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: letsplot 4.10.1 | Python 3.13.13
Quality: 88/100 | Updated: 2026-06-09
"""
import os
import numpy as np
import pandas as pd
from lets_plot import (
LetsPlot,
aes,
arrow,
element_blank,
element_line,
element_rect,
element_text,
geom_path,
geom_point,
geom_segment,
geom_text,
ggplot,
ggsave,
ggsize,
labs,
layer_tooltips,
scale_color_gradient,
scale_fill_gradient,
scale_x_continuous,
scale_y_continuous,
theme,
theme_minimal,
)
LetsPlot.setup_html()
# Theme tokens — Imprint palette, theme-adaptive chrome
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"
GRID = "rgba(26,26,23,0.12)" if THEME == "light" else "rgba(240,239,232,0.12)"
# Imprint sequential colormap: brand green (early) → blue (recent)
SEQ_LOW = "#009E73" # Imprint position 1 — start of temporal path
SEQ_HIGH = "#4467A3" # Imprint position 3 — end of temporal path
# Data — Phillips curve dynamics, unemployment vs inflation 1990–2023
np.random.seed(42)
years = np.arange(1990, 2024)
n = len(years)
unemployment = np.concatenate(
[
np.linspace(5.6, 4.0, 10) + np.random.randn(10) * 0.3, # 1990s decline
np.linspace(4.0, 6.3, 4) + np.random.randn(4) * 0.2, # 2001 recession
np.linspace(6.3, 4.4, 6) + np.random.randn(6) * 0.2, # mid-2000s recovery
np.linspace(4.4, 10.0, 3) + np.random.randn(3) * 0.3, # 2008 crisis
np.linspace(10.0, 3.5, 11) + np.random.randn(11) * 0.3, # long recovery
]
)
inflation = np.concatenate(
[
np.linspace(5.4, 2.3, 10) + np.random.randn(10) * 0.4, # 1990s disinflation
np.linspace(2.3, 1.6, 4) + np.random.randn(4) * 0.3, # low inflation
np.linspace(1.6, 3.8, 6) + np.random.randn(6) * 0.3, # rising
np.linspace(3.8, -0.4, 3) + np.random.randn(3) * 0.4, # deflation scare
np.linspace(-0.4, 6.5, 11) + np.random.randn(11) * 0.5, # recovery to post-covid
]
)
df = pd.DataFrame(
{
"unemployment": unemployment,
"inflation": inflation,
"year": years,
"year_label": [str(y) for y in years],
"time_idx": np.arange(n),
}
)
# Annotate key economic turning points
key_years = {1990, 2000, 2007, 2009, 2020, 2023}
df_labels = df[df["year"].isin(key_years)].copy()
nudge_map = {
1990: (0.35, 0.7),
2000: (0.35, -0.7),
2007: (0.35, -0.7),
2009: (-0.55, 0.7),
2020: (-0.35, 0.7),
2023: (0.35, 0.7),
}
df_endpoints = df[df["year"].isin([1990, 2023])].copy()
df_labels["label_x"] = df_labels.apply(lambda r: r["unemployment"] + nudge_map.get(r["year"], (0, 0))[0], axis=1)
df_labels["label_y"] = df_labels.apply(lambda r: r["inflation"] + nudge_map.get(r["year"], (0, 0))[1], axis=1)
# Direction arrow at terminal segment of the path
last = df.iloc[-1]
prev = df.iloc[-2]
arrow_df = pd.DataFrame(
{"x": [prev["unemployment"]], "y": [prev["inflation"]], "xend": [last["unemployment"]], "yend": [last["inflation"]]}
)
title = "scatter-connected-temporal · python · letsplot · anyplot.ai"
# Plot
plot = (
ggplot(df, aes(x="unemployment", y="inflation"))
+ geom_path(aes(color="time_idx"), size=1.5, alpha=0.75, tooltips="none")
+ geom_segment(
data=arrow_df,
mapping=aes(x="x", y="y", xend="xend", yend="yend"),
color=SEQ_HIGH,
size=2.2,
arrow=arrow(angle=25, length=10, type="closed"),
)
+ geom_point(
aes(fill="time_idx"),
color=PAGE_BG,
size=3.5,
stroke=1.0,
shape=21,
alpha=0.9,
tooltips=layer_tooltips()
.line("Year|@year")
.line("Unemployment|@{unemployment}{.1f}%")
.line("Inflation|@{inflation}{.1f}%"),
)
+ geom_point(
data=df_endpoints,
mapping=aes(x="unemployment", y="inflation", fill="time_idx"),
color=INK,
size=6.0,
stroke=1.8,
shape=21,
alpha=1.0,
)
+ geom_text(
data=df_labels,
mapping=aes(x="label_x", y="label_y", label="year_label"),
size=6,
color=INK,
family="monospace",
fontface="bold",
)
+ scale_color_gradient(
low=SEQ_LOW, high=SEQ_HIGH, name="Year", breaks=[0, (n - 1) / 2, n - 1], labels=["1990", "2006", "2023"]
)
+ scale_fill_gradient(low=SEQ_LOW, high=SEQ_HIGH, guide="none")
+ scale_x_continuous(expand=[0.06, 0])
+ scale_y_continuous(expand=[0.08, 0])
+ labs(
x="Unemployment Rate (%)",
y="Inflation Rate (%)",
title=title,
subtitle="Phillips Curve: unemployment vs inflation, 1990–2023",
)
+ ggsize(800, 450)
+ theme_minimal()
+ theme(
axis_text=element_text(size=10, color=INK_SOFT),
axis_title=element_text(size=12, color=INK),
plot_title=element_text(size=16, color=INK),
plot_subtitle=element_text(size=10, color=INK_SOFT),
legend_text=element_text(size=10, color=INK_SOFT),
legend_title=element_text(size=12, color=INK),
panel_grid_major=element_line(color=GRID, size=0.3),
panel_grid_minor=element_blank(),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
panel_border=element_blank(),
plot_margin=[20, 20, 10, 10],
)
)
# Save — PNG at 3200×1800 (800×450 × scale=4), plus interactive HTML
ggsave(plot, f"plot-{THEME}.png", path=".", scale=4)
ggsave(plot, f"plot-{THEME}.html", path=".")
Part of Connected Scatter Plot with Temporal Path on anyplot.ai.