13

To pass schema to a json file we do this: 

from pyspark.sql.types import (StructField, StringType, StructType, IntegerType)
data_schema = [StructField('age', IntegerType(), True), StructField('name', StringType(), True)]
final_struc = StructType(fields = data_schema)
df =spark.read.json('people.json', schema=final_struc)

The above code works as expected. However now, I have data in table which I display by: 

df = sqlContext.sql("SELECT * FROM people_json")

But if I try to pass a new schema to it by using following command it does not work. 

df2 = spark.sql("SELECT * FROM people_json", schema=final_struc)

It gives the following error:

sql() got an unexpected keyword argument 'schema'

NOTE: I am using Databrics Community Edition

  • What am I missing?
  • How do I pass the new schema if I have data in the table instead of some JSON file?
koiralo
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BlackBeard
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  • doesn't sql() takes only one parameter as the string? – koiralo Feb 12 '18 at 05:28
  • @ShankarKoirala Yes. That is the issue I'm trying to figure a way out of. My question is how do I pass the new schema if I have data in the table instead of some `json file`? – BlackBeard Feb 12 '18 at 05:31

2 Answers2

18

You cannot apply a new schema to already created dataframe. However, you can change the schema of each column by casting to another datatype as below.

df.withColumn("column_name", $"column_name".cast("new_datatype"))

If you need to apply a new schema, you need to convert to RDD and create a new dataframe again as below 

df = sqlContext.sql("SELECT * FROM people_json")
val newDF = spark.createDataFrame(df.rdd, schema=schema)

Hope this helps!

koiralo
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  • Thank you for your answer. But, doing `newDF = spark.createDataFrame(df.rdd, data_schema)` gives `AttributeError: 'StructField' object has no attribute 'encode'` **Error** – BlackBeard Feb 12 '18 at 05:58
  • I passed `final_struc` instead of `data_schema` in `spark.createDataFrame(df.rdd, schema=final_struc)` – BlackBeard Feb 12 '18 at 06:33
1

There is already one answer available but still I want to add something.

  1. Create DF from RDD

  • using toDF

    newDf = rdd.toDF(schema, column_name_list)

  • using createDataFrame

    newDF = spark.createDataFrame(rdd ,schema, [list_of_column_name])

  1. Create DF from other DF

suppose I have DataFrame with columns|data type - name|string, marks|string, gender|string.

if I want to get only marks as integer.

newDF = oldDF.select("marks")
newDF_with_int = newDF.withColumn("marks", df['marks'].cast('Integer'))

This will convert marks to integer.

bhargav3vedi
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