pyspark convert row to json with nulls - json

Question

Goal:
For a dataframe with schema
id:string
Cold:string
Medium:string
Hot:string
IsNull:string
annual_sales_c:string
average_check_c:string
credit_rating_c:string
cuisine_c:string
dayparts_c:string
location_name_c:string
market_category_c:string
market_segment_list_c:string
menu_items_c:string
msa_name_c:string
name:string
number_of_employees_c:string
number_of_rooms_c:string
Months In Role:integer
Tenured Status:string
IsCustomer:integer
units_c:string
years_in_business_c:string
medium_interactions_c:string
hot_interactions_c:string
cold_interactions_c:string
is_null_interactions_c:string
I want to add a new column that is a JSON string of all keys and values for the columns. I have used the approach in this post PySpark - Convert to JSON row by row and related questions.
My code
df = df.withColumn("JSON",func.to_json(func.struct([df[x] for x in small_df.columns])))
I am having one issue:
Issue:
When any row has a null value for a column (and my data has many...) the Json string doesn't contain the key. I.e. if only 9 out of the 27 columns have values then the JSON string only has 9 keys... What I would like to do is maintain all keys but for the null values just pass an empty string ""
Any tips?

Answer 1

You should be able to just modify the answer on the question you linked using pyspark.sql.functions.when.
Consider the following example DataFrame:
data = [
('one', 1, 10),
(None, 2, 20),
('three', None, 30),
(None, None, 40)
]
sdf = spark.createDataFrame(data, ["A", "B", "C"])
sdf.printSchema()
#root
# |-- A: string (nullable = true)
# |-- B: long (nullable = true)
# |-- C: long (nullable = true)
Use when to implement if-then-else logic. Use the column if it is not null. Otherwise return an empty string.
from pyspark.sql.functions import col, to_json, struct, when, lit
sdf = sdf.withColumn(
"JSON",
to_json(
struct(
[
when(
col(x).isNotNull(),
col(x)
).otherwise(lit("")).alias(x)
for x in sdf.columns
]
)
)
)
sdf.show()
#+-----+----+---+-----------------------------+
#|A |B |C |JSON |
#+-----+----+---+-----------------------------+
#|one |1 |10 |{"A":"one","B":"1","C":"10"} |
#|null |2 |20 |{"A":"","B":"2","C":"20"} |
#|three|null|30 |{"A":"three","B":"","C":"30"}|
#|null |null|40 |{"A":"","B":"","C":"40"} |
#+-----+----+---+-----------------------------+
Another option is to use pyspark.sql.functions.coalesce instead of when:
from pyspark.sql.functions import coalesce
sdf.withColumn(
"JSON",
to_json(
struct(
[coalesce(col(x), lit("")).alias(x) for x in sdf.columns]
)
)
).show(truncate=False)
## Same as above