>>> from pyspark.sql import functions as F
>>> from pyspark.sql import Window
>>> w = Window.partitionBy('ID').orderBy('Date')

//Input data frame
>>> df.show()
+---+----+-----+----+
| ID|Date|Place|City|
+---+----+-----+----+
|  1|2017|   UK|Birm|
|  2|2014|   US|  LA|
|  1|2018|  SIN| Sin|
|  1|2019|  MAL|  KL|
|  2|2015|   US|  SF|
|  3|2019|   UK| Lon|
+---+----+-----+----+

>>> df2 = df.withColumn("Place",F.collect_list("Place").over(w)).withColumn("City",F.collect_list("City").over(w)).groupBy("ID").agg(F.max("Place").alias("Place"), F.max("City").alias("City"))

 //Data value in List
>>> df2.show()
+---+--------------+---------------+
| ID|         Place|           City|
+---+--------------+---------------+
|  3|          [UK]|          [Lon]|
|  1|[UK, SIN, MAL]|[Birm, Sin, KL]|
|  2|      [US, US]|       [LA, SF]|
+---+--------------+---------------+

//If you want value in String 
>>> df2.withColumn("Place", F.concat_ws(" ", "Place")).withColumn("City", F.concat_ws(" ", "City")).show()
+---+----------+-----------+
| ID|     Place|       City|
+---+----------+-----------+
|  3|        UK|        Lon|
|  1|UK SIN MAL|Birm Sin KL|
|  2|     US US|      LA SF|
+---+----------+-----------+