Merge branch 'doc-prod' into offsetgroup-alignmentgroup

Author: Mahdis-z

.circleci/config.yml

@@ -425,8 +425,8 @@ jobs:
             . venv/bin/activate
             echo ${mapbox_token} > python/.mapbox_token
             make -kj8 || make -kj8
-            curl https://raw.githubusercontent.com/plotly/documentation/source-design-merge/front-matter-ci.py > front-matter-ci.py
-            curl https://raw.githubusercontent.com/plotly/documentation/source-design-merge/check-or-enforce-order.py > check-or-enforce-order.py
+            curl https://raw.githubusercontent.com/plotly/graphing-library-docs/master/front-matter-ci.py > front-matter-ci.py
+            curl https://raw.githubusercontent.com/plotly/graphing-library-docs/master/check-or-enforce-order.py > check-or-enforce-order.py
             python front-matter-ci.py build/html
             python check-or-enforce-order.py build/html
             if [ "${CIRCLE_BRANCH}" == "doc-prod" ]; then
@@ -448,14 +448,14 @@ jobs:
           name: trigger doc build
           command: |
             if [ "${CIRCLE_BRANCH}" == "doc-prod" ]; then
-              git clone --depth=1 --branch=source-design-merge https://github.com/plotly/documentation.git
-              cd documentation
+              git clone --depth=1 https://github.com/plotly/graphing-library-docs.git
+              cd graphing-library-docs
               git config user.name plotlydocbot
               git config user.email [email protected]
               git commit --allow-empty -m "deploying https://github.com/plotly/plotly.py/commit/${CIRCLE_SHA1}"
               git push
               cd ..
-              rm -rf documentation
+              rm -rf graphing-library-docs
             fi
 
       - run:

binder/requirements.txt

@@ -12,3 +12,5 @@ psutil
 requests
 networkx
 scikit-image
+datashader
+pyarrow

doc/python/3d-scatter-plots.md

@@ -35,7 +35,7 @@ jupyter:
 
 ## 3D scatter plot with Plotly Express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 Like the [2D scatter plot](https://plot.ly/python/line-and-scatter/) `px.scatter`, the 3D function `px.scatter_3d` plots individual data in three-dimensional space.
 

doc/python/bar-charts.md

@@ -35,7 +35,7 @@ jupyter:
 
 ### Bar chart with Plotly Express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 With `px.bar`, each row of the DataFrame is represented as a rectangular mark.
 

doc/python/box-plots.md

@@ -5,7 +5,7 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.2'
+      format_version: "1.2"
       jupytext_version: 1.3.1
   kernelspec:
     display_name: Python 3
@@ -31,16 +31,16 @@ jupyter:
     page_type: example_index
     permalink: python/box-plots/
     redirect_from:
-    - /python/box/
-    - /python/basic_statistics/
+      - /python/box/
+      - /python/basic_statistics/
     thumbnail: thumbnail/box.jpg
 ---
 
 A [box plot](https://en.wikipedia.org/wiki/Box_plot) is a statistical representation of numerical data through their quartiles. The ends of the box represent the lower and upper quartiles, while the median (second quartile) is marked by a line inside the box. For other statistical representations of numerical data, see [other statistical charts](https://plot.ly/python/statistical-charts/).
 
 ## Box Plot with `plotly.express`
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 In a box plot created by `px.box`, the distribution of the column given as `y` argument is represented.
 
@@ -73,13 +73,13 @@ fig.show()
 
 ### Choosing The Algorithm For Computing Quartiles
 
-By default, quartiles for box plots are computed using the `linear` method (for more about linear interpolation, see #10 listed on [http://www.amstat.org/publications/jse/v14n3/langford.html](http://www.amstat.org/publications/jse/v14n3/langford.html) and [https://en.wikipedia.org/wiki/Quartile](https://en.wikipedia.org/wiki/Quartile) for more details). 
+By default, quartiles for box plots are computed using the `linear` method (for more about linear interpolation, see #10 listed on [http://www.amstat.org/publications/jse/v14n3/langford.html](http://www.amstat.org/publications/jse/v14n3/langford.html) and [https://en.wikipedia.org/wiki/Quartile](https://en.wikipedia.org/wiki/Quartile) for more details).
 
