diff --git a/doc/python/histograms.md b/doc/python/histograms.md
index 3d72758005b..6e5d98f84fe 100644
--- a/doc/python/histograms.md
+++ b/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 Dash Enterprise.**
+
+
+```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`.
diff --git a/doc/python/legend.md b/doc/python/legend.md
index 65efd16c447..cdc1605d2d4 100644
--- a/doc/python/legend.md
+++ b/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 Dash Enterprise.**
+
+
+```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.
diff --git a/doc/python/subplots.md b/doc/python/subplots.md
index 1687a2ae800..3d40b239dea 100644
--- a/doc/python/subplots.md
+++ b/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 Dash Enterprise.**
+
+
+```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)
-```
\ No newline at end of file
+```
diff --git a/doc/python/text-and-annotations.md b/doc/python/text-and-annotations.md
index b3bb9b6da9b..8f9af6bed53 100644
--- a/doc/python/text-and-annotations.md
+++ b/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 Dash Enterprise.**
+
+
+```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.
diff --git a/doc/python/time-series.md b/doc/python/time-series.md
index 691cbf19e30..466f0647550 100644
--- a/doc/python/time-series.md
+++ b/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 Dash Enterprise.**
+
+
+```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.