is there a way to implement pandas wide_to_long in Polars?

I use Pandas wide to long to stack survey data and it works beautifully with regex and stub names, is this possible to do in Polars ?

e.g. in Pandas -

import pandas as pd
df = pd.DataFrame({
'famid': [1, 1, 1, 2, 2, 2, 3, 3, 3],
'birth': [1, 2, 3, 1, 2, 3, 1, 2, 3],
'ht_one': [2.8, 2.9, 2.2, 2, 1.8, 1.9, 2.2, 2.3, 2.1],
'ht_two': [3.4, 3.8, 2.9, 3.2, 2.8, 2.4, 3.3, 3.4, 2.9]
})
changed_df = pd.wide_to_long(df, 
                              stubnames='ht', 
                              i=['famid', 'birth'], 
                              j='age',
                              sep='_', 
                              suffix=r'\w+')

stubnames can take a list as well.

Edit- Added code after taking inspiration from Jqurious -

import pandas as pd
import numpy as np
import polars as pl
import re

# Create age group data
age_groups = np.random.choice(['0-18', '19-35', '36-50', '51-65', '65+'], size=10)

# Create gender data
genders = np.random.choice(['Male', 'Female', 'Other'], size=10)

# Create familiarity and affinity data
fam_aff = np.random.rand(10, 4)

# Create column names
cols = ['Age_group', 'Gender', 'Familiarity_loop1', 'Familiarity_loop2', 'Affinity_loop1', 'Affinity_loop2']

# Combine data into dataframe
data = np.column_stack([age_groups, genders, fam_aff])
df = pd.DataFrame(data=data, columns=cols)
df["unique_records"] = np.arange(len(df))

regex_pattern = '^.*_loop\d'

# get polars DF
pl_df = pl.from_pandas(df)

# get all columns list
col_list = pl_df.columns

loop_list = [] # list of columns which contains _loop
sans_loop_list = [] # list of columns which do not contain _loop

for col in col_list:
    if re.search(regex_pattern, col):
        loop_list.append(col)
    else:
        sans_loop_list.append(col)
        

pl_melt_df = (pl_df
 .melt(
    id_vars = pl_df.select(sans_loop_list).columns, 
    variable_name = "master_stack")
    .with_columns(pl.col("master_stack").str.replace(r"_loop\d","")) 
)

pl_melt_df.pivot(index=sans_loop_list, columns="master_stack", values="value")

I want to see Affinity and Familiarity as their own columns, but I am not able to achieve it.

Edit 2 - Added Polars output and Pandas output

Polars -

Pandas output -

Solution

If we look at the .unpivot() result. (previously known as melt)

df.unpivot(index = ["famid", "birth"], variable_name = "age").head(1)

shape: (1, 4)
┌───────┬───────┬────────┬───────┐
│ famid ┆ birth ┆ age    ┆ value │
│ ---   ┆ ---   ┆ ---    ┆ ---   │
│ i64   ┆ i64   ┆ str    ┆ f64   │
╞═══════╪═══════╪════════╪═══════╡
│ 1     ┆ 1     ┆ ht_one ┆ 2.8   │
└───────┴───────┴────────┴───────┘

The sep="_" and suffix=r"\w+" params used in wide_to_long are just extracting one from ht_one.

One way to do this in Polars could be .str.extract()

df.unpivot(
   index = ["famid", "birth"],
   variable_name = "age"
).with_columns(
   pl.col("age").str.extract(r"_(\w+)$")
)

shape: (18, 4)
┌───────┬───────┬─────┬───────┐
│ famid ┆ birth ┆ age ┆ value │
│ ---   ┆ ---   ┆ --- ┆ ---   │
│ i64   ┆ i64   ┆ str ┆ f64   │
╞═══════╪═══════╪═════╪═══════╡
│ 1     ┆ 1     ┆ one ┆ 2.8   │
│ 1     ┆ 2     ┆ one ┆ 2.9   │
│ 1     ┆ 3     ┆ one ┆ 2.2   │
│ 2     ┆ 1     ┆ one ┆ 2.0   │
│ 2     ┆ 2     ┆ one ┆ 1.8   │
│ …     ┆ …     ┆ …   ┆ …     │
│ 2     ┆ 2     ┆ two ┆ 2.8   │
│ 2     ┆ 3     ┆ two ┆ 2.4   │
│ 3     ┆ 1     ┆ two ┆ 3.3   │
│ 3     ┆ 2     ┆ two ┆ 3.4   │
│ 3     ┆ 3     ┆ two ┆ 2.9   │
└───────┴───────┴─────┴───────┘

EDIT: As per the updated example:

The pattern I have been using for this is to .unpivot() and then .pivot() back.

