Python-Programming-Exercises-by-EKIKA/dict.md

📘 Python Dictionary Exercises

These exercises will allow you working with Python dict. You’ll learn nested structures, lookups, merges, transformations, filtering, and frequency maps — foundational for JSON, API, and data processing.

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Invert a Dictionary (Simple)

Problem

Write a function invert_dict(d) that swaps keys and values.

Example

invert_dict({'a': 1, 'b': 2})
# Output: {1: 'a', 2: 'b'}

Why? (Why it matters)

• Useful in reverse lookups.
• Found in config parsers, ENUM-like mappings, and translation systems.
• Reversing data relationships is common in APIs and normalization.

Pro Constraints

• Assume all values are unique and hashable.
• Must not mutate the input dictionary.
• Bonus: Extend to many-to-one values: {1: ['a', 'b']} if both a and b mapped to 1.

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Merge Dictionaries with Conflict Resolution

Problem

Write merge_dicts(a, b) where b overrides values in a.

Add a mode argument:

• 'override' (default): b overwrites a.
• 'sum': if values are numeric, sum them.
• 'append': create lists for conflicting keys.

Example

merge_dicts({'x': 2}, {'x': 3}, mode='sum')
# Output: {'x': 5}

Why?

• Merging config files, environment overrides.
• Inventory and accounting systems often need conflict handling logic.
• Avoids accidental overwrites by making intent explicit.

Pro Constraints

• Must not mutate original dicts.
• Use pure dict operations only (no collections.ChainMap, etc.).
• Bonus: Support deeply nested dictionaries.

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Count Frequencies of Elements

Problem

Write a function freq_map(lst) that returns frequency of elements in a list using a dictionary.

Example

freq_map(['a', 'b', 'a'])
# Output: {'a': 2, 'b': 1}

Why?

• Foundational for log analysis, NLP, and aggregation pipelines.
• Used in dashboards, tag clouds, voting systems.
• Also forms the basis of histograms and feature vectors.

Pro Constraints

• Do not use collections.Counter.
• Bonus: Make function case-sensitive toggle via case_sensitive=True.

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Nested Key Lookup

Problem

Write nested_get(d, keys) where keys is a list like ['a', 'b', 'c'] that returns d['a']['b']['c'].

Example

nested_get({'a': {'b': {'c': 42}}}, ['a', 'b', 'c'])
# Output: 42

Why?

• Vital in parsing JSON, API responses, settings files.
• Common in recursive data structures, permission systems.
• Avoids KeyError and nested try blocks.

Pro Constraints

• Must not crash on missing keys.
• Return None or optional default value.
• Must work with arbitrary depth.

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Top N Frequent Elements

Problem

Write top_n_frequent(lst, n) to return top n most frequent items.

Example

top_n_frequent(['x', 'y', 'x', 'z'], 2)
# Output: [('x', 2), ('y', 1)]

Why?

• Used in autocomplete, search trends, error analysis.
• Found in ecommerce (top sold items), NLP (most used terms), and telemetry.
• Teaches sorting dicts by value and memory-efficient counting.

Pro Constraints

• No third-party modules.
• Handle ties deterministically (alphabetical or insertion).
• Bonus: Make output format toggleable (dict or list of tuples).

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