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Merged
merged 13 commits into from
Jun 21, 2019
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[MRG] Candlestick andothers #11

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27ecd50
candlestick notebook
emmanuelle Jun 12, 2019
75fca75
table notebook
emmanuelle Jun 12, 2019
7c9edb6
box-plots notebook
emmanuelle Jun 12, 2019
2c8e46d
gantt notebook
emmanuelle Jun 12, 2019
991a6b2
sankey-diagram notebook
emmanuelle Jun 12, 2019
295631e
filled-area-plots notebook
emmanuelle Jun 12, 2019
4d68915
distplot
emmanuelle Jun 12, 2019
7d0bd9f
tree-plots
emmanuelle Jun 12, 2019
37284c3
bubble-charts notebook
emmanuelle Jun 12, 2019
1260c02
more inlining, plotly.express, v4upgrade
emmanuelle Jun 16, 2019
4082a8c
more inlining, plotly.express, v4upgrade
emmanuelle Jun 16, 2019
e778e0b
graph_objects, update_layout
emmanuelle Jun 20, 2019
bad179b
minor improvements
emmanuelle Jun 21, 2019
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501 changes: 239 additions & 262 deletions notebooks/box-plots.md
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274 changes: 94 additions & 180 deletions notebooks/bubble-charts.md
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Original file line number Diff line number Diff line change
@@ -1,15 +1,26 @@
---
jupyter:
jupytext:
notebook_metadata_filter: all
text_representation:
extension: .md
format_name: markdown
format_version: '1.1'
jupytext_version: 1.1.1
kernelspec:
display_name: Python 2
display_name: Python 3
language: python
name: python2
name: python3
language_info:
codemirror_mode:
name: ipython
version: 3
file_extension: .py
mimetype: text/x-python
name: python
nbconvert_exporter: python
pygments_lexer: ipython3
version: 3.6.7
plotly:
description: How to make bubble charts in Python with Plotly.
display_as: basic
Expand All @@ -24,59 +35,60 @@ jupyter:
redirect_from: python/bubble-charts-tutorial/
thumbnail: thumbnail/bubble.jpg
title: Bubble Charts | plotly
v4upgrade: true
---

#### New to Plotly?
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/).
<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).
<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!
#### Version Check
Plotly's python package is updated frequently. Run `pip install plotly --upgrade` to use the latest version.
## Bubble chart with plotly.express

A [bubble chart](https://en.wikipedia.org/wiki/Bubble_chart) is a scatter plot in which a third dimension of the data is shown through the size of markers. For other types of scatter plot, see the [line and scatter page](https://plot.ly/python/line-and-scatter/).

We first show a bubble chart example using plotly express. Plotly express functions take as argument a tidy [pandas DataFrame](https://pandas.pydata.org/pandas-docs/stable/getting_started/10min.html). The size of markers is set from the dataframe column given as the `size` parameter.

```python
import plotly
plotly.__version__
import plotly.express as px
gapminder = px.data.gapminder()

fig = px.scatter(gapminder.query("year==2007"), x="gdpPercap", y="lifeExp",
size="pop", color="continent",
hover_name="country", log_x=True, size_max=60)
fig.show()
```

## Bubble Chart with plotly.graph_objects

When data are not available as tidy dataframes, it is also possible to use the more generic `go.Scatter` from `plotly.graph_objects`, and define the size of markers to create a bubble chart. All of the available options are described in the scatter section of the reference page: https://plot.ly/python/reference#scatter.

### Simple Bubble Chart

```python
import plotly.plotly as py
import plotly.graph_objs as go
import plotly.graph_objects as go

trace0 = go.Scatter(
x=[1, 2, 3, 4],
y=[10, 11, 12, 13],
fig = go.Figure(data=[go.Scatter(
x=[1, 2, 3, 4], y=[10, 11, 12, 13],
mode='markers',
marker=dict(
size=[40, 60, 80, 100],
)
)

data = [trace0]
py.iplot(data, filename='bubblechart-size')
marker_size=[40, 60, 80, 100])
])

fig.show()
```

### Setting Marker Size and Color

```python
import plotly.plotly as py
import plotly.graph_objs as go
import plotly.graph_objects as go

trace0 = go.Scatter(
x=[1, 2, 3, 4],
y=[10, 11, 12, 13],
fig = go.Figure(data=[go.Scatter(
x=[1, 2, 3, 4], y=[10, 11, 12, 13],
mode='markers',
marker=dict(
color=['rgb(93, 164, 214)', 'rgb(255, 144, 14)',
'rgb(44, 160, 101)', 'rgb(255, 65, 54)'],
opacity=[1, 0.8, 0.6, 0.4],
size=[40, 60, 80, 100],
)
)
)])

data = [trace0]
py.iplot(data, filename='bubblechart-color')
fig.show()
```

