ls1248659692/leetcode

# Sliding windows code template is most used in substring match or maximum/minimum problems.
# It uses two-pointer as boundary of sliding window to traverse, and use a counter(dict) maintain current state,
# and a count as condition checker, update it when trigger some key changes.
#
# Time:  O(n)
# Space: O(k) k = len(set(p))
from collections import Counter

# s - target sequence, p - pattern or restrict sequence
def sliding_window_template_with_examples(s, p):
    # initialize the hash map here
    # counter is used to record current state, could use defaultdict in some situation, for example, no p restrict
    counter = Counter(p)

    # two pointers, boundary of sliding window
    start, end = 0, 0
    # condition checker, update it when trigger some key changes, init value depend on your situation
    count = 0
    # result, return int(such as max or min) or list(such as all index)
    res = 0

    # loop the source string from begin to end
    while end < len(s):
        counter[s[end]] += 1
        # update count based on some condition
        if counter[s[end]] > 1:
            count += 1
        end += 1

        # count condition here
        while count > 0:
            '''
            update res here if finding minimum
            '''

            # increase start to make it invalid or valid again
            counter[s[start]] -= 1
            # update count based on some condition
            if counter[s[start]] > 0:
                count -= 1
            start += 1

        '''
        update res here if finding maximum
        '''
        res = max(res, end - start)

    return res

# refer to https://leetcode.com/problems/minimum-window-substring/discuss/26808/here-is-a-10-line-template-that-can-solve-most-substring-problems

# two pointers scenario, famous applications such as binary search, quick sort and sliding window.

'''
Classification:
1. old & new state: old, new = new, cur_result
2. slow & fast runner: slow-> fast->->
3. left & right boundary or index: left-> <-right
4. p1 & p2 from two sequences: p1-> p2->
5. start & end sliding window boundary: start-> end->
'''


# old & new state: old, new = new, cur_result
def old_new_state(self, seq):
    # initialize state
    old, new = default_val1, default_val2
    for element in seq:
        '''
        process current element with old state
        '''
        old, new = new, self.process_logic(element, old)


# slow & fast runner: slow-> fast->->
def slow_fast_runner(self, seq):
    # initialize slow runner
    slow = seq[0]   # for index: slow = 0
    # fast-runner grows each iteration generally
    for fast in range(seq):     # for index: range(len(seq))
        '''
        slow-runner grows with some restrictions
        '''
        if self.slow_condition(slow):
            slow = slow.next    # for index: slow += 1
        '''
        process logic before or after pointers movement
        '''
        self.process_logic(slow, fast)


# left & right boundary or index: left-> <-right
def left_right_boundary(self, seq):
    left, right = 0, len(seq) - 1
    while left < right:
        '''
        left index moves when satisfy the condition
        '''
        if self.left_condition(left):
            left += 1

        '''
        right index move when satisfy the condition
        '''
        if self.right_condition(right):
            right -= 1

        '''
        process logic before or after pointers movement
        '''
        self.process_logic(left, right)


# p1 & p2 from two sequences: p1-> p2->
def pointers_from_two_seq(self, seq1, seq2):
    # init pointers
    p1, p2 = 0, 0       # or seq1[0], seq2[0]

    # or other condition
    while p1 < len(seq1) and p2 < len(seq2):
        '''
        p1 index moves when satisfy the condition
        '''
        if self.p1_condition(p1):
            p1 += 1         # or p1 = next(seq1)

        '''
        p2 index move when satisfy the condition
        '''
        if self.p2_condition(p2):
            p2 += 1         # or p2 = next(seq2)

        '''
        process logic before or after pointers movement
        '''
        self.process_logic(p1, p2)


# start & end of sliding window: |start-> ... end->|
# more details in sliding windows templates, here is just about two-pointers part
def start_end_sliding_window(self, seq):
    start, end = 0, 0

    while end < len(seq):
        # end grows in the outer loop
        end += 1

        # start grows with some restrict
        while self.start_condition(start):
            '''
            process logic before pointers movement
            '''
            self.process_logic1(start, end)

            # start grows in the inner loop
            start += 1

        '''
        or process logic after pointers movement
        '''
        self.process_logic2(start, end)

