Aggregate one column, but show all columns in select - apache-spark

Question

I try to show maximum value from column while I group rows by date column.
So i tried this code
maxVal = dfSelect.select('*')\
.groupBy('DATE')\
.agg(max('CLOSE'))
But output looks like that:
+----------+----------+
| DATE|max(CLOSE)|
+----------+----------+
|1987-05-08| 43.51|
|1987-05-29| 39.061|
+----------+----------+
I wanna have output like below
+------+---+----------+------+------+------+------+------+---+----------+
|TICKER|PER| DATE| TIME| OPEN| HIGH| LOW| CLOSE|VOL|max(CLOSE)|
+------+---+----------+------+------+------+------+------+---+----------+
| CDG| D|1987-01-02|000000|50.666|51.441|49.896|50.666| 0| 50.666|
| ABC| D|1987-01-05|000000|51.441| 52.02|51.441|51.441| 0| 51.441|
+------+---+----------+------+------+------+------+------+---+----------+
So my question is how to change the code to have output with all columns and aggregated 'CLOSE' column?
Scheme of my data looks like below:
root
|-- TICKER: string (nullable = true)
|-- PER: string (nullable = true)
|-- DATE: date (nullable = true)
|-- TIME: string (nullable = true)
|-- OPEN: float (nullable = true)
|-- HIGH: float (nullable = true)
|-- LOW: float (nullable = true)
|-- CLOSE: float (nullable = true)
|-- VOL: integer (nullable = true)
|-- OPENINT: string (nullable = true)

Answer 1

If you want the same aggregation all your columns in the original dataframe, then you can do something like,
import pyspark.sql.functions as F
expr = [F.max(coln).alias(coln) for coln in df.columns if 'date' not in coln] # df is your datafram
df_res = df.groupby('date').agg(*expr)
If you want multiple aggregations, then you can do like,
sub_col1 = # define
sub_col2=# define
expr1 = [F.max(coln).alias(coln) for coln in sub_col1 if 'date' not in coln]
expr2 = [F.first(coln).alias(coln) for coln in sub_col2 if 'date' not in coln]
expr=expr1+expr2
df_res = df.groupby('date').agg(*expr)
If you want only one of the columns aggregated and added to your original dataframe, then you can do a selfjoin after aggregating
df_agg = df.groupby('date').agg(F.max('close').alias('close_agg')).withColumn("dummy",F.lit("dummmy")) # dummy column is needed as a workaround in spark issues of self join
df_join = df.join(df_agg,on='date',how='left')
or you can use a windowing function
from pyspark.sql import Window
w= Window.partitionBy('date')
df_res = df.withColumn("max_close",F.max('close').over(w))