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Returns a new DataFrame which null values are filled with new value. DataFrame.fillna and DataFrameNaFunctions.fill are aliases of each other.
Syntax
fillna(value: Union["LiteralType", Dict[str, "LiteralType"]], subset: Optional[Union[str, Tuple[str, ...], List[str]]] = None)
Parameters
| Parameter | Type | Description |
|---|---|---|
value |
int, float, string, bool or dict | the value to replace null values with. If the value is a dict, then subset is ignored and value must be a mapping from column name (string) to replacement value. The replacement value must be an int, float, boolean, or string. |
subset |
str, tuple or list, optional | optional list of column names to consider. Columns specified in subset that do not have matching data types are ignored. |
Returns
DataFrame: DataFrame with replaced null values.
Examples
df = spark.createDataFrame([
(10, 80.5, "Alice", None),
(5, None, "Bob", None),
(None, None, "Tom", None),
(None, None, None, True)],
schema=["age", "height", "name", "bool"])
df.na.fill(50).show()
# +---+------+-----+----+
# |age|height| name|bool|
# +---+------+-----+----+
# | 10| 80.5|Alice|NULL|
# | 5| 50.0| Bob|NULL|
# | 50| 50.0| Tom|NULL|
# | 50| 50.0| NULL|true|
# +---+------+-----+----+
df.na.fill(False).show()
# +----+------+-----+-----+
# | age|height| name| bool|
# +----+------+-----+-----+
# | 10| 80.5|Alice|false|
# | 5| NULL| Bob|false|
# |NULL| NULL| Tom|false|
# |NULL| NULL| NULL| true|
# +----+------+-----+-----+
df.na.fill({'age': 50, 'name': 'unknown'}).show()
# +---+------+-------+----+
# |age|height| name|bool|
# +---+------+-------+----+
# | 10| 80.5| Alice|NULL|
# | 5| NULL| Bob|NULL|
# | 50| NULL| Tom|NULL|
# | 50| NULL|unknown|true|
# +---+------+-------+----+