Ganti nama kolom

Tabel Orders memiliki beberapa kolom dengan nama yang bukan standar atau singkatan dari kata yang lebih panjang. Untuk meningkatkan kejelasan dan pemahaman terhadap tabel Orders, Anda diminta mengganti nama kolom-kolom tersebut dengan nama lengkapnya.

Namun, Anda perlu berhati-hati saat mengganti nama kolom. Hindari mengganti nama kolom jika namanya penting untuk node di hilir dalam sebuah workflow, karena beberapa node bergantung pada nama kolom tertentu agar dapat berfungsi dengan baik. Dalam kasus ini, penggantian nama diperlukan karena nama kolom kurang jelas dan dapat menimbulkan kebingungan.

Jika Anda kehilangan progres, telusuri Local space Anda, klik folder data-transformation-in-knime, dan muat workflow 1_2_rename_columns.

Latihan ini adalah bagian dari kursus

Transformasi Data di KNIME

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Transformasi Data di 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: Pernyataan kondisional dan operator logika Exercise 2: Tetapkan nilai inflasi Exercise 3: Perkaya data mahasiswa Exercise 4: Pisahkan menjadi city dan quantity Exercise 5: Ganti nama kolom

Latihan Saat Ini

Exercise 6: Tetapkan persentase diskon

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: Data integration Exercise 2: Find the most expensive food item Exercise 3: Integrate and analyze students data Exercise 4: Append and merge columns Exercise 5: Add date and reorder columns Exercise 6: Sort the table

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