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9. Pandas Series: Type Conversion & Copying

Status

Done

TABLE OF CONTENT

1. Type Conversion

df['series'].astype(dtype) → Force cast to specified dtype (e.g., 'int', 'float', 'str', 'category')
df['series'].convert_dtypes() → Convert to best possible nullable dtype (Pandas 1.0+)
df['series'].infer_objects() → Infer better dtype for object columns (soft conversion)

2. Memory & Copy Management

df['series'].copy(deep=True) → Create a copy (deep=True for full memory copy)
df['series'].bool() → Return the bool value (only for single-element boolean Series)

Key Notes:

astype() vs convert_dtypes():

astype() forces conversion (may lose info)
convert_dtypes() intelligently chooses nullable dtypes (e.g., Int64 instead of float for missing values)

Use infer_objects() when dealing with mixed-type object columns
bool() raises ValueError if Series doesn't have exactly 1 boolean element

Best Practices:

Prefer convert_dtypes() for modern nullable types
Use astype() when you need specific control
Always copy() before modifying if you need the original
bool() is mainly for scalar boolean extraction

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1. Type Conversion Methods

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2. Memory & Copy Management

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Comparison Table

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Best Practices