merging doc-prod to master

Author: nicolaskruchten

.circleci/create_conda_optional_env.sh

@@ -19,5 +19,9 @@ if [ ! -d $HOME/miniconda/envs/circle_optional ]; then
 requests nbformat six retrying psutil pandas decorator pytest mock nose poppler xarray scikit-image ipython jupyter ipykernel ipywidgets
 
     # Install orca into environment
+<<<<<<< HEAD
     $HOME/miniconda/bin/conda install --yes -n circle_optional -c plotly plotly-orca==1.3.1
+=======
+    $HOME/miniconda/bin/conda install --yes -n circle_optional -c plotly plotly-orca==1.2.1
+>>>>>>> doc-prod
 fi

binder/requirements.txt

@@ -14,3 +14,4 @@ networkx
 scikit-image
 datashader
 pyarrow
+cufflinks==0.17.3

doc/Makefile

@@ -30,6 +30,7 @@ $(IPYNB_FILES): $(IPYNB_DIR)/.mapbox_token
 $(IPYNB_DIR)/%.ipynb: $(MD_DIR)/%.md
 	@mkdir -p $(IPYNB_DIR)
 	@echo "[jupytext]   $<"
+	@cat what_about_dash.md >> $<
 	@jupytext $< --to notebook --quiet --output $@
 
 $(HTML_DIR)/2019-07-03-%.html: $(IPYNB_DIR)/%.ipynb

doc/python/2D-Histogram.md

@@ -71,7 +71,7 @@ fig = go.Figure(go.Histogram2d(x=x, y=y, histnorm='probability',
 fig.show()
 ```
 ### Sharing bin settings between 2D Histograms
-This example shows how to use [bingroup](https://plot.ly/python/reference/#histogram-bingroup) attribute to have a compatible bin settings for both histograms. To define `start`, `end` and `size` value of x-axis and y-axis seperatly, set [ybins](https://plot.ly/python/reference/#histogram2dcontour-ybins) and `xbins`.
+This example shows how to use [bingroup](https://plotly.com/python/reference/#histogram-bingroup) attribute to have a compatible bin settings for both histograms. To define `start`, `end` and `size` value of x-axis and y-axis seperatly, set [ybins](https://plotly.com/python/reference/#histogram2dcontour-ybins) and `xbins`.
 
 ```python
 import plotly.graph_objects as go
@@ -170,4 +170,4 @@ fig.show()
 ```
 
 #### Reference
-See https://plot.ly/python/reference/#histogram2d for more information and chart attribute options!
+See https://plotly.com/python/reference/#histogram2d for more information and chart attribute options!

doc/python/2d-histogram-contour.md

@@ -185,4 +185,4 @@ fig.show()
 ```
 
 #### Reference
-See https://plot.ly/python/reference/#histogram2dcontour for more information and chart attribute options!
+See https://plotly.com/python/reference/#histogram2dcontour for more information and chart attribute options!

doc/python/3d-axes.md

@@ -39,7 +39,7 @@ jupyter:
 attributes such as `xaxis`, `yaxis` and `zaxis` parameters, in order to
 set the range, title, ticks, color etc. of the axes.
 
