Manage Null Values

In general terms, a null value is a definition that points to nothing. A container for a value, such as a row-column combination or a variable, exists, but the container points to no actual value.

Important notes on null values

NOTE: In the platform, null values are a subset of the category identifying missing values. For technical reasons, however, Cloud Dataprep displays null values as missing values and visually treats them as the same. Internally, they are understood to be different values.


  • Null values are visually represented as missing values.
    • In the data quality bar, null and missing values are represented in the dark bar (missing values).
  • Computationally, they are different types of values.
    • Most functions applied to null and missing values return the same results.
      • For example, the ISMISSING function returns true for null and missing values.
      • However, the ISNULL function returns true for a null value and false for a missing value. See below.
    • If you use a function to generate null values, they are displayed as missing values, although they are recorded as nulls.
      • For example, the following transform generates a column of null values, which are represented as missing values in the data quality bar.

        derive value:NULL()

        You can paste Wrangle steps into the Transform Builder.

  • When a set of results is generated, both null and missing values are written as missing values, unless the output format has a specific schema associated with it.

Locate null values

Null values are displayed with missing values in the Missing values category of the data quality bar (in black).

You can use the following transform to distinguish between null and missing values. This transform generates a new column of values, which are set to true if the value in isActive is a null value:

derive value:ISNULL(isActive)

High percentage of nulls

On import, if a column has a high enough percentage of null values, the platform may retype the column as a String column, which may yield mismatched values in addition to the missing values that were imported from null values.

Fix null values

See Find Missing Data.

Write null values

If needed, you can write a null value to a set of data. In the following example, all missing values in a column are replaced by nulls, using the NULL function:

NOTE: The NULL function is typically used to pass null values into functions that have been designed to specifically address them.

set col: My_Column value: NULL() row: ISMISSING([My_Column])

The above transform writes null values, but these values are converted to missing values on export.

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Google Cloud Dataprep Documentation