7 Essential Python One-Liners for Quick Data Cleaning

Manikandan

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1. Capitalize Strings

Ensure uniform string formatting across datasets by capitalizing text fields for consistency.

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2. Convert Data Types

Standardize data types to maintain accuracy—easily convert fields like age to integers.

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3. Validate Numeric Ranges

Set acceptable value ranges, such as checking that ages are within 18 to 60, and apply defaults for outliers.

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4. Validate Email

Quickly check email formats for validity, replacing any invalid emails with a standard placeholder.

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5. Handle Missing Values

Identify and fill missing data points (e.g., set default salary values for missing entries) to keep datasets complete.

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6. Standardize Date Formats

Convert various date formats to a single standard for easier comparisons and analysis.

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7. Remove Negative Values

Replace negative values in fields like salary or age with zero or a specified default for logical consistency.

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