data-analysis

Describe the issue linked to the documentation

Many legitimate notebook style examples have been broken, and specifically by the following PR
scikit-learn/scikit-learn#9061

Here are all the examples that use patterns like # ####### (found by ag -l '# #####*\s#' examples | sort, note there may be false positives ... for example examples/impute/plot_missing_val

The Mixed Time-Series chart type allows for configuring the title of the primary and the secondary y-axis.
However, while only the title of the primary axis is shown next to the axis, the title of the secondary one is placed at the upper end of the axis where it gets hidden by bar values and zoom controls.

How to reproduce the bug

1. Create a mixed time-series chart
2. Configure axi

Pandas version checks

• I have checked that the issue still exists on the latest versions of the docs on master here

Location of the documentation

https://pandas.pydata.org/docs/dev/reference/api/pandas.DataFrame.to_hdf.html?highlight=to_hdf#pandas.DataFrame.to_hdf

Documentation problem

• dropna
• index

are missing from the docst

Summary

Aesthetically trivial, yet I've spotted a discrepancy with font sizes in our tooltip (front-end + back-end screenshots below).
I believe sections #1 and #2 should have the same font size?

![image](https://user-images.githubusercontent.com/27242399/139825179-4d62e3

Is your feature request related to a problem? Please describe.
When working with a big piece of text, I sometimes scroll down and copy some text into another tab. When switching back to the first tab, both the input and the output pane is back on top. So I don't know where I was working just now.

Describe the solution you'd like
After tab switching, scroll position should be remembere

The variable columnNames is not available in Jython, whereas it is available in GREL. It is useful to iterate over all cells in a row, for instance with this expression:
forEach(row.columnNames,cn,cells[cn].value)
(see https://groups.google.com/g/openrefine/c/gV0bLUz7p7o)

Proposed solution

We could make this variable available in Jython too.
It would also be good to [document](https

• Base README.md
• Quizzes
• Introduction base README
  • Defining Data Science README
  • Defining Data Science assignment
  • Ethics README
  • Ethics assignment
  • Defining Data README
  • Defining Data assignment
  • Stats and Probability README
  • Stats and Probability assignment
• Working with Data base README
  • Rel

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

Tell us about the new connector you’d like to have

• Which source and which destination?
  Source: mambu (https://api.mambu.com/)
  Destination: S3, RDS, Redshift
• Do you need a specific version of the underlying data source e.g: you specifically need support for an older version of the API or DB?
  Want to support for since 1.0.

Describe the context around this new connector

• Why do yo

See:
gonum/gonum#845 (comment)

The official instructions say to use joblib for pickling PyOD models.

This fails for AutoEncoders, or any other TensorFlow-backed model as far as I can tell. The error is:

>>> dump(model, 'model.joblib')
...
TypeError: can't pickle _thread.RLock objects

Note that it's not sufficient to save the underlying Keras S

Is your feature request related to a problem? Please describe.
It's not clear what inputs are optional when you are using a Gradio Interface

Describe the solution you'd like
One idea: the default label for an optional component could include "(Optional)" at the end to designate it at such.

Additional context
Here's an example Space where I think it would be useful to know wh

Is your feature request related to a problem? Please describe.
While reviewing PR #9817 to introduce DataFrame.diff, I noticed that it is restricted to acting on numeric types.

A time-series diff is probably a very common user need, if provided a series of timestamps and seeking the durations between observations.

Pandas supports diffs on non-numeric types like timestamps: