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plotting module

Base plotting functions.

Provides functions for visualizing data in an efficient and convenient way. Each creates a figure widget that is compatible with ipywidgets and enables interactive data visualization in Jupyter Notebook and JupyterLab environments. For more details on using Plotly, see Getting Started with Plotly in Python.

Warning

Errors related to plotting in Jupyter environment usually appear in the logs, not under the cell.

clean_labels function

clean_labels(
    labels
)

Clean labels.

Plotly doesn't support multi-indexes.

Bar class

Bar(
    data=None,
    trace_names=None,
    x_labels=None,
    trace_kwargs=None,
    add_trace_kwargs=None,
    make_figure_kwargs=None,
    fig=None,
    **layout_kwargs
)

Class with an initialization config.

All subclasses of Configured are initialized using Config, which makes it easier to pickle.

Settings are defined under configured.

Warning

If any attribute has been overwritten that isn't listed in Configured._writeable_attrs, or if any Configured.__init__ argument depends upon global defaults, their values won't be copied over. Make sure to pass them explicitly to make that the saved & loaded / copied instance is resilient to any changes in globals.

Create a bar plot.

Args

data : array_like

Data in any format that can be converted to NumPy.

Must be of shape (x_labels, trace_names).

trace_names : str or list of str
Trace names, corresponding to columns in pandas.
x_labels : array_like
X-axis labels, corresponding to index in pandas.
trace_kwargs : dict or list of dict

Keyword arguments passed to plotly.graph_objects.Bar.

Can be specified per trace as a sequence of dicts.

add_trace_kwargs : dict
Keyword arguments passed to add_trace.
make_figure_kwargs : dict
Keyword arguments passed to make_figure().
fig : Figure or FigureWidget
Figure to add traces to.
**layout_kwargs
Keyword arguments for layout.

Usage

>>> from vectorbtpro import *

>>> bar = vbt.Bar(
...     data=[[1, 2], [3, 4]],
...     trace_names=['a', 'b'],
...     x_labels=['x', 'y']
... )
>>> bar.fig.show()

Superclasses

Inherited members

Box class

Box(
    data=None,
    trace_names=None,
    horizontal=False,
    remove_nan=True,
    from_quantile=None,
    to_quantile=None,
    trace_kwargs=None,
    add_trace_kwargs=None,
    make_figure_kwargs=None,
    fig=None,
    **layout_kwargs
)

Class with an initialization config.

All subclasses of Configured are initialized using Config, which makes it easier to pickle.

Settings are defined under configured.

Warning

If any attribute has been overwritten that isn't listed in Configured._writeable_attrs, or if any Configured.__init__ argument depends upon global defaults, their values won't be copied over. Make sure to pass them explicitly to make that the saved & loaded / copied instance is resilient to any changes in globals.

Create a box plot.

For keyword arguments, see Histogram.

Usage

>>> from vectorbtpro import *

>>> box = vbt.Box(
...     data=[[1, 2], [3, 4], [2, 1]],
...     trace_names=['a', 'b']
... )
>>> box.fig.show()

Superclasses

Inherited members

from_quantile property

Filter out data points before this quantile.

horizontal property

Whether to plot horizontally.

remove_nan property

Whether to remove NaN values.

to_quantile property

Filter out data points after this quantile.

Gauge class

Gauge(
    value=None,
    label=None,
    value_range=None,
    cmap_name='Spectral',
    trace_kwargs=None,
    add_trace_kwargs=None,
    make_figure_kwargs=None,
    fig=None,
    **layout_kwargs
)

Class with an initialization config.

All subclasses of Configured are initialized using Config, which makes it easier to pickle.

Settings are defined under configured.

Warning

If any attribute has been overwritten that isn't listed in Configured._writeable_attrs, or if any Configured.__init__ argument depends upon global defaults, their values won't be copied over. Make sure to pass them explicitly to make that the saved & loaded / copied instance is resilient to any changes in globals.

Create a gauge plot.

Args

value : float
The value to be displayed.
label : str
The label to be displayed.
value_range : tuple of float
The value range of the gauge.
cmap_name : str

A matplotlib-compatible colormap name.

