Pyspark groupBy DataFrame without aggregation or count

Can it iterate through the Pyspark groupBy dataframe without aggregation or count?

For example code in Pandas:

for i, d in df2:
      mycode ....

^^ if using pandas ^^
Is there a difference in how to iterate groupby in Pyspark or have to use aggregation and count?

Answer

At best you can use .first , .last to get respective values from the groupBy but not all in the way you can get in pandas.

ex:

from pyspark.sql import functions as f
df.groupBy(df['some_col']).agg(f.first(df['col1']), f.first(df['col2'])).show()

Since their is a basic difference between the way the data is handled in pandas and spark not all functionalities can be used in the same way.

Their are a few work arounds to get what you want like:

for diamonds DataFrame:

+---+-----+---------+-----+-------+-----+-----+-----+----+----+----+
|_c0|carat|      cut|color|clarity|depth|table|price|   x|   y|   z|
+---+-----+---------+-----+-------+-----+-----+-----+----+----+----+
|  1| 0.23|    Ideal|    E|    SI2| 61.5| 55.0|  326|3.95|3.98|2.43|
|  2| 0.21|  Premium|    E|    SI1| 59.8| 61.0|  326|3.89|3.84|2.31|
|  3| 0.23|     Good|    E|    VS1| 56.9| 65.0|  327|4.05|4.07|2.31|
|  4| 0.29|  Premium|    I|    VS2| 62.4| 58.0|  334| 4.2|4.23|2.63|
|  5| 0.31|     Good|    J|    SI2| 63.3| 58.0|  335|4.34|4.35|2.75|
+---+-----+---------+-----+-------+-----+-----+-----+----+----+----+

You can use:

l=[x.cut for x in diamonds.select("cut").distinct().rdd.collect()]
def groups(df,i):
  import pyspark.sql.functions as f
  return df.filter(f.col("cut")==i)

#for multi grouping
def groups_multi(df,i):
  import pyspark.sql.functions as f
  return df.filter((f.col("cut")==i) & (f.col("color")=='E'))# use | for or.

for i in l:
  groups(diamonds,i).show(2)

output :

+---+-----+-------+-----+-------+-----+-----+-----+----+----+----+
|_c0|carat|    cut|color|clarity|depth|table|price|   x|   y|   z|
+---+-----+-------+-----+-------+-----+-----+-----+----+----+----+
|  2| 0.21|Premium|    E|    SI1| 59.8| 61.0|  326|3.89|3.84|2.31|
|  4| 0.29|Premium|    I|    VS2| 62.4| 58.0|  334| 4.2|4.23|2.63|
+---+-----+-------+-----+-------+-----+-----+-----+----+----+----+
only showing top 2 rows

+---+-----+-----+-----+-------+-----+-----+-----+----+----+----+
|_c0|carat|  cut|color|clarity|depth|table|price|   x|   y|   z|
+---+-----+-----+-----+-------+-----+-----+-----+----+----+----+
|  1| 0.23|Ideal|    E|    SI2| 61.5| 55.0|  326|3.95|3.98|2.43|
| 12| 0.23|Ideal|    J|    VS1| 62.8| 56.0|  340|3.93| 3.9|2.46|
+---+-----+-----+-----+-------+-----+-----+-----+----+----+----+

...

In Function groups you can decide what kind of grouping you want for the data. It is a simple filter condition but it will get you all the groups separately.