Merge branch 'master' into select-traces-function-selector

Author: nicholas-esterer

.circleci/config.yml

@@ -440,6 +440,15 @@ jobs:
               git push --force [email protected]:plotly/plotly.py-docs.git master:built
               rm -rf .git
               cd ../..
+              cd build/ipynb
+              git init
+              git config user.name plotlydocbot
+              git config user.email [email protected]
+              git add *
+              git commit -m "build of https://github.com/plotly/plotly.py/commit/${CIRCLE_SHA1}"
+              git push --force [email protected]:plotly/plotly.py-docs.git master:built_ipynb
+              rm -rf .git
+              cd ../..
             fi
             tar -zcf build/html.tgz build/html
             rm -rf build/html build/ipynb

CHANGELOG.md

@@ -2,6 +2,12 @@
 All notable changes to this project will be documented in this file.
 This project adheres to [Semantic Versioning](http://semver.org/).
 
+## [4.12.0] - unreleased
+
+### Updated
+
+- Updated Plotly.js to version 1.57.0. See the [plotly.js CHANGELOG](https://github.com/plotly/plotly.js/blob/v1.57.0/CHANGELOG.md) for more information. These changes are reflected in the auto-generated `plotly.graph_objects` module.
+
 ## [4.11.0] - 2020-10-01
 
 ### Updated

README.md

@@ -31,6 +31,10 @@
     </tr>
 </table>
 
+## Data Science Workspaces
+
+Our recommended IDE for Plotly’s Python graphing library is Dash Enterprise’s [Data Science Workspaces](https://plotly.com/dash/workspaces/), which has both Jupyter notebook and Python code file support.
+
 ## Quickstart
 
 `pip install plotly==4.11.0`

binder/requirements.txt

@@ -18,3 +18,4 @@ cufflinks==0.17.3
 kaleido
 scikit-learn
 umap-learn
+wget

doc/python/bar-charts.md

@@ -287,6 +287,56 @@ fig = go.Figure(data=[go.Bar(
 fig.show()
 ```
 
+Bar charts with custom widths can be used to make mekko charts (also known as marimekko charts, mosaic plots, or variwide charts).
+
+```python
+import plotly.graph_objects as go
+import numpy as np
+
+labels = ["apples","oranges","pears","bananas"]
+widths = np.array([10,20,20,50])
+
+data = {
+    "South": [50,80,60,70],
+    "North": [50,20,40,30]
+}
+
+fig = go.Figure()
+for key in data:
+    fig.add_trace(go.Bar(
+        name=key,
+        y=data[key],
+        x=np.cumsum(widths)-widths,
+        width=widths,
+        offset=0,
+        customdata=np.transpose([labels, widths*data[key]]),
+        texttemplate="%{y} x %{width} =<br>%{customdata[1]}",
+        textposition="inside",
+        textangle=0,
+        textfont_color="white",
+        hovertemplate="<br>".join([
+            "label: %{customdata[0]}",
+            "width: %{width}",
+            "height: %{y}",
+            "area: %{customdata[1]}",
+        ])
+    ))
+
+fig.update_xaxes( 
+    tickvals=np.cumsum(widths)-widths/2, 
+    ticktext= ["%s<br>%d" % (l, w) for l, w in zip(labels, widths)]
+)
+
+fig.update_xaxes(range=[0,100])
+fig.update_yaxes(range=[0,100])
+
+fig.update_layout(
+    title_text="Marimekko Chart",
+    barmode="stack",
+    uniformtext=dict(mode="hide", minsize=10),
+)
+```
+
 ### Customizing Individual Bar Base
 
 ```python

doc/python/choropleth-maps.md

@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.2'
-      jupytext_version: 1.3.1
+      jupytext_version: 1.4.2
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.8
+    version: 3.7.7
   plotly:
     description: How to make choropleth maps in Python with Plotly.
     display_as: maps
@@ -164,7 +164,25 @@ fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
 fig.show()
 ```
 
-<!-- #region -->
+### Using GeoPandas Data Frames
+
+`px.choropleth` accepts the `geometry` of a [GeoPandas](https://geopandas.org/) data frame as the input to `geojson` if the `geometry` contains polygons.
+
+```python
+import plotly.express as px
+import geopandas as gpd
+
+geo_df = gpd.read_file(gpd.datasets.get_path('nybb')).to_crs("EPSG:4326")
+
+fig = px.choropleth(geo_df,
+                   geojson=geo_df.geometry,
+                   locations=geo_df.index,
+                   color='Shape_Leng',
+                   hover_name="BoroName")
+fig.update_geos(fitbounds="locations", visible=False)
+fig.show()
+```
+
 
 ### Using Built-in Country and State Geometries
 
@@ -179,7 +197,6 @@ Plotly comes with two built-in geometries which do not require an external GeoJS
 
 To use the built-in countries geometry, provide `locations` as [three-letter ISO country codes](https://en.wikipedia.org/wiki/ISO_3166-1_alpha-3).
 
-<!-- #endregion -->
 
