pyxplor.plot_time_series

Module Contents

Functions

plot_time_series(, output, super_title, super_title_font)

Generates line plot visualizations for multiple time-series variables in a DataFrame.
pyxplor.plot_time_series.plot_time_series(input_df: pandas.DataFrame, date_column: str, value_columns: list, freq: str = 'D', figsize: tuple = (10, 6), output: bool = False, super_title: str = 'Time Series Analysis', super_title_font: int = 14) → tuple[source]

Generates line plot visualizations for multiple time-series variables in a DataFrame.

This function will construct a multiple lineplot of different numerical values over time. The time frequency will be adjustable and the function will return the figure and axes object.

Parameters:

  • input_df (pd.DataFrame) – DataFrame containing the time-series data with date and numeric columns.

  • date_column (str) – Name of the column containing date/time information in datetime format.

  • value_columns (list) – List of column names containing numeric values to plot as time series.

  • freq (str, optional) – Frequency of data aggregation (‘D’ for daily, ‘W’ for weekly, etc.). Default is ‘D’.

  • figsize (tuple[int, int], optional) – Size of the plot as (width, height). Default is (10, 6).

  • output (bool, optional) – Whether to output the figure to the current working directory. Default is False.

  • super_title (str, optional) – Title for the entire plot. Default is “Time Series Analysis”.

  • super_title_font (int, optional) – Font size for the super title. Default is 14.

Returns:

  • fig (matplotlib.figure.Figure) – The matplotlib Figure object.

  • ax (matplotlib.axes.Axes or array of Axes) – The matplotlib Axes object(s).

Examples

datetime_variable = df[“datetime_variable”] numerical_variables = [“num_var1”, “num_var2”, “num_var3”] fig, ax = plot_time_series(df, datetime_variable, numerical_variables)