5 Python bugs that every developer is still facing in 2024 (and how to fix them)

Written by Rupesh Sharma AKA @hackyrupesh

Python, with its simplicity and beauty, is one of the most popular programming languages in the world. However, even in 2024, certain flaws continue to trouble developers. These problems aren’t always due to weaknesses in Python, but rather to its design, behavior, or common misconceptions that result in unanticipated outcomes. In this blog article, we’ll look at the top 5 Python issues that every developer still encounters in 2024, as well as their remedies.

1. Mutable Default Arguments: A Silent Trap

The Problem

One of the most infamous Python bugs is the mutable default argument. When a mutable object (like a list or dictionary) is used as a default argument in a function, Python only evaluates this default argument once when the function is defined, not each time the function is called. This leads to unexpected behavior when the function modifies the object.

Example


<span>def</span> <span>append_to_list</span><span>(</span><span>value</span><span>,</span> <span>my_list</span><span>=</span><span>[]):</span>
    <span>my_list</span><span>.</span><span>append</span><span>(</span><span>value</span><span>)</span>
    <span>return</span> <span>my_list</span>
<span>print</span><span>(</span><span>append_to_list</span><span>(</span><span>1</span><span>))</span>  <span># Outputs: [1] </span><span>print</span><span>(</span><span>append_to_list</span><span>(</span><span>2</span><span>))</span>  <span># Outputs: [1, 2] - Unexpected! </span><span>print</span><span>(</span><span>append_to_list</span><span>(</span><span>3</span><span>))</span>  <span># Outputs: [1, 2, 3] - Even more unexpected! </span>
<span>def</span> <span>append_to_list</span><span>(</span><span>value</span><span>,</span> <span>my_list</span><span>=</span><span>[]):</span>
    <span>my_list</span><span>.</span><span>append</span><span>(</span><span>value</span><span>)</span>
    <span>return</span> <span>my_list</span>

<span>print</span><span>(</span><span>append_to_list</span><span>(</span><span>1</span><span>))</span>  <span># Outputs: [1] </span><span>print</span><span>(</span><span>append_to_list</span><span>(</span><span>2</span><span>))</span>  <span># Outputs: [1, 2] - Unexpected! </span><span>print</span><span>(</span><span>append_to_list</span><span>(</span><span>3</span><span>))</span>  <span># Outputs: [1, 2, 3] - Even more unexpected! </span>
def append_to_list(value, my_list=[]):
    my_list.append(value)
    return my_list

print(append_to_list(1))  # Outputs: [1] print(append_to_list(2))  # Outputs: [1, 2] - Unexpected! print(append_to_list(3))  # Outputs: [1, 2, 3] - Even more unexpected!

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The Solution

To avoid this, use None as the default argument and create a new list inside the function if needed.


<span>def</span> <span>append_to_list</span><span>(</span><span>value</span><span>,</span> <span>my_list</span><span>=</span><span>None</span><span>):</span>
    <span>if</span> <span>my_list</span> <span>is</span> <span>None</span><span>:</span>
        <span>my_list</span> <span>=</span> <span>[]</span>
    <span>my_list</span><span>.</span><span>append</span><span>(</span><span>value</span><span>)</span>
    <span>return</span> <span>my_list</span>
<span>print</span><span>(</span><span>append_to_list</span><span>(</span><span>1</span><span>))</span>  <span># Outputs: [1] </span><span>print</span><span>(</span><span>append_to_list</span><span>(</span><span>2</span><span>))</span>  <span># Outputs: [2] </span><span>print</span><span>(</span><span>append_to_list</span><span>(</span><span>3</span><span>))</span>  <span># Outputs: [3] </span>
<span>def</span> <span>append_to_list</span><span>(</span><span>value</span><span>,</span> <span>my_list</span><span>=</span><span>None</span><span>):</span>
    <span>if</span> <span>my_list</span> <span>is</span> <span>None</span><span>:</span>
        <span>my_list</span> <span>=</span> <span>[]</span>
    <span>my_list</span><span>.</span><span>append</span><span>(</span><span>value</span><span>)</span>
    <span>return</span> <span>my_list</span>

