Write a pyspark.sql.dataframe.DataFrame without losing information

Question

I am trying to save an pyspark.sql.dataframe.DataFrame in CSV format (could also be another format, as long as it is easily readable).

So far, I found a couple of examples to save the DataFrame. However, it is losing information everytime that I write it.

Dataset example:

# Create an example Pyspark DataFrame

from pyspark.sql import Row

Employee = Row("firstName", "lastName", "email", "salary")
employee1 = Employee('A', 'AA', 'mail1', 100000)
employee2 = Employee('B', 'BB', 'mail2', 120000 )
employee3 = Employee('C', None, 'mail3', 140000 )
employee4 = Employee('D', 'DD', 'mail4', 160000 )
employee5 = Employee('E', 'EE', 'mail5', 160000 )

department1 = Row(id='123', name='HR')
department2 = Row(id='456', name='OPS')
department3 = Row(id='789', name='FN')
department4 = Row(id='101112', name='DEV')

departmentWithEmployees1 = Row(department=department1, employees=[employee1, employee2, employee5])
departmentWithEmployees2 = Row(department=department2, employees=[employee3, employee4])
departmentWithEmployees3 = Row(department=department3, employees=[employee1, employee4, employee3])
departmentWithEmployees4 = Row(department=department4, employees=[employee2, employee3])

departmentsWithEmployees_Seq = [departmentWithEmployees1, departmentWithEmployees2]
dframe = spark.createDataFrame(departmentsWithEmployees_Seq)

In order to save this file as CSV, I firstly tried this solution:

type(dframe)
Out[]: pyspark.sql.dataframe.DataFrame
dframe.write.csv('junk_mycsv.csv')

Unfortunately, that result in this error:

org.apache.spark.sql.AnalysisException: CSV data source does not support struct<id:string,name:string> data type.; 

That is the reason why I tried another possibility, to convert the spark dataframe into a pandas dataframe, and save it then. As mentioned in this example.

pandas_df = dframe.toPandas()

Works good! However, If I show my data, it is missing data:

print(pandas_df.head())

department                                          employees
0   (123, HR)  [(A, AA, mail1, 100000), (B, BB, mail2, 120000...
1  (456, OPS)  [(C, None, mail3, 140000), (D, DD, mail4, 1600...

As you can see in the snapshot below, we are missing information. Because the data should be like this:

department              employees
0  id:123, name:HR      firstName: A, lastName: AA, email: mail1, salary: 100000

# Info is missing like 'id', 'name', 'firstName', 'lastName', 'email' etc. 
# For the complete expected example, see screenshow below. 

Just for information: I am working in Databricks, with Python.

Therefore, how can I write my data (dframe from the example above) without losing information?

Many thanks in advance!

Edit Adding a picture for Pault, to show the format of the csv (and the headers).

Edit2 Replacing the picture for example csv output:

After running Pault's code:

from pyspark.sql.functions import to_json
dframe.select(*[to_json(c).alias(c) for c in dframe.columns])\
    .repartition(1).write.csv("junk_mycsv.csv", header= True)

The output is not tidy, since most column headers are empty (due the nested format?). Only copying the first row:

department           employees              (empty ColName)     (empty ColName)   (and so on)
{\id\":\"123\"       \"name\":\"HR\"}"     [{\firstName\":\"A\"  \"lastName\":\"AA\"    (...)

Answer 1

Your dataframe has the following schema:

dframe.printSchema()
#root
# |-- department: struct (nullable = true)
# |    |-- id: string (nullable = true)
# |    |-- name: string (nullable = true)
# |-- employees: array (nullable = true)
# |    |-- element: struct (containsNull = true)
# |    |    |-- firstName: string (nullable = true)
# |    |    |-- lastName: string (nullable = true)
# |    |    |-- email: string (nullable = true)
# |    |    |-- salary: long (nullable = true)

So the department column is a StructType with two named fields and the employees column is an array of structs with four named fields. It appears what you want is to write the data in a format that saves both the key and the value for each record.

One option is to write the file in JSON format instead of CSV:

dframe.write.json("junk.json")

Which produces the following output:

{"department":{"id":"123","name":"HR"},"employees":[{"firstName":"A","lastName":"AA","email":"mail1","salary":100000},{"firstName":"B","lastName":"BB","email":"mail2","salary":120000},{"firstName":"E","lastName":"EE","email":"mail5","salary":160000}]}
{"department":{"id":"456","name":"OPS"},"employees":[{"firstName":"C","email":"mail3","salary":140000},{"firstName":"D","lastName":"DD","email":"mail4","salary":160000}]}

Or if you wanted to keep it in CSV format, you can use to_json to convert each column to JSON before writing the CSV.

# looping over all columns
# but you can also just limit this to the columns you want to convert

from pyspark.sql.functions import to_json
dframe.select(*[to_json(c).alias(c) for c in dframe.columns])\
    .write.csv("junk_mycsv.csv")

This produces the following output:

"{\"id\":\"123\",\"name\":\"HR\"}","[{\"firstName\":\"A\",\"lastName\":\"AA\",\"email\":\"mail1\",\"salary\":100000},{\"firstName\":\"B\",\"lastName\":\"BB\",\"email\":\"mail2\",\"salary\":120000},{\"firstName\":\"E\",\"lastName\":\"EE\",\"email\":\"mail5\",\"salary\":160000}]"
"{\"id\":\"456\",\"name\":\"OPS\"}","[{\"firstName\":\"C\",\"email\":\"mail3\",\"salary\":140000},{\"firstName\":\"D\",\"lastName\":\"DD\",\"email\":\"mail4\",\"salary\":160000}]"

Note that the double-quotes are escaped.