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Nov 30, 2020
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Add 5 new dash snippets #2937

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19 changes: 16 additions & 3 deletions doc/python/creating-and-updating-figures.md
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Expand Up @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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.
Expand Down Expand Up @@ -652,4 +665,4 @@ fig.data[0].marker.line.width = 4
fig.data[0].marker.line.color = "black"

fig.show()
```
```
21 changes: 17 additions & 4 deletions doc/python/distplot.md
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Expand Up @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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.
Expand Down Expand Up @@ -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)
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)
21 changes: 17 additions & 4 deletions doc/python/filled-area-plots.md
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Expand Up @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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
Expand Down Expand Up @@ -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!
for more information and attribute options!
19 changes: 16 additions & 3 deletions doc/python/ml-regression.md
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Expand Up @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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.
Expand Down Expand Up @@ -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
* http://www.scikit-yb.org/zh/latest/api/regressor/peplot.html
19 changes: 16 additions & 3 deletions doc/python/tick-formatting.md
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Expand Up @@ -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
Expand All @@ -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
Expand Down Expand Up @@ -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


Expand Down