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02_​Transform_​Using_​the_​Pivoting_​Node

Transform Data using Pivoting node

Create a pivot table with one or more group columns and one or more pivot columns. Apply basic aggregation methods like sum and count, statistical aggregation methods, and aggregation methods available for columns of type Date&Time. Apply multiple aggregation methods to one or more aggregation columns.







Basic example: a pivot table with one group and one pivotcolumnQuestion: How many male/female customers belong to each age bin?Group: Age BinPivot: GenderAggregation Column: CustomerKey or any otherAggregation Operator: Count Two pivot columnsQuestion: What is the most common product among customers accordingto their sentiment, gender and marital status?Group: Sentiment AnalysisPivot: MaritalStatus, GenderAggregation Column: ProductAggregation Operator: Mode Two group columnsQuestion: Which web activity classes are represented in groups accordingto marital status, gender and sentiment?Group: MaritalStatus, GenderPivot: Sentiment AnalysisAggregation Column: WebActivityAggregation Operator: Unique concatenate Aggregation Column of Type Date and TimeQuestion: What is the age dispersion of the customers according todifferent product and sentiment groups? Group: ProductPivot: Sentiment AnalysisAggregation Column: birthdayAggregation Operator: Date range(day) Two aggregation operatorsQuestion: What is the mean and standard deviation of income according todifferent sentiment and gender groups?Group: Sentiment AnalysisPivot: GenderAggregation Column: EstimatedYearlyIncomeAggregation Operator: Mean, Standard deviation Count of customersMode of productUnique concatenate of web activity classesDate range(day)of birthdaysMean and standarddeviation of incomeCustomer dataCustomer dataCustomer dataCustomer dataCustomer data Pivoting Pivoting Pivoting Pivoting Pivoting Table Reader Table Reader Table Reader Table Reader Table Reader Basic example: a pivot table with one group and one pivotcolumnQuestion: How many male/female customers belong to each age bin?Group: Age BinPivot: GenderAggregation Column: CustomerKey or any otherAggregation Operator: Count Two pivot columnsQuestion: What is the most common product among customers accordingto their sentiment, gender and marital status?Group: Sentiment AnalysisPivot: MaritalStatus, GenderAggregation Column: ProductAggregation Operator: Mode Two group columnsQuestion: Which web activity classes are represented in groups accordingto marital status, gender and sentiment?Group: MaritalStatus, GenderPivot: Sentiment AnalysisAggregation Column: WebActivityAggregation Operator: Unique concatenate Aggregation Column of Type Date and TimeQuestion: What is the age dispersion of the customers according todifferent product and sentiment groups? Group: ProductPivot: Sentiment AnalysisAggregation Column: birthdayAggregation Operator: Date range(day) Two aggregation operatorsQuestion: What is the mean and standard deviation of income according todifferent sentiment and gender groups?Group: Sentiment AnalysisPivot: GenderAggregation Column: EstimatedYearlyIncomeAggregation Operator: Mean, Standard deviation Count of customersMode of productUnique concatenate of web activity classesDate range(day)of birthdaysMean and standarddeviation of incomeCustomer dataCustomer dataCustomer dataCustomer dataCustomer data Pivoting Pivoting Pivoting Pivoting Pivoting Table Reader Table Reader Table Reader Table Reader Table Reader

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To use this workflow in KNIME, download it from the below URL and open it in KNIME:

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Created by: Ferry
Created at: 2016-04-19
On NodePit since: 2023-12-24
Last update: 2024-05-05
Created with KNIME version: v4.5.2
Tags: ETLdata aggregationpivotinggroupingdata transformationdata preprocessingsummarizing dataorganizing dataBeginnerBeginner Cheat SheetBeginners Space

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