See the list of available colormaps.

trace_kwargs : dict
Keyword arguments passed to the plotly.graph_objects.Indicator.
add_trace_kwargs : dict
Keyword arguments passed to add_trace.
make_figure_kwargs : dict
Keyword arguments passed to make_figure().
fig : Figure or FigureWidget
Figure to add traces to.
**layout_kwargs
Keyword arguments for layout.

Usage

>>> from vectorbtpro import *

>>> gauge = vbt.Gauge(
...     value=2,
...     value_range=(1, 3),
...     label='My Gauge'
... )
>>> gauge.fig.show()

Superclasses

Inherited members

cmap_name property

A matplotlib-compatible colormap name.

value_range property

The value range of the gauge.

Heatmap class

Heatmap(
    data=None,
    x_labels=None,
    y_labels=None,
    is_x_category=False,
    is_y_category=False,
    trace_kwargs=None,
    add_trace_kwargs=None,
    make_figure_kwargs=None,
    fig=None,
    **layout_kwargs
)

Class with an initialization config.

All subclasses of Configured are initialized using Config, which makes it easier to pickle.

Settings are defined under configured.

Warning

If any attribute has been overwritten that isn't listed in Configured._writeable_attrs, or if any Configured.__init__ argument depends upon global defaults, their values won't be copied over. Make sure to pass them explicitly to make that the saved & loaded / copied instance is resilient to any changes in globals.

Create a heatmap plot.

Args

data : array_like

Data in any format that can be converted to NumPy.

Must be of shape (y_labels, x_labels).

x_labels : array_like
X-axis labels, corresponding to columns in pandas.
y_labels : array_like
Y-axis labels, corresponding to index in pandas.
is_x_category : bool
Whether X-axis is a categorical axis.
is_y_category : bool
Whether Y-axis is a categorical axis.
trace_kwargs : dict
Keyword arguments passed to plotly.graph_objects.Heatmap.
add_trace_kwargs : dict
Keyword arguments passed to add_trace.
make_figure_kwargs : dict
Keyword arguments passed to make_figure().
fig : Figure or FigureWidget
Figure to add traces to.
**layout_kwargs
Keyword arguments for layout.

Usage

>>> from vectorbtpro import *

>>> heatmap = vbt.Heatmap(
...     data=[[1, 2], [3, 4]],
...     x_labels=['a', 'b'],
...     y_labels=['x', 'y']
... )
>>> heatmap.fig.show()

Superclasses

Inherited members

Histogram class

Histogram(
    data=None,
    trace_names=None,
    horizontal=False,
    remove_nan=True,
    from_quantile=None,
    to_quantile=None,
    trace_kwargs=None,
    add_trace_kwargs=None,
    make_figure_kwargs=None,
    fig=None,
    **layout_kwargs
)

Class with an initialization config.

All subclasses of Configured are initialized using Config, which makes it easier to pickle.

Settings are defined under configured.

Warning

If any attribute has been overwritten that isn't listed in Configured._writeable_attrs, or if any Configured.__init__ argument depends upon global defaults, their values won't be copied over. Make sure to pass them explicitly to make that the saved & loaded / copied instance is resilient to any changes in globals.

Create a histogram plot.

Args

data : array_like

Data in any format that can be converted to NumPy.

Must be of shape (any, trace_names).

trace_names : str or list of str
Trace names, corresponding to columns in pandas.
horizontal : bool
Whether to plot horizontally.
remove_nan : bool
Whether to remove NaN values.
from_quantile : float

Filter out data points before this quantile.

Must be in range [0, 1].

to_quantile : float

Filter out data points after this quantile.

Must be in range [0, 1].

trace_kwargs : dict or list of dict

Keyword arguments passed to plotly.graph_objects.Histogram.

Can be specified per trace as a sequence of dicts.

add_trace_kwargs : dict
Keyword arguments passed to add_trace.
make_figure_kwargs : dict
Keyword arguments passed to make_figure().
fig : Figure or FigureWidget
Figure to add traces to.
**layout_kwargs
Keyword arguments for layout.

