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Tag: pyspark

Translating pyspark into sql

I’m experiencing an issue with the following function. I’m trying to translate this to a SQL statement so I can have a better idea of exactly what’s happening, so I can more effectively work on my actual issue. I know that this contains a join between valid_data to ri_data, a filter, and a select statement. I’m primarily having issue understanding

Splitting each Multi-Category columns to Multiple columns with counts

date Value1 Value2 Value3 16-08-2022 a b e 16-08-2022 a b f 16-08-2022 c d f output date Value1_a Value1_c Value2_b Value2_d Value3_e Value3_f 16-08-2022 2 1 2 1 1 2 continues like this for more columns maybe 10, I will aggregate on date and split the categorical columns with counts for each category , currently doing like this Need

Pyspark, iteratively get values from column containing json string

I wonder how you would iteratively get the values from a json string in pyspark. I have the following format of my data and would like to create the “value” column: id_1 id_2 json_string value 1 1001 {“1001”:106, “2200”:101} 106 1 2200 {“1001”:106, “2200”:101} 101 Which gives the error Column is not iterable However, just inserting the key manually works,

Spark SQL column doesn’t exist

I am using Spark in databricks for this SQL command. In the input_data table, I have a string for the st column. Here I want to do some calculations of the string length. However, after I assign the length_s alias to the first column, I can not call it in the following columns. SQL engine gives out Column ‘length_s1’ does

How can I write an SQL query as a template in PySpark?

I want to write a function that takes a column, a dataframe containing that column and a query template as arguments that outputs the result of the query when run on the column. Something like: func_sql(df_tbl,’age’,’select count(distinct {col}) from df_tbl’) Here, {col} should get replace with ‘age’ and output should be the result of the query run on ‘age’, i.e.

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