Dropping nested column of Dataframe with PySpark - dataframe

Question

I'm trying to drop some nested columns from structs in a Spark dataframe using PySpark.
I found this for Scala that seems to be doing exactly what I want to, but I'm not familiar with Scala and don't know how to write it in Python.
https://stackoverflow.com/a/39943812/5706548

Answer 1

Example for pyspark:
def drop_col(df, struct_nm, delete_struct_child_col_nm):
fields_to_keep = filter(lambda x: x != delete_struct_child_col_nm, df.select("{}.*".format(struct_nm)).columns)
fields_to_keep = list(map(lambda x: "{}.{}".format(struct_nm, x), fields_to_keep))
return df.withColumn(struct_nm, struct(fields_to_keep))

Answer 2

A method that I found using pyspark is by first converting the nested column into json and then parse the converted json with a new nested schema with the unwanted columns filtered out.
Suppose I have the following schema and I want to drop d, e and j (a.b.d, a.e, a.h.j) from the dataframe:
root
|-- a: struct (nullable = true)
| |-- b: struct (nullable = true)
| | |-- c: long (nullable = true)
| | |-- d: string (nullable = true)
| |-- e: struct (nullable = true)
| | |-- f: long (nullable = true)
| | |-- g: string (nullable = true)
| |-- h: array (nullable = true)
| | |-- element: struct (containsNull = true)
| | | |-- i: string (nullable = true)
| | | |-- j: string (nullable = true)
|-- k: string (nullable = true)
I used the following approach:
Create new schema for a by excluding d, e and j. A quick way to do this is by manually select the fields that you want from df.select("a").schema and create a new schema from the selected fields using StructType. Or, you can do this programmatically by traversing the schema tree and exclude the unwanted fields, something like:
def exclude_nested_field(schema, unwanted_fields, parent=""):
new_schema = []
for field in schema:
full_field_name = field.name
if parent:
full_field_name = parent + "." + full_field_name
if full_field_name not in unwanted_fields:
if isinstance(field.dataType, StructType):
inner_schema = exclude_nested_field(field.dataType, unwanted_fields, full_field_name)
new_schema.append(StructField(field.name, inner_schema))
elif isinstance(field.dataType, ArrayType):
new_schema.append(StructField(field.name, ArrayType(field.dataType.elementType)))
else:
new_schema.append(StructField(field.name, field.dataType))
return StructType(new_schema)
new_schema = exclude_nested_field(df.schema["a"].dataType, ["b.d", "e", "h.j"])
Convert a column to json: .withColumn("json", F.to_json("a")).drop("a")
Parse the json-converted a column from step 2 with the new schema found in step 1: .withColumn("a", F.from_json("json", new_schema)).drop("json")

Answer 3

We can now do it natively with Spark version >= 3.1
https://spark.apache.org/docs/3.1.1/api/python/reference/api/pyspark.sql.Column.dropFields.html

Answer 4

Althoug I've no solution for PySpark, maybe it's easier to translate this into python. Consider a dataframe df with schema:
root
|-- employee: struct (nullable = false)
| |-- name: string (nullable = false)
| |-- age: integer (nullable = false)
Then if you want e.g. to drop name,
you can do:
val fieldsToKeep = df.select($"employee.*").columns
.filter(_!="name") // the nested column you want to drop
.map(n => "employee."+n)
// overwite column with subset of fields
df
.withColumn("employee",struct(fieldsToKeep.head,fieldsToKeep.tail:_*))

Answer 5

Having the below dataframe, the aim is to drop d, e and j.
from pyspark.sql import functions as F
df = spark.createDataFrame([], "a struct<b:struct<c:bigint,d:string>,e:struct<f:bigint,g:string>,h:array<struct<i:string,j:string>>>, k string")
df.printSchema()
# root
# |-- a: struct (nullable = true)
# | |-- b: struct (nullable = true)
# | | |-- c: long (nullable = true)
# | | |-- d: string (nullable = true) # <<--- to be dropped
# | |-- e: struct (nullable = true) # <<--- to be dropped
# | | |-- f: long (nullable = true)
# | | |-- g: string (nullable = true)
# | |-- h: array (nullable = true)
# | | |-- element: struct (containsNull = true)
# | | | |-- i: string (nullable = true)
# | | | |-- j: string (nullable = true) # <<--- to be dropped
# |-- k: string (nullable = true)
e is the easiest:
df = df.withColumn("a", F.col("a").dropFields("e"))
df.printSchema()
# root
# |-- a: struct (nullable = true)
# | |-- b: struct (nullable = true)
# | | |-- c: long (nullable = true)
# | | |-- d: string (nullable = true)
# | |-- h: array (nullable = true)
# | | |-- element: struct (containsNull = true)
# | | | |-- i: string (nullable = true)
# | | | |-- j: string (nullable = true)
# |-- k: string (nullable = true)
In order to drop d, we must go inside b:
df = df.withColumn("a", F.col("a").withField("b", F.col("a.b").dropFields("d")))
df.printSchema()
# root
# |-- a: struct (nullable = true)
# | |-- b: struct (nullable = true)
# | | |-- c: long (nullable = true)
# | |-- h: array (nullable = true)
# | | |-- element: struct (containsNull = true)
# | | | |-- i: string (nullable = true)
# | | | |-- j: string (nullable = true)
# |-- k: string (nullable = true)
j is inside array, so transform must also be used. It "loops" through every array's elements (in this case, the element is a struct) and transforms it (removes a field).
df = df.withColumn("a", F.col("a").withField(
"h",
F.transform(
F.col("a.h"),
lambda x: x.dropFields("j")
)
))
df.printSchema()
# root
# |-- a: struct (nullable = true)
# | |-- b: struct (nullable = true)
# | | |-- c: long (nullable = true)
# | |-- h: array (nullable = true)
# | | |-- element: struct (containsNull = true)
# | | | |-- i: string (nullable = true)
# |-- k: string (nullable = true)

Answer 6

Pyspark version of Raphaels Scala answer.
This runs at a certain depth, discards everything above that depth and filters on the row below it.
def remove_columns(df,root):
from pyspark.sql.functions import col
cols = df.select(root).columns
fields_filter = filter(lambda x: x[0]!= "$", cols) # use your own lambda here.
fieldsToKeep = list(map(lambda x: root[:-1] + x, fields_filter))
return df.select(fieldsToKeep)
df = remove_columns(raw_df, root="level1.level2.*")