Strategies for data cleansing and transformation in Python
Python’s rich ecosystem of data-centric libraries, such as Pandas and NumPy, allows the seamless detection and correction of inconsistencies, errors, or missing values, leading to better data integrity and reliability. In transformation, data is reshaped, normalized, or aggregated to suit specific needs. Python’s flexibility enables complex transformations and operations such as merging datasets, grouping data, or creating pivot tables, which are often necessary for advanced analytics or machine learning models.
Preliminary tasks – the importance of staging data
The extracted data is sent to a temporary storage area called the data staging area prior to the transformation and cleansing process. This is done to avoid the need to extract data again, should any problem occur (reference: A Five-Layered Business Intelligence Architecture by In Ong et al.).
