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Python Docs Dash Snippets: hover-text-and-formatting, pie-charts, choropleth-maps, box-plots, discrete-color #2895

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Python Docs Dash Snippets: hover-text-and-formatting, pie-charts, choropleth-maps, box-plots, discrete-color #2895

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16 changes: 14 additions & 2 deletions doc/python/box-plots.md
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
jupytext_version: 1.3.1
jupytext_version: 1.6.0
kernelspec:
display_name: Python 3
language: python
Expand All @@ -20,7 +20,7 @@ jupyter:
name: python
nbconvert_exporter: python
pygments_lexer: ipython3
version: 3.6.8
version: 3.7.6
plotly:
description: How to make Box Plots in Python with Plotly.
display_as: statistical
Expand Down Expand Up @@ -60,6 +60,18 @@ fig = px.box(df, x="time", y="total_bill")
fig.show()
```

### Box Plots in Dash

[Dash](https://plotly.com/dash/) is the best way to build analytical apps in Python using Plotly figures. To run the app below, run `pip install dash`, click "Download" to get the code and run `python app.py`.

Get started with [the official Dash docs](https://dash.plotly.com/installation) and **learn how to effortlessly [style](https://plotly.com/dash/design-kit/) & [deploy](https://plotly.com/dash/app-manager/) apps like this with <a style="color:red;" href="https://plotly.com/dash/">Dash Enterprise</a>.**

```python hide_code=true
from IPython.display import IFrame
snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/'
IFrame(snippet_url + 'box-plots', width='100%', height=630)
```

### Display the underlying data

With the `points` argument, display underlying data points with either all points (`all`), outliers only (`outliers`, default), or none of them (`False`).
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16 changes: 14 additions & 2 deletions doc/python/choropleth-maps.md
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
jupytext_version: 1.4.2
jupytext_version: 1.6.0
kernelspec:
display_name: Python 3
language: python
Expand All @@ -20,7 +20,7 @@ jupyter:
name: python
nbconvert_exporter: python
pygments_lexer: ipython3
version: 3.7.7
version: 3.7.6
plotly:
description: How to make choropleth maps in Python with Plotly.
display_as: maps
Expand Down Expand Up @@ -143,6 +143,18 @@ fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
fig.show()
```

### Choropleth maps in Dash

[Dash](https://plotly.com/dash/) is the best way to build analytical apps in Python using Plotly figures. To run the app below, run `pip install dash`, click "Download" to get the code and run `python app.py`.

Get started with [the official Dash docs](https://dash.plotly.com/installation) and **learn how to effortlessly [style](https://plotly.com/dash/design-kit/) & [deploy](https://plotly.com/dash/app-manager/) apps like this with <a style="color:red;" href="https://plotly.com/dash/">Dash Enterprise</a>.**

```python hide_code=true
from IPython.display import IFrame
snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/'
IFrame(snippet_url + 'choropleth-maps', width='100%', height=630)
```

### Discrete Colors

In addition to [continuous colors](/python/colorscales/), we can [discretely-color](/python/discrete-color/) our choropleth maps by setting `color` to a non-numerical column, like the name of the winner of an election.
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6 changes: 2 additions & 4 deletions doc/python/discrete-color.md
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Expand Up @@ -101,11 +101,9 @@ fig.show()

### Discrete Colors in Dash

[Dash](https://plotly.com/dash/) is the best way to build analytical apps in Python using Plotly figures. With Dash, you can add radio buttons to control the color mode of your graph.
[Dash](https://plotly.com/dash/) is the best way to build analytical apps in Python using Plotly figures. To run the app below, run `pip install dash`, click "Download" to get the code and run `python app.py`.

To run the app below, run `pip install dash`, click "Download" to get the code and run `python app.py`.

