host = 127.0.0.1

port = 3306

import configparser

config = configparser.ConfigParser()

config.read('config.ini')

database_host = config.get('Database', 'host')

database_port = config.getint('Database', 'port')

print(f"Database Host: {database_host}")

print(f"Database Port: {database_port}")

config['Database']['host'] = 'localhost'

config['Database']['port'] = '5432'

with open('config.ini', 'w') as configfile:

config.write(configfile)

from dotenv import load_dotenv

import os

load_dotenv()

database_host = os.getenv('DATABASE_HOST')

database_port = os.getenv('DATABASE_PORT')

print(f"Database Host: {database_host}")

print(f"Database Port: {database_port}")

This repo includes scripts for profiling memory usage and performance in Python.

Memory profiling is the process of analyzing a program to understand its memory consumption patterns. This involves tracking how much memory your application uses and identifying memory-intensive parts of your code.

1. **Optimize Memory Usage**: Memory profiling helps identify memory leaks and inefficient memory usage, which can lead to improved performance and reduced memory consumption.

2. **Enhance Performance**: By understanding which parts of your code are using the most memory, you can refactor or optimize those areas to make your application more efficient.

3. **Resource Management**: For applications running on systems with limited resources, efficient memory usage is critical to maintaining stability and performance.

Performance profiling is the process of analyzing a program to determine its execution time and identify bottlenecks. This involves measuring how long different parts of your code take to execute.

1. **Identify Bottlenecks**: Performance profiling helps pinpoint parts of your code that are slow or inefficient, allowing you to focus optimization efforts where they are needed most.

2. **Improve Efficiency**: By understanding execution times, you can make informed decisions about code improvements to enhance overall performance.

3. **Benchmarking**: Profiling provides benchmarks that help you measure the impact of optimizations and compare different approaches.

- `memory_profiler_example.py`: Demonstrates how to use the `memory_profiler` library to track memory usage of a Python function.

- `performance_profiling.py`: Uses the `cProfile` module to profile the execution time of a Python function.

To use these profiling scripts, make sure you have the necessary libraries installed:

pip install memory_profiler

from memory_profiler import profile

import time

def compute_sum(n):

total = 0

for i in range(n):

total += i

time.sleep(0.01) # some delay

return total

if __name__ == "__main__":

result = compute_sum(1000)

print(f"Sum is {result}")

import cProfile

import pstats

import io

def compute_sum(n):

total = 0

for i in range(n):

total += i

return total

def main():

result = compute_sum(100000)

print(f"Sum is {result}")

if __name__ == "__main__":

pr = cProfile.Profile()

pr.enable()

main()

pr.disable()

s = io.StringIO()

sortby = 'cumulative'

ps = pstats.Stats(pr, stream=s).sort_stats(sortby)

ps.print_stats()

print(s.getvalue())