I trying to make into using only SQL queries. It is kind of similar to using groupByKey() in pyspark. Is there a way to do this? Answer Just use conditional aggregation. One method is: In Postgres, this would be phrased using the standard filter clause:
Tag: pyspark
How to count unique data occuring in multiple categorical columns from a pyspark dataframe
The Problem: In a ML based scenario, I am trying to see the occurrence of data from multiple columns in an Inference file versus the files that were provided to me for Training. I need this to be found only for categorical variables, since numerical attributes are scaled. The Expectation: I’ve got some success in doing the following in Standard
Daily forecast on a PySpark dataframe
I have the following dataframe in PySpark: DT_BORD_REF: Date column for the month REF_DATE: A date reference for current day separating past and future PROD_ID: Product ID COMPANY_CODE: Company ID CUSTOMER_CODE: Customer ID MTD_WD: Month to Date count of working days (Date = DT_BORD_REF) QUANTITY: Number of items sold QTE_MTD: Number of items month to date for DT_BORD_REF < REF_DATE
How I can select a column where in another column I need a specific things
I have a pyspark data frame. How I can select a column where in another column I need a specific things. suppose I have n columns. for 2 columns I have A. B. a b a c d f I want all column B. …
Converting query from SQL to pyspark
I am trying to convert the following SQL query into pyspark: The code I have in PySpark right now is this: However, this is simply returning the number of rows in the “data” dataframe, and I know this isn’t correct. I am very new at PySpark, can anyone help me solve this? Answer You need to collect the result into
How to deal with ambiguous column reference in sql column name reference?
I have some code: I then try but I get an error Error in SQL statement: AnalysisException: Reference ‘A.CDE_WR’ is ambiguous, could be: A.CDE_WR, A.CDE_WR.; line 6 pos 4 in databricks. How can I deal with this? Answer This query: is using SELECT *. The * is shorthand for all columns from both tables. Obviously the combined columns from the
How do I specify a default value when the value is “null” in a spark dataframe?
I have a data frame like the picture below. In the case of “null” among the values of the “item_param” column, I want to replace the string’test’. How can I do it? df = sv_df….
How to add a ranking to a pyspark dataframe
I have a pyspark dataframe with 2 columns – id and count. I want to add a ranking to this by reverse count. So the highest count has rank 1, second highest rank 2, etc. testDF = spark.createDataFrame([(DJS232,437232)], [“id”, “count”]) I first tried using and this worked, ish. It had monotonically increasing id numbers but the jump from the first
how to join two hive tables with embedded array of struct and array on pyspark
I am trying to join two hive tables on databricks. tab1: The schema of “some_questions” “some_questions” example: tab2: I need to join tab1 and tab2 by “question_id” such that I get a new table I try to join them by pyspark. But, I am not sure how to decompose the array with embedded struct/array. thanks Answer For SparkSQL, you can
Sum of column returning all null values in PySpark SQL
I am new to Spark and this might be a straightforward problem. I’ve a SQL with name sql_left which is in the format: Here is a sample data generated using sql_left.take(1): Note: Age column has ‘XXX’,’NUll’ and other integer values as 023,034 etc. The printSchema shows Age,Total Cas as integers. I’ve tried the below code to first join two tables: