Hash Map

In [10]: d = {'a': 1, 'b': 2}
    ...: d['a']
Out[10]: 1

In [11]: d.get('a')
Out[11]: 1

In [12]: d.get('c', 0)
Out[12]: 0

In [13]: d.get('z')

In [14]:

#-------------------------------------------------------------------------------
# setdefault : will creatte key if key NOT existed (with value as well if defined)
#-------------------------------------------------------------------------------

# syntax
d.setdefault(new_key)
d.setdefault(new_key, new_value)

# 662 Maximum Width of Binary Tree
car = {
  "brand": "Ford",
  "model": "Mustang",
  "year": 1964
}

# example 1) insert key "my_key", since my_key not existed, -> make it as new key and value as None (since not defined)
car.setdefault("my_key")
print (car)
# In [18]: car
# Out[18]: {'brand': 'Ford', 'model': 'Mustang', 'year': 1964, 'my_key': None}

# example 2) insert key "color", since my_key not existed, -> make it as new key and value as white
car.setdefault("color", "white")
print (car)
# Out[22]:
# {'brand': 'Ford',
#  'model': 'Mustang',
#  'year': 1964,
#  'my_key': None,
#  'color': 'white'}

# https://stackoverflow.com/questions/613183/how-do-i-sort-a-dictionary-by-value

x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
In [11]: x.items()
Out[11]: dict_items([(1, 2), (3, 4), (4, 3), (2, 1), (0, 0)])

#----------------------------------
# Sort hashMap by key/value !!!
#----------------------------------
x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
# note : have to use sorted(xxx, key=yyy), instead of xxx.sorted(....)
### NOTE this !!! : x.items()
sorted_x = sorted(x.items(), key=lambda kv: kv[1])
print (sorted_x)
# [(0, 0), (2, 1), (1, 2), (4, 3), (3, 4)]

x = {1: 2, 3: 4, 4: 3, 2: 1, 0: 0}
sorted_x = sorted(x.items(), key=lambda kv: kv[0])
print (sorted_x)
# [(0, 0), (1, 2), (2, 1), (3, 4), (4, 3)]

# 451  Sort Characters By Frequency
import collections
class Solution(object):
    def frequencySort(self, s):
        count = collections.Counter(s)
        count_dict = dict(count)
        """
        NOTE this !!!
            1. use sorted()
            2. count_dict.items()
        """
        count_tuple_sorted = sorted(count_dict.items(), key=lambda kv : -kv[1])
        res = ''
        for item in count_tuple_sorted:
            res += item[0] * item[1]
        return res

# dict values -> array
In [6]:
   ...: mydict = {'a':['a1','a2','a3'], 'b':['b1','b2','b3']}
   ...:
   ...: res = [mydict[x] for x in mydict]
   ...:
   ...: print (res)
[['a1', 'a2', 'a3'], ['b1', 'b2', 'b3']]

# Universal Counting Template
def counting_pattern(arr):
    count = {}  # or collections.defaultdict(int)
    result = []
    
    # Count frequency
    for item in arr:
        count[item] = count.get(item, 0) + 1
        # or count[item] += 1 with defaultdict
    
    # Process based on frequency
    for key, freq in count.items():
        if meets_condition(freq):
            result.append(key)
    
    return result

# Examples: LC 49, LC 242, LC 451, LC 347, LC 692

last_idx - first_idx + 1 == number_of_occurrences

# python
# LC 4038 - Count Integers Appearing in a Single Block
# IDEA: {val: [idx, ...]}, then a value is special iff its indices are consecutive
# time = O(n), space = O(n)
from collections import defaultdict

def countSpecialIntegers(nums: list) -> int:
    my_map = defaultdict(list)
    for i, v in enumerate(nums):
        my_map[v].append(i)

    cnt = 0
    for k in my_map:
        idxs = my_map[k]
        # NOTE !!! span == count  <=>  indices are consecutive
        if idxs[-1] - idxs[0] + 1 == len(idxs):
            cnt += 1
    return cnt

# [1,1,2,2,3] -> 1:[0,1] 2:[2,3] 3:[4]     -> every span == count -> 3
# [1,2,1]     -> 1:[0,2] -> 2-0+1 = 3 != 2 -> broken -> only 2 is special -> 1

