Big Data Engineering

  Troubleshooting “Column Not Callable” Error in PySpark While working with PySpark, you might encounter the “Column not callable” error. This error can occur due to various reasons, including incorrect syntax or a misspelled keyword. In this blog post, we’ll explore one specific cause of this error: misspelling the  alias  keyword in the  agg  function, and how to fix it. The Misspelling Issue One specific cause of this error is  misspelling.  In this scenario,   the  alias  keyword when using the agg function. For example: Reading the data from pyspark.sql.functions import avg temp_data = [( "Monday" , 20.0 ), ( "Tuesday" , 25.0 ), ( "Wednesday" , 22.5 )] temp_df = spark.createDataFrame(temp_data,[ "day" , "temp" ]) Output of the above code Now let us perform the aggregation: from pyspark.sql.functions import avg temp_data = [("Monday", 20.0 ), ("Tuesday", 25.0 ), ("Wednesday", 22.5 )] temp_df = spar...

Handling a PySpark TypeError: ‘unsupported operand type(s) for +: ‘int’ and ‘str’ You’ve been working with PySpark, trying to calculate the rolling sum of a column in your DataFrame, and you’ve set everything up perfectly. However, when you run your code, you face the dreaded TypeError: ‘unsupported operand type(s) for +: ‘int’ and ‘str’ error. The Reason: The error indicates that there’s a type mismatch in your DataFrame. Specifically, you’re trying to perform arithmetic operations on columns with different data types — an integer (int) and a string (str). In this scenario, you forgot a tiny but essential step: importing the ‘ sum ’ function! Here is the sample Hospital data, from pyspark.sql.window import Window as w from pyspark.sql.functions import col df_hospital = spark.read.options(header= 'True' ,inferSchema= 'True' ).csv( 'C:/Users/hp/Python_Sums_20thfeb/pyspark/hospital.csv' ) ovr_sp = w.partitionBy( 'department' ).orderBy( 'discharge_...

Troubleshooting PySparkTypeError:  [NOT_ITERABLE]   Ever encountered a confusing error message like 'Column is not iterable' while working with PySpark? Here's a relatable scenario: You're trying to find the highest salary from a list of employees using PySpark . You've got your DataFrame set up, but when you run your code, The error pops up, leaving you scratching your head. The reason? You forgot a tiny but essential step: importing the ' max ' function! Here's the fix, in plain language: # Don't forget to import the ' max ' function! from pyspark.sql.functions import max # Now, let's find that maximum salary: data = [("John", 2500.0), ("Alice", 3000.0), ("Bob", 2200.0)] usr_data = spark.createDataFrame(data,["name","salary"]) +-----+----------+ | name|salary    | +-----+----------+ | John| 2500.0   | |Alice | 3000.0  | | Bob | 2200.0   | +-----+----------+ max_sal = usr_data.agg(max(us...