Problem Statement
When should you prefer vectorized operations over DataFrame.apply, and what are practical tips?
Explanation
Prefer vectorized operations and built-ins like where, clip, str, and dt for series-wise transforms. They run in optimized C and avoid per-row Python overhead. apply is flexible but slower since it calls Python for each row or column.
If you must use apply, limit it to column-wise functions and avoid row-wise lambdas. For very large data, consider cython, numba, or pushing work into database engines when possible.
Code Solution
SolutionRead Only
df['z'] = (df['x']+df['y']).clip(lower=0) # slower # df['z']=df.apply(lambda r: max(r.x+r.y,0), axis=1)
Practice Sets
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