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: plotnine 0.15.5 | Python 3.13.13
Quality: 88/100 | Updated: 2026-06-09
"""
import os
import sys
# This file is named after the library it imports — remove its directory from
# sys.path so Python finds the installed plotnine package, not this script.
_here = os.path.dirname(os.path.abspath(__file__))
if sys.path and os.path.abspath(sys.path[0]) == _here:
sys.path.pop(0)
import numpy as np
import pandas as pd
from plotnine import (
aes,
annotate,
arrow,
element_blank,
element_line,
element_rect,
element_text,
geom_path,
geom_point,
geom_segment,
geom_text,
ggplot,
labs,
scale_color_gradient,
scale_fill_gradient,
scale_x_continuous,
scale_y_continuous,
theme,
theme_minimal,
)
# Theme-adaptive chrome tokens (Imprint palette — see prompts/default-style-guide.md)
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 gradient: brand green → blue (temporal progression low→high)
SEQ_LOW = "#009E73"
SEQ_HIGH = "#4467A3"
RECESSION_COLOR = "#AE3030" # Imprint matte red — semantic bad/crisis anchor
# Data: US unemployment rate vs inflation rate (Phillips curve), 1990-2020
np.random.seed(42)
years = np.arange(1990, 2021)
n = len(years)
unemployment = np.array(
[
5.6,
6.8,
7.5,
6.9,
6.1,
5.6,
5.4,
4.9,
4.5,
4.2,
4.0,
4.7,
5.8,
6.0,
5.5,
5.1,
4.6,
4.6,
5.8,
9.3,
9.6,
8.9,
8.1,
7.4,
6.2,
5.3,
4.9,
4.4,
3.9,
3.7,
8.1,
]
)
inflation = np.array(
[
5.4,
4.2,
3.0,
3.0,
2.6,
2.8,
3.0,
2.3,
1.6,
2.2,
3.4,
2.8,
1.6,
2.3,
2.7,
3.4,
3.2,
2.8,
3.8,
-0.4,
1.6,
3.2,
2.1,
1.5,
1.6,
0.1,
1.3,
2.1,
2.4,
1.8,
1.2,
]
)
df = pd.DataFrame(
{"Unemployment": unemployment, "Inflation": inflation, "Year": years, "Year_num": np.arange(n, dtype=float)}
)
# Four well-separated key years to anchor temporal reading (avoids central congestion)
label_config = {1990: (-0.42, 0.55), 2000: (0.32, 0.55), 2010: (0.42, -0.60), 2020: (0.42, 0.55)}
label_rows = []
for yr, (dx, dy) in label_config.items():
row = df[df["Year"] == yr].iloc[0]
label_rows.append({"x_label": row["Unemployment"] + dx, "y_label": row["Inflation"] + dy, "Label": str(yr)})
df_labels = pd.DataFrame(label_rows)
recession_point = df[df["Year"] == 2009].copy()
# Directional arrow segments at inflection points to reinforce temporal flow
arrow_years = [1992, 1997, 2011, 2017]
arrow_rows = []
for yr in arrow_years:
r1 = df[df["Year"] == yr].iloc[0]
r2 = df[df["Year"] == yr + 1].iloc[0]
arrow_rows.append(
{
"x": r1["Unemployment"],
"y": r1["Inflation"],
"xend": r2["Unemployment"],
"yend": r2["Inflation"],
"Year_num": r1["Year_num"],
}
)
df_arrows = pd.DataFrame(arrow_rows)
# Plot — geom_path preserves temporal ordering (not geom_line which sorts by x)
plot = (
ggplot(df, aes(x="Unemployment", y="Inflation"))
+ geom_path(aes(color="Year_num"), size=1.2)
+ geom_segment(
data=df_arrows,
mapping=aes(x="x", y="y", xend="xend", yend="yend", color="Year_num"),
arrow=arrow(length=0.1, type="open"),
show_legend=False,
size=1.2,
)
+ geom_point(aes(fill="Year_num"), size=4.0, color=PAGE_BG, stroke=0.6, show_legend=False)
+ geom_point(
data=recession_point,
mapping=aes(x="Unemployment", y="Inflation"),
size=7.5,
color=RECESSION_COLOR,
fill="none",
stroke=1.8,
)
+ annotate(
"text",
x=recession_point["Unemployment"].values[0] - 0.9,
y=recession_point["Inflation"].values[0] + 0.75,
label="2009 Recession",
size=3.5,
fontweight="bold",
color=RECESSION_COLOR,
)
+ geom_text(
aes(x="x_label", y="y_label", label="Label"),
data=df_labels,
size=3.5,
fontweight="bold",
color=INK_SOFT,
inherit_aes=False,
)
+ scale_color_gradient(
low=SEQ_LOW, high=SEQ_HIGH, name="Year", breaks=[0, 10, 20, 30], labels=["1990", "2000", "2010", "2020"]
)
+ scale_fill_gradient(low=SEQ_LOW, high=SEQ_HIGH)
+ scale_x_continuous(breaks=range(3, 11), expand=(0.05, 0.3))
+ scale_y_continuous(breaks=range(-1, 7), expand=(0.05, 0.3))
+ labs(
x="Unemployment Rate (%)",
y="Inflation Rate (%)",
title="scatter-connected-temporal · python · plotnine · anyplot.ai",
)
+ theme_minimal()
+ theme(
figure_size=(8, 4.5),
text=element_text(size=7, color=INK),
axis_title=element_text(size=10, color=INK),
axis_text=element_text(size=8, color=INK_SOFT),
plot_title=element_text(size=12, fontweight="bold", color=INK),
legend_title=element_text(size=8, color=INK),
legend_text=element_text(size=7, color=INK_SOFT),
legend_position="right",
legend_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
panel_background=element_rect(fill=PAGE_BG),
panel_grid_major=element_line(color=INK, size=0.3, alpha=0.15),
panel_grid_minor=element_blank(),
panel_border=element_blank(),
axis_line=element_blank(),
)
)
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Part of Connected Scatter Plot with Temporal Path on anyplot.ai.