Data Clock Visualisation Library

Introduction

This library allows the user to create data clock graphs, using the matplotlib Python library.

Data clocks visually summarise temporal data in two dimensions, revealing seasonal or cyclical patterns and trends over time. A data clock is a circular chart that divides a larger unit of time into rings and subdivides it by a smaller unit of time into wedges, creating a set of temporal bins.

These temporal bins are symbolised using graduated colors that correspond to a count or aggregated value taking place in each time period.

The table below details the currently supported chart modes and the corresponding rings and wedges:

The full documentation can be viewed on the project GitHub Page.

Example charts

Chart examples have been generated using UK Department for Transport data 2010 - 2015.

import pandas as pd
from dataclocklib.charts import dataclock

data = pd.read_parquet(
    "https://raw.githubusercontent.com/andyrids/dataclocklib/main/tests/data/traffic_data.parquet.gzip"
)

chart_data, fig, ax = dataclock(
    data=data,
    date_column="Date_Time",
    mode="DOW_HOUR",
    spine_color="darkslategrey",
    grid_color="black",
    default_text=False,
)

import pandas as pd
from dataclocklib.charts import dataclock

data = pd.read_parquet(
    "https://raw.githubusercontent.com/andyrids/dataclocklib/main/tests/data/traffic_data.parquet.gzip"
)

chart_data, fig, ax = dataclock(
    data=data,
    date_column="Date_Time",
    mode="DOW_HOUR",
    spine_color="darkslategrey",
    grid_color="black",
    default_text=True,
)

import pandas as pd
from dataclocklib.charts import dataclock

data = pd.read_parquet(
    "https://raw.githubusercontent.com/andyrids/dataclocklib/main/tests/data/traffic_data.parquet.gzip"
)

chart_data, fig, ax = dataclock(
    data=data,
    date_column="Date_Time",
    mode="DOW_HOUR",
    default_text=True,
    spine_color="darkslategrey",
    grid_color="black",
    chart_title="**CUSTOM TITLE**",
    chart_subtitle="**CUSTOM SUBTITLE**",
    chart_period="**CUSTOM PERIOD**",
    chart_source="Source: UK Department for Transport",
    dpi=150,
)

import pandas as pd
from dataclocklib.charts import dataclock

data = pd.read_parquet(
    "https://raw.githubusercontent.com/andyrids/dataclocklib/main/tests/data/traffic_data.parquet.gzip"
)

chart_data, fig, ax = dataclock(
    data=data.query("Date_Time.dt.year.eq(2010)"),
    date_column="Date_Time",
    agg_column="Number_of_Casualties",
    agg="sum",
    mode="DOW_HOUR",
    cmap_name="X26",
    cmap_reverse=True,
    spine_color="honeydew",
    grid_color="honeydew",
    default_text=True,
    chart_title="UK Traffic Accident Casualties",
    chart_subtitle=None,
    chart_period="Period: 2010",
    chart_source="Source: https://data.dft.gov.uk/road-accidents-safety-data/dft-road-casualty-statistics-collision-last-5-years.csv",
    dpi=300,
)

Installation

You can install using pip:

python -m pip install dataclocklib

To install from GitHub use:

python -m pip install git+https://github.com/andyrids/dataclocklib.git

Development Installation

Astral uv is used as the Python package manager. To install uv see the installation guide @ uv documentation.

Clone the repository:

git clone git@github.com:andyrids/dataclocklib.git
cd dataclocklib

Sync the dependencies, including the dev dependency group and optional dependencies with uv:

uv sync --all-extras

Activate the virtual environment:

. .venv/bin/activate

Common development tasks are just recipes (run just to list them):

just setup      # uv sync --all-extras, install the prek git hooks, update .secrets.baseline
just coverage   # run the tests with a coverage report

Sphinx documentation

Build the HTML documentation into docs/build/html (warnings are treated as errors):

just docs

Serve it locally with live reload, or remove the build output:

just docs-serve
just docs-clean