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Multiple Chart Types in Python

How to design figures with multiple chart types in python.


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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.

Chart Types versus Trace Types

Plotly's figure data structure supports defining subplots of various types (e.g. cartesian, polar, 3-dimensional, maps etc) with attached traces of various compatible types (e.g. scatter, bar, choropleth, surface etc). This means that Plotly figures are not constrained to representing a fixed set of "chart types" such as scatter plots only or bar charts only or line charts only: any subplot can contain multiple traces of different types.

Multiple Trace Types 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.

Plotly Express exposes a number of functions such as px.scatter() and px.choropleth() which generally speaking only contain traces of the same type, with exceptions made for trendlines and marginal distribution plots.

Figures produced with Plotly Express functions support the add_trace() method documented below, just like figures created with graph objects so it is easy to start with a Plotly Express figure containing only traces of a given type, and add traces of another type.

In [1]:
import plotly.express as px

fruits = ["apples", "oranges", "bananas"]
fig = px.line(x=fruits, y=[1,3,2], color=px.Constant("This year"),
             labels=dict(x="Fruit", y="Amount", color="Time Period"))
fig.add_bar(x=fruits, y=[2,1,3], name="Last year")
fig.show()

Line Chart and a Bar Chart

In [2]:
import plotly.graph_objects as go

fig = go.Figure()

fig.add_trace(
    go.Scatter(
        x=[0, 1, 2, 3, 4, 5],
        y=[1.5, 1, 1.3, 0.7, 0.8, 0.9]
    ))

fig.add_trace(
    go.Bar(
        x=[0, 1, 2, 3, 4, 5],
        y=[1, 0.5, 0.7, -1.2, 0.3, 0.4]
    ))

fig.show()

A Contour and Scatter Plot of the Method of Steepest Descent

In [3]:
import plotly.graph_objects as go

# Load data
import json
import six.moves.urllib

response = six.moves.urllib.request.urlopen(
    "https://raw.githubusercontent.com/plotly/datasets/master/steepest.json")

data = json.load(response)

# Create figure
fig = go.Figure()

fig.add_trace(
    go.Contour(
        z=data["contour_z"][0],
        y=data["contour_y"][0],
        x=data["contour_x"][0],
        ncontours=30,
        showscale=False
    )
)

fig.add_trace(
    go.Scatter(
        x=data["trace_x"],
        y=data["trace_y"],
        mode="markers+lines",
        name="steepest",
        line=dict(
            color="black"
        )
    )
)

fig.show()

Reference

See https://plotly.com/python/reference/ for more information and 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( ... )

import dash
import dash_core_components as dcc
import dash_html_components as html

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

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