When you want to count values without null in polars

pl.count

Most of the time, when you’re counting the number of values in a column, you’re not interested in including empty (null) values. Therefore, simply counting the number of rows in the dataframe isn’t enough. Luckily, Polars makes this kind of count easy to perform. Below is a dataframe showing department managers.

shape: (5, 2)

Manager	Department
str	str
"Mae C"	"Hospitality"
"Lauren K"	"Legal"
"Sahill H"	"Finance"
null	"Advertising"
"Titus P"	"Sales"

Count values in each column

To get the number of non-null values in each column, you can use the Polars expression pl.count like this:

(df
 .select(pl.count('Manager','Department'))
 )

shape: (1, 2)

Manager	Department
u32	u32
4	5

As you can see, the number of values in the two columns is not the same. Manager has only 4 values because it contains a null value.

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