Aggiungi la data e riordina le colonne

I dati degli ordini vengono raccolti ogni giorno per analizzare le tendenze di acquisto, quindi aggiungere una colonna con la data è essenziale. I dati provengono dalle vendite dell'11 marzo 2023. L'azienda di arredamento ha bisogno che tu aggiunga al dataset una colonna data con il valore "11.Mar.2023". Inoltre, ripulisci il dataset rimuovendo le colonne non necessarie e riordinando le rimanenti.

Se hai perso dei progressi, apri il tuo Local space, fai clic sulla cartella data-transformation-in-knime e carica il workflow 3_2_reorder_columns.

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Trasformazione dei dati in KNIME

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Trasformazione dei dati in KNIME

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In this chapter, you will learn how to split columns based on cell values, allowing you to separate combined data into more manageable parts. Next, you will explore the use of conditional statements for data transformation, enabling you to apply specific rules to your dataset. Additionally, you will learn how to use logical operators to formulate logical conditions. Finally, you will discover how to rename one or multiple columns, ensuring your dataset is well-organized and easy to understand.

Exercise 1: Conditional statements and logical operators Exercise 2: Assign inflation value Exercise 3: Enhance student data Exercise 4: Split into city and quantity Exercise 5: Rename columns Exercise 6: Assign discount percentage

Delve into Expression- Based Data Transformation, a crucial skill for data manipulation. This lesson covers understanding and utilizing string expressions for various string manipulations. Additionally, you will master mathematical expressions using formulas and operators, enabling them to create custom columns tailored to their needs. The chapter culminates with the ability to build mixed expressions that seamlessly combine mathematical and string expressions, providing a comprehensive toolkit for advanced data transformation.

Exercise 1: String and math expressions Exercise 2: Calculate the bill value of Items Exercise 3: Calculate student scores Exercise 4: Convert sales column to numeric Exercise 5: Calculate discount value Exercise 6: Calculate profit

Learn how to append and merge columns from multiple tables, enabling you to consolidate data from various sources. Discover how to add constant value columns and reorder columns to customize your dataset layout. Finally, delve into sorting tables by specifying a list of criteria, ensuring your data is organized and easily accessible for analysis. These skills will enhance your data management proficiency, making complex datasets more manageable and insightful.

Exercise 1: Integrazione dei dati Exercise 2: Trova l’alimento più costoso Exercise 3: Integra e analizza i dati degli studenti Exercise 4: Accoda e unisci colonne Exercise 5: Aggiungi la data e riordina le colonne

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Exercise 6: Ordina la tabella

This final chapter focuses on enhancing KNIME workflows by adding detailed descriptions and tags to represent their themes. Further, learn how to abstract workflow segments into reusable KNIME components, bundle functionality, and add visual elements for better usability. Lastly, learn to share your workflows and components on the KNIME Community Hub, ensuring they are valuable and accessible to the community.

Exercise 1: Share workflows & collaborate Exercise 2: Component characteristics Exercise 3: Share & collaborate student's workflow Exercise 4: Add workflow details Exercise 5: Create component Exercise 6: Sharing the component Exercise 7: Course wrap up