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How to make scatter plots in Python with Plotly.
basic
python
base
Scatter Plots
1
example_index
python/line-and-scatter/
python/line-and-scatter-plots-tutorial/
thumbnail/line-and-scatter.jpg

Scatter plot with Plotly Express

Plotly Express is the easy-to-use, high-level interface to Plotly, which operates on a variety of types of data and produces easy-to-style figures.

With px.scatter, each data point is represented as a marker point, whose location is given by the x and y columns.

# x and y given as array_like objects
import plotly.express as px
fig = px.scatter(x=[0, 1, 2, 3, 4], y=[0, 1, 4, 9, 16])
fig.show()
# x and y given as DataFrame columns
import plotly.express as px
df = px.data.iris() # iris is a pandas DataFrame
fig = px.scatter(df, x="sepal_width", y="sepal_length")
fig.show()

Set size and color with column names

Note that color and size data are added to hover information. You can add other columns to hover data with the hover_data argument of px.scatter.

import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
                 size='petal_length', hover_data=['petal_width'])
fig.show()

Scatter plot 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.

from IPython.display import IFrame
snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/'
IFrame(snippet_url + 'line-and-scatter', width='100%', height=630)

Line plot with Plotly Express

import plotly.express as px
import numpy as np

t = np.linspace(0, 2*np.pi, 100)

fig = px.line(x=t, y=np.cos(t), labels={'x':'t', 'y':'cos(t)'})
fig.show()
import plotly.express as px
df = px.data.gapminder().query("continent == 'Oceania'")
fig = px.line(df, x='year', y='lifeExp', color='country')
fig.show()

Scatter and line plot with go.Scatter

If Plotly Express does not provide a good starting point, it is possible to use the more generic go.Scatter class from plotly.graph_objects. Whereas plotly.express has two functions scatter and line, go.Scatter can be used both for plotting points (makers) or lines, depending on the value of mode. The different options of go.Scatter are documented in its reference page.

Simple Scatter Plot

import plotly.graph_objects as go
import numpy as np

N = 1000
t = np.linspace(0, 10, 100)
y = np.sin(t)

fig = go.Figure(data=go.Scatter(x=t, y=y, mode='markers'))

fig.show()

Line and Scatter Plots

Use mode argument to choose between markers, lines, or a combination of both. For more options about line plots, see also the line charts notebook and the filled area plots notebook.

import plotly.graph_objects as go

# Create random data with numpy
import numpy as np
np.random.seed(1)

N = 100
random_x = np.linspace(0, 1, N)
random_y0 = np.random.randn(N) + 5
random_y1 = np.random.randn(N)
random_y2 = np.random.randn(N) - 5

fig = go.Figure()

# Add traces
fig.add_trace(go.Scatter(x=random_x, y=random_y0,
                    mode='markers',
                    name='markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y1,
                    mode='lines+markers',
                    name='lines+markers'))
fig.add_trace(go.Scatter(x=random_x, y=random_y2,
                    mode='lines',
                    name='lines'))

fig.show()

Bubble Scatter Plots

In bubble charts, a third dimension of the data is shown through the size of markers. For more examples, see the bubble chart notebook

import plotly.graph_objects as go

fig = go.Figure(data=go.Scatter(
    x=[1, 2, 3, 4],
    y=[10, 11, 12, 13],
    mode='markers',
    marker=dict(size=[40, 60, 80, 100],
                color=[0, 1, 2, 3])
))

fig.show()

Style Scatter Plots

import plotly.graph_objects as go
import numpy as np


t = np.linspace(0, 10, 100)

fig = go.Figure()

fig.add_trace(go.Scatter(
    x=t, y=np.sin(t),
    name='sin',
    mode='markers',
    marker_color='rgba(152, 0, 0, .8)'
))

fig.add_trace(go.Scatter(
    x=t, y=np.cos(t),
    name='cos',
    marker_color='rgba(255, 182, 193, .9)'
))

# Set options common to all traces with fig.update_traces
fig.update_traces(mode='markers', marker_line_width=2, marker_size=10)
fig.update_layout(title='Styled Scatter',
                  yaxis_zeroline=False, xaxis_zeroline=False)


fig.show()

Data Labels on Hover

import plotly.graph_objects as go
import pandas as pd

data= pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/2014_usa_states.csv")

fig = go.Figure(data=go.Scatter(x=data['Postal'],
                                y=data['Population'],
                                mode='markers',
                                marker_color=data['Population'],
                                text=data['State'])) # hover text goes here

fig.update_layout(title='Population of USA States')
fig.show()

Scatter with a Color Dimension

import plotly.graph_objects as go
import numpy as np

fig = go.Figure(data=go.Scatter(
    y = np.random.randn(500),
    mode='markers',
    marker=dict(
        size=16,
        color=np.random.randn(500), #set color equal to a variable
        colorscale='Viridis', # one of plotly colorscales
        showscale=True
    )
))

fig.show()

Large Data Sets

Now in Ploty you can implement WebGL with Scattergl() in place of Scatter()
for increased speed, improved interactivity, and the ability to plot even more data!

import plotly.graph_objects as go
import numpy as np

N = 100000
fig = go.Figure(data=go.Scattergl(
    x = np.random.randn(N),
    y = np.random.randn(N),
    mode='markers',
    marker=dict(
        color=np.random.randn(N),
        colorscale='Viridis',
        line_width=1
    )
))

fig.show()
import plotly.graph_objects as go
import numpy as np

N = 100000
r = np.random.uniform(0, 1, N)
theta = np.random.uniform(0, 2*np.pi, N)

fig = go.Figure(data=go.Scattergl(
    x = r * np.cos(theta), # non-uniform distribution
    y = r * np.sin(theta), # zoom to see more points at the center
    mode='markers',
    marker=dict(
        color=np.random.randn(N),
        colorscale='Viridis',
        line_width=1
    )
))

fig.show()

Reference

See function reference for px.scatter() or https://plotly.com/python/reference/scatter/ or https://plotly.com/python/reference/scattergl/ for more information and chart attribute options!