Fixed small missing import and variable errors. Also added 'getting-started.md' to the ToC.

Author: daexs

doc/python/creating-and-updating-figures.md

@@ -239,6 +239,7 @@ You can also add traces to a figure produced by a figure factory or Plotly Expre
 
 ```python
 import plotly.express as px
+import plotly.graph_objects as go
 
 df = px.data.iris()
 
@@ -371,6 +372,10 @@ fig.show()
 Note that the following `update_layout()` operations are equivalent:
 
 ```python
+import plotly.graph_objects as go
+
+fig = go.Figure(data=go.Bar(x=[1, 2, 3], y=[1, 3, 2]))
+
 fig.update_layout(title_text="update_layout() Syntax Example",
                   title_font_size=30)
 

doc/python/ecdf-plots.md

@@ -96,6 +96,7 @@ In `standard` mode (the default), the right-most point is at 1 (or the total cou
 
 ```python
 import plotly.express as px
+df = px.data.tips()
 fig = px.ecdf(df, x=[1,2,3,4], markers=True, ecdfmode="standard",
               title="ecdfmode='standard' (Y=fraction at or below X value, this the default)")
 fig.show()
@@ -105,6 +106,7 @@ In `reversed` mode, the right-most point is at 1 (or the total count/sum, depend
 
 ```python
 import plotly.express as px
+df = px.data.tips()
 fig = px.ecdf(df, x=[1,2,3,4], markers=True, ecdfmode="reversed",
               title="ecdfmode='reversed' (Y=fraction at or above X value)")
 fig.show()
@@ -114,6 +116,7 @@ In `complementary` mode, the right-most point is at 0 and no points are at 1 (or
 
 ```python
 import plotly.express as px
+df = px.data.tips()
 fig = px.ecdf(df, x=[1,2,3,4], markers=True, ecdfmode="complementary",
               title="ecdfmode='complementary' (Y=fraction above X value)")
 fig.show()

doc/python/figure-introspection.md

@@ -117,6 +117,7 @@ print(full_fig.data[0])
 We see that this is an instance of `go.Scatter` (as expected, given the input) and that it has an attribute we've maybe never heard of called `cliponaxis` which by default seems to be set to `True` in this case. Let's find out more about this attribute using the built-in Python `help()` function
 
 ```python
+import plotly.graph_objects as go
 help(go.Scatter.cliponaxis)
 ```
 
@@ -130,6 +131,7 @@ fig.show()
 We can use this technique (of making a figure, and querying Plotly.js for the "full" version of that figure, and then exploring the attributes that are automatically set for us) to learn more about the range of possibilities that the figure schema makes available. We can drill down into `layout` attributes also:
 
 ```python
+import plotly.graph_objects as go
 help(go.layout.XAxis.autorange)
 ```
 

doc/python/figurewidget.md

@@ -56,14 +56,26 @@ f
 Add traces or update the layout and then watch the output above update in real time.
 
 ```python
-f.add_scatter(y=[2, 1, 4, 3]);
+import plotly.graph_objects as go
+f = go.FigureWidget()
+
+f.add_scatter(y=[2, 1, 4, 3])
 ```
 
 ```python
-f.add_bar(y=[1, 4, 3, 2]);
+import plotly.graph_objects as go
+f = go.FigureWidget()
+f.add_scatter(y=[2, 1, 4, 3])
+
+f.add_bar(y=[1, 4, 3, 2])
 ```
 
 ```python
+import plotly.graph_objects as go
+f = go.FigureWidget()
+f.add_scatter(y=[2, 1, 4, 3])
+f.add_bar(y=[1, 4, 3, 2])
+
 f.layout.title = 'Hello FigureWidget'
 ```
 
@@ -73,18 +85,42 @@ f.layout.title = 'Hello FigureWidget'
 #### Update the Data and the Layout
 
 ```python
+import plotly.graph_objects as go
+f = go.FigureWidget()
+f.add_scatter(y=[2, 1, 4, 3])
+f.add_bar(y=[1, 4, 3, 2])
+f.layout.title = 'Hello FigureWidget'
+
 # update scatter data
 scatter = f.data[0]
 scatter.y = [3, 1, 4, 3]
 ```
 
 ```python
+import plotly.graph_objects as go
+f = go.FigureWidget()
+f.add_scatter(y=[2, 1, 4, 3])
+f.add_bar(y=[1, 4, 3, 2])
+f.layout.title = 'Hello FigureWidget'
+scatter = f.data[0]
+scatter.y = [3, 1, 4, 3]
+
 # update bar data
 bar = f.data[1]
 bar.y = [5, 3, 2, 8]
 ```
 
 ```python
+import plotly.graph_objects as go
+f = go.FigureWidget()
+f.add_scatter(y=[2, 1, 4, 3])
+f.add_bar(y=[1, 4, 3, 2])
+f.layout.title = 'Hello FigureWidget'
+scatter = f.data[0]
+scatter.y = [3, 1, 4, 3]
+bar = f.data[1]
+bar.y = [5, 3, 2, 8]
+
 f.layout.title.text = 'This is a new title'
 ```
 
@@ -114,5 +150,7 @@ f2
 See [these Jupyter notebooks](https://github.com/jonmmease/plotly_ipywidget_notebooks) for even more FigureWidget examples.
 
