CODEWITHSUNDEEP
pythongroup-bypysparkapache-spark-sqlwindow
Sascha
Sascha

Reputation: 687

How to retrieve unique values in each window in pyspark dataframe

I have the following spark dataframe: 

from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('').getOrCreate()
df = spark.createDataFrame([(1, "a", "2"), (2, "b", "2"),(3, "c", "2"), (4, "d", "2"),
                (5, "b", "3"), (6, "b", "3"),(7, "c", "2")], ["nr", "column2", "quant"])

which returns me: 

+---+-------+------+
| nr|column2|quant |
+---+-------+------+
|  1|      a|     2|
|  2|      b|     2|
|  3|      c|     2|
|  4|      d|     2|
|  5|      b|     3|
|  6|      b|     3|
|  7|      c|     2|
+---+-------+------+

I would like to retrieve the rows where for each 3 groupped rows (from each window where window size is 3) quant column has unique values. as in the following pic:

enter image description here

Here red is window size and each window i keep only green rows where quant is unique:

The ouptput that i would like to get is as following:

+---+-------+------+
| nr|column2|values|
+---+-------+------+
|  1|      a|     2|
|  4|      d|     2|
|  5|      b|     3|
|  7|      c|     2|
+---+-------+------+

I am new in spark so, I would appreciate any help. Thanks

Upvotes: 0

Views: 1302

Answers (1)

Ranga Vure
Ranga Vure

Reputation: 1932

This approach should work for you, assuming grouping 3 records are based on 'nr' column.

Using udf, which decides whether a record should be selected or not and lag, is used to get prev rows data.

def tag_selected(index, current_quant, prev_quant1, prev_quant2):                                                                                                    
    if index % 3 == 1:  # first record in each group is always selected                                                                                              
        return True                                                                                                                                                  
    if index % 3 == 2 and current_quant != prev_quant1: # second record will be selected if prev quant is not same as current                                        
        return True                                                                                                                                                  
    if index % 3 == 0 and current_quant != prev_quant1 and current_quant != prev_quant2: # third record will be selected if prev quant are not same as current       
        return True                                                                                                                                                  
    return False                                                                                                                                                     

tag_selected_udf = udf(tag_selected, BooleanType())                                                                                                                  


df = spark.createDataFrame([(1, "a", "2"), (2, "b", "2"),(3, "c", "2"), (4, "d", "2"),
                (5, "b", "3"), (6, "b", "3"),(7, "c", "2")], ["nr", "column2", "quant"])

window = Window.orderBy("nr")

df = df.withColumn("prev_quant1", lag(col("quant"),1, None).over(window))\
       .withColumn("prev_quant2", lag(col("quant"),2, None).over(window)) \
       .withColumn("selected", 
                   tag_selected_udf(col('nr'),col('quant'),col('prev_quant1'),col('prev_quant2')))\
       .filter(col('selected') == True).drop("prev_quant1","prev_quant2","selected")
df.show()

which results

+---+-------+-----+
| nr|column2|quant|
+---+-------+-----+
|  1|      a|    2|
|  4|      d|    2|
|  5|      b|    3|
|  7|      c|    2|
+---+-------+-----+

Upvotes: 2

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