Merge pull request #2895 from plotly/add-five-snippets

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

Author: nicolaskruchten

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 <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`).

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 <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.

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 <a style="color:red;" href="https://plotly.com/dash/">Dash Enterprise</a>.**
 
 ```python hide_code=true
 from IPython.display import IFrame

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 <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/).

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 <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
@@ -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!