doc tweaks

Author: nicolaskruchten

doc/python/hover-text-and-formatting.md

@@ -6,7 +6,7 @@ jupyter:
       extension: .md
       format_name: markdown
       format_version: '1.2'
-      jupytext_version: 1.3.0
+      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 use hover text and formatting in Python with Plotly.
     display_as: file_settings

doc/python/imshow.md

@@ -298,15 +298,15 @@ examples on how to use plotly and datashader.
 
 _introduced in plotly 4.7_
 
-It can be useful to add shapes to an image trace, for highlighting an object, drawing bounding boxes as part of a machine learning training set, or identifying seeds for a segmentation algorithm. 
+It can be useful to add shapes to an image trace, for highlighting an object, drawing bounding boxes as part of a machine learning training set, or identifying seeds for a segmentation algorithm.
 
 In order to enable shape drawing, you need to
 - define a dragmode corresponding to a drawing tool (`'drawline'`,`'drawopenpath'`, `'drawclosedpath'`, `'drawcircle'`, or `'drawrect'`)
 - add modebar buttons corresponding to the drawing tools you wish to use.
 
 The style of new shapes is specified by the `newshape` layout attribute. Shapes can be selected and modified after they have been drawn. More details and examples are given in the [tutorial on shapes](/python/shapes#drawing-shapes-on-cartesian-plots).
 
-Drawing or modifying a shape triggers a `relayout` event, which [can be captured by a callback inside a Dash application](https://dash.plotly.com/interactive-graphing). 
+Drawing or modifying a shape triggers a `relayout` event, which [can be captured by a callback inside a Dash application](https://dash.plotly.com/interactive-graphing).
 
 ```python
 import plotly.express as px
@@ -400,8 +400,5 @@ fig.show()
 ```
 
 #### Reference
-<<<<<<< HEAD
-See https://plotly.com/python/reference/#image for more information and chart attribute options!
-=======
+
 See https://plotly.com/python/reference/image/ for more information and chart attribute options!
->>>>>>> doc-prod

doc/python/time-series.md

@@ -87,10 +87,14 @@ fig.show()
 
 By default, the tick labels (and optional ticks) are associated with a specific grid-line, and represent an *instant* in time, for example, "midnight on February 1, 2018". Tick labels can be formatted using the `tickformat` attribute (which accepts the [d3 time-format formatting strings](https://github.com/d3/d3-time-format)) to display only the month and year, but they still represent an instant by default, so in the figure below, the text of the label "Feb 2018" spans part of the month of January and part of the month of February. The `dtick` attribute controls the spacing between gridlines, and the `"M1"` setting means "1 month". This attribute also accepts a number of milliseconds, which can be scaled up to days by multiplying by `24*60*60*1000`.
 
+Note that by default, the formatting of values of X and Y values in the hover label matches that of the tick labels of the corresponding axes, so when customizing the tick labels to something broad like "month", it's usually necessary to [customize the hover label](/python/hover-text-and-formatting/) to something narrower like the acutal date, as below.
+
 ```python
 import plotly.express as px
-df = px.data.stocks(indexed=True)
-fig = px.line(df, title="custom tick labels")
+df = px.data.stocks()
+fig = px.line(df, x="date", y=df.columns, 
+              hover_data={"date": "|%B %d, %Y"}, 
+              title='custom tick labels')
 fig.update_xaxes(
     dtick="M1", 
     tickformat="%b %Y", 
@@ -106,8 +110,10 @@ By setting the `ticklabelmode` attribute to `"period"` (the default is `"instant
 
 ```python
 import plotly.express as px
-df = px.data.stocks(indexed=True)
-fig = px.line(df, title='custom tick labels with ticklabelmode="period"')
+df = px.data.stocks()
+fig = px.line(df, x="date", y=df.columns, 
+              hover_data={"date": "|%B %d, %Y"}, 
+              title='custom tick labels with ticklabelmode="period"')
 fig.update_xaxes(
     dtick="M1", 
     tickformat="%b %Y",