-For creating 3D charts, see [this page](https://plot.ly/python/3d-charts/).
+For creating 3D charts, see [this page](https://plotly.com/python/3d-charts/).
 
 ```python
 import plotly.graph_objects as go
@@ -141,7 +141,7 @@ fig.show()
 import plotly.graph_objects as go
 import numpy as np
 
-# Define random surface
+# Define random surface
 N = 50
 fig = go.Figure(data=[go.Mesh3d(x=(60*np.random.randn(N)),
                    y=(25*np.random.randn(N)),
@@ -150,7 +150,7 @@ fig = go.Figure(data=[go.Mesh3d(x=(60*np.random.randn(N)),
                    color='rgba(100,22,200,0.5)'
                   )])
 
-# Different types of customized ticks
+# Different types of customized ticks
 fig.update_layout(scene = dict(
                     xaxis = dict(
                         ticktext= ['TICKS','MESH','PLOTLY','PYTHON'],
@@ -184,7 +184,7 @@ fig = go.Figure(data=[go.Mesh3d(x=(30*np.random.randn(N)),
                    opacity=0.5,)])
 
 
-# xaxis.backgroundcolor is used to set background color
+# xaxis.backgroundcolor is used to set background color
 fig.update_layout(scene = dict(
                     xaxis = dict(
                          backgroundcolor="rgb(200, 200, 230)",

doc/python/3d-bubble-charts.md

@@ -108,7 +108,7 @@ fig = go.Figure(data=go.Scatter3d(
     mode = 'markers',
     marker = dict(
         sizemode = 'diameter',
-        sizeref = 750, # info on sizeref: https://plot.ly/python/reference/#scatter-marker-sizeref
+        sizeref = 750, # info on sizeref: https://plotly.com/python/reference/#scatter-marker-sizeref
         size = planet_diameter,
         color = planet_colors,
         )
@@ -147,7 +147,7 @@ fig = go.Figure(go.Scatter3d(
     mode = 'markers',
     marker = dict(
         sizemode = 'diameter',
-        sizeref = 750, # info on sizeref: https://plot.ly/python/reference/#scatter-marker-sizeref
+        sizeref = 750, # info on sizeref: https://plotly.com/python/reference/#scatter-marker-sizeref
         size = planet_diameter,
         color = temperatures,
         colorbar_title = 'Mean<br>Temperature',
@@ -167,4 +167,4 @@ fig.show()
 
 #### Reference
 
-See https://plot.ly/python/reference/#scatter3d and https://plot.ly/python/reference/#scatter-marker-sizeref <br>for more information and chart attribute options!
+See https://plotly.com/python/reference/#scatter3d and https://plotly.com/python/reference/#scatter-marker-sizeref <br>for more information and chart attribute options!

doc/python/3d-camera-controls.md

@@ -290,4 +290,4 @@ fig.show()
 #### Reference
 
 
-See https://plot.ly/python/reference/#layout-scene-camera for more information and chart attribute options!
+See https://plotly.com/python/reference/#layout-scene-camera for more information and chart attribute options!

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

@@ -235,5 +235,5 @@ fig.show()
 ```
 
 #### Reference
-See https://plot.ly/python/reference/#isosurface for more information and chart attribute options!
+See https://plotly.com/python/reference/#isosurface for more information and chart attribute options!
 

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

@@ -120,4 +120,4 @@ fig.show()
 
 #### Reference
 
-See https://plot.ly/python/reference/#scatter3d-marker-line for more information and chart attribute options!
+See https://plotly.com/python/reference/#scatter3d-marker-line for more information and chart attribute options!

doc/python/3d-mesh.md

@@ -163,4 +163,4 @@ fig.show()
 ```
 
 ## Reference
-See https://plot.ly/python/reference/#mesh3d for more information and chart attribute options!
+See https://plotly.com/python/reference/#mesh3d for more information and chart attribute options!

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

@@ -37,7 +37,7 @@ jupyter:
 
 [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.
+Like the [2D scatter plot](https://plotly.com/python/line-and-scatter/) `px.scatter`, the 3D function `px.scatter_3d` plots individual data in three-dimensional space.
 
 ```python
 import plotly.express as px
@@ -77,7 +77,7 @@ fig.update_layout(margin=dict(l=0, r=0, b=0, t=0))
 #### Basic 3D Scatter Plot
 
 If Plotly Express does not provide a good starting point, it is also possible to use the more generic `go.Scatter3D` from `plotly.graph_objs`.
-Like the [2D scatter plot](https://plot.ly/python/line-and-scatter/) `go.Scatter`, `go.Scatter3d` plots individual data in three-dimensional space.
+Like the [2D scatter plot](https://plotly.com/python/line-and-scatter/) `go.Scatter`, `go.Scatter3d` plots individual data in three-dimensional space.
 
 ```python
 import plotly.graph_objects as go
@@ -122,7 +122,7 @@ fig.show()
 
 ### Dash App
 
-[Dash](https://plot.ly/products/dash/) is an Open Source Python library which can help you convert plotly figures into a reactive, web-based application. Below is a simple example of a dashboard created using Dash. Its [source code](https://github.com/plotly/simple-example-chart-apps/tree/master/dash-3dscatterplot) can easily be deployed to a PaaS.
+[Dash](https://plotly.com/products/dash/) is an Open Source Python library which can help you convert plotly figures into a reactive, web-based application. Below is a simple example of a dashboard created using Dash. Its [source code](https://github.com/plotly/simple-example-chart-apps/tree/master/dash-3dscatterplot) can easily be deployed to a PaaS.
 