Usage

>>> from vectorbtpro import *

>>> hist = vbt.Histogram(
...     data=[[1, 2], [3, 4], [2, 1]],
...     trace_names=['a', 'b']
... )
>>> hist.fig.show()

Superclasses

Inherited members

from_quantile property

Filter out data points before this quantile.

horizontal property

Whether to plot horizontally.

remove_nan property

Whether to remove NaN values.

to_quantile property

Filter out data points after this quantile.

Scatter class

Scatter(
    data=None,
    trace_names=None,
    x_labels=None,
    trace_kwargs=None,
    add_trace_kwargs=None,
    make_figure_kwargs=None,
    fig=None,
    use_gl=None,
    **layout_kwargs
)

Class with an initialization config.

All subclasses of Configured are initialized using Config, which makes it easier to pickle.

Settings are defined under configured.

Warning

If any attribute has been overwritten that isn't listed in Configured._writeable_attrs, or if any Configured.__init__ argument depends upon global defaults, their values won't be copied over. Make sure to pass them explicitly to make that the saved & loaded / copied instance is resilient to any changes in globals.

Create a scatter plot.

Args

data : array_like

Data in any format that can be converted to NumPy.

Must be of shape (x_labels, trace_names).

trace_names : str or list of str
Trace names, corresponding to columns in pandas.
x_labels : array_like
X-axis labels, corresponding to index in pandas.
trace_kwargs : dict or list of dict

Keyword arguments passed to plotly.graph_objects.Scatter.

Can be specified per trace as a sequence of dicts.

add_trace_kwargs : dict
Keyword arguments passed to add_trace.
make_figure_kwargs : dict
Keyword arguments passed to make_figure().
fig : Figure or FigureWidget
Figure to add traces to.
use_gl : bool

Whether to use plotly.graph_objects.Scattergl.

Defaults to the global setting. If the global setting is None, becomes True if there are more than 10,000 data points.

**layout_kwargs
Keyword arguments for layout.

Usage

>>> from vectorbtpro import *

>>> scatter = vbt.Scatter(
...     data=[[1, 2], [3, 4]],
...     trace_names=['a', 'b'],
...     x_labels=['x', 'y']
... )
>>> scatter.fig.show()

Superclasses

Inherited members

TraceUpdater class

TraceUpdater(
    fig,
    traces
)

Base trace updating class.

Subclasses

fig property

Figure.

traces property

Traces to update.

update method

TraceUpdater.update(
    *args,
    **kwargs
)

Update all traces using new data.

update_trace class method

TraceUpdater.update_trace(
    trace,
    data,
    *args,
    **kwargs
)

Update one trace.

Volume class

Volume(
    data=None,
    x_labels=None,
    y_labels=None,
    z_labels=None,
    trace_kwargs=None,
    add_trace_kwargs=None,
    scene_name='scene',
    make_figure_kwargs=None,
    fig=None,
    **layout_kwargs
)

Class with an initialization config.

All subclasses of Configured are initialized using Config, which makes it easier to pickle.

Settings are defined under configured.

Warning

If any attribute has been overwritten that isn't listed in Configured._writeable_attrs, or if any Configured.__init__ argument depends upon global defaults, their values won't be copied over. Make sure to pass them explicitly to make that the saved & loaded / copied instance is resilient to any changes in globals.

Create a volume plot.

Args

data : array_like

Data in any format that can be converted to NumPy.

Must be a 3-dim array.

x_labels : array_like
X-axis labels.
y_labels : array_like
Y-axis labels.
z_labels : array_like
Z-axis labels.
trace_kwargs : dict
Keyword arguments passed to plotly.graph_objects.Volume.
add_trace_kwargs : dict
Keyword arguments passed to add_trace.
scene_name : str
Reference to the 3D scene.
make_figure_kwargs : dict
Keyword arguments passed to make_figure().
fig : Figure or FigureWidget
Figure to add traces to.
**layout_kwargs
Keyword arguments for layout.

Note

Figure widgets have currently problems displaying NaNs. Use .show() method for rendering.

Usage

>>> from vectorbtpro import *

>>> volume = vbt.Volume(
...     data=np.random.randint(1, 10, size=(3, 3, 3)),
...     x_labels=['a', 'b', 'c'],
...     y_labels=['d', 'e', 'f'],
...     z_labels=['g', 'h', 'i']
... )
>>> volume.fig.show()

Superclasses

Inherited members