# python
# LC 4038 - keep only (first_idx, last_idx, count) per value
# time = O(n), space = O(k), k = number of distinct values
def countSpecialIntegers(nums: list) -> int:
    info = {}                                  # {val: [first, last, cnt]}
    for i, v in enumerate(nums):
        if v not in info:
            info[v] = [i, i, 1]
        else:
            info[v][1] = i
            info[v][2] += 1
    return sum(1 for first, last, cnt in info.values() if last - first + 1 == cnt)

# Two Sum Pattern Template
def two_sum_pattern(nums, target):
    seen = {}  # {value: index}
    
    for i, num in enumerate(nums):
        complement = target - num
        
        if complement in seen:
            return [seen[complement], i]
        
        seen[num] = i
    
    return []

# Variations:
# - Multiple pairs: collect all instead of returning first
# - K-diff pairs: check for num+k and num-k
# - Examples: LC 1, LC 167, LC 15, LC 532, LC 1010

# LC 049 Group Anagrams
# IDEA : HASH TABLE
class Solution:
    def groupAnagrams(self, strs):
        res = {}
        for item in strs:
            k = ''.join(sorted(item))  # sort the string 
            if k not in res:  #  check if exists in res 
                res[k] = []
            res[k].append(item)  # if same, put all the same string into dict k 
        return [res[x] for x in res]  # output the result 

// java
// LC 149 - Max Points on a Line
// IDEA: anchor at each point, group the others by a gcd-normalized slope key
// time = O(n^2), space = O(n)
public int maxPoints(int[][] points) {
    int n = points.length;
    if (n <= 2) return n;
    int best = 1;
    for (int i = 0; i < n; i++) {
        Map<String, Integer> slopeCount = new HashMap<>();
        for (int j = i + 1; j < n; j++) {
            int dx = points[j][0] - points[i][0];
            int dy = points[j][1] - points[i][1];
            int g = gcd(Math.abs(dx), Math.abs(dy));
            if (g != 0) { dx /= g; dy /= g; }
            // canonical direction: force dx > 0, or dx == 0 && dy > 0
            if (dx < 0 || (dx == 0 && dy < 0)) { dx = -dx; dy = -dy; }
            String key = dx + "/" + dy;
            int cnt = slopeCount.merge(key, 1, Integer::sum);
            best = Math.max(best, cnt + 1);   // +1 for the anchor point itself
        }
    }
    return best;
}

private int gcd(int a, int b) { return b == 0 ? a : gcd(b, a % b); }

# python
# LC 149 - Max Points on a Line
# IDEA: anchor at each point, group the others by a gcd-normalized slope key
# time = O(n^2), space = O(n)
from collections import defaultdict
from math import gcd

def maxPoints(points: list) -> int:
    n = len(points)
    if n <= 2:
        return n
    best = 1
    for i in range(n):
        slope_count = defaultdict(int)
        x1, y1 = points[i]
        for j in range(i + 1, n):
            dx, dy = points[j][0] - x1, points[j][1] - y1
            g = gcd(abs(dx), abs(dy))
            if g:
                dx, dy = dx // g, dy // g
            if dx < 0 or (dx == 0 and dy < 0):     # canonical sign
                dx, dy = -dx, -dy
            slope_count[(dx, dy)] += 1
            best = max(best, slope_count[(dx, dy)] + 1)   # +1 = anchor point
    return best

# (algorithm book (labu) p.350)
my_array = [1,2,3,4,5]
my_array_pre = [0] * (len(my_array)+1)
cur = 0
for i in range(len(my_array)):
    cur += my_array[i]
    my_array_pre[i+1] += cur

# In [17]: print ("my_array = " + str(my_array))
#     ...: print ("my_array_pre = " + str(my_array_pre))
# my_array = [1, 2, 3, 4, 5]
# my_array_pre = [0, 1, 3, 6, 10, 15]

#-----------------------------------------------
# Get sub array sum !!!!!!!
#    -> nums[i..j] sum = preSum[j+1] - preSum[i]
#-----------------------------------------------

