输入 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的melt会传回下列内容：

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

print(df_pivot_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

在PySpark中，我会这样做：

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|
# +----+------------------+------+