A fundamental 2D scatter plot that displays the relationship between two numeric variables by plotting points on a Cartesian coordinate system. This visualization is essential for exploring correlations, identifying patterns, detecting outliers, and understanding the distribution of paired data points.
""" anyplot.ai
scatter-basic: Basic Scatter Plot
Library: matplotlib 3.11.0 | Python 3.13.14
Quality: 91/100 | Updated: 2026-06-25
"""
import os
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
# Theme tokens
THEME = os.getenv("ANYPLOT_THEME", "light")
PAGE_BG = "#FAF8F1" if THEME == "light" else "#1A1A17"
INK = "#1A1A17" if THEME == "light" else "#F0EFE8"
INK_SOFT = "#4A4A44" if THEME == "light" else "#B8B7B0"
INK_MUTED = "#6B6A63" if THEME == "light" else "#A8A79F"
BRAND = "#009E73" # Imprint palette position 1 — ALWAYS first series
mpl.rcParams.update(
{"font.family": "DejaVu Sans", "axes.titlepad": 18, "axes.labelpad": 12, "axes.unicode_minus": True}
)
# Data — study hours vs exam scores (r ~ 0.7)
np.random.seed(42)
study_hours = np.random.uniform(1, 12, 180)
exam_scores = np.clip(38 + study_hours * 4.5 + np.random.normal(0, 12, 180), 35, 100)
# Title
title = "scatter-basic · python · matplotlib · anyplot.ai"
n = len(title)
title_fontsize = max(8, round(12 * 67 / n)) if n > 67 else 12
# Plot
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400, facecolor=PAGE_BG)
ax.set_facecolor(PAGE_BG)
ax.scatter(study_hours, exam_scores, s=130, alpha=0.65, color=BRAND, edgecolors=PAGE_BG, linewidths=0.6, zorder=3)
# Trend line + 95% confidence band — showcase fill_between analytical capability
coeffs = np.polyfit(study_hours, exam_scores, 1)
x_line = np.linspace(study_hours.min(), study_hours.max(), 200)
y_line = np.polyval(coeffs, x_line)
n_pts = len(study_hours)
x_mean = np.mean(study_hours)
Sxx = np.sum((study_hours - x_mean) ** 2)
s_res = np.sqrt(np.sum((exam_scores - np.polyval(coeffs, study_hours)) ** 2) / (n_pts - 2))
se_band = s_res * np.sqrt(1 / n_pts + (x_line - x_mean) ** 2 / Sxx)
ax.fill_between(x_line, y_line - 1.96 * se_band, y_line + 1.96 * se_band, color=INK_SOFT, alpha=0.12, zorder=0)
ax.plot(x_line, y_line, color=INK_SOFT, linewidth=2.2, alpha=0.75, zorder=1)
# Style
ax.set_xlabel("Study Hours per Week", fontsize=10, color=INK)
ax.set_ylabel("Exam Score (%)", fontsize=10, color=INK)
ax.set_title(title, fontsize=title_fontsize, fontweight="medium", color=INK)
ax.tick_params(axis="both", which="both", labelsize=8, colors=INK_SOFT, length=0, pad=8)
ax.spines["top"].set_visible(False)
ax.spines["right"].set_visible(False)
for spine in ("left", "bottom"):
ax.spines[spine].set_color(INK_SOFT)
ax.spines[spine].set_linewidth(0.8)
ax.yaxis.grid(True, alpha=0.12, linewidth=0.6, color=INK)
ax.set_axisbelow(True)
ax.margins(x=0.04, y=0.08)
# Pearson r footnote
r = np.corrcoef(study_hours, exam_scores)[0, 1]
fig.text(
0.985, 0.03, f"n = {len(study_hours)} · Pearson r = {r:.2f}", fontsize=8, color=INK_MUTED, ha="right", va="bottom"
)
fig.subplots_adjust(left=0.09, right=0.97, top=0.90, bottom=0.14)
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
Part of Basic Scatter Plot on anyplot.ai.