Shapes in Python

How to make SVG shapes in python. Examples of lines, circle, rectangle, and path.

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Adding Lines and Polygons to Figures¶

As a general rule, there are two ways to add shapes (lines or polygons) to figures:

Trace types in the scatter family (e.g. scatter, scatter3d, scattergeo etc) can be drawn with mode="lines" and optionally support a fill="self" attribute, and so can be used to draw open or closed shapes on figures.
Standalone lines, ellipses and rectangles can be added to figures using fig.add_shape(), and they can be positioned absolutely within the figure, or they can be positioned relative to the axes of 2d cartesian subplots i.e. in data coordinates.

Note: there are special methods add_hline, add_vline, add_hrect and add_vrect for the common cases of wanting to draw horizontal or vertical lines or rectangles that are fixed to data coordinates in one axis and absolutely positioned in another.

The differences between these two approaches are that:

Traces can optionally support hover labels and can appear in legends.
Shapes can be positioned absolutely or relative to data coordinates in 2d cartesian subplots only.
Traces cannot be positioned absolutely but can be positioned relative to date coordinates in any subplot type.
Traces also support optional text, although there is a textual equivalent to shapes in text annotations.

Shape-drawing with Scatter traces¶

There are two ways to draw filled shapes: scatter traces and layout.shapes which is mostly useful for the 2d subplots, and defines the shape type to be drawn, and can be rectangle, circle, line, or path (a custom SVG path). You also can use scatterpolar, scattergeo, scattermapbox to draw filled shapes on any kind of subplots. To set an area to be filled with a solid color, you need to define Scatter.fill="toself" that connects the endpoints of the trace into a closed shape. If mode=line (default value), then you need to repeat the initial point of a shape at the end of the sequence to have a closed shape.

In [1]:

import plotly.graph_objects as go

fig = go.Figure(go.Scatter(x=[0,1,2,0], y=[0,2,0,0], fill="toself"))
fig.show()

You can have more shapes either by adding more traces or interrupting the series with None.

In [2]:

import plotly.graph_objects as go

fig = go.Figure(go.Scatter(x=[0,1,2,0,None,3,3,5,5,3], y=[0,2,0,0,None,0.5,1.5,1.5,0.5,0.5], fill="toself"))
fig.show()

Shapes in Dash¶

Dash is the best way to build analytical apps in Python using Plotly figures. To run the app below, run pip install dash, click "Download" to get the code and run python app.py.

Get started with the official Dash docs and learn how to effortlessly style & deploy apps like this with Dash Enterprise.

Out[3]:

Sign up for Dash Club → Free cheat sheets plus updates from Chris Parmer and Adam Schroeder delivered to your inbox every two months. Includes tips and tricks, community apps, and deep dives into the Dash architecture. Join now.

Vertical and Horizontal Lines Positioned Relative to the Axis Data¶

In [4]:

import plotly.graph_objects as go

fig = go.Figure()

# Create scatter trace of text labels
fig.add_trace(go.Scatter(
    x=[2, 3.5, 6],
    y=[1, 1.5, 1],
    text=["Vertical Line",
          "Horizontal Dashed Line",
          "Diagonal dotted Line"],
    mode="text",
))

# Set axes ranges
fig.update_xaxes(range=[0, 7])
fig.update_yaxes(range=[0, 2.5])

# Add shapes
fig.add_shape(type="line",
    x0=1, y0=0, x1=1, y1=2,
    line=dict(color="RoyalBlue",width=3)
)
fig.add_shape(type="line",
    x0=2, y0=2, x1=5, y1=2,
    line=dict(
        color="LightSeaGreen",
        width=4,
        dash="dashdot",
    )
)
fig.add_shape(type="line",
    x0=4, y0=0, x1=6, y1=2,
    line=dict(
        color="MediumPurple",
        width=4,
        dash="dot",
    )
)
fig.update_shapes(dict(xref='x', yref='y'))
fig.show()

Lines Positioned Relative to the Plot & to the Axis Data¶

In [5]:

import plotly.graph_objects as go

fig = go.Figure()

