Spark add new column with value form previous some columns

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I have DataFrame like this:

|   code   |idn|
|   [I0478]|  0|
|   [B0527]|  1|
|   [C0798]|  2|
|   [C0059]|  3|
|   [I0767]|  4|
|   [I1001]|  5|
|   [C0446]|  6|

And i want to add new column to DataFrame

|   code   |idn| item |
|   [I0478]|  0| I0478|
|   [B0527]|  1| B0527|
|   [C0798]|  2| C0798|
|   [C0059]|  3| C0059|
|   [I0767]|  4| I0767|
|   [I1001]|  5| I1001|
|   [C0446]|  6| C0446|

Please help me do this!

Use []:

df.withColumn("item", df["item"][0])

Spark - How to add a new column to DataFrame, I solved your problem using lag window function. Just go through the code below: >>> from pyspark.sql.functions import lag, col. >>> from  I manage to generally "append" new columns to a dataframe by using something like: df.withColumn ("new_Col", df.num * 10) However I have no idea on how I can achieve this "shift of rows" for the new column, so that the new column has the value of a field from the previous row (as shown in the example above).

So the problem will be evident if you look at the schema - the column you are trying to subset is not an array. So the solution is to .* expand the column.'code.*', 'idn')

5 Ways to add a new column in a PySpark Dataframe, Adding a new column or multiple columns to Spark DataFrame can be adding a constant or literal value and finally adding a list column to  When we ingest data from source to Hadoop data lake, we used to add some additional columns with the existing data source. These columns basically help to validate and analyze the data. So, in this post, we will walk through how we can add some additional columns with the source data.


import pandas as pd

array = {'code': [['I0478'],['B0527'], ['C0798'], ['C0059'], ['I0767'], ['I1001'], ['C0446']], 'idn':[0, 1, 2, 3, 4, 5, 6]}

df = pd.DataFrame(array)

df['item'] = df.apply(lambda row: str(row.code).lstrip('[').rstrip(']').strip("'").strip(), axis= 1)


Spark dataframe add column based on other columns, In my last post on Spark, I explained how to work with PySpark RDDs and Dataframes. create a new column, so this is the first place I go whenever I want to do some on a PySpark Dataframe to a single column or multiple columns. Add a new key in the dictionary with the new column name and value. The first parameter “sum” is the name of the new column, the second parameter is the call to the UDF “addColumnUDF”. To the udf “addColumnUDF” we pass 2 columns of the DataFrame “inputDataFrame”. The udf will be invoked on every row of the DataFrame and adds a new column “sum” which is addition of the existing 2 columns.

df.withColumn("item", df["code"][0])

If the "item" column is Array type, if it's Struct of string, you may need to inspect the key of the element of item by"code").collect()[0], see what key(string) it has.

How to create new column with function in Spark Dataframe , Spark – Add new column to Dataset A new column could be added to an existing Let In the previous section, we showed how you can augment a Spark If both dataframes has some different columns, then based on this value, it will be  from pyspark. sql. functions import udf from pyspark. sql. types import * def valueToCategory (value): if value == 1: return 1 elif value == 2: return 2 else: return 0 # NOTE: it seems that calls to udf() must be after SparkContext() is called udfValueToCategory = udf (valueToCategory, StringType ()) df_with_cat = df. withColumn ("category", udfValueToCategory ("c1"))

pyspark.sql module, I am facing an issue here that I have a dataframe with 2 columns, "ID" and Email me at this address if my answer is selected or commented I guess withColumn is the right way to add a column. from pyspark.sql.functions import udf from pyspark.sql.types import * def valueToCategory(value): if value  Column // The target type triggers the implicit conversion to Column scala> val idCol: Column = $ "id" idCol: org.apache.spark.sql. Column = id Beside using the implicits conversions, you can create columns using col and column functions.

Spark SQL Upgrading Guide, SparkSession.builder.config("spark.some.config.option", "some-value") Interface through which the user may create, drop, alter or query underlying Can be a single column name, or a list of names for multiple columns. For example, an offset of one will return the previous row at any given point in the window partition. There are generally two ways to dynamically add columns to a dataframe in Spark. A foldLeft or a map (passing a RowEncoder). The foldLeft way is quite popular (and elegant) but recently I came across an issue regarding its performance when the number of columns to add is not trivial.

[PDF] Cheat sheet PySpark SQL Python.indd, This can cause some change in behavior and are illustrated in the table below. In previous versions, instead, the fields of the struct were compared to the output filterPushdown change their default values to native and true respectively. Since Spark 2.4, creating a managed table with nonempty location is not allowed. I need to concatenate two columns in a dataframe. Is there any function in spark sql to do careers to become a Big Data Developer or Architect!

  • AnalysisException: u"Field name should be String Literal, but it's 0;"
  • The question is about apache-spark, not pandas.