pytrnsys_process.api.scatter_plot#

pytrnsys_process.api.scatter_plot(df: DataFrame, x_column: str, y_column: str, group_by_color: str | None = None, group_by_marker: str | None = None, use_legend: bool = True, size: tuple[float, float] = (7.8, 3.9), **kwargs: Any) → tuple[Figure, Axes][source]#

Create a scatter plot with up to two grouping variables. This visualization allows simultaneous analysis of:

  • Numerical relationships between x and y variables

  • Categorical grouping through color encoding

  • Secondary categorical grouping through marker styles

Note

The way to changing colors depends on how this function is used. Categorical grouping -> use eg: cmap=”viridis” No grouping -> use eg: color=”red”

Parameters:

  • df (pandas.DataFrame) – the dataframe to plot

  • x_column (str) – coloumn name for x-axis values

  • y_column (str) – coloumn name for y-axis values

  • group_by_color (str, optional) – column name for color grouping

  • group_by_marker (str, optional) – column name for marker style grouping

  • use_legend (bool, default 'True') – whether to show the legend or not

  • size (tuple of (float, float)) – size of the figure (width, height)

  • **kwargs – Additional keyword arguments to pass on to pandas.DataFrame.plot().

Return type:

tuple of (matplotlib.figure.Figure, matplotlib.axes.Axes)

Examples

Simple scatter plot

>>> api.scatter_plot(
...     simulation.monthly, x_column="QSnk60dQlossTess", y_column="QSnk60dQ"
... )

Compare plot

>>> api.scatter_plot(
...     comparison_data,
...     "VIceSscaled",
...     "VIceRatioMax",
...     "yearly_demand_GWh",
...     "ratioDHWtoSH_allSinks",
... )