### Scaling the Size of Bubble Charts
Expand All @@ -86,11 +98,10 @@ Note that setting 'sizeref' to a value greater than 1, decreases the rendered ma
Additionally, we recommend setting the sizemode attribute: https://plot.ly/python/reference/#scatter-marker-sizemode to area.

```python
import plotly.plotly as py
import plotly.graph_objs as go
import plotly.graph_objects as go

size = [20, 40, 60, 80, 100, 80, 60, 40, 20, 40]
trace0 = go.Scatter(
fig = go.Figure(data=[go.Scatter(
x=[1, 2, 3, 4, 5, 6, 7, 8, 9, 10],
y=[11, 12, 10, 11, 12, 11, 12, 13, 12, 11],
mode='markers',
Expand All @@ -100,68 +111,61 @@ trace0 = go.Scatter(
sizeref=2.*max(size)/(40.**2),
sizemin=4
)
)
)])

data = [trace0]
py.iplot(data, filename='bubblechart-size-ref')
fig.show()
```

### Hover Text with Bubble Charts

```python
import plotly.plotly as py
import plotly.graph_objs as go
import plotly.graph_objects as go

trace0 = go.Scatter(
x=[1, 2, 3, 4],
y=[10, 11, 12, 13],
fig = go.Figure(data=[go.Scatter(
x=[1, 2, 3, 4], y=[10, 11, 12, 13],
text=['A<br>size: 40', 'B<br>size: 60', 'C<br>size: 80', 'D<br>size: 100'],
mode='markers',
marker=dict(
color=['rgb(93, 164, 214)', 'rgb(255, 144, 14)', 'rgb(44, 160, 101)', 'rgb(255, 65, 54)'],
size=[40, 60, 80, 100],
)
)
)])

data = [trace0]
py.iplot(data, filename='bubblechart-text')
fig.show()
```

### Bubble Charts with Colorscale

```python
import plotly.plotly as py
import plotly.graph_objs as go

data = [
{
'x': [1, 3.2, 5.4, 7.6, 9.8, 12.5],
'y': [1, 3.2, 5.4, 7.6, 9.8, 12.5],
'mode': 'markers',
'marker': {
'color': [120, 125, 130, 135, 140, 145],
'size': [15, 30, 55, 70, 90, 110],
'showscale': True
}
}
]

py.iplot(data, filename='scatter-colorscale')
import plotly.graph_objects as go

fig = go.Figure(data=[go.Scatter(
x=[1, 3.2, 5.4, 7.6, 9.8, 12.5],
y=[1, 3.2, 5.4, 7.6, 9.8, 12.5],
mode='markers',
marker=dict(
color=[120, 125, 130, 135, 140, 145],
size=[15, 30, 55, 70, 90, 110],
showscale=True
)
)])

fig.show()
```

