How To Set Pyspark Dataframe Headers To Another Row?

I have a dataframe that looks like this: # +----+------+---------+ # |col1| col2 | col3 | # +----+------+---------+ # | id| name | val | # | 1 | a01 | X | # | 2 |

Solution 1:

Assuming that there is only one row with id in col1, name in col2 and val in col3, you can use the following logic (commented for clarity and explanation)

#select the row with the header name 
header = df.filter((df['col1'] == 'id') & (df['col2'] == 'name') & (df['col3'] == 'val'))

#selecting the rest of the rows except the first one 
restDF = df.subtract(header)

#converting the header row into Row 
headerColumn = header.first()

#looping columns for renaming 
for column in restDF.columns:
    restDF = restDF.withColumnRenamed(column, headerColumn[column])

restDF.show(truncate=False)

this should give you

+---+----+---+
|id |name|val|
+---+----+---+
|1  |a01 |X  |
|2  |a02 |Y  |
+---+----+---+

But the best option would be read it with header option set to true while reading the dataframe using sqlContext from source

Solution 2:

Did you try this? header=True

from pyspark.sql import SparkSession
spark = SparkSession \
    .builder \
    .appName("Python Spark SQL basic example") \
    .getOrCreate()
df = spark.read.csv("TSCAINV_062020.csv",header=True)

Pyspark sets the column names as _c0, _c1, _c2 if the header is not set to True and it pushes the column down by one row.