# example 1 : sum of [1,2]
my_array_pre[1+1] - my_array_pre[0]  # 1's index is 0, and 2's index is 1. (my_array = [1, 2, 3, 4, 5])

# example 2 : sum of [2,3,4]
my_array_pre[3+1] - my_array_pre[1] # 2's index is 1, and 4's index is 3. (my_array = [1, 2, 3, 4, 5])

# Prefix Sum Pattern Template
def prefix_sum_pattern(nums, target):
    prefix_sum = 0
    sum_count = {0: 1}  # {sum: count/index}
    result = 0

    for num in nums:
        prefix_sum += num

        # Check if (prefix_sum - target) exists
        if prefix_sum - target in sum_count:
            result += sum_count[prefix_sum - target]

        # Update current prefix sum count
        sum_count[prefix_sum] = sum_count.get(prefix_sum, 0) + 1

    return result

# For max length problems, store index instead of count:
# sum_index = {0: -1}, then calculate i - sum_index[prefix_sum - target]
# Examples: LC 560, LC 325, LC 525, LC 523

If we want subarray[i,j] to sum to k:
  presum[j] - presum[i-1] = k
  → presum[i-1] = presum[j] - k

So at index j, check if (presum[j] - k) exists in map!

i=0: num=1, presum=1
  - Check: (1-2)=-1 not in map → count=0
  - Update: map={0:1, 1:1}

i=1: num=1, presum=2
  - Check: (2-2)=0 in map, count += map[0] = 1 → count=1
  - Update: map={0:1, 1:1, 2:1}

i=2: num=1, presum=3
  - Check: (3-2)=1 in map, count += map[1] = 1 → count=2
  - Update: map={0:1, 1:1, 2:1, 3:1}

Result: count=2 (subarrays [1,1] and [1,1])

# Sliding Window with HashMap Template
def sliding_window_hashmap(s, pattern):
    if len(pattern) > len(s):
        return []
    
    pattern_count = {}
    window_count = {}
    
    # Count pattern frequency
    for char in pattern:
        pattern_count[char] = pattern_count.get(char, 0) + 1
    
    left = 0
    result = []
    
    for right in range(len(s)):
        # Expand window
        char = s[right]
        window_count[char] = window_count.get(char, 0) + 1
        
        # Contract window if needed
        while window_size_condition_met():
            # Check if current window is valid
            if window_count == pattern_count:
                result.append(left)
            
            # Remove leftmost character
            left_char = s[left]
            window_count[left_char] -= 1
            if window_count[left_char] == 0:
                del window_count[left_char]
            left += 1
    
    return result

# Examples: LC 3, LC 76, LC 438, LC 567

// LC 567
// ...
     /** NOTE !!!
     *
     *  we use below trick to
     *
     *  -> 1) check if `new reached s2 val` is in s1 map
     *  -> 2) check if 2 map are equal
     *
     *  -> so we have more simple code, and clean logic
     */
    if (map2.equals(map1)) {
        return true;
    }
// ...

// java
// LC 953 - Verifying an Alien Dictionary
// IDEA: char -> rank map turns an arbitrary alphabet into comparable ints
// time = O(total chars), space = O(1)  (26 keys)
public boolean isAlienSorted(String[] words, String order) {
    int[] rank = new int[26];                       // char -> position in `order`
    for (int i = 0; i < order.length(); i++) rank[order.charAt(i) - 'a'] = i;
    for (int i = 0; i + 1 < words.length; i++) {
        if (!inOrder(words[i], words[i + 1], rank)) return false;
    }
    return true;
}

private boolean inOrder(String a, String b, int[] rank) {
    int n = Math.min(a.length(), b.length());
    for (int i = 0; i < n; i++) {
        int ra = rank[a.charAt(i) - 'a'], rb = rank[b.charAt(i) - 'a'];
        if (ra != rb) return ra < rb;
    }
    return a.length() <= b.length();                // prefix must come first: "app" < "apple"
}

