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Setting up input in Pyspark
I am trying to create a pivot table in pyspark that has customers as one input column and other columns as the pivot columns. The output is a dataframe with a customer id column as the unique identifier, and the rest of the columns as the pivoting columns.
Code I have tried is
from pyspark.sql import functions as F
df_src = spark.createDataFrame(source_df)
pivot_df = df_src.groupby([‘CustomerId’]).agg(F.count([‘ProductId’], name=’ProductCnt’))
df_pivot = pivot_df.sort(‘ProductCnt’, ascending=False)
#df_pivot.groupby(‘CustomerId’).count()
df_pivot = pivot_df.sort(‘ProductCnt’, ascending=False)
However, the pivot table only has a customer id column, and no other columns that I need in the final dataframe.
Please let me know how I can achieve this.
Also, I am using spark 2.4.
A:
Try to rename the columns before you call pivot_df.sort
Like this:
from pyspark.sql.functions import col, lit
from pyspark.sql.types import ArrayType
df_src = spark.createDataFrame(source_df)
df_pivot = df_src.groupby(‘CustomerId’)\
.agg(F.count([‘ProductId’], name=’ProductCnt’).alias(‘ProductCnt’), lit(1.0)
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