Merge branch 'doc-prod'

Author: nicolaskruchten

doc/python/annotated-heatmap.md

@@ -43,6 +43,8 @@ As of version 5.5.0 of `plotly`, the **recommended way to [display annotated hea
 
 #### Basic Annotated Heatmap for z-annotations
 
+*New in v5.5*
+
 After creating a figure with `px.imshow`, you can add z-annotations with `.update_traces(texttemplate="%{z}")`.
 
 ```python
@@ -246,7 +248,7 @@ fig = px.imshow(color, color_continuous_scale=colorscale, aspect="auto",
                title='Periodic Table')
 fig.update_traces(
     text=symbol, texttemplate="%{text}", textfont_size=12,
-    customdata=np.moveaxis([element, atomic_mass], 0,-1), 
+    customdata=np.moveaxis([element, atomic_mass], 0,-1),
     hovertemplate="%{customdata[0]}<br>Atomic Mass: %{customdata[1]:.2f}<extra></extra>"
 )
 fig.update_xaxes(visible=False)

doc/python/axes.md

@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.9.0
+    version: 3.9.7
   plotly:
     description: How to adjust axes properties in Python - axes titles, styling and
       coloring axes and grid lines, ticks, tick labels and more.
@@ -293,6 +293,8 @@ fig.show()
 
 ##### Step for tick labels
 
+*New in v5.6*
+
 You can set a step for tick labels with `ticklabelstep`. In this example, we hide labels between every `2` ticks on the y axes. Similarly, this can be used with `fig.update_xaxes` for x axes: `fig.update_xaxes(ticklabelstep=2)`.
 
 ```python
@@ -384,12 +386,10 @@ fig.show()
 
 _new in 5.8_
 
-You can position and style minor ticks on a Cartesian axis using `minor`. This takes a `dict` of properties to apply to minor ticks. Available properties include: `tickmode`, `tickvals`, `tickcolor`, `ticklen`, `tickwidth`, `dtick`, `tick0`, `nticks`, `ticks`, `showgrid`, `gridcolor`, `griddash`, and `gridwidth`.
+You can position and style minor ticks on a Cartesian axis using the `minor` attribute. This takes a `dict` of properties to apply to minor ticks. See the [figure reference](https://plotly.com/python/reference/layout/xaxis/#layout-xaxis-minor) for full details on the accepted keys in this dict.
 
 In the following example, we add minor ticks to the x-axis and then to the y-axis. For the y-axis we add ticks on the inside: `ticks="inside"`. On the x-axis we've specified some additional properties to style the minor ticks, setting the length of the ticks with `ticklen` and the color with `tickcolor`. We've also turned on grid lines for the x-axis minor ticks using `showgrid`.
 
-Note: Minor ticks and grid lines are not currently supported on color bars, ternary plots, polar charts, geo plots, or on multi-categorical, or 3D axes.
-
 ```python
 import plotly.express as px
 import pandas as pd
@@ -399,7 +399,7 @@ fig = px.scatter(df, x="total_bill", y="tip", color="sex")
 
 
 fig.update_xaxes(minor=dict(ticklen=6, tickcolor="black", showgrid=True))
-fig.update_yaxes(minor=dict(ticks="inside"))
+fig.update_yaxes(minor_ticks="inside")
 
 fig.show()
 ```
@@ -490,15 +490,14 @@ fig.show()
 
 _new in 5.8_
 
-By default grid lines are `solid`. Set the `griddash` property to change this style. In this example we display the x-axis grid lines as `dot`. It can also be set to `dash`, `longdash`, `dashdot`, or `longdashdot`.
+By default grid lines are `solid`. Set the `griddash` property to change this style. In this example we display the x-axis grid lines as `dash` and the minor grid lines as `dot`. Other allowable values are `longdash`, `dashdot`, or `longdashdot`.
 
 ```python
 import plotly.express as px
 df = px.data.iris()
 
 fig = px.scatter(df, x="sepal_width", y="sepal_length", facet_col="species")
-fig.update_xaxes(showgrid=True, gridwidth=1, gridcolor='LightPink', griddash='dot')
-fig.update_yaxes(showgrid=True, gridwidth=1, gridcolor='LightPink')
+fig.update_xaxes(gridcolor='black', griddash='dash', minor_griddash="dot")
 
 fig.show()
 ```

doc/python/filled-area-plots.md

@@ -63,7 +63,7 @@ IFrame(snippet_url + 'filled-area-plots', width='100%', height=1200)
 
