cross-linking between tutorials

emmanuelle · emmanuelle · commit 5a9efc631bf3 · 2019-11-12T16:02:28.000-05:00
diff --git a/python/creating-and-updating-figures.md b/python/creating-and-updating-figures.md
@@ -487,6 +487,10 @@ fig.show()
 
 There are also `for_each_xaxis` and `for_each_yaxis` methods that are analogous to the `for_each_trace` method described above. For non-cartesian subplot types (e.g. polar), there are additional `update_{type}` and `for_each_{type}` methods (e.g. `update_polar`, `for_each_polar`).
 
+### Other update methods
+
+`go` figures also support `update_layout_images` in order to [update background layout images](/python/images/), `update_annotations` in order to [update annotations](/python/text-and-annotations/#multiple-annotations), and `update-shapes` in order to [update shapes](/python/shapes/).
+
 
 #### Chaining figure operations
 All of the figure update operations described above are methods that return a reference to the figure being modified.  This makes it possible the chain multiple figure modification operations together into a single expression.
diff --git a/python/histograms.md b/python/histograms.md
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.7
+    version: 3.7.3
   plotly:
     description: How to make Histograms in Python with Plotly.
     display_as: statistical
@@ -331,6 +331,21 @@ fig.append_trace(trace5, 3, 2)
 fig.show()
 ```
 
+### See also: Bar Charts
+
+If you want to display information about the individual items within each histogram bar, then create a stacked bar chart with hover information as shown below. Note that this is not technically the histogram chart type, but it will have a similar effect as shown below by comparing the output of `px.histogram` and `px.bar`. For more information, see the [tutorial on bar charts](/python/bar-charts/).
+
+```python
+import plotly.express as px
+tips = px.data.tips()
+fig1 = px.bar(tips, x='day', y='tip', height=300,
+              title='Stacked Bar Chart - Hover on individual items')
+fig2 = px.histogram(tips, x='day', y='tip', histfunc='sum', height=300,
+                    title='Histogram Chart')
+fig1.show()
+fig2.show()
+```
+
 ### Dash Example
 
 
diff --git a/python/parallel-categories-diagram.md b/python/parallel-categories-diagram.md
@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.1'
-      jupytext_version: 1.2.1
+      jupytext_version: 1.1.1
   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: 3.7.3
   plotly:
     description: How to make parallel categories diagrams in Python with Plotly.
     display_as: statistical
@@ -38,6 +38,8 @@ The parallel categories diagram is a visualization of multi-dimensional categori
 
 Combinations of category rectangles across dimensions are connected by ribbons, where the height of the ribbon corresponds to the relative frequency of occurrence of the combination of categories in the data set.
 
+For other representations of multivariate data, also see [parallel coordinates](/python/parallel-coordinates-plot/), [radar charts](/python/radar-chart/) and [scatterplot matrix (SPLOM)](/python/splom/).
+
 
 #### Basic Parallel Category Diagram with plotly.express
 
diff --git a/python/parallel-coordinates-plot.md b/python/parallel-coordinates-plot.md
@@ -22,7 +22,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.7
+    version: 3.7.3
   plotly:
     description: How to make parallel coorindates plots in Python with Plotly.
     display_as: scientific
@@ -37,7 +37,7 @@ jupyter:
 
 ## Parallel Coordinates 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/). In a parallel coordinates plot with `px.parallel_coordinates`, each row of the DataFrame is represented by a polyline mark which traverses a set of parallel axes, one for each of the dimensions. For other representations of multivariate data, also see [radar charts](/python/radar-chart/) and [scatterplot matrix (SPLOM)](/python/splom/).
+[Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on "tidy" data](/python/px-arguments/). In a parallel coordinates plot with `px.parallel_coordinates`, each row of the DataFrame is represented by a polyline mark which traverses a set of parallel axes, one for each of the dimensions. For other representations of multivariate data, also see [parallel categories](/python/parallel-categories-diagram/), [radar charts](/python/radar-chart/) and [scatterplot matrix (SPLOM)](/python/splom/).
 
 ```python
 import plotly.express as px
diff --git a/python/pie-charts.md b/python/pie-charts.md
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.6.7
+    version: 3.7.3
   plotly:
     description: How to make Pie Charts.
     display_as: basic
@@ -164,6 +164,24 @@ fig.update_layout(title_text='World GDP')
 fig.show()
 ```
 
+### See Also: Sunburst charts
+
+For multilevel pie charts representing hierarchical data, you can use the `Sunburst` chart. A simple example is given below, for more information see the [tutorial on Sunburst charts](/python/sunburst-charts/).
+
+```python
+import plotly.graph_objects as go
+
+fig =go.Figure(go.Sunburst(
+    labels=["Eve", "Cain", "Seth", "Enos", "Noam", "Abel", "Awan", "Enoch", "Azura"],
+    parents=["", "Eve", "Eve", "Seth", "Seth", "Eve", "Eve", "Awan", "Eve" ],
+    values=[10, 14, 12, 10, 2, 6, 6, 4, 4],
+))
+fig.update_layout(margin = dict(t=0, l=0, r=0, b=0))
+
+fig.show()
+
+```
+
 ### Dash Example
 
 
