What is data transformation?

Data transformation is the process of converting data from one format to another.

Data transformation may involve:

• Data Cleansing: Removing inconsistencies and errors.
  • https://github.com/rhyslwells/DE_Tools/blob/main/Explorations/Cleaning
• Structuring: Organizing raw data.
  • https://github.com/rhyslwells/DE_Tools/blob/main/Explorations/Transformation/multi_index.ipynb
  • https://github.com/rhyslwells/DE_Tools/blob/main/Explorations/Transformation/reshaping.ipynb
• Aggregation: Summarizing data for analysis (Pandas Pivot Table).
  • https://github.com/rhyslwells/DE_Tools/blob/main/Explorations/Transformation/group_by.ipynb
  • https://github.com/rhyslwells/DE_Tools/blob/main/Explorations/Transformation/pivot_table.ipynb
• Selection:
  • Data Selection
• Joining: Merging datasets for completeness (also see SQL Joins).
  • https://github.com/rhyslwells/DE_Tools/blob/main/Explorations/Transformation/Joining.ipynb

Others:

• Normalizing: Standardizing data distributions for consistency. See Normalisation
• Sorting: Arranging data in a logical order.
• Enriching: Adding external or missing information.
• Validating: Ensuring data integrity and accuracy.

Key Aspects

• Normalization & Scaling: Adjusting numerical values to a consistent range. See: Normalisation of data
• Data Type Conversion: Changing data types (e.g., converting strings to integers).
• Schema Normalization: Ensuring a consistent data structure for efficiency.
• File Format Conversion: Transforming data between formats (e.g., .xls to .csv).

Related:

• Data Transformation with Pandas
• Data transformation in Data Engineering
• Data transformation in Machine Learning
• Benefits of Data Transformation