<span>print</span><span>(</span><span>append_to_list</span><span>(</span><span>1</span><span>))</span>  <span># Outputs: [1] </span><span>print</span><span>(</span><span>append_to_list</span><span>(</span><span>2</span><span>))</span>  <span># Outputs: [2] </span><span>print</span><span>(</span><span>append_to_list</span><span>(</span><span>3</span><span>))</span>  <span># Outputs: [3] </span>
def append_to_list(value, my_list=None):
    if my_list is None:
        my_list = []
    my_list.append(value)
    return my_list

print(append_to_list(1))  # Outputs: [1] print(append_to_list(2))  # Outputs: [2] print(append_to_list(3))  # Outputs: [3]

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References

Python’s default argument gotcha

2. The Elusive `KeyError` in Dictionaries

The Problem

KeyError occurs when trying to access a dictionary key that doesn’t exist. This can be especially tricky when working with nested dictionaries or when dealing with data whose structure isn’t guaranteed.

Example


<span>data</span> <span>=</span> <span>{</span><span>'</span><span>name</span><span>'</span><span>:</span> <span>'</span><span>Alice</span><span>'</span><span>}</span>
<span>print</span><span>(</span><span>data</span><span>[</span><span>'</span><span>age</span><span>'</span><span>])</span>  <span># Raises KeyError: 'age' </span>
<span>data</span> <span>=</span> <span>{</span><span>'</span><span>name</span><span>'</span><span>:</span> <span>'</span><span>Alice</span><span>'</span><span>}</span>
<span>print</span><span>(</span><span>data</span><span>[</span><span>'</span><span>age</span><span>'</span><span>])</span>  <span># Raises KeyError: 'age' </span>
data = {'name': 'Alice'}
print(data['age'])  # Raises KeyError: 'age'

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The Solution

To prevent KeyError, use the get() method, which returns None (or a specified default value) if the key is not found.


<span>print</span><span>(</span><span>data</span><span>.</span><span>get</span><span>(</span><span>'</span><span>age</span><span>'</span><span>))</span>  <span># Outputs: None </span><span>print</span><span>(</span><span>data</span><span>.</span><span>get</span><span>(</span><span>'</span><span>age</span><span>'</span><span>,</span> <span>'</span><span>Unknown</span><span>'</span><span>))</span>  <span># Outputs: Unknown </span>
<span>print</span><span>(</span><span>data</span><span>.</span><span>get</span><span>(</span><span>'</span><span>age</span><span>'</span><span>))</span>  <span># Outputs: None </span><span>print</span><span>(</span><span>data</span><span>.</span><span>get</span><span>(</span><span>'</span><span>age</span><span>'</span><span>,</span> <span>'</span><span>Unknown</span><span>'</span><span>))</span>  <span># Outputs: Unknown </span>
print(data.get('age'))  # Outputs: None print(data.get('age', 'Unknown'))  # Outputs: Unknown

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For nested dictionaries, consider using the defaultdict from the collections module or libraries like dotmap or pydash.


<span>from</span> <span>collections</span> <span>import</span> <span>defaultdict</span>
<span>nested_data</span> <span>=</span> <span>defaultdict</span><span>(</span><span>lambda</span><span>:</span> <span>'</span><span>Unknown</span><span>'</span><span>)</span>
<span>nested_data</span><span>[</span><span>'</span><span>name</span><span>'</span><span>]</span> <span>=</span> <span>'</span><span>Alice</span><span>'</span>
<span>print</span><span>(</span><span>nested_data</span><span>[</span><span>'</span><span>age</span><span>'</span><span>])</span>  <span># Outputs: Unknown </span>
<span>from</span> <span>collections</span> <span>import</span> <span>defaultdict</span>

<span>nested_data</span> <span>=</span> <span>defaultdict</span><span>(</span><span>lambda</span><span>:</span> <span>'</span><span>Unknown</span><span>'</span><span>)</span>
<span>nested_data</span><span>[</span><span>'</span><span>name</span><span>'</span><span>]</span> <span>=</span> <span>'</span><span>Alice</span><span>'</span>
<span>print</span><span>(</span><span>nested_data</span><span>[</span><span>'</span><span>age</span><span>'</span><span>])</span>  <span># Outputs: Unknown </span>
from collections import defaultdict

nested_data = defaultdict(lambda: 'Unknown')
nested_data['name'] = 'Alice'
print(nested_data['age'])  # Outputs: Unknown

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References

Python KeyError and how to handle it

3. Silent Errors with `try-except` Overuse

The Problem

Overusing or misusing try-except blocks can lead to silent errors, where exceptions are caught but not properly handled. This can make bugs difficult to detect and debug.

