The `polars.DataFrame.product()`

function is used to aggregate the columns of a DataFrame to their product values. This function calculates the product of all elements in each column and returns a new DataFrame with a single row containing the product values for each column.

Here’s the syntax of the `product()`

function:

DataFrame.product()

The resulting DataFrame will have a single row, with the product of each column as its values:

We will use the `product()`

function, to check its functionality in Polars. For this, we will take four columns named as `p1`

, `p2`

, `p3`

, and `p4`

. Each of them contains different values.

import polars as pldf= pl.DataFrame({"p1": [5, 10, 3, 2, 9],"p2": [48, 0.5, 7, 4, 0.2],"p3": [True, False, True, True, False],"p4": [7, 9, 34, 2, 1],})result = df.product();print (result)

We will now explain the above code step by step:

**Lines 3–11:**We created a DataFrame (`df`

) using the Polars library. The DataFrame has four columns (`p1`

,`p2`

,`p3`

,`p4`

) with corresponding data.`p1`

and`p4`

contain integer values,`p2`

contains floating-point values, and`p3`

contains boolean values.**Line 12:**We use the`product()`

function, the product function will calculate the product of each column:`p1`

**:**The product of the values in the column (`5`

*`10`

*`3`

*`2`

*`9`

) is`2700`

.`p2`

**:**The product of the values in the column (`48`

*`0.5`

*`7`

*`4`

*`0.2`

) is`134.0`

.`p3`

**:**The product of the boolean values in the column (`True`

*`False`

*`True`

*`True`

*`False`

) is`false`

. In this way,`True`

is considered as`1`

and`False`

as`0`

.`p4`

**:**The product of the values in this column is (`7`

*`9`

*`34`

*`2`

*`1`

) is`4284`

.

**Line 13:**We printed the`result`

. It will show a newly generated DataFrame consisting of a single row that displays the product values for each column.

The `polars.DataFrame.product()`

function aggregates the columns within a DataFrame by computing their product values. Subsequently, it generates a new DataFrame presenting a single row that encapsulates these calculated product values for each column.

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