This package provides a simple way to visualize patterns in timeseries data mapping 24 hours onto a polar plot
Project description
clock_plot
clock_plot provides a simple way to visualize timeseries data mapping 24 hours onto the 360 degrees of a polar plot. For usage, please see the examples.ipynb Jupyter notebook
Time features are automatically generated for your timeseries These features include:
Feature | Type | Description | Example Values |
---|---|---|---|
year | int | Calendar year | 2022 |
month | str | Calendar month | "January" |
year_month | int | Calendar year and month in the format YYYYMM | 202201 |
day | int | Day of calendar year | 25 |
date | str | Expressed in the format YYYY-MM-DD | "2022-01-25" |
week | int | ISO week of the calendar year | 5 |
dayofweek | str | Short version of day of week | "Tue" |
weekend | str | Either "weekday" or "weekend", where "weekend" is Saturday and Sunday | "weekend" (Sat/Sun) "weekday" (Mon-Fri) |
hour | int | Hour of the day in 24 clock | 14 |
minute | int | Minute of the hour | 42 |
degrees | int | Angle around 24 hour clock-face measured in degrees | 341 |
season | str | Season of the year defined based on month, with Winter being Dec-Feb | "Winter" (Dec-Feb) "Spring" (Mar-May) "Summer" (Jun-Aug) "Autumn" (Sep-Nov) |
These can be used to filter your data and format your plot.
For example you could filter for a particular year, plot seasons with different colors and weekday vs weekend days with different line dashes. Examples of this are given in examples.ipynb
Installation
To install this package run: pip install clock_plot
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