Python Performance Optimization | Actionable Guide

Roman Latyshenko


Why Optimize Python Code?

As the needs of digital companies are continuously growing, becoming more and more complicated, especially when building a large scale product, upgrading the expertise and searching for fresh solutions to solve customer pains are a must for software development companies. 

Python is one of the most popular and progressive backend programming languages, thus, many tend to use it in the creation of large-scale high load applications. 

However, after being inspired by the new ground that will probably be broken using Python to build a backend, many companies face the reality where the greatest Python drawback— loss of performance—is revealed. This Python flaw may ruin the impression of even the clearest code.

All software developers are always interested in the maximum speed with minimal code size 


How to Optimize Python Performance?

Even with the comparatively low program performance, Python is still one of the most attractive programming languages for thousands of developers across the globe due to its advantages. 

That’s why there is little practical value in preferring another programming language over Python just because of its performance flaws. Any team of Python developers (provided that they are professional enough) is definitely able to give you several possible solutions to this problem on the spot. As long as this issue is still relevant, the team doesn’t want to stand aside offering you the options to improve the Python performance.

Usually, we offer several solutions to customers discussing the pros and cons of each for the specific application. 

Methods of Optimization 

In general, all the existing methods of Python optimization are based on two approaches: 

  • Another environment for the programming language. 
  • Refactoring of the existing Python code in order to increase its performance.

Each of the ways to improve code performance we’re about to recommend is based on one of the above-mentioned approaches:


Cython is a Python superset, a special version of the language that compiles to C and interfaces with C/C++ code. This is one of the ways to write C extensions for Python (to implement the blocks of code that requires the particular performance). Besides, it can be used separately from Python. The flip side of this method is a non-automatic code database migration, thus, technically, not exactly Python is used. 

Cython is common enough to be used in numerous libraries. For instance, some algorithms in scikit-learn are developed using Cython to increase the performance. 


Among all the candidates contributing to the increase of a Python performance, PyPy is one of the most prominent. For instance, Quora uses it in production. PyPy probably has all the chances to become a good default environment due to the excellent code compatibility. 

PyPy uses the JIT compiler as well as a Google Chrome engine to support JavaScript that is exactly the reason for performance improvement. However, we have some bad news for all lovers to stay ahead of the others using the latest possibilities the Python language offers, as PyPy provides support of the new language versions with a considerable delay.


The project sponsored by Dropbox uses LLVM (Low Level Virtual Machine) infrastructure for a compiler that uses JIT as well. If to compare with PyPy, Pyston is still on the first stage of development with the actual version 0.5.1 that doesn’t support all the possibilities of the programming language.


Instead of increasing the performance of the Python interpreter and bytecode compiler, some web developers suggested that Python code to be translated into the other languages that haven’t any performance problem. 

The same has done the team of Nuitka: its project compiles the Python applications to the C++ code. The realization is largely based on the existing binary files of the Python environment that imposes certain limitations on project migration but you shouldn’t underestimate a performance increase that is ensured by such a translation. In the future, the team is about to translate Python directly into C (this plan is actually for the far future) that will increase the performance drastically.

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    Numba combines two above-mentioned approaches. It takes the idea of accelerating only certain programming language blocks (for example, math calculations connected to the central processor) from Cython. Numba does it with the help of LLVM as well as other projects, discussed in the post. The functions compiled with the help of Numba can be marked by the specific decorators so that the tool will be working on improving their performance together with NumPy.

    A few other ways to speed up Python:

    Python C Extensions 

    The Python creators made a great present to those developers who work with this programming language, namely Python/C API that provides the possibility of relatively transparent integration of C code into Python applications. There are only two drawbacks of this solution:

    • The barrier to entry of C and Python/C API is higher than in bare Python that makes it impossible for developers who aren’t familiar with C to use this method. 
    • One of the Python great advantages is the speed of development. Using C to create the part of the application slows this process down.


    SciPy is the package of applied mathematical procedures based on the Numpy Python extension. A Python session turns into the real environment of data processing and sophisticated systems prototyping. 

    The module scipy.optimize allows the realization of the following procedures:

    • Conditional and unconditional minimization of scalar functions of several variables (minim) using the different algorithms (Nelder–Mead method, BFGS, COBYLA, SLSQP, and Newton’s method).
    • Global optimization (for example, basinhopping, diff_evolution).
    • Minimization of scalar functions by one variable (minimum_scalar) and root search (root_scalar).
    • Multidimensional solvers of a system of equations (root) using the different algorithms (Powell’s method, Levenberg–Marquardt algorithm). 

    Basic Optimization Principles

    Considering a code optimization makes sense only after the program has produced the valid output. 

    If you find out your software is slow, profiling is a way to identify the parts to optimize. 

    The first thing to do is to determine the bottlenecks. Related blocks of code such as cycles and subprograms.

    Then, decide on the approach to code optimization. The only way to choose is to proceed from the technical characteristics of your project and the technical capacities of your team. As a rule, a software architect or back-end team lead is responsible for taking this decision together with a customer.

    Looking for a solution to improve Python performance? Ask an experienced software architect:

      Considering the relevance of Python optimization, bear in mind the key success factors:

      1. Simplicity. The code should be clean, modular, and easy-to-read. Each module should be naturally integrated into the program with the possibility to edit or delete separate parts without interfering with the other parts of a program. 
      2. Performance. You have to get the performance growth. As a rule, a successfully optimized program increases the speed by 20-30 percent compared to the initial version. 
      3. Time. Optimization and further debugging shouldn’t take much time. A period not exceeding 10-15 percent of all time spent on product development. Otherwise, it would not be cost-effective.  

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