Data Transformation for E-Commerce
A case study of a Python script designed to transform product data from CSV files into a structure suitable for an e-commerce platform
Data transformation is a critical process in the data pipeline for e-commerce businesses that add large numbers of new skus on a consistent basis. The data transformation broadly involves converting data from the supplier format or structure into the internal one, ensuring compatibility with end-use requirements.
When doing research into this, I didn't find any real case studies of a process of this kind. I'm hoping that this post will fill that lacuna, and be helpful. Specific details have been removed.
With Python and its powerful data manipulation library pandas, complex transformations can be performed relatively easily. The IDE that I use is Google Colab, a Jupyter notebook that runs in the cloud and is very user-friendly.
The Data
The data comprises two CSV files with product information. The first file contains detailed product specifications, including unique identifiers, brand names, model codes, and various attributes. The second file serves as a reference, offering additional information that assists in describing and categorizing the products.
The Transformation
The transformation process in the script consists of several steps, which are outlined below:
Data Cleaning
Before the actual transformation, the script performs a few crucial data cleaning steps. It ensures that all product identifiers are correctly formatted and have the required length. This step is particularly important because the correctness of these identifiers is critical to prevent errors and to maintain consistency across the product pages and catalog.
Creating New Columns
The script generates new columns based on the existing ones. For instance, it creates a new column for the model name by extracting it from the complete product name. This step helps to normalize the data and makes it easier to perform operations on specific product attributes.
Categorizing Products
This is perhaps the most complex part of the transformation process. The script uses the reference dataframe to match product attributes to category IDs. These IDs are then used to populate new columns in the product dataframe, providing a way to easily categorize the products on the e-commerce platform. By associating each product with a specific category, the script enables more effective product filtering and searching, which is crucial for an excellent user experience on any e-commerce website.
Feature Identification
In addition to categorization, the script identifies specific features of products based on their attributes. If a product has a specific feature, the script records this in a new column. This data can be used to highlight these features to customers or to enable more detailed product filtering.