Merge branch 'doc-prod'

nicolaskruchten · nicolaskruchten · commit 97ad010f665c · 2023-01-22T22:07:27.000-05:00
diff --git a/doc/python/aggregations.md b/doc/python/aggregations.md
@@ -191,7 +191,7 @@ layout = dict(
             method = 'restyle',
         ), dict(
             args = ['xbins.size', 'M3'],
-            label = 'Quater',
+            label = 'Quarter',
             method = 'restyle',
         ), dict(
             args = ['xbins.size', 'M6'],
diff --git a/doc/python/builtin-colorscales.md b/doc/python/builtin-colorscales.md
@@ -41,7 +41,7 @@ jupyter:
 Many Plotly Express functions accept a `color_continuous_scale` argument and many trace
 types have a `colorscale` attribute in their schema. Plotly comes with a large number of
 built-in continuous color scales, which can be referred to in Python code when setting the above arguments,
-either by name in a case-insensitive string e.g. `px.scatter(continuous_color_scale="Viridis"`) or by reference e.g.
+either by name in a case-insensitive string e.g. `px.scatter(color_continuous_scale="Viridis"`) or by reference e.g.
 `go.Scatter(marker_colorscale=plotly.colors.sequential.Viridis)`. They can also be reversed by adding `_r` at the end
 e.g. `"Viridis_r"` or `plotly.colors.sequential.Viridis_r`.
 
diff --git a/doc/python/dumbbell-plots.md b/doc/python/dumbbell-plots.md
@@ -55,47 +55,59 @@ countries = (
     .unique()
 )
 
-data = {"x": [], "y": [], "colors": [], "years": []}
+data = {"line_x": [], "line_y": [], "1952": [], "2002": [], "colors": [], "years": [], "countries": []}
 
 for country in countries:
-    data["x"].extend(
+    data["1952"].extend([df.loc[(df.year == 1952) & (df.country == country)]["lifeExp"].values[0]])
+    data["2002"].extend([df.loc[(df.year == 2002) & (df.country == country)]["lifeExp"].values[0]])
+    data["line_x"].extend(
         [
             df.loc[(df.year == 1952) & (df.country == country)]["lifeExp"].values[0],
             df.loc[(df.year == 2002) & (df.country == country)]["lifeExp"].values[0],
             None,
         ]
     )
-    data["y"].extend([country, country, None]),
-    data["colors"].extend(["green", "blue", "brown"]),
-    data["years"].extend(["1952", "2002", None])
+    data["line_y"].extend([country, country, None]),
 
 fig = go.Figure(
     data=[
         go.Scatter(
-            x=data["x"],
-            y=data["y"],
+            x=data["line_x"],
+            y=data["line_y"],
             mode="lines",
+            showlegend=False,
             marker=dict(
-                color="grey",
-            ),
+                color="grey"
+            )
         ),
         go.Scatter(
-            x=data["x"],
-            y=data["y"],
-            mode="markers+text",
+            x=data["1952"],
+            y=countries,
+            mode="markers",
+            name="1952",
             marker=dict(
-                color=data["colors"],
-                size=10,
-            ),
-            hovertemplate="""Country: %{y} <br> Life Expectancy: %{x} <br><extra></extra>""",
+                color="green",
+                size=10
+            )
+            
+        ),
+        go.Scatter(
+            x=data["2002"],
+            y=countries,
+            mode="markers",
+            name="2002",
+            marker=dict(
+                color="blue",
+                size=10
+            )   
         ),
     ]
 )
 
 fig.update_layout(
     title="Life Expectancy in Europe: 1952 and 2002",
     height=1000,
-    showlegend=False,
+    legend_itemclick=False
 )
 
 fig.show()
@@ -124,48 +136,62 @@ countries = (
     .unique()
 )
 
-data = {"x": [], "y": [], "colors": [], "years": []}
+data = {"line_x": [], "line_y": [], "1952": [], "2002": [], "colors": [], "years": [], "countries": []}
 
 for country in countries:
-    data["x"].extend(
+    data["1952"].extend([df.loc[(df.year == 1952) & (df.country == country)]["lifeExp"].values[0]])
+    data["2002"].extend([df.loc[(df.year == 2002) & (df.country == country)]["lifeExp"].values[0]])
+    data["line_x"].extend(
         [
             df.loc[(df.year == 1952) & (df.country == country)]["lifeExp"].values[0],
             df.loc[(df.year == 2002) & (df.country == country)]["lifeExp"].values[0],
             None,
         ]
     )
-    data["y"].extend([country, country, None]),
-    data["colors"].extend(["silver", "lightskyblue", "white"]),
-    data["years"].extend(["1952", "2002", None])
+    data["line_y"].extend([country, country, None]),
 
 fig = go.Figure(
     data=[
         go.Scatter(
-            x=data["x"],
-            y=data["y"],
+            x=data["line_x"],
+            y=data["line_y"],
             mode="markers+lines",
+            showlegend=False,
             marker=dict(
-                symbol="arrow", color="black", size=16, angleref="previous", standoff=8
-            ),
+                symbol="arrow", 
+                color="black", 
+                size=16, 
+                angleref="previous", 
+                standoff=8
+            )
+        ),
+        go.Scatter(
+            x=data["1952"],
+            y=countries, 
+            name="1952",
+            mode="markers",
+            marker=dict(
+                color="silver",
+                size=16,
+            )
         ),
         go.Scatter(
-            x=data["x"],
-            y=data["y"],
-            text=data["years"],
+            x=data["2002"],
+            y=countries,
+            name="2002",
             mode="markers",
             marker=dict(
-                color=data["colors"],
+                color="lightskyblue",
                 size=16,
             ),
-            hovertemplate="""Country: %{y} <br> Life Expectancy: %{x} <br> Year: %{text} <br><extra></extra>""",
         ),
     ]
 )
 
