diff --git a/doc/python/creating-and-updating-figures.md b/doc/python/creating-and-updating-figures.md
index 928df618692..e63f559184a 100644
--- a/doc/python/creating-and-updating-figures.md
+++ b/doc/python/creating-and-updating-figures.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: 3.7.0
+    version: 3.7.6
   plotly:
     description: Creating and Updating Figures with Plotly's Python graphing library
     display_as: file_settings
@@ -127,6 +127,19 @@ print("JSON Representation of A Graph Object:\n\n" + str(fig.to_json()))
 print("\n\n")
 ```
 
+### Representing Figures 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 class="plotly-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 + 'figure-structure', width='100%', height=630)
+```
+
 ### Creating Figures
 
 This section summarizes several ways to create new graph object figures with the `plotly.py` graphing library.
@@ -652,4 +665,4 @@ fig.data[0].marker.line.width = 4
 fig.data[0].marker.line.color = "black"
 
 fig.show()
-```
\ No newline at end of file
+```
diff --git a/doc/python/distplot.md b/doc/python/distplot.md
index e27c5af35ed..efa7dfc4acb 100644
--- a/doc/python/distplot.md
+++ b/doc/python/distplot.md
@@ -5,8 +5,8 @@ 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
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.7
+    version: 3.7.6
   plotly:
     description: How to make interactive Distplots in Python with Plotly.
     display_as: statistical
@@ -56,6 +56,19 @@ fig = px.histogram(df, x="total_bill", y="tip", color="sex",
 fig.show()
 ```
 
+### Combined statistical representations 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 class="plotly-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 + 'distplot', width='100%', height=630)
+```
+
 ## Combined statistical representations with distplot figure factory
 
 The distplot [figure factory](/python/figure-factories/) displays a combination of statistical representations of numerical data, such as histogram, kernel density estimation or normal curve, and rug plot.
@@ -287,4 +300,4 @@ fig.show()
 #### Reference
 
 
-For more info on `ff.create_distplot()`, see the [full function reference](https://plotly.com/python-api-reference/generated/plotly.figure_factory.create_distplot.html)
\ No newline at end of file
+For more info on `ff.create_distplot()`, see the [full function reference](https://plotly.com/python-api-reference/generated/plotly.figure_factory.create_distplot.html)
diff --git a/doc/python/filled-area-plots.md b/doc/python/filled-area-plots.md
index b93ba0f8b07..b12dbb00eea 100644
--- a/doc/python/filled-area-plots.md
+++ b/doc/python/filled-area-plots.md
@@ -5,8 +5,8 @@ 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
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.7
+    version: 3.7.6
   plotly:
     description: How to make filled area plots in Python with Plotly.
     display_as: basic
@@ -49,6 +49,19 @@ fig = px.area(df, x="year", y="pop", color="continent",
 fig.show()
 ```
 
+## Filled area plot 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 class="plotly-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 + 'filled-area-plots', width='100%', height=630)
+```
+
 ### Filled area chart with plotly.graph_objects
 
 #### Basic Overlaid Area Chart
@@ -212,4 +225,4 @@ fig.show()
 
 See https://plotly.com/python/reference/scatter/#scatter-line
 and https://plotly.com/python/reference/scatter/#scatter-fill
-for more information and attribute options!
\ No newline at end of file
+for more information and attribute options!
diff --git a/doc/python/ml-regression.md b/doc/python/ml-regression.md
index f6b2bf2330e..faee293eef4 100644
--- a/doc/python/ml-regression.md
+++ b/doc/python/ml-regression.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: Visualize regression in scikit-learn with Plotly.
     display_as: ai_ml
@@ -91,6 +91,19 @@ fig.add_traces(go.Scatter(x=x_range, y=y_range, name='Regression Fit'))
 fig.show()
 ```
 
+### ML Regression 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 class="plotly-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 + 'ml-regression', width='100%', height=630)
+```
+
 ## Model generalization on unseen data
 
 With `go.Scatter`, you can easily color your plot based on a predefined data split. By coloring the training and the testing data points with different colors, you can easily see if whether the model generalizes well to the test data or not.
@@ -534,4 +547,4 @@ Learn more about the Machine Learning models used in this tutorial:
 Other tutorials that inspired this notebook:
 * https://seaborn.pydata.org/examples/residplot.html
 * https://scikit-learn.org/stable/auto_examples/linear_model/plot_lasso_model_selection.html
-* http://www.scikit-yb.org/zh/latest/api/regressor/peplot.html
\ No newline at end of file
+* http://www.scikit-yb.org/zh/latest/api/regressor/peplot.html
diff --git a/doc/python/tick-formatting.md b/doc/python/tick-formatting.md
index ed5324c3c4a..408bf36e1f0 100644
--- a/doc/python/tick-formatting.md
+++ b/doc/python/tick-formatting.md
@@ -5,8 +5,8 @@ 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
@@ -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 format axes ticks in Python with Plotly.
     display_as: file_settings
@@ -80,6 +80,19 @@ fig.update_layout(
 fig.show()
 ```
 
+### Dynamic tickmode 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 class="plotly-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 + 'tick-formatting', width='100%', height=630)
+```
+
 #### Using Tickformat Attribute