Improving polars statement that adds a column applying a lambda function on each row

I am trying to add a column using apply in polars. The equivalent of pandas is as follows:

>>> import pandas as pd
>>> df = pd.DataFrame({"ref": [-1, 2, 8], "v1": [-1, 5, 0], "v2": [-1, 5, 8]})
>>> df['count'] = df.apply(lambda r: len([i for i in r if i == r[0]]) - 1, axis=1)
>>> df = df.drop('ref', axis=1)
>>> df
   v1  v2  count
0  -1  -1      2
1   5   5      0
2   0   8      1
>>>

The following is the sample code that I have with polars. Though it works as desired, it looks ugly and probably can be improved as well.

>>> import polars as pl
>>>
>>> df = pl.DataFrame({"ref": [-1, 2, 8], "v1": [-1, 5, 0], "v2": [-1, 5, 8]})
>>>
>>> x = df.apply(lambda r: len([i for i in r if i == r[0]]) - 1).rename({'apply': 'count'})
>>> df = df.hstack([x.to_series()]).drop('ref')
>>>
>>> df
shape: (3, 3)
┌─────┬─────┬───────┐
│ v1  ┆ v2  ┆ count │
│ --- ┆ --- ┆ ---   │
│ i64 ┆ i64 ┆ i64   │
╞═════╪═════╪═══════╡
│ -1  ┆ -1  ┆ 2     │
│ 5   ┆ 5   ┆ 0     │
│ 0   ┆ 8   ┆ 1     │
└─────┴─────┴───────┘
>>>

What bothers me is the renaming part and hstack that I clobbered together to work. I have seen some examples where .with_column() was used but that method is not present in my version of polars (0.17.14).

I would be grateful for any improvements in the above code.

TIA

Previously, .with_column and .with_columns both existed, it's just .with_columns now.

It looks like you're trying to count when ref and another column have the same value.

You can do this directly with expressions in polars:

df.with_columns(count = pl.sum(pl.col('ref') == pl.exclude('ref')))

shape: (3, 4)
┌─────┬─────┬─────┬───────┐
│ ref ┆ v1  ┆ v2  ┆ count │
│ --- ┆ --- ┆ --- ┆ ---   │
│ i64 ┆ i64 ┆ i64 ┆ u32   │
╞═════╪═════╪═════╪═══════╡
│ -1  ┆ -1  ┆ -1  ┆ 2     │
│ 2   ┆ 5   ┆ 5   ┆ 0     │
│ 8   ┆ 0   ┆ 8   ┆ 1     │
└─────┴─────┴─────┴───────┘