Will Python Get Faster

Python Programming

As a programmer who regularly uses Python, I have always wondered if the performance of this popular programming language will improve over time. Python has gained immense popularity due to its simplicity and readability, but it is often criticized for its execution speed. In this article, I will explore the question: will Python get faster?

The Need for Speed

Python’s execution speed has been a topic of debate among developers for quite some time. While Python is known for its ease of use and productivity, it has been perceived as slower compared to other programming languages like C or Java. This performance gap becomes more apparent when dealing with computationally intensive tasks or running large-scale applications.

However, it’s important to note that Python has come a long way in terms of performance improvements. The development community has been actively working on enhancing the language’s execution speed, resulting in various optimizations and advancements.

Python Implementations

Python has different implementations, such as CPython, Jython, IronPython, and PyPy, each with its own performance characteristics. CPython, the reference implementation, is written in C and is widely used. It provides good compatibility and stability, but its performance can be a limiting factor.

On the other hand, PyPy, a Just-in-Time (JIT) compiler implementation of Python, focuses on improving execution speed. It achieves this by dynamically optimizing the code at runtime, which can lead to significant performance gains compared to CPython.

Compiler Optimizations

Python 3 introduced many performance optimizations, such as faster function calls, improved string handling, and enhanced memory management. These improvements help reduce the overhead and make Python faster in various scenarios.

Furthermore, there are numerous tools and libraries available that can help optimize Python code. Profilers, like cProfile, allow developers to identify performance bottlenecks and make targeted optimizations. Libraries like NumPy and Pandas provide high-performance data processing capabilities by leveraging optimized C or Fortran code under the hood.

The Future of Python’s Speed

Given the ongoing efforts to improve Python’s performance, it is safe to say that Python will likely continue to get faster in the future. The Python development community is highly active and devoted to enhancing the language’s capabilities.

Some upcoming developments, such as static type checking with tools like MyPy and gradual typing, aim to improve performance by enabling more advanced static analysis and optimization techniques. These advancements could potentially narrow the performance gap between Python and statically-typed languages.

Additionally, the adoption of new hardware architectures, such as GPUs and vectorized processors, opens up opportunities for further performance improvements. Libraries like TensorFlow and PyTorch already benefit from these hardware accelerators, allowing Python to harness their power for faster execution.


While Python may have historically been perceived as a slower language, it is important to recognize the ongoing efforts to improve its performance. With advancements in implementations, compiler optimizations, and the adoption of new technologies, Python’s execution speed is expected to continue to increase.

As a Python developer, I am excited about the future prospects of the language. Python’s simplicity and versatility have made it a favorite choice for many developers, and the continuous improvements in speed will only enhance its appeal. So, yes, Python will get faster, and I look forward to the exciting possibilities it will bring.