 ```python
 from IPython.display import IFrame
@@ -136,4 +136,4 @@ IFrame(src= "https://dash-simple-apps.plotly.host/dash-3dscatterplot/code", widt
 
 #### Reference
 
-See https://plot.ly/python/reference/#scatter3d for more information and chart attribute options!
+See https://plotly.com/python/reference/#scatter3d for more information and chart attribute options!

doc/python/3d-subplots.md

@@ -82,4 +82,4 @@ fig.show()
 #### Reference
 
 
-See https://plot.ly/python/subplots/ for more information regarding subplots!
+See https://plotly.com/python/subplots/ for more information regarding subplots!

doc/python/3d-surface-coloring.md

@@ -60,5 +60,5 @@ fig.show()
 #### Reference
 
 
-See https://plot.ly/python/reference/#surface-surfacecolor for more information!
+See https://plotly.com/python/reference/#surface-surfacecolor for more information!
 

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

@@ -76,7 +76,7 @@ fig.show()
 #### Surface Plot With Contours
 
 
-Display and customize contour data for each axis using the `contours` attribute ([reference](plot.ly/python/reference/#surface-contours)).
+Display and customize contour data for each axis using the `contours` attribute ([reference](plotly.com/python/reference/#surface-contours)).
 
 ```python
 import plotly.graph_objects as go
@@ -98,7 +98,7 @@ fig.update_layout(title='Mt Bruno Elevation', autosize=False,
 fig.show()
 ```
 #### Configure Surface Contour Levels
-This example shows how to slice the surface graph on the desired position for each of x, y and z axis. [contours.x.start](https://plot.ly/python/reference/#surface-contours-x-start) sets the starting contour level value, `end` sets the end of it, and `size` sets the step between each contour level. 
+This example shows how to slice the surface graph on the desired position for each of x, y and z axis. [contours.x.start](https://plotly.com/python/reference/#surface-contours-x-start) sets the starting contour level value, `end` sets the end of it, and `size` sets the step between each contour level. 
 
 ```python
 import plotly.graph_objects as go
@@ -166,4 +166,4 @@ fig.show()
 #### Reference
 
 
-See https://plot.ly/python/reference/#surface for more information!
+See https://plotly.com/python/reference/#surface for more information!

doc/python/3d-volume.md

@@ -190,7 +190,7 @@ fig = go.Figure(data=go.Volume(
     ))
 
 # Change camera view for a better view of the sides, XZ plane
-# (see https://plot.ly/python/v3/3d-camera-controls/)
+# (see https://plotly.com/python/v3/3d-camera-controls/)
 fig.update_layout(scene_camera = dict(
     up=dict(x=0, y=0, z=1),
     center=dict(x=0, y=0, z=0),
@@ -254,7 +254,7 @@ fig.show()
 ```
 
 #### Reference
-See https://plot.ly/python/reference/#volume for more information and chart attribute options!
+See https://plotly.com/python/reference/#volume for more information and chart attribute options!
 
 #### See also
 [3D isosurface documentation](/python/3d-isosurface-plots/)

doc/python/aggregations.md

@@ -142,7 +142,7 @@ import plotly.io as pio
 
 import pandas as pd
 
-df = pd.read_csv("https://plot.ly/~public.health/17.csv")
+df = pd.read_csv("https://plotly.com/~public.health/17.csv")
 