// LC 791 - Custom Sort String  (counting sort, no comparator needed)
// time = O(n + m), space = O(1)
public String customSortString(String order, String s) {
    int[] cnt = new int[26];
    for (char c : s.toCharArray()) cnt[c - 'a']++;
    StringBuilder sb = new StringBuilder();
    for (char c : order.toCharArray()) {            // ranked chars first, in rank order
        while (cnt[c - 'a'] > 0) { sb.append(c); cnt[c - 'a']--; }
    }
    for (char c = 'a'; c <= 'z'; c++) {             // unranked chars: any order
        while (cnt[c - 'a'] > 0) { sb.append(c); cnt[c - 'a']--; }
    }
    return sb.toString();
}

# python
# LC 953 - Verifying an Alien Dictionary
# IDEA: map every word to a list of ranks, then plain list comparison does the lexicographic work
# time = O(total chars), space = O(total chars)
def isAlienSorted(words: list, order: str) -> bool:
    rank = {c: i for i, c in enumerate(order)}
    keys = [[rank[c] for c in w] for w in words]
    # python list compare == lexicographic compare, and [1,2] < [1,2,3] handles the prefix rule
    return all(keys[i] <= keys[i + 1] for i in range(len(keys) - 1))

# python
# LC 791 - Custom Sort String
# IDEA: rank map as a sort key; unranked chars get rank len(order) (stable sort keeps them last)
# time = O(n log n + m), space = O(n)
def customSortString(order: str, s: str) -> str:
    rank = {c: i for i, c in enumerate(order)}
    return "".join(sorted(s, key=lambda c: rank.get(c, len(order))))

# python
# LC 105 - Construct Binary Tree from Preorder and Inorder Traversal
# IDEA: {value: index in inorder} → O(1) root split
# time = O(n), space = O(n)
def buildTree(preorder: list, inorder: list):
    idx = {v: i for i, v in enumerate(inorder)}   # values are unique (given)
    pre = [0]                                     # pointer into preorder

    def build(lo, hi):
        if lo > hi:
            return None
        node = TreeNode(preorder[pre[0]])
        pre[0] += 1
        mid = idx[node.val]                       # O(1) instead of inorder.index(...)
        node.left = build(lo, mid - 1)
        node.right = build(mid + 1, hi)
        return node

    return build(0, len(inorder) - 1)

# LC 205 Isomorphic Strings
def isIsomorphic(s: str, t: str) -> bool:
    s2t, t2s = {}, {}
    for a, b in zip(s, t):
        if s2t.get(a, b) != b or t2s.get(b, a) != a:
            return False
        s2t[a] = b
        t2s[b] = a
    return True

# LC 290 Word Pattern
def wordPattern(pattern: str, s: str) -> bool:
    words = s.split()
    if len(pattern) != len(words):
        return False
    p2w, w2p = {}, {}
    for p, w in zip(pattern, words):
        if p2w.get(p, w) != w or w2p.get(w, p) != p:
            return False
        p2w[p] = w
        w2p[w] = p
    return True

# Caching/Memoization Template
class CacheTemplate:
    def __init__(self, capacity):
        self.capacity = capacity
        self.cache = {}  # key -> value
        self.usage = {}  # key -> usage_info
    
    def get(self, key):
        if key in self.cache:
            self.update_usage(key)
            return self.cache[key]
        return -1
    
    def put(self, key, value):
        if len(self.cache) >= self.capacity:
            self.evict()
        
        self.cache[key] = value
        self.update_usage(key)
    
    def update_usage(self, key):
        # Update usage tracking
        pass
    
    def evict(self):
        # Remove least recently/frequently used
        pass

# Examples: LC 146 (LRU), LC 460 (LFU)

# Graph with HashMap Template
def graph_hashmap_pattern(graph_input):
    # Build adjacency list/map
    graph = {}  # node -> [neighbors] or node -> {neighbor: weight}
    
    for edge in graph_input:
        node1, node2 = edge[0], edge[1]
        if node1 not in graph:
            graph[node1] = []
        if node2 not in graph:
            graph[node2] = []
        
        graph[node1].append(node2)
        graph[node2].append(node1)  # for undirected
    
    # Process using DFS/BFS with visited tracking
    visited = set()
    result = []
    
    def dfs(node):
        if node in visited:
            return
        
        visited.add(node)
        result.append(node)
        
        for neighbor in graph.get(node, []):
            dfs(neighbor)
    
    return result

# Examples: LC 133, LC 200, LC 694, LC 1257

n=10, blacklist=[2,3,5,8]   →   bound = 10 - 4 = 6

RANDOM RANGE  [0, bound)
|----|----|----|----|----|----|
  0    1    2    3    4    5
             X    X         X
             ↑bad inside range — must remap

