Skip to content

pie-charts.md

Latest commit

 

History

History
204 lines (157 loc) · 7 KB

pie-charts.md

File metadata and controls

204 lines (157 loc) · 7 KB
jupyter
jupytext kernelspec language_info plotly
notebook_metadata_filter text_representation
all
extension format_name format_version jupytext_version
.md
markdown
1.1
1.1.1
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
3.7.3
description display_as language layout name order page_type permalink thumbnail
How to make Pie Charts.
basic
python
base
Pie Charts
4
example_index
python/pie-charts/
thumbnail/pie-chart.jpg

Basic Pie Chart

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

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.

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()

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

Pie Charts in subplots

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()
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.

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()

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.

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()

Dash Example

Dash is an Open Source Python library which can help you convert plotly figures into a reactive, web-based application. Below is a simple example of a dashboard created using Dash. Its source code can easily be deployed to a PaaS.

from IPython.display import IFrame
IFrame(src= "https://dash-simple-apps.plotly.host/dash-pieplot", width="100%", height="650px" ,frameBorder="0")
from IPython.display import IFrame
IFrame(src= "https://dash-simple-apps.plotly.host/dash-pieplot/code", width="100%", height=500 ,frameBorder="0")

Reference

See https://plot.ly/python/reference/#pie for more information and chart attribute options!