How to add a column to a polars DataFrame using .with_columns()

Question

I am currently creating a new column in a polars data frame using

predictions = [10, 20, 30, 40, 50]
df['predictions'] = predictions

where predictions is a numpy array or list containing values I computed with another tool.

However, polars throws a warning, that this option will be deprecated. How can the same result be achieved using .with_columns()?

This should answer your question: https://stackoverflow.com/questions/72245243/polars-how-to-add-a-column-with-numerical/72245435#72245435 — , Aug 01 '22 at 13:15
Thankd, yes that answers it. IMO it would be great to add the linked answer to the "Coming from Pandas" guide: https://pola-rs.github.io/polars-book/user-guide/coming_from_pandas.html — Felix.B, Aug 01 '22 at 13:57
It is there, see https://pola-rs.github.io/polars-book/user-guide/coming_from_pandas.html#column-assignment — jvz, Aug 01 '22 at 20:05

score 17 · Accepted Answer · answered Aug 25 '22 at 13:10

17

The values in the numpy array or list predictions can be add to a polars table using:

predictions = [10, 20, 30, 40, 50]
df.with_column(pl.Series(name="predictions", values=predictions))

answered Aug 25 '22 at 13:10

Felix.B

6

Just a heads up, with_column is now deprecated. The correct form is now with_columns (plural). – Artur Dutra Mar 09 '23 at 14:47

1 Answers1