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# dfConverted.select("education").limit(5).explain("extended")
== Analyzed Logical Plan ==
education: string
GlobalLimit 5
+- LocalLimit 5
+- Project [education#238]
+- Project [id#236, year_birth#237, education#238, count_kid#239, count_teen#240, date_customer#241, days_last_login#242, add_months(to_date('date_customer, Some(d-M-yyyy)), 72) AS date_joined#257]
+- Project [ID#16 AS id#236, Year_Birth#17 AS year_birth#237, Education#18 AS education#238, Kidhome#21 AS count_kid#239, Teenhome#22 AS count_teen#240, Dt_Customer#23 AS date_customer#241, Recency#24 AS days_last_login#242]
+- Relation[ID#16,Year_Birth#17,Education#18,Marital_Status#19,Income#20,Kidhome#21,Teenhome#22,Dt_Customer#23,Recency#24,MntWines#25,MntFruits#26,MntMeatProducts#27,MntFishProducts#28,MntSweetProducts#29,MntGoldProds#30,NumDealsPurchases#31,NumWebPurchases#32,NumCatalogPurchases#33,NumStorePurchases#34,NumWebVisitsMonth#35,AcceptedCmp3#36,AcceptedCmp4#37,AcceptedCmp5#38,AcceptedCmp1#39,... 5 more fields] csv
== Optimized Logical Plan ==
GlobalLimit 5
+- LocalLimit 5
+- Project [Education#18 AS education#238]
+- Relation[ID#16,Year_Birth#17,Education#18,Marital_Status#19,Income#20,Kidhome#21,Teenhome#22,Dt_Customer#23,Recency#24,MntWines#25,MntFruits#26,MntMeatProducts#27,MntFishProducts#28,MntSweetProducts#29,MntGoldProds#30,NumDealsPurchases#31,NumWebPurchases#32,NumCatalogPurchases#33,NumStorePurchases#34,NumWebVisitsMonth#35,AcceptedCmp3#36,AcceptedCmp4#37,AcceptedCmp5#38,AcceptedCmp1#39,... 5 more fields] csv
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