# Create scatter trace of text labels
fig.add_trace(go.Scatter(
    x=[2, 6], y=[1, 1],
    text=["Line positioned relative to the plot",
          "Line positioned relative to the axes"],
    mode="text",
))

# Set axes ranges
fig.update_xaxes(range=[0, 8])
fig.update_yaxes(range=[0, 2])

fig.add_shape(type="line",
    xref="x", yref="y",
    x0=4, y0=0, x1=8, y1=1,
    line=dict(
        color="LightSeaGreen",
        width=3,
    ),
)
fig.add_shape(type="line",
    xref="paper", yref="paper",
    x0=0, y0=0, x1=0.5,
    y1=0.5,
    line=dict(
        color="DarkOrange",
        width=3,
    ),
)

fig.show()

Rectangles Positioned Relative to the Axis Data¶

In [6]:

import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=[1.5, 4.5],
    y=[0.75, 0.75],
    text=["Unfilled Rectangle", "Filled Rectangle"],
    mode="text",
))

# Set axes properties
fig.update_xaxes(range=[0, 7], showgrid=False)
fig.update_yaxes(range=[0, 3.5])

# Add shapes
fig.add_shape(type="rect",
    x0=1, y0=1, x1=2, y1=3,
    line=dict(color="RoyalBlue"),
)
fig.add_shape(type="rect",
    x0=3, y0=1, x1=6, y1=2,
    line=dict(
        color="RoyalBlue",
        width=2,
    ),
    fillcolor="LightSkyBlue",
)
fig.update_shapes(dict(xref='x', yref='y'))
fig.show()

Rectangle Positioned Relative to the Plot & to the Axis Data¶

In [7]:

import plotly.graph_objects as go

fig = go.Figure()

# Create scatter trace of text labels
fig.add_trace(go.Scatter(
    x=[1.5, 3],
    y=[2.5, 2.5],
    text=["Rectangle reference to the plot",
          "Rectangle reference to the axes"],
    mode="text",
))

# Set axes properties
fig.update_xaxes(range=[0, 4])
fig.update_yaxes(range=[0, 4])

# Add shapes
fig.add_shape(type="rect",
    xref="x", yref="y",
    x0=2.5, y0=0,
    x1=3.5, y1=2,
    line=dict(
        color="RoyalBlue",
        width=3,
    ),
    fillcolor="LightSkyBlue",
)
fig.add_shape(type="rect",
    xref="paper", yref="paper",
    x0=0.25, y0=0,
    x1=0.5, y1=0.5,
    line=dict(
        color="LightSeaGreen",
        width=3,
    ),
    fillcolor="PaleTurquoise",
)

fig.show()

A Rectangle Placed Relative to the Axis Position and Length¶

A shape can be placed relative to an axis's position on the plot by adding the string ' domain' to the axis reference in the xref or yref attributes for shapes. The following code places a rectangle that starts at 60% and ends at 70% along the x-axis, starting from the left, and starts at 80% and ends at 90% along the y-axis, starting from the bottom.

In [8]:

import plotly.graph_objects as go
import plotly.express as px

df = px.data.wind()
fig = px.scatter(df, y="frequency")

fig.update_layout(xaxis=dict(domain=[0, 0.5]), yaxis=dict(domain=[0.25, 0.75]))

# Add a shape whose x and y coordinates refer to the domains of the x and y axes
fig.add_shape(type="rect",
    xref="x domain", yref="y domain",
    x0=0.6, x1=0.7, y0=0.8, y1=0.9,
)

fig.show()

Highlighting Time Series Regions with Rectangle Shapes¶

In [9]:

import plotly.graph_objects as go

fig = go.Figure()

# Add scatter trace for line
fig.add_trace(go.Scatter(
    x=["2015-02-01", "2015-02-02", "2015-02-03", "2015-02-04", "2015-02-05",
       "2015-02-06", "2015-02-07", "2015-02-08", "2015-02-09", "2015-02-10",
       "2015-02-11", "2015-02-12", "2015-02-13", "2015-02-14", "2015-02-15",
       "2015-02-16", "2015-02-17", "2015-02-18", "2015-02-19", "2015-02-20",
       "2015-02-21", "2015-02-22", "2015-02-23", "2015-02-24", "2015-02-25",
       "2015-02-26", "2015-02-27", "2015-02-28"],
    y=[-14, -17, -8, -4, -7, -10, -12, -14, -12, -7, -11, -7, -18, -14, -14,
       -16, -13, -7, -8, -14, -8, -3, -9, -9, -4, -13, -9, -6],
    mode="lines",
    name="temperature"
))

