A data scientist is processing a large dataset of e-commerce transactions from a web API. The data is in a JSON format where each object represents a customer and contains a customer_id and a nested list of orders. Each object within the orders list has an order_id and a nested list of products. The goal is to load this data into a relational database for cohort analysis, which requires normalizing the structure into separate, related tables. Which of the following is the MOST effective and scalable approach to flatten this nested JSON?
Utilize the pandas json_normalize function, specifying the record_path to the nested 'products' list and using the meta argument to include customer_id and order_id for relational mapping.
Develop a custom, deeply nested loop that iterates through each customer, their orders, and products, appending the data row-by-row into a single denormalized list before converting to a table.
Read the entire JSON file as a single text string and apply a complex regular expression with multiple capture groups to extract all customer, order, and product details simultaneously.
First, convert the JSON structure to an equivalent XML format, then use an XSLT stylesheet to define the transformation rules for flattening the data into a tabular structure.
The correct answer is to use the pandas json_normalize function with the record_path and meta arguments. This function is specifically designed and optimized for flattening semi-structured JSON data into a tabular format. The record_path argument allows you to specify the path to the nested list that you want to unnest into separate rows (in this case, the 'products'). The meta argument is crucial for including parent-level data (like customer_id and order_id) in each of the new rows, thereby preserving the relational links needed for database normalization.
A custom loop is inefficient and not scalable for large datasets compared to optimized library functions like json_normalize. Using regular expressions to parse structured data like JSON is strongly discouraged because it is error-prone, difficult to maintain, and fails to handle the inherent structure of the format. Converting JSON to XML to then use XSLT adds unnecessary complexity and processing overhead; it is more direct and efficient to process the JSON data natively.
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