 ```python
+import plotly.graph_objects as go
+
 help(go.FigureWidget)
 ```

doc/python/imshow.md

@@ -239,6 +239,7 @@ These two modes can be used for single- and multichannel data. The default value
 
 ```python
 import plotly.express as px
+import numpy as np
 img = np.arange(100, dtype=np.uint8).reshape((10, 10))
 fig = px.imshow(img, contrast_rescaling='infer')
 fig.show()

doc/python/ml-tsne-umap-projections.md

@@ -110,7 +110,7 @@ Just like t-SNE, [UMAP](https://umap-learn.readthedocs.io/en/latest/index.html)
 In the example below, we see how easy it is to use UMAP as a drop-in replacement for scikit-learn's `manifold.TSNE`.
 
 ```python
-from umap import UMAP
+from umap.umap_ import UMAP
 import plotly.express as px
 
 df = px.data.iris()
@@ -146,7 +146,7 @@ Although there's over 1000 data points, and many more dimensions than the previo
 ```python
 import plotly.express as px
 from sklearn.datasets import load_digits
-from umap import UMAP
+from umap.umap_ import UMAP
 
 digits = load_digits()
 

doc/python/network-graphs.md

@@ -57,8 +57,13 @@ G = nx.random_geometric_graph(200, 0.125)
 Add edges as disconnected lines in a single trace and nodes as a scatter trace
 
 ```python
+import plotly.graph_objects as go
+import networkx as nx
+
+G = nx.random_geometric_graph(200, 0.125)
 edge_x = []
 edge_y = []
+
 for edge in G.edges():
     x0, y0 = G.nodes[edge[0]]['pos']
     x1, y1 = G.nodes[edge[1]]['pos']
@@ -115,8 +120,12 @@ Another option would be to size points by the number of connections
 i.e. ```node_trace.marker.size = node_adjacencies```
 
 ```python
+import networkx as nx
+
+G = nx.random_geometric_graph(200, 0.125)
 node_adjacencies = []
 node_text = []
+
 for node, adjacencies in enumerate(G.adjacency()):
     node_adjacencies.append(len(adjacencies[1]))
     node_text.append('# of connections: '+str(len(adjacencies[1])))

doc/python/px-arguments.md

@@ -55,6 +55,8 @@ df.head()
 By default `px.data` functions return a pandas `DataFrame` object, but you can specify an alternative dataframe type using `return_type`. `pandas`, `polars`, `pyarrow`, `modin`, and `cuDF` are supported return types.
 
 ```python
+import plotly.express as px
+
 df = px.data.iris(return_type='polars')
 df.head()
 ```

doc/python/static-image-export.md

@@ -204,13 +204,23 @@ Here's the bytes object displayed using `IPython.display.Image`:
 
 ```python
 from IPython.display import Image
+import plotly.express as px
+data_canada = px.data.gapminder().query("country == 'Canada'")
+fig = px.bar(data_canada, x='year', y='pop')
+img_bytes = fig.to_image(format="png")
+
 Image(img_bytes)
 ```
 
 ## Specify Image Dimensions and Scale
 In addition to the image format, the `to_image` and `write_image` functions provide arguments to specify the image `width` and `height` in logical pixels. They also provide a `scale` parameter that can be used to increase (`scale` > 1) or decrease (`scale` < 1) the physical resolution of the resulting image.
 
 ```python
+from IPython.display import Image
+import plotly.express as px
+data_canada = px.data.gapminder().query("country == 'Canada'")
+fig = px.bar(data_canada, x='year', y='pop')
+
 img_bytes = fig.to_image(format="png", width=600, height=350, scale=2)
 Image(img_bytes)
 ```

doc/python/ternary-contour.md

@@ -128,6 +128,9 @@ fig.show()
 Two modes are available in order to interpolate between data points: interpolation in Cartesian space (`interp_mode='cartesian'`) or interpolation using the [isometric log-ratio transformation](https://link.springer.com/article/10.1023/A:1023818214614) (see also [preprint](https://www.researchgate.net/profile/Leon_Parent2/post/What_is_the_best_approach_for_diagnosing_nutrient_disorders_and_formulating_fertilizer_recommendations/attachment/59d62a69c49f478072e9cf3f/AS%3A272541220835360%401441990298625/download/Egozcue+et+al+2003.pdf)),  `interp_mode='ilr'`. The `ilr` transformation preserves metrics in the [simplex](https://en.wikipedia.org/wiki/Simplex) but is not defined on its edges.
 
 ```python
+import numpy as np
+import plotly.figure_factory as ff
+
 a, b = np.mgrid[0:1:20j, 0:1:20j]
 mask = a + b <= 1
 a, b = a[mask], b[mask]
@@ -138,6 +141,9 @@ fig.show()
 ```
 
 ```python
+import numpy as np
+import plotly.figure_factory as ff
+
 a, b = np.mgrid[0:1:20j, 0:1:20j]
 mask = a + b <= 1
 a, b = a[mask], b[mask]