-However, you can also choose to use an `exclusive` or an `inclusive` algorithm to compute quartiles. 
+However, you can also choose to use an `exclusive` or an `inclusive` algorithm to compute quartiles.
 
-The *exclusive* algorithm uses the median to divide the ordered dataset into two halves. If the sample is odd, it does not include the median in either half. Q1 is then the median of the lower half and Q3 is the median of the upper half.
+The _exclusive_ algorithm uses the median to divide the ordered dataset into two halves. If the sample is odd, it does not include the median in either half. Q1 is then the median of the lower half and Q3 is the median of the upper half.
 
-The *inclusive* algorithm also uses the median to divide the ordered dataset into two halves, but if the sample is odd, it includes the median in both halves. Q1 is then the median of the lower half and Q3 the median of the upper half.
+The _inclusive_ algorithm also uses the median to divide the ordered dataset into two halves, but if the sample is odd, it includes the median in both halves. Q1 is then the median of the lower half and Q3 the median of the upper half.
 
 ```python
 import plotly.express as px
@@ -92,7 +92,8 @@ fig.show()
 ```
 
 #### Difference Between Quartile Algorithms
-It can sometimes be difficult to see the difference between the linear, inclusive, and exclusive algorithms for computing quartiles. In the following example, the same dataset is visualized using each of the three different quartile computation algorithms. 
+
+It can sometimes be difficult to see the difference between the linear, inclusive, and exclusive algorithms for computing quartiles. In the following example, the same dataset is visualized using each of the three different quartile computation algorithms.
 
 ```python
 import plotly.express as px
@@ -103,7 +104,7 @@ df = pd.DataFrame(dict(
     linear=data,
     inclusive=data,
     exclusive=data
-)).melt(var_name="quartilemethod") 
+)).melt(var_name="quartilemethod")
 
 
 fig = px.box(df, y="value", facet_col="quartilemethod", color="quartilemethod",
@@ -204,7 +205,7 @@ fig.show()
 
 You can specify precomputed quartile attributes rather than using a built-in quartile computation algorithm.
 
-This could be useful if you have already pre-computed those values or if you need to use a different algorithm than the ones provided. 
+This could be useful if you have already pre-computed those values or if you need to use a different algorithm than the ones provided.
 
 ```python
 import plotly.graph_objects as go
@@ -217,9 +218,9 @@ fig.add_trace(go.Box(y=[
         [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
       ], name="Precompiled Quartiles"))
 
-fig.update_traces(q1=[ 1, 2, 3 ], median=[ 4, 5, 6 ], 
-                  q3=[ 7, 8, 9 ], lowerfence=[-1, 0, 1], 
-                  upperfence=[5, 6, 7], mean=[ 2.2, 2.8, 3.2 ], 
+fig.update_traces(q1=[ 1, 2, 3 ], median=[ 4, 5, 6 ],
+                  q3=[ 7, 8, 9 ], lowerfence=[-1, 0, 1],
+                  upperfence=[5, 6, 7], mean=[ 2.2, 2.8, 3.2 ],
                   sd=[ 0.2, 0.4, 0.6 ], notchspan=[ 0.2, 0.4, 0.6 ] )
 
 fig.show()

doc/python/bubble-charts.md

@@ -38,7 +38,7 @@ jupyter:
 
 A [bubble chart](https://en.wikipedia.org/wiki/Bubble_chart) is a scatter plot in which a third dimension of the data is shown through the size of markers. For other types of scatter plot, see the [line and scatter page](https://plot.ly/python/line-and-scatter/).
 
-We first show a bubble chart example using Plotly Express. [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/). The size of markers is set from the dataframe column given as the `size` parameter.
+We first show a bubble chart example using Plotly Express. [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/). The size of markers is set from the dataframe column given as the `size` parameter.
 
 ```python
 import plotly.express as px

doc/python/bubble-maps.md

@@ -39,7 +39,7 @@ Plotly figures made with `px.scatter_geo`, `px.line_geo` or `px.choropleth` func
 
 ### Bubble map with Plotly Express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/). With `px.scatter_geo`, each line of the dataframe is represented as a marker point. The column set as the `size` argument gives the size of markers.
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/). With `px.scatter_geo`, each line of the dataframe is represented as a marker point. The column set as the `size` argument gives the size of markers.
 
