maui.visualizations.polar_bar_plot¶

maui.visualizations.polar_bar_plot(df, date_time_col, categories_col, percent=False, show_plot=True, **kwargs)[source]¶

Generate a polar bar plot to visualize category occurrences over the year. It will group data by day of the year, keep this in mind if you have more than one year of data.

Return type:

Figure

Parameters:

dfpandas.DataFrame: The input dataframe containing the data to plot.
date_time_colstr: The name of the column in df containing date or datetime values.
categories_colstr: The name of the column in df representing the categorical variable.
percentbool, optional, default: False: If True, the plot will display the data as percentages of total occurrences for each day. If False, raw counts will be used.
show_plotbool, optional, default: True: If True, the plot will be displayed. If False, the plot will be returned without displaying it.
**kwargsdict, optional: Additional keyword arguments passed to the plot layout, such as height and width for figure dimensions.

Returns:

plotly.graph_objects.Figure: The generated polar bar plot figure.

Raises:

AssertionError: If date_time_col or categories_col is not in df.
AttributeError: If categories_col contains continuous data instead of discrete categories.

Warns:

UserWarning: If date_time_col contains invalid date values, a warning is issued, and those rows are ignored in the plot.

Notes

The date_time_col is converted to the day of the year (1 to 366, to account for leap years).
If percent=True, the data is normalized by day to represent the proportion of occurrences.

Examples

>>> df = pd.DataFrame({
>>>     'date': pd.date_range(start='2023-01-01', periods=366, freq='D'),
>>>     'category': ['A', 'B', 'C'] * 122
>>> })
>>> fig = polar_bar_plot(df, 'date', 'category', percent=True)
>>> fig.show()