Shubhanshu Mishra Shubhanshu Mishra - 3 months ago 213
Python Question

How to change dataframe column names in pyspark?

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

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.

    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 *
    
    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|
    #+-------+---+
    
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