How to change dataframe column names in PySpark?

Question

I come from pandas background and am used to reading data from CSV files into a dataframe and then simply changing the column names to something useful using the simple command:

df.columns = new_column_name_list

However, the same doesn't work in PySpark dataframes created using sqlContext. The only solution I could figure out to do this easily is the following:

df = sqlContext.read.format("com.databricks.spark.csv").options(header='false', inferschema='true', delimiter='\t').load("data.txt")
oldSchema = df.schema
for i,k in enumerate(oldSchema.fields):
  k.name = new_column_name_list[i]
df = sqlContext.read.format("com.databricks.spark.csv").options(header='false', delimiter='\t').load("data.txt", schema=oldSchema)

This is basically defining the variable twice and inferring the schema first then renaming the column names and then loading the dataframe again with the updated schema.

Is there a better and more efficient way to do this like we do in pandas?

My Spark version is 1.5.0

Answer 1

There are many ways to do that:

• Option 1. Using selectExpr.

   data = sqlContext.createDataFrame([("Alberto", 2), ("Dakota", 2)], 
                                     ["Name", "askdaosdka"])
   data.show()
   data.printSchema()
  
   # Output
   #+-------+----------+
   #|   Name|askdaosdka|
   #+-------+----------+
   #|Alberto|         2|
   #| Dakota|         2|
   #+-------+----------+
  
   #root
   # |-- Name: string (nullable = true)
   # |-- askdaosdka: long (nullable = true)
  
   df = data.selectExpr("Name as name", "askdaosdka as age")
   df.show()
   df.printSchema()
  
   # Output
   #+-------+---+
   #|   name|age|
   #+-------+---+
   #|Alberto|  2|
   #| Dakota|  2|
   #+-------+---+
  
   #root
   # |-- name: string (nullable = true)
   # |-- age: long (nullable = true)
  

• Option 2. Using withColumnRenamed, notice that this method allows you to "overwrite" the same column. For Python3, replace xrange with range.

   from functools import reduce
  
   oldColumns = data.schema.names
   newColumns = ["name", "age"]
  
   df = reduce(lambda data, idx: data.withColumnRenamed(oldColumns[idx], newColumns[idx]), xrange(len(oldColumns)), data)
   df.printSchema()
   df.show()
  

• Option 3. using alias, in Scala you can also use as.

   from pyspark.sql.functions import col
  
   data = data.select(col("Name").alias("name"), col("askdaosdka").alias("age"))
   data.show()
  
   # Output
   #+-------+---+
   #|   name|age|
   #+-------+---+
   #|Alberto|  2|
   #| Dakota|  2|
   #+-------+---+
  

• Option 4. Using sqlContext.sql, which lets you use SQL queries on DataFrames registered as tables.

   sqlContext.registerDataFrameAsTable(data, "myTable")
   df2 = sqlContext.sql("SELECT Name AS name, askdaosdka as age from myTable")
  
   df2.show()
  
   # Output
   #+-------+---+
   #|   name|age|
   #+-------+---+
   #|Alberto|  2|
   #| Dakota|  2|
   #+-------+---+