Get started now with [the official docs](https://dash.plotly.com/installation) and learn how you can effortlessly [style](https://plotly.com/dash/design-kit/) and [deploy](https://plotly.com/dash/app-manager/) apps like this with [Dash Enterprise](https://plotly.com/dash/).
Get started with [the official Dash docs](https://dash.plotly.com/installation) and **learn how to effortlessly [style](https://plotly.com/dash/design-kit/) & [deploy](https://plotly.com/dash/app-manager/) apps like this with <a style="color:red;" href="https://plotly.com/dash/">Dash Enterprise</a>.**

```python hide_code=true
from IPython.display import IFrame
Expand Down
18 changes: 16 additions & 2 deletions doc/python/hover-text-and-formatting.md
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
jupytext_version: 1.4.2
jupytext_version: 1.6.0
kernelspec:
display_name: Python 3
language: python
Expand All @@ -20,7 +20,7 @@ jupyter:
name: python
nbconvert_exporter: python
pygments_lexer: ipython3
version: 3.7.7
version: 3.7.6
plotly:
description: How to use hover text and formatting in Python with Plotly.
display_as: file_settings
Expand Down Expand Up @@ -83,6 +83,20 @@ fig.update_layout(hovermode="x unified")
fig.show()
```

#### Control hovermode with Dash

[Dash](https://plotly.com/dash/) is the best way to build analytical apps in Python using Plotly figures. To run the app below, run `pip install dash`, click "Download" to get the code and run `python app.py`.

Get started with [the official Dash docs](https://dash.plotly.com/installation) and **learn how to effortlessly [style](https://plotly.com/dash/design-kit/) & [deploy](https://plotly.com/dash/app-manager/) apps like this with <a style="color:red;" href="https://plotly.com/dash/">Dash Enterprise</a>.**

Change the hovermode below and try hovering over the points:

```python hide_code=true
from IPython.display import IFrame
snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/'
IFrame(snippet_url + 'hover-text-and-formatting', width='100%', height=630)
```

#### Selecting a hovermode in a figure created with `plotly.graph_objects`

The hovermode is a property of the figure layout, so you can select a hovermode no matter how you created the figure, either with `plotly.express` or with `plotly.graph_objects`. Below is an example with a figure created with `plotly.graph_objects`. If you're not familiar with the structure of plotly figures, you can read [the tutorial on creating and updating plotly figures](/python/creating-and-updating-figures/).
Expand Down
18 changes: 15 additions & 3 deletions doc/python/pie-charts.md
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Original file line number Diff line number Diff line change
Expand Up @@ -6,7 +6,7 @@ jupyter:
extension: .md
format_name: markdown
format_version: '1.2'
jupytext_version: 1.3.0
jupytext_version: 1.6.0
kernelspec:
display_name: Python 3
language: python
Expand All @@ -20,7 +20,7 @@ jupyter:
name: python
nbconvert_exporter: python
pygments_lexer: ipython3
version: 3.7.3
version: 3.7.6
plotly:
description: How to make Pie Charts.
display_as: basic
Expand Down Expand Up @@ -64,6 +64,18 @@ fig = px.pie(df, values='tip', names='day')
fig.show()
```

### Pie chart in Dash

[Dash](https://plotly.com/dash/) is the best way to build analytical apps in Python using Plotly figures. To run the app below, run `pip install dash`, click "Download" to get the code and run `python app.py`.

Get started with [the official Dash docs](https://dash.plotly.com/installation) and **learn how to effortlessly [style](https://plotly.com/dash/design-kit/) & [deploy](https://plotly.com/dash/app-manager/) apps like this with <a style="color:red;" href="https://plotly.com/dash/">Dash Enterprise</a>.**

```python hide_code=true
from IPython.display import IFrame
snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/'
IFrame(snippet_url + 'pie-charts', width='100%', height=630)
```

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

```python
Expand Down Expand Up @@ -304,4 +316,4 @@ fig.show()

#### Reference

See https://plotly.com/python/reference/pie/ for more information and chart attribute options!
See https://plotly.com/python/reference/pie/ for more information and chart attribute options!