How do I add a new column to a Spark DataFrame using PySpark

Question

I have a Spark DataFrame  using PySpark 1 5 1  and would like to add a new column   I ve tried the following without any success   type randomed hours      gt  list    Create in Python and transform to RDD  new col   pd DataFrame randomed hours  columns   new col     spark new col   sqlContext createDataFrame new col   my df spark withColumn  hours   spark new col  new col      Also got an error using this   my df spark withColumn  hours    sc parallelize randomed hours     So how do I add a new column  based on Python vector  to an existing DataFrame with PySpark

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You can define a new udf when adding a column name   u f   F udf lambda  yourstring StringType    a select u f   alias  column name

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For Spark 2 0    assumes schema has  age  column  df select       df age   10  alias  agePlusTen

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We can add additional columns to DataFrame directly with below steps   from pyspark sql functions import when df   spark createDataFrame    amit   30     rohit   45     sameer   50      name    age    df   df withColumn  profile   when df age  gt   40   Senior   otherwise  Executive    df show

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You cannot add an arbitrary column to a DataFrame in Spark  New columns can be created only by using literals  other literal types are described in How to add a constant column in a Spark DataFrame    from pyspark sql functions import lit  df   sqlContext createDataFrame        1   a   23 0    3   B   -23 0      x1    x2    x3     df with x4   df withColumn  x4   lit 0   df with x4 show        --- --- ----- ---       x1  x2    x3  x4      --- --- ----- ---        1   a  23 0   0        3   B -23 0   0      --- --- ----- ---    transforming an existing column   from pyspark sql functions import exp  df with x5   df with x4 withColumn  x5   exp  x3    df with x5 show        --- --- ----- --- --------------------       x1  x2    x3  x4                   x5      --- --- ----- --- --------------------        1   a  23 0   0  9 744803446248903E9        3   B -23 0   0 1 026187963170189         --- --- ----- --- --------------------    included using join   from pyspark sql functions import exp  lookup   sqlContext createDataFrame   1   foo     2   bar       k    v    df with x6    df with x5      join lookup  col  x1      col  k     leftouter        drop  k        withColumnRenamed  v    x6         --- --- ----- --- -------------------- ----       x1  x2    x3  x4                   x5   x6      --- --- ----- --- -------------------- ----        1   a  23 0   0  9 744803446248903E9  foo        3   B -23 0   0 1 026187963170189    null      --- --- ----- --- -------------------- ----    or generated with function   udf   from pyspark sql functions import rand  df with x7   df with x6 withColumn  x7   rand    df with x7 show        --- --- ----- --- -------------------- ---- -------------------       x1  x2    x3  x4                   x5   x6                  x7      --- --- ----- --- -------------------- ---- -------------------        1   a  23 0   0  9 744803446248903E9  foo 0 41930610446846617        3   B -23 0   0 1 026187963170189    null 0 37801881545497873      --- --- ----- --- -------------------- ---- -------------------    Performance-wise  built-in functions  pyspark sql functions   which map to Catalyst expression  are usually preferred over Python user defined functions   If you want to add content of an arbitrary RDD as a column you can    add row numbers to existing data frame call zipWithIndex on RDD and convert it to data frame join both using index as a join key

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There are multiple ways we can add a new column in pySpark   Let s first create a simple DataFrame   date    27  28  29  None  30  31  df   spark createDataFrame date  IntegerType      Now let s try to double the column value and store it in a new column  PFB few different approaches to achieve the same     Approach - 1   using withColumn function df withColumn  double   df value   2  show      Approach - 2   using select with alias function  df select       df value   2  alias  double    show      Approach - 3   using selectExpr function with as clause  df selectExpr       value   2 as double   show      Approach - 4   Using as clause in SQL statement  df createTempView  temp   spark sql  select    value   2 as double from temp   show     For more examples and explanation on spark DataFrame functions  you can visit my blog   I hope this helps

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from pyspark sql functions import udf from pyspark sql types import   func name   udf      lambda val  val    do sth to val     StringType     df withColumn  new col   func name df old col

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I would like to offer a generalized example for a very similar use case   Use Case  I have a csv consisting of   First Third Fifth data data data data data data    billion more lines   I need to perform some transformations and the final csv needs to look like   First Second Third Fourth Fifth data null data null data data null data null data    billion more lines   I need to do this because this is the schema defined by some model and I need for my final data to be interoperable with SQL Bulk Inserts and such things   so   1  I read the original csv using spark read and call it  df    2  I do something to the data   3  I add the null columns using this script   outcols      for column in MY COLUMN LIST      if column in df columns          outcols append column      else          outcols append lit None  cast StringType    alias   0   format column     df   df select outcols    In this way  you can structure your schema after loading a csv  would also work for reordering columns if you have to do this for many tables

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To add a column using a UDF   df   sqlContext createDataFrame        1   a   23 0    3   B   -23 0      x1    x2    x3     from pyspark sql functions import udf from pyspark sql types import    def valueToCategory value      if   value    1  return  cat1     elif value    2  return  cat2            else  return  n a     NOTE  it seems that calls to udf   must be after SparkContext   is called udfValueToCategory   udf valueToCategory  StringType    df with cat   df withColumn  category   udfValueToCategory  x1    df with cat show        --- --- ----- ---------       x1  x2    x3  category      --- --- ----- ---------        1   a  23 0      cat1        3   B -23 0       n a      --- --- ----- ---------

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To add new column with some custom value or dynamic value calculation which will be populated based on the existing columns  e g   ColumnA   ColumnB    -------- ---------    10       15          10       20          10       30         and new ColumnC as ColumnA ColumnB  ColumnA   ColumnB   ColumnC   -------- --------- --------    10       15        25         10       20        30         10       30        40        using  to add new column def customColumnVal row   rd row asDict   rd  quot ColumnC quot   row  quot ColumnA quot     row  quot ColumnB quot    new row Row   rd  return new row ----------------------------  convert DF to RDD df rdd  input dataframe rdd   apply new fucntion to rdd output dataframe df rdd map customColumnVal  toDF    input dataframe is the dataframe which will get modified and customColumnVal function is having code to add new column

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The simplest way to add a column is to use  withColumn   Since the dataframe is created using sqlContext  you have to specify the schema or by default can be available in the dataset  If the schema is specified  the workload becomes tedious when changing every time    Below is an example that you can consider   from pyspark sql import SQLContext from pyspark sql types import   sqlContext   SQLContext sc    SparkContext will be sc by default     Read the dataset of your choice  Already loaded with schema  Data   sqlContext read csv   path   header   True False  schema    infer   sep    delimiter      For instance the data has 30 columns from col1  col2      col30  If you want to add a 31st column  you can do so by the following  Data   Data withColumn  col31    Code goes here      Check the change  Data printSchema

[python] How do I add a new column to a Spark DataFrame (using PySpark)?

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