Merge changes from doc-prod to master

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

@@ -5,10 +5,10 @@ jupyter:
     text_representation:
       extension: .md
       format_name: markdown
-      format_version: '1.1'
-      jupytext_version: 1.2.3
+      format_version: '1.3'
+      jupytext_version: 1.16.4
   kernelspec:
-    display_name: Python 3
+    display_name: Python 3 (ipykernel)
     language: python
     name: python3
   language_info:
@@ -20,7 +20,7 @@ jupyter:
     name: python
     nbconvert_exporter: python
     pygments_lexer: ipython3
-    version: 3.7.3
+    version: 3.11.10
   plotly:
     description: How to make 3D Bubble Charts in Python with Plotly. Three examples
       of 3D Bubble Charts.
@@ -113,12 +113,39 @@ fig = go.Figure(data=go.Scatter3d(
         )
 ))
 
-fig.update_layout(width=800, height=800, title = 'Planets!',
-                  scene = dict(xaxis=dict(title='Distance from Sun', titlefont_color='white'),
-                               yaxis=dict(title='Density', titlefont_color='white'),
-                               zaxis=dict(title='Gravity', titlefont_color='white'),
-                               bgcolor = 'rgb(20, 24, 54)'
-                           ))
+fig.update_layout(
+    width=800,
+    height=800,
+    title="Planets!",
+    scene=dict(
+        xaxis=dict(
+            title=dict(
+                text="Distance from Sun",
+                font=dict(
+                    color="white"
+                )
+            )
+        ),
+        yaxis=dict(
+            title=dict(
+                text="Density",
+                font=dict(
+                    color="white"
+                )
+            )
+        ),
+        zaxis=dict(
+            title=dict(
+                text="Gravity",
+                font=dict(
+                    color="white"
+                )
+            )
+        ),
+        bgcolor="rgb(20, 24, 54)"
+    )
+)
+
 
 fig.show()
 ```
@@ -154,16 +181,42 @@ fig = go.Figure(go.Scatter3d(
         )
 ))
 
-fig.update_layout(width=800, height=800, title = 'Planets!',
-                  scene = dict(xaxis=dict(title='Distance from Sun', titlefont_color='white'),
-                               yaxis=dict(title='Density', titlefont_color='white'),
-                               zaxis=dict(title='Gravity', titlefont_color='white'),
-                               bgcolor = 'rgb(20, 24, 54)'
-                           ))
+fig.update_layout(
+    width=800,
+    height=800,
+    title="Planets!",
+    scene=dict(
+        xaxis=dict(
+            title=dict(
+                text="Distance from Sun",
+                font=dict(
+                    color="white"
+                )
+            )
+        ),
+        yaxis=dict(
+            title=dict(
+                text="Density",
+                font=dict(
+                    color="white"
+                )
+            )
+        ),
+        zaxis=dict(
+            title=dict(
+                text="Gravity",
+                font=dict(
+                    color="white"
+                )
+            )
+        ),
+        bgcolor="rgb(20, 24, 54)"
+    )
+)
 
 fig.show()
 ```
 
 #### Reference
 
-See https://plotly.com/python/reference/scatter3d/ and https://plotly.com/python/reference/scatter/#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/#scatter-marker-sizeref <br>for more information and chart attribute options!

doc/python/animations.md

@@ -145,9 +145,7 @@ fig.show()
 
 ```python
 import plotly.graph_objects as go
-
 import numpy as np
-
 # Generate curve data
 t = np.linspace(-1, 1, 100)
 x = t + t ** 2
@@ -156,7 +154,7 @@ xm = np.min(x) - 1.5
 xM = np.max(x) + 1.5
 ym = np.min(y) - 1.5
 yM = np.max(y) + 1.5
-N = 50
+N = 25
 s = np.linspace(-1, 1, N)
 xx = s + s ** 2
 yy = s - s ** 2
@@ -167,26 +165,29 @@ fig = go.Figure(
     data=[go.Scatter(x=x, y=y,
                      mode="lines",
                      line=dict(width=2, color="blue")),
-          go.Scatter(x=x, y=y,
-                     mode="lines",
-                     line=dict(width=2, color="blue"))],
-    layout=go.Layout(
+          go.Scatter(x=[xx[0]], y=[yy[0]],
+                     mode="markers",
+                     marker=dict(color="red", size=10))])
+fig.update_layout(width=600, height=450,
         xaxis=dict(range=[xm, xM], autorange=False, zeroline=False),
         yaxis=dict(range=[ym, yM], autorange=False, zeroline=False),
-        title_text="Kinematic Generation of a Planar Curve", hovermode="closest",
-        updatemenus=[dict(type="buttons",
-                          buttons=[dict(label="Play",
-                                        method="animate",
-                                        args=[None])])]),
-    frames=[go.Frame(
-        data=[go.Scatter(
-            x=[xx[k]],
-            y=[yy[k]],
-            mode="markers",
-            marker=dict(color="red", size=10))])
-
-        for k in range(N)]
-)
+        title_text="Kinematic Generation of a Planar Curve", title_x=0.5, 
+        updatemenus = [dict(type = "buttons",
+        buttons = [
+            dict(
+                args = [None, {"frame": {"duration": 10, "redraw": False},
+                                "fromcurrent": True, "transition": {"duration": 10}}],
+                label = "Play",
+                method = "animate",
+
+                )])])
+
+fig.update(frames=[go.Frame(
+                        data=[go.Scatter(
+                                   x=[xx[k]],
+                                   y=[yy[k]])],
+                        traces=[1]) # fig.data[1] is updated by each frame
+        for k in range(N)])
 
 fig.show()
 ```