# a Linked list is a linear collection of data elements, whose order is not given by their physical placement in memory.
# Instead, each element points to the next.
#
# when operation with linked list, we can choose to change list node or change node value.
# change node value is more easy-understanding, and may need extra space to store.
#
# Time:  O(1) to insert and delete, O(n) to random access
# Space: O(n)


# Definition for singly-linked list.
class ListNode:
    def __init__(self, x):
        self.val = x
        self.next = None


# add dummy node to help flag the head, usually used in head may changed or merging None edge case.
def dummy_node_assist(self, head):
    dummy = ListNode(0)
    cur = dummy.next = head  # or cur = dummy

    while cur:
        '''
        process current node logic 
        '''
        self.process_logic(cur)

    return dummy.next


# another solution is use self.next, more pythonic
def self_assist(self, head):
    pre, pre.next = self, head

    while pre.next:
        '''
        process current node logic 
        '''
        self.process_logic(pre.next)

    return self.next


# add node
def add_node(pre_node, new_node):
    pre_node.next, new_node = new_node, pre_node.next


# delete node
def delete_node(pre_node):
    pre_node.next = pre_node.next.next


# reverse linked list iteratively
def reverse_iteratively(head: ListNode) -> ListNode:
    prev, cur = None, head
    while cur:
        cur.next, cur, prev = prev, cur.next, cur
    return prev


# reverse linked list recursively
def reverse_recursively(head: ListNode) -> ListNode:
    def recursive(cur, pre=None):
        if not cur:
            return pre
        pre, cur.next = cur.next, pre
        return recursive(pre, cur)

    return recursive(head)

# a Linked list is a linear collection of data elements, whose order is not given by their physical placement in memory.
# Instead, each element points to the next.
#
# when operation with linked list, we can choose to change list node or change node value.
# change node value is more easy-understanding, and may need extra space to store.
#
# Time:  O(1) to insert and delete, O(n) to random access
# Space: O(n)


# reverse linked list iteratively
def reverse_iteratively(head: ListNode) -> ListNode:
    prev, cur = None, head
    while cur:
        cur.next, cur, prev = prev, cur.next, cur
    return prev


# reverse linked list recursively
def reverse_recursively(head: ListNode) -> ListNode:
    def recursive(cur, pre=None):
        if not cur:
            return pre
        pre, cur.next = cur.next, pre
        return recursive(pre, cur)

    return recursive(head)

todo Merge Intervals algorithm template 

todo 无序限定范围的数组元素查找O(N） algorithm template 

# Breadth-first search is traversing or searching tree or graph data structures, including graph-like solution space.
# it explores all of the neighbor nodes at the present depth prior to moving on to the nodes at the next depth level.
#
# Time:  O(V+E)     V: vertex, E: edges
# Space: O(V)
from collections import deque


# iteration version, using deque
def bfs_iteratively_by_queue(self, start, target=None):
    queue, visited = deque([start]), {start}

    while queue:
        node = queue.popleft()
        visited.add(node)
        '''
        process current node logic here
        '''
        self.process_logic(node)

        # target is optional
        if node == target:
            '''
            reach the goal and break out
            '''
            self.process_target_logic(target)
            break
        for next_node in node.get_successors():
            if next_node not in visited:
                queue.append(next_node)


# iteration version, using list, pythonic-style
# conciser but more memory, mainly used when you want to collect the whole list
def bfs_iteratively_by_list(self, start, target=None):
    node_list, visited = [start], {start}

    # append while traversing
    for node in node_list:
        visited.add(node)
        '''
        process current node logic here
        '''
        self.process_logic(node)

        # target is optional
        if node == target:
            '''
            reach the goal and break out
            '''
            self.process_target_logic(target)
            break
        for next_node in node.get_successors():
            if next_node not in visited:
                node_list += node

    # basically the node_list is useful here
    return node_list


# recursion version, uncommon
def bfs_recursively(self, queue: deque, visited: set, target=None):
    if not queue:
        return

    node = queue.popleft()
    visited.add(node)
    '''
    process current node logic here
    '''
    self.process_logic(node)

