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Commiteaca0e9

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Merge pull request#4 from plotly/pie
pie chart notebook
2 parentsb718c8b +646c4c3 commiteaca0e9

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‎notebooks/pie-charts.md

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display_name:Python 3
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language:python
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name:python3
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language_info:
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codemirror_mode:
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name:ipython
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version:3
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file_extension:.py
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mimetype:text/x-python
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name:python
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nbconvert_exporter:python
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pygments_lexer:ipython3
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version:3.6.7
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plotly:
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description:How to make Pie Charts.
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display_as:basic
@@ -24,210 +34,138 @@ jupyter:
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permalink:python/pie-charts/
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thumbnail:thumbnail/pie-chart.jpg
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title:Pie Charts in Python | plotly
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v4upgrade:true
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---
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####New to Plotly?
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Plotly's Python library is free and open source![Get started](https://plot.ly/python/getting-started/) by downloading the client and[reading the primer](https://plot.ly/python/getting-started/).
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<br>You can set up Plotly to work in[online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or[offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in[jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
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<br>We also have a quick-reference[cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started!
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###Basic Pie Chart ###
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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`.
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```python
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import plotly.graph_objectsas go
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labels= ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']
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values= [4500,2500,1053,500]
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####Version Check
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Note: Pie Charts are available in version <b>1.9.12+</b><br>
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Run`pip install plotly --upgrade` to update your Plotly version
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fig= go.Figure(data=[go.Pie(labels=labels,values=values)])
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fig.show()
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```
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###Styled Pie Chart
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Colors can be given as RGB triplets or hexadecimal strings, or with[CSS color names](https://www.w3schools.com/cssref/css_colors.asp) as below.
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```python
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import plotly
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plotly.__version__
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import plotly.graph_objectsas go
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colors= ['gold','mediumturquoise','darkorange','lightgreen']
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fig= go.Figure(data=[go.Pie(labels=['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen'],
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values=[4500,2500,1053,500])])
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fig.update_traces(hoverinfo='label+percent',textinfo='value',textfont_size=20,
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marker=dict(colors=colors,line=dict(color='#000000',width=2)))
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fig.show()
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```
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###Basic Pie Chart ###
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###Donut Chart
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```python
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import plotly.plotlyas py
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import plotly.graph_objsas go
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import plotly.graph_objectsas go
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labels= ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']
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values= [4500,2500,1053,500]
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values= [4500,2500,1053,500]
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trace= go.Pie(labels=labels,values=values)
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py.iplot([trace],filename='basic_pie_chart')
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# Use `hole` to create a donut-like pie chart
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fig= go.Figure(data=[go.Pie(labels=labels,values=values,hole=.3)])
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fig.show()
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```
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###StyledPieChart
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###PieCharts in subplots
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```python
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import plotly.plotlyas py
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import plotly.graph_objsas go
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labels= ['Oxygen','Hydrogen','Carbon_Dioxide','Nitrogen']
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values= [4500,2500,1053,500]
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colors= ['#FEBFB3','#E1396C','#96D38C','#D0F9B1']
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trace= go.Pie(labels=labels,values=values,
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hoverinfo='label+percent',textinfo='value',
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textfont=dict(size=20),
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marker=dict(colors=colors,
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line=dict(color='#000000',width=2)))
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py.iplot([trace],filename='styled_pie_chart')
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import plotly.graph_objectsas go
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from plotly.subplotsimport make_subplots
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labels= ["US","China","European Union","Russian Federation","Brazil","India",
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"Rest of World"]
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# Create subplots: use 'domain' type for Pie subplot
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fig= make_subplots(rows=1,cols=2,specs=[[{'type':'domain'}, {'type':'domain'}]])
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fig.add_trace(go.Pie(labels=labels,values=[16,15,12,6,5,4,42],name="GHG Emissions"),
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1,1)
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fig.add_trace(go.Pie(labels=labels,values=[27,11,25,8,1,3,25],name="CO2 Emissions"),
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1,2)
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# Use `hole` to create a donut-like pie chart
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fig.update_traces(hole=.4,hoverinfo="label+percent+name")
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fig.update_layout(
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title_text="Global Emissions 1990-2011",
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# Add annotations in the center of the donut pies.
