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Handling Missing Data | Handling Garbage Values | Data Preprocessing in Python | Data Science
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📊 Are you struggling with data that's got more than just numbers? In this tutorial as we learn how to handle garbage entries versus recognized missing values in our datasets.
🚫 Garbage entries, like special characters and text in a numeric column, can wreak havoc on your analyses. We'll show you why this happens and how it leads to pandas categorizing a column as an 'object'.
🔍 Discover the key difference between these garbage entries and recognized missing values like NaN, NULL, and NA. Understanding this distinction is crucial for accurate data analysis.
✅ Once you've cleaned up your dataset, you can choose the right missing value treatment. Ensure your data is ready for analysis with our step-by-step approach.
📚 Happy Learning!
🚫 Garbage entries, like special characters and text in a numeric column, can wreak havoc on your analyses. We'll show you why this happens and how it leads to pandas categorizing a column as an 'object'.
🔍 Discover the key difference between these garbage entries and recognized missing values like NaN, NULL, and NA. Understanding this distinction is crucial for accurate data analysis.
✅ Once you've cleaned up your dataset, you can choose the right missing value treatment. Ensure your data is ready for analysis with our step-by-step approach.
📚 Happy Learning!
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