Comparing two dictionaries and checking how many (key, value) pairs are equal

Diving deeper into dictionary comparison

Shared items in dictionaries

Find matching key-value pairs using dictionary comprehension:

shared_items = {k: dict1[k] for k in dict1 if k in dict2 and dict1[k] == dict2[k]}  # "Pairs unite!" - Random matching pair
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shared_items contains keys present in both dict1 and dict2, with identical values.

Identifying common keys

Use set to determine common keys:

common_keys = dict1.keys() & dict2.keys()  # Sherlock Holmes of dictionaries
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Here, & performs intersection of keys, finding those that exist in both dicts.

Viewing added or removed keys

The set difference helps uncover keys that have been added to or removed from dictionaries:

added_keys = dict2.keys() - dict1.keys()    # What's new, doc?
removed_keys = dict1.keys() - dict2.keys()  # Missing in action
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This lets you monitor the change in dictionary contents over time.

An approach for nested dictionaries

For nested structures, use the deepdiff library:

from deepdiff import DeepDiff
difference = DeepDiff(dict1, dict2)  # Dive in, it's not so deep
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Use json.dumps() or pprint.pprint() to get a print-friendly version of the differences.

Visualization

dict1 = {'color': 'red', 'size': 'M', 'pattern': 'stripes'}
dict2 = {'color': 'red', 'size': 'L', 'pattern': 'stripes'}
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Matching 🧦

dict1 🔍 dict2: [('color', 'red'), ('pattern', 'stripes')]
# Confessions of a matchmaker: "2 out of 3 attributes match!"

Let's unwrap the tricks

Understanding comparison logic

== checks if two dictionaries have identical keys and values, without depending on order.

Expanding comparison

Zipping dictionaries

zip helps form dictionaries from keys and values lists:

dict3 = dict(zip(keys, values1))
dict4 = dict(zip(keys, values2))  # Zip it good
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Special case of unique keys

Ensure dictionary keys are valid identifiers to evade syntax errors when used as keyword arguments:

dict_chars = {k.strip("$"): v for k, v in dict1.items()}  # Housekeeping!
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Advancing comparison concepts

The concept of similarity could be stretched beyond exact matches to accommodate custom thresholds or criteria-based evaluations.