Python: apply() & map() VERSUS for loops

apply() and map() are more efficient than for loops when dealing with DataFrames in Pandas for several reasons:

1. Vectorized Operations: apply() and map() operations are implemented as vectorized operations. This means that they are optimized and performed efficiently at the C level in Pandas, which is significantly faster than interpreting Python code in a for loop. Vectorized operations process entire arrays of data at once, resulting in improved performance.
2. Optimized Code Paths: Pandas internally optimizes the code paths for apply() and map() operations, making them faster than manually iterating over DataFrame rows or elements in a loop. This optimization takes advantage of low-level optimizations in the underlying libraries like NumPy.
3. Parallelization: In certain cases, Pandas can leverage multi-core processors and parallelize operations when using apply(). This can lead to substantial speed improvements, especially for large DataFrames.
4. Code Readability and Maintainability: Using apply() and map() can lead to more concise and readable code. It abstracts away the low-level iteration details, making the code easier to understand and maintain.
5. Memory Efficiency: Vectorized operations typically use less memory compared to for loops because they don't require the creation…