 fig.update_layout(
     title="Life Expectancy in Europe: 1952 and 2002",
     height=1000,
-    showlegend=False,
+    legend_itemclick=False
 )
 
 
diff --git a/doc/python/histograms.md b/doc/python/histograms.md
@@ -40,7 +40,7 @@ jupyter:
 In statistics, a [histogram](https://en.wikipedia.org/wiki/Histogram) is representation of the distribution of numerical data, where the data are binned and the count for each bin is represented. More generally, in Plotly a histogram is an aggregated bar chart, with several possible aggregation functions (e.g. sum, average, count...) which can be used to visualize data on categorical and date axes as well as linear axes.
 
 
-Alternatives to violin plots for visualizing distributions include [violin plots](https://plotly.com/python/violin/), [box plots](https://plotly.com/python/box-plots/), [ECDF plots](https://plotly.com/python/ecdf-plots/) and [strip charts](https://plotly.com/python/strip-charts/).
+Alternatives to histogram plots for visualizing distributions include [violin plots](https://plotly.com/python/violin/), [box plots](https://plotly.com/python/box-plots/), [ECDF plots](https://plotly.com/python/ecdf-plots/) and [strip charts](https://plotly.com/python/strip-charts/).
 
 > If you're looking instead for bar charts, i.e. representing *raw, unaggregated* data with rectangular
 bar, go to the [Bar Chart tutorial](/python/bar-charts/).
diff --git a/doc/python/ml-pca.md b/doc/python/ml-pca.md
@@ -250,11 +250,16 @@ loadings = pca.components_.T * np.sqrt(pca.explained_variance_)
 fig = px.scatter(components, x=0, y=1, color=df['species'])
 
 for i, feature in enumerate(features):
-    fig.add_shape(
-        type='line',
-        x0=0, y0=0,
-        x1=loadings[i, 0],
-        y1=loadings[i, 1]
+    fig.add_annotation(
+        ax=0, ay=0,
+        axref="x", ayref="y",
+        x=loadings[i, 0],
+        y=loadings[i, 1],
+        showarrow=True,
+        arrowsize=2,
+        arrowhead=2,
+        xanchor="right",
+        yanchor="top"
     )
     fig.add_annotation(
         x=loadings[i, 0],
@@ -263,6 +268,7 @@ for i, feature in enumerate(features):
         xanchor="center",
         yanchor="bottom",
         text=feature,
+        yshift=5,
     )
 fig.show()
 ```
diff --git a/doc/python/shapes.md b/doc/python/shapes.md
@@ -51,7 +51,7 @@ The differences between these two approaches are that:
 
 ### Shape-drawing with Scatter traces
 
-There are two ways to draw filled shapes: scatter traces and [layout.shapes](https://plotly.com/python/reference/layout/shapes/#layout-shapes-items-shape-type) which is mostly useful for the 2d subplots, and defines the shape type to be drawn, and can be rectangle, circle, line, or path (a custom SVG path). You also can use [scatterpolar](https://plotly.com/python/polar-chart/#categorical-polar-chart), scattergeo, [scattermapbox](https://plotly.com/python/filled-area-on-mapbox/#filled-scattermapbox-trace) to draw filled shapes on any kind of subplots. To set an area to be filled with a solid color, you need to define [Scatter.fill="toself"](https://plotly.com/python/reference/scatter/#scatter-fill) that connects the endpoints of the trace into a closed shape. If `mode=line` (default value), then you need to repeat the initial point of a shape at the of the sequence to have a closed shape.
+There are two ways to draw filled shapes: scatter traces and [layout.shapes](https://plotly.com/python/reference/layout/shapes/#layout-shapes-items-shape-type) which is mostly useful for the 2d subplots, and defines the shape type to be drawn, and can be rectangle, circle, line, or path (a custom SVG path). You also can use [scatterpolar](https://plotly.com/python/polar-chart/#categorical-polar-chart), scattergeo, [scattermapbox](https://plotly.com/python/filled-area-on-mapbox/#filled-scattermapbox-trace) to draw filled shapes on any kind of subplots. To set an area to be filled with a solid color, you need to define [Scatter.fill="toself"](https://plotly.com/python/reference/scatter/#scatter-fill) that connects the endpoints of the trace into a closed shape. If `mode=line` (default value), then you need to repeat the initial point of a shape at the end of the sequence to have a closed shape.
 
 ```python
 import plotly.graph_objects as go
diff --git a/doc/python/splom.md b/doc/python/splom.md
@@ -56,7 +56,7 @@ Specify the columns to be represented with the `dimensions` argument, and set co
 import plotly.express as px
 df = px.data.iris()
 fig = px.scatter_matrix(df,
-    dimensions=["sepal_width", "sepal_length", "petal_width", "petal_length"],
+    dimensions=["sepal_length", "sepal_width", "petal_length", "petal_width"],
     color="species")
 fig.show()
 ```
@@ -69,7 +69,7 @@ The scatter matrix plot can be configured thanks to the parameters of `px.scatte
 import plotly.express as px
 df = px.data.iris()
 fig = px.scatter_matrix(df,
-    dimensions=["sepal_width", "sepal_length", "petal_width", "petal_length"],
+    dimensions=["sepal_length", "sepal_width", "petal_length", "petal_width"],
     color="species", symbol="species",
     title="Scatter matrix of iris data set",
     labels={col:col.replace('_', ' ') for col in df.columns}) # remove underscore
