Plotly#

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The Plotly pane displays Plotly plots within a Panel application. It enhances the speed of plot updates by using binary serialization for array data contained in the Plotly object.

Please remember that to use the Plotly pane in a Jupyter notebook, you must activate the Panel extension and include "plotly" as an argument. This step ensures that plotly.js is properly set up.

import panel as pn

pn.extension("plotly")



Parameters:#


For details on other options for customizing the component see the general layout and styling how-to guides as well as the specific Style Plotly Plots how-to guide.




Core#


    

  • object (object): The Plotly Figure or dictionary object being displayed.
    • 

  • config (dict): Additional configuration of the plot. See Plotly configuration options.
    • 







Update in Place#


    

  • link_figure (bool, default: True): Update the displayed Plotly figure when the Plotly Figure is modified in place.
    • 







Events#


    

  • click_data (dict): Click event data from plotly_click event.
    • 

  • clickannotation_data (dict): Clickannotation event data from plotly_clickannotation event.
    • 

  • hover_data (dict): Hover event data from plotly_hover and plotly_unhover events.
    • 

  • relayout_data (dict): Relayout event data from plotly_relayout event
    • 

  • restyle_data (dict): Restyle event data from plotly_restyle event
    • 

  • selected_data (dict): Selected event data from plotly_selected and plotly_deselect events.
    • 

  • viewport (dict): Current viewport state, i.e. the x- and y-axis limits of the displayed plot. Updated on plotly_relayout, plotly_relayouting and plotly_restyle events.
    • 

  • viewport_update_policy (str, default = ‘mouseup’): Policy by which the viewport parameter is updated during user interactions
    

      

    • mouseup: updates are synchronized when mouse button is released after panning
      • 

    • continuous: updates are synchronized continually while panning
      • 

    • throttle: updates are synchronized while panning, at intervals determined by the viewport_update_throttle parameter
      • 

    

    • 

  • viewport_update_throttle (int, default = 200, bounds = (0, None)): Time interval in milliseconds at which viewport updates are synchronized when viewport_update_policy is “throttle”.
    • 





As with most other types Panel will automatically convert a Plotly Figure to a Plotly pane if it is passed to the pn.panel function or a Panel layout, but a Plotly pane can also be constructed directly using the pn.pane.Plotly constructor:








Basic Example#


Lets create a basic example


import numpy as np
import plotly.graph_objs as go

import panel as pn

pn.extension("plotly")

xx = np.linspace(-3.5, 3.5, 100)
yy = np.linspace(-3.5, 3.5, 100)
x, y = np.meshgrid(xx, yy)
z = np.exp(-((x - 1) ** 2) - y**2) - (x**3 + y**4 - x / 5) * np.exp(-(x**2 + y**2))

surface=go.Surface(z=z)
fig = go.Figure(data=[surface])

fig.update_layout(
    title="Plotly 3D Plot",
    width=500,
    height=500,
    margin=dict(t=50, b=50, r=50, l=50),
)

plotly_pane = pn.pane.Plotly(fig)
plotly_pane







            
            
Once created Plotly pane can be updated by assigning a new figure object


new_fig = go.Figure(data=[go.Surface(z=np.sin(z+1))])
new_fig.update_layout(
    title="Plotly 3D Plot",
    width=500,
    height=500,
    margin=dict(t=50, b=50, r=50, l=50),
)

plotly_pane.object = new_fig





Lets reset the Plotly pane


plotly_pane.object = fig









Layout Example#


The Plotly pane supports layouts and subplots of arbitrary complexity, allowing even deeply nested Plotly figures to be displayed:


import panel as pn
from plotly import subplots

pn.extension("plotly")

heatmap = go.Heatmap(
    z=[[1, 20, 30],
       [20, 1, 60],
       [30, 60, 1]],
    showscale=False)

y0 = np.random.randn(50)
y1 = np.random.randn(50)+1

box_1 = go.Box(y=y0)
box_2 = go.Box(y=y1)
data = [heatmap, box_1, box_2]

fig_layout = subplots.make_subplots(
    rows=2, cols=2, specs=[[{}, {}], [{'colspan': 2}, None]],
    subplot_titles=('First Subplot','Second Subplot', 'Third Subplot')
)

fig_layout.append_trace(box_1, 1, 1)
fig_layout.append_trace(box_2, 1, 2)
fig_layout.append_trace(heatmap, 2, 1)

fig_layout['layout'].update(height=600, width=600, title='i <3 subplots')

pn.pane.Plotly(fig_layout)







