How do I specify a default value when the value is “null” in a spark dataframe?

I have a data frame like the picture below.

In the case of “null” among the values of the “item_param” column, I want to replace the string’test’. How can I do it?

df = sv_df.withColumn("srv_name", col('col.srv_name'))
      .withColumn("srv_serial", col('col.srv_serial'))
      .withColumn("col2",explode('col.groups'))
      .withColumn("groups_id", col('col2.group_id'))
      .withColumn("col3", explode('col2.items'))
      .withColumn("item_id", col('col3.item_id'))
      .withColumn("item_param", from_json(col("col3.item_param"), MapType(StringType(), StringType())) ) 
      .withColumn("item_param", map_values(col("item_param"))[0])
      .withColumn("item_time", col('col3.item_time'))
      .withColumn("item_time", from_unixtime( col('col3.item_time')/10000000 - 11644473600))
      .withColumn("item_value",col('col3.item_value'))
      .drop("servers","col","col2", "col3")
df.show(truncate=False)
df.printSchema()

Answer

Use coalesce:

.withColumn("item_param", coalesce(col("item_param"), lit("someDefaultValue"))

It will apply the first column/expression which is not null