feat(seaborn): implement heatmap-cohort-retention

plots/heatmap-cohort-retention/implementations/seaborn.py

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+""" pyplots.ai
+heatmap-cohort-retention: Cohort Retention Heatmap
+Library: seaborn 0.13.2 | Python 3.14.3
+Quality: 90/100 | Created: 2026-03-16
+"""
+
+import matplotlib.colors as mcolors
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import seaborn as sns
+
+
+# Seaborn styling
+sns.set_context("talk", font_scale=1.1)
+sns.set_style("white")
+
+# Data
+np.random.seed(42)
+
+cohort_labels = [
+    "Jan 2024",
+    "Feb 2024",
+    "Mar 2024",
+    "Apr 2024",
+    "May 2024",
+    "Jun 2024",
+    "Jul 2024",
+    "Aug 2024",
+    "Sep 2024",
+    "Oct 2024",
+]
+n_cohorts = len(cohort_labels)
+n_periods = n_cohorts
+cohort_sizes = np.random.randint(800, 2500, size=n_cohorts)
+
+retention_data = np.full((n_cohorts, n_periods), np.nan)
+for i in range(n_cohorts):
+    max_periods = n_periods - i
+    retention_data[i, 0] = 100.0
+    base_decay = np.random.uniform(0.72, 0.85)
+    for j in range(1, max_periods):
+        prev = retention_data[i, j - 1]
+        decay = base_decay + np.random.uniform(-0.03, 0.03)
+        decay = min(decay, 0.98)
+        retention_data[i, j] = round(prev * decay, 1)
+
+period_labels = [f"Month {i}" for i in range(n_periods)]
+df_heatmap = pd.DataFrame(retention_data, index=cohort_labels, columns=period_labels)
+
+# Custom colormap: warm tones for low retention, cool tones for high
+cmap = mcolors.LinearSegmentedColormap.from_list("retention", ["#f7e1d0", "#e8c4a0", "#7fbf7b", "#1b7837", "#00441b"])
+
+# Plot
+fig, ax = plt.subplots(figsize=(16, 9))
+fig.set_facecolor("#fafafa")
+ax.set_facecolor("#f0f0f0")
+
+mask = df_heatmap.isna()
+
+# Annotation array with "%" suffix
+annot_array = df_heatmap.copy()
+annot_strings = annot_array.map(lambda v: f"{v:.0f}%" if not np.isnan(v) else "")
+
+sns.heatmap(
+    df_heatmap,
+    mask=mask,
+    annot=annot_strings,
+    fmt="",
+    cmap=cmap,
+    vmin=0,
+    vmax=100,
+    linewidths=2.5,
+    linecolor="#fafafa",
+    ax=ax,
+    annot_kws={"fontsize": 13, "fontweight": "bold"},
+    cbar_kws={"label": "Retention %", "shrink": 0.75, "aspect": 30, "pad": 0.02},
+    square=False,
+)
+
+# Style
+y_labels = [f"{label}  (n={size:,})" for label, size in zip(cohort_labels, cohort_sizes, strict=True)]
+ax.set_yticklabels(y_labels, rotation=0, fontsize=16)
+ax.set_xticklabels(period_labels, rotation=0, fontsize=16)
+ax.set_xlabel("Periods Since Signup", fontsize=20, labelpad=12)
+ax.set_ylabel("Signup Cohort", fontsize=20, labelpad=12)
+ax.set_title(
+    "heatmap-cohort-retention · seaborn · pyplots.ai", fontsize=24, fontweight="medium", pad=24, color="#333333"
+)
+
+# Colorbar styling
+cbar = ax.collections[0].colorbar
+cbar.ax.tick_params(labelsize=16)
+cbar.set_label("Retention %", fontsize=18, labelpad=10)
+cbar.outline.set_visible(False)
+
+# X-axis at top
+ax.xaxis.tick_top()
+ax.xaxis.set_label_position("top")
+
+# Remove all spines
+sns.despine(ax=ax, top=True, right=True, bottom=True, left=True)
+ax.tick_params(axis="both", length=0)
+
+plt.tight_layout()
+plt.savefig("plot.png", dpi=300, bbox_inches="tight", facecolor=fig.get_facecolor())

plots/heatmap-cohort-retention/metadata/seaborn.yaml

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+library: seaborn
+specification_id: heatmap-cohort-retention
+created: '2026-03-16T20:46:04Z'
