RDD to DataFrame in pyspark (columns from rdd's first element)

Question

I have created a rdd from a csv file and the first row is the header line in that csv file. Now I want to create dataframe from that rdd and retain the column from 1st element of rdd. Problem is I am able to create the dataframe and with column from rdd.first(), but the created dataframe has its first row as the headers itself. How to remove that? ``` lines = sc.textFile('/path/data.csv') rdd = lines.map(lambda x: x.split('#####')) ###multiple char sep can be there #### or #@# , so can't directly read csv to a dataframe #rdd: [[u'mailid', u'age', u'address'], [u'satya', u'23', u'Mumbai'], [u'abc', u'27', u'Goa']] ###first element is the header df = rdd.toDF(rdd.first()) ###retaing te column from rdd.first() df.show() #mailid age address mailid age address ####I don't want this as dataframe data satya 23 Mumbai abc 27 Goa ``` How to avoid that first element moving to dataframe data. Can I give any option in rdd.toDF(rdd.first()) to get that done?? Note: I can't collect rdd to form list…

Accepted Answer

You will have to remove the header from your `RDD`. One way to do it is the following considering your `rdd` variable :

```
>>> header = rdd.first()
>>> header
# ['mailid', 'age', 'address']
>>> data = rdd.filter(lambda row : row != header).toDF(header)
>>> data.show()
# +------+---+-------+
# |mailid|age|address|
# +------+---+-------+
# | satya| 23| Mumbai|
# |   abc| 27|    Goa|
# +------+---+-------+ 
```

RDD to DataFrame in pyspark (columns from rdd's first element)

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