The same plot in 14 other libraries — Python: Altair, Bokeh, lets-plot, Matplotlib, Plotly, plotnine, Pygal; R: ggplot2; Julia: Makie.jl; JavaScript: Chart.js, D3.js, Apache ECharts, Highcharts, MUI X Charts. Compare all 15 side by side: Connected Scatter Plot with Temporal Path in Python, R, Julia and JavaScript.
A scatter plot where data points are connected by lines in temporal order, revealing how two variables co-evolve over time. Unlike standard time series plots that show one variable against time, this plot encodes time as movement through 2D space, making cyclical patterns, regime changes, and directional trends visible. Popularized by the New York Times, it is a powerful tool for narrative data visualization.
""" anyplot.ai
scatter-connected-temporal: Connected Scatter Plot with Temporal Path
Library: seaborn 0.13.2 | Python 3.13.16
Quality: 86/100 | Updated: 2026-10-08
"""
import os
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd
import seaborn as sns
from matplotlib.collections import LineCollection
from matplotlib.colors import LinearSegmentedColormap
# Theme-adaptive chrome tokens (Imprint palette)
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: brand green (early) → blue (recent)
imprint_seq = LinearSegmentedColormap.from_list("imprint_seq", ["#009E73", "#4467A3"])
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,
"grid.color": INK,
"grid.alpha": 0.15,
"legend.facecolor": ELEVATED_BG,
"legend.edgecolor": INK_SOFT,
},
)
# Monthly closing price vs. average daily trading volume for a fictional stock, Jan 2023 – Dec 2024
months = pd.period_range("2023-01", periods=24, freq="M")
t = np.arange(len(months), dtype=float)
n = len(months)
price = np.array(
[
102,
108,
115,
121,
118,
109,
98,
92,
95,
104,
113,
125,
134,
129,
121,
117,
124,
136,
148,
155,
149,
141,
146,
158,
],
dtype=float,
)
volume = np.array(
[
4.1,
4.6,
5.8,
6.4,
5.9,
7.8,
9.6,
10.4,
8.2,
6.1,
5.3,
5.9,
7.1,
6.3,
5.5,
5.0,
5.4,
6.8,
8.1,
9.0,
7.4,
6.2,
5.8,
7.5,
]
)
X_LABEL = "Average Daily Volume (million shares)"
Y_LABEL = "Closing Price (USD)"
df = pd.DataFrame({X_LABEL: volume, Y_LABEL: price, "Month": t})
# Canvas: landscape 16:9 → exactly 3200×1800 px
fig, ax = plt.subplots(figsize=(8, 4.5), dpi=400)
fig.subplots_adjust(left=0.11, bottom=0.13, right=0.88, top=0.82)
# Background: seaborn KDE contours showing where price/volume spent most of the two years
sns.kdeplot(data=df, x=X_LABEL, y=Y_LABEL, levels=3, color=INK_SOFT, alpha=0.2, linewidths=0.7, bw_adjust=1.2, ax=ax)
# Temporal path: LineCollection with Imprint sequential gradient
norm = plt.Normalize(t[0], t[-1])
points = np.column_stack([volume, price])
segments = np.array([[points[i], points[i + 1]] for i in range(n - 1)])
lc = LineCollection(segments, cmap=imprint_seq, norm=norm, linewidths=1.8, zorder=2, alpha=0.85)
lc.set_array(t[:-1])
ax.add_collection(lc)
# Scatter markers with temporal hue encoding via seaborn continuous palette
sns.scatterplot(
data=df,
x=X_LABEL,
y=Y_LABEL,
hue="Month",
hue_norm=(t[0], t[-1]),
palette=imprint_seq,
s=120,
edgecolor=PAGE_BG,
linewidth=0.6,
legend=False,
zorder=3,
ax=ax,
)
# Directional arrow on temporal path (Jun→Jul 2023 segment makes time direction explicit)
i_dir = 5
ax.annotate(
"",
xy=(volume[i_dir + 1], price[i_dir + 1]),
xytext=(volume[i_dir], price[i_dir]),
arrowprops={"arrowstyle": "-|>", "color": INK, "lw": 1.4, "mutation_scale": 14},
zorder=4,
)
# Key time-point annotations: start, volume spike, rally peak, end
key_points = {0: (-8, -22), 7: (-6, -24), 11: (-34, 14), 19: (6, 14), n - 1: (14, 8)}
for idx, offset in key_points.items():
ax.annotate(
months[idx].strftime("%b %Y"),
(volume[idx], price[idx]),
textcoords="offset points",
xytext=offset,
fontsize=9,
fontweight="bold",
color=INK,
arrowprops={"arrowstyle": "->", "color": INK_SOFT, "lw": 0.9, "connectionstyle": "arc3,rad=0.2"},
)
# Narrative subtitle
ax.text(
0.5,
1.03,
"Fictional stock: how price and trading volume moved together, Jan 2023 – Dec 2024",
transform=ax.transAxes,
fontsize=8,
color=INK_SOFT,
ha="center",
va="bottom",
style="italic",
)
ax.set_xlabel(X_LABEL, fontsize=10)
ax.set_ylabel(Y_LABEL, fontsize=10)
ax.set_title(
"scatter-connected-temporal · python · seaborn · anyplot.ai", fontsize=12, fontweight="medium", pad=22, color=INK
)
ax.tick_params(axis="both", labelsize=8)
sns.despine(ax=ax)
ax.yaxis.grid(True, alpha=0.15, linewidth=0.6, color=INK)
ax.set_xlim(volume.min() - 0.8, volume.max() + 0.8)
ax.set_ylim(price.min() - 10, price.max() + 10)
# Colorbar for temporal scale
sm = plt.cm.ScalarMappable(cmap=imprint_seq, norm=norm)
sm.set_array([])
cbar = fig.colorbar(sm, ax=ax, pad=0.02, aspect=30, shrink=0.85)
cbar.set_ticks([0, 6, 12, 18, 23])
cbar.set_ticklabels([months[i].strftime("%b %Y") for i in (0, 6, 12, 18, 23)])
cbar.set_label("Month", fontsize=8, color=INK)
cbar.ax.tick_params(labelsize=7, colors=INK_SOFT)
cbar.outline.set_edgecolor(INK_SOFT)
plt.savefig(f"plot-{THEME}.png", dpi=400, facecolor=PAGE_BG)
Runnable source as JSON, for any HTTP client: https://api.anyplot.ai/specs/scatter-connected-temporal/seaborn/code. Any spec id and library id listed in llms-full.txt fit the same URL shape; every URL below is complete and callable.
{
"spec_id": "scatter-connected-temporal",
"language": "python",
"library": "seaborn",
"page": "https://anyplot.ai/scatter-connected-temporal/python/seaborn",
"hub": "https://anyplot.ai/scatter-connected-temporal",
"code_json": "https://api.anyplot.ai/specs/scatter-connected-temporal/seaborn/code",
"spec_json": "https://api.anyplot.ai/specs/scatter-connected-temporal",
"render_light_png": "https://storage.googleapis.com/anyplot-images/plots/scatter-connected-temporal/python/seaborn/plot-light.png",
"render_dark_png": "https://storage.googleapis.com/anyplot-images/plots/scatter-connected-temporal/python/seaborn/plot-dark.png",
"quality_score": 86.0,
"license": "MIT",
"guide": "https://anyplot.ai/llms.txt"
}Part of Connected Scatter Plot with Temporal Path on anyplot.ai.