Merge pull request #4290 from plotly/doc-prod

Merge docs changes since last release into master

Author: LiamConnors

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

@@ -70,6 +70,7 @@ fig = px.scatter_3d(df, x='sepal_length', y='sepal_width', z='petal_width',
 
 # tight layout
 fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))
+fig.show()
 ```
 
 #### 3d scatter plots in Dash

doc/python/axes.md

@@ -33,7 +33,7 @@ jupyter:
     thumbnail: thumbnail/axes.png
 ---
 
-This tutorial explain how to set the properties of [2-dimensional Cartesian axes](/python/figure-structure/#2d-cartesian-trace-types-and-subplots), namely [`go.layout.XAxis`](/python/reference/layout/xaxis/) and [`go.layout.YAxis`](python/reference/layout/xaxis/).
+This tutorial explain how to set the properties of [2-dimensional Cartesian axes](/python/figure-structure/#2d-cartesian-trace-types-and-subplots), namely [`go.layout.XAxis`](/python/reference/layout/xaxis/) and [`go.layout.YAxis`](/python/reference/layout/xaxis/).
 
 Other kinds of subplots and axes are described in other tutorials:
 
@@ -154,7 +154,7 @@ fig.update_yaxes(ticklabelposition="inside top", title=None)
 fig.show()
 ```
 
-#### Specifying Label Aliases 
+#### Specifying Label Aliases
 
 *New in 5.14*
 

doc/python/colorscales.md

@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.3'
-      jupytext_version: 1.14.5
+      jupytext_version: 1.14.6
   kernelspec:
     display_name: Python 3 (ipykernel)
     language: python
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.10.11
+    version: 3.10.8
   plotly:
     description: How to set, create and control continuous color scales and color
       bars in scatter, bar, map and heatmap figures.
@@ -307,11 +307,11 @@ Using `labelalias` you can replace some labels on the `colorbar` with alternativ
 ```python
 import plotly.graph_objects as go
 
-import urllib
+import urllib.request as request
 import json
 
 # Load heatmap data
-response = urllib.request.urlopen(
+response = request.urlopen(
     "https://raw.githubusercontent.com/plotly/datasets/master/custom_heatmap_colorscale.json")
 dataset = json.load(response)
 

doc/python/configuration-options.md

@@ -53,7 +53,7 @@ import plotly.graph_objects as go
 
 fig = go.Figure()
 
-config = dict({'scrollZoom': True})
+config = {'scrollZoom': True}
 
 fig.add_trace(
     go.Scatter(
@@ -244,6 +244,8 @@ fig.add_trace(
         y=[1, 3, 1]))
 
 fig.update_layout(modebar_remove=['zoom', 'pan'])
+
+fig.show()
 ```
 
 ### Add optional shape-drawing buttons to modebar
@@ -253,16 +255,19 @@ fig.update_layout(modebar_remove=['zoom', 'pan'])
 Some modebar buttons of Cartesian plots are optional and have to be added explicitly, using the `modeBarButtonsToAdd` config attribute. These buttons are used for drawing or erasing shapes. See [the tutorial on shapes and shape drawing](python/shapes#drawing-shapes-on-cartesian-plots) for more details.
 
 ```python
-import plotly.graph_objects as go
 import plotly.express as px
+
 df = px.data.iris()
+
 fig = px.scatter(df, x='petal_width', y='sepal_length', color='species')
+
 fig.update_layout(
     dragmode='drawopenpath',
     newshape_line_color='cyan',
     title_text='Draw a path to separate versicolor and virginica'
 )
-fig.show(config={'modeBarButtonsToAdd':['drawline',
+
+fig.show(config={'modeBarButtonsToAdd': ['drawline',
                                         'drawopenpath',
                                         'drawclosedpath',
                                         'drawcircle',
@@ -276,10 +281,12 @@ fig.show(config={'modeBarButtonsToAdd':['drawline',
 The `layout.modebar.add` attribute can be used instead of the approach used above:
 
