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.

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

Answer 2

df = df.withColumnRenamed("colName", "newColName")
       .withColumnRenamed("colName2", "newColName2")

Advantage of using this way: With long list of columns you would like to change only few column names. This can be very convenient in these scenarios. Very useful when joining tables with duplicate column names.

Answer 3

If you want to change all columns names, try df.toDF(*cols)

Answer 4

In case you would like to apply a simple transformation on all column names, this code does the trick: (I am replacing all spaces with underscore)

new_column_name_list= list(map(lambda x: x.replace(" ", "_"), df.columns))

df = df.toDF(*new_column_name_list)

Thanks to @user8117731 for toDf trick.

Answer 5

If you want to rename a single column and keep the rest as it is:

from pyspark.sql.functions import col
new_df = old_df.select(*[col(s).alias(new_name) if s == column_to_change else s for s in old_df.columns])

Answer 6

df.withColumnRenamed('age', 'age2')

Answer 7

Another way to rename just one column (using import pyspark.sql.functions as F):

df = df.select( '*', F.col('count').alias('new_count') ).drop('count')

Answer 8

this is the approach that I used:

create pyspark session:

import pyspark
from pyspark.sql import SparkSession
spark = SparkSession.builder.appName('changeColNames').getOrCreate()

create dataframe:

df = spark.createDataFrame(data = [('Bob', 5.62,'juice'),  ('Sue',0.85,'milk')], schema = ["Name", "Amount","Item"])

view df with column names:

df.show()
+----+------+-----+
|Name|Amount| Item|
+----+------+-----+
| Bob|  5.62|juice|
| Sue|  0.85| milk|
+----+------+-----+

create a list with new column names:

newcolnames = ['NameNew','AmountNew','ItemNew']

change the column names of the df:

for c,n in zip(df.columns,newcolnames):
    df=df.withColumnRenamed(c,n)

view df with new column names:

df.show()
+-------+---------+-------+
|NameNew|AmountNew|ItemNew|
+-------+---------+-------+
|    Bob|     5.62|  juice|
|    Sue|     0.85|   milk|
+-------+---------+-------+

Answer 9

I made an easy to use function to rename multiple columns for a pyspark dataframe, in case anyone wants to use it:

def renameCols(df, old_columns, new_columns):
    for old_col,new_col in zip(old_columns,new_columns):
        df = df.withColumnRenamed(old_col,new_col)
    return df

old_columns = ['old_name1','old_name2']
new_columns = ['new_name1', 'new_name2']
df_renamed = renameCols(df, old_columns, new_columns)

Be careful, both lists must be the same lenght.

Answer 10

I use this one:

from pyspark.sql.functions import col
df.select(['vin',col('timeStamp').alias('Date')]).show()

Answer 11

For a single column rename, you can still use toDF(). For example,

df1.selectExpr("SALARY*2").toDF("REVISED_SALARY").show()