PySpark Drop Rows

A straightforward way to achieve this in PySpark (Python API), assuming you are using Python 3:

noHeaderRDD = rawRDD.zipWithIndex().filter(lambda row_index: row_index[1] > 0).keys()

I did some profiling with various solutions and have the following

Cluster Configuration

Clusters

• Cluster 1 : 4 Cores 16 GB
• Cluster 2 : 4 Cores 16 GB
• Cluster 3 : 4 Cores 16 GB
• Cluster 4 : 2 Cores 8 GB

Data

7 million rows, 4 columns

#Solution 1
# Time Taken : 40 ms
data=sc.TextFile('file1.txt')
firstRow=data.first()
data=data.filter(lambda row:row != firstRow)

#Solution 2
# Time Taken : 3 seconds
data=sc.TextFile('file1.txt')
def dropFirstRow(index,iterator):
     return iter(list(iterator)[1:]) if index==0 else iterator
data=data.mapPartitionsWithIndex(dropFirstRow)

#Solution 3
# Time Taken : 0.3 seconds
data=sc.TextFile('file1.txt')
def dropFirstRow(index,iterator):
     if(index==0):
          for subIndex,item in enumerate(iterator):
               if subIndex > 0:
                    yield item
     else:
          yield iterator

data=data.mapPartitionsWithIndex(dropFirstRow)

I think that Solution 3 is the most scalable

Specific to PySpark:

As per @maasg, you could do this:

header = rdd.first()
rdd.filter(lambda line: line != header)

but it's not technically correct, as it's possible you exclude lines containing data as well as the header. However, this seems to work for me:

def remove_header(itr_index, itr):
    return iter(list(itr)[1:]) if itr_index == 0 else itr
rdd.mapPartitionsWithIndex(remove_header)

Similarly:

rdd.zipWithIndex().filter(lambda tup: tup[1] > 0).map(lambda tup: tup[0])

I'm new to Spark, so can't intelligently comment about which will be fastest.

AFAIK there's no 'easy' way to do this.

This should do the trick, though:

val header = data.first
val rows = data.filter(line => line != header)