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jupytext kernelspec plotly
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extension format_name format_version jupytext_version
.md
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1.2
1.4.2
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Julia 1.6.0
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julia-1.6
description display_as language layout name order page_type permalink thumbnail
How to make Pie Charts.
basic
julia
base
Pie Charts
4
example_index
julia/pie-charts/
thumbnail/pie-chart.jpg

A pie chart is a circular statistical chart, which is divided into sectors to illustrate numerical proportion.

If you're looking instead for a multilevel hierarchical pie-like chart, go to the Sunburst tutorial.

Pie chart with plotly express

In .pie, data visualized by the sectors of the pie is set in values. The sector labels are set in labels.

using PlotlyJS, CSV, DataFrames

df = dataset(DataFrame, "gapminder")
df_2007 = df[df.year .== 2007, :]
europe = df_2007[df_2007.continent .== "Europe", :]

europe[europe.pop .< 2e6,:country] .= "Other contries"

plot(pie( values=europe.pop, labels=europe.country), Layout(title="Population of European continent"))

Pie chart with repeated labels

Lines of the dataframe with the same value for labels are grouped together in the same sector.

import plotly.express as px
# This dataframe has 244 lines, but 4 distinct values for `day`
df = px.data.tips()
fig = px.pie(df, values='tip', names='day')
fig.show()
using PlotlyJS, CSV, DataFrames

df = dataset(DataFrame, "tips")

plot(
  pie(
    values=df.tip,
    labels=df.day
  )
)

Setting the color of pie sectors with pie

import plotly.express as px
df = px.data.tips()
fig = px.pie(df, values='tip', names='day', color_discrete_sequence=px.colors.sequential.RdBu)
fig.show()
using PlotlyJS, CSV, DataFrames

df = dataset(DataFrame, "tips")
plot(
  pie(
    values=df.tip,
    labels=df.day,
    color_discrete_sequence=colors.RdBu_8
  )
)

Using an explicit mapping for discrete colors

For more information about discrete colors, see the dedicated page.

import plotly.express as px
df = px.data.tips()
fig = px.pie(df, values='tip', names='day', color='day',
             color_discrete_map={'Thur':'lightcyan',
                                 'Fri':'cyan',
                                 'Sat':'royalblue',
                                 'Sun':'darkblue'})
fig.show()
using PlotlyJS, CSV, DataFrames

df = dataset(DataFrame, "tips")
plot(
  pie(
    values=df.tip,
    labels=df.day,
    color_discrete_map=attr(
      Thur="lightcyan",
      Fri="cyan",
      Sat="royalblue",
      Sun="darkblue"
    )
  )
)

Customizing a pie chart created with pie

In the example below, we first create a pie chart with pie, using some of its options such as hover_data (which columns should appear in the hover) or labels (renaming column names). For further tuning, we call restyle! to set other parameters of the chart (you can also use relayout! for changing the layout).

using PlotlyJS, CSV, DataFrames

df = dataset(DataFrame, "gapminder")
df_2007 = df[df.year .== 2007, :]
americas = df_2007[df_2007.continent .== "Americas", :]

fig = plot(
  pie(
    values=americas.pop, labels=americas.country,
    hover_data=americas.lifeExp
  ),
  Layout(
    title="Population of American Continent"
  )
)
restyle!(fig, textposition="inside", textinfo="percent+label")
fig

Styled Pie Chart

Colors can be given as RGB triplets or hexadecimal strings, or with CSS color names as below.

using PlotlyJS

cols = ["gold", "mediumturquoise", "darkorange", "lightgreen"]

fig = plot(
  pie(
    labels=["Oxygen","Hydrogen","Carbon_Dioxide","Nitrogen"],
    values=[4500,2500,1053,500]
  )
)
restyle!(fig, hoverinfo="label+percent", textinfo="value", textfont_size=20,
                  marker=attr(colors=cols, line=attr(color="#000000", width=2)))
fig

Controlling text fontsize with uniformtext

If you want all the text labels to have the same size, you can use the uniformtext layout parameter. The minsize attribute sets the font size, and the mode attribute sets what happens for labels which cannot fit with the desired fontsize: either hide them or show them with overflow. In the example below we also force the text to be inside with textposition, otherwise text labels which do not fit are displayed outside of pie sectors.

using PlotlyJS, CSV, DataFrames
df = dataset(DataFrame, "gapminder")
asia = df[df.continent .== "Asia", :]

fig = plot(pie(values=asia.pop, labels=asia.country))
restyle!(fig, textposition="inside")
relayout!(fig, uniformtext_minsize=12, uniformtext_mode="hide")
fig

Controlling text orientation inside pie sectors

The insidetextorientation attribute controls the orientation of text inside sectors. With "auto" the texts may automatically be rotated to fit with the maximum size inside the slice. Using "horizontal" (resp. "radial", "tangential") forces text to be horizontal (resp. radial or tangential)

For a figure fig created with plotly express, use restyle!(insidetextorientation='...') to change the text orientation.

using PlotlyJS

labels = ["Oxygen","Hydrogen","Carbon_Dioxide","Nitrogen"]
values = [4500, 2500, 1053, 500]

plot(pie(labels=labels, values=values, textinfo="label+percent",
                             insidetextorientation="radial"
                            ))