+updated: '2026-03-16T20:57:29Z'
+generated_by: claude-opus-4-5-20251101
+workflow_run: 23165008151
+issue: 4570
+python_version: 3.14.3
+library_version: 0.13.2
+preview_url: https://storage.googleapis.com/pyplots-images/plots/heatmap-cohort-retention/seaborn/plot.png
+preview_thumb: https://storage.googleapis.com/pyplots-images/plots/heatmap-cohort-retention/seaborn/plot_thumb.png
+preview_html: null
+quality_score: 90
+review:
+  strengths:
+  - Excellent spec compliance — all required features implemented correctly
+  - Custom colormap with warm-to-green transition creates a distinctive professional
+    look
+  - 'Clean visual refinement: all spines removed, tick marks hidden, custom background,
+    styled colorbar'
+  - Idiomatic seaborn usage with mask, annotations, and context/style configuration
+  - Well-structured readable code with proper reproducibility
+  weaknesses:
+  - Annotation font size (13pt) could be slightly larger for better readability at
+    full resolution
+  - Color palette is not perceptually uniform — mid-range retention values may be
+    harder to distinguish
+  - Some cohorts show very similar decay patterns limiting cross-cohort comparison
+    insight
+  image_description: The plot displays a triangular cohort retention heatmap with
+    10 monthly cohorts (Jan 2024 – Oct 2024) on the y-axis and 10 periods (Month 0
+    – Month 9) on the x-axis positioned at the top. Each cell shows bold retention
+    percentages with a "%" suffix. The color scheme transitions from light tan/peach
+    (low retention) through medium green to dark forest green (high retention), using
+    a custom colormap. Cohort sizes (n=921 to n=2,438) are shown in parentheses next
+    to each cohort label. The triangular shape is correctly formed — earlier cohorts
+    have more columns. The background is light gray (#fafafa), all spines are removed,
+    tick marks are hidden, and the colorbar on the right displays "Retention %" from
+    0–100 with its outline removed. The title reads "heatmap-cohort-retention · seaborn
+    · pyplots.ai" in medium weight.
+  criteria_checklist:
+    visual_quality:
+      score: 28
+      max: 30
+      items:
+      - id: VQ-01
+        name: Text Legibility
+        score: 7
+        max: 8
+        passed: true
+        comment: All font sizes explicitly set (title 24pt, labels 20pt, ticks 16pt,
+          annotations 13pt). Annotations slightly small but readable.
+      - id: VQ-02
+        name: No Overlap
+        score: 6
+        max: 6
+        passed: true
+        comment: No overlapping text. Clean cell spacing with linewidths=2.5.
+      - id: VQ-03
+        name: Element Visibility
+        score: 6
+        max: 6
+        passed: true
+        comment: Heatmap cells well-sized with clear separation and good contrast.
+      - id: VQ-04
+        name: Color Accessibility
+        score: 3
+        max: 4
+        passed: true
+        comment: Custom tan-to-green palette avoids red-green issues but is not perceptually
+          uniform.
+      - id: VQ-05
+        name: Layout & Canvas
+        score: 4
+        max: 4
+        passed: true
+        comment: Plot fills canvas well, colorbar appropriately sized and positioned.
+      - id: VQ-06
+        name: Axis Labels & Title
+        score: 2
+        max: 2
+        passed: true
+        comment: 'Descriptive labels: Periods Since Signup, Signup Cohort.'
