Plotting

Style

You can apply your own custom plotting styles in addition to the MindLab default style via the tool.mindlab.styles list in pyproject.toml, via the MINDLAB_STYLES environment variable or by using the mindlab.use_mindlab_styles() function.

mindlab.use_mindlab_styles(style_install_path: Optional[Path] = None, font_install_path: Optional[Path] = None, apply_mindlab_styles: bool = True, project_styles: Optional[Union[bool, Iterable[str]]] = None) → bool

Install and use MindLab and project styles.

Parameters

style_install_path – The path to install the MindLab styles to. Defaults to the Matplotlib package style library directory.
font_install_path – The path to install the style fonts to. Defaults to $XDG_DATA_HOME/fonts.
apply_mindlab_styles – Whether to use the MindLab styles. Defaults to tool.mindlab.apply_mindlab_styles in pyproject.toml or $MINDLAB_APPLY_MINDLAB_STYLES.
project_styles – The project styles to use in addition to the MindLab style. Defaults to tool.mindlab.styles in pyproject.toml or $MINDLAB_STYLES.

Returns

True if the styles were applied and False otherwise.

Reference

class mindlab.Figure(size: Optional[Tuple[float, float]] = None, title: Optional[str] = None, xlabel: Optional[str] = None, ylabel: Optional[str] = None, xtics: Optional[str] = None, ytics: Optional[str] = None, xscale: Optional[str] = None, yscale: Optional[str] = None, xlim: Optional[Tuple[Optional[float], Optional[float]]] = None, ylim: Optional[Tuple[Optional[float], Optional[float]]] = None, legend: Optional[str] = 'best')

Create professional plots easily.

Note that in addition to the overwritten methods described below, a Figure instance also exposes all the same methods as the underlying matplotlib.axes.Axes instance.

Parameters

size – The size of the figure (in inches).
title – The title of the figure.
xlabel – The x axis label of the figure.
ylabel – The y axis label of the figure.
xtics – The x tick locators (one of eng, auto, log, percent, year, month, week or day). Defaults to eng in linear scale and log in log scale.
ytics – The y tick locators (same choices as for xtics).
xscale – The scale of the x axis (one of linear or log).
yscale – The scale of the y axis (same choices as for xscale).
xlim – The x axis limits.
ylim – The y axis limits.
legend – The location of the legend (either best, or a combination of top/center/bottom and left/center/right.

figure

The underlying figure instance.

Type: matplotlib.figure.Figure

axes

The underlying axes instance.

Type: matplotlib.axes.Axes

save(output: Union[Path, IOBase], **kwargs: Any) → None

Save the figure.

Parameters

output – A path or object to use for saving.
**kwargs – Arguments to forward to matplotlib.figure.Figure.savefig().

line(*args: Any, **kwargs: Any) → None

Draw a line chart.

Parameters

*args – Arguments to forward to matplotlib.axes.Axes.plot(). If the first argument is a grouped data frame (see GroupBy) we draw a line for each group.
**kwargs – Arguments to forward to matplotlib.axes.Axes.plot().

scatter(*args: Any, **kwargs: Any) → None

Draw a scatter plot.

Parameters

*args – Arguments to forward to matplotlib.axes.Axes.scatter(). If the first argument is a grouped data frame (see GroupBy) we draw a scatter plot for each group.
**kwargs – Arguments to forward to matplotlib.axes.Axes.scatter().

bar(data: DataFrameGroupBy, **kwargs: Any) → None

Draw a stacked bar chart.

Parameters

data – The grouped data frame to plot (see GroupBy).
**kwargs – Arguments to forward to matplotlib.axes.Axes.bar().

kde(series: Series, rug: bool = True, **kwargs: Any) → None

Draw a kernel density estimation plot.

Parameters

series – The series for which to calculate the kernel density estimate.
rug – Whether to add a rug plot.
**kwargs – Arguments to forward to pandas.Series.plot().