Replace null values, alias for na.fill(). DataFrame.fillna() and DataFrameNaFunctions.fill() are aliases of each other. Parameters value –int, long, float, string, bool or dict. Value to replace null values with. If the value is a dict, then subset is ignored and value must be a ...
In PySpark,fillna() from DataFrame class or fill() from DataFrameNaFunctions is used to replace NULL/None values on all or selected multiple columns with either zero(0), empty string, space, or any constant literal values. AdvertisementsWhile working on PySpark DataFrame we often need to ...
3.Replace Column Values Conditionally #Replace string column value conditionallyfrompyspark.sql.functionsimportwhen df.withColumn('address', when(df.address.endswith('Rd'),regexp_replace(df.address,'Rd','Road')) \ .when(df.address.endswith('St'),regexp_replace(df.address,'St','Street')) ...
3.Replace Column Values Conditionally #Replace string column value conditionally frompyspark.sql.functionsimportwhen df.withColumn('address', when(df.address.endswith('Rd'),regexp_replace(df.address,'Rd','Road')) \ .when(df.address.endswith('St'),regexp_replace(df.address,'St','Street'))...
6. Replace All or Multiple Column Values If you want to replace values on all or selected DataFrame columns, refer toHow to Replace NULL/None values on all column in PySparkor How to replaceempty string with NULL/None value 7. Using overlay() Function ...
Text preprocessing:regexp_replacecan be used as part of text preprocessing tasks, such as removing punctuation, special characters, or stop words. This can be particularly useful when working with natural language processing (NLP) tasks. Handling missing or null values:regexp_replacecan be used to...