    # target is optional
    if node == target:
        '''
        reach the goal and break out
        '''
        self.process_target_logic(target)
        return
    for next_node in node.get_successors():
        if next_node not in visited:
            queue.append(next_node)
    self.bfs_recursively(queue, visited)


# bfs list comprehension in row of binary tree
def bfs_row(self, root):
    row = [root]
    while row:
        '''
        process current node logic here
        '''
        # process logic separately
        row = [child for node in row for child in (node.left, node.right) if node]

# Deep-first search is traversing or searching tree or graph data structures, including graph-like solution space.
# it starts at the root node and explores as far as possible along each branch before backtracking.
#
# Time:  O(V+E)     V: vertex, E: edges
# Space: O(V)


# recursion version
def dfs_recursively(self, node, visited: set):
    visited.add(node)
    '''
    process current node logic here
    '''
    self.process_logic(node)

    for next_node in node.get_successors():
        if next_node not in visited:
            self.dfs_recursively(next_node, visited)


# iteration version, uncommon
def dfs_iteratively(self, root):
    stack, visited = [root], set()
    while stack:
        node = stack.pop()
        visited.add(node)
        '''
        process current node logic here
        '''
        self.process_logic(node)

        for next_node in node.get_successors():
            if next_node not in visited:
                stack.append(next_node)

# Backtracking is a general algorithm for finding all (or some) solutions to some computational problems, notably
#  constraint satisfaction problems, that incrementally builds candidates to the solutions, and abandons a candidate
#  ("backtracks") as soon as it determines that the candidate cannot possibly be completed to a valid solution.
#
# backtracking vs. dfs
# traverse in solution space vs. traverse in data structure space, pruned of dfs

def backtracking(self, data, candidate):
    # pruning
    if self.reject(data, candidate):
        return

    # reach the end
    if self.accept(data, candidate):
        return self.output(data, candidate)

    # drill down
    for cur_candidate in self.all_extension(data, candidate):
        # or you can choose to prune here, recursion depth - 1
        if not self.should_to_be_pruned(cur_candidate):
            self.backtracking(data, cur_candidate)

# binary search requires sorted iterator. Common variation is find left-most or right-most index.
#
# Time:  O(log(n))
# Space: O(1)


# [lo, hi] version
def binary_search(arr, target):
    lo, hi = 0, len(arr) - 1
    while lo <= hi:
        # no overflow problem in python, (lo + hi) // 2 is better.
        # mid = lo + (hi - lo) // 2
        mid = (lo + hi) // 2
        '''
        change to your comparison condition as you need
        '''
        # find the target
        if arr[mid] == target:
            break
        elif arr[mid] < target:
            lo = mid + 1
        else:
            hi = mid - 1


# [lo, hi) version
def binary_search2(arr, target):
    lo, hi = 0, len(arr)
    while lo < hi:
        mid = (lo + hi) // 2
        # find the target
        if arr[mid] == target:
            break
        elif arr[mid] < target:
            lo = mid + 1
        else:
            hi = mid


# result in [0, hi]
def bisect_left(arr, target, lo, hi):
    while lo < hi:
        mid = (lo + hi) // 2
        # left-most, so equal will be on the right side
        if arr[mid] < target:
            lo = mid + 1
        else:
            hi = mid
        '''
        # bisect right code
        if arr[mid] > target:
            hi = mid
        else:
            lo = mid + 1
        '''
    return lo

# heap, also called priority queue
# heap common operations: heapify, top, heappush, heappop, heappushpop, heapreplace, _siftup/_siftdown,
# nlargest/nsmallest, merge
# heapq in Python, there is only min heap, if you want to a max heap, you should reverse the key or implement __lt__.
#
# Time:  O(1) to find, O(log(n)) to push/pop/sift, O(nlog(n)) to heapify, O(n) to merge
# Space: O(n)
#
# from Queue import PriorityQueue is alternative way, which is a thread-safe class
import heapq


def heap_operations():
    heap = [4, 2, 1, 3]