 data = [dict(
   x = df['date'],
@@ -270,4 +270,4 @@ pio.show(fig_dict, validate=False)
 ```
 
 #### Reference
-See https://plot.ly/python/reference/ for more information and chart attribute options!
+See https://plotly.com/python/reference/ for more information and chart attribute options!

doc/python/animations.md

@@ -63,7 +63,7 @@ Along with `data` and `layout`, `frames` can be added as a key in a figure objec
 
 #### Adding Control Buttons to Animations
 
-You can add play and pause buttons to control your animated charts by adding an `updatemenus` array to the `layout` of your `figure`. More information on style and placement of the buttons is available in Plotly's [`updatemenus` reference](https://plot.ly/python/reference/#layout-updatemenus).
+You can add play and pause buttons to control your animated charts by adding an `updatemenus` array to the `layout` of your `figure`. More information on style and placement of the buttons is available in Plotly's [`updatemenus` reference](https://plotly.com/python/reference/#layout-updatemenus).
 <br>
 The buttons are defined as follows:
 
@@ -389,5 +389,5 @@ fig.show()
 
 #### Reference
 
-For additional information and attributes for creating bubble charts in Plotly see: https://plot.ly/python/bubble-charts/.
-For more documentation on creating animations with Plotly, see https://plot.ly/python/#animations.
+For additional information and attributes for creating bubble charts in Plotly see: https://plotly.com/python/bubble-charts/.
+For more documentation on creating animations with Plotly, see https://plotly.com/python/#animations.

doc/python/annotated-heatmap.md

@@ -202,7 +202,7 @@ fig.show()
 ```
 
 #### Reference
-For more info on Plotly heatmaps, see: https://plot.ly/python/reference/#heatmap.<br> For more info on using colorscales with Plotly see: https://plot.ly/python/heatmap-and-contour-colorscales/ <br>For more info on annotated_heatmaps, see:
+For more info on Plotly heatmaps, see: https://plotly.com/python/reference/#heatmap.<br> For more info on using colorscales with Plotly see: https://plotly.com/python/heatmap-and-contour-colorscales/ <br>For more info on annotated_heatmaps, see:
 
 ```python
 help(ff.create_annotated_heatmap)

doc/python/axes.md

@@ -477,7 +477,7 @@ fig.show()
 
 ### Set axis title position
 
-This example sets `standoff` attribute to cartesian axes to determine the distance between the tick labels and the axis title. Note that the axis title position is always constrained within the margins, so the actual standoff distance is always less than the set or default value. By default [automargin](https://plot.ly/python/setting-graph-size/#automatically-adjust-margins) is `True` in Plotly template for the cartesian axis, so the margins will be pushed to fit the axis title at given standoff distance.
+This example sets `standoff` attribute to cartesian axes to determine the distance between the tick labels and the axis title. Note that the axis title position is always constrained within the margins, so the actual standoff distance is always less than the set or default value. By default [automargin](https://plotly.com/python/setting-graph-size/#automatically-adjust-margins) is `True` in Plotly template for the cartesian axis, so the margins will be pushed to fit the axis title at given standoff distance.
 
 ```python
 import plotly.graph_objects as go
@@ -743,7 +743,7 @@ fig.show()
 
 #### Synchronizing axes in subplots with `matches`
 
-Using `facet_col` from `plotly.express` let [zoom](https://help.plot.ly/zoom-pan-hover-controls/#step-3-zoom-in-and-zoom-out-autoscale-the-plot) and [pan](https://help.plot.ly/zoom-pan-hover-controls/#step-6-pan-along-axes) each facet to the same range implicitly. However, if the subplots are created with `make_subplots`, the axis needs to be updated with `matches` parameter to update all the subplots accordingly. 
+Using `facet_col` from `plotly.express` let [zoom](https://help.plotly.com/zoom-pan-hover-controls/#step-3-zoom-in-and-zoom-out-autoscale-the-plot) and [pan](https://help.plotly.com/zoom-pan-hover-controls/#step-6-pan-along-axes) each facet to the same range implicitly. However, if the subplots are created with `make_subplots`, the axis needs to be updated with `matches` parameter to update all the subplots accordingly. 
 