# Add shape regions
fig.add_vrect(
    x0="2015-02-04", x1="2015-02-06",
    fillcolor="LightSalmon", opacity=0.5,
    layer="below", line_width=0,
),

fig.add_vrect(
    x0="2015-02-20", x1="2015-02-22",
    fillcolor="LightSalmon", opacity=0.5,
    layer="below", line_width=0,
)

fig.show()

Circles Positioned Relative to the Axis Data¶

In [10]:

import plotly.graph_objects as go

fig = go.Figure()

# Create scatter trace of text labels
fig.add_trace(go.Scatter(
    x=[1.5, 3.5],
    y=[0.75, 2.5],
    text=["Unfilled Circle",
          "Filled Circle"],
    mode="text",
))

# Set axes properties
fig.update_xaxes(range=[0, 4.5], zeroline=False)
fig.update_yaxes(range=[0, 4.5])

# Add circles
fig.add_shape(type="circle",
    xref="x", yref="y",
    x0=1, y0=1, x1=3, y1=3,
    line_color="LightSeaGreen",
)
fig.add_shape(type="circle",
    xref="x", yref="y",
    fillcolor="PaleTurquoise",
    x0=3, y0=3, x1=4, y1=4,
    line_color="LightSeaGreen",
)

# Set figure size
fig.update_layout(width=800, height=800)

fig.show()

Highlighting Clusters of Scatter Points with Circle Shapes¶

In [11]:

import plotly.graph_objects as go

import numpy as np
np.random.seed(1)

# Generate data
x0 = np.random.normal(2, 0.45, 300)
y0 = np.random.normal(2, 0.45, 300)

x1 = np.random.normal(6, 0.4, 200)
y1 = np.random.normal(6, 0.4, 200)

# Create figure
fig = go.Figure()

# Add scatter traces
fig.add_trace(go.Scatter(x=x0, y=y0, mode="markers"))
fig.add_trace(go.Scatter(x=x1, y=y1, mode="markers"))

# Add shapes
fig.add_shape(type="circle",
    xref="x", yref="y",
    x0=min(x0), y0=min(y0),
    x1=max(x0), y1=max(y0),
    opacity=0.2,
    fillcolor="blue",
    line_color="blue",
)

fig.add_shape(type="circle",
    xref="x", yref="y",
    x0=min(x1), y0=min(y1),
    x1=max(x1), y1=max(y1),
    opacity=0.2,
    fillcolor="orange",
    line_color="orange",
)


# Hide legend
fig.update_layout(showlegend=False)

fig.show()

Venn Diagram with Circle Shapes¶

In [12]:

import plotly.graph_objects as go

fig = go.Figure()

# Create scatter trace of text labels
fig.add_trace(go.Scatter(
    x=[1, 1.75, 2.5],
    y=[1, 1, 1],
    text=["$A$", "$A+B$", "$B$"],
    mode="text",
    textfont=dict(
        color="black",
        size=18,
        family="Arail",
    )
))

# Update axes properties
fig.update_xaxes(
    showticklabels=False,
    showgrid=False,
    zeroline=False,
)

fig.update_yaxes(
    showticklabels=False,
    showgrid=False,
    zeroline=False,
)

# Add circles
fig.add_shape(type="circle",
    line_color="blue", fillcolor="blue",
    x0=0, y0=0, x1=2, y1=2
)
fig.add_shape(type="circle",
    line_color="gray", fillcolor="gray",
    x0=1.5, y0=0, x1=3.5, y1=2
)
fig.update_shapes(opacity=0.3, xref="x", yref="y")

fig.update_layout(
    margin=dict(l=20, r=20, b=100),
    height=600, width=800,
    plot_bgcolor="white"
)

fig.show()

Adding Shapes to Subplots¶

Here we use the different axes (x1, x2) created by make_subplots as reference in order to draw shapes in figure subplots.