 ```python
 import plotly.express as px

doc/python/choropleth-maps.md

@@ -56,7 +56,7 @@ The GeoJSON data is passed to the `geojson` argument, and the data is passed int
 
 ### Choropleth Map with plotly.express
 
-[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
 #### GeoJSON with `feature.id`
 

doc/python/custom-buttons.md

@@ -5,8 +5,8 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.1.7
+      format_version: '1.2'
+      jupytext_version: 1.3.2
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.7.2
+    version: 3.7.3
   plotly:
     description: How to add custom buttons to update Plotly chart attributes in Python.
     display_as: controls
@@ -78,7 +78,7 @@ fig.update_scenes(
 # Add dropdown
 fig.update_layout(
     updatemenus=[
-        go.layout.Updatemenu(
+        dict(
             type = "buttons",
             direction = "left",
             buttons=list([
@@ -106,7 +106,7 @@ fig.update_layout(
 # Add annotation
 fig.update_layout(
     annotations=[
-        go.layout.Annotation(text="Trace type:", showarrow=False,
+        dict(text="Trace type:", showarrow=False,
                              x=0, y=1.08, yref="paper", align="left")
     ]
 )
@@ -153,7 +153,7 @@ button_layer_2_height = 1.065
 
 fig.update_layout(
     updatemenus=[
-        go.layout.Updatemenu(
+        dict(
             buttons=list([
                 dict(
                     args=["colorscale", "Viridis"],
@@ -185,7 +185,7 @@ fig.update_layout(
             y=button_layer_1_height,
             yanchor="top"
         ),
-        go.layout.Updatemenu(
+        dict(
             buttons=list([
                 dict(
                     args=["reversescale", False],
@@ -207,7 +207,7 @@ fig.update_layout(
             y=button_layer_2_height,
             yanchor="top"
         ),
-        go.layout.Updatemenu(
+        dict(
             buttons=list([
                 dict(
                     args=[{"contours.showlines": False, "type": "contour"}],
@@ -234,11 +234,11 @@ fig.update_layout(
 
 fig.update_layout(
     annotations=[
-        go.layout.Annotation(text="colorscale", x=0, xref="paper", y=1.1, yref="paper",
+        dict(text="colorscale", x=0, xref="paper", y=1.1, yref="paper",
                              align="left", showarrow=False),
-        go.layout.Annotation(text="Reverse<br>Colorscale", x=0, xref="paper", y=1.06,
+        dict(text="Reverse<br>Colorscale", x=0, xref="paper", y=1.06,
                              yref="paper", showarrow=False),
-        go.layout.Annotation(text="Lines", x=0.47, xref="paper", y=1.045, yref="paper",
+        dict(text="Lines", x=0.47, xref="paper", y=1.045, yref="paper",
                              showarrow=False)
     ])
 
@@ -296,25 +296,25 @@ fig.add_trace(
 )
 
 # Add buttons that add shapes
-cluster0 = [go.layout.Shape(type="circle",
+cluster0 = [dict(type="circle",
                             xref="x", yref="y",
                             x0=min(x0), y0=min(y0),
                             x1=max(x0), y1=max(y0),
                             line=dict(color="DarkOrange"))]
-cluster1 = [go.layout.Shape(type="circle",
+cluster1 = [dict(type="circle",
                             xref="x", yref="y",
                             x0=min(x1), y0=min(y1),
                             x1=max(x1), y1=max(y1),
                             line=dict(color="Crimson"))]
-cluster2 = [go.layout.Shape(type="circle",
+cluster2 = [dict(type="circle",
                             xref="x", yref="y",
                             x0=min(x2), y0=min(y2),
                             x1=max(x2), y1=max(y2),
                             line=dict(color="RebeccaPurple"))]
 
 fig.update_layout(
     updatemenus=[
-        go.layout.Updatemenu(
+        dict(
             type="buttons",
             buttons=[
                 dict(label="None",
@@ -415,7 +415,7 @@ low_annotations = [dict(x="2015-05-01",
 
 fig.update_layout(
     updatemenus=[
-        go.layout.Updatemenu(
+        dict(
             type="buttons",
             direction="right",
             active=0,