    # heapify
    heapq.heapify(heap)

    # top
    top = heap[0]

    # heappop
    top = heapq.heappop(heap)

    # heappush
    heapq.heappush(heap, 5)

    # heappushpop = push + pop
    heapq.heappushpop(heap, 0)

    # heapreplace = pop + push
    heapq.heapreplace(heap, 0)

    data = [10, 5, 18, 2, 37, 3, 8, 7, 19, 1]
    heapq.heapify(data)
    old, new = 8, 22  # increase the 8 to 22
    i = data.index(old)
    data[i] = new
    # _siftup, from root to leaf, when increase
    heapq._siftup(data, i)

    old, new = 10, 4  # decrease the 10 to 4
    i = data.index(old)
    data[i] = new
    # _siftdown, from leaf to root, when decrease
    heapq._siftdown(data, 0, i)

    # find n largest by queue
    heapq.nlargest(data, 3)

    # find n smallest by queue
    heapq.nsmallest(data, 3)

    # Merge multiple sorted inputs into a single sorted output
    # e.g. merge timestamped entries from multiple log files
    heapq.merge([1, 3, 5, 7], [0, 2, 4, 8], [5, 10, 15, 20], [], [25])

todo K路归并 algorithm template

# simplifying a complicated problem by breaking it down into simpler sub-problems in a recursive manner
# dynamic programming = recursion + memorized
# state definition, state transition equation, optimal substructure
# direction: bottom-up or top-down


def dynamic_programming_template_with_example(word1, word2):
    m, n = len(word1), len(word2)

    # states definition: cur_min_edit_dist
    # states compressed to two states: old and new
    dp = [[0] * (m + 1) for _ in range(2)]

    # initialize the initial edit distance, add one more state for init state
    for i in range(0, m + 1):
        dp[0][i] = i

    cur = 1
    for i in range(n):
        # initialize the init state
        dp[cur][0] = i + 1
        for j in range(m):
            # state transition equation
            # if char matched, this is the min dist.
            if word1[j] == word2[i]:
                dp[cur][j + 1] = dp[cur ^ 1][j]
            # otherwise, 1 + minimum of edit/remove/add operations
            else:
                dp[cur][j + 1] = 1 + min(dp[cur ^ 1][j], dp[cur ^ 1][j + 1], dp[cur][j])
        # switch between two states
        cur ^= 1
    # return the last state as result
    return dp[cur ^ 1][-1]
    

# all kinds of sort algorithm, just some common versions to show its idea.
# here I unify the api as xxx_sort(arr), changed in-place
#
# currently include:
# quick, merge, insert, bubble, selection, heap, shell, bucket, counting, radix, topological
#
# constant space vs extra space (merge O(n), counting O(k), bucket O(n+k), radix O(n+k))
#
# in place vs out place (merge, counting, bucket, radix)
#
# stable vs unstable (selection, quick, heap, shell)
#
# comparison based vs non-comparison based (radix, bucket, counting)
#
# comparison of counting, bucket and radix sort: all use bucket idea
# counting sort:  store the single key in bucket
# bucket sort:    store a range in bucket
# radix sort:     store each part of element in bucket

# sort      average     best        worst       space   place       stable      comparison
# bubble    O(n^2)      O(n)        O(n^2)      O(1)    in-place    stable      comparison
# selection O(n^2)      O(n^2)      O(n^2)      O(1)    in-place    unstable    comparison
# insert    O(n^2)      O(n)        O(n^2)      O(1)    in-place    stable      comparison
# shell     O(nlogn)    O(nlogn^2)  O(nlogn^2)  O(1)    in-place    unstable    comparison
# merge     O(nlogn)    O(nlogn)    O(nlogn)    O(n)    out-place   stable      comparison
# quick     O(nlogn)    O(nlogn)    O(n^2)      O(logn) in-place    unstable    comparison
# heap      O(nlogn)    O(nlogn)    O(nlogn)    O(1)    in-place    unstable    comparison
# counting  O(n+k)      O(n+k)      O(n+k)      O(k)    out-place   stable      non-comparison
# bucket    O(n+k)      O(n+k)      O(n^2)      O(n+k)  out-place   stable      non-comparison
# radix     O(nk)       O(nk)       O(nk)       O(n+k)  out-place   stable      non-comparison

import random
import time
from collections import deque
from collections import defaultdict