TAIL RANGE  [bound, n)
|----|----|----|----|
  6    7    8    9
             X            ← also blacklisted, skip it

Remapping (last starts at 9, walks left skipping blacklisted):
  b=2  →  last=9 (valid)  → map 2→9,  last=8
  b=3  →  last=8 (blacklisted, skip) → last=7 (valid) → map 3→7, last=6
  b=5  →  last=6 (valid)  → map 5→6,  last=5

Final mapping: { 2→9, 3→7, 5→6 }

pick() result for each index in [0,5]:
  0 → 0   (not mapped, return directly)
  1 → 1
  2 → 9   (remapped)
  3 → 7   (remapped)
  4 → 4
  5 → 6   (remapped)

Valid numbers returned: {0,1,4,6,7,9} ✓ uniformly distributed

// LC 710 - Random Pick with Blacklist
class Solution {
    private Map<Integer, Integer> mapping = new HashMap<>();
    private Random random = new Random();
    private int bound;

    public Solution(int n, int[] blacklist) {
        bound = n - blacklist.length;

        Set<Integer> blackSet = new HashSet<>();
        for (int b : blacklist) blackSet.add(b);

        int last = n - 1;
        for (int b : blacklist) {
            if (b < bound) {
                // Skip tail values that are also blacklisted
                while (blackSet.contains(last)) last--;
                mapping.put(b, last);
                last--;
            }
        }
    }

    public int pick() {
        int idx = random.nextInt(bound);
        return mapping.getOrDefault(idx, idx);  // remap if blacklisted, else return directly
    }
}

import random

class Solution:
    def __init__(self, n: int, blacklist: list[int]):
        self.bound = n - len(blacklist)
        black_set = set(blacklist)
        self.mapping = {}

        last = n - 1
        for b in blacklist:
            if b < self.bound:
                while last in black_set:
                    last -= 1
                self.mapping[b] = last
                last -= 1

    def pick(self) -> int:
        idx = random.randrange(self.bound)
        return self.mapping.get(idx, idx)

# Track multiple relationships simultaneously
def complex_problem(arr):
    index_map = {}      # value -> index
    freq_map = {}       # value -> frequency
    reverse_map = {}    # index -> value
    
    for i, val in enumerate(arr):
        index_map[val] = i
        freq_map[val] = freq_map.get(val, 0) + 1
        reverse_map[i] = val

# Hash Map + Priority Queue (Heap)
import heapq
from collections import defaultdict

def top_k_frequent(nums, k):
    count = defaultdict(int)
    for num in nums:
        count[num] += 1
    
    # Use heap with frequency
    heap = []
    for num, freq in count.items():
        heapq.heappush(heap, (-freq, num))  # Max heap using negative values
    
    result = []
    for _ in range(k):
        result.append(heapq.heappop(heap)[1])
    return result

# 1. Use defaultdict for cleaner counting code
from collections import defaultdict
count = defaultdict(int)  # No need for get(key, 0)

# 2. Use Counter for frequency problems
from collections import Counter
freq = Counter(arr)  # Automatically counts frequencies

# 3. Handle edge cases with dict.get()
value = my_dict.get(key, default_value)

# 4. Clean up zero counts to save space
if count[key] == 0:
    del count[key]

# 5. Use enumerate when you need both index and value
for i, val in enumerate(arr):
    ...        # use both i and val

// 1. Use getOrDefault to avoid null checks
map.put(key, map.getOrDefault(key, 0) + 1);

// 2. Use containsKey for existence checks
if (map.containsKey(key)) { /* ... */ }

// 3. Initialize with appropriate capacity
Map<String, Integer> map = new HashMap<>(expectedSize);

// 4. Use putIfAbsent for first occurrence
map.putIfAbsent(key, index);  // Only puts if key doesn't exist