### Categorical Bubble Charts

```python
import plotly.plotly as py
import plotly.graph_objs as go

import plotly.graph_objects as go
import plotly.express as px
import pandas as pd
import math

data = pd.read_csv("https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv")
# Load data, define hover text and bubble size
data = px.data.gapminder()
df_2007 = data[data['year']==2007]
df_2007 = df_2007.sort_values(['continent', 'country'])
slope = 2.666051223553066e-05

hover_text = []
bubble_size = []

Expand All @@ -175,140 +179,50 @@ for index, row in df_2007.iterrows():
gdp=row['gdpPercap'],
pop=row['pop'],
year=row['year']))
bubble_size.append(math.sqrt(row['pop']*slope))
bubble_size.append(math.sqrt(row['pop']))

df_2007['text'] = hover_text
df_2007['size'] = bubble_size
sizeref = 2.*max(df_2007['size'])/(100**2)

trace0 = go.Scatter(
x=df_2007['gdpPercap'][df_2007['continent'] == 'Africa'],
y=df_2007['lifeExp'][df_2007['continent'] == 'Africa'],
mode='markers',
name='Africa',
text=df_2007['text'][df_2007['continent'] == 'Africa'],
marker=dict(
symbol='circle',
sizemode='area',
sizeref=sizeref,
size=df_2007['size'][df_2007['continent'] == 'Africa'],
line=dict(
width=2
),
)
)
trace1 = go.Scatter(
x=df_2007['gdpPercap'][df_2007['continent'] == 'Americas'],
y=df_2007['lifeExp'][df_2007['continent'] == 'Americas'],
mode='markers',
name='Americas',
text=df_2007['text'][df_2007['continent'] == 'Americas'],
marker=dict(
sizemode='area',
sizeref=sizeref,
size=df_2007['size'][df_2007['continent'] == 'Americas'],
line=dict(
width=2
),
)
)
trace2 = go.Scatter(
x=df_2007['gdpPercap'][df_2007['continent'] == 'Asia'],
y=df_2007['lifeExp'][df_2007['continent'] == 'Asia'],
mode='markers',
name='Asia',
text=df_2007['text'][df_2007['continent'] == 'Asia'],
marker=dict(
sizemode='area',
sizeref=sizeref,
size=df_2007['size'][df_2007['continent'] == 'Asia'],
line=dict(
width=2
),
)
)
trace3 = go.Scatter(
x=df_2007['gdpPercap'][df_2007['continent'] == 'Europe'],
y=df_2007['lifeExp'][df_2007['continent'] == 'Europe'],
mode='markers',
name='Europe',
text=df_2007['text'][df_2007['continent'] == 'Europe'],
marker=dict(
sizemode='area',
sizeref=sizeref,
size=df_2007['size'][df_2007['continent'] == 'Europe'],
line=dict(
width=2
),
)
)
trace4 = go.Scatter(
x=df_2007['gdpPercap'][df_2007['continent'] == 'Oceania'],
y=df_2007['lifeExp'][df_2007['continent'] == 'Oceania'],
mode='markers',
name='Oceania',
text=df_2007['text'][df_2007['continent'] == 'Oceania'],
marker=dict(
sizemode='area',
sizeref=sizeref,
size=df_2007['size'][df_2007['continent'] == 'Oceania'],
line=dict(
width=2
),
)
)

data = [trace0, trace1, trace2, trace3, trace4]
layout = go.Layout(
# Dictionary with dataframes for each continent
continent_names = ['Africa', 'Americas', 'Asia', 'Europe', 'Oceania']
continent_data = {continent:df_2007.query("continent == '%s'" %continent)
for continent in continent_names}

# Create figure
fig = go.Figure()

for continent_name, continent in continent_data.items():
fig.add_trace(go.Scatter(
x=continent['gdpPercap'], y=continent['lifeExp'],
name=continent_name, text=continent['text'],
marker_size=continent['size'],
))

# Tune marker appearance and layout
fig.update_traces(mode='markers', marker=dict(sizemode='area',
sizeref=sizeref, line_width=2))

fig.update_layout(
title='Life Expectancy v. Per Capita GDP, 2007',
xaxis=dict(
title='GDP per capita (2000 dollars)',
gridcolor='rgb(255, 255, 255)',
range=[2.003297660701705, 5.191505530708712],
gridcolor='white',
type='log',
zerolinewidth=1,
ticklen=5,
gridwidth=2,
),
yaxis=dict(
title='Life Expectancy (years)',
gridcolor='rgb(255, 255, 255)',
range=[36.12621671352166, 91.72921793264332],
zerolinewidth=1,
ticklen=5,
gridcolor='white',
gridwidth=2,
),
paper_bgcolor='rgb(243, 243, 243)',
plot_bgcolor='rgb(243, 243, 243)',
)

fig = go.Figure(data=data, layout=layout)
py.iplot(fig, filename='life-expectancy-per-GDP-2007')
fig.show()
```

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

```python
from IPython.display import display, HTML

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" />'))
display(HTML('<link rel="stylesheet" type="text/css" href="http://help.plot.ly/documentation/all_static/css/ipython-notebook-custom.css">'))

! pip install git+https://github.com/plotly/publisher.git --upgrade
import publisher
publisher.publish(
'bubble.ipynb', 'python/bubble-charts/', 'Python Bubble Charts | plotly',
'How to make bubble charts in Python with Plotly.',
title = 'Bubble Charts | plotly',
name = 'Bubble Charts', language='python',
has_thumbnail='true', thumbnail='thumbnail/bubble.jpg',
display_as='basic', order=3,
ipynb= '~notebook_demo/1/new-to-plotly-plotlys-python-library-i',
redirect_from='python/bubble-charts-tutorial/',
)
```

```python

```
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