Drop nulls

Drop nulls — `dropNulls`

Remove rows with missing values.

Use cases

Discard records that are missing a required field (e.g. no amount).
Drop fully-empty rows while keeping partially-populated ones.

What it does

Drops rows where the target column(s) are null. With subset: ["amount"] only rows missing an amount are removed — rows with other nulls (like region) survive.

Before

order_id	region	amount
1001	North	120.5
1002	South	null
1003	null	89
1004	South	42.25

4 rows · 3 cols

Drop nulls (subset=amount)

After

order_id	region	amount
1001	North	120.5
1003	null	89
1004	South	42.25

3 rows · 3 cols

Configuration

Config key	Type	Required	Description
`how`	string	No	`any` (default) drops a row with any null; `all` only if every value is null
`subset`	string[]	No	Only consider these columns

Generated Python code

df_2 = df_1.dropna(subset='amount')

Tips & common mistakes

how: all needs a subset to be meaningful row-wise — pair it with the columns that define an "empty" row.
To keep rows and fill the gaps instead, use Fill nulls.