In this exploration of optimizing Python code, we look at common issues that impede performance and cause overhead. We look at two issues here: one related to nested loops and the other related to memory/allocation issues caused by reading large data sets.
With the nested loops issue, we look at an example use case to understand the nested loops dilemma and then move on to a solution that serves as an alternative to avoid the performance issues caused by nested loops.
With memory/allocation issues encountered with large data sets, we explored multiple data reading strategies and compared the performance of each strategy. Let's explore further.
While nested loops are a common programming construct, their inefficient implementation can lead to suboptimal performance. One notable challenge one may encounter with nested loops is the “kernel still running” problem. This happens when the code has nested loops that are implemented inefficiently, leading to long execution times; and in most cases, an infinite loop. Nested loops are easy to implement, but optimizing performance sometimes requires sacrificing the simplicity of nested structures. Nested loops can contribute to greater algorithmic complexity, leading to longer execution times, especially when dealing with large data sets. It's important to note that while nested loops may not be inherently “bad,” understanding their implications and considering alternative approaches can lead to more efficient Python code. In this case, it is good to consider Python functions and libraries effectively.
We have two files where some records are duplicates of each other. There is an identifier column in…