 ```python
-import plotly.graph_objects as go
 import plotly.express as px
+
 df = px.data.iris()
+
 fig = px.scatter(df, x='petal_width', y='sepal_length', color='species')
+
 fig.update_layout(
     dragmode='drawopenpath',
     newshape_line_color='cyan',
@@ -292,6 +299,8 @@ fig.update_layout(
         'eraseshape'
        ]
 )
+
+fig.show()
 ```
 
 ### Double-Click Delay
@@ -304,12 +313,12 @@ import plotly.graph_objects as go
 config = {'doubleClickDelay': 1000}
 
 fig = go.Figure(go.Bar(
-    y = [3, 5, 3, 2],
-    x = ["2019-09-02", "2019-10-10", "2019-11-12", "2019-12-22"],
-    texttemplate = "%{label}",
-    textposition = "inside"))
+    y=[3, 5, 3, 2],
+    x=["2019-09-02", "2019-10-10", "2019-11-12", "2019-12-22"],
+    texttemplate="%{label}",
+    textposition="inside"))
 
-fig.update_layout(xaxis = {'type': 'date'})
+fig.update_layout(xaxis={'type': 'date'})
 
 fig.show(config=config)
 ```
@@ -320,4 +329,4 @@ The same configuration dictionary that you pass to the `config` parameter of the
 
 #### Reference
 
-See config options at https://github.com/plotly/plotly.js/blob/master/src/plot_api/plot_config.js#L6
+See config options at https://github.com/plotly/plotly.js/blob/master/src/plot_api/plot_config.js

doc/python/figure-factory-subplots.md

@@ -61,7 +61,7 @@ Y, X = np.meshgrid(x, y)
 u = -1 - X**2 + Y
 v = 1 + X - Y**2
 
-fig2 = ff.create_streamline(x, y, u, v, arrow_scale=.1, name='Steamline')
+fig2 = ff.create_streamline(x, y, u, v, arrow_scale=.1, name='Streamline')
 ```
 
 Edit the figures' x and y axes attributes to create subplots:

doc/python/legend.md

@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.3'
-      jupytext_version: 1.14.5
+      jupytext_version: 1.14.6
   kernelspec:
     display_name: Python 3 (ipykernel)
     language: python
@@ -580,7 +580,7 @@ fig.show()
 
 By default, all traces appear on one legend. To have multiple legends, specify an alternative legend for a trace using the `legend` property. For a second legend, set `legend="legend2"`. Specify more legends with `legend="legend3"`, `legend="legend4"` and so on.
 
-In this example, the last two scatter traces display on the second legend, "legend2". On the figure's layout, we then position and style this legend to display on the right of the graph below the first legend.
+In this example, the last two scatter traces display on the second legend, "legend2". On the figure's layout, we then position and style each legend.
 
 
 ```python
@@ -622,20 +622,25 @@ fig = go.Figure(
     ],
     layout=dict(
         title="GDP Per Capita",
-        legend={"title": "By country", "bgcolor": "Orange",},
+        legend={
+            "title": "By country",
+            "xref": "container",
+            "yref": "container",
+            "y": 0.65,
+            "bgcolor": "Orange",
+        },
         legend2={
-            "x": 1.155,
-            "y": 0.55,
-            "xanchor": "right",
-            "yanchor": "middle",
+            "title": "By continent",
+            "xref": "container",
+            "yref": "container",
+            "y": 0.85,
             "bgcolor": "Gold",
-            "title": {"text": "By continent"},
+
         },
     ),
 )
 
