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# Pie Charts in Python

How to make Pie Charts.

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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.

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¶

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.

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

In :
import plotly.express as px
df = px.data.gapminder().query("year == 2007").query("continent == 'Europe'")
df.loc[df['pop'] < 2.e6, 'country'] = 'Other countries' # Represent only large countries
fig = px.pie(df, values='pop', names='country', title='Population of European continent')
fig.show()


### Pie chart with repeated labels¶

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

In :
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()


### Setting the color of pie sectors with px.pie¶

In :
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 an explicit mapping for discrete colors¶

In :
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()


### Customizing a pie chart created with px.pie¶

In the example below, we first create a pie chart with px,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 fig.update_traces to set other parameters of the chart (you can also use fig.update_layout for changing the layout).

In :
import plotly.express as px
df = px.data.gapminder().query("year == 2007").query("continent == 'Americas'")
fig = px.pie(df, values='pop', names='country',
title='Population of American continent',
hover_data=['lifeExp'], labels={'lifeExp':'life expectancy'})
fig.update_traces(textposition='inside', textinfo='percent+label')
fig.show()


### Basic Pie Chart with go.Pie¶

If Plotly Express does not provide a good starting point, it is also possible to use the more generic go.Pie class from plotly.graph_objects.

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

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

In :
import plotly.graph_objects as go

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

fig = go.Figure(data=[go.Pie(labels=labels, values=values)])
fig.show()


### Styled Pie Chart¶

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

In :
import plotly.graph_objects as go
colors = ['gold', 'mediumturquoise', 'darkorange', 'lightgreen']

fig = go.Figure(data=[go.Pie(labels=['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen'],
values=[4500,2500,1053,500])])
fig.update_traces(hoverinfo='label+percent', textinfo='value', textfont_size=20,
marker=dict(colors=colors, line=dict(color='#000000', width=2)))
fig.show()


### 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.

In :
import plotly.express as px

df = px.data.gapminder().query("continent == 'Asia'")
fig = px.pie(df, values='pop', names='country')
fig.update_traces(textposition='inside')
fig.update_layout(uniformtext_minsize=12, uniformtext_mode='hide')
fig.show()


#### 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 fig.update_traces(insidetextorientation='...') to change the text orientation.

In :
import plotly.graph_objects as go

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

fig = go.Figure(data=[go.Pie(labels=labels, values=values, textinfo='label+percent',
)])
fig.show()


### Donut Chart¶

In :
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 = go.Figure(data=[go.Pie(labels=labels, values=values, hole=.3)])
fig.show()


### 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.

In :
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 = go.Figure(data=[go.Pie(labels=labels, values=values, pull=[0, 0, 0.2, 0])])
fig.show()


### Pie Charts in subplots¶

In :
import plotly.graph_objects as go
from plotly.subplots import make_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=[[{'type':'domain'}, {'type':'domain'}]])
fig.add_trace(go.Pie(labels=labels, values=[16, 15, 12, 6, 5, 4, 42], name="GHG Emissions"),
1, 1)
fig.add_trace(go.Pie(labels=labels, values=[27, 11, 25, 8, 1, 3, 25], name="CO2 Emissions"),
1, 2)

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

fig.update_layout(
title_text="Global Emissions 1990-2011",
# Add annotations in the center of the donut pies.
annotations=[dict(text='GHG', x=0.18, y=0.5, font_size=20, showarrow=False),
dict(text='CO2', x=0.82, y=0.5, font_size=20, showarrow=False)])
fig.show()

In :
import plotly.graph_objects as go
from plotly.subplots import make_subplots

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
specs = [[{'type':'domain'}, {'type':'domain'}], [{'type':'domain'}, {'type':'domain'}]]
fig = make_subplots(rows=2, cols=2, specs=specs)

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

# Tune layout and hover info
fig.update_traces(hoverinfo='label+percent+name', textinfo='none')
fig.update(layout_title_text='Van Gogh: 5 Most Prominent Colors Shown Proportionally',
layout_showlegend=False)

fig = go.Figure(fig)
fig.show()


#### Plot chart with area proportional to total count¶

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

In :
import plotly.graph_objects as go
from plotly.subplots import make_subplots

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

fig = make_subplots(1, 2, specs=[[{'type':'domain'}, {'type':'domain'}]],
subplot_titles=['1980', '2007'])
fig.add_trace(go.Pie(labels=labels, values=[4, 7, 1, 7, 0.5], scalegroup='one',
name="World GDP 1980"), 1, 1)
fig.add_trace(go.Pie(labels=labels, values=[21, 15, 3, 19, 1], scalegroup='one',
name="World GDP 2007"), 1, 2)

fig.update_layout(title_text='World GDP')
fig.show()


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.

In :
import plotly.graph_objects as go

fig =go.Figure(go.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],
))
fig.update_layout(margin = dict(t=0, l=0, r=0, b=0))

fig.show()


#### Reference¶

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.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)
]) 