Example


<span>try</span><span>:</span>
    <span>result</span> <span>=</span> <span>1</span> <span>/</span> <span>0</span>
<span>except</span><span>:</span>
    <span>pass</span>  <span># Silently ignores the error </span><span>print</span><span>(</span><span>"</span><span>Continuing execution...</span><span>"</span><span>)</span>
<span>try</span><span>:</span>
    <span>result</span> <span>=</span> <span>1</span> <span>/</span> <span>0</span>
<span>except</span><span>:</span>
    <span>pass</span>  <span># Silently ignores the error </span><span>print</span><span>(</span><span>"</span><span>Continuing execution...</span><span>"</span><span>)</span>
try:
    result = 1 / 0
except:
    pass  # Silently ignores the error print("Continuing execution...")

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In the above example, the ZeroDivisionError is caught and ignored, but this can mask the underlying issue.

The Solution

Always specify the exception type you are catching, and handle it appropriately. Logging the error can also help in tracking down issues.


<span>try</span><span>:</span>
    <span>result</span> <span>=</span> <span>1</span> <span>/</span> <span>0</span>
<span>except</span> <span>ZeroDivisionError</span> <span>as</span> <span>e</span><span>:</span>
    <span>print</span><span>(</span><span>f</span><span>"</span><span>Error: </span><span>{</span><span>e</span><span>}</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Continuing execution...</span><span>"</span><span>)</span>
<span>try</span><span>:</span>
    <span>result</span> <span>=</span> <span>1</span> <span>/</span> <span>0</span>
<span>except</span> <span>ZeroDivisionError</span> <span>as</span> <span>e</span><span>:</span>
    <span>print</span><span>(</span><span>f</span><span>"</span><span>Error: </span><span>{</span><span>e</span><span>}</span><span>"</span><span>)</span>
<span>print</span><span>(</span><span>"</span><span>Continuing execution...</span><span>"</span><span>)</span>
try:
    result = 1 / 0
except ZeroDivisionError as e:
    print(f"Error: {e}")
print("Continuing execution...")

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For broader exception handling, you can use logging instead of pass:


<span>import</span> <span>logging</span>
<span>try</span><span>:</span>
    <span>result</span> <span>=</span> <span>1</span> <span>/</span> <span>0</span>
<span>except</span> <span>Exception</span> <span>as</span> <span>e</span><span>:</span>
    <span>logging</span><span>.</span><span>error</span><span>(</span><span>f</span><span>"</span><span>Unexpected error: </span><span>{</span><span>e</span><span>}</span><span>"</span><span>)</span>
<span>import</span> <span>logging</span>

<span>try</span><span>:</span>
    <span>result</span> <span>=</span> <span>1</span> <span>/</span> <span>0</span>
<span>except</span> <span>Exception</span> <span>as</span> <span>e</span><span>:</span>
    <span>logging</span><span>.</span><span>error</span><span>(</span><span>f</span><span>"</span><span>Unexpected error: </span><span>{</span><span>e</span><span>}</span><span>"</span><span>)</span>
import logging

try:
    result = 1 / 0
except Exception as e:
    logging.error(f"Unexpected error: {e}")

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References

Python’s try-except best practices

4. Integer Division: The Trap of Truncation

The Problem

Before Python 3, the division of two integers performed floor division by default, truncating the result to an integer. Although Python 3 resolved this with true division (/), some developers still face issues when unintentionally using floor division (//).