            
            




Responsive Plots#


Plotly plots can be made responsive using the autosize option on a Plotly layout and a responsive sizing_mode argument to the Plotly pane:


import pandas as pd
import panel as pn
import plotly.express as px

pn.extension("plotly")

data = pd.DataFrame([
    ('Monday', 7), ('Tuesday', 4), ('Wednesday', 9), ('Thursday', 4),
    ('Friday', 4), ('Saturday', 4), ('Sunday', 4)], columns=['Day', 'Orders']
)

fig_responsive = px.line(data, x="Day", y="Orders")
fig_responsive.update_traces(mode="lines+markers", marker=dict(size=10), line=dict(width=4))
fig_responsive.layout.autosize = True

responsive = pn.pane.Plotly(fig_responsive, height=300)

pn.Column('## A responsive plot', responsive, sizing_mode='stretch_width')







            
            




Plot Configuration#


You can set the Plotly configuration options via the config parameter. Lets try to configure scrollZoom:


responsive_with_zoom = pn.pane.Plotly(fig_responsive, config={"scrollZoom": True}, height=300)

pn.Column('## A responsive and scroll zoomable plot', responsive_with_zoom, sizing_mode='stretch_width')







            
            
Try scrolling with the mouse over the plot!






Patching#


Instead of updating the entire Figure you can efficiently patch traces or the layout if you use a dictionary instead of a Plotly Figure.


Note patching will only be efficient if the Figure is defined as a dictionary, since Plotly will make copies of the traces, which means that modifying them in place has no effect. Modifying an array will send just the array using a binary protocol, leading to fast and efficient updates.


import numpy as np
import plotly.graph_objs as go

import panel as pn

pn.extension("plotly")

xx = np.linspace(-3.5, 3.5, 100)
yy = np.linspace(-3.5, 3.5, 100)
x, y = np.meshgrid(xx, yy)
z = np.exp(-((x - 1) ** 2) - y**2) - (x**3 + y**4 - x / 5) * np.exp(-(x**2 + y**2))

surface = go.Surface(z=z)
layout = go.Layout(
    title='Plotly 3D Plot',
    autosize=False,
    width=500,
    height=500,
    margin=dict(t=50, b=50, r=50, l=50)
)

fig_patch = dict(data=[surface], layout=layout)

plotly_pane_patch = pn.pane.Plotly(fig_patch)
plotly_pane_patch







            
            
surface.z = np.sin(z+1)
plotly_pane_patch.object = fig_patch





Similarly, modifying the plot layout will only modify the layout, leaving the traces unaffected.


fig_patch['layout']['width'] = 800

plotly_pane_patch.object = fig_patch





Lets reset the Plotly pane


surface.z = z
fig_patch['layout']['width'] = 500

plotly_pane_patch.object = fig_patch









Streaming#


You can stream updates by replacing the entire Figure object. To stream efficiently though you should use patching technique described above.


You can stream periodically using pn.state.add_periodic_callback.