 ```python
 import plotly.express as px

doc/python/mapbox-county-choropleth.md

@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.2'
-      jupytext_version: 1.3.1
+      jupytext_version: 1.4.2
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.8
+    version: 3.7.7
   plotly:
     description: How to make a Mapbox Choropleth Map of US Counties in Python with
       Plotly.
@@ -157,6 +157,27 @@ fig.update_layout(margin={"r":0,"t":0,"l":0,"b":0})
 fig.show()
 ```
 
+### Using GeoPandas Data Frames
+
+`px.choropleth_mapbox` accepts the `geometry` of a [GeoPandas](https://geopandas.org/) data frame as the input to `geojson` if the `geometry` contains polygons.
+
+```python
+import plotly.express as px
+import geopandas as gpd
+
+geo_df = gpd.read_file(gpd.datasets.get_path('nybb')).to_crs("EPSG:4326")
+
+fig = px.choropleth_mapbox(geo_df,
+                           geojson=geo_df.geometry,
+                           locations=geo_df.index,
+                           color='Shape_Leng',
+                           hover_name="BoroName",
+                           center={"lat": 40.71, "lon": -74.00},
+                           mapbox_style="open-street-map",
+                           zoom=8)
+fig.show()
+```
+
 ### Choropleth map using plotly.graph_objects and carto base map (no token needed)
 
 If Plotly Express does not provide a good starting point, it is also possible to use [the more generic `go.Choroplethmapbox` class from `plotly.graph_objects`](/python/graph-objects/).
@@ -209,4 +230,4 @@ fig.show()
 
 #### Reference
 
-See https://plotly.com/python/reference/choroplethmapbox/ for more information about mapbox and their attribute options.
+See https://plotly.com/python/reference/choroplethmapbox/ for more information about mapbox and their attribute options.

doc/python/scatter-plots-on-maps.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: 1.4.2
   kernelspec:
     display_name: Python 3
     language: python
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.7
+    version: 3.7.7
   plotly:
     description: How to make scatter plots on maps in Python. Scatter plots on maps
       highlight geographic areas and can be colored by value.
@@ -66,6 +66,24 @@ fig = px.scatter_geo(df, locations="iso_alpha",
 fig.show()
 ```
 
+### Basic Example with GeoPandas
+
+`px.scatter_geo` can work well with [GeoPandas](https://geopandas.org/) dataframes whose `geometry` is of type `Point`.
+
+```python
+import plotly.express as px
+import geopandas as gpd
+
+geo_df = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
+
+px.set_mapbox_access_token(open(".mapbox_token").read())
+fig = px.scatter_geo(geo_df,
+                    lat=geo_df.geometry.y,
+                    lon=geo_df.geometry.x,
+                    hover_name="name")
+fig.show()
+```
+
 ### U.S. Airports Map
 
 Here we show how to use `go.Scattergeo` from `plotly.graph_objects`.

doc/python/scattermapbox.md

@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.2'
-      jupytext_version: 1.3.2
+      jupytext_version: 1.4.2
   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: 3.7.7
   plotly:
     description: How to make scatter plots on Mapbox maps in Python.
     display_as: maps
@@ -52,6 +52,25 @@ fig = px.scatter_mapbox(df, lat="centroid_lat", lon="centroid_lon",     color="p
 fig.show()
 ```
 
+### Basic Example with GeoPandas
+
+`px.scatter_mapbox` can work well with [GeoPandas](https://geopandas.org/) dataframes whose `geometry` is of type `Point`.
+
+```python
+import plotly.express as px
+import geopandas as gpd
+
+geo_df = gpd.read_file(gpd.datasets.get_path('naturalearth_cities'))
+
+px.set_mapbox_access_token(open(".mapbox_token").read())
+fig = px.scatter_mapbox(geo_df,
+                        lat=geo_df.geometry.y,
+                        lon=geo_df.geometry.x,
+                        hover_name="name",
+                        zoom=1)
+fig.show()
+```
+
 #### Basic Example
 
 ```python
@@ -228,4 +247,4 @@ fig.show()
 
 #### Reference
 
-See https://plotly.com/python/reference/scattermapbox/ for more information and options!
+See https://plotly.com/python/reference/scattermapbox/ for more information and options!

doc/requirements.txt

@@ -28,3 +28,4 @@ pyarrow
 cufflinks==0.17.3
 kaleido
 umap-learn
+wget