diff --git a/doc/python/box-plots.md b/doc/python/box-plots.md
index 659863faed5..6733f53e2d4 100644
--- a/doc/python/box-plots.md
+++ b/doc/python/box-plots.md
@@ -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
@@ -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
@@ -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 Dash Enterprise.**
+
+```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`).
diff --git a/doc/python/choropleth-maps.md b/doc/python/choropleth-maps.md
index 4e848ef009e..3245533cf2f 100644
--- a/doc/python/choropleth-maps.md
+++ b/doc/python/choropleth-maps.md
@@ -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
@@ -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
@@ -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 Dash Enterprise.**
+
+```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.
diff --git a/doc/python/discrete-color.md b/doc/python/discrete-color.md
index 90cb4911d60..dfae44ed96a 100644
--- a/doc/python/discrete-color.md
+++ b/doc/python/discrete-color.md
@@ -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 Dash Enterprise.**
```python hide_code=true
from IPython.display import IFrame
diff --git a/doc/python/hover-text-and-formatting.md b/doc/python/hover-text-and-formatting.md
index c656248a5e3..b4db3b96c28 100644
--- a/doc/python/hover-text-and-formatting.md
+++ b/doc/python/hover-text-and-formatting.md
@@ -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
@@ -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
@@ -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 Dash Enterprise.**
+
+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/).
diff --git a/doc/python/pie-charts.md b/doc/python/pie-charts.md
index 1899fe7be31..e202036e687 100644
--- a/doc/python/pie-charts.md
+++ b/doc/python/pie-charts.md
@@ -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
@@ -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
@@ -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 Dash Enterprise.**
+
+```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
@@ -304,4 +316,4 @@ fig.show()
#### Reference
-See https://plotly.com/python/reference/pie/ for more information and chart attribute options!
\ No newline at end of file
+See https://plotly.com/python/reference/pie/ for more information and chart attribute options!