Example


<span>print</span><span>(</span><span>5</span> <span>/</span> <span>2</span><span>)</span>  <span># Outputs: 2.5 in Python 3, but would be 2 in Python 2 </span><span>print</span><span>(</span><span>5</span> <span>//</span> <span>2</span><span>)</span>  <span># Outputs: 2 </span>
<span>print</span><span>(</span><span>5</span> <span>/</span> <span>2</span><span>)</span>  <span># Outputs: 2.5 in Python 3, but would be 2 in Python 2 </span><span>print</span><span>(</span><span>5</span> <span>//</span> <span>2</span><span>)</span>  <span># Outputs: 2 </span>
print(5 / 2)  # Outputs: 2.5 in Python 3, but would be 2 in Python 2 print(5 // 2)  # Outputs: 2

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The Solution

Always use / for division unless you specifically need floor division. Be cautious when porting code from Python 2 to Python 3.


<span>print</span><span>(</span><span>5</span> <span>/</span> <span>2</span><span>)</span>  <span># Outputs: 2.5 </span><span>print</span><span>(</span><span>5</span> <span>//</span> <span>2</span><span>)</span>  <span># Outputs: 2 </span>
<span>print</span><span>(</span><span>5</span> <span>/</span> <span>2</span><span>)</span>  <span># Outputs: 2.5 </span><span>print</span><span>(</span><span>5</span> <span>//</span> <span>2</span><span>)</span>  <span># Outputs: 2 </span>
print(5 / 2)  # Outputs: 2.5 print(5 // 2)  # Outputs: 2

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For clear and predictable code, consider using decimal.Decimal for more accurate arithmetic operations, especially in financial calculations.


<span>from</span> <span>decimal</span> <span>import</span> <span>Decimal</span>
<span>print</span><span>(</span><span>Decimal</span><span>(</span><span>'</span><span>5</span><span>'</span><span>)</span> <span>/</span> <span>Decimal</span><span>(</span><span>'</span><span>2</span><span>'</span><span>))</span>  <span># Outputs: 2.5 </span>
<span>from</span> <span>decimal</span> <span>import</span> <span>Decimal</span>

<span>print</span><span>(</span><span>Decimal</span><span>(</span><span>'</span><span>5</span><span>'</span><span>)</span> <span>/</span> <span>Decimal</span><span>(</span><span>'</span><span>2</span><span>'</span><span>))</span>  <span># Outputs: 2.5 </span>
from decimal import Decimal

print(Decimal('5') / Decimal('2'))  # Outputs: 2.5

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References

Python Division: / vs //

5. Memory Leaks with Circular References

The Problem

Python’s garbage collector handles most memory management, but circular references can cause memory leaks if not handled correctly. When two or more objects reference each other, they may never be garbage collected, leading to increased memory usage.

Example


<span>class</span> <span>Node</span><span>:</span>
    <span>def</span> <span>__init__</span><span>(</span><span>self</span><span>,</span> <span>value</span><span>):</span>
        <span>self</span><span>.</span><span>value</span> <span>=</span> <span>value</span>
        <span>self</span><span>.</span><span>next</span> <span>=</span> <span>None</span>
<span>node1</span> <span>=</span> <span>Node</span><span>(</span><span>1</span><span>)</span>
<span>node2</span> <span>=</span> <span>Node</span><span>(</span><span>2</span><span>)</span>
<span>node1</span><span>.</span><span>next</span> <span>=</span> <span>node2</span>
<span>node2</span><span>.</span><span>next</span> <span>=</span> <span>node1</span>  <span># Circular reference </span>
<span>del</span> <span>node1</span>
<span>del</span> <span>node2</span>  <span># Memory not freed due to circular reference </span>
<span>class</span> <span>Node</span><span>:</span>
    <span>def</span> <span>__init__</span><span>(</span><span>self</span><span>,</span> <span>value</span><span>):</span>
        <span>self</span><span>.</span><span>value</span> <span>=</span> <span>value</span>
        <span>self</span><span>.</span><span>next</span> <span>=</span> <span>None</span>