Donut Chart

import plotly.graph_objects as go

labels = ["Oxygen","Hydrogen","Carbon_Dioxide","Nitrogen"]
values = [4500, 2500, 1053, 500]

# Use `hole` to create a donut-like pie chart
fig = plot(pie(labels=labels, values=values, hole=.3))

Pulling sectors out from the center

For a "pulled-out" or "exploded" layout of the pie chart, use the pull argument. It can be a scalar for pulling all sectors or an array to pull only some of the sectors.

import plotly.graph_objects as go

labels = ["Oxygen","Hydrogen","Carbon_Dioxide","Nitrogen"]
values = [4500, 2500, 1053, 500]

# pull is given as a fraction of the pie radius
fig = plot(pie(labels=labels, values=values, pull=[0, 0, 0.2, 0]))

Pie Charts in subplots

labels = ["US", "China", "European Union", "Russian Federation", "Brazil", "India",
          "Rest of World"]

# Create subplots: use 'domain' type for Pie subplot
fig = make_subplots(rows=1, cols=2, specs=fill(Spec(kind="domain"), 1,2))
add_trace!(fig, pie(labels=labels, values=[16, 15, 12, 6, 5, 4, 42], name="GHG Emissions"),
              row=1, col=1)
add_trace!(fig, pie(labels=labels, values=[27, 11, 25, 8, 1, 3, 25], name="CO2 Emissions"),
              row=1, col=2)

# Use `hole` to create a donut-like pie chart
restyle!(fig, hole=.4, hoverinfo="label+percent+name")

relayout!(fig,
    title_text="Global Emissions 1990-2011",
    # Add annotations in the center of the donut pies.
    annotations=[attr(text="GHG", x=0.18, y=0.5, font_size=20, showarrow=false),
                 attr(text="CO2", x=0.82, y=0.5, font_size=20, showarrow=false)])

fig
using PlotlyJS

labels = ["1st", "2nd", "3rd", "4th", "5th"]

# Define color sets of paintings
night_colors = ["rgb(56, 75, 126)", "rgb(18, 36, 37)", "rgb(34, 53, 101)",
                "rgb(36, 55, 57)", "rgb(6, 4, 4)"]
sunflowers_colors = ["rgb(177, 127, 38)", "rgb(205, 152, 36)", "rgb(99, 79, 37)",
                     "rgb(129, 180, 179)", "rgb(124, 103, 37)"]
irises_colors = ["rgb(33, 75, 99)", "rgb(79, 129, 102)", "rgb(151, 179, 100)",
                 "rgb(175, 49, 35)", "rgb(36, 73, 147)"]
cafe_colors =  ["rgb(146, 123, 21)", "rgb(177, 180, 34)", "rgb(206, 206, 40)",
                "rgb(175, 51, 21)", "rgb(35, 36, 21)"]

# Create subplots, using "domain" type for pie charts
fig = make_subplots(rows=2, cols=2, specs=fill(Spec(kind="domain"), 2,2))

# Define pie charts
add_trace!(fig, pie(labels=labels, values=[38, 27, 18, 10, 7], name="Starry Night",
                     marker_colors=night_colors), row=1, col=1)
add_trace!(fig, pie(labels=labels, values=[28, 26, 21, 15, 10], name="Sunflowers",
                     marker_colors=sunflowers_colors), row=1, col=2)
add_trace!(fig, pie(labels=labels, values=[38, 19, 16, 14, 13], name="Irises",
                     marker_colors=irises_colors), row=2, col=1)
add_trace!(fig, pie(labels=labels, values=[31, 24, 19, 18, 8], name="The Night Café",
                     marker_colors=cafe_colors), row=2, col=2)

# Tune layout and hover info
restyle!(fig, hoverinfo="label+percent+name", textinfo="none")
relayout!(fig, title_text="Van Gogh: 5 Most Prominent Colors Shown Proportionally",
           showlegend=false)

fig

Plot chart with area proportional to total count

Plots in the same scalegroup are represented with an area proportional to their total size.

using PlotlyJS
labels = ["Asia", "Europe", "Africa", "Americas", "Oceania"]

fig = make_subplots(rows=1, cols=2, specs=fill(Spec(kind="domain"), 1,2),
                    subplot_titles=["1980" "2007"])
add_trace!(fig, pie(labels=labels, values=[4, 7, 1, 7, 0.5], scalegroup="one",
                     name="World GDP 1980"), row=1, col=1)
add_trace!(fig, pie(labels=labels, values=[21, 15, 3, 19, 1], scalegroup="one",
                     name="World GDP 2007"), row=1, col=2)

relayout!(fig, title_text="World GDP")
fig

See Also: Sunburst charts

For multilevel pie charts representing hierarchical data, you can use the Sunburst chart. A simple example is given below, for more information see the tutorial on Sunburst charts.

using PlotlyJS

fig =plot(sunburst(
    labels=["Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"],
    parents=["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve" ],
    values=[10, 14, 12, 10, 2, 6, 6, 4, 4],
))
relayout!(fig, margin = attr(t=0, l=0, r=0, b=0))

fig

Reference

See function reference for pie() or https://plotly.com/julia/reference/pie/ for more information and chart attribute options!