'''
quick sort 
avg: O(nlogn), best: O(nlogn), worst: O(n^2), space: O(logn), in-place, unstable, comparison
fastest in large-scale and out-of-order data, divide & conquer
'''


def quick_sort(arr):
    quick_sort_rec(arr, 0, len(arr) - 1)


# hi is included, in range [lo, hi]
def quick_sort_rec(arr, lo, hi):
    if lo < hi:
        pivot = partition(arr, lo, hi)

        quick_sort_rec(arr, lo, pivot - 1)
        quick_sort_rec(arr, pivot + 1, hi)


def partition(arr, lo, hi):
    # choose pivot by median-of-three
    x, idx = sorted(((arr[lo], lo), (arr[hi], hi), (arr[lo + hi >> 1], lo + hi >> 1)))[1]
    arr[lo], arr[idx] = arr[idx], arr[lo]
    while lo < hi:
        while lo < hi and arr[hi] >= x:
            hi -= 1
        arr[lo] = arr[hi]
        while lo < hi and arr[lo] <= x:
            lo += 1
        arr[hi] = arr[lo]
    arr[lo] = x
    return lo


'''
merge sort
avg: O(nlogn), best: O(nlogn), worst: O(nlogn), space: O(n), out-place, stable, comparison
divide & conquer
'''


# the version with a fixed temp array
def merge_sort(arr):
    merge_sort_rec(arr, 0, len(arr) - 1, [0] * len(arr))


def merge_sort_rec(arr, first, last, temp):
    if first < last:
        mid = (first + last) // 2
        merge_sort_rec(arr, first, mid, temp)
        merge_sort_rec(arr, mid + 1, last, temp)
        merge(arr, first, mid, last, temp)


def merge(arr, first, mid, last, temp):
    i, j = first, mid + 1
    m, n = mid, last
    k = 0
    while i <= m and j <= n:
        if arr[i] <= arr[j]:
            temp[k] = arr[i]
            i += 1
        else:
            temp[k] = arr[j]
            j += 1
        k += 1

    while i <= m:
        temp[k] = arr[i]
        i += 1
        k += 1

    while j <= n:
        temp[k] = arr[j]
        j += 1
        k += 1

    for i in range(k):
        arr[first + i] = temp[i]


def merge_sort2(arr):
    res = merge_sort_rec2(arr)
    for i in range(len(arr)):
        arr[i] = res[i]


def merge_sort_rec2(arr):
    if len(arr) <= 1:
        return arr
    mid = len(arr) // 2
    left = merge_sort_rec2(arr[:mid])
    right = merge_sort_rec2(arr[mid:])
    return merge1(left, right)


def merge1(left, right):
    res = []
    i = j = 0
    while i < len(left) and j < len(right):
        if left[i] <= right[j]:
            res.append(left[i])
            i += 1
        else:
            res.append(right[j])
            j += 1

    while i < len(left):
        res.append(left[i])
        i += 1

    while j < len(right):
        res.append(right[j])
        j += 1

    return res


# more pythonic but slower
def merge2(left, right):
    res = []
    while left and right:
        if left[0] <= right[0]:
            res.append(left.pop(0))
        else:
            res.append(right.pop(0))
    return res + (left or right)


# slowest
def merge3(left, right):
    res = []
    while left and right:
        if left[-1] > right[-1]:
            res.insert(0, left.pop())
        else:
            res.insert(0, right.pop())
    return (left or right) + res


'''
insert sort
avg: O(n^2), best: O(n), worst: O(n^2), space: O(1), in-place, stable, comparison
fast in small-scale and almost sorted data 
'''


def insert_sort(arr):
    for i in range(1, len(arr)):
        x = arr[i]
        j = i - 1
        while j >= 0 and arr[j] > x:
            arr[j + 1] = arr[j]
            j = j - 1
        arr[j + 1] = x


'''
bubble sort
avg: O(n^2), best:O(n), worst: O(n^2), space:O(1), in-place, stable, comparison
optimization: 
1. flag whether swap last round, if not, end sort
2. flag the last swap place, [last_swap, j] has been sorted, just skip it.
'''


# bubble the max to right most
def bubble_sort(arr):
    for i in range(len(arr) - 1):
        for j in range(len(arr) - i - 1):
            if arr[j] > arr[j + 1]:
                arr[j], arr[j + 1] = arr[j + 1], arr[j]


