Heatmap Calendar

This example demonstrates how to use plot_heatmap_calendar to visualize daily aggregated energy consumption in a calendar layout.

Each cell represents one day. Rows are weekdays (Mon–Sun), columns are calendar weeks, and each year is shown as a separate row.

All plots are interactive — hover to see exact date and value.

# Configure Plotly to render as HTML (required for Jupyter Book)
import plotly.io as pio
pio.renderers.default = "notebook_connected"

import pandas as pd
from importlib import resources

# Load the bundled sample data
data_path = resources.files("pyedautils") / "data" / "ele_meter.csv"
df = pd.read_csv(data_path, sep=";")
df.head()

	timestamp	value
0	2015-01-01 00:00:00	40.0
1	2015-01-01 00:15:00	32.0
2	2015-01-01 00:30:00	32.0
3	2015-01-01 00:45:00	34.0
4	2015-01-01 01:00:00	34.0

Basic Usage

from pyedautils.plots import plot_heatmap_calendar

fig = plot_heatmap_calendar(
    data=df,
    title="Calendar Plot Energy Consumption",
    ylab="Energy Consumption (Wh/d)",
)
fig.show()

Customization

You can change the color scale:

fig = plot_heatmap_calendar(
    data=df,
    title="Calendar Heatmap (Viridis)",
    ylab="Energy Consumption (Wh/d)",
    colorscale="Viridis",
)
fig.show()