Merge pull request #2898 from plotly/dash-snippets

Add 5 more Dash Snippets

Author: nicolaskruchten

doc/python/colorscales.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 set, create and control continous color scales and color bars
       in scatter, bar, map and heatmap figures.
@@ -90,6 +90,19 @@ fig = px.scatter(df, x="total_bill", y="tip", color="size",
 fig.show()
 ```
 
+### Colorscales 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 + 'colorscales', width='100%', height=630)
+```
+
 ### Color Scales in Plotly Express
 
 By default, [Plotly Express](/python/plotly-express/) will use the color scale from the active [template](/python/templates/)'s `layout.colorscales.sequential` attribute, and the default active template is `plotly` which uses the `Plasma` color scale. You can choose any of the [built-in color scales](/python/builtin-colorscales/), however, or define your own.
@@ -557,4 +570,4 @@ fig.show()
 
 ### Reference
 
-See https://plotly.com/python/reference/ for more information and chart attribute options!
+See https://plotly.com/python/reference/ for more information and chart attribute options!

doc/python/marker-style.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 style markers in Python with Plotly.
     display_as: file_settings
@@ -107,6 +107,19 @@ fig.show()
 
 Fully opaque, the default setting, is useful for non-overlapping markers. When many points overlap it can be hard to observe density.
 
+
+### Control Marker Border 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>.**
+
+```python hide_code=true
+from IPython.display import IFrame
+snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/'
+IFrame(snippet_url + 'marker-style', width='100%', height=630)
+```
+
 ### Opacity
 
 Setting opacity outside the marker will set the opacity of the trace. Thus, it will allow greater visbility of additional traces but like fully opaque it is hard to distinguish density.

doc/python/setting-graph-size.md

@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.2'
-      jupytext_version: 1.3.2
+      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: 3.7.3
+    version: 3.7.6
   plotly:
     description: How to manipulate the graph size, margins and background color.
     display_as: file_settings
@@ -51,6 +51,18 @@ fig.update_layout(
 fig.show()
 ```
 
+### Adjusting graph size 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>.**
+
+```python hide_code=true
+from IPython.display import IFrame
+snippet_url = 'https://dash-gallery.plotly.host/python-docs-dash-snippets/'
+IFrame(snippet_url + 'setting-graph-size', width='100%', height=630)
+```
+
 ### Adjusting Height, Width, & Margins With Graph Objects
 
 [Graph objects](/python/graph-objects/) are the low-level building blocks of figures which you can use instead of Plotly Express for greater control.
@@ -117,4 +129,4 @@ fig.show()
 
 #### Reference
 
-See https://plotly.com/python/reference/layout/ for more information and chart attribute options!
+See https://plotly.com/python/reference/layout/ for more information and chart attribute options!

doc/python/static-image-export.md

@@ -5,8 +5,8 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.1.6
+      format_version: '1.2'
+      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: 3.7.3
+    version: 3.7.6
   plotly:
     description: Plotly allows you to save static images of your plots. Save the image
       to your local computer, or embed it inside your Jupyter notebooks as a static
@@ -148,6 +148,20 @@ fig.write_image("images/fig1.eps")
 
 **Note:** It is important to note that any figures containing WebGL traces (i.e. of type `scattergl`, `heatmapgl`, `contourgl`, `scatter3d`, `surface`, `mesh3d`, `scatterpolargl`, `cone`, `streamtube`, `splom`, or `parcoords`) that are exported in a vector format will include encapsulated rasters, instead of vectors, for some parts of the image.
 
+
+### Image Export 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 + 'static-image-export', width='100%', height=630)
+```
+
 ### Get Image as Bytes
 
 The `plotly.io.to_image` function is used to return an image as a bytes object. You can also use the `.to_image` graph object figure method.
@@ -221,4 +235,4 @@ Here is a complete listing of the available image export settings:
 See the [Orca Management](/python/orca-management/) section for information on how to specify image export settings when using orca.
 
 ### Summary
-In summary, to export high-quality static images from plotly.py, all you need to do is install the `kaleido` package and then use the `plotly.io.write_image` and `plotly.io.to_image` functions (or the `.write_image` and `.to_image` graph object figure methods).
+In summary, to export high-quality static images from plotly.py, all you need to do is install the `kaleido` package and then use the `plotly.io.write_image` and `plotly.io.to_image` functions (or the `.write_image` and `.to_image` graph object figure methods).

doc/python/table.md

@@ -5,12 +5,22 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.1.1
+      format_version: '1.2'
+      jupytext_version: 1.6.0
   kernelspec:
     display_name: Python 3
     language: python
     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: 3.7.6
   plotly:
     description: How to make tables in Python with Plotly.
     display_as: basic
@@ -80,6 +90,18 @@ fig = go.Figure(data=[go.Table(
 fig.show()
 ```
 
+#### Tables 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 + 'table', width='100%', height=630)
+```
+
 #### Changing Row and Column Size
 
 ```python
@@ -211,4 +233,4 @@ fig.show()
 ```
 
 #### Reference
-For more information on tables and table attributes see: https://plotly.com/python/reference/table/.
+For more information on tables and table attributes see: https://plotly.com/python/reference/table/.