 ### Pattern Fills
 
-*New in v5.0*
+*New in v5.7*
 
 Area charts afford the use of [patterns (also known as hatching or texture)](/python/pattern-hatching-texture/) in addition to color:
 

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

@@ -313,7 +313,7 @@ for continent_name, continent in continent_data.items():
         x=continent['gdpPercap'],
         y=continent['lifeExp'],
         name=continent_name,
-        text=df_2007['continent'],
+        text=continent['continent'],
         hovertemplate=
         "<b>%{text}</b><br><br>" +
         "GDP per Capita: %{x:$,.0f}<br>" +

doc/python/legend.md

@@ -99,7 +99,7 @@ fig.show()
 
 *New in v5.0*
 
-The `legendrank` attribute of a trace can be used to control its placement within the legend, without regard for its placement in the `data` list. 
+The `legendrank` attribute of a trace can be used to control its placement within the legend, without regard for its placement in the `data` list.
 
 The default `legendrank` for traces is 1000 and ties are broken as described above, meaning that any trace can be pulled up to the top if it is the only one with a legend rank less than 1000 and pushed to the bottom if it is the only one with a rank greater than 1000.
 
@@ -447,6 +447,8 @@ fig.show()
 
 #### Group click toggle behavior
 
+*New in v5.3*
+
 You can also define the toggle behavior for when a user clicks an item in a group. Here we set the `groupclick` for the `legend` to `toggleitem`. This toggles the visibility of just the item clicked on by the user. Set to `togglegroup` and it toggles the visibility of all items in the same group as the item clicked on.
 
 ```python

doc/python/line-and-scatter.md

@@ -100,7 +100,7 @@ IFrame(snippet_url + 'line-and-scatter', width='100%', height=1200)
 
 ### Scatter plots and Categorical Axes
 
-Scatters plots can be made on using any type of cartesian axis, including [linear](https://plotly.com/python/axes/), [logarithmic](https://plotly.com/python/log-plot/), [categorical](https://plotly.com/python/categorical-axes/) or [date](https://plotly.com/python/time-series/) axes.
+Scatter plots can be made using any type of cartesian axis, including [linear](https://plotly.com/python/axes/), [logarithmic](https://plotly.com/python/log-plot/), [categorical](https://plotly.com/python/categorical-axes/) or [date](https://plotly.com/python/time-series/) axes.
 
 Scatter plots where one axis is categorical are often known as [dot plots](https://plotly.com/python/dot-plots/).
 

doc/python/log-plot.md

@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.9.0
+    version: 3.9.7
   plotly:
     description: How to make Log plots in Python with Plotly.
     display_as: scientific
@@ -64,7 +64,7 @@ fig.show()
 
 _new in 5.8_
 
-You can position and style minor ticks using `minor`. This takes a `dict` of properties to apply to minor ticks. Available properties include: `tickmode`, `tickvals`, `tickcolor`, `ticklen`, `tickwidth`, `dtick`, `tick0`, `nticks`, `ticks`, `showgrid`, `gridcolor`, `griddash`, and `gridwidth`. 
+You can position and style minor ticks using `minor`. This takes a `dict` of properties to apply to minor ticks. See the [figure reference](https://plotly.com/python/reference/layout/xaxis/#layout-xaxis-minor) for full details on the accepted keys in this dict.
 
 In this example we set the tick length with `ticklen`, add the ticks on the inside with `ticks="inside"`, and turn grid lines on with `howgrid=True`.
 
@@ -75,7 +75,7 @@ df = px.data.gapminder().query("year == 2007")
 fig = px.scatter(df, x="gdpPercap", y="lifeExp", hover_name="country",
                  log_x=True, range_x=[1,100000], range_y=[0,100])
 
-fig.update_xaxes(minor=dict(ticks="inside", ticklen=6, showgrid=True))# {"ticks": "inside", "ticklen": 6, "showgrid": True})
+fig.update_xaxes(minor=dict(ticks="inside", ticklen=6, showgrid=True))
 
 fig.show()
 ```

doc/python/smith-charts.md

@@ -33,6 +33,8 @@ jupyter:
     thumbnail: thumbnail/contourcarpet.jpg
 ---
 
+*New in v5.4*
+
 A [Smith Chart](https://en.wikipedia.org/wiki/Smith_chart) is a specialized chart for visualizing [complex numbers](https://en.wikipedia.org/wiki/Complex_number): numbers with both a real and imaginary part.
 
