Da Type Conversion — Free Data Analytics Tutorial
Learn Da Type Conversion in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
TL;DR: Learn Da Type Conversion in Data Analytics with a free, beginner-friendly tutorial, examples and practice for Indian students on Syllab.in.
Written & reviewed by the Syllab.in Academic Team (CBSE/NCERT subject experts) · Updated
Da Type Conversion in Data Analytics
Convert data types with pd.to_numeric(), pd.to_datetime(), astype().
pd.to_numeric() with errors='coerce' converts invalid values to NaN.
astype() for simple conversions: df['col'].astype('int'), .astype('float'), .astype('str')
Categorical type reduces memory usage for repeated values.
Check data types with df.dtypes or df.info().
Da Type Conversion — Syntax
# Basic conversion: df['col'].astype('int')
# Safe numeric: pd.to_numeric(df['col'], errors='coerce')
# To datetime: pd.to_datetime(df['col'], format='%Y-%m-%d')
# To category: df['col'].astype('category')
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