Convert Pandas pivot_table function into Polars pivot Function

Question

I'm trying to convert some python pandas into polars. I'm stuck trying to convert pandas pivot_table function into polars. The following is the working pandas code. I can't seem to get the same behavior with the Polars pivot function. The polars pivot function forces the column parameter and uses the column values as headers instead of the column label as a header. I'm going for the same output below but with Polars instead of Pandas.

df = pd.DataFrame({"obj" : ["ring", "shoe", "ring"], "price":["65", "42", "65"], "value":["53", "55", "54"], "date":["2022-02-07", "2022-01-07", "2022-03-07"]})

table = pd.pivot_table(df, values=['price','value','date'],index=['obj'], aggfunc={'price': pd.Series.nunique,'value':pd.Series.nunique,'date':pd.Series.nunique})

print(table)

Outputs the following:

        date    price     value  
obj  
ring    2       1         2  
shoe    1       1         1

Answer 1

In Polars, we would not use a pivot table for this. Instead, we would use the groupby and agg functions. Using your data, it would be:

import polars as pl
df = pl.from_pandas(df)

df.groupby("obj").agg(pl.all().n_unique())

shape: (2, 4)
┌──────┬───────┬───────┬──────┐
│ obj  ┆ price ┆ value ┆ date │
│ ---  ┆ ---   ┆ ---   ┆ ---  │
│ str  ┆ u32   ┆ u32   ┆ u32  │
╞══════╪═══════╪═══════╪══════╡
│ ring ┆ 1     ┆ 2     ┆ 2    │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌┤
│ shoe ┆ 1     ┆ 1     ┆ 1    │
└──────┴───────┴───────┴──────┘

pivot and melt

Where we would use the pivot function in Polars is to summarize a dataset in 'long' format to a dataset in 'wide' format. As an example, let's convert your original dataset to 'long' format using the melt function.

df2 = df.melt(id_vars="obj", value_vars=["price", "date", "value"])
print(df2)

shape: (9, 3)
┌──────┬──────────┬────────────┐
│ obj  ┆ variable ┆ value      │
│ ---  ┆ ---      ┆ ---        │
│ str  ┆ str      ┆ str        │
╞══════╪══════════╪════════════╡
│ ring ┆ price    ┆ 65         │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ shoe ┆ price    ┆ 42         │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ ring ┆ price    ┆ 65         │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ ring ┆ date     ┆ 2022-02-07 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ shoe ┆ date     ┆ 2022-01-07 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ ring ┆ date     ┆ 2022-03-07 │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ ring ┆ value    ┆ 53         │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ shoe ┆ value    ┆ 55         │
├╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌╌╌╌╌╌┤
│ ring ┆ value    ┆ 54         │
└──────┴──────────┴────────────┘

Now let's use pivot to summarize this 'long' format dataset back to one in "wide" format and simply count the number of values.

df2.pivot(values='value', index='obj', columns='variable', aggregate_fn='count')

shape: (2, 4)
┌──────┬──────┬───────┬───────┐
│ obj  ┆ date ┆ price ┆ value │
│ ---  ┆ ---  ┆ ---   ┆ ---   │
│ str  ┆ u32  ┆ u32   ┆ u32   │
╞══════╪══════╪═══════╪═══════╡
│ ring ┆ 2    ┆ 2     ┆ 2     │
├╌╌╌╌╌╌┼╌╌╌╌╌╌┼╌╌╌╌╌╌╌┼╌╌╌╌╌╌╌┤
│ shoe ┆ 1    ┆ 1     ┆ 1     │
└──────┴──────┴───────┴───────┘

Does this help clarify the use of the pivot functionality?