Add 5 more Dash Snippets

doc/python/histograms.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: 3.7.3
+    version: 3.7.6
   plotly:
     description: How to make Histograms in Python with Plotly.
     display_as: statistical
@@ -30,9 +30,9 @@ jupyter:
     order: 3
     page_type: example_index
     permalink: python/histograms/
-    redirect_from: 
-      - /python/histogram-tutorial/
-      - /python/histogram/
+    redirect_from:
+    - /python/histogram-tutorial/
+    - /python/histogram/
     thumbnail: thumbnail/histogram.jpg
 ---
 
@@ -71,6 +71,19 @@ fig = px.histogram(df, x="total_bill", nbins=20)
 fig.show()
 ```
 
+#### Histograms 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 + 'histograms', width='100%', height=630)
+```
+
 #### Accessing the counts (y-axis) values
 
 JavaScript calculates the y-axis (count) values on the fly in the browser, so it's not accessible in the `fig`. You can manually calculate it using `np.histogram`.

doc/python/legend.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 configure and style the legend in Plotly with Python.
     display_as: file_settings
@@ -133,6 +133,19 @@ fig.update_layout(legend=dict(
 fig.show()
 ```
 
+#### Legends 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 + 'legend', width='100%', height=630)
+```
+
 #### Horizontal Legends
 
 The `layout.legend.orientation` attribute can be set to `"h"` for a horizontal legend. Here we also position it above the plotting area.

doc/python/subplots.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 subplots in with Plotly's Python graphing library. Examples
       of stacked, custom-sized, gridded, and annotated subplots.
@@ -211,6 +211,19 @@ fig.add_trace(go.Scatter(x=[20, 30, 40], y=[50, 60, 70]),
 fig.show()
 ```
 
+#### Subplots 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 + 'subplots', width='100%', height=630)
+```
+
 #### Customizing Subplot Axes
 After a figure with subplots is created using the `make_subplots` function, its axis properties (title, font, range, grid style, etc.) can be customized using the `update_xaxes` and `update_yaxes` graph object figure methods.  By default, these methods apply to all of the x axes or y axes in the figure. The `row` and `col` arguments can be used to control which axes are targeted by the update.
 
@@ -588,4 +601,4 @@ All of the y-axis properties are found here: https://plotly.com/python/reference
 ```python
 from plotly.subplots import make_subplots
 help(make_subplots)
-```
+```

doc/python/text-and-annotations.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 text labels and annotations to plots in python.
     display_as: file_settings
@@ -103,6 +103,19 @@ fig.add_trace(go.Scatter(
 fig.show()
 ```
 
+### Text positioning 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 + 'text-and-annotations', width='100%', height=630)
+```
+
 ### Controlling text fontsize with uniformtext
 
 For the [pie](/python/pie-charts), [bar](/python/bar-charts), [sunburst](/python/sunburst-charts) and [treemap](/python/treemap-charts) traces, it is possible to force all the text labels to have the same size thanks to the `uniformtext` layout parameter. The `minsize` attribute sets the font size, and the `mode` attribute sets what happens for labels which cannot fit with the desired fontsize: either `hide` them or `show` them with overflow.

doc/python/time-series.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 plot date and time in python.
     display_as: financial
@@ -61,6 +61,19 @@ fig = go.Figure([go.Scatter(x=df['Date'], y=df['AAPL.High'])])
 fig.show()
 ```
 
+### Time Series 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 + 'time-series', width='100%', height=630)
+```
+
 ### Different Chart Types on Date Axes
 
 Any kind of cartesian chart can be placed on `date` axes, for example this bar chart of relative stock ticker values.