Find the columns names not ending in the suffix to use as id_vars / index:

suffix = r"_loop\d+$"
id_vars = df.select(pl.exclude("^.+" + suffix)).columns

['Age_group', 'Gender', 'unique_records']

(df.unpivot(index=id_vars)
   .with_columns(pl.col("variable").str.replace(suffix, ""))
)

shape: (40, 5)
┌───────────┬────────┬────────────────┬─────────────┬─────────────────────┐
│ Age_group ┆ Gender ┆ unique_records ┆ variable    ┆ value               │
│ ---       ┆ ---    ┆ ---            ┆ ---         ┆ ---                 │
│ str       ┆ str    ┆ i64            ┆ str         ┆ str                 │
╞═══════════╪════════╪════════════════╪═════════════╪═════════════════════╡
│ 19-35     ┆ Female ┆ 0              ┆ Familiarity ┆ 0.9458448571805742  │
│ 65+       ┆ Other  ┆ 1              ┆ Familiarity ┆ 0.29898349718902584 │
│ 36-50     ┆ Other  ┆ 2              ┆ Familiarity ┆ 0.6698438749905085  │
│ 0-18      ┆ Female ┆ 3              ┆ Familiarity ┆ 0.9589949988835984  │
│ 36-50     ┆ Female ┆ 4              ┆ Familiarity ┆ 0.8738576462244922  │
│ …         ┆ …      ┆ …              ┆ …           ┆ …                   │
│ 0-18      ┆ Female ┆ 5              ┆ Affinity    ┆ 0.13593940132707893 │
│ 36-50     ┆ Female ┆ 6              ┆ Affinity    ┆ 0.37172205023277705 │
│ 19-35     ┆ Other  ┆ 7              ┆ Affinity    ┆ 0.5024658713377818  │
│ 51-65     ┆ Other  ┆ 8              ┆ Affinity    ┆ 0.00582736048275978 │
│ 36-50     ┆ Female ┆ 9              ┆ Affinity    ┆ 0.34380158652767634 │
└───────────┴────────┴────────────────┴─────────────┴─────────────────────┘

We end up with 40 rows and 2 variables (Familiarity, Affinity).

In order to pivot into 20 rows, you can add a "row number" per variable and use it as part of the index.

(df.unpivot(index=id_vars)
   .with_columns(pl.col("variable").str.replace(suffix, ""))
   .with_columns(index = pl.int_range(pl.len()).over("variable"))
   .pivot(on="variable", index=id_vars + ["index"])
)

shape: (20, 6)
┌───────────┬────────┬────────────────┬───────┬─────────────────────┬─────────────────────┐
│ Age_group ┆ Gender ┆ unique_records ┆ index ┆ Familiarity         ┆ Affinity            │
│ ---       ┆ ---    ┆ ---            ┆ ---   ┆ ---                 ┆ ---                 │
│ str       ┆ str    ┆ i64            ┆ i64   ┆ str                 ┆ str                 │
╞═══════════╪════════╪════════════════╪═══════╪═════════════════════╪═════════════════════╡
│ 19-35     ┆ Female ┆ 0              ┆ 0     ┆ 0.9458448571805742  ┆ 0.8318885018762573  │
│ 65+       ┆ Other  ┆ 1              ┆ 1     ┆ 0.29898349718902584 ┆ 0.5932787653850062  │
│ 36-50     ┆ Other  ┆ 2              ┆ 2     ┆ 0.6698438749905085  ┆ 0.3322678195709319  │
│ 0-18      ┆ Female ┆ 3              ┆ 3     ┆ 0.9589949988835984  ┆ 0.2252757821730993  │
│ 36-50     ┆ Female ┆ 4              ┆ 4     ┆ 0.8738576462244922  ┆ 0.42281089740408706 │
│ …         ┆ …      ┆ …              ┆ …     ┆ …                   ┆ …                   │
│ 0-18      ┆ Female ┆ 5              ┆ 15    ┆ 0.17803848283413837 ┆ 0.13593940132707893 │
│ 36-50     ┆ Female ┆ 6              ┆ 16    ┆ 0.5390844456218246  ┆ 0.37172205023277705 │
│ 19-35     ┆ Other  ┆ 7              ┆ 17    ┆ 0.7692067698388259  ┆ 0.5024658713377818  │
│ 51-65     ┆ Other  ┆ 8              ┆ 18    ┆ 0.6569518159892904  ┆ 0.00582736048275978 │
│ 36-50     ┆ Female ┆ 9              ┆ 19    ┆ 0.6946040879238368  ┆ 0.34380158652767634 │
└───────────┴────────┴────────────────┴───────┴─────────────────────┴─────────────────────┘