Flatten

The Flatten functionality in Edilitics empowers users to seamlessly transform nested data structures into a tabular format, facilitating streamlined analysis and processing. This feature is particularly advantageous for columns containing complex data types such as objects, lists, dictionaries, or structs. Below is a comprehensive guide on leveraging the Flatten functionality, including detailed steps, practical considerations, and explanations of the supported data types.

Step-by-Step Guide to Utilizing Flatten#

1. Select Columns to Flatten
   • Choose the columns you would like to flatten from the list of column names provided. The columns listed will include only those with data types: Object, List, Dict, or Struct.

2. Submit
   • Submit the operation to flatten the selected columns.

Note: This operation only flattens the first level of nesting. If the column contains multiple levels of nesting, you will need to repeat the operation with the newly created columns to handle additional levels of nesting. Continue this process until you achieve the desired level of flattening.

Retention of Original Columns#

The original columns are not automatically dropped. This allows you to backtrack if needed without reworking multiple operations. If you wish to remove the original columns, you will need to do so manually.

Supported Data Types#

• Object
  • Description: A generic container for data that can hold various types of values.
  • Example: A column containing JSON objects like {"name": "John", "age": 30}.
  • Use Case: Flatten to extract individual attributes such as name and age.
• List
  • Description: A sequence of values, typically of the same type, enclosed in square brackets.
  • Example: A column containing lists like ["apple", "banana", "cherry"].
  • Use Case: Flatten to create separate columns for each list item or aggregate them into a single column.
• Dict (Dictionary)
  • Description: A collection of key-value pairs, where each key is unique.
  • Example: A column containing dictionaries like {"key1": "value1", "key2": "value2"}.
  • Use Case: Flatten to extract individual key-value pairs into separate columns.
• Struct
  • Description: A complex data type that groups related data together, similar to a record or a row in a table.
  • Example: A column containing structures like {"first_name": "Jane", "last_name": "Doe", "address": {"city": "New York", "zip": "10001"}}.
  • Use Case: Flatten to break down the structure into its constituent fields and sub-fields.

Real-World Applications of Flatten#

Here are five real-world scenarios across various industries:

1. Retail Industry#

_ Objective: Flatten nested product attributes for detailed analysis. _ Scenario: _ Column: ProductDetails (containing nested JSON objects with attributes such as color, size, and material). _ Use Case: Transform nested product attributes into a tabular format for enhanced analysis and reporting. Sample Data:

• Example: Flattening the ProductDetails column to extract attributes like color, size, and material.

1. Healthcare Industry _ Objective: Flatten patient records for streamlined reporting. _ Scenario: _ Column: PatientInfo (containing nested structures with patient demographics, contact information, and medical history). _ Use Case: Simplify patient records for easier access and analysis. Sample Data:

• Example: Flattening the PatientInfo column to extract nested fields such as demographics_age, contact_phone, and medical_history_conditions.

1. Finance Industry _ Objective: Flatten transaction details for comprehensive auditing. _ Scenario: _ Column: TransactionDetails (containing nested objects with transaction metadata, itemized charges, and payment methods). _ Use Case: Transform complex transaction records into a tabular format for thorough auditing. Sample Data:

Example: Flattening the TransactionDetails column to extract itemized charges and payment methods.

1. Manufacturing Industry _ Objective: Flatten production batch data for quality control analysis. _ Scenario: _ Column: BatchData (containing nested lists and structures with batch parameters, test results, and timestamps). _ Use Case: Convert nested batch data into a flat structure for efficient quality control analysis.

Example: Flattening the BatchData column to extract parameters such as test_results and timestamps.

1. Education Industry _ Objective: Flatten student performance data for academic insights. _ Scenario: _ Column: PerformanceData (containing nested structures with scores, attendance, and extracurricular activities). _ Use Case: Simplify student performance records for comprehensive academic analysis. Sample Data:

• Example: Flattening the PerformanceData column to extract nested fields like scores_math, attendance_days_present, and activities_clubs.

The Flatten functionality in Edilitics provides a robust, user-friendly solution for transforming nested data into a tabular format. By supporting complex data types and offering iterative flattening for multiple levels of nesting, users can efficiently manage and analyze their data. This feature enhances data accessibility and usability, making it an essential tool for all users.