Common Performance Problems in Python and How to Solve Them

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1. Inefficient Looping

Loops can be the Achilles’ heel of your Python code. Writing loops the wrong way can make your code as slow as waiting for a Windows update.

Its better to just get another job.. Maybe become a farmer.. at least you will get to go outside..

(if you are thinking “What is an ‘outside’?” - i want you to turn off your computer right now.. and run out your front door while singing classical music)

Problem: Looping through a massive list with clunky operations can turn your code into a tortoise.

Solution: Swap out those clunky loops for sleek list comprehensions or generator expressions. They’re like the sports cars of looping—fast and stylish.

Example:

Inefficient approach (using for loop):

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numbers = [i for i in range(1000000)]
squared_numbers = []
for num in numbers:
    squared_numbers.append(num ** 2)

Optimized approach (using list comprehension):

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numbers = [i for i in range(1000000)]
squared_numbers = [num ** 2 for num in numbers]

List comprehensions are like the express lane at the grocery store—get in, get out, and get on with your life.

2. Excessive Memory Usage

Ever felt like your program is a memory hoarder? Keeping unnecessary data around can bloat your memory usage faster than my inbox after a weekend.

Problem: Storing huge datasets in memory can make your program as sluggish as me before my morning coffee.

Solution: Use Python’s gc module to collect garbage (not the smelly kind) or opt for memory-efficient data structures like deque from the collections module.

Example:

Inefficient approach (using lists):

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import random
large_list = [random.randint(0, 100) for _ in range(10000000)]

Optimized approach (using deque):

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from collections import deque
import random

large_deque = deque(random.randint(0, 100) for _ in range(10000000))

Using deque is like upgrading from a tricycle to a Harley—smooth and efficient.

3. Inefficient Data Structures

Choosing the wrong data structure is like bringing a spoon to a knife fight—not very effective.

Problem: Using a list for operations that require fast lookups can slow you down.

Solution: Use dictionaries or sets for faster lookups. They’re like the ninjas of data structures—quick and stealthy.

Example:

Inefficient approach (searching in a list):

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items = ['apple', 'banana', 'orange', 'pear']
if 'banana' in items:
    print("Found banana!")

Optimized approach (using a set for fast lookups):

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items = {'apple', 'banana', 'orange', 'pear'}
if 'banana' in items:
    print("Found banana!")

Sets are like the express checkout lane—no waiting, no hassle.

4. Global Variable Access

Global variables can be like that one friend who always slows down the group—best to avoid them when possible.

Problem: Accessing global variables inside functions can make your code sluggish.

Solution: Pass variables as parameters or encapsulate them in classes. Keep it local, keep it speedy.

Example:

Inefficient approach (using global variables):

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x = 10
y = 20

def add():
    return x + y  # Accesses global variables

print(add())

Optimized approach (passing arguments):

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def add(x, y):
    return x + y

print(add(10, 20))  # Passing variables as arguments

Passing parameters is like using a GPS—direct and efficient.

5. Unnecessary Function Calls

Calling functions unnecessarily is like taking the scenic route when you’re late—not ideal.

Problem: Frequent function calls, especially in loops, can add overhead.

Solution: Inline simple logic or use functools.lru_cache to cache results of expensive function calls.

Example:

Inefficient approach (repeated function calls):

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def square(x):
    return x * x

result = [square(i) for i in range(1000000)]

Optimized approach (avoiding repeated function calls):

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result = [i * i for i in range(1000000)]  # Inlining the logic

Inlining is like taking the shortcut you wish you’d known about sooner.

6. Inefficient String Concatenation

Using + for string concatenation in loops is like using duct tape for everything—it works, but there’s a better way.

Problem: Concatenating strings with + in a loop can be slow and memory-intensive.

Solution: Use str.join() for efficient string concatenation.

Example:

Inefficient approach (using + in loops):

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result = ""
for i in range(1000000):
    result += str(i)

Optimized approach (using join()):

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result = ''.join(str(i) for i in range(1000000))

Using join() is like upgrading from dial-up to fiber optic—blazing fast.

7. Inefficient Database Queries

Querying the database inefficiently is like asking your grandma for tech support—bless her heart, but it’s going to take a while.

Problem: Executing multiple queries in a loop can bog down your application.

Solution: Use batch inserts and prepared statements to minimize database calls.

Example:

Inefficient approach (executing queries in a loop):

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import sqlite3
conn = sqlite3.connect('example.db')
cursor = conn.cursor()

for i in range(1000):
    cursor.execute("INSERT INTO my_table (value) VALUES (?)", (i,))
conn.commit()

Optimized approach (using batch insert):

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import sqlite3
conn = sqlite3.connect('example.db')
cursor = conn.cursor()

data = [(i,) for i in range(1000)]
cursor.executemany("INSERT INTO my_table (value) VALUES (?)", data)
conn.commit()

Batch inserts are like ordering pizza for the whole team instead of one slice at a time—efficient and satisfying.