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annotations=[dict(text='GHG',x=0.18,y=0.5,font_size=20,showarrow=False),
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dict(text='CO2',x=0.82,y=0.5,font_size=20,showarrow=False)])
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fig.show()
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```
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###Donut Chart
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This example uses a[plotly grid attribute](https://plot.ly/python/reference/#layout-grid) for the suplots. Reference the row and column destination using the[domain](https://plot.ly/python/reference/#pie-domain) attribute.
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```python
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import plotly.plotlyas py
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import plotly.graph_objsas go
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fig= {
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"data": [
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{
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"values": [16,15,12,6,5,4,42],
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"labels": [
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"US",
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"China",
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"European Union",
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"Russian Federation",
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"Brazil",
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"India",
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"Rest of World"
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],
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"domain": {"column":0},
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"name":"GHG Emissions",
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"hoverinfo":"label+percent+name",
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"hole":.4,
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"type":"pie"
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},
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{
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"values": [27,11,25,8,1,3,25],
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"labels": [
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"US",
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"China",
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"European Union",
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"Russian Federation",
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"Brazil",
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"India",
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"Rest of World"
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],
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"text":["CO2"],
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"textposition":"inside",
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"domain": {"column":1},
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"name":"CO2 Emissions",
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"hoverinfo":"label+percent+name",
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"hole":.4,
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"type":"pie"
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}],
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"layout": {
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"title":"Global Emissions 1990-2011",
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"grid": {"rows":1,"columns":2},
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"annotations": [
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{
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"font": {
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"size":20
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},
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"showarrow":False,
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"text":"GHG",
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"x":0.20,
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"y":0.5
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},
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{
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"font": {
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"size":20
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},
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"showarrow":False,
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"text":"CO2",
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"x":0.8,
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"y":0.5
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}
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]
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}
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}
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py.iplot(fig,filename='donut')
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import plotly.graph_objectsas go
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from plotly.subplotsimport make_subplots
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labels= ['1st','2nd','3rd','4th','5th']
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# Define color sets of paintings
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night_colors= ['rgb(56, 75, 126)','rgb(18, 36, 37)','rgb(34, 53, 101)',
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'rgb(36, 55, 57)','rgb(6, 4, 4)']
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sunflowers_colors= ['rgb(177, 127, 38)','rgb(205, 152, 36)','rgb(99, 79, 37)',
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'rgb(129, 180, 179)','rgb(124, 103, 37)']
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irises_colors= ['rgb(33, 75, 99)','rgb(79, 129, 102)','rgb(151, 179, 100)',
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'rgb(175, 49, 35)','rgb(36, 73, 147)']
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cafe_colors= ['rgb(146, 123, 21)','rgb(177, 180, 34)','rgb(206, 206, 40)',
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'rgb(175, 51, 21)','rgb(35, 36, 21)']
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# Create subplots, using 'domain' type for pie charts
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specs= [[{'type':'domain'}, {'type':'domain'}], [{'type':'domain'}, {'type':'domain'}]]
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fig= make_subplots(rows=2,cols=2,specs=specs)
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# Define pie charts
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fig.add_trace(go.Pie(labels=labels,values=[38,27,18,10,7],name='Starry Night',
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marker_colors=night_colors),1,1)
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fig.add_trace(go.Pie(labels=labels,values=[28,26,21,15,10],name='Sunflowers',
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marker_colors=sunflowers_colors),1,2)
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fig.add_trace(go.Pie(labels=labels,values=[38,19,16,14,13],name='Irises',
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marker_colors=irises_colors),2,1)
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fig.add_trace(go.Pie(labels=labels,values=[31,24,19,18,8],name='The Night Café',
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marker_colors=cafe_colors),2,2)
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# Tune layout and hover info
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fig.update_traces(hoverinfo='label+percent+name',textinfo='none')
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fig.update(layout_title_text='Van Gogh: 5 Most Prominent Colors Shown Proportionally',
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layout_showlegend=False)
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fig= go.Figure(fig)
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fig.show()
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```
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###Pie Chart Subplots ###
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####Plot chart with area proportional to total count
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In order to create pie chart subplots, you need to usethe[domain](https://plot.ly/python/reference/#pie-domain) attribute. It is important to note that the`X` array set the horizontal position whilst the`Y` array sets the vertical. For example,`x: [0,0.5], y: [0, 0.5]` would mean the bottom left position of the plot.
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Plots inthesame`scalegroup` are represented with an area proportional to their total size.
