Describe the bug

In grid_to_graph, you expect the vertices to correspond to the implicit order defined by the mask. This is not always the case, due to the occurrence of isolated vertices that are dismissed in the reindexing of the vertices.

Steps/Code to Reproduce

import numpy as np
from sklearn.feature_extraction import grid_to_graph

mask = np.zeros((2, 3)).ast

Collection of follow-ups to #5827. These can/should be broken out into individual PRs. Many are relatively straightforward and would make a good first PR.

General

• Documentation (none was added in original PR).
• Release notes.
• Example notebook.
• Double-check how sm.tsa.arima.ARIMA works with fix_params (it should fail except when the fit method is statespace

Improve examples such that they are more incremental (in the import etc) without following strictly PEP8. It will make it nicer to read on the gallery generated online.

See:
gonum/gonum#845 (comment)

In X-ray crystallography, the most important prior distributions include two special cases of the generalized gamma distribtion. I am very keen to try this parameterization of the variational distritribution in my research project. How hard would it be for the TFP devs to implement this distr

1. "Conclusion" section of "Getting started with Tablesaw" page contains broken link to "Java Docs".
   https://jtablesaw.github.io/tablesaw/gettingstarted#conclusion
2. "Exploring tables" section of "Getting started with Tablesaw" page contains broken link to "plotting".
   https://jtablesaw.github.io/tablesaw/gettingstarted.html#exploring-tables

I found several useful applications of pseudo-random number sampling in the past. In particular:

• Inverse transform sampling
• Gibbs sampling

(This issue serves a reminder to add the respective methods. Pull requests always welcome.)

Since the default output is meant to be human-readable, would it make sense to add thousands separators to make the output more easily readable?