To normalize data across multiple datasets, start by identifying common variables and applying consistent scaling or transformation methods. The goal is to ensure data from different sources follows the same statistical distribution or scale, making comparisons and analyses valid. Common techniques include min-max scaling (adjusting values to a 0-1 range) and z-score standardization (centering data around a mean of 0 with a standard deviation of 1). For example, if Dataset A measures temperature in Celsius (0–100) and Dataset B uses Fahrenheit (32–212), converting both to a 0-1 scale using min-max normalization would allow direct comparison. Tools like Python’s scikit-learn provide built-in functions (e.g., MinMaxScaler, StandardScaler) to automate this process. Always fit scalers on a reference dataset or a combined dataset to avoid introducing bias when merging.
Next, address structural inconsistencies and categorical data. Datasets often use different formats for the same information—such as dates stored as strings in one dataset and Unix timestamps in another. Convert these to a shared format (e.g., ISO 8601 dates) before merging. For categorical variables like product categories, ensure labels are harmonized. For example, if one dataset uses “Electronics” and another uses “E-devices,” map both to a common term. Missing data handling is also critical: decide whether to impute missing values (using mean, median, or machine learning models) or exclude incomplete records. Tools like pandas in Python can merge datasets while aligning columns, and libraries like category_encoders handle categorical encoding consistently across datasets.
Finally, validate and document the process. After normalization, check for outliers, unexpected value ranges, or misaligned categories. Use summary statistics (mean, variance) and visualizations (histograms, boxplots) to compare distributions across datasets. For example, if a normalized income field in one dataset has a mean of 0.5 (min-max scaled) but another has a mean of 0.1, investigate whether this reflects true differences or normalization errors. Automate checks using unit tests or data validation frameworks like Great Expectations. Document all steps—including scaling methods, category mappings, and imputation rules—so others can reproduce the workflow. This ensures transparency and simplifies updates when new datasets are added.