CODEWITHSUNDEEP
dataframeapache-sparkpysparkunpivotmelt
ar_mm18
ar_mm18

Reputation: 465

Unpivot the data frame from wide to long in PySpark using melt

I am trying to perform melting operation on my data frame. I have tried the code below, but I am getting an error:

A DataFrame object does not have an attribute melt. Please check the spelling and/or the datatype of the object.

df_pivot_jp = JP_ch.melt(id_vars=['c_id'], var_name='views_on_character', value_name='answer')
df_pivot_gj = GJ_ch.melt(id_vars=['c_id'], var_name='views_on_character', value_name='answer')

Can someone please tell me what is this attribute that I am missing?

Upvotes: 1

Views: 1521

Answers (1)

ZygD
ZygD

Reputation: 24488

Input dataframe:

from pyspark.sql import functions as F
JP_ch = spark.createDataFrame(
    [('c1', 111, 1111),
     ('c2', 222, 2222),
     ('c3', 333, 3333)],
    ['c_id', 'col2', 'col3'])

Pandas' melt returns this:

JP_ch = JP_ch.toPandas()
df_melted_jp = JP_ch.melt(id_vars=['c_id'], var_name='views_on_character', value_name='answer')

print(df_melted_jp)
#   c_id views_on_character  answer
# 0   c1               col2     111
# 1   c2               col2     222
# 2   c3               col2     333
# 3   c1               col3    1111
# 4   c2               col3    2222
# 5   c3               col3    3333

Spark 3.4+

df = JP_ch.melt(['c_id'], ['col2', 'col3'], 'views_on_character', 'answer')

df.show()
# +----+------------------+------+
# |c_id|views_on_character|answer|
# +----+------------------+------+
# |  c1|              col2|   111|
# |  c1|              col3|  1111|
# |  c2|              col2|   222|
# |  c2|              col3|  2222|
# |  c3|              col2|   333|
# |  c3|              col3|  3333|
# +----+------------------+------+

or

to_melt = [c for c in JP_ch.columns if c not in {'c_id'}]
df = JP_ch.melt(['c_id'], to_melt, 'views_on_character', 'answer')

df.show()
# +----+------------------+------+
# |c_id|views_on_character|answer|
# +----+------------------+------+
# |  c1|              col2|   111|
# |  c1|              col3|  1111|
# |  c2|              col2|   222|
# |  c2|              col3|  2222|
# |  c3|              col2|   333|
# |  c3|              col3|  3333|
# +----+------------------+------+

Older Spark versions:

to_melt = {c for c in JP_ch.columns if c not in ['c_id']}
new_names = '(views_on_character, answer)'

melt_list = [f"\'{c}\', `{c}`" for c in to_melt]
df = JP_ch.select(
    *(set(JP_ch.columns) - to_melt),
    F.expr(f"stack({len(melt_list)}, {','.join(melt_list)}) {new_names}")
)
df.show()
# +----+------------------+------+
# |c_id|views_on_character|answer|
# +----+------------------+------+
# |  c1|              col3|  1111|
# |  c1|              col2|   111|
# |  c2|              col3|  2222|
# |  c2|              col2|   222|
# |  c3|              col3|  3333|
# |  c3|              col2|   333|
# +----+------------------+------+

Upvotes: 2

Related Questions