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Commit acfd037

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emmanuelleemmanuelle
authored
px examples for pie, funnel/funnelarea, sunburst/treemap (plotly#1995)
* pie chart tutorial using px * funnel chart * examples for sunburst and treemap * px intro
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‎doc/python/funnel-charts.md

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---
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jupyter:
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jupytext:
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notebook_metadata_filter: plotly
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text_representation:
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extension: .md
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format_name: markdown
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format_version: '1.1'
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jupytext_version: 1.1.1
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format_version: '1.2'
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jupytext_version: 1.3.0
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kernelspec:
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display_name: Python 3
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language: python
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name: python3
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plotly:
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permalink: python/funnel-charts/
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redirect_from: python/funnel-chart/
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description: How to make funnel-chart plots in Python with Plotly.
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name: Funnel Chart
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thumbnail: thumbnail/funnel.jpg
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language: python
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display_as: financial
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order: 4
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language: python
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layout: base
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name: Funnel Chart
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order: 4
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page_type: example_index
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permalink: python/funnel-charts/
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redirect_from: python/funnel-chart/
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thumbnail: thumbnail/funnel.jpg
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---
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Funnel charts are often used to represent data in different stages of a business process. It’s an important mechanism in Business Intelligence to identify potential problem areas of a process. For example, it’s used to observe the revenue or loss in a sales process for each stage, and displays values that are decreasing progressively. Each stage is illustrated as a percentage of the total of all values.
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### Basic Funnel Plot
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### Basic Funnel Plot with plotly.express
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[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
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With `px.funnel`, each row of the DataFrame is represented as a stage of the funnel.
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```python
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import plotly.express as px
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data = dict(
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number=[39, 27.4, 20.6, 11, 2],
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stage=["Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent"])
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fig = px.funnel(data, x='number', y='stage')
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fig.show()
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```
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### Stacked Funnel Plot with plotly.express
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```python
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import plotly.express as px
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import pandas as pd
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stages = ["Website visit", "Downloads", "Potential customers", "Requested price", "invoice sent"]
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df_mtl = pd.DataFrame(dict(number=[39, 27.4, 20.6, 11, 3], stage=stages))
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df_mtl['office'] = 'Montreal'
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df_toronto = pd.DataFrame(dict(number=[52, 36, 18, 14, 5], stage=stages))
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df_toronto['office'] = 'Toronto'
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df = pd.concat([df_mtl, df_toronto], axis=0)
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fig = px.funnel(df, x='number', y='stage', color='office')
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fig.show()
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```
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### Basic Funnel Chart with graph_objects trace go.Funnel
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If Plotly Express does not provide a good starting point, it is also possible to use the more generic `go.Funnel` function from `plotly.graph_objects`.
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```python
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from plotly import graph_objects as go
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fig.show()
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```
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### Stacked Funnel Plot
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### Stacked Funnel Plot with go.Funnel
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```python
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from plotly import graph_objects as go
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fig.show()
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```
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#### Basic Area Funnel Plot
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### Basic Area Funnel Plot with plotly.express
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With `px.funnel_area`, each row of the DataFrame is represented as a stage of
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the funnel.
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```python
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import plotly.express as px
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fig = px.funnel_area(names=["The 1st","The 2nd", "The 3rd", "The 4th", "The 5th"],
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values=[5, 4, 3, 2, 1])
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fig.show()
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```
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### Basic Area Funnel Plot with go.Funnelarea
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If Plotly Express does not provide a good starting point, it is also possible to use the more generic `go.Funnelarea` function from `plotly.graph_objects`.
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```python
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from plotly import graph_objects as go

