graphing-multiple-chart-types.md

jupyter

jupytext kernelspec language_info plotly notebook_metadata_filter text_representation all extension format_name format_version jupytext_version .md markdown 1.2 1.4.2 display_name language name Python 3 python python3 codemirror_mode file_extension mimetype name nbconvert_exporter pygments_lexer version name version ipython 3 .py text/x-python python python ipython3 3.7.7 description display_as language layout name order page_type permalink thumbnail How to design figures with multiple chart types in python. file_settings python base Multiple Chart Types 18 u-guide python/graphing-multiple-chart-types/ thumbnail/multiple-chart-type.jpg

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.

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

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

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!