Scatter plots 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.

In [1]:

# 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()

In [2]:

# 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()

Setting size and color with column names¶

Scatter plots with variable-sized circular markers are often known as bubble charts. 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.

In [3]:

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()

Color can be continuous as follows, or discrete/categorical as above.

In [4]:

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

The symbol argument can be mapped to a column as well. A wide variety of symbols are available.

In [5]:

import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", symbol="species")
fig.show()

Scatter plots 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[6]:

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.

Scatter plots and Categorical Axes¶

Scatter plots can be made using any type of cartesian axis, including linear, logarithmic, categorical or date axes.

Scatter plots where one axis is categorical are often known as dot plots.

In [7]:

import plotly.express as px
df = px.data.medals_long()

fig = px.scatter(df, y="nation", x="count", color="medal", symbol="medal")
fig.update_traces(marker_size=10)
fig.show()

Grouped Scatter Points¶

New in 5.12

By default, scatter points at the same location are overlayed. We can see this in the previous example, with the values for Canada for bronze and silver. Set scattermode='group' to plot scatter points next to one another, centered around the shared location.

In [8]:

import plotly.express as px

df = px.data.medals_long()

fig = px.scatter(df, y="count", x="nation", color="medal")
fig.update_traces(marker_size=10)
fig.update_layout(scattermode="group")
fig.show()

New in 5.12

You can configure the gap between groups of scatter points using scattergap. Here we set it to 0.75, which brings the points closer together by allocating more space to the gap between groups. If you don't set scattergap, a default value of 0 is used, unless you have bargap set. If you have bargap set, the scattergap defaults to that value.

In [9]:

import plotly.express as px

df = px.data.medals_long()

fig = px.scatter(df, y="count", x="nation", color="medal")
fig.update_traces(marker_size=10)
fig.update_layout(scattermode="group", scattergap=0.75)
fig.show()

Error Bars¶

Scatter plots support error bars.

In [10]:

import plotly.express as px
df = px.data.iris()
df["e"] = df["sepal_width"]/100
fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species",
                 error_x="e", error_y="e")
fig.show()

Marginal Distribution Plots¶

Scatter plots support marginal distribution plots

In [11]:

import plotly.express as px
df = px.data.iris()
fig = px.scatter(df, x="sepal_length", y="sepal_width", marginal_x="histogram", marginal_y="rug")
fig.show()

Facetting¶

Scatter plots support faceting.

In [12]:

import plotly.express as px
df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", color="smoker", facet_col="sex", facet_row="time")
fig.show()

Linear Regression and Other Trendlines¶

Scatter plots support linear and non-linear trendlines.

In [13]:

import plotly.express as px

df = px.data.tips()
fig = px.scatter(df, x="total_bill", y="tip", trendline="ols")
fig.show()

Line plots with Plotly Express¶

In [14]:

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()

In [15]:

import plotly.express as px
df = px.data.gapminder().query("continent == 'Oceania'")
fig = px.line(df, x='year', y='lifeExp', color='country')
fig.show()

The markers argument can be set to True to show markers on lines.

In [16]:

import plotly.express as px
df = px.data.gapminder().query("continent == 'Oceania'")
fig = px.line(df, x='year', y='lifeExp', color='country', markers=True)
fig.show()

The symbol argument can be used to map a data field to the marker symbol. A wide variety of symbols are available.

In [17]:

import plotly.express as px
df = px.data.gapminder().query("continent == 'Oceania'")
fig = px.line(df, x='year', y='lifeExp', color='country', symbol="country")
fig.show()

Line plots on Date axes¶

Line plots can be made on using any type of cartesian axis, including linear, logarithmic, categorical or date axes. Line plots on date axes are often called time-series charts.

Plotly auto-sets the axis type to a date format when the corresponding data are either ISO-formatted date strings or if they're a date pandas column or datetime NumPy array.

In [18]:

import plotly.express as px

df = px.data.stocks()
fig = px.line(df, x='date', y="GOOG")
fig.show()

Data Order in Scatter and Line Charts¶

Plotly line charts are implemented as connected scatterplots (see below), meaning that the points are plotted and connected with lines in the order they are provided, with no automatic reordering.

This makes it possible to make charts like the one below, but also means that it may be required to explicitly sort data before passing it to Plotly to avoid lines moving "backwards" across the chart.

In [19]:

import plotly.express as px
import pandas as pd

df = pd.DataFrame(dict(
    x = [1, 3, 2, 4],
    y = [1, 2, 3, 4]
))
fig = px.line(df, x="x", y="y", title="Unsorted Input")
fig.show()

df = df.sort_values(by="x")
fig = px.line(df, x="x", y="y", title="Sorted Input")
fig.show()

Connected Scatterplots¶

In a connected scatterplot, two continuous variables are plotted against each other, with a line connecting them in some meaningful order, usually a time variable. In the plot below, we show the "trajectory" of a pair of countries through a space defined by GDP per Capita and Life Expectancy. Botswana's life expectancy

In [20]:

import plotly.express as px

df = px.data.gapminder().query("country in ['Canada', 'Botswana']")

fig = px.line(df, x="lifeExp", y="gdpPercap", color="country", text="year")
fig.update_traces(textposition="bottom right")
fig.show()

Scatter and line plots 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¶

In [21]:

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.

In [22]:

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

In [23]:

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¶

In [24]:

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=dict(text='Styled Scatter'),
                  yaxis_zeroline=False, xaxis_zeroline=False)


fig.show()

Data Labels on Hover¶

In [25]:

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=dict(text='Population of USA States'))
fig.show()

Scatter with a Color Dimension¶

In [26]:

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()

Trace Zorder¶

New in 5.21

For many trace types, including go.Scatter, you can define the order traces are drawn in by setting a zorder. Traces with a higher zorder appear at the front, with traces with a lower zorder at the back. In this example, we give our trace for 'France' the highest zorder, meaning it is drawn in front of the other two traces:

In [27]:

import plotly.graph_objects as go
import plotly.data as data

df = data.gapminder()

df_europe = df[df['continent'] == 'Europe']

trace1 = go.Scatter(x=df_europe[df_europe['country'] == 'France']['year'],
                    y=df_europe[df_europe['country'] == 'France']['lifeExp'],
                    mode='lines+markers',
                    zorder=3,
                    name='France',
                    marker=dict(size=15))

trace2 = go.Scatter(x=df_europe[df_europe['country'] == 'Germany']['year'],
                    y=df_europe[df_europe['country'] == 'Germany']['lifeExp'],
                    mode='lines+markers',
                    zorder=1,
                    name='Germany',
                    marker=dict(size=15))

trace3 = go.Scatter(x=df_europe[df_europe['country'] == 'Spain']['year'],
                    y=df_europe[df_europe['country'] == 'Spain']['lifeExp'],
                    mode='lines+markers',
                    zorder=2,
                    name='Spain',
                    marker=dict(size=15))

layout = go.Layout(title=dict(text='Life Expectancy in Europe Over Time'))

fig = go.Figure(data=[trace1, trace2, trace3], layout=layout)

fig.show()

Large Data Sets¶

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

In [28]:

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()

In [29]:

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!

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