A choropleth map visualizes data by shading geographic regions (countries, states, or counties) according to a measured variable. This technique is ideal for showing regional patterns and spatial distributions, making it easy to identify areas with high or low values at a glance. The color intensity represents the data magnitude, creating an intuitive way to understand geographic variation.
""" anyplot.ai
choropleth-basic: Choropleth Map with Regional Coloring
Library: seaborn 0.13.2 | Python 3.13.13
Quality: 82/100 | Updated: 2026-05-15
"""
import matplotlib.patches as mpatches
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
# Data: US states tile grid map with economic data (GDP growth rate %)
# Tile grid maps are a recognized cartogram technique that ensures equal visual weight per region
np.random.seed(42)
# State data with grid positions (row, col) approximating US map layout
# Values represent GDP growth rate (%) - None indicates missing data
states_data = {
# Row 0 (top - Pacific Northwest, Northern states)
"WA": (0, 1, 3.2),
"MT": (0, 3, 1.8),
"ND": (0, 5, 2.1),
"MN": (0, 6, 2.9),
"WI": (0, 7, 2.3),
"MI": (0, 8, 1.9),
"NY": (0, 10, 3.5),
"VT": (0, 11, 1.5),
"ME": (0, 12, 1.2),
# Row 1
"OR": (1, 1, 2.8),
"ID": (1, 2, 3.1),
"WY": (1, 3, 0.9),
"SD": (1, 5, 1.7),
"IA": (1, 6, 2.0),
"IL": (1, 7, 2.5),
"IN": (1, 8, 2.2),
"OH": (1, 9, 1.8),
"PA": (1, 10, 2.1),
"MA": (1, 11, 3.8),
"NH": (1, 12, 2.4),
# Row 2
"NV": (2, 1, 4.1),
"UT": (2, 2, 4.5),
"CO": (2, 3, 3.9),
"NE": (2, 5, 1.6),
"KS": (2, 6, 1.4),
"MO": (2, 7, 1.9),
"KY": (2, 8, 2.0),
"WV": (2, 9, 0.5),
"VA": (2, 10, 3.2),
"MD": (2, 11, 2.8),
"NJ": (2, 12, 2.6),
# Row 3
"CA": (3, 1, 3.7),
"AZ": (3, 2, 4.2),
"NM": (3, 3, 1.3),
"OK": (3, 5, 1.1),
"AR": (3, 6, 1.5),
"TN": (3, 7, 3.0),
"NC": (3, 9, 3.4),
"SC": (3, 10, 2.7),
"DE": (3, 11, 2.3),
"CT": (3, 12, 2.9),
# Row 4 (bottom - Southern states)
"TX": (4, 3, 3.6),
"LA": (4, 5, 0.8),
"MS": (4, 6, 0.6),
"AL": (4, 7, 1.7),
"GA": (4, 8, 3.3),
"FL": (4, 10, 3.8),
"RI": (4, 12, 2.0),
# Missing data examples (show as gray/hatched pattern per spec)
"PR": (4, 13, None), # Puerto Rico - no data
# Alaska and Hawaii (offset)
"AK": (5, 0, 0.4),
"HI": (5, 2, 2.5),
"DC": (3, 13, None), # DC - no data available
}
# Create DataFrame
rows = []
for state, (r, c, val) in states_data.items():
rows.append({"state": state, "row": r, "col": c, "gdp_growth": val})
df = pd.DataFrame(rows)
# Create grid matrix for heatmap (6 rows x 14 cols)
n_rows, n_cols = 6, 14
grid = np.full((n_rows, n_cols), np.nan)
state_labels = np.full((n_rows, n_cols), "", dtype=object)
missing_mask = np.zeros((n_rows, n_cols), dtype=bool)
for _, row in df.iterrows():
r, c = int(row["row"]), int(row["col"])
if row["gdp_growth"] is not None and not pd.isna(row["gdp_growth"]):
grid[r, c] = row["gdp_growth"]
else:
missing_mask[r, c] = True # Mark as missing data
state_labels[r, c] = row["state"]
# Set up seaborn styling
sns.set_theme(style="white", context="talk", font_scale=1.2)
# Create figure with appropriate size for 4800x2700 output
fig, ax = plt.subplots(figsize=(16, 9))
# Create heatmap using seaborn with masked values for empty cells
empty_mask = np.isnan(grid) & ~missing_mask # True for truly empty cells (no state)
heatmap = sns.heatmap(
grid,
mask=empty_mask,
cmap="YlGnBu",
annot=False, # We'll add custom annotations
cbar=True,
cbar_kws={"label": "GDP Growth Rate (%)", "shrink": 0.7, "aspect": 20, "pad": 0.02},
linewidths=1.5, # Reduced from 3 to be less overwhelming
linecolor="white",
square=True,
vmin=0,
vmax=5,
ax=ax,
)
# Add gray cells for missing data with hatching pattern
for i in range(n_rows):
for j in range(n_cols):
if missing_mask[i, j]:
# Draw gray rectangle with hatching for missing data
rect = mpatches.Rectangle(
(j, i), 1, 1, fill=True, facecolor="#d0d0d0", edgecolor="white", linewidth=1.5, hatch="///", zorder=2
)
ax.add_patch(rect)
# Customize colorbar
cbar = heatmap.collections[0].colorbar
cbar.ax.tick_params(labelsize=18)
cbar.set_label("GDP Growth Rate (%)", fontsize=20, labelpad=10)
# Add state code and value annotations
for i in range(n_rows):
for j in range(n_cols):
if state_labels[i, j]: # If there's a state here
# State code (larger, bold)
ax.text(
j + 0.5,
i + 0.35,
state_labels[i, j],
ha="center",
va="center",
fontsize=20,
fontweight="bold",
color="#1a1a1a" if not missing_mask[i, j] else "#666666",
)
# Value or "N/A" for missing data (increased from 13pt to 16pt)
if not missing_mask[i, j] and not np.isnan(grid[i, j]):
ax.text(
j + 0.5,
i + 0.7,
f"{grid[i, j]:.1f}%",
ha="center",
va="center",
fontsize=16,
color="#333333",
fontweight="medium",
)
else:
ax.text(
j + 0.5, i + 0.7, "N/A", ha="center", va="center", fontsize=16, color="#888888", fontstyle="italic"
)
# Styling
ax.set_title("choropleth-basic · seaborn · pyplots.ai", fontsize=26, pad=20, fontweight="bold")
# Remove axis labels and ticks (tile grid doesn't need them)
ax.set_xticks([])
ax.set_yticks([])
ax.set_xlabel("")
ax.set_ylabel("")
# Add legend for missing data
missing_patch = mpatches.Patch(facecolor="#d0d0d0", edgecolor="white", hatch="///", label="No Data Available")
ax.legend(handles=[missing_patch], loc="lower left", fontsize=16, framealpha=0.9)
# Add subtitle explaining the visualization
ax.text(
0.5,
-0.06,
"US States GDP Growth Rate (%) — Tile Grid Choropleth Map",
ha="center",
va="top",
fontsize=18,
color="#555555",
transform=ax.transAxes,
)
plt.tight_layout()
plt.savefig("plot.png", dpi=300, bbox_inches="tight")
Part of Choropleth Map with Regional Coloring on anyplot.ai.