[IMP] Few dict exercises.

dict.md

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+
+# 📘 Python Dictionary Exercises by EKIKA
+
+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.
+
+---
+
+## PYEKX-2ce5579c
+
+**Invert a Dictionary (Simple)**
+
+### Problem  
+Write a function `invert_dict(d)` that swaps keys and values.
+
+### Example  
+```python
+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.
+
+---
+
+## PYEKX-7b549e55
+
+**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  
+```python
+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.
+
+---
+
+## PYEKX-8be01ef0
+
+**Count Frequencies of Elements**
+
+### Problem  
+Write a function `freq_map(lst)` that returns frequency of elements in a list using a dictionary.
+
+### Example  
+```python
+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`.
+
+---
+
+## PYEKX-4ffdfbd7
+
+**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  
+```python
+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.
+
+---
+
+## PYEKX-2d3191a8
+
+**Top N Frequent Elements**
+
+### Problem  
+Write `top_n_frequent(lst, n)` to return top `n` most frequent items.
+
+### Example  
+```python
+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).
+
+---