 
@@ -53,7 +55,7 @@ import plotly.graph_objects as go
 fig = go.Figure()
 
 fig.add_trace(go.Scattersmith(
-    imag=[1], 
+    imag=[1],
     real=[1],
     marker_symbol='x',
     marker_size=30,
@@ -62,7 +64,7 @@ fig.add_trace(go.Scattersmith(
 ))
 
 fig.add_trace(go.Scattersmith(
-    imag=[1], 
+    imag=[1],
     real=[1],
     marker_symbol='x',
     marker_size=30,

doc/python/time-series.md

@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.9.0
+    version: 3.9.7
   plotly:
     description: How to plot date and time in python.
     display_as: financial
@@ -139,9 +139,9 @@ fig.show()
 
 _new in 5.8_
 
-You can add minor ticks to an axis with `minor`. This takes a `dict` of properties to apply to minor ticks. Available properties include: `tickmode`, `tickvals`, `tickcolor`, `ticklen`, `tickwidth`, `dtick`, `tick0`, `nticks`, `ticks`, `showgrid`, `gridcolor`, `griddash`, and `gridwidth`.
+You can add minor ticks to an axis with `minor`. This takes a `dict` of properties to apply to minor ticks. See the [figure reference](https://plotly.com/python/reference/layout/xaxis/#layout-xaxis-minor) for full details on the accepted keys in this dict.
 
-In this example, we've added minor ticks to the inside of the x-axis and turned on grid lines.
+In this example, we've added minor ticks to the inside of the x-axis and turned on minor grid lines.
 
 ```python
 import pandas as pd
@@ -159,7 +159,7 @@ fig.show()
 
 _new in 5.8_
 
-You can set `dtick` on `minor` to control the spacing for minor ticks and grid lines. In the following example, by setting `dtick=7*24*3.6e6` (the number of milliseconds in a week) and setting `tick0="2016-07-04"` (the first Monday in our data), a minor tick and grid line is displayed for the start of each week. When zoomed out, we can see where each month and week begins and ends.
+You can set `dtick` on `minor` to control the spacing for minor ticks and grid lines. In the following example, by setting `dtick=7*24*60*60*1000` (the number of milliseconds in a week) and setting `tick0="2016-07-03"` (the first Sunday in our data), a minor tick and grid line is displayed for the start of each week. When zoomed out, we can see where each month and week begins and ends.
 
 ```python
 import pandas as pd
@@ -169,7 +169,17 @@ df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/finan
 df = df.loc[(df["Date"] >= "2016-07-01") & (df["Date"] <= "2016-12-01")]
 
 fig = px.line(df, x='Date', y='AAPL.High')
-fig.update_xaxes(ticks= "outside", ticklabelmode= "period", tickcolor= "black", tickwidth=2, ticklen=10, minor=dict(ticks="outside", dtick=7*24*3.6e6,  tick0="2016-07-04", griddash='dot', gridcolor='pink'))
+fig.update_xaxes(ticks= "outside",
+                 ticklabelmode= "period", 
+                 tickcolor= "black", 
+                 ticklen=10, 
+                 minor=dict(
+                     ticklen=4,  
+                     dtick=7*24*60*60*1000,  
+                     tick0="2016-07-03", 
+                     griddash='dot', 
+                     gridcolor='white')
+                )
 
 fig.show()
 ```

release.md

@@ -155,7 +155,6 @@ to features in the release.
 
 ## Release *Candidate* process - `plotly` package
 
-
 ### Bump to release candidate version
 
  1) Manually update the versions to `X.Y.Z-rc.1` in the files