---
title: "How do you improve performance of large Python applications?"  
description: "How do you improve performance of large Python applications?"  
author: "Anubhav Sharma"  
published: 2025-11-06  
updated: 2025-11-06  
canonical: https://www.mindstick.com/interview/34405/how-do-you-improve-performance-of-large-python-applications  
category: "python"  
tags: ["python-3.4", "Python 3"]  
reading_time: 1 minute  

---

# How do you improve performance of large Python applications?

## Answer:

- Use [**NumPy**](https://www.mindstick.com/interview/34402/introduction-to-numpy), **Pandas**, or **Cython** for heavy computations.
- Use **multiprocessing** or **asyncio** for parallelism.
- Cache results via `functools.lru_cache`.
- Use **PyPy** or **Just-In-Time (JIT)** compilers.
- Profile code using `cProfile` or `line_profiler`.

Example:

```python
from functools import lru_cache

@lru_cache(maxsize=None)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)
```

## Answers

### Answer by Anubhav Sharma

## Answer:

- Use [**NumPy**](https://www.mindstick.com/interview/34402/introduction-to-numpy), **Pandas**, or **Cython** for heavy computations.
- Use **multiprocessing** or **asyncio** for parallelism.
- Cache results via `functools.lru_cache`.
- Use **PyPy** or **Just-In-Time (JIT)** compilers.
- Profile code using `cProfile` or `line_profiler`.

Example:

```python
from functools import lru_cache

@lru_cache(maxsize=None)
def fibonacci(n):
    if n < 2:
        return n
    return fibonacci(n-1) + fibonacci(n-2)
```


---

Original Source: https://www.mindstick.com/interview/34405/how-do-you-improve-performance-of-large-python-applications

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