feat(plotnine): implement bubble-map-geographic (#3635)

## Implementation: `bubble-map-geographic` - plotnine

Implements the **plotnine** version of `bubble-map-geographic`.

**File:** `plots/bubble-map-geographic/implementations/plotnine.py`

**Parent Issue:** #3625

---
:robot: *[impl-generate
workflow](https://github.com/MarkusNeusinger/pyplots/actions/runs/20873959857)*

---------

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>

Author: github-actions[bot]

plots/bubble-map-geographic/implementations/plotnine.py

@@ -0,0 +1,308 @@
+""" pyplots.ai
+bubble-map-geographic: Bubble Map with Sized Geographic Markers
+Library: plotnine 0.15.2 | Python 3.13.11
+Quality: 91/100 | Created: 2026-01-10
+"""
+
+import numpy as np
+import pandas as pd
+from plotnine import (
+    aes,
+    coord_fixed,
+    element_blank,
+    element_line,
+    element_rect,
+    element_text,
+    geom_point,
+    geom_polygon,
+    ggplot,
+    labs,
+    scale_color_manual,
+    scale_size_area,
+    theme,
+    theme_minimal,
+)
+
+
+# Seed for reproducibility
+np.random.seed(42)
+
+# Major world cities with population data (in millions)
+cities_data = {
+    "city": [
+        "Tokyo",
+        "Delhi",
+        "Shanghai",
+        "Sao Paulo",
+        "Mexico City",
+        "Cairo",
+        "Mumbai",
+        "Beijing",
+        "Dhaka",
+        "Osaka",
+        "New York",
+        "Karachi",
+        "Buenos Aires",
+        "Istanbul",
+        "Kolkata",
+        "Lagos",
+        "Rio de Janeiro",
+        "Los Angeles",
+        "Moscow",
+        "Paris",
+        "Bangkok",
+        "Seoul",
+        "London",
+        "Lima",
+        "Chicago",
+        "Santiago",
+        "Sydney",
+        "Toronto",
+        "Singapore",
+        "Dubai",
+    ],
+    "latitude": [
+        35.68,
+        28.61,
+        31.23,
+        -23.55,
+        19.43,
+        30.04,
+        19.08,
+        39.90,
+        23.81,
+        34.69,
+        40.71,
+        24.86,
+        -34.60,
+        41.01,
+        22.57,
+        6.52,
+        -22.91,
+        34.05,
+        55.76,
+        48.86,
+        13.76,
+        37.57,
+        51.51,
+        -12.05,
+        41.88,
+        -33.45,
+        -33.87,
+        43.65,
+        1.35,
+        25.20,
+    ],
+    "longitude": [
+        139.69,
+        77.21,
+        121.47,
+        -46.63,
+        -99.13,
+        31.24,
+        72.88,
+        116.41,
+        90.41,
+        135.50,
+        -74.01,
+        67.01,
+        -58.38,
+        28.98,
+        88.36,
+        3.38,
+        -43.17,
+        -118.24,
+        37.62,
+        2.35,
+        100.50,
+        127.00,
+        -0.13,
+        -77.04,
+        -87.63,
+        -70.67,
+        151.21,
+        -79.38,
+        103.82,
+        55.27,
+    ],
+    "population": [
+        37.4,
+        32.9,
+        29.2,
+        22.4,
+        21.8,
+        21.3,
+        21.0,
+        20.9,
+        22.5,
+        19.1,
+        18.8,
+        16.8,
+        15.4,
+        15.6,
+        15.1,
+        15.3,
+        13.5,
+        12.5,
+        12.5,
+        11.0,
+        10.7,
+        9.9,
+        9.5,
+        11.0,
+        8.9,
+        6.8,
+        5.3,
+        6.3,
+        5.9,
+        3.4,
+    ],
+    "region": [
+        "Asia",
+        "Asia",
+        "Asia",
+        "S. America",
+        "N. America",
+        "Africa",
+        "Asia",
+        "Asia",
+        "Asia",
+        "Asia",
+        "N. America",
+        "Asia",
+        "S. America",
+        "Europe",
+        "Asia",
+        "Africa",
+        "S. America",
+        "N. America",
+        "Europe",
+        "Europe",
+        "Asia",
+        "Asia",
+        "Europe",
+        "S. America",
+        "N. America",
+        "S. America",
+        "Oceania",
+        "N. America",
+        "Asia",
+        "Asia",
+    ],
+}
+
