Status
Done
TABLE OF CONTENT
1. Type Conversion
df['series'].astype(dtype) → Force cast to specified dtype (e.g.,Â'int',Â'float',Â'str',Â'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() vsÂconvert_dtypes():astype() forces conversion (may lose info)convert_dtypes() intelligently chooses nullable dtypes (e.g.,ÂInt64 instead ofÂfloat for missing values)- UseÂ
infer_objects()Â when dealing with mixed-type object columns bool()Â raises ValueError if Series doesn't have exactly 1 boolean element
Best Practices:
- PreferÂ
convert_dtypes()Â for modern nullable types - UseÂ
astype()Â when you need specific control - AlwaysÂ
copy()Â before modifying if you need the original bool()Â 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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