Update value of a nested dictionary of varying depth

Understanding the depth issue

These nested dictionaries are like onion layers, only less teary. Here, each key is a path and can lead to either another layer or the value we seek.

Keep an eye out for:

• Oops moments of overwriting existing values
• Freak-outs when faced with non-existent keys
• Confusion when mixed types like lists or non-mapping types muscle in

Fear not, time for some advanced implementation.

Advanced techniques

Recursion with checks

We'll ride alongside recursion but have the seatbelts of isinstance checks, so we don't go off-route assuming data types:

from collections.abc import Mapping  # No Maps, No Problems!

def update_nested(d, keys, value):
    if not isinstance(d, Mapping):
        raise ValueError("Err...That's not a dictionary, mate!")
    if keys:
        key = keys[0]
        if len(keys) == 1:
            d[key] = value
        else:
            new_key = keys[1:]
            # Are we there yet? If not, let's keep going
            d[key] = update_nested(d.get(key, {}), new_key, value)
    return d

nested_dict = {'a': {'b': {}}}
update_nested(nested_dict, ['a', 'b', 'c'], 3)
print(nested_dict)  # Outputs: {'a': {'b': {'c': 3}}}
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For nested keys nesting in with lists, we might need some extra list-leverage logic.

Built-in methodologies

For code minimalists not into external dependencies, 'ready-to-roll' methods like pydantic.deep_update could be a lifesaver.

# Sample extraction code:

def deep_dive_update(d, u):
    # Just copy the borrowed magic here and adapt it to your circumstances, gotcha?
    pass

# Talking about borrowed magic, peek into library's licensing before borrowing
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Varying dictionary depths

You want your code to be the Indiana Jones of nested dictionaries, going deep into any depth like a pro.

No more overwrites

No one likes when fresh {} wipes out the hard-earned, non-dictionary data. Here's how to handle these bullies:

# Add check that blocks `{}` from picking up fights with non-dictionary items
def update_nested_no_overwrite(d, keys, value):
    # Let's kick the bug outta here
    pass
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Love Test cases

They say mockery is the best form of flattery. The update_nested function begs to differ. Test cases are where it soaks up all the applause:

# Sample test case
def test_update_nested():
    # Gather all your edge cases and tricky scenarios here for a showdown
    pass
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More on lists within nested dictionaries

When lists join the nested dictionaries gang, things get a bit messy:

• Indexing: Ensure you aren't pointing at something that's not there.
• Updates vs. Appends: Should you replace the oldie or invite a newbie if the index is out of bounds?

Edge case: Lists as keys

Here's how you deal with those surprise twists when keys lead to a list:

def update_nested_with_lists(d, keys, value):
    # A safe space to handle lists as keys
    pass
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Update fidelity at all levels

Stay loyal to your updates at all levels. Python 3 users, remember items() and iteritems() aren't the same couple.

No data left behind

The goal is updates, not data loss. Your task is to give the missing keys a home without thrashing the existing ones:

def safe_update_nested(d, keys, value):
    for key in keys[:-1]:
        d = d.setdefault(key, {})
    # Value assignment without any tantrums
    pass
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Graceful type validation with abstract base classes

Embrace the flexible and robust isinstance check for type validation:

def is_valid_structure(d):
    # Validate the hero before the game begins
    pass
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Comprehensive solutions

A master function that delegates tasks to other functions. Now, that's a captain right there! Gather all specific handlers and edge case managers under one umbrella function. This function should form a unified front to contain the unpredictables of a wild dataset.