Merge doc prod to main

doc/python/animations.md

@@ -339,27 +339,6 @@ sliders_dict = {
     "steps": []
 }
 
-# make data
-year = 1952
-for continent in continents:
-    dataset_by_year = dataset[dataset["year"] == year]
-    dataset_by_year_and_cont = dataset_by_year[
-        dataset_by_year["continent"] == continent]
-
-    data_dict = {
-        "x": list(dataset_by_year_and_cont["lifeExp"]),
-        "y": list(dataset_by_year_and_cont["gdpPercap"]),
-        "mode": "markers",
-        "text": list(dataset_by_year_and_cont["country"]),
-        "marker": {
-            "sizemode": "area",
-            "sizeref": 200000,
-            "size": list(dataset_by_year_and_cont["pop"])
-        },
-        "name": continent
-    }
-    fig_dict["data"].append(data_dict)
-
 # make frames
 for year in years:
     frame = {"data": [], "name": str(year)}
@@ -396,6 +375,9 @@ for year in years:
 
 fig_dict["layout"]["sliders"] = [sliders_dict]
 
+# make data
+fig_dict["data"] = fig_dict["frames"][0]['data']
+
 fig = go.Figure(fig_dict)
 
 fig.show()

doc/python/choropleth-maps.md

@@ -216,7 +216,7 @@ In **Plotly.py 6.3 and later**, the built-in countries geometry is created from
 
 In **earlier versions of Plotly.py**, the built-in countries geometry is based on Natural Earth data only. Plotly includes data from Natural Earth "as-is". This dataset draws boundaries of countries according to de facto status. See the [Natural Earth page for more details](https://www.naturalearthdata.com/downloads/50m-cultural-vectors/50m-admin-0-countries-2/).
 
-To use the built-in countries geometry, provide `locations` as [three-letter ISO country codes](https://en.wikipedia.org/wiki/ISO_3166-1_alpha-3).
+To use the built-in countries geometry, provide `locations` as [three-letter ISO country codes](/python/outline-map-locations/#supported-iso-codes).
 
 ```python
 import plotly.express as px
@@ -229,7 +229,7 @@ fig = px.choropleth(df, locations="iso_alpha",
 fig.show()
 ```
 
-To use the USA States geometry, set `locationmode='USA-states'` and provide `locations` as two-letter state abbreviations:
+To use the USA States geometry, set `locationmode='USA-states'` and provide `locations` as [two-letter state abbreviations](/python/outline-map-locations/#supported-us-state-codes):
 
 ```python
 import plotly.express as px

doc/python/dendrogram.md

@@ -79,7 +79,7 @@ fig.show()
 
 #### Plot a Dendrogram with a Heatmap
 
-See also the [Dash Bio demo](https://dash-bio.plotly.host/dash-clustergram/).
+This example uses randomly generated sample data to demonstrate how to plot a dendrogram with a heatmap.
 
 ```python
 import plotly.graph_objects as go
@@ -89,12 +89,11 @@ import numpy as np
 from scipy.spatial.distance import pdist, squareform
 
 
-# get data
-data = np.genfromtxt("http://files.figshare.com/2133304/ExpRawData_E_TABM_84_A_AFFY_44.tab",
-                     names=True,usecols=tuple(range(1,30)),dtype=float, delimiter="\t")
-data_array = data.view((float, len(data.dtype.names)))
-data_array = data_array.transpose()
-labels = data.dtype.names
+# Generate sample data
+np.random.seed(1)
+X = np.random.rand(15, 15)
+labels = [f'Sample_{i}' for i in range(15)]
+data_array = X
 
 # Initialize figure by creating upper dendrogram
 fig = ff.create_dendrogram(data_array, orientation='bottom', labels=labels)