doc/python/bar-charts.md

@@ -37,7 +37,7 @@ jupyter:
 
 [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/).
 
-With `px.bar`, **each row of the DataFrame is represented as a rectangular mark**. To aggregate multiple data points into the same rectangular mark, please refer to the [histogram documentation](/python/histograms). 
+With `px.bar`, **each row of the DataFrame is represented as a rectangular mark**. To aggregate multiple data points into the same rectangular mark, please refer to the [histogram documentation](/python/histograms).
 
 In the example below, there is only a single row of data per year, so a single bar is displayed per year.
 
@@ -152,7 +152,7 @@ fig.show()
 
 ### Aggregating into Single Colored Bars
 
-As noted above `px.bar()` will result in **one rectangle drawn per row of input**. This can sometimes result in a striped look as in the examples above. To combine these rectangles into one per color per position, you can use `px.histogram()`, which has [its own detailed documentation page](/python/histogram). 
+As noted above `px.bar()` will result in **one rectangle drawn per row of input**. This can sometimes result in a striped look as in the examples above. To combine these rectangles into one per color per position, you can use `px.histogram()`, which has [its own detailed documentation page](/python/histogram).
 
 > `px.bar` and `px.histogram` are designed  to be nearly interchangeable in their call signatures, so as to be able to switch between aggregated and disaggregated bar representations.
 
@@ -304,7 +304,7 @@ fig.update_layout(barmode='stack')
 fig.show()
 ```
 
-### Stacked Bar Chart From Aggregating a DataFrame 
+### Stacked Bar Chart From Aggregating a DataFrame
 
 Stacked bar charts are a powerful way to present results summarizing categories generated using the Pandas aggregate commands. `pandas.DataFrame.agg` produces a wide data set format incompatible with `px.bar`. Transposing and updating the indexes to achieve `px.bar` compatibility is a somewhat involved option. Here is one straightforward alternative, which presents the aggregated data as a stacked bar using plotly.graph_objects.
 
@@ -326,19 +326,19 @@ df_summarized["percent of world population"]=100*df_summarized["pop"]/df_summari
 df_summarized["percent of world GDP"]=100*df_summarized["gdp"]/df_summarized["gdp"].sum()
 
 
-df = df_summarized[["continent", 
+df = df_summarized[["continent",
 "percent of world population",
 "percent of world GDP",
 ]]
 
 # We now have a wide data frame, but it's in the opposite orientation from the one that px is designed to deal with.
-# Transposing it and rebuilding the indexes is an option, but iterating through the DF using graph objects is more succinct. 
+# Transposing it and rebuilding the indexes is an option, but iterating through the DF using graph objects is more succinct.
 
 fig=go.Figure()
 for category in df_summarized["continent"].values:
     fig.add_trace(go.Bar(
             x=df.columns[1:],
-            # We need to get a pandas series that contains just the values to graph; 
+            # We need to get a pandas series that contains just the values to graph;
             # We do so by selecting the right row, selecting the right columns
             # and then transposing and using iloc to convert to a series
             # Here, we assume that the bar element category variable is in column 0
@@ -619,8 +619,12 @@ fig.update_layout(
     title='US Export of Plastic Scrap',
     xaxis_tickfont_size=14,
     yaxis=dict(
-        title='USD (millions)',
-        titlefont_size=16,
+        title=dict(
+            text="USD (millions)",
+            font=dict(
+                size=16
+            )
+        ),
         tickfont_size=14,
     ),
     legend=dict(

doc/python/box-plots.md

@@ -233,16 +233,10 @@ import plotly.graph_objects as go
 
 fig = go.Figure()
 
-fig.add_trace(go.Box(y=[
-        [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ],
-        [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ],
-        [ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9 ]
-      ], name="Precompiled Quartiles"))
-
-fig.update_traces(q1=[ 1, 2, 3 ], median=[ 4, 5, 6 ],
+fig.add_trace(go.Box(q1=[ 1, 2, 3 ], median=[ 4, 5, 6 ],
                   q3=[ 7, 8, 9 ], lowerfence=[-1, 0, 1],
-                  upperfence=[5, 6, 7], mean=[ 2.2, 2.8, 3.2 ],
-                  sd=[ 0.2, 0.4, 0.6 ], notchspan=[ 0.2, 0.4, 0.6 ] )
+                  upperfence=[7, 8, 9], mean=[ 2.2, 2.8, 3.2 ],
+                  sd=[ 0.2, 0.4, 0.6 ], notchspan=[ 0.2, 0.4, 0.6 ], name="Precompiled Quartiles"))
 
 fig.show()
 ```