+    design_excellence:
+      score: 15
+      max: 20
+      items:
+      - id: DE-01
+        name: Aesthetic Sophistication
+        score: 6
+        max: 8
+        passed: true
+        comment: Custom colormap, custom background, styled colorbar — clearly above
+          defaults.
+      - id: DE-02
+        name: Visual Refinement
+        score: 5
+        max: 6
+        passed: true
+        comment: All spines removed, tick marks hidden, custom background, colorbar
+          outline removed.
+      - id: DE-03
+        name: Data Storytelling
+        score: 4
+        max: 6
+        passed: true
+        comment: Triangular shape communicates cohort narrative, color intensity shows
+          retention decay.
+    spec_compliance:
+      score: 15
+      max: 15
+      items:
+      - id: SC-01
+        name: Plot Type
+        score: 5
+        max: 5
+        passed: true
+        comment: Correct triangular cohort retention heatmap.
+      - id: SC-02
+        name: Required Features
+        score: 4
+        max: 4
+        passed: true
+        comment: 'All spec features present: triangular shape, Period 0 = 100%, colormap,
+          annotations, cohort sizes, Month N labels, colorbar.'
+      - id: SC-03
+        name: Data Mapping
+        score: 3
+        max: 3
+        passed: true
+        comment: Cohorts on Y, periods on X, retention as color. Correct.
+      - id: SC-04
+        name: Title & Legend
+        score: 3
+        max: 3
+        passed: true
+        comment: Title format correct. Colorbar serves as legend.
+    data_quality:
+      score: 14
+      max: 15
+      items:
+      - id: DQ-01
+        name: Feature Coverage
+        score: 5
+        max: 6
+        passed: true
+        comment: Shows varying decay rates, different cohort sizes, full triangular
+          pattern. Some similar decay profiles.
+      - id: DQ-02
+        name: Realistic Context
+        score: 5
+        max: 5
+        passed: true
+        comment: Monthly SaaS cohort retention — real-world neutral business scenario.
+      - id: DQ-03
+        name: Appropriate Scale
+        score: 4
+        max: 4
+        passed: true
+        comment: Realistic retention values and cohort sizes.
+    code_quality:
+      score: 10
+      max: 10
+      items:
+      - id: CQ-01
+        name: KISS Structure
+        score: 3
+        max: 3
+        passed: true
+        comment: 'Linear flow: imports, data, plot, save. No functions or classes.'
+      - id: CQ-02
+        name: Reproducibility
+        score: 2
+        max: 2
+        passed: true
+        comment: np.random.seed(42) set.
+      - id: CQ-03
+        name: Clean Imports
+        score: 2
+        max: 2
+        passed: true
+        comment: All imports used.
+      - id: CQ-04
+        name: Code Elegance
+        score: 2
+        max: 2
+        passed: true
+        comment: Clean and Pythonic. Elegant .map() for annotations.
+      - id: CQ-05
+        name: Output & API
+        score: 1
+        max: 1
+        passed: true
+        comment: Saves as plot.png with dpi=300. No deprecated API.
+    library_mastery:
+      score: 8
+      max: 10
+      items:
+      - id: LM-01
+        name: Idiomatic Usage
+        score: 5
+        max: 5
+        passed: true
+        comment: Expert use of sns.heatmap with mask, annot, fmt, cbar_kws. Idiomatic
+          set_context, set_style, despine.
+      - id: LM-02
+        name: Distinctive Features
+        score: 3
+        max: 5
+        passed: true
+        comment: Leverages seaborn-specific mask, built-in annotations, cbar_kws,
+          sns.despine.
+  verdict: APPROVED
+impl_tags:
+  dependencies: []
+  techniques:
+  - colorbar
+  - annotations
+  patterns:
+  - data-generation
+  - matrix-construction
+  dataprep: []
+  styling:
+  - custom-colormap
+  - edge-highlighting