How can I avoid "RuntimeError: dictionary changed size during iteration" error?

Unraveling the snapshot mystery

Iterating over a dictionary is like taking a leisurely stroll in a park, step by step. If you remove items (or plant life, metaphorically speaking), the park's layout changes unexpectedly causing a RuntimeError. The use of a snapshot of keys is like carrying a reliable park map, saving you from any sudden changes.

Note that in Python 3, dict.keys() returns a dynamic view object, not a list. So ensure your safety belt is fastened by wrapping it in list().

To copy or to deepcopy, That's the question

The copy() function creates a surface-level copy which is suitable for simple one-tier dictionaries. However, if your dictionary holds other mutable objects like nested dictionaries or lists, you might need the more powerful copy.deepcopy:

import copy
my_deep_copy = copy.deepcopy(my_orig_dict)


For creating copies of lists, shiny Python provides a quick slicing method:

my_list_copy = my_list[:]


Let's not forget that different Python versions have various modus operandi for copying data structures, so ensure you're familiar with your Python version's quirks.

Key removal tactics

Impel and pop

For removal of items whilst iterating, pop() is your friend. Use it with a separate list of keys or items to steer clear of fluctuating dictionary real estate:

for key in list(my_dict.keys()):
    value = my_dict.pop(key, None)  # Adios, key, nice knowing y'a!


Transformation, not elimination

When feasible, opt to modify values instead of exterminating keys. This way, the dictionary's size remains unchanged:

for key in my_dict:
    # Hey key, fancy a makeover?
    if should_modify_value(key):
        my_dict[key] = new_value


Out with the old, in with the new

For massive reshuffles or changes, dictionary comprehensions are exceptionally handy:

my_dict = {k: v for k, v in my_dict.items() if not should_remove(k)}


This clever method simultaneously iterates and births a new dictionary in a single breath. Efficiency, thy name is Python.

Understanding concurrent modifications

Thread-safe jukebox

When dealing with simultaneous modifications, use locks or thread-safe collections like collections.deque or queue.Queue to regulate access to shared resources.

Keeping state corruption at bay

In multi-threaded environments, preserving atomicity is the key. Ensure only one thread gets to interact with your dictionary at a time to prevent state corruption. Good news is, Python's Global Interpreter Lock (GIL) got your back on this, well, most of the time!