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```python
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import plotly.plotlyas py
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import plotly.graph_objsas go
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fig= {
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'data': [
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{
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'labels': ['1st','2nd','3rd','4th','5th'],
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'values': [38,27,18,10,7],
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'type':'pie',
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'name':'Starry Night',
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'marker': {'colors': ['rgb(56, 75, 126)',
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'rgb(18, 36, 37)',
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'rgb(34, 53, 101)',
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'rgb(36, 55, 57)',
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'rgb(6, 4, 4)']},
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'domain': {'x': [0,.48],
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'y': [0,.49]},
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'hoverinfo':'label+percent+name',
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'textinfo':'none'
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},
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{
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'labels': ['1st','2nd','3rd','4th','5th'],
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'values': [28,26,21,15,10],
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'marker': {'colors': ['rgb(177, 127, 38)',
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'rgb(205, 152, 36)',
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'rgb(99, 79, 37)',
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'rgb(129, 180, 179)',
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'rgb(124, 103, 37)']},
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'type':'pie',
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'name':'Sunflowers',
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'domain': {'x': [.52,1],
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'y': [0,.49]},
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'hoverinfo':'label+percent+name',
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'textinfo':'none'
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},
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{
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'labels': ['1st','2nd','3rd','4th','5th'],
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'values': [38,19,16,14,13],
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'marker': {'colors': ['rgb(33, 75, 99)',
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'rgb(79, 129, 102)',
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'rgb(151, 179, 100)',
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'rgb(175, 49, 35)',
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'rgb(36, 73, 147)']},
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'type':'pie',
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'name':'Irises',
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'domain': {'x': [0,.48],
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'y': [.51,1]},
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'hoverinfo':'label+percent+name',
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'textinfo':'none'
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},
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{
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'labels': ['1st','2nd','3rd','4th','5th'],
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'values': [31,24,19,18,8],
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'marker': {'colors': ['rgb(146, 123, 21)',
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'rgb(177, 180, 34)',
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'rgb(206, 206, 40)',
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'rgb(175, 51, 21)',
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'rgb(35, 36, 21)']},
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'type':'pie',
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'name':'The Night Café',
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'domain': {'x': [.52,1],
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'y': [.51,1]},
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'hoverinfo':'label+percent+name',
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'textinfo':'none'
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}
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],
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'layout': {'title':'Van Gogh: 5 Most Prominent Colors Shown Proportionally',
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'showlegend':False}
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}
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py.iplot(fig,filename='pie_chart_subplots')
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import plotly.graph_objectsas go
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from plotly.subplotsimport make_subplots
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labels= ["Asia","Europe","Africa","Americas","Oceania"]
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fig= make_subplots(1,2,specs=[[{'type':'domain'}, {'type':'domain'}]],
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subplot_titles=['1980','2007'])
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fig.add_trace(go.Pie(labels=labels,values=[4,7,1,7,0.5],scalegroup='one',
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name="World GDP 1980"),1,1)
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fig.add_trace(go.Pie(labels=labels,values=[21,15,3,19,1],scalegroup='one',
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name="World GDP 2007"),1,2)
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fig.update_layout(title_text='World GDP')
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fig.show()
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```
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###Dash Example
@@ -247,21 +185,3 @@ IFrame(src= "https://dash-simple-apps.plotly.host/dash-pieplot/code", width="100
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####Reference
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Seehttps://plot.ly/python/reference/#pie for more information and chart attribute options!
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```python
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from IPython.displayimport display,HTML
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display(HTML('<link href="//fonts.googleapis.com/css?family=Open+Sans:600,400,300,200|Inconsolata|Ubuntu+Mono:400,700" rel="stylesheet" type="text/css" />'))
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display(HTML('<link rel="stylesheet" type="text/css" href="http://help.plot.ly/documentation/all_static/css/ipython-notebook-custom.css">'))
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! pip install git+https://github.com/plotly/publisher.git--upgrade
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import publisher
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publisher.publish(
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'pie-charts.ipynb','python/pie-charts/','Pie Charts',
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'How to make Pie Charts.',
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title='Pie Charts in Python | plotly',
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has_thumbnail='true',thumbnail='thumbnail/pie-chart.jpg',
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language='python',page_type='example_index',
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display_as='basic',order=6,
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ipynb='~notebook_demo/7/')
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```

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