Mapping

 Let's ease into things. provide code for a world map that shows(with color) - by region - if there are more men than women, or more women than men, or roughly equal...

Here’s a starter Python script that will generate a world map, coloring each region based on whether there are more men than women, more women than men, or roughly equal.

import matplotlib.pyplot as plt

import cartopy.crs as ccrs

import cartopy.feature as cfeature

# Simplified regional sex ratio data (males per 100 females)

# Source: UN World Population Prospects 2025

region_ratios = {

    "Asia": 103,        # More men

    "Europe": 97,       # More women

    "Africa": 101,      # Slightly more men

    "North America": 98, # More women

    "South America": 99, # Roughly equal

    "Oceania": 100,     # Equal

}

# Define colors

def get_color(ratio):

    if ratio > 101:   # More men

        return "blue"

    elif ratio < 99:  # More women

        return "pink"

    else:             # Roughly equal

        return "gray"

region_colors = {region: get_color(ratio) for region, ratio in region_ratios.items()}

# Create world map

fig, ax = plt.subplots(figsize=(12, 6), subplot_kw={'projection': ccrs.Robinson()})

ax.add_feature(cfeature.LAND, facecolor="lightgray")

ax.add_feature(cfeature.BORDERS, linestyle=":")

ax.coastlines()

# Annotate regions with color markers

region_coords = {

    "Asia": (90, 30),

    "Europe": (15, 50),

    "Africa": (20, 0),

    "North America": (-100, 40),

    "South America": (-60, -15),

    "Oceania": (140, -25),

}

for region, (lon, lat) in region_coords.items():

    ax.plot(lon, lat, marker="o", markersize=20,

            color=region_colors[region],

            transform=ccrs.PlateCarree())

    ax.text(lon, lat, region, fontsize=9,

            transform=ccrs.PlateCarree(),

            ha="center", va="center", color="white")

plt.title("World Regions by Sex Ratio (2025)")

plt.show()

🔍 How It Works

Data source: UN estimates for 2025 show the global sex ratio is 101.07 males per 100 females, with regional variation.

Color logic:

Blue: More men than women (ratio > 101).

Pink: More women than men (ratio < 99).

Gray: Roughly equal (99–101).

Markers: Each region is represented by a colored circle placed approximately at its geographic center.

⚖️ Notes

This is a simplified regional view. For country-level detail, you’d replace the region_ratios dictionary with actual country data (e.g., from World Population Review or StatisticsTimes).

                                                                           

You can refine the thresholds (e.g., ±0.5 instead of ±1) depending on how strict you want “roughly equal” to be.

For a more polished visualization, you could overlay actual polygons (continents or countries) instead of markers.

📌 Takeaway: This script gives you a quick, visual overview of sex ratio differences by region. You can expand it to country-level maps for finer detail.

Would you like me to adapt this to country-level choropleth shading (so each country is colored according to its sex ratio, not just regions)? That would give a much richer picture.

Is there an alternative to PlateCarree() that would still work?

Yes — PlateCarree() is just one of several coordinate reference systems (CRS) available in Cartopy. It’s the simplest because it assumes latitude/longitude are already in degrees, but you can swap it for other projections depending on your needs.

                                                                                 

PlateCarree() is identity mapping: it directly interprets your lat/lon coordinates.

Other projections (like Robinson or Mercator) transform coordinates onto a curved surface, which can change how distances, areas, and shapes look.

                                                                           

📌 Takeaway: You can swap PlateCarree() for any Cartopy projection class (ccrs.Mercator(), ccrs.Robinson(), etc.), depending on whether you want accuracy (equal-area, conformal) or aesthetics (Robinson, Orthographic).

Would you like me to show the same sex-ratio map in two different projections side by side (e.g., PlateCarree vs Robinson), so you can see how the choice changes the look?