doc/python/carpet-contour.md

@@ -159,9 +159,10 @@ fig.add_trace(go.Contourcarpet(
     colorbar = dict(
       y = 0,
       yanchor = "bottom",
-      titleside = "right",
       len = 0.75,
-      title = "Pressure coefficient, c<sub>p</sub>"
+      title = dict(
+        text="Pressure coefficient, c<sub>p</sub>",
+        side="right")
     ),
     contours = dict(
       start = -1,

doc/python/colorscales.md

@@ -321,8 +321,10 @@ fig = go.Figure()
 fig.add_trace(go.Heatmap(
     z=dataset["z"],
     colorbar=dict(
-        title="Surface Heat",
-        titleside="top",
+        title=dict(
+            text="Surface Heat",
+            side="top",
+        ),
         tickmode="array",
         tickvals=[2, 25, 50, 75, 100],
         labelalias={100: "Hot", 50: "Mild", 2: "Cold"},
@@ -545,8 +547,10 @@ fig = go.Figure()
 fig.add_trace(go.Heatmap(
     z=dataset["z"],
     colorbar=dict(
-        title="Surface Heat",
-        titleside="top",
+        title=dict(
+            text="Surface Heat",
+            side="top",
+        ),
         tickmode="array",
         tickvals=[2, 50, 100],
         ticktext=["Cool", "Mild", "Hot"],

doc/python/configuration-options.md

@@ -207,7 +207,7 @@ fig.show(config=config)
 To delete buttons from the modebar, pass an array of strings containing the names of the buttons you want to remove to the `modeBarButtonsToRemove` attribute in the figure's configuration dictionary. Note that different chart types have different default modebars. The following is a list of all the modebar buttons and the chart types they are associated with:
 
   - **High-level**: `zoom`, `pan`, `select`, `zoomIn`, `zoomOut`, `autoScale`, `resetScale`
-  - **2D**: `zoom2d`, `pan2d`, `select2d`, `lasso2d`, `zoomIn2d`, `zoomOut2d`, `autoScale2d`, `resetScale2d`
+  - **2D**: `zoom2d`, `pan2d`, `select2d`, `lasso2d`, `zoomIn2d`, `zoomOut2d`, `autoScale2d`, `resetScale2d`, `v1hovermode`
   - **2D Shape Drawing**: `drawline`, `drawopenpath`, `drawclosedpath`, `drawcircle`, `drawrect`, `eraseshape`
   - **3D**: `zoom3d`, `pan3d`, `orbitRotation`, `tableRotation`, `handleDrag3d`, `resetCameraDefault3d`, `resetCameraLastSave3d`, `hoverClosest3d`
   - **Cartesian**: `hoverClosestCartesian`, `hoverCompareCartesian`

doc/python/contour-plots.md

@@ -281,12 +281,15 @@ fig = go.Figure(data=
            [0.625, 1.25, 3.125, 6.25, 10.625],
            [0, 0.625, 2.5, 5.625, 10]],
         colorbar=dict(
-            title='Color bar title', # title here
-            titleside='right',
-            titlefont=dict(
-                size=14,
-                family='Arial, sans-serif')
-        )))
+            title=dict(
+                text='Color bar title', # title here
+                side='right',
+                font=dict(
+                    size=14,
+                    family='Arial, sans-serif')
+                )
+            ),
+        ))
 
 fig.show()
 ```