AABA GP

Day 1 Workflow2_Customer Transactions Analysis

Step 1.1. Access the Data from File(s)

1. Read data

   Reads GoGoX's booking export and URA's subzone map from the workflow's data folder and profiles the raw bookings; the 25 hand-matched area names are a table typed into the workflow. Sends the bookings to step 2, and the map and the names to step 3.

Step 3.1. Transform the Data

1. Create a new column AgeGroup with values Adolescent, Adult, Older Adult according to the age value: <18 Adolescents, 18-65 Adults, >65 Older Adults

2. Replace the character "_" with a space " " from the column country Country

3. Split the column CustomerID on the character "_" with the Cell Splitter node

4. Rename the columns created by the split with the Column Renamer node

5. Merge the columns Email and CorporateEmail with the Column Merger node. Keep the Email when both are available.

6. Convert the column Newsletter with the Number to String node

Perform instructions 1 and 2 with two Expression nodes. Click Ask K-AI and copy paste the text of the instruction in the prompt.

Learning objectives: Access, clean, transform, merge and aggregate data to report and visualize the results.

Workflow description: This workflow accesses customer and transactions data, transforms the data and generates an interactive view with multiple visualizations.

You’ll find the use case information in the blue annotations and the instructions to the exercises in the yellow annotations.

Use Case

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Your continuous project will be to help Pauline build a workflow to report the monthly insights, by completing each step of the process she repeats every month below.

Data Merging

With the data cleaned and in the correct shape, you can now help Pauline to merge the different data sources, by stacking tables and matching or joining others to add the necessary columns.

Step 4.1. Merge the Data

1. Concatenate the two customer tables with the Concatenate node

2. Append the StoreType (in Stores.csv) and Price (from the ProductNrAndPrice DB table) to each transaction (stored in the Transactions DB table) with two Value Lookup nodes

3. Join the Customer and Transaction tables on the CustomerID column with the Joiner node.

Note: The first output port of the Joiner shows the rows with a match in the two tables. Use only these rows in the next steps.