+df = pd.DataFrame(cities_data)
+
+# Simplified continent outlines for basemap
+continents = []
+
+# North America
+na_lon = [
+    -170,
+    -168,
+    -140,
+    -125,
+    -124,
+    -117,
+    -105,
+    -97,
+    -82,
+    -77,
+    -68,
+    -55,
+    -52,
+    -80,
+    -87,
+    -97,
+    -105,
+    -125,
+    -145,
+    -165,
+    -170,
+]
+na_lat = [60, 65, 70, 55, 48, 33, 25, 26, 25, 35, 45, 48, 45, 27, 30, 20, 22, 50, 60, 55, 60]
+for i in range(len(na_lon)):
+    continents.append({"continent": "N. America", "order": i, "lon": na_lon[i], "lat": na_lat[i]})
+
+# South America
+sa_lon = [-80, -68, -60, -50, -35, -40, -50, -55, -68, -72, -75, -80, -82, -80]
+sa_lat = [10, 12, 5, 0, -5, -22, -35, -52, -55, -18, -5, 0, 8, 10]
+for i in range(len(sa_lon)):
+    continents.append({"continent": "S. America", "order": i, "lon": sa_lon[i], "lat": sa_lat[i]})
+
+# Europe
+eu_lon = [-10, 0, 10, 20, 30, 40, 50, 60, 50, 35, 25, 20, 10, 0, -10, -10]
+eu_lat = [35, 37, 36, 35, 35, 40, 45, 55, 70, 70, 70, 65, 60, 50, 40, 35]
+for i in range(len(eu_lon)):
+    continents.append({"continent": "Europe", "order": i, "lon": eu_lon[i], "lat": eu_lat[i]})
+
+# Africa
+af_lon = [-17, -5, 10, 35, 50, 52, 43, 35, 30, 15, 0, -17, -17]
+af_lat = [15, 37, 37, 32, 12, 0, -25, -35, -35, -25, 5, 20, 15]
+for i in range(len(af_lon)):
+    continents.append({"continent": "Africa", "order": i, "lon": af_lon[i], "lat": af_lat[i]})
+
+# Asia
+as_lon = [60, 80, 100, 120, 140, 145, 140, 130, 105, 100, 80, 60, 45, 30, 25, 30, 35, 50, 60]
+as_lat = [55, 70, 75, 70, 55, 45, 35, 30, 0, 5, 10, 25, 30, 35, 42, 55, 70, 70, 55]
+for i in range(len(as_lon)):
+    continents.append({"continent": "Asia", "order": i, "lon": as_lon[i], "lat": as_lat[i]})
+
+# Australia
+au_lon = [113, 125, 135, 145, 152, 150, 140, 130, 115, 113]
+au_lat = [-22, -15, -12, -15, -25, -38, -38, -33, -35, -22]
+for i in range(len(au_lon)):
+    continents.append({"continent": "Australia", "order": i, "lon": au_lon[i], "lat": au_lat[i]})
+
+df_continents = pd.DataFrame(continents)
+
+# Region color palette (colorblind-safe, alphabetically ordered for plotnine)
+region_colors = [
+    "#9467BD",  # Africa - Purple
+    "#306998",  # Asia - Python Blue
+    "#FFD43B",  # Europe - Python Yellow
+    "#2CA02C",  # N. America - Green
+    "#17BECF",  # Oceania - Cyan
+    "#D62728",  # S. America - Red
+]
+
+# Create the bubble map
+plot = (
+    ggplot()
+    # Draw continent polygons as basemap
+    + geom_polygon(
+        aes(x="lon", y="lat", group="continent"),
+        data=df_continents,
+        fill="#E0E0E0",
+        color="#A0A0A0",
+        size=0.5,
+        alpha=0.8,
+    )
+    # Draw bubble markers sized by population
+    + geom_point(aes(x="longitude", y="latitude", color="region", size="population"), data=df, alpha=0.7, stroke=0.5)
+    # Scale size by area for accurate perception (bubble area proportional to value)
+    + scale_size_area(max_size=20, name="Population (M)")
+    + scale_color_manual(values=region_colors, name="Region")
+    + coord_fixed(ratio=1.0, xlim=(-180, 180), ylim=(-60, 80))
+    + labs(
+        title="World City Populations · bubble-map-geographic · plotnine · pyplots.ai",
+        x="Longitude (°)",
+        y="Latitude (°)",
+    )
+    + theme_minimal()
+    + theme(
+        figure_size=(16, 9),
+        plot_title=element_text(size=24, weight="bold"),
+        axis_title=element_text(size=20),
+        axis_text=element_text(size=16),
+        legend_title=element_text(size=18),
+        legend_text=element_text(size=14),
+        legend_position="right",
+        panel_grid_major=element_line(color="#CCCCCC", size=0.3, alpha=0.5),
+        panel_grid_minor=element_blank(),
+        panel_background=element_rect(fill="#D4E8F7", alpha=0.5),  # Ocean color
+    )
+)
+
+# Save at 300 DPI for 4800x2700 px output
+plot.save("plot.png", dpi=300, verbose=False)