In [13]:

import plotly.graph_objects as go
from plotly.subplots import make_subplots

# Create Subplots
fig = make_subplots(rows=2, cols=2)

fig.add_trace(go.Scatter(x=[2, 6], y=[1,1]), row=1, col=1)
fig.add_trace(go.Bar(x=[1,2,3], y=[4,5,6]), row=1, col=2)
fig.add_trace(go.Scatter(x=[10,20], y=[40,50]), row=2, col=1)
fig.add_trace(go.Bar(x=[11,13,15], y=[8,11,20]), row=2, col=2)

# Add shapes
fig.update_layout(
    shapes=[
        dict(type="line", xref="x", yref="y",
            x0=3, y0=0.5, x1=5, y1=0.8, line_width=3),
        dict(type="rect", xref="x2", yref='y2',
             x0=4, y0=2, x1=5, y1=6),
        dict(type="rect", xref="x3", yref="y3",
             x0=10, y0=20, x1=15, y1=30),
        dict(type="circle", xref="x4", yref="y4",
             x0=5, y0=12, x1=10, y1=18)])
fig.show()

Adding the Same Shapes to Multiple Subplots¶

The same shape can be added to multiple facets by using the 'all' keyword in the row and col arguments. For example

In [14]:

import plotly.express as px

df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", facet_row="smoker", facet_col="sex")
# Adds a rectangle to all facets
fig.add_shape(
    dict(type="rect", x0=25, x1=35, y0=4, y1=6, line_color="purple"),
    row="all",
    col="all",
)
# Adds a line to all the rows of the second column
fig.add_shape(
    dict(type="line", x0=20, x1=25, y0=5, y1=6, line_color="yellow"), row="all", col=2
)

# Adds a circle to all the columns of the first row
fig.add_shape(
    dict(type="circle", x0=10, y0=2, x1=20, y1=7), row=1, col="all", line_color="green"
)
fig.show()

SVG Paths¶

In [15]:

import plotly.graph_objects as go

fig = go.Figure()

# Create scatter trace of text labels
fig.add_trace(go.Scatter(
    x=[2, 1, 8, 8],
    y=[0.25, 9, 2, 6],
    text=["Filled Triangle",
          "Filled Polygon",
          "Quadratic Bezier Curves",
          "Cubic Bezier Curves"],
    mode="text",
))

# Update axes properties
fig.update_xaxes(
    range=[0, 9],
    zeroline=False,
)

fig.update_yaxes(
    range=[0, 11],
    zeroline=False,
)

# Add shapes
fig.update_layout(
    shapes=[
        # Quadratic Bezier Curves
        dict(
            type="path",
            path="M 4,4 Q 6,0 8,4",
            line_color="RoyalBlue",
        ),
        # Cubic Bezier Curves
        dict(
            type="path",
            path="M 1,4 C 2,8 6,4 8,8",
            line_color="MediumPurple",
        ),
        # filled Triangle
        dict(
            type="path",
            path=" M 1 1 L 1 3 L 4 1 Z",
            fillcolor="LightPink",
            line_color="Crimson",
        ),
        # filled Polygon
        dict(
            type="path",
            path=" M 3,7 L2,8 L2,9 L3,10 L4,10 L5,9 L5,8 L4,7 Z",
            fillcolor="PaleTurquoise",
            line_color="LightSeaGreen",
        ),
    ]
)

fig.show()

Shifting Shapes on Categorical Axes¶

New in 5.23

When drawing shapes where xref or yref reference axes of type category or multicategory, you can shift x0, x1, y0, and y1 away from the center of the category using x0shift, x1shift, y0shift, and y1shift by specifying a value between -1 and 1.

-1 is the center of the previous category, 0 is the center of the referenced category, and 1 is the center of the next category.

In the following example, the x0 and x1 values for both shapes reference category values on the x-axis.