 fig.show()
-
 ```
 
 ### Positioning Legends
@@ -666,7 +671,6 @@ fig = go.Figure(
             "xref": "container",
             "yref": "container",
             "bgcolor": "Gold",
-            "title": {"text": "By continent"},
         },
     ),
 )

doc/python/lines-on-mapbox.md

@@ -37,7 +37,7 @@ jupyter:
 
 To plot on Mapbox maps with Plotly you _may_ need a Mapbox account and a public [Mapbox Access Token](https://www.mapbox.com/studio). See our [Mapbox Map Layers](/python/mapbox-layers/) documentation for more information.
 
-To draw a line on your map, you either can use [`px.line_mapbox()`](https://www.plotly.express/plotly_express/#plotly_express.line_mapbox) in Plotly Express, or [`Scattermapbox`](https://plotly.com/python/reference/scattermapbox/) traces. Below we show you how to draw a line on Mapbox using Plotly Express.
+To draw a line on your map, you either can use [`px.line_mapbox()`](https://plotly.com/python-api-reference/generated/plotly.express.line_mapbox.html) in Plotly Express, or [`Scattermapbox`](https://plotly.com/python/reference/scattermapbox/) traces. Below we show you how to draw a line on Mapbox using Plotly Express.
 
 ### Lines on Mapbox maps using Plotly Express
 
@@ -132,5 +132,5 @@ fig.show()
 
 #### Reference
 
-See [function reference for `px.(line_mapbox)`](https://plotly.com/python-api-reference/generated/plotly.express.line_mapbox) or 
+See [function reference for `px.(line_mapbox)`](https://plotly.com/python-api-reference/generated/plotly.express.line_mapbox) or
 https://plotly.com/python/reference/scattermapbox/ for more information about mapbox and their attribute options.

doc/python/map-configuration.md

@@ -5,10 +5,10 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.2'
-      jupytext_version: 1.3.1
+      format_version: '1.3'
+      jupytext_version: 1.14.7
   kernelspec:
-    display_name: Python 3
+    display_name: Python 3 (ipykernel)
     language: python
     name: python3
   language_info:
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.8
+    version: 3.10.4
   plotly:
     description: How to configure and style base maps for Choropleths and Bubble Maps.
     display_as: maps
@@ -119,9 +119,7 @@ fig.show()
 
 ### Map Projections
 
-Geo maps are drawn according to a given map [projection](https://en.wikipedia.org/wiki/Map_projection) that flattens the Earth's roughly-spherical surface into a 2-dimensional space.
-
-The available projections are `'equirectangular'`, `'mercator'`, `'orthographic'`, `'natural earth'`, `'kavrayskiy7'`, `'miller'`, `'robinson'`, `'eckert4'`, `'azimuthal equal area'`, `'azimuthal equidistant'`, `'conic equal area'`, `'conic conformal'`, `'conic equidistant'`, `'gnomonic'`, `'stereographic'`, `'mollweide'`, `'hammer'`, `'transverse mercator'`, `'albers usa'`, `'winkel tripel'`, `'aitoff'` and `'sinusoidal'`.
+Geo maps are drawn according to a given map [projection](https://en.wikipedia.org/wiki/Map_projection) that flattens the Earth's roughly-spherical surface into a 2-dimensional space. In the following examples, we show the `'orthographic'` and `'natural earth'` projections, two of the many projection types available. For a full list of available projection types, see the [layout.geo reference documentation](https://plotly.com/python/reference/layout/geo/#layout-geo-projection-type).
 
 ```python
 import plotly.graph_objects as go
@@ -221,7 +219,3 @@ fig.show()
 ### Reference
 
 See https://plotly.com/python/reference/layout/geo/ for more information and chart attribute options!
-
-```python
-
-```

doc/python/marker-style.md

@@ -326,7 +326,7 @@ fig.show()
 
 The `marker_symbol` attribute allows you to choose from a wide array of symbols to represent markers in your figures.
 
-The basic symbols are: `circle`, `square`, `diamond`, `cross`, `x`, `triangle`, `pentagon`, `hexagram`, `star`, `diamond`, `hourglass`, `bowtie`, `asterisk`, `hash`, `y`, and `line`.
+The basic symbols are: `circle`, `square`, `diamond`, `cross`, `x`, `triangle`, `pentagon`, `hexagram`, `star`, `hourglass`, `bowtie`, `asterisk`, `hash`, `y`, and `line`.
 
 Each basic symbol is also represented by a number. Adding 100 to that number is equivalent to appending the suffix "-open" to a symbol name. Adding 200 is equivalent to appending "-dot" to a symbol name. Adding 300 is equivalent to appending "-open-dot" or "dot-open" to a symbol name.
 

doc/python/parallel-categories-diagram.md

@@ -45,7 +45,7 @@ For other representations of multivariate data, also see [parallel coordinates](
 
 This example visualizes the restaurant bills of a sample of 244 people. Hovering over a category rectangle (sex, smoker, etc) displays a tooltip with the number of people with that single trait. Hovering over a ribbon in the diagram displays a tooltip with the number of people with a particular combination of the five traits connected by the ribbon.
 
-By default, `px.parallel_categories` will display any column in the `data_frame` that has a cardinality (or number of unique values) of less than 50. This can be overridden either by passing in a specific list of columns to `dimensions` or by setting `dimensions_max_cardinality` to something other than 50. 
+By default, `px.parallel_categories` will display any column in the `data_frame` that has a cardinality (or number of unique values) of less than 50. This can be overridden either by passing in a specific list of columns to `dimensions` or by setting `dimensions_max_cardinality` to something other than 50.
 
 ```python
 import plotly.express as px
@@ -58,7 +58,7 @@ fig.show()
 
 #### Style Diagram
 
-In this example `dimensions` represents a list of stings or the columns of data frame, and `labels` is a dictionary with string keys (column name) and string values ('desired label to be displayed'). See [Plotly express reference page](https://www.plotly.express/plotly_express/#plotly_express.parallel_categories) for more information.
+In this example `dimensions` represents a list of stings or the columns of data frame, and `labels` is a dictionary with string keys (column name) and string values ('desired label to be displayed'). See [Plotly express reference page](https://plotly.com/python-api-reference/generated/plotly.express.parallel_categories) for more information.
 
 ```python
 import plotly.express as px