graphing-multiple-chart-types.md

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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()

Grouped Bar and Scatter Chart

New in 5.12

In this example, we display individual data points with a grouped scatter chart and show averages using a grouped bar chart. offsetgroup links the bar trace for smoker with the scatter trace for smoker, and the bar trace for non-smoker with the scatter trace for non-smoker. If you deselect a trace using the legend, other traces maintain the position of the traces they are linked to.

import plotly.graph_objects as go
from plotly import data

df = data.tips()[data.tips()["day"] == "Sun"]

mean_values_df = df.groupby(by=["sex", "smoker"], as_index=False).mean(
    numeric_only=True
)

smoker_mean = mean_values_df[mean_values_df.smoker == "Yes"].sort_values(
    by="tip", ascending=False
)
non_smoker_mean = mean_values_df[mean_values_df.smoker == "No"].sort_values(
    by="tip", ascending=False
)

smoker = df[df.smoker == "Yes"].sort_values(by="tip", ascending=False)
non_smoker = df[df.smoker == "No"].sort_values(by="tip", ascending=False)

fig = go.Figure(
    layout=dict(
        xaxis=dict(categoryorder="category descending"),
        yaxis=dict(range=[0, 7]),
        scattermode="group",
        legend=dict(groupclick="toggleitem"),
    )
)

fig.add_trace(
    go.Bar(
        x=smoker_mean.sex,
        y=smoker_mean.tip,
        name="Average",
        marker_color="IndianRed",
        offsetgroup="smoker",
        legendgroup="smoker",
        legendgrouptitle_text="Smoker",
    )
)


fig.add_trace(
    go.Scatter(
        x=smoker.sex,
        y=smoker.tip,
        mode="markers",
        name="Individual tips",
        marker=dict(color="LightSlateGrey", size=5),
        offsetgroup="smoker",
        legendgroup="smoker",
    )
)

fig.add_trace(
    go.Bar(
        x=non_smoker_mean.sex,
        y=non_smoker_mean.tip,
        name="Average",
        marker_color="LightSalmon",
        offsetgroup="non-smoker",
        legendgroup="non-smoker",
        legendgrouptitle_text="Non-Smoker",
    )
)


fig.add_trace(
    go.Scatter(
        x=non_smoker.sex,
        y=non_smoker.tip,
        mode="markers",
        name="Individual tips",
        marker=dict(color="LightSteelBlue", size=5),
        offsetgroup="non-smoker",
        legendgroup="non-smoker",
    )
)

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 urllib

response = 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()

Trace Zorder

New in 5.21

You can move a trace in front of or behind another trace by setting its zorder. All traces have a default zorder of 0. In the following example, we set zorder on the bar trace to 1 to move it in front of the scatter trace.

import plotly.graph_objects as go

x = ["Jan", "Feb", "Mar", "Apr", "May", "Jun", "Jul", "Aug", "Sep", "Oct", "Nov", "Dec"]
y_bar = [10, 15, 7, 10, 17, 15, 14, 20, 16, 19, 15, 17]
y_area = [12, 13, 10, 14, 15, 13, 16, 18, 15, 17, 14, 16]

area_trace = go.Scatter(
    x=x,
    y=y_area,
    fill="tozeroy",
    mode="lines+markers",
    name="Area Trace with default `zorder` of 0",
    line=dict(color="lightsteelblue"),
)

bar_trace = go.Bar(
    x=x,
    y=y_bar,
    name="Bar Trace with `zorder` of 1",
    zorder=1,
    marker=dict(color="lightslategray"),
)

fig = go.Figure(data=[area_trace, bar_trace])

fig.show()

Reference

See https://plotly.com/python/reference/ for more information and attribute options!