Basic Contour Plot — Seaborn

A contour plot displays isolines (level curves) of a 2D scalar field, connecting points of equal value across a surface. It transforms 3D data into an intuitive 2D representation, making it easy to identify regions of high and low values, gradients, and patterns. Contour plots are essential for visualizing continuous surfaces where the relationship between X, Y coordinates and a Z value needs to be understood.

Basic Contour Plot rendered with Seaborn

Python source (Seaborn)

""" anyplot.ai
contour-basic: Basic Contour Plot
Library: seaborn 0.13.2 | Python 3.13.14
Quality: 89/100 | Updated: 2026-06-25
"""

import os

import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.colors import LinearSegmentedColormap
from scipy.stats import gaussian_kde


# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
ELEVATED_BG = "#FFFDF6" if THEME == "light" else "#242420"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"

# Imprint sequential colormap for continuous density data
imprint_seq = LinearSegmentedColormap.from_list("imprint_seq", ["#009E73", "#4467A3"])

# Data — bivariate distribution of weather-station readings across two synoptic regimes
np.random.seed(42)
cold_front = np.random.multivariate_normal(mean=[4.8, 1021.5], cov=[[3.2, -1.3], [-1.3, 7.8]], size=1500)
warm_front = np.random.multivariate_normal(mean=[11.6, 1013.2], cov=[[6.0, 2.0], [2.0, 5.0]], size=900)
readings = pd.DataFrame(np.vstack([cold_front, warm_front]), columns=["Wind Speed (m/s)", "Barometric Pressure (hPa)"])

# Theme
sns.set_theme(
    style="ticks",
    rc={
        "figure.facecolor": PAGE_BG,
        "axes.facecolor": PAGE_BG,
        "axes.edgecolor": INK_SOFT,
        "axes.labelcolor": INK,
        "text.color": INK,
        "xtick.color": INK_SOFT,
        "ytick.color": INK_SOFT,
        "axes.linewidth": 0.9,
        "axes.axisbelow": True,
    },
)

fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400)
fig.set_facecolor(PAGE_BG)
ax.set_facecolor(PAGE_BG)

# Filled KDE contours — seaborn's kdeplot with integrated colorbar (Imprint sequential cmap)
sns.kdeplot(
    data=readings,
    x="Wind Speed (m/s)",
    y="Barometric Pressure (hPa)",
    fill=True,
    cmap=imprint_seq,
    thresh=0.02,
    levels=12,
    cbar=True,
    cbar_kws={"shrink": 0.85, "pad": 0.02, "label": "Reading Density"},
    ax=ax,
)

# Isoline overlay via scipy KDE grid — returns ContourSet needed for ax.clabel
x_vals = readings["Wind Speed (m/s)"].values
y_vals = readings["Barometric Pressure (hPa)"].values
kde = gaussian_kde(np.vstack([x_vals, y_vals]))
x_grid = np.linspace(x_vals.min() - 1, x_vals.max() + 1, 80)
y_grid = np.linspace(y_vals.min() - 2, y_vals.max() + 2, 80)
XX, YY = np.meshgrid(x_grid, y_grid)
ZZ = kde(np.vstack([XX.ravel(), YY.ravel()])).reshape(XX.shape)
cs = ax.contour(XX, YY, ZZ, levels=10, colors=INK, alpha=0.35, linewidths=0.7)
ax.clabel(cs, levels=cs.levels[2::3], inline=True, fontsize=8, fmt="%.3f", colors=INK)

# Subtle reference line separating the two regimes
ax.axhline(1017, color=INK_SOFT, linewidth=0.8, linestyle=":", alpha=0.5)

# Mode annotations to guide the viewer
ax.text(4.8, 1020.2, "Cold Front", fontsize=8, color=INK_SOFT, ha="center", style="italic")
ax.text(11.6, 1014.5, "Warm Front", fontsize=8, color=INK_SOFT, ha="center", style="italic")

# Subtle grid for value reading aid
ax.yaxis.grid(True, color=INK, alpha=0.12, linewidth=0.6, linestyle="--")
ax.set_axisbelow(True)

# Style
title = "contour-basic · python · seaborn · anyplot.ai"
ax.set_title(title, fontsize=12, fontweight="medium", color=INK, pad=14)
ax.set_xlabel("Wind Speed (m/s)", fontsize=10, color=INK, labelpad=10)
ax.set_ylabel("Barometric Pressure (hPa)", fontsize=10, color=INK, labelpad=10)
ax.tick_params(axis="both", labelsize=8, colors=INK_SOFT, length=0)
sns.despine(ax=ax)

# Colorbar chrome (theme-adaptive)
cbar_ax = fig.axes[-1]
cbar_ax.tick_params(labelsize=8, colors=INK_SOFT, length=0)
cbar_ax.yaxis.label.set_color(INK)
cbar_ax.yaxis.label.set_fontsize(9)
cbar_ax.set_facecolor(ELEVATED_BG)
for spine in cbar_ax.spines.values():
    spine.set_color(INK_SOFT)
    spine.set_linewidth(0.8)

plt.tight_layout()
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)

Part of Basic Contour Plot on anyplot.ai.

Other implementations