‎doc/python/pie-charts.md

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---
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### Basic Pie Chart ###
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A pie chart is a circular statistical chart, which is divided into sectors to illustrate numerical proportion.
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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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If you're looking instead for a multilevel hierarchical pie-like chart, go to the
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[Sunburst tutorial](/python/sunburst-charts/).
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### Pie chart with plotly express
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[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
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In `px.pie`, data visualized by the sectors of the pie is set in `values`. The sector labels are set in `names`.
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```python
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import plotly.express as px
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df = px.data.gapminder().query("year == 2007").query("continent == 'Europe'")
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df.loc[df['pop'] < 2.e6, 'country'] = 'Other countries' # Represent only large countries
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fig = px.pie(df, values='pop', names='country', title='Population of European continent')
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fig.show()
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```
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### Pie chart with repeated labels
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Lines of the dataframe with the same value for `names` are grouped together in the same sector.
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```python
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import plotly.express as px
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# This dataframe has 244 lines, but 4 distinct values for `day`
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df = px.data.tips()
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fig = px.pie(df, values='tip', names='day')
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fig.show()
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```
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### Setting the color of pie sectors with px.pie
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```python
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import plotly.express as px
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df = px.data.tips()
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fig = px.pie(df, values='tip', names='day', color_discrete_sequence=px.colors.sequential.RdBu)
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fig.show()
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```
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### Customizing a pie chart created with px.pie
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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).
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```python
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import plotly.express as px
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df = px.data.gapminder().query("year == 2007").query("continent == 'Americas'")
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fig = px.pie(df, values='pop', names='country',
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title='Population of American continent',
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hover_data=['lifeExp'], labels={'lifeExp':'life expectancy'})
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fig.update_traces(textposition='inside', textinfo='percent+label')
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fig.show()
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```
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### Basic Pie Chart with go.Pie
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If Plotly Express does not provide a good starting point, it is also possible to use the more generic `go.Pie` function from `plotly.graph_objects`.
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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`.
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‎doc/python/sunburst-charts.md

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### Basic Sunburst Plot ###
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Sunburst plots visualize hierarchical data spanning outwards radially from root to leaves. The sunburst sector hierarchy is determined by the entries in `labels` and in `parents`. The root starts from the center and children are added to the outer rings.
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Sunburst plots visualize hierarchical data spanning outwards radially from root to leaves. The sunburst sector hierarchy is determined by the entries in `labels` (`names` in `px.sunburst`) and in `parents`. The root starts from the center and children are added to the outer rings.
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Main arguments:
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1. `labels`: sets the labels of sunburst sectors.
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1. `labels` (`names` in `px.sunburst` since `labels` is reserved for overriding columns names): sets the labels of sunburst sectors.
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2. `parents`: sets the parent sectors of sunburst sectors. An empty string `''` is used for the root node in the hierarchy. In this example, the root is "Eve".
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3. `values`: sets the values associated with sunburst sectors, determining their width (See the `branchvalues` section below for different modes for setting the width).
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### Basic Sunburst Plot with plotly.express
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[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
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With `px.sunburst`, each row of the DataFrame is represented as a sector of the sunburst.
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```python
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data = dict(
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character=["Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"],
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parent=["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve" ],
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value=[10, 14, 12, 10, 2, 6, 6, 4, 4])
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fig =px.sunburst(
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data,
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names='character',
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parents='parent',
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values='value',
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)
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fig.show()
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```
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### Basic Sunburst Plot with go.Sunburst
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If Plotly Express does not provide a good starting point, it is also possible to use the more generic `go.Sunburst` function from `plotly.graph_objects`.
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‎doc/python/treemaps.md

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### Basic Treemap
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[Treemap charts](https://en.wikipedia.org/wiki/Treemapping) visualize hierarchical data using nested rectangles. Same as [Sunburst](https://plot.ly/python/sunburst-charts/) the hierarchy is defined by [labels](https://plot.ly/python/reference/#treemap-labels) and [parents](https://plot.ly/python/reference/#treemap-parents) attributes. Click on one sector to zoom in/out, which also displays a pathbar in the upper-left corner of your treemap. To zoom out you can use the path bar as well.
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[Treemap charts](https://en.wikipedia.org/wiki/Treemapping) visualize hierarchical data using nested rectangles. Same as [Sunburst](https://plot.ly/python/sunburst-charts/) the hierarchy is defined by [labels](https://plot.ly/python/reference/#treemap-labels) (`names` for `px.treemap`) and [parents](https://plot.ly/python/reference/#treemap-parents) attributes. Click on one sector to zoom in/out, which also displays a pathbar in the upper-left corner of your treemap. To zoom out you can use the path bar as well.
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### Basic Treemap with plotly.express
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[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
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With `px.treemap`, each row of the DataFrame is represented as a sector of the treemap.
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```python
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import plotly.express as px
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fig = px.treemap(
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names = ["Eve","Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"],
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parents = ["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve"]
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)
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fig.show()
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```
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### Basic Treemap with go.Treemap
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If Plotly Express does not provide a good starting point, it is also possible to use the more generic `go.Treemap` function from `plotly.graph_objects`.
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#### Reference
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See https://plot.ly/python/reference/#treemap for more information and chart attribute options!
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See https://plot.ly/python/reference/#treemap for more information and chart attribute options!

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