Python > Fundamentals > Styling Markers
How to style markers in Python with Plotly.
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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.
Add Marker Border¶
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 pxdf = 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 dataimport numpy as npnp.random.seed(1)x = np.random.uniform(low=3, high=6, size=(500,))y = np.random.uniform(low=3, high=6, size=(500,))# Build figurefig = go.Figure()# Add scatter trace with medium sized markersfig.add_trace( 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 markerfig.add_trace( 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.
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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 dataimport numpy as npx = 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 figurefig = go.Figure()# Add first scatter trace with medium sized markersfig.add_trace( 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 1.0fig.add_trace( 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 markersfig.add_trace( 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 dataimport numpy as npx = np.random.uniform(low=3, high=6, size=(500,))y = np.random.uniform(low=3, high=6, size=(500,))# Build figurefig = go.Figure()# Add scatter trace with medium sized markersfig.add_trace( 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 markersfig.add_trace( 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 dataimport numpy as npx = np.random.uniform(low=3, high=6, size=(500,))y = np.random.uniform(low=3, high=6, size=(500,))# Build figurefig = go.Figure()# Add scatter trace with medium sized markersfig.add_trace( 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 markersfig.add_trace( 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, 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.
The
arrow-wideandarrowmarker symbols are new in 5.11
In[7]:
import plotly.graph_objects as gofrom plotly.validators.scatter.marker import SymbolValidatorraw_symbols = SymbolValidator().valuesnamestems = []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()
Using a Custom Marker¶
To use a custom marker, set the symbol on the marker. Here we set it to diamond.
In[8]:
import plotly.express as pxdf = px.data.iris()fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")fig.update_traces( marker=dict(size=8, symbol="diamond", line=dict(width=2, color="DarkSlateGrey")), selector=dict(mode="markers"),)fig.show()
Open Marker Colors¶
In the previous example, each marker has two colors, a marker color (set in Plotly Express with color="species") and a line color (set on the line with color="DarkSlateGrey". All open markers, like "diamond-open" in the following example, have a transparent fill, which means you can specify only one color. Specify this color using the marker color parameter. This controls the outline color and any dot or cross. For open markers, the line color does nothing.
In[9]:
import plotly.express as pxdf = px.data.iris()fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")fig.update_traces( marker=dict( size=8, symbol="diamond-open", line=dict( width=2, # color="DarkSlateGrey" Line colors don't apply to open markers ) ), selector=dict(mode="markers"),)fig.show()
Setting Marker Angles¶
New in 5.11
Change the angle of markers by setting angle. Here we set the angle on the arrow markers to 45.
In[10]:
import plotly.express as pxdf = px.data.iris()fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species")fig.update_traces( marker=dict( size=12, symbol="arrow", angle=45, line=dict(width=2, color="DarkSlateGrey") ), selector=dict(mode="markers"),)fig.show()
Setting Angle Reference¶
New in 5.11
In the previous example the angle reference is the default up, whichmeans all makers start at the angle reference point of 0. Set angleref to previous and a marker will take its angle reference from the previous data point.
In[11]:
import pandas as pdimport plotly.express as pximport plotly.graph_objects as godf = px.data.gapminder()fig = go.Figure()for x in df.loc[df.continent.isin(["Europe"])].country.unique()[:5]: fil = df.loc[(df.country.str.contains(x))] fig.add_trace( go.Scatter( x=fil["year"], y=fil["pop"], mode="lines+markers", marker=dict( symbol="arrow", size=15, angleref="previous", ), name=x, ) )fig.show()
Using Standoff to Position a Marker¶
New in 5.11
When you have multiple markers at one location, you can use standoff on a marker to move it away from the other marker in the direction of the angle. In this example, we set standoff=8 on the arrow marker, which is half the size of the other circle marker, meaning it points exactly at the circle.
In[12]:
import pandas as pdimport plotly.graph_objects as gofrom plotly import datadf = data.gapminder()df = df.loc[(df.continent == "Americas") & (df.year.isin([1987, 2007]))]countries = ( df.loc[(df.continent == "Americas") & (df.year.isin([2007]))] .sort_values(by=["pop"], ascending=True)["country"] .unique())[5:-10]data = {"x": [], "y": [], "colors": [], "years": []}for country in countries: data["x"].extend( [ df.loc[(df.year == 1987) & (df.country == country)]["pop"].values[0], df.loc[(df.year == 2007) & (df.country == country)]["pop"].values[0], None, ] ) data["y"].extend([country, country, None]), data["colors"].extend(["cyan", "darkblue", "white"]), data["years"].extend(["1987", "2007", None])fig = go.Figure( data=[ go.Scatter( x=data["x"], y=data["y"], mode="markers+lines", marker=dict( symbol="arrow", color="royalblue", size=16, angleref="previous", standoff=8, ), ), go.Scatter( x=data["x"], y=data["y"], text=data["years"], mode="markers", marker=dict( color=data["colors"], size=16, ), hovertemplate="""Country: %{y} <br> Population: %{x} <br> Year: %{text} <br><extra></extra>""", ), ])fig.update_layout( title="Population changes 1987 to 2007", width=1000, height=1000, showlegend=False,)fig.show()
Reference¶
See https://plotly.com/python/reference/ 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 pxfig = go.Figure() # or any Plotly Express function e.g. px.bar(...)# fig.add_trace( ... )# fig.update_layout( ... )from dash import Dash, dcc, htmlapp = Dash()app.layout = html.Div([ dcc.Graph(figure=fig)])app.run_server(debug=True, use_reloader=False) # Turn off reloader if inside Jupyter