In this example, the first shape:

Shifts x0 half way between the center of category "Germany" and the center of the previous category by setting x0shift=-0.5
Shifts x1half way between the center of category "Germany" and the center of the next category by setting x1shift=0.5

The second shape:

Shifts x0 back to the center of the previous category by setting x0shift=-1
Shifts x1forward to the center of the next category by setting x1shift=1

In [16]:

import plotly.graph_objects as go
import plotly.express as px

df = px.data.gapminder().query("continent == 'Europe' and year == 1952")

fig = go.Figure(
    data=go.Bar(x=df["country"], y=df["lifeExp"], marker_color="LightSalmon"),
    layout=dict(
        shapes=[
            dict(
                type="rect",
                x0="Germany",
                y0=0,
                x1="Germany",
                y1=0.5,
                xref="x",
                yref="paper",
                x0shift=-0.5,
                x1shift=0.5,
                line=dict(color="LightGreen", width=4),
            ),
            dict(
                type="rect",
                x0="Spain",
                y0=0,
                x1="Spain",
                y1=0.5,
                xref="x",
                yref="paper",
                x0shift=-1,
                x1shift=1,
                line=dict(color="MediumTurquoise", width=4),
            ),
        ]
    ),
)

fig.update_layout(
    title=dict(
        text="GDP per Capita in Europe (1972)"
    ),
    xaxis=dict(
        title=dict(
            text="Country"
        )
    ),
    yaxis=dict(
        title=dict(
            text="GDP per Capita"
        )
    ),
)

fig.show()

Drawing shapes with a Mouse on Cartesian plots¶

introduced in plotly 4.7

You can create layout shapes programmatically, but you can also draw shapes manually by setting the dragmode to one of the shape-drawing modes: 'drawline','drawopenpath', 'drawclosedpath', 'drawcircle', or 'drawrect'. If you need to switch between different shape-drawing or other dragmodes (panning, selecting, etc.), modebar buttons can be added in the config to select the dragmode. If you switch to a different dragmode such as pan or zoom, you will need to select the drawing tool in the modebar to go back to shape drawing.

This shape-drawing feature is particularly interesting for annotating graphs, in particular image traces or layout images.

Once you have drawn shapes, you can select and modify an existing shape by clicking on its boundary (note the arrow pointer). Its fillcolor turns to pink to highlight the activated shape and then you can

drag and resize it for lines, rectangles and circles/ellipses
drag and move individual vertices for closed paths
move individual vertices for open paths.

An activated shape is deleted by clicking on the eraseshape button.

Drawing or modifying a shape triggers a relayout event, which can be captured by a callback inside a Dash application.

In [17]:

import plotly.graph_objects as go
fig = go.Figure()
text="Click and drag here <br> to draw a rectangle <br><br> or select another shape <br>in the modebar"
fig.add_annotation(
            x=0.5,
            y=0.5,
            text=text,
            xref="paper",
            yref="paper",
            showarrow=False,
            font_size=20
)
# shape defined programatically
fig.add_shape(editable=True,
              x0=-1, x1=0, y0=2, y1=3,
              xref='x', yref='y')
# define dragmode and add modebar buttons
fig.update_layout(dragmode='drawrect')
fig.show(config={'modeBarButtonsToAdd':['drawline',
                                        'drawopenpath',
                                        'drawclosedpath',
                                        'drawcircle',
                                        'drawrect',
                                        'eraseshape'
                                       ]})

Style of user-drawn shapes¶

The layout newshape attribute controls the visual appearance of new shapes drawn by the user. newshape attributes have the same names as layout shapes.

Note on shape opacity: having a new shape's opacity > 0.5 makes it possible to activate a shape by clicking inside the shape (for opacity <= 0.5 you have to click on the border of the shape), but you cannot start a new shape within an existing shape (which is possible for an opacity <= 0.5).

In [18]:

import plotly.graph_objects as go
fig = go.Figure()
text="Click and drag<br> to draw a rectangle <br><br> or select another shape <br>in the modebar"
fig.add_annotation(
            x=0.5,
            y=0.5,
            text=text,
            xref="paper",
            yref="paper",
            showarrow=False,
            font_size=20
)
# shape defined programatically
fig.add_shape(line_color='yellow',
              fillcolor='turquoise',
              opacity=0.4,
              editable=True,
              x0=0, x1=1, y0=2, y1=3,
              xref='x', yref='y'
)
fig.update_layout(dragmode='drawrect',
                  # style of new shapes
                  newshape=dict(line_color='yellow',
                                fillcolor='turquoise',
                                opacity=0.5))
fig.show(config={'modeBarButtonsToAdd':['drawline',
                                        'drawopenpath',
                                        'drawclosedpath',
                                        'drawcircle',
                                        'drawrect',
                                        'eraseshape'
                                       ]})

Adding Text Labels to Shapes¶

New in 5.14

Add a text label to a shape by adding a label property to a shape with text. In this example, we add a rect and line shape and add a text label to both.