# optimization
def bubble_sort2(arr):
    i = len(arr) - 1
    while i > 0:
        last_swap = 0
        for j in range(i):
            if arr[j] > arr[j + 1]:
                arr[j], arr[j + 1] = arr[j + 1], arr[j]
                last_swap = j
        # if last_swap=0, it means no swap in last round, end sort
        # [last_swap, i] has been sorted, start from last_swap
        i = last_swap


'''
selection sort
avg: O(n^2), best: O(n^2), worst: O(n^2), space: O(1), in-place, unstable, comparison
most stable in time cost, always O(n^2), and easy understanding, used in small scale data
'''


def selection_sort(arr):
    for i in range(len(arr)):
        min_i = i
        for j in range(i+1, len(arr)):
            if arr[j] < arr[min_i]:
                min_i = j
        arr[i], arr[min_i] = arr[min_i], arr[i]


'''
heap sort
avg: O(nlogn), best: O(nlogn), worst: O(nlogn), space: O(1), in-place, unstable, comparison
not only a sort algorithm, this structure (priority queue) can do more thing.
'''


def heap_sort(arr):
    # build max heap
    for i in range(len(arr) // 2)[::-1]:
        heap_adjust(arr, i, len(arr))

    # adjust from last element
    for i in range(1, len(arr))[::-1]:
        # swap first with the last, make the right most is maximum
        arr[0], arr[i] = arr[i], arr[0]
        heap_adjust(arr, 0, i)


def heap_adjust(arr, i, n):
    cur = arr[i]
    while 2 * i + 1 < n:
        # get child index in heap
        child = 2 * i + 1

        # choose the larger child
        if child < n - 1 and arr[child + 1] > arr[child]:
            child += 1

        # if child is larger than parent, sift up
        if cur < arr[child]:
            arr[i], arr[child] = arr[child], cur
            i = child
        else:
            break


'''
shell sort
avg: O(nlogn), best: O(nlogn^2), worst: O(nlogn^2), space: O(1), in-place, unstable, comparison
improvement of insertion sort
'''


def shell_sort(arr):
    # Shell sort using Shell's (original) gap sequence: n/2, n/4, ..., 1.
    gap = len(arr) // 2
    # loop over the gaps
    while gap > 0:
        # do the insertion sort
        for i in range(gap, len(arr)):
            val = arr[i]
            j = i
            while j >= gap and arr[j - gap] > val:
                arr[j] = arr[j - gap]
                j -= gap
            arr[j] = val
        gap //= 2


'''
counting sort
avg: O(n+k), best:O(n+k), worst:O(n+k), space:O(k), out-place, stable, non-comparison
relatively concentrated values
'''


def counting_sort(arr):
    min_val, max_val = min(arr), max(arr)
    size = max_val - min_val + 1
    counter = [0] * size

    for i in range(len(arr)):
        counter[arr[i] - min_val] += 1

    idx = 0
    for i, v in enumerate(counter):
        # use slice assignment
        arr[idx:idx + v] = [i + min_val] * v
        idx += v


'''
bucket sort
avg: O(n+k), best:O(n+k), worst:O(n^2), space:O(n+k), out-place, stable, non-comparison
improvement of counting sort, usually used with hash 
'''
DEFAULT_BUCKET_SIZE = 5


def bucket_sort(arr, size=DEFAULT_BUCKET_SIZE):
    min_val, max_val = min(arr), max(arr)

    # initialize buckets
    count = (max_val - min_val) // size + 1
    buckets = [[] for _ in range(count)]

    # put values in buckets
    for i in range(len(arr)):
        buckets[(arr[i] - min_val) // size].append(arr[i])

    # sort buckets and place back into input array
    i = 0
    for bucket in buckets:
        insert_sort(bucket)
        # use slice assignment
        arr[i:i + len(bucket)] = bucket
        i += len(bucket)