import pandas as pd
import plotly.graph_objects as go

import panel as pn

pn.extension("plotly")


df = pn.cache(pd.read_csv)(
    "https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv"
)

start_index = 50

data = go.Ohlc(
    x=df.loc[:start_index, "Date"],
    open=df.loc[:start_index, "AAPL.Open"],
    high=df.loc[:start_index, "AAPL.High"],
    low=df.loc[:start_index, "AAPL.Low"],
    close=df.loc[:start_index, "AAPL.Close"],
)

fig_stream = {"data": data, "layout": go.Layout(xaxis_rangeslider_visible=False)}

plotly_pane_stream = pn.pane.Plotly(fig_stream)


def stream():
    index = len(data.x)
    if index == len(df):
        index = 0

    data["x"] = df.loc[:index, "Date"]
    data["open"] = df.loc[:index, "AAPL.Open"]
    data["high"] = df.loc[:index, "AAPL.High"]
    data["low"] = df.loc[:index, "AAPL.Low"]
    data["close"] = df.loc[:index, "AAPL.Close"]
    plotly_pane_stream.object = fig_stream


pn.state.add_periodic_callback(stream, period=100, count=50)

plotly_pane_stream







            
            
You can also stream via a generator or async generator function:


from asyncio import sleep

import pandas as pd
import plotly.graph_objects as go

import panel as pn

pn.extension("plotly")


df = pn.cache(pd.read_csv)(
    "https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv"
)

start_index = 50

data = go.Ohlc(
    x=df.loc[:start_index, "Date"],
    open=df.loc[:start_index, "AAPL.Open"],
    high=df.loc[:start_index, "AAPL.High"],
    low=df.loc[:start_index, "AAPL.Low"],
    close=df.loc[:start_index, "AAPL.Close"],
)
layout = go.Layout(xaxis_rangeslider_visible=False)


async def stream_generator():
    for _ in range(start_index, start_index+50):
        index = len(data.x)
        if index == len(df):
            index = 0

        data["x"] = df.loc[:index, "Date"]
        data["open"] = df.loc[:index, "AAPL.Open"]
        data["high"] = df.loc[:index, "AAPL.High"]
        data["low"] = df.loc[:index, "AAPL.Low"]
        data["close"] = df.loc[:index, "AAPL.Close"]
        
        yield  {"data": data, "layout": layout}
        await sleep(0.05)


pn.pane.Plotly(stream_generator)







            
            




Update in Place#


An alternative to updating the figure dictionary is updating the Plotly Figure in place, i.e. via its attributes and methods.


import plotly.graph_objects as go

import panel as pn

pn.extension("plotly")


fig_in_place = go.Figure()

button = pn.widgets.Button(name="Create", button_type="primary")


def handle_click(clicks):
    mod = clicks % 3
    if mod == 1:
        button.name = "Update"
        fig_in_place.add_scatter(y=[2, 1, 4, 3])
        fig_in_place.add_bar(y=[2, 1, 4, 3])
        fig_in_place.layout.title = "New Figure"
    elif mod == 2:
        button.name = "Reset"
        scatter = fig_in_place.data[0]
        scatter.y = [3, 1, 4, 3]
        bar = fig_in_place.data[1]
        bar.y = [5, 3, 2, 8]
        fig_in_place.layout.title.text = "Updated Figure"
    else:
        fig_in_place.data = []
        fig_in_place.layout = {"title": ""}
        button.name = "Create"

pn.bind(handle_click, button.param.clicks, watch=True)
button.clicks=1

plotly_pane_in_place = pn.pane.Plotly(
    fig_in_place,
    height=400,
    width=700,
    # link_figure=False
)

pn.Column(
    button,
    plotly_pane_in_place,
)







            
            
This enables you to use the Plotly Figure in the same way as you would have been using the Plotly FigureWidget.


If you for some reason want to disable in place updates, you can set link_figure=False when you create the Plotly pane. You cannot change this when the pane has been created.






Events#


The Plotly pane enables you to bind to most of the click, hover, selection and other events described in Plotly Event Handlers.




