Merge branch 'doc-prod'

Author: nicolaskruchten

doc/python/builtin-colorscales.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: A reference for the built-in named continuous (sequential, diverging
       and cylclical) color scales in Plotly.
@@ -74,6 +74,19 @@ import plotly.express as px
 print(px.colors.sequential.Plasma)
 ```
 
+### Continuous Color Scales 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 + 'builtin-colorscales', width='100%', height=630)
+```
+
 ### Built-In Sequential Color scales
 
 A collection of predefined sequential colorscales is provided in the `plotly.colors.sequential` module. Sequential color scales are appropriate for most continuous data, but in some cases it can be helpful to use a diverging or cyclical color scale (see below).

doc/python/heatmaps.md

@@ -102,7 +102,7 @@ fig.show()
 
 ### Heatmap with Categorical Axis Labels
 
-In this example we also show how to ignore [hovertext](https://plotly.com/python/hover-text-and-formatting/) when we have [missing values](https://plotly.com/python/missing_values) in the data by setting the [hoverongaps](https://plotly.com/python/reference/heatmap/#heatmap-hoverongaps) to False.
+In this example we also show how to ignore [hovertext](https://plotly.com/python/hover-text-and-formatting/) when we have missing values in the data by setting the [hoverongaps](https://plotly.com/python/reference/heatmap/#heatmap-hoverongaps) to False.
 
 ```python
 import plotly.graph_objects as go

doc/python/horizontal-vertical-shapes.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 add annotated horizontal and vertical lines in Python.
     display_as: file_settings
@@ -67,6 +67,19 @@ fig.add_hrect(y0=0.9, y1=2.6, line_width=0, fillcolor="red", opacity=0.2)
 fig.show()
 ```
 
+#### Horizontal and vertical lines 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 + 'horizontal-vertical-shapes', width='100%', height=630)
+```
+
 #### Adding Text Annotations
 
 [Text annotations](/python/text-and-annotations) can optionally be added to an autoshape

doc/python/hover-text-and-formatting.md

@@ -34,7 +34,7 @@ jupyter:
 
 ### Hover Labels
 
-One of the most deceptively-power features of interactive visualization using Plotly is the ability for the user to reveal more information about a data point by moving their mouse cursor over the point and having a hover label appear.
+One of the most deceptively-powerful features of interactive visualization using Plotly is the ability for the user to reveal more information about a data point by moving their mouse cursor over the point and having a hover label appear.
 
 There are three hover modes available in Plotly. The default setting is `layout.hovermode='closest'`, wherein a single hover label appears for the point directly underneath the cursor.
 

doc/python/imshow.md

@@ -450,7 +450,7 @@ from skimage.data import image_fetcher
 path = image_fetcher.fetch('data/cells.tif')
 data = io.imread(path)
 img = data[25:40]
-fig = px.imshow(img, animation_frame=0, binary_string=True, labels=dict(animation_frame="yo"))
+fig = px.imshow(img, animation_frame=0, binary_string=True, labels=dict(animation_frame="slice"))
 fig.show()
 ```
 

doc/python/interactive-html-export.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: Plotly allows you to save interactive HTML versions of your figures
       to your local disk.
@@ -56,10 +56,23 @@ fig.write_html("path/to/file.html")
 By default, the resulting HTML file is a fully self-contained HTML file which can be uploaded to a web server or shared via email or other file-sharing mechanisms. The downside to this approach is that the file is very large (5Mb+) because it contains an inlined copy of the Plotly.js library required to make the figure interactive. This can be controlled via the `include_plotlyjs` argument (see below).
 
 
+### HTML 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 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 + 'interactive-html-export', width='100%', height=630)
+```
+
 ### Full Parameter Documentation
 
 ```python
 import plotly.graph_objects as go
 
 help(go.Figure.write_html)
-```
+```

doc/python/mapbox-county-choropleth.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 a Mapbox Choropleth Map of US Counties in Python with
       Plotly.
@@ -107,6 +107,19 @@ 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 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 + 'mapbox-county-choropleth', width='100%', height=630)
+```
+
 ### Indexing by GeoJSON Properties
 
 If the GeoJSON you are using either does not have an `id` field or you wish you use one of the keys in the `properties` field, you may use the `featureidkey` parameter to specify where to match the values of `locations`.

doc/python/ml-knn.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 scikit-learn's k-Nearest Neighbors (kNN) classification
       in Python with Plotly.
@@ -236,6 +236,19 @@ fig.add_trace(
 fig.show()
 ```
 