# with hash
def bucket_sort2(arr):
    # get hash codes
    code = hashing(arr)
    buckets = [[] for _ in range(code[1])]
    # distribute data into buckets: O(n)
    for i in arr:
        x = re_hashing(i, code)
        buckets[x].append(i)

    i = 0
    # merge the buckets: O(n)
    for bucket in buckets:
        insert_sort(bucket)
        arr[i:i + len(bucket)] = bucket
        i += len(bucket)


def hashing(arr):
    return [max(arr), int(len(arr) ** 0.5)]


def re_hashing(i, code):
    return int(i / code[0] * (code[1] - 1))


'''
radix sort
avg: O(nk), best:O(nk), worst:O(nk), space:O(n+k), out-place, stable, non-comparison
improvement of counting sort
'''


def radix_sort(arr):
    radix = 10
    max_length = False
    placement = 1

    while not max_length:
        max_length = True
        # declare and initialize buckets
        buckets = [[] for _ in range(radix)]

        # split arr between lists
        for i in arr:
            temp = i // placement
            buckets[temp % radix].append(i)
            if max_length and temp > 0:
                max_length = False

        # assign from bucket to arr
        i = 0
        for bucket in buckets:
            insert_sort(bucket)
            # use slice assignment
            arr[i:i + len(bucket)] = bucket
            i += len(bucket)

        # move to next digit
        placement *= radix
    return arr


'''
topological sort
in a directed graph, a linear ordering of its vertices such that for every directed edge uv from vertex u to vertex v, 
u comes before v in the ordering. 
'''

GRAY, BLACK = 0, 1


def topological_sort(graph):
    order, enter, state = deque(), set(graph), {}

    def dfs(node):
        state[node] = GRAY
        for k in graph.get(node, ()):
            sk = state.get(k, None)
            if sk == GRAY:
                raise ValueError("cycle")
            if sk == BLACK:
                continue
            enter.discard(k)
            dfs(k)
        order.appendleft(node)
        state[node] = BLACK

    while enter:
        dfs(enter.pop())
    return order


if __name__ == '__main__':
    def sort_test(sort_func):
        start = time.time()
        for _ in range(100):
            nums = [random.randrange(1, 250) for _ in range(1000)]
            nums1 = sorted(nums)
            sort_func(nums)
            if nums1 != nums:
                print('incorrect', )
        end = time.time()
        print('{:15s} time: {}'.format(sort_func.__name__, end - start))


    sort_test(quick_sort)
    sort_test(merge_sort)
    sort_test(merge_sort2)
    sort_test(heap_sort)
    sort_test(shell_sort)
    sort_test(counting_sort)
    sort_test(bucket_sort)
    sort_test(bucket_sort2)
    sort_test(radix_sort)

    # put turtle algorithms to the tail
    sort_test(insert_sort)
    sort_test(bubble_sort)
    sort_test(bubble_sort2)
    sort_test(selection_sort)

    graph = defaultdict(list)
    graph['A'].append('B')
    graph['B'].append('D')
    graph['C'].append('A')
    print(topological_sort(graph))

# Classification: binary tree, complete binary tree, full binary tree, binary search tree, AVL tree(self-balancing)
#
# Time:  O(n)
# Space: O(h) h: height of tree
from collections import deque


# Definition for a binary tree node
class TreeNode:
    def __init__(self, x):
        self.val = x
        self.left = None
        self.right = None


'''
traverse binary tree recursively
'''


# pre-order: root->left->right
def preorder_traversal_recursively(self, root: 'TreeNode'):
    # recursion terminator here
    # you can check if none when add left and right child, it will decrease one recursion depth,
    # but you have to check whether root is None outside.
    if not root:
        return
    '''
    add current node logic here
    '''
    self.process_logic(root)

    self.preorder_traversal_recursively(root.left)
    self.preorder_traversal_recursively(root.right)


# in-order: left->root->right
def inorder_traversal_recursively(self, root: 'TreeNode'):
    if not root:
        return

    self.inorder_traversal_recursively(root.left)
    '''
    add current node logic here
    '''
    self.process_logic(root)

    self.inorder_traversal_recursively(root.right)


# post-order: left->right->root
def postorder_traversal_recursively(self, root: 'TreeNode'):
    if not root:
        return

    self.postorder_traversal_recursively(root.left)
    self.postorder_traversal_recursively(root.right)
    '''
    add current node logic here
    '''
    self.process_logic(root)


