How can one partition a dataframe in python using polars and then pivot and concatenate it?

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To partition a dataframe in Polars, you can use the groupby method, which groups the rows based on one or more columns.

Here's an example code snippet:

import polars as pl

df = pl.DataFrame({
    'day': [1, 1, 2, 2, 3, 3],
    'category': ['A', 'B', 'A', 'B', 'A', 'B'],
    'value': [10, 20, 30, 40, 50, 60]
})

# group by day and category
gb = df.groupby(['day', 'category'])

# aggregate the values column by sum
aggregated = gb.agg({'value': 'sum'})

# pivot the table
pivoted = aggregated.pivot(index='day', columns='category')

# concatenate the two columns
result = pl.concat([pivoted['value']['A'], pivoted['value']['B']], axis=1)

In this example, we have a dataframe df with columns day, category, and value. We group the rows by day and category, and aggregate the value column by summing it up.

Then, we pivot the resulting table by specifying the index and columns, and finally, concatenate the two columns A and B using the concat function.

Note that we accessed the value column in the pivoted table using pivoted['value']['A'] and pivoted['value']['B']. The first index value is the name of the aggregated column, and the second index A and B are the values of the category column.