CODEWITHSUNDEEP
scalaapache-sparkdataframeapache-spark-sql
RudyVerboven
RudyVerboven

Reputation: 1274

Copy schema from one dataframe to another dataframe

I'm trying to change the schema of an existing dataframe to the schema of another dataframe.

DataFrame 1:

Column A | Column B | Column C | Column D
   "a"   |    1     |   2.0    |   300
   "b"   |    2     |   3.0    |   400
   "c"   |    3     |   4.0    |   500

DataFrame 2:

Column K | Column B | Column F
   "c"   |    4     |   5.0
   "b"   |    5     |   6.0
   "f"   |    6     |   7.0

So I want to apply the schema of the first dataframe on the second. So all the columns which are the same remain. The columns in dataframe 2 that are not in 1 get deleted. The others become "NULL".

Output

Column A | Column B | Column C | Column D
 "NULL"  |    4     |   "NULL" |  "NULL"
 "NULL"  |    5     |   "NULL" |  "NULL"
 "NULL"  |    6     |   "NULL" |  "NULL"

So I came with a possible solution:

val schema = df1.schema
val newRows: RDD[Row] = df2.map(row => {
  val values = row.schema.fields.map(s => {
    if(schema.fields.contains(s)){
      row.getAs(s.name).toString
    }else{
      "NULL"
    }
  })
  Row.fromSeq(values)
})
sqlContext.createDataFrame(newRows, schema)}

Now as you can see this will not work because the schema contains String, Int and Double. And all my rows have String values.

This is where I'm stuck, is there a way to automatically convert the type of my values to the schema?

Upvotes: 7

Views: 27519

Answers (4)

sidmore
sidmore

Reputation: 21

Below are simple PYSPARK steps to achieve same:

df = <dataframe whose schema needs to be copied>
df_tmp = <dataframe with result with fewer fields> 
#Note: field names from df_tmp must match with field names from df

df_tmp_cols = [colmn.lower() for colmn in df_tmp.columns]
for col_dtls in df.dtypes:
  col_name, dtype = col_dtls
  if col_name.lower() in df_tmp_cols:
    df_tmp = df_tmp.withColumn(col_name,f.col(col_name).cast(dtype))
  else:
    df_tmp = df_tmp.withColumn(col_name,f.lit(None).cast(dtype)) 
df_fin = df_tmp.select(df.columns) #Final dataframe

Upvotes: 2

Antonio Cachuan
Antonio Cachuan

Reputation: 485

Working in 2018 (Spark 2.3) reading a .sas7bdat

Scala

val sasFile = "file.sas7bdat"
val dfSas = spark.sqlContext.sasFile(sasFile)
val myManualSchema = dfSas.schema //getting the schema from another dataframe
val df = spark.read.format("csv").option("header","true").schema(myManualSchema).load(csvFile)

PD: spark.sqlContext.sasFile use saurfang library, you could skip that part of code and get the schema from another dataframe.

Upvotes: 1

zero323
zero323

Reputation: 330063

If schema is flat I would use simply map over per-existing schema and select required columns:

val exprs = df1.schema.fields.map { f => 
  if (df2.schema.fields.contains(f)) col(f.name)
  else lit(null).cast(f.dataType).alias(f.name) 
}

df2.select(exprs: _*).printSchema

// root
//  |-- A: string (nullable = true)
//  |-- B: integer (nullable = false)
//  |-- C: double (nullable = true)
//  |-- D: integer (nullable = true)

Upvotes: 12

charles gomes
charles gomes

Reputation: 2155

You could simply do Left Join on your dataframes with query like this:-

SELECT Column A, Column B, Column C, Column D FROM foo LEFT JOIN BAR ON Column C = Column C

Please checkout the answer by @zero323 in this post:-

Spark specify multiple column conditions for dataframe join

Thanks, Charles.

Upvotes: 0

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