In [19]:

import plotly.graph_objects as go

fig = go.Figure()

fig.add_shape(
    type="rect",
    fillcolor='turquoise',
    x0=1,
    y0=1,
    x1=2,
    y1=3,
    label=dict(text="Text in rectangle")
)
fig.add_shape(
    type="line",
    x0=3,
    y0=0.5,
    x1=5,
    y1=0.8,
    line_width=3,
    label=dict(text="Text above line")
)

fig.show()

Styling Text Labels¶

Use the font property to configure the color, size, and family of the label font. In this example, we change the label color of the first rectangle to "DarkOrange", set the size of the text above the line to 20, and change the font family and set the font size on the second rectangle.

In [20]:

import plotly.graph_objects as go

fig = go.Figure()

fig.add_shape(
    type="rect",
    fillcolor='MediumSlateBlue',
    x0=1,
    y0=1,
    x1=2,
    y1=3,
    label=dict(text="Text in rectangle", font=dict(color="DarkOrange")),
)
fig.add_shape(
    type="line",
    x0=3,
    y0=0.5,
    x1=5,
    y1=0.8,
    line_width=3,
    label=dict(text="Text above line", font=dict(size=20)),
)
fig.add_shape(
    type="rect",
    fillcolor='Lavender',
    x0=2.5,
    y0=2.5,
    x1=5,
    y1=3.5,
    label=dict(
        text="Text in rectangle 2", font=dict(family="Courier New, monospace", size=20)
    ),
)

fig.show()

Setting Label Position¶

Set a label's position relative to the shape by setting textposition. The default position for lines is middle. The default position for other shapes is middle center.

In [21]:

import plotly.graph_objects as go

fig = go.Figure()

fig.add_shape(
    type="rect",
    fillcolor='Lavender',
    x0=0,
    y0=0,
    x1=1.5,
    y1=1.5,
    label=dict(text="Text at middle center"),
)

fig.add_shape(
    type="rect",
    fillcolor='Lavender',
    x0=3,
    y0=0,
    x1=4.5,
    y1=1.5,
    label=dict(text="Text at top left", textposition="top left"),
)


fig.add_shape(
    type="line",
    line_color="MediumSlateBlue",
    x0=3,
    y0=2,
    x1=5,
    y1=3,
    line_width=3,
    label=dict(text="Text at start", textposition="start"),
)


fig.add_shape(
    type="line",
    line_color="MediumSlateBlue",
    x0=0,
    y0=2,
    x1=2,
    y1=3,
    line_width=3,
    label=dict(text="Text at middle"),
)

fig.show()

Setting Label Angle¶

Use textangle to rotate a label by setting a value between -180 and 180. The default angle for a label on a line is the angle of the line. The default angle for a label on other shapes is 0. In this example, in the first shape, the label is at 45 degrees, and in the second, the label is at -45 degrees.

In [22]:

import plotly.graph_objects as go

fig = go.Figure()

fig.add_shape(
    type="rect",
    fillcolor='LightGreen',
    x0=0,
    y0=0,
    x1=2,
    y1=2,
    label=dict(text="Text at 45", textangle=45),
)

fig.add_shape(
    type="rect",
    fillcolor='Gold',
    x0=3,
    y0=0,
    x1=5,
    y1=2,
    label=dict(text="Text at -45", textangle=-45),
)

fig.show()

Setting Label Padding¶

padding adds padding between the label and shape. This example shows one line with padding of 30px and another with the default padding, which is 3px.

