with Python libraries like pandas, we can automate many common cleaning tasks to create a reliable, reproducible pipeline
Before we begin any cleaning, we need to understand the quality of the data we're working with. So the first step involves assessing the current state of your data.
Run Basic Data Quality Checks
Standardize Data Types
One of the most common issues in raw data is inconsistent data types.
Handle Missing Values
Missing values can significantly impact our analysis. Rather than dropping data records with missing values, we can use imputation strategies:
Detect and Handle Outliers
Outliers can skew our analysis, so we need to handle them carefully.
Validate the Results
After cleaning, we need to verify that our pipeline worked as expected:
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