Dropping nested column of Dataframe with PySpark

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:

1. 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"])
   

2. Convert a column to json: .withColumn("json", F.to_json("a")).drop("a")

3. 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

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:_*))