In [23]:

import plotly.graph_objects as go

fig = go.Figure()

fig.add_shape(
    type="line",
    line_color="RoyalBlue",
    x0=3,
    y0=0,
    x1=5,
    y1=3,
    line_width=3,
    label=dict(text="Label padding of 30px", padding=30),
)

fig.add_shape(
    type="line",
    line_color="RoyalBlue",
    x0=0,
    y0=0,
    x1=2,
    y1=3,
    line_width=3,
    label=dict(text="Default label padding of 3px"),
)

fig.show()

Setting Label Anchors¶

xanchor sets a label's horizontal positional anchor and yanchor sets its vertical position anchor. Use xanchor to bind the textposition to the "left", "center" or "right" of the label text and yanchor to bind textposition to the "top", "middle" or "bottom" of the label text.

In this example, yanchoris set to "top", instead of the default of "bottom" for lines, meaning the text displays below the line.

In [24]:

import plotly.express as px

df = px.data.stocks(indexed=True)
fig = px.line(df)

fig.add_shape(
    type="rect",
    x0="2018-09-24",
    y0=0,
    x1="2018-12-18",
    y1=3,
    line_width=0,
    label=dict(text="Decline", textposition="top center", font=dict(size=20)),
    fillcolor="green",
    opacity=0.25,
)

fig.add_shape(
    type="line",
    x0=min(df.index),
    y0=1,
    x1=max(df.index),
    y1=1,
    line_width=3,
    line_dash="dot",
    label=dict(
        text="Jan 1 2018 Baseline",
        textposition="end",
        font=dict(size=20, color="blue"),
        yanchor="top",
    ),
)

fig.show()

Variables in Shape Label Text¶

New in 5.15

Use texttemplate to add text with variables to shapes. You have access to raw variables (x0, x1, y0, y1), which use raw data values from the shape definition, and the following calculated variables:

xcenter: (x0 + x1) / 2
ycenter: (y0 + y1) / 2
dx: x1 - x0
dy: y1 - y0
width: abs(x1 - x0)
height: abs(y1 - y0)
length (for lines only): sqrt(dx^2 + dy^2)
slope: (y1 - y0) / (x1 - x0)

texttemplate supports d3 number and date formatting.

Add a variable with "%{variable}". This example adds the raw variables x0 and y0 to a rectangle and shows the calculated variables height, slope, length, and width on three other shapes.

In [25]:

import plotly.graph_objects as go

fig = go.Figure()


fig.add_shape(
    type="rect",
    fillcolor="MediumSlateBlue",
    x0=-0.5,
    y0=-0.5,
    x1=1,
    y1=1,
    label=dict(
        texttemplate="x0 is %{x0:.3f}, y0 is %{y0:.3f}", font=dict(color="DarkOrange")
    ),
)

fig.add_shape(
    type="rect",
    fillcolor="LightGreen",
    x0=1,
    y0=1.75,
    x1=2.25,
    y1=3,
    label=dict(texttemplate="Height: %{height:.3f}", font=dict(color="DarkOrange")),
)
fig.add_shape(
    type="line",
    x0=3,
    y0=0.5,
    x1=5,
    y1=1.5,
    line_width=3,
    label=dict(
        texttemplate="Slope of %{slope:.3f} and length of %{length:.3f}",
        font=dict(size=20),
    ),
)
fig.add_shape(
    type="rect",
    fillcolor="Lavender",
    x0=2.5,
    y0=2.5,
    x1=5,
    y1=3.5,
    label=dict(
        texttemplate="Width: %{width:.3f}",
        font=dict(family="Courier New, monospace", size=20),
    ),
)

fig.show()

Variables in Shape Label Text for New Shapes¶

New in 5.15

You can also use texttemplate to add text with variables to new shapes drawn on the graph.

In this example, we enable drawing lines on the figure by adding drawline to modeBarButtonsToAdd in config. We then define a texttemplate for shapes that shows the calculated variable dy. Select Draw line in the modebar to try it out.

