Converting from polars to pandas dataframe is as easy as 1, 2, 3

to_pandas

Almost anything you can do in Pandas can also be done in Polars. However, there are certain outputs that are unique to Pandas, such as creating a hierarchical column table. Fortunately, converting a Polars dataframe to a Pandas dataframe is very easy. Below is a Polars dataframe showing some popular clothing brands.

shape: (4, 3)

Brand	Category	Price
str	str	i64
"Gucci"	"Premium"	1000
"Versace"	"Premium"	5000
"Forever21"	"Basic"	39
"Gap"	"Basic"	56

Change to pandas dataframe

Suppose you want to pivot your data to create a hierarchical column table where Category is at the top, followed by Brand. Currently, this cannot be done in Polars. However, you can use the to_pandas method to convert your Polars dataframe into a Pandas dataframe. From there, you can pivot the data to achieve the desired output as shown below:

(df
 .to_pandas()
 .pivot(columns=['Category', 'Brand'], values='Price')
 )

Category	Premium		Basic
Brand	Gucci	Versace	Forever21	Gap
0	1000.0	NaN	NaN	NaN
1	NaN	5000.0	NaN	NaN
2	NaN	NaN	39.0	NaN
3	NaN	NaN	NaN	56.0

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