Spark dataframe transform multiple rows to column

Using zero323's dataframe,

df = sqlContext.createDataFrame([
("a", 1, "m1"), ("a", 1, "m2"), ("a", 2, "m3"),
("a", 3, "m4"), ("b", 4, "m1"), ("b", 1, "m2"),
("b", 2, "m3"), ("c", 3, "m1"), ("c", 4, "m3"),
("c", 5, "m4"), ("d", 6, "m1"), ("d", 1, "m2"),
("d", 2, "m3"), ("d", 3, "m4"), ("d", 4, "m5"),
("e", 4, "m1"), ("e", 5, "m2"), ("e", 1, "m3"),
("e", 1, "m4"), ("e", 1, "m5")], 
("a", "cnt", "major"))

you could also use

reshaped_df = df.groupby('a').pivot('major').max('cnt').fillna(0)

Lets start with example data:

df = sqlContext.createDataFrame([
    ("a", 1, "m1"), ("a", 1, "m2"), ("a", 2, "m3"),
    ("a", 3, "m4"), ("b", 4, "m1"), ("b", 1, "m2"),
    ("b", 2, "m3"), ("c", 3, "m1"), ("c", 4, "m3"),
    ("c", 5, "m4"), ("d", 6, "m1"), ("d", 1, "m2"),
    ("d", 2, "m3"), ("d", 3, "m4"), ("d", 4, "m5"),
    ("e", 4, "m1"), ("e", 5, "m2"), ("e", 1, "m3"),
    ("e", 1, "m4"), ("e", 1, "m5")], 
    ("a", "cnt", "major"))

Please note that I've changed count to cnt. Count is a reserved keyword in most of the SQL dialects and it is not a good choice for a column name.

There are at least two ways to reshape this data:

  • aggregating over DataFrame

    from pyspark.sql.functions import col, when, max
    
    majors = sorted(df.select("major")
        .distinct()
        .map(lambda row: row[0])
        .collect())
    
    cols = [when(col("major") == m, col("cnt")).otherwise(None).alias(m) 
        for m in  majors]
    maxs = [max(col(m)).alias(m) for m in majors]
    
    reshaped1 = (df
        .select(col("a"), *cols)
        .groupBy("a")
        .agg(*maxs)
        .na.fill(0))
    
    reshaped1.show()
    
    ## +---+---+---+---+---+---+
    ## |  a| m1| m2| m3| m4| m5|
    ## +---+---+---+---+---+---+
    ## |  a|  1|  1|  2|  3|  0|
    ## |  b|  4|  1|  2|  0|  0|
    ## |  c|  3|  0|  4|  5|  0|
    ## |  d|  6|  1|  2|  3|  4|
    ## |  e|  4|  5|  1|  1|  1|
    ## +---+---+---+---+---+---+
    
  • groupBy over RDD

    from pyspark.sql import Row
    
    grouped = (df
        .map(lambda row: (row.a, (row.major, row.cnt)))
        .groupByKey())
    
    def make_row(kv):
        k, vs = kv
        tmp = dict(list(vs) + [("a", k)])
        return Row(**{k: tmp.get(k, 0) for k in ["a"] + majors})
    
    reshaped2 = sqlContext.createDataFrame(grouped.map(make_row))
    
    reshaped2.show()
    
    ## +---+---+---+---+---+---+
    ## |  a| m1| m2| m3| m4| m5|
    ## +---+---+---+---+---+---+
    ## |  a|  1|  1|  2|  3|  0|
    ## |  e|  4|  5|  1|  1|  1|
    ## |  c|  3|  0|  4|  5|  0|
    ## |  b|  4|  1|  2|  0|  0|
    ## |  d|  6|  1|  2|  3|  4|
    ## +---+---+---+---+---+---+