In [26]:

import plotly.graph_objects as go
from plotly import data

df = data.stocks()

fig = go.Figure(
    data=go.Scatter(
        x=df.date,
        y=df.GOOG,
    ),
    layout=go.Layout(
        yaxis=dict(
            title=dict(
                text="Price in USD"
            )),
        newshape=dict(
            label=dict(texttemplate="Change: %{dy:.2f}")
        ),
        title=dict(text="Google Share Price 2018/2019"),
    ),
)


fig.show(
    config={
        "modeBarButtonsToAdd": [
            "drawline",
        ]
    }
)

Shapes in the Legend¶

New in 5.16

You can add a shape to the legend by setting showlegend=True on the shape. In this example, we add the second shape to the legend. The name that appears for the shape in the legend is the shape's name if it is provided. If no name is provided, the shape label's text is used. If neither is provided, the legend item appears as "shape \". For example, "shape 1".

In [27]:

import plotly.express as px

df = px.data.stocks(indexed=True)

fig = px.line(df)

fig.add_shape(
    type="rect",
    x0="2018-09-24",
    y0=0,
    x1="2018-12-18",
    y1=3,
    line_width=0,
    label=dict(text="Decline", textposition="top center", font=dict(size=20)),
    fillcolor="green",
    opacity=0.25,
)

fig.add_shape(
    showlegend=True,
    type="line",
    x0=min(df.index),
    y0=1,
    x1=max(df.index),
    y1=1,
    line_width=3,
    line_dash="dot",
    label=dict(
        text="Jan 1 2018 Baseline",
        textposition="end",
        font=dict(size=20, color="blue"),
        yanchor="top",
    ),
)

fig.show()

newshape also supports showlegend. In this example, each new line drawn on the graph appears in the legend.

In [28]:

import plotly.graph_objects as go
from plotly import data

df = data.stocks()

fig = go.Figure(
    data=go.Scatter(
        x=df.date,
        y=df.AAPL,
        name="Apple"
    ),
    layout=go.Layout(
        yaxis=dict(
            title=dict(text="Price in USD"),
            ),
        newshape=dict(
            showlegend=True,
            label=dict(texttemplate="Change: %{dy:.2f}")
        ),
        title=dict(text="Apple Share Price 2018/2019"),
    ),
)


fig.show(
    config={
        "modeBarButtonsToAdd": [
            "drawline",
        ]
    }
)

Shape Layer¶

By default, shapes are drawn above traces. You can also configure them to be drawn between traces and gridlines with layer="between" (new in 5.21), or below gridlines with layer="below".

In [29]:

import plotly.express as px

df = px.data.stocks(indexed=True)

fig = px.line(df)

fig.add_shape(
    type="rect",
    x0="2018-03-01",
    y0=0,
    x1="2018-08-01",
    y1=3,
    line_width=0,
    layer="above",
    label=dict(text="Above", textposition="top center", font=dict(size=15)),
    fillcolor="LightGreen",
    opacity=0.80,
)

fig.add_shape(
    type="rect",
    x0="2018-10-01",
    y0=0,
    x1="2019-03-01",
    y1=3,
    line_width=0,
    layer="between",
    label=dict(text="Between", textposition="top center", font=dict(size=15)),
    fillcolor="LightGreen",
    opacity=0.80,
)

fig.add_shape(
    type="rect",
    x0="2019-05-01",
    y0=0,
    x1="2019-10-01",
    y1=3,
    line_width=0,
    layer="below",
    label=dict(text="Below", textposition="top center", font=dict(size=15)),
    fillcolor="LightGreen",
    opacity=0.80,
)

fig.show()

Reference¶

See https://plotly.com/python/reference/layout/shapes/ for more information and chart attribute options!

What About Dash?¶

Dash is an open-source framework for building analytical applications, with no Javascript required, and it is tightly integrated with the Plotly graphing library.

Learn about how to install Dash at https://dash.plot.ly/installation.

Everywhere in this page that you see fig.show(), you can display the same figure in a Dash application by passing it to the figure argument of the Graph component from the built-in dash_core_components package like this:

import plotly.graph_objects as go # or plotly.express as px
fig = go.Figure() # or any Plotly Express function e.g. px.bar(...)
# fig.add_trace( ... )
# fig.update_layout( ... )

from dash import Dash, dcc, html

app = Dash()
app.layout = html.Div([
    dcc.Graph(figure=fig)
])

app.run_server(debug=True, use_reloader=False)  # Turn off reloader if inside Jupyter