If you really want to apply python function then you can use map_elements(). However, using native polars expression is always preferrable.

In your case I'd suggest to look at replace() or replace_strict().

If you would want to just search by AH_PROC_REALIZADO column you could use simple replace_strict():

Copydf = pl.DataFrame({
    "AH_PROC_REALIZADO": ["30408", "410010065", "410010111", "XXXX"]
})

┌───────────────────┐
│ AH_PROC_REALIZADO │
│ ---               │
│ str               │
╞═══════════════════╡
│ 30408             │
│ 410010065         │
│ 410010111         │
│ XXXX              │
└───────────────────┘

df.with_columns(
    pl.col("AH_PROC_REALIZADO")
    .replace_strict(proceds, default=None)
    .alias("proced_descr")
)

┌───────────────────┬────────────────────────────────┐
│ AH_PROC_REALIZADO ┆ proced_descr                   │
│ ---               ┆ ---                            │
│ str               ┆ str                            │
╞═══════════════════╪════════════════════════════════╡
│ 30408             ┆ QUIMIOTERAPIA                  │
│ 410010065         ┆ MASTECTOMIA SIMPLES            │
│ 410010111         ┆ SETORECTOMIA / QUADRANTECTOMIA │
│ XXXX              ┆ null                           │
└───────────────────┴────────────────────────────────┘

The problem with your use case is that, as far as I understand, you want to search by prefix of the strings in AH_PROC_REALIZADO column. In that case you could probably adjust the solution to:

• itertools.groupby() to transform proceds dictionary into dictionary of dictionaries where high level keys are length of the key.
• replace_strict() to search for product description.
• coalesce() to combine results into final column.

Copyfrom itertools import groupby

mappings = {k: dict(g) for k, g in groupby(proceds.items(), lambda x: len(x[0]))}

df = pl.DataFrame({
    "AH_PROC_REALIZADO": ["30408_____", "410010065_____", "410010111____", "XXXX"]
})

┌───────────────────┐
│ AH_PROC_REALIZADO │
│ ---               │
│ str               │
╞═══════════════════╡
│ 30408_____        │
│ 410010065_____    │
│ 410010111____     │
│ XXXX              │
└───────────────────┘

df.with_columns(
    pl.coalesce(
        pl.col("AH_PROC_REALIZADO").str.head(k).replace_strict(m, default=None) for k, m in mappings.items()
    )
    .alias("proced_descr")
)

┌───────────────────┬────────────────────────────────┐
│ AH_PROC_REALIZADO ┆ proced_descr                   │
│ ---               ┆ ---                            │
│ str               ┆ str                            │
╞═══════════════════╪════════════════════════════════╡
│ 30408_____        ┆ QUIMIOTERAPIA                  │
│ 410010065_____    ┆ MASTECTOMIA SIMPLES            │
│ 410010111____     ┆ SETORECTOMIA / QUADRANTECTOMIA │
│ XXXX              ┆ null                           │
└───────────────────┴────────────────────────────────┘

I think the column name that contains "Number" is something like " Number" or "Number ". I'm assuming you might have a residual space in the column name. Please run print "<{}>".format(data.columns[1]) and see what you get. If it's something like < Number>, it can be fixed with:

data.columns = data.columns.str.strip()

See pandas.Series.str.strip

In general, AttributeError: 'DataFrame' object has no attribute '...', where ... is some column name, is caused because . notation has been used to reference a nonexistent column name or pandas method.

pandas methods are accessed with a .. pandas columns can also be accessed with a . (e.g. data.col) or with brackets (e.g. ['col'] or [['col1', 'col2']]).

data.columns = data.columns.str.strip() is a fast way to quickly remove leading and trailing spaces from all column names. Otherwise verify the column or attribute is correctly spelled.

It's an issue with how you're calling the data and if it's actually there.

train.loc[:,['Survived','Sex']]

tells me that there's a DataFrame (which is from pandas, hence the error) called train and this line is trying to access parts of that dataframe (it's just a type of an array). Specifically, it's trying to access columns named Survived and Sex.

Similarly, this line tells me there's another dataframe (df) known as test with a column named Sex and this is access that data.

test.loc[:,['Sex']]

The error code also informs me of some things

predictions = pandas.DataFrame({'PassengerId': test.PassengerId,'Survived': hypothesis.Survived.tolist()})

There's another df called predictions that's of dict type which is trying to access information from the another hypothesis df. The attribute it's tryin to access in the second key of the dict is

hypothesis.Survived.tolist()

which is a way of calling a column from that df. That is, when the predictions line is executed, it's trying to pull all the values from the Survived column of the hypothesis df.

The error is that the df doesn't actually have a column named Survived. So either there's missing data, or you're calling it wrong, or there's a missing reference.

Without knowing more about your code and your question, I can't really extrapolate much more.