Optimization Techniques for High-Performance Python Code in Data Science Applications

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@article{1707601,
      author = {Praggnya Kanungo},
      title = {Optimization Techniques for High-Performance Python Code in Data Science Applications},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {4},
      number = {10},
      pages = {269-274},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1707601.pdf},
      abstract = {This research paper explores various optimization techniques for enhancing the performance of Python code in data science applications. As data science continues to grow in importance across industries, the need for efficient and high-performance code becomes increasingly critical. This study investigates multiple approaches to optimize Python code, including vectorization, just-in-time compilation, parallel processing, and memory management techniques. We present a comprehensive analysis of these methods, their implementation, and their impact on code performance. Through a series of benchmarks and case studies, we demonstrate significant improvements in execution time and resource utilization. Our findings provide valuable insights for data scientists and developers seeking to optimize their Python code for large-scale data processing and analysis tasks.},
      keywords = {Python, Optimization, Vectorization, JIT, Parallel Processing, Memory Management, Performance, Data Science},
      month = {April},
  }