Manual review of builtin functions: list-dict

Author: heikkitoivonen

docs/builtins/dict_func.md

@@ -203,70 +203,6 @@ for i in range(100):
 data = {f"key_{i}": i for i in range(100)}  # O(n)
 ```
 
-## Practical Examples
-
-### Parse Configuration
-
-```python
-# O(n) - convert pairs to config dict
-config_pairs = [
-    ("timeout", 30),
-    ("retries", 3),
-    ("debug", True),
-]
-
-config = dict(config_pairs)
-# {'timeout': 30, 'retries': 3, 'debug': True}
-```
-
-### Map Names to Values
-
-```python
-# O(n) - create mapping
-names = ["Alice", "Bob", "Charlie"]
-values = [1, 2, 3]
-
-mapping = dict(zip(names, values))
-# {'Alice': 1, 'Bob': 2, 'Charlie': 3}
-
-# Useful for CSV data
-headers = ["name", "age", "city"]
-row = ["Alice", 30, "NYC"]
-record = dict(zip(headers, row))
-# {'name': 'Alice', 'age': 30, 'city': 'NYC'}
-```
-
-### Create Lookup Table
-
-```python
-# O(n) - build lookup from data
-items = ["apple", "banana", "cherry"]
-lookup = dict(enumerate(items))
-# {0: 'apple', 1: 'banana', 2: 'cherry'}
-
-# Or reverse
-reverse_lookup = {v: k for k, v in lookup.items()}
-# {'apple': 0, 'banana': 1, 'cherry': 2}
-```
-
-### Initialize with Defaults
-
-```python
-# O(n) - create with initial values
-keys = ["a", "b", "c", "d"]
-initial_value = 0
-
-# Using dict comprehension
-defaults = {k: initial_value for k in keys}  # O(n)
-
-# Or with dict() - but requires pairs
-defaults = dict((k, initial_value) for k in keys)  # O(n)
-
-# defaultdict is O(1) for access, but setup is similar
-from collections import defaultdict
-defaults = defaultdict(int)  # O(1) setup, O(1) per access
-```
-
 ## Edge Cases
 
 ### Empty Dictionary

docs/builtins/list_func.md

@@ -71,8 +71,9 @@ original = [1, 2, 3, 4, 5]
 copy = list(original)  # O(5) - shallow copy
 
 # Each element is accessed once
+copy = []
 for item in iterable:  # O(n)
-    append to list
+    copy.append(item)
 
 # vs slicing - also O(n)
 copy = original[:]  # O(n) - same complexity
@@ -187,58 +188,6 @@ lst = [x**2 for x in range(100)]  # O(100)
 # Comprehensions often faster
 ```
 
-## Practical Examples
-
-### Parse CSV
-
-```python
-# O(n) - convert strings to list of values
-csv_line = "1,2,3,4,5"
-values = csv_line.split(",")  # ['1', '2', '3', '4', '5']
-
-# Convert to integers
-numbers = list(map(int, values))  # O(n)
-```
-
-### Remove Duplicates (Preserve Order)
-
-```python
-# O(n) - convert to set then back
-items = [1, 2, 2, 3, 3, 3, 4]
-unique = list(dict.fromkeys(items))  # [1, 2, 3, 4] - O(n)
-
-# Preserves first occurrence order
-```
-
-### Flatten Nested Lists
-
-```python
-# O(n) - flatten one level
-nested = [[1, 2], [3, 4], [5, 6]]
-flat = []
-for sublist in nested:
-    flat.extend(sublist)  # O(n) total
-
-# Using list concatenation
-flat = sum(nested, [])  # O(n) - but slower due to copying
-
-# Better - list comprehension
-flat = [x for sublist in nested for x in sublist]  # O(n)
-```
-
-### Read File Lines
-
-```python
-# O(n) - read all lines
-with open("file.txt") as f:
-    lines = list(f)  # O(n) - each line is one item
-    # lines = ["line1\n", "line2\n", ...]
-
-# vs
-with open("file.txt") as f:
-    lines = f.readlines()  # O(n) - same result
-```
-
 ## Edge Cases
 
 ### Empty Iterable