Updating a dataframe column in spark
Looking at the new spark dataframe api, it is unclear whether it is possible to modify dataframe columns.
How would I go about changing a value in row
y of a dataframe?
pandas this would be
df.ix[x,y] = new_value
Edit: Consolidating what was said below, you can't modify the existing dataframe as it is immutable, but you can return a new dataframe with the desired modifications.
If you just want to replace a value in a column based on a condition, like
from pyspark.sql import functions as F update_func = (F.when(F.col('update_col') == replace_val, new_value) .otherwise(F.col('update_col'))) df = df.withColumn('new_column_name', update_func)
If you want to perform some operation on a column and create a new column that is added to the dataframe:
import pyspark.sql.functions as F import pyspark.sql.types as T def my_func(col): do stuff to column here return transformed_value # if we assume that my_func returns a string my_udf = F.UserDefinedFunction(my_func, T.StringType()) df = df.withColumn('new_column_name', my_udf('update_col'))
If you want the new column to have the same name as the old column, you could add the additional step:
df = df.drop('update_col').withColumnRenamed('new_column_name', 'update_col')
While you cannot modify a column as such, you may operate on a column and return a new DataFrame reflecting that change. For that you'd first create a
UserDefinedFunction implementing the operation to apply and then selectively apply that function to the targeted column only. In Python:
from pyspark.sql.functions import UserDefinedFunction from pyspark.sql.types import StringType name = 'target_column' udf = UserDefinedFunction(lambda x: 'new_value', StringType()) new_df = old_df.select(*[udf(column).alias(name) if column == name else column for column in old_df.columns])
new_df now has the same schema as
old_df (assuming that
old_df.target_column was of type
StringType as well) but all values in column
target_column will be
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