 Zoom in one trace below, to see the other subplots zoomed to the same x-axis range. To pan all the subplots, click and drag from the center of x-axis to the side:
 
@@ -764,4 +764,4 @@ fig.show()
 
 #### Reference
 
-See https://plot.ly/python/reference/#layout-xaxis and https://plot.ly/python/reference/#layout-yaxis for more information and chart attribute options!
+See https://plotly.com/python/reference/#layout-xaxis and https://plotly.com/python/reference/#layout-yaxis for more information and chart attribute options!

doc/python/bar-charts.md

@@ -50,7 +50,7 @@ fig.show()
 data_canada
 ```
 
-### Customize bar chart with Plotly Express
+### Customize bar chart with Plotly Express
 
 The bar plot can be customized using keyword arguments.
 
@@ -121,7 +121,7 @@ fig = go.Figure(data=[
     go.Bar(name='SF Zoo', x=animals, y=[20, 14, 23]),
     go.Bar(name='LA Zoo', x=animals, y=[12, 18, 29])
 ])
-# Change the bar mode
+# Change the bar mode
 fig.update_layout(barmode='group')
 fig.show()
 ```
@@ -213,7 +213,7 @@ fig.add_trace(go.Bar(
     marker_color='lightsalmon'
 ))
 
-# Here we modify the tickangle of the xaxis, resulting in rotated labels.
+# Here we modify the tickangle of the xaxis, resulting in rotated labels.
 fig.update_layout(barmode='group', xaxis_tickangle=-45)
 fig.show()
 ```
@@ -336,7 +336,7 @@ fig.show()
 
 ### Bar Chart with Sorted or Ordered Categories
 
-Set `categoryorder` to `"category ascending"` or `"category descending"` for the alphanumerical order of the category names or `"total ascending"` or `"total descending"` for numerical order of values. [categoryorder](https://plot.ly/python/reference/#layout-xaxis-categoryorder) for more information. Note that sorting the bars by a particular trace isn't possible right now - it's only possible to sort by the total values. Of course, you can always sort your data _before_ plotting it if you need more customization.
+Set `categoryorder` to `"category ascending"` or `"category descending"` for the alphanumerical order of the category names or `"total ascending"` or `"total descending"` for numerical order of values. [categoryorder](https://plotly.com/python/reference/#layout-xaxis-categoryorder) for more information. Note that sorting the bars by a particular trace isn't possible right now - it's only possible to sort by the total values. Of course, you can always sort your data _before_ plotting it if you need more customization.
 
 This example orders the bar chart alphabetically with `categoryorder: 'category ascending'`
 
@@ -382,8 +382,8 @@ fig.show()
 
 ### Horizontal Bar Charts
 
-See examples of horizontal bar charts [here](https://plot.ly/python/horizontal-bar-charts/).
+See examples of horizontal bar charts [here](https://plotly.com/python/horizontal-bar-charts/).
 
 ### Reference
 
-See https://plot.ly/python/reference/#bar for more information and chart attribute options!
+See https://plotly.com/python/reference/#bar for more information and chart attribute options!

doc/python/box-plots.md

@@ -36,7 +36,7 @@ jupyter:
     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/).
+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://plotly.com/python/statistical-charts/).
 
 ## Box Plot with `plotly.express`
 
@@ -134,7 +134,7 @@ fig.show()
 
 ## Box plot with go.Box
 
-If Plotly Express does not provide a good starting point, it is also possible to use the more generic `go.Box` function from `plotly.graph_objects`. All available options for `go.Box` are described in the reference page https://plot.ly/python/reference/#box.
+If Plotly Express does not provide a good starting point, it is also possible to use the more generic `go.Box` function from `plotly.graph_objects`. All available options for `go.Box` are described in the reference page https://plotly.com/python/reference/#box.
 
 ### Basic Box Plot
 
@@ -280,7 +280,7 @@ fig.add_trace(go.Box(
     name="All Points",
     jitter=0.3,
     pointpos=-1.8,
-    boxpoints='all', # represent all points
+    boxpoints='all', # represent all points
     marker_color='rgb(7,40,89)',
     line_color='rgb(7,40,89)'
 ))
@@ -353,7 +353,7 @@ fig.add_trace(go.Box(
 
 fig.update_layout(
     yaxis_title='normalized moisture',
-    boxmode='group' # group together boxes of the different traces for each value of x
+    boxmode='group' # group together boxes of the different traces for each value of x
 )
 fig.show()
 ```
@@ -491,4 +491,4 @@ fig.show()
 
 #### Reference
 
-See https://plot.ly/python/reference/#box for more information and chart attribute options!
+See https://plotly.com/python/reference/#box for more information and chart attribute options!