Describe the workflow you want to enable

A parameter accepting custom bin edges as an array.

Describe your proposed solution

Use pd.cut() under the hood or any other computa

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

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Is your feature request related to a problem? Please describe

convert classification report to dataframe as sklearn classification report

Describe the solution you'd like

to add o

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?