+## k-NN classification 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 + 'knn-classification', width='100%', height=630)
+```
+
 ## Multi-class prediction confidence with [`go.Heatmap`](https://plotly.com/python/heatmaps/)
 
 It is also possible to visualize the prediction confidence of the model using [heatmaps](https://plotly.com/python/heatmaps/). In this example, you can see how to compute how confident the model is about its prediction at every point in the 2D grid. Here, we define the confidence as the difference between the highest score and the score of the other classes summed, at a certain point.
@@ -321,4 +334,4 @@ Learn more about `px`, `go.Contour`, and `go.Heatmap` here:
 This tutorial was inspired by amazing examples from the official scikit-learn docs:
 * https://scikit-learn.org/stable/auto_examples/neighbors/plot_classification.html
 * https://scikit-learn.org/stable/auto_examples/classification/plot_classifier_comparison.html
-* https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html
+* https://scikit-learn.org/stable/auto_examples/datasets/plot_iris_dataset.html

doc/python/ml-pca.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: 3.7.7
+    version: 3.7.6
   plotly:
     description: Visualize Principle Component Analysis (PCA) of your high-dimensional
       data in Python with Plotly.
@@ -136,6 +136,19 @@ fig.update_traces(diagonal_visible=False)
 fig.show()
 ```
 
+## PCA analysis 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 + 'pca-visualization', width='100%', height=630)
+```
+
 ## 2D PCA Scatter Plot
 
 In the previous examples, you saw how to visualize high-dimensional PCs. In this example, we show you how to simply visualize the first two principal components of a PCA, by reducing a dataset of 4 dimensions to 2D.
@@ -262,4 +275,4 @@ The following resources offer an in-depth overview of PCA and explained variance
 * https://scikit-learn.org/stable/modules/decomposition.html#pca
 * https://stats.stackexchange.com/questions/2691/making-sense-of-principal-component-analysis-eigenvectors-eigenvalues/140579#140579
 * https://stats.stackexchange.com/questions/143905/loadings-vs-eigenvectors-in-pca-when-to-use-one-or-another
-* https://stats.stackexchange.com/questions/22569/pca-and-proportion-of-variance-explained
+* https://stats.stackexchange.com/questions/22569/pca-and-proportion-of-variance-explained

doc/python/ml-roc-pr.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: 3.7.7
+    version: 3.7.6
   plotly:
     description: Interpret the results of your classification using Receiver Operating
       Characteristics (ROC) and Precision-Recall (PR) Curves in Python with Plotly.
@@ -119,6 +119,19 @@ fig.update_xaxes(constrain='domain')
 fig.show()
 ```
 
+## ROC curve 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 + 'roc-and-pr-curves', width='100%', height=630)
+```
+
 ## Multiclass ROC Curve
 
 When you have more than 2 classes, you will need to plot the ROC curve for each class separately. Make sure that you use a [one-versus-rest](https://scikit-learn.org/stable/modules/multiclass.html#one-vs-the-rest) model, or make sure that your problem has a [multi-label](https://scikit-learn.org/stable/modules/multiclass.html#multilabel-classification-format) format; otherwise, your ROC curve might not return the expected results.

doc/python/multiple-axes.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 a graph with multiple axes (dual y-axis plots, plots
       with secondary axes) in python.
@@ -75,6 +75,19 @@ fig.update_yaxes(title_text="<b>secondary</b> yaxis title", secondary_y=True)
 fig.show()
 ```
 
+### Multiple axes 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 + 'multiple-axes', width='100%', height=630)
+```
+
 #### Muliple Y-Axes Subplots
 
 ```python
@@ -233,4 +246,4 @@ fig.show()
 ```
 
 #### Reference
-All of the y-axis properties are found here: https://plotly.com/python/reference/YAxis/.  For more information on creating subplots see the [Subplots in Python](/python/subplots/) section.
+All of the y-axis properties are found here: https://plotly.com/python/reference/YAxis/.  For more information on creating subplots see the [Subplots in Python](/python/subplots/) section.