Type Conversion

Converting between data types is essential for proper data analysis. Pandas provides several methods for type conversion.

Checking Data Types

Inspect column types before converting:

Using astype()

Convert columns to specific types:

String to Numeric

Handle string-to-number conversion:

pd.to_numeric()

Safely convert to numbers with error handling:

Boolean Conversion

Convert to boolean type:

Category Type

Convert to categorical for efficiency:

Multiple Column Conversion

Convert multiple columns at once:

Exercise: Convert Strings

Exercise: Safe Numeric Conversion

Key Points

• .dtypes shows column types
• .astype() converts to specified type
• pd.to_numeric() with errors='coerce' for safe conversion
• .map() for custom value mapping
• category type saves memory for repeated values
• Pass dict to .astype() for multiple columns