
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df["y"] = positions_y
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# Assign colors based on frequency tiers
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colors = []
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for freq in df["frequency"]:
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if freq >= 65:
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colors.append("#306998") # Python Blue - high frequency
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elif freq >= 35:
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colors.append("#FFD43B") # Python Yellow - medium frequency
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elif freq >= 15:
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colors.append("#4ECDC4") # Teal - lower medium
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else:
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colors.append("#95E1A3") # Light green - low frequency
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df["color"] = colors
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# Create plot
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plot = (
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ggplot(df, aes(x="x", y="y", label="word", size="size", color="color"))
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+ geom_text(family="sans-serif", fontstyle="normal", show_legend=False)
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+ scale_size_identity()
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+ scale_color_identity()
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+ labs(title="Tech Survey Keywords · wordcloud-basic · plotnine · pyplots.ai")
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+ theme(
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figure_size=(16, 9),
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plot_title=element_text(size=24, ha="center", weight="bold", margin={"b": 15}),
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panel_background=element_rect(fill="white"),
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plot_background=element_rect(fill="white"),
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panel_grid_major=element_blank(),
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panel_grid_minor=element_blank(),
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axis_text=element_blank(),
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axis_title=element_blank(),
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axis_ticks=element_blank(),
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)
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)
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# Save
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plot.save("plot.png", dpi=300, verbose=False)

‎plots/wordcloud-basic/metadata/plotnine.yaml‎

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# Per-library metadata for plotnine implementation of wordcloud-basic
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# Auto-generated by impl-generate.yml
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library: plotnine
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specification_id: wordcloud-basic
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# Preview URLs (filled by workflow)
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preview_url: https://storage.googleapis.com/pyplots-images/plots/wordcloud-basic/plotnine/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/wordcloud-basic/plotnine/plot_thumb.png
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preview_html: null
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current:
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version: 0
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generated_at: 2025-12-16T05:30:06Z
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20257492021
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issue: 1012
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quality_score: 92
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# Version info (filled by workflow)
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python_version: "3.13.11"
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library_version: "unknown"
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history: []

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