Python allocates memory for integers frequently, which can significantly impact performance. Each integer in CPython is represented as a PyLongObject, stored in the heap. This leads to many allocations, especially since integers are widely used in programming. Initial experiments showed that adding integers and printing them can trigger hundreds of thousands of allocations, but optimizations exist to reduce this number during addition operations. When testing the addition of integers, it was found that printing caused the majority of the allocations. Without the print statement, the script allocated only 905 PyLongObjects during 100,000 iterations, compared to over 100,000 when printing. The internal method used for addition, `long_add`, checks if both integers are compact and can optimize allocations for small integers. For larger integers, a freelist is employed to reuse previously allocated objects, minimizing the need for new allocations. The memory management in CPython utilizes a pool allocator, which organizes memory into fixed-size chunks. This approach speeds up allocation and reduces fragmentation compared to traditional malloc. Overall, while Python does allocate memory for integers often, its internal optimizations help manage these allocations effectively, especially in arithmetic operations.