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feat(plotly): implement subplot-grid (#2823)
## Implementation: `subplot-grid` - plotly Implements the **plotly** version of `subplot-grid`. **File:** `plots/subplot-grid/implementations/plotly.py` --- :robot: *[impl-generate workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20602446641)* --------- Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com> Co-authored-by: Claude Opus 4.5 <noreply@anthropic.com> Co-authored-by: github-actions[bot] <41898282+github-actions[bot]@users.noreply.github.com>
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‎plots/subplot-grid/implementations/plotly.py‎

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""" pyplots.ai
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subplot-grid: Subplot Grid Layout
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Library: plotly 6.5.0 | Python 3.13.11
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Quality: 92/100 | Created: 2025-12-30
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"""
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import numpy as np
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import pandas as pd
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import plotly.graph_objects as go
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from plotly.subplots import make_subplots
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# Data - Financial dashboard example
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np.random.seed(42)
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# Generate 60 days of stock data
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days = 60
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dates = pd.date_range("2024-01-01", periods=days, freq="D")
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# Stock price with realistic walk
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returns = np.random.normal(0.001, 0.02, days)
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price = 100 * np.cumprod(1 + returns)
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# Volume data (correlated with absolute price movement)
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base_volume = 1000000
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volume = base_volume + np.abs(returns) * 50000000 + np.random.normal(0, 200000, days)
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volume = np.clip(volume, 500000, 3000000)
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# Daily returns for histogram
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daily_returns = np.diff(price) / price[:-1] * 100
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# Moving averages
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ma_20 = pd.Series(price).rolling(20).mean().values
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# Create 2x2 subplot grid
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fig = make_subplots(
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rows=2,
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cols=2,
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subplot_titles=("Stock Price & Moving Average", "Trading Volume", "Daily Returns Distribution", "Price vs Volume"),
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horizontal_spacing=0.1,
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vertical_spacing=0.12,
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specs=[[{"type": "scatter"}, {"type": "bar"}], [{"type": "histogram"}, {"type": "scatter"}]],
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)
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# Colors
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python_blue = "#306998"
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python_yellow = "#FFD43B"
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accent_green = "#2E7D32"
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accent_red = "#C62828"
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# Subplot 1: Line chart - Stock price with moving average
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fig.add_trace(
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go.Scatter(x=dates, y=price, mode="lines", name="Price", line={"color": python_blue, "width": 3}), row=1, col=1
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)
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fig.add_trace(
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go.Scatter(
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x=dates, y=ma_20, mode="lines", name="20-day MA", line={"color": python_yellow, "width": 2, "dash": "dash"}
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),
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row=1,
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col=1,
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)
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# Subplot 2: Bar chart - Volume
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volume_colors = [accent_green if r >= 0 else accent_red for r in returns]
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fig.add_trace(go.Bar(x=dates, y=volume, name="Volume", marker={"color": volume_colors, "opacity": 0.7}), row=1, col=2)
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# Subplot 3: Histogram - Daily returns distribution
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fig.add_trace(
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go.Histogram(
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x=daily_returns,
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nbinsx=20,
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name="Returns",
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marker={"color": python_blue, "opacity": 0.7, "line": {"color": "white", "width": 1}},
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),
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row=2,
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col=1,
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)
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# Subplot 4: Scatter - Price vs Volume relationship
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fig.add_trace(
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go.Scatter(
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x=volume,
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y=price,
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mode="markers",
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name="Price-Volume",
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marker={"color": python_blue, "size": 10, "opacity": 0.6, "line": {"color": "white", "width": 1}},
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),
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row=2,
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col=2,
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)
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# Update layout
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fig.update_layout(
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title={"text": "subplot-grid \u00b7 plotly \u00b7 pyplots.ai", "font": {"size": 32}, "x": 0.5, "xanchor": "center"},
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showlegend=True,
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legend={"font": {"size": 16}, "x": 1.02, "y": 1, "xanchor": "left", "yanchor": "top"},
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template="plotly_white",
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margin={"l": 80, "r": 150, "t": 120, "b": 80},
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)
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# Update all axes fonts
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fig.update_xaxes(tickfont={"size": 14}, title_font={"size": 18})
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fig.update_yaxes(tickfont={"size": 14}, title_font={"size": 18})
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# Specific axis labels
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fig.update_xaxes(title_text="Date", row=1, col=1)
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fig.update_yaxes(title_text="Price ($)", row=1, col=1)
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fig.update_xaxes(title_text="Date", row=1, col=2)
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fig.update_yaxes(title_text="Volume", row=1, col=2)
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fig.update_xaxes(title_text="Daily Return (%)", row=2, col=1)
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fig.update_yaxes(title_text="Frequency", row=2, col=1)
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fig.update_xaxes(title_text="Volume", row=2, col=2)
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fig.update_yaxes(title_text="Price ($)", row=2, col=2)
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# Update subplot titles font size
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for annotation in fig["layout"]["annotations"]:
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annotation["font"] = {"size": 20}
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# Save as PNG (4800x2700 via scale=3)
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fig.write_image("plot.png", width=1600, height=900, scale=3)
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# Save interactive HTML
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fig.write_html("plot.html", include_plotlyjs=True)

‎plots/subplot-grid/metadata/plotly.yaml‎

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library: plotly
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specification_id: subplot-grid
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created: '2025-12-30T17:49:58Z'
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updated: '2025-12-30T18:01:57Z'
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generated_by: claude-opus-4-5-20251101
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workflow_run: 20602446641
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issue: 0
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python_version: 3.13.11
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library_version: 6.5.0
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preview_url: https://storage.googleapis.com/pyplots-images/plots/subplot-grid/plotly/plot.png
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preview_thumb: https://storage.googleapis.com/pyplots-images/plots/subplot-grid/plotly/plot_thumb.png
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preview_html: https://storage.googleapis.com/pyplots-images/plots/subplot-grid/plotly/plot.html
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quality_score: 92
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review:
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strengths:
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- Excellent demonstration of Plotly's make_subplots capability with different chart
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types in each cell
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- Well-chosen financial dashboard scenario that naturally requires multiple visualization
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types
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- Clean color scheme using Python blue (#306998) and yellow (#FFD43B) consistently
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- Proper use of specs parameter to define chart types per cell
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- Good axis labeling with units throughout
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- Interactive HTML export alongside static PNG
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weaknesses:
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- Missing grid lines would improve readability of individual subplots (add gridcolor
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and showgrid to axes)
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- Scatter markers in bottom-right subplot could be slightly larger (12-14 instead
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of 10) for better visibility

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