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I'd like to enumerate grouped values just like with Pandas:
Enumerate each row for each group in a DataFrame
What is a way in Spark/Python?
493
In Spark/PySpark, you can use show() action to get the top/first N (5,10,100 ..) rows of the DataFrame and display them on a console or a log, there are also several Spark Actions like take() , tail() , collect() , head() , first() that return top and last n rows as a list of Rows (Array[Row] for Scala).
When we perform groupBy() on PySpark Dataframe, it returns GroupedData object which contains below aggregate functions. count() – Use groupBy() count() to return the number of rows for each group. mean() – Returns the mean of values for each group. max() – Returns the maximum of values for each group.
With row_number window function:
from pyspark.sql.functions import row_number
from pyspark.sql import Window
w = Window.partitionBy("some_column").orderBy("some_other_column")
df.withColumn("rn", row_number().over(w))
You can achieve this on rdd level by doing:
rdd = sc.parallelize(['a', 'b', 'c'])
df = spark.createDataFrame(rdd.zipWithIndex())
df.show()
It will result:
+---+---+
| _1| _2|
+---+---+
| a| 0|
| b| 1|
| c| 2|
+---+---+
If you only need unique ID, not real continuous indexing, you may also use
zipWithUniqueId() which is more efficient, since done locally on each partition.
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