<span>node1</span> <span>=</span> <span>Node</span><span>(</span><span>1</span><span>)</span>
<span>node2</span> <span>=</span> <span>Node</span><span>(</span><span>2</span><span>)</span>
<span>node1</span><span>.</span><span>next</span> <span>=</span> <span>node2</span>
<span>node2</span><span>.</span><span>next</span> <span>=</span> <span>node1</span>  <span># Circular reference </span>
<span>del</span> <span>node1</span>
<span>del</span> <span>node2</span>  <span># Memory not freed due to circular reference </span>
class Node:
    def __init__(self, value):
        self.value = value
        self.next = None

node1 = Node(1)
node2 = Node(2)
node1.next = node2
node2.next = node1  # Circular reference 
del node1
del node2  # Memory not freed due to circular reference

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The Solution

To avoid circular references, consider using weak references via the weakref module, which allows references to be garbage collected when no strong references exist.


<span>import</span> <span>weakref</span>
<span>class</span> <span>Node</span><span>:</span>
    <span>def</span> <span>__init__</span><span>(</span><span>self</span><span>,</span> <span>value</span><span>):</span>
        <span>self</span><span>.</span><span>value</span> <span>=</span> <span>value</span>
        <span>self</span><span>.</span><span>next</span> <span>=</span> <span>None</span>
<span>node1</span> <span>=</span> <span>Node</span><span>(</span><span>1</span><span>)</span>
<span>node2</span> <span>=</span> <span>Node</span><span>(</span><span>2</span><span>)</span>
<span>node1</span><span>.</span><span>next</span> <span>=</span> <span>weakref</span><span>.</span><span>ref</span><span>(</span><span>node2</span><span>)</span>
<span>node2</span><span>.</span><span>next</span> <span>=</span> <span>weakref</span><span>.</span><span>ref</span><span>(</span><span>node1</span><span>)</span>  <span># No circular reference now </span>
<span>import</span> <span>weakref</span>

<span>class</span> <span>Node</span><span>:</span>
    <span>def</span> <span>__init__</span><span>(</span><span>self</span><span>,</span> <span>value</span><span>):</span>
        <span>self</span><span>.</span><span>value</span> <span>=</span> <span>value</span>
        <span>self</span><span>.</span><span>next</span> <span>=</span> <span>None</span>

<span>node1</span> <span>=</span> <span>Node</span><span>(</span><span>1</span><span>)</span>
<span>node2</span> <span>=</span> <span>Node</span><span>(</span><span>2</span><span>)</span>
<span>node1</span><span>.</span><span>next</span> <span>=</span> <span>weakref</span><span>.</span><span>ref</span><span>(</span><span>node2</span><span>)</span>
<span>node2</span><span>.</span><span>next</span> <span>=</span> <span>weakref</span><span>.</span><span>ref</span><span>(</span><span>node1</span><span>)</span>  <span># No circular reference now </span>
import weakref

class Node:
    def __init__(self, value):
        self.value = value
        self.next = None

node1 = Node(1)
node2 = Node(2)
node1.next = weakref.ref(node2)
node2.next = weakref.ref(node1)  # No circular reference now

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Alternatively, you can manually break the cycle by setting references to None before deleting the objects.


<span>node1</span><span>.</span><span>next</span> <span>=</span> <span>None</span>
<span>node2</span><span>.</span><span>next</span> <span>=</span> <span>None</span>
<span>del</span> <span>node1</span>
<span>del</span> <span>node2</span>  <span># Memory is freed </span>
<span>node1</span><span>.</span><span>next</span> <span>=</span> <span>None</span>
<span>node2</span><span>.</span><span>next</span> <span>=</span> <span>None</span>
<span>del</span> <span>node1</span>
<span>del</span> <span>node2</span>  <span># Memory is freed </span>
node1.next = None
node2.next = None
del node1
del node2  # Memory is freed

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References

Python Memory Management and Garbage Collection

Conclusion

Even in 2024, Python developers continue to encounter these common bugs. While the language has evolved and improved over the years, these issues are often tied to fundamental aspects of how Python works. By understanding these pitfalls and applying the appropriate solutions, you can write more robust, error-free code. Happy coding!

Written by Rupesh Sharma AKA @hackyrupesh

原文链接：5 Python bugs that every developer is still facing in 2024 (and how to fix them)

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