A dashboard layout displaying multiple metric tiles in a responsive grid, where each tile shows a KPI value with its label, an embedded sparkline showing recent trend, and a change indicator (up/down arrow with percentage). This visualization is essential for operations monitoring and business dashboards where multiple metrics need to be tracked simultaneously at a glance. The combination of current value, trend visualization, and change direction provides comprehensive metric context in a compact format.
""" anyplot.ai
dashboard-metrics-tiles: Real-Time Dashboard Tiles
Library: plotnine 0.15.4 | Python 3.13.13
Quality: 86/100 | Updated: 2026-05-21
"""
import os
import numpy as np
import pandas as pd
from plotnine import (
aes,
element_blank,
element_rect,
element_text,
facet_wrap,
geom_line,
geom_rect,
geom_text,
ggplot,
labs,
scale_color_identity,
scale_x_continuous,
scale_y_continuous,
theme,
theme_void,
)
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"
SPARKLINE_BG = "#EDEAE0" if THEME == "light" else "#2A2A26"
# imprint semantic anchors
STATUS_COLORS = {
"good": "#009E73", # green
"warning": "#DDCC77", # amber
"critical": "#AE3030", # red
}
np.random.seed(42)
metrics = [
{"name": "CPU Usage", "value": 45, "unit": "%", "change": -5.2, "status": "good"},
{"name": "Memory", "value": 72, "unit": "%", "change": 8.3, "status": "warning"},
{"name": "Response Time", "value": 120, "unit": "ms", "change": -15.4, "status": "good"},
{"name": "Active Users", "value": 1284, "unit": "", "change": 12.7, "status": "good"},
{"name": "Error Rate", "value": 0.8, "unit": "%", "change": 45.2, "status": "critical"},
{"name": "Throughput", "value": 3450, "unit": "req/s", "change": -2.1, "status": "good"},
]
# Generate sparkline history for each metric
n_points = 20
sparkline_data = []
for metric in metrics:
base_value = metric["value"]
trend_direction = -1 if metric["change"] < 0 else 1
noise = np.random.randn(n_points) * (base_value * 0.1)
trend = np.linspace(0, trend_direction * abs(metric["change"]) / 100 * base_value, n_points)
history = base_value - trend + noise
hist_min, hist_max = history.min(), history.max()
history_norm = (history - hist_min) / (hist_max - hist_min) if hist_max > hist_min else np.ones(n_points) * 0.5
history_scaled = history_norm * 0.22 + 0.03
for j, val in enumerate(history_scaled):
sparkline_data.append(
{
"metric_name": metric["name"],
"x": j / (n_points - 1) * 18 + 1,
"y": val,
"line_color": STATUS_COLORS[metric["status"]],
}
)
df_sparkline = pd.DataFrame(sparkline_data)
# Build label rows with theme-adaptive and status-aware colors
label_data = []
for metric in metrics:
value = metric["value"]
value_str = f"{value:,.0f}" if value >= 1000 else (f"{value:.1f}" if value < 1 else f"{value:.0f}")
value_display = f"{value_str}{metric['unit']}"
change = metric["change"]
arrow = "▲" if change >= 0 else "▼"
change_str = f"{arrow} {abs(change):.1f}%"
# Context-aware change color: for error rate, up = bad
if metric["name"] == "Error Rate":
change_color = "#C475FD" if change >= 0 else "#009E73"
else:
change_color = "#009E73" if change >= 0 else "#C475FD"
label_data.append(
{
"metric_name": metric["name"],
"metric_label": metric["name"],
"value_display": value_display,
"change_str": change_str,
"status_color": STATUS_COLORS[metric["status"]],
"change_color": change_color,
"ink_color": INK,
"label_x": 10,
"label_y": 0.88,
"value_x": 10,
"value_y": 0.62,
"change_x": 10,
"change_y": 0.38,
}
)
df_labels = pd.DataFrame(label_data)
all_metrics = [m["name"] for m in metrics]
df_sparkline["metric_name"] = pd.Categorical(df_sparkline["metric_name"], categories=all_metrics, ordered=True)
df_labels["metric_name"] = pd.Categorical(df_labels["metric_name"], categories=all_metrics, ordered=True)
bg_data = [{"metric_name": m["name"], "xmin": 0, "xmax": 20, "ymin": 0, "ymax": 0.28} for m in metrics]
df_bg = pd.DataFrame(bg_data)
df_bg["metric_name"] = pd.Categorical(df_bg["metric_name"], categories=all_metrics, ordered=True)
# Plot
plot = (
ggplot()
# Sparkline area background
+ geom_rect(df_bg, aes(xmin="xmin", xmax="xmax", ymin="ymin", ymax="ymax"), fill=SPARKLINE_BG)
# Sparklines — thicker for visibility, colored by status
+ geom_line(df_sparkline, aes(x="x", y="y", color="line_color"), size=1.4, alpha=0.9)
# Metric label
+ geom_text(
df_labels,
aes(x="label_x", y="label_y", label="metric_label", color="ink_color"),
size=7,
ha="center",
va="center",
fontweight="bold",
)
# Main value (colored by status)
+ geom_text(
df_labels,
aes(x="value_x", y="value_y", label="value_display", color="status_color"),
size=13,
ha="center",
va="center",
fontweight="bold",
)
# Change indicator (colored green/red by favorable/unfavorable direction)
+ geom_text(
df_labels,
aes(x="change_x", y="change_y", label="change_str", color="change_color"),
size=6,
ha="center",
va="center",
)
# Use hex values directly from data columns
+ scale_color_identity()
+ facet_wrap("~metric_name", ncol=3)
+ scale_x_continuous(limits=(0, 20), expand=(0.02, 0.02))
+ scale_y_continuous(limits=(0, 1), expand=(0.02, 0.02))
+ labs(title="dashboard-metrics-tiles · python · plotnine · anyplot.ai")
+ theme_void()
+ theme(
figure_size=(8, 4.5),
plot_title=element_text(size=10, ha="center", color=INK, margin={"b": 12}),
strip_text=element_blank(),
strip_background=element_blank(),
panel_spacing=0.12,
panel_background=element_rect(fill=ELEVATED_BG, color=INK_SOFT, size=0.5),
plot_background=element_rect(fill=PAGE_BG, color=PAGE_BG),
legend_position="none",
)
)
plot.save(f"plot-{THEME}.png", dpi=400, width=8, height=4.5, units="in", verbose=False)
Part of Real-Time Dashboard Tiles on anyplot.ai.