Format standardization scheme using Quadratic unified data sources
The common problems of conflicting fields and confusing formats when integrating data across systems can be resolved in the following ways:
- Intelligent type inference: Automatic recognition of special formats such as dates/values
- Conversion libraries: Built-in common data cleaning functions
- Visual mapping tool: intuitively match different source fields
Specific implementation:
- Access to all data sources through Data Connections
- Write conversion scripts using python's pandas or Rust's polars libraries.
- Applying built-in conversion functions such as q.str.to_datetime()
- Create formatting templates and apply them to newly imported data
- Verify consistency using the Data Quality Check feature
Advanced tips: custom parser plug-ins can be created to handle special formats, and regularly running quality check scripts can be set as automated tasks. For same-source data it is recommended to create a data pipeline for continuous synchronization.
This answer comes from the articleQuadratic: an online data analysis form that integrates AI dialog and code executionThe
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