​
Core Implementation

Python strings are immutable sequences of Unicode characters:

# Basic string creation
str1 = "Hello, World!"
str2 = 'Hello, World!'  # Single quotes
str3 = '''Multiline
string'''  # Triple quotes

# String methods
upper_case = str1.upper()
lower_case = str1.lower()
stripped = str1.strip()
split_words = str1.split(',')

​
String Buffer Implementation (for mutable strings)

from io import StringIO

class StringBuffer:
    def __init__(self):
        self.buffer = StringIO()
    
    def append(self, string):
        self.buffer.write(string)
    
    def toString(self):
        return self.buffer.getvalue()
    
    def clear(self):
        self.buffer.seek(0)
        self.buffer.truncate(0)

​
Implementation Variations

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1. Rope Data Structure (for large strings)

class RopeNode:
    def __init__(self, text=""):
        self.weight = len(text)
        self.text = text
        self.left = None
        self.right = None

class Rope:
    def __init__(self, text=""):
        self.root = self._build_rope(text)
    
    def _build_rope(self, text):
        if len(text) <= 8:  # Threshold for leaf nodes
            return RopeNode(text)
        
        mid = len(text) // 2
        node = RopeNode()
        node.weight = mid
        node.left = self._build_rope(text[:mid])
        node.right = self._build_rope(text[mid:])
        return node

​
2. Trie (for string prefix operations)

class TrieNode:
    def __init__(self):
        self.children = {}
        self.is_end = False

class Trie:
    def __init__(self):
        self.root = TrieNode()
    
    def insert(self, word):
        node = self.root
        for char in word:
            if char not in node.children:
                node.children[char] = TrieNode()
            node = node.children[char]
        node.is_end = True

​
Time Complexities

OperationStringStringBufferRopeTrie
Access by IndexO(1)O(n)O(log n)N/A
AppendO(n)O(1)*O(log n)O(m)**
ConcatenationO(n+m)O(1)*O(log n)N/A
SubstringO(k)O(n)O(log n)N/A
Find/SearchO(nm)**O(n*m)O(n)O(m)
InsertO(n)O(1)*O(log n)O(m)
DeleteO(n)O(n)O(log n)O(m)

* Amortized time complexity ** Where m is the length of the word *** Using naive search. KMP algorithm improves this to O(n+m)

​
Space Complexities

ImplementationSpace ComplexityAdditional Notes
StringO(n)Immutable, requires new copy for changes
StringBufferO(n)Mutable, grows dynamically
RopeO(n)Additional O(log n) for tree structure
TrieO(ALPHABET_SIZE * m * n)Where m is average string length

​
Common Data Structure Patterns

  1. Sliding Window

    • Fixed size window
    • Variable size window
    • Window with constraints

  2. Two Pointers

    • Start and end
    • Same direction
    • Opposite directions

  3. String Matching Patterns

    • KMP Algorithm
    • Rabin-Karp
    • Boyer-Moore

  4. State Machine

    • String parsing
    • Pattern matching

  5. Dynamic Programming

    • Longest common substring
    • Edit distance
    • String alignment

  6. Hash-based Patterns

    • Rolling hash
    • Substring hash

​
Edge Cases to Consider

  1. Empty String
s = ""
# Handle: if not s: return None
  1. Single Character
s = "a"
# Handle: if len(s) == 1: return s
  1. Unicode Characters
s = "Hello, 世界"  # Mixed ASCII and Unicode
s = "🌟"  # Emoji
# Use s.encode().decode('utf-8') for proper handling
  1. Special Characters
s = "Hello\n\tWorld"  # Escape sequences
s = r"Hello\n\tWorld"  # Raw string
  1. Case Sensitivity
s1 = "Hello"
s2 = "hello"
# Consider: s1.lower() == s2.lower()

​
Optimization Techniques

  1. String Interning
# Python automatically interns small strings
a = 'hello'
b = 'hello'
# id(a) == id(b) is True

# Force interning
from sys import intern
c = intern('hello'*100)
d = intern('hello'*100)
# id(c) == id(d) is True
  1. Join vs Concatenation
# Bad
result = ""
for s in string_list:
    result += s  # Creates new string each time

# Good
result = "".join(string_list)
  1. Format String Optimization
# Bad
s = "Hello " + name + ", you are " + str(age) + " years old"

# Good
s = f"Hello {name}, you are {age} years old"
# or
s = "Hello {}, you are {} years old".format(name, age)

​
Memory Management

  1. String Deduplication
# Using weakref for string deduplication
from weakref import WeakValueDictionary

string_cache = WeakValueDictionary()
def deduplicate(s):
    if s not in string_cache:
        string_cache[s] = s
    return string_cache[s]
  1. Memory-Efficient String Storage
# Using __slots__ for string containers
class StringContainer:
    __slots__ = ['value']
    def __init__(self, value):
        self.value = value
  1. Garbage Collection
import gc

# Force garbage collection after string operations
large_string = "x" * 10**6
del large_string
gc.collect()

​
Performance Considerations

  1. String Methods vs Regular Expressions
# Slower (regex for simple case)
import re
result = re.sub(r'\s+', ' ', text)

# Faster (string methods)
result = ' '.join(text.split())
  1. String Views
# Memory efficient string slicing
from memory_view import memoryview
text = bytearray(b'Hello, World!')
view = memoryview(text)
  1. Batch Processing
def process_large_string(s, chunk_size=1024):
    for i in range(0, len(s), chunk_size):
        chunk = s[i:i + chunk_size]
        # Process chunk

​
Common Pitfalls

  1. String Immutability
# Common mistake
s = "hello"
s[0] = 'H'  # TypeError: strings are immutable

# Correct way
s = 'H' + s[1:]
  1. Character vs String Comparison
# Pitfall
if c == "a":  # c is a single character
    pass

# Better
if c == 'a':  # Single quotes for characters
    pass
  1. Unicode Handling
# Potential issue
s = "Hello, 世界"
length = len(s)  # May not give expected length

# Better
import unicodedata
length = len(unicodedata.normalize('NFC', s))
  1. Memory Usage in String Formatting
# Memory inefficient
large_list = ["item"] * 1000000
result = ", ".join(large_list)  # Creates large temporary string

# Better (using generator)
def generate_items():
    for item in large_list:
        yield item
result = ", ".join(generate_items())
  1. String Pool References
# Unexpected behavior
a = "hello" * 100
b = "hello" * 100
print(a is b)  # False, different objects

# Expected behavior
a = "hello"
b = "hello"
print(a is b)  # True, same object from string pool
ArrayLinked list