# Styling Markers in Python

How to style markers in Python with Plotly.

New to Plotly?

Plotly is a free and open-source graphing library for Python. We recommend you read our Getting Started guide for the latest installation or upgrade instructions, then move on to our Plotly Fundamentals tutorials or dive straight in to some Basic Charts tutorials.

In order to make markers look more distinct, you can add a border to the markers. This can be achieved by adding the line property to the marker object.

Here is an example of adding a marker border to a faceted scatter plot created using Plotly Express.

In [1]:
import plotly.express as px

df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")

fig.update_traces(marker=dict(size=12,
line=dict(width=2,
color='DarkSlateGrey')),
selector=dict(mode='markers'))
fig.show()


Here is an example that creates an empty graph object figure, and then adds two scatter traces with a marker border.

In [2]:
import plotly.graph_objects as go

# Generate example data
import numpy as np
np.random.seed(1)

x = np.random.uniform(low=3, high=6, size=(500,))
y = np.random.uniform(low=3, high=6, size=(500,))

# Build figure
fig = go.Figure()

# Add scatter trace with medium sized markers
go.Scatter(
mode='markers',
x=x,
y=y,
marker=dict(
color='LightSkyBlue',
size=20,
line=dict(
color='MediumPurple',
width=2
)
),
showlegend=False
)
)

# Add trace with large marker
go.Scatter(
mode='markers',
x=[2],
y=[4.5],
marker=dict(
color='LightSkyBlue',
size=120,
line=dict(
color='MediumPurple',
width=12
)
),
showlegend=False
)
)

fig.show()


Fully opaque, the default setting, is useful for non-overlapping markers. When many points overlap it can be hard to observe density.

### Control Marker Border with 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]:

### Opacity¶

Setting opacity outside the marker will set the opacity of the trace. Thus, it will allow greater visibility of additional traces but like fully opaque it is hard to distinguish density.

In [4]:
import plotly.graph_objects as go

# Generate example data
import numpy as np

x = np.random.uniform(low=3, high=6, size=(500,))
y = np.random.uniform(low=3, high=4.5, size=(500,))
x2 = np.random.uniform(low=3, high=6, size=(500,))
y2 = np.random.uniform(low=4.5, high=6, size=(500,))

# Build figure
fig = go.Figure()

# Add first scatter trace with medium sized markers
go.Scatter(
mode='markers',
x=x,
y=y,
opacity=0.5,
marker=dict(
color='LightSkyBlue',
size=20,
line=dict(
color='MediumPurple',
width=2
)
),
name='Opacity 0.5'
)
)

# Add second scatter trace with medium sized markers
# and opacity 1.0
go.Scatter(
mode='markers',
x=x2,
y=y2,
marker=dict(
color='LightSkyBlue',
size=20,
line=dict(
color='MediumPurple',
width=2
)
),
name='Opacity 1.0'
)
)

# Add trace with large markers
go.Scatter(
mode='markers',
x=[2, 2],
y=[4.25, 4.75],
opacity=0.5,
marker=dict(
color='LightSkyBlue',
size=80,
line=dict(
color='MediumPurple',
width=8
)
),
showlegend=False
)
)

fig.show()


### Marker Opacity¶

To maximise visibility of density, it is recommended to set the opacity inside the marker marker:{opacity:0.5}. If multiple traces exist with high density, consider using marker opacity in conjunction with trace opacity.

In [5]:
import plotly.graph_objects as go

# Generate example data
import numpy as np

x = np.random.uniform(low=3, high=6, size=(500,))
y = np.random.uniform(low=3, high=6, size=(500,))

# Build figure
fig = go.Figure()

# Add scatter trace with medium sized markers
go.Scatter(
mode='markers',
x=x,
y=y,
marker=dict(
color='LightSkyBlue',
size=20,
opacity=0.5,
line=dict(
color='MediumPurple',
width=2
)
),
showlegend=False
)
)

# Add trace with large markers
go.Scatter(
mode='markers',
x=[2, 2],
y=[4.25, 4.75],
marker=dict(
color='LightSkyBlue',
size=80,
opacity=0.5,
line=dict(
color='MediumPurple',
width=8
)
),
showlegend=False
)
)

fig.show()


### Color Opacity¶

To maximise visibility of each point, set the color as an rgba string that includes an alpha value of 0.5.

This example sets the marker color to 'rgba(135, 206, 250, 0.5)'. The rgb values of 135, 206, and 250 are from the definition of the LightSkyBlue named CSS color that is is used in the previous examples (See https://www.color-hex.com/color/87cefa). The marker line will remain opaque.

In [6]:
import plotly.graph_objects as go

# Generate example data
import numpy as np

x = np.random.uniform(low=3, high=6, size=(500,))
y = np.random.uniform(low=3, high=6, size=(500,))

# Build figure
fig = go.Figure()

# Add scatter trace with medium sized markers
go.Scatter(
mode='markers',
x=x,
y=y,
marker=dict(
color='rgba(135, 206, 250, 0.5)',
size=20,
line=dict(
color='MediumPurple',
width=2
)
),
showlegend=False
)
)

# Add trace with large markers
go.Scatter(
mode='markers',
x=[2, 2],
y=[4.25, 4.75],
marker=dict(
color='rgba(135, 206, 250, 0.5)',
size=80,
line=dict(
color='MediumPurple',
width=8
)
),
showlegend=False
)
)

fig.show()


### Custom Marker Symbols¶

The marker_symbol attribute allows you to choose from a wide array of symbols to represent markers in your figures.

The basic symbols are: circle, square, diamond, cross, x, triangle, pentagon, hexagram, star, diamond, hourglass, bowtie, asterisk, hash, y, and line.

Each basic symbol is also represented by a number. Adding 100 to that number is equivalent to appending the suffix "-open" to a symbol name. Adding 200 is equivalent to appending "-dot" to a symbol name. Adding 300 is equivalent to appending "-open-dot" or "dot-open" to a symbol name.

In the following figure, hover over a symbol to see its name or number. Set the marker_symbol attribute equal to that name or number to change the marker symbol in your figure.

In [7]:
import plotly.graph_objects as go
from plotly.validators.scatter.marker import SymbolValidator

raw_symbols = SymbolValidator().values
namestems = []
namevariants = []
symbols = []
for i in range(0,len(raw_symbols),3):
name = raw_symbols[i+2]
symbols.append(raw_symbols[i])
namestems.append(name.replace("-open", "").replace("-dot", ""))
namevariants.append(name[len(namestems[-1]):])

fig = go.Figure(go.Scatter(mode="markers", x=namevariants, y=namestems, marker_symbol=symbols,
marker_line_color="midnightblue", marker_color="lightskyblue",
marker_line_width=2, marker_size=15,
hovertemplate="name: %{y}%{x}<br>number: %{marker.symbol}<extra></extra>"))
fig.update_layout(title="Mouse over symbols for name & number!",
xaxis_range=[-1,4], yaxis_range=[len(set(namestems)),-1],
margin=dict(b=0,r=0), xaxis_side="top", height=1400, width=400)
fig.show()


### Reference¶

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(...)