Count null values in every column in polars

null_count

Everyone working in the data field knows that data is never perfectly clean, and missing values are almost inevitable. That’s why it’s important to check whether your dataset contains any null values before you start analyzing it. Below is a dataframe showing visits to fast food stores.

shape: (4, 3)

Store	Visits	Opinion
str	i64	str
"Burger King"	4	"It's good"
"McDonald's"	7	null
"Shake Shack"	10	"Loved it"
"Pizza Hut"	null	null

Check number of nulls

To check how many null values are in each column, use the Polars method null_count like this:

(df
 .null_count()
 )

shape: (1, 3)

Store	Visits	Opinion
u32	u32	u32
0	1	2

You can now see the number of null values in each column. For instance, Opinion has 2 null values.

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