Simple Event Example#


import plotly.express as px
import panel as pn
import pandas as pd

pn.extension("plotly")

# Create dataframe
x = [1, 2, 3, 4, 5]
y = [10, 20, 30, 20, 10]
df = pd.DataFrame({'x': x, 'y': y})

# Create scatter plot
fig_events = px.scatter(df, x='x', y='y', title='Click on a Point!', hover_name='x',)
fig_events.update_traces(marker=dict(size=20))
fig_events.update_layout(autosize=True, hovermode='closest')

plotly_pane_event=pn.pane.Plotly(fig_events, height=400, max_width=1200, sizing_mode="stretch_width")

# Define Child View
def child_view(event):
    if not event:
        return "No point clicked"
    try:
        point = event["points"][0]
        index = point['pointIndex']
        x = point['x']
        y = point['y']
    except Exception as ex:
        return f"You clicked the Plotly Chart! I could not determine the point: {ex}"
    
    return f"**You clicked point {index} at ({x}, {y}) on the Plotly Chart!**"

ichild_view = pn.bind(child_view, plotly_pane_event.param.click_data)

# Put things together
pn.Column(plotly_pane_event, ichild_view)







            
            




Event Inspection#


The be able to work with the events its a good idea to inspect them. You can do that by printing them or including them in your visualization.


Lets display them.


event_parameters = ["click_data", "click_annotation_data", "hover_data", "relayout_data", "restyle_data", "selected_data", "viewport"]

pn.Param(plotly_pane_event, parameters=event_parameters, max_width=1100, name="Plotly Event Parameters")







            
            
In the plot above, try hovering, clicking, selecting and changing the viewport by panning. Watch how the parameter values just above changes.






Parent-Child Views#


A common technique for exploring higher-dimensional datasets is the use of Parent-Child views. This approach allows you to employ a top-down method to initially exing thehree most important dimensions in the parent plot. You can then select a subset of the data points and examine them in greater detail and across additional dimensions.


Let’s create a plot where Box or Lasso selections in the parent plot update a child plot. We will also customize the action bars using the config parameter.


import pandas as pd
import plotly.express as px

import panel as pn

pn.extension("plotly")
df = (
    pd.read_csv("https://datasets.holoviz.org/penguins/v1/penguins.csv")
    .dropna()
    .reset_index(drop=True)
)
df["index"] = df.index # Used to filter rows for child view

color_map = {"Adelie": "blue", "Chinstrap": "red", "Gentoo": "green"}

fig_parent = px.scatter(
    df,
    x="flipper_length_mm",
    y="body_mass_g",
    color_discrete_map=color_map,
    custom_data="index",  # Used to filter rows for child view
    color="species",
    title="<b>Parent Plot</b>: Box or Lasso Select Points",
)


def fig_child(selectedData):
    if selectedData:
        indices = [point["customdata"][0] for point in selectedData["points"]]
        filtered_df = df.iloc[indices]
        title = f"<b>Child Plot</b>: Selected Points({len(indices)})"
    else:
        filtered_df = df
        title = f"<b>Child Plot</b>: All Points ({len(filtered_df)})"

    fig = px.scatter(
        filtered_df,
        x="bill_length_mm",
        y="bill_depth_mm",
        color_discrete_map=color_map,
        color="species",
        hover_data={"flipper_length_mm": True, "body_mass_g": True},
        title=title,
    )
    return fig


parent_config = {
    "modeBarButtonsToAdd": ["select2d", "lasso2d"],
    "modeBarButtonsToRemove": [
        "zoomIn2d",
        "zoomOut2d",
        "pan2d",
        "zoom2d",
        "autoScale2d",
    ],
    "displayModeBar": True,
    "displaylogo": False,
}
parent_pane = pn.pane.Plotly(fig_parent, config=parent_config)

ifig_child = pn.bind(fig_child, parent_pane.param.selected_data)
child_config = {
    "modeBarButtonsToRemove": [
        "select2d",
        "lasso2d",
        "zoomIn2d",
        "zoomOut2d",
        "pan2d",
        "zoom2d",
        "autoScale2d",
    ],
    "displayModeBar": True,
    "displaylogo": False,
}
child_pane = pn.pane.Plotly(ifig_child, config=child_config)

pn.FlexBox(parent_pane, child_pane)







            
            






Controls#


The Plotly pane exposes a number of options which can be changed from both Python and Javascript try out the effect of these parameters interactively:


pn.Row(responsive.controls(jslink=True), responsive)







            
            


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