# level-order
# to traverse recursively, need help from extra data structure or dfs level by level
def level_order_traversal_recursively(self, root: 'TreeNode') -> 'List[List[int]]':
    res = []

    def dfs(root, level):
        if not root:
            return
        if len(res) < level + 1:
            res.append([])
        '''
        add current node logic here
        '''
        self.process_logic(root)

        res[level].append(root.val)
        dfs(root.left, level + 1)
        dfs(root.right, level + 1)

    dfs(root, 0)
    return res


'''
traverse binary tree iteratively
'''


# There are too many version to traverse iteratively, just choose the one you like it.
# pre-order: root->left->right
def preorder_traversal_iteratively(self, root: 'TreeNode'):
    if not root:
        return []
    stack = [root]
    while stack:
        root = stack.pop()
        '''
        add current node logic here
        '''
        self.process_logic(root)

        # push right child first because of FILO
        if root.right:
            stack.append(root.right)
        if root.left:
            stack.append(root.left)


# in-order: left->root->right
def inorder_traversal_iteratively(self, root: 'TreeNode'):
    stack = []
    # keep stack empty and compare root too, compatible with edge case: root=None
    while stack or root:
        # push itself and all left children into stack iteratively
        while root:
            stack.append(root)
            root = root.left
        root = stack.pop()
        '''
        add current node logic here
        '''
        self.process_logic(root)

        # point to right child
        root = root.right


# here I choose to not use
# post-order: left->right->root
def postorder_traversal_iteratively(self, root: 'TreeNode'):
    if not root:
        return []
    stack = [root]
    # used to record whether left or right child has been visited
    last = None

    while stack:
        root = stack[-1]
        # if current node has no left right child, or left child or right child has been visited, then process and pop it
        if not root.left and not root.right or last and (root.left == last or root.right == last):
            '''
            add current node logic here
            '''
            self.process_logic(root)

            stack.pop()
            last = root
        # if not, push right and left child in stack
        else:
            # push right first because of FILO
            if root.right:
                stack.append(root.right)
            if root.left:
                stack.append(root.left)


# post-order: left->right->root
# use visit flag
def postorder_traversal_iteratively2(self, root: 'TreeNode'):
    if not root:
        return []
    stack = [root]
    while stack:
        root = stack[-1]
        # use visited attribute to judge whether it has been visited
        if hasattr(root, 'visited'):
            '''
            add current node logic here
            '''
            self.process_logic(root)

            stack.pop()
            del root.visited
        else:
            if root.right:
                stack.append(root.right)
            if root.left:
                stack.append(root.left)
            root.visited = True


# level-order
def level_order_traversal_iteratively(self, root: 'TreeNode'):
    if not root:
        return []
    queue = deque([root])
    while queue:
        for _ in range(len(queue)):
            cur = queue.popleft()
            '''
            add current node logic here
            '''
            self.process_logic(cur)

            if cur.left:
                queue.append(cur.left)
            if cur.right:
                queue.append(cur.right)
        '''
        add level logic here if you need
        '''

相关算法模板同上树和链表结合

# Bit manipulation is the act of algorithmically manipulating bits or other pieces of data shorter than a word.
#
# common bit-wise operation
#
# operations priority:
# {}[]() -> ** -> ~ -> -x -> *,/,% -> +,- -> <<,>> -> & -> ^ -> | -> <>!= -> is -> in -> not x -> and -> or


def bit_wise_operations(a, b):
    # not
    ~a

    # or
    a | b

    # and
    a & b

    # xor
    a ^ b

    # shift operators
    a << b
    a >> b

    # subtraction
    a & ~b

    # set bit, assign to 1
    a |= 1 << b

    # clear bit, assign to 0
    a &= ~(1 << b)

    # test bit
    if a & 1 << b: pass

    # extract last bit
    a & -a

    # remove last bit
    a & (a - 1)

    # check is odd or even
    if a & 1: print('odd')

    # clear right n bit
    a & (~0 << b)

    # clear left until to n
    a & ((1 << b) - 1)

# reference
# https://leetcode.com/problems/sum-of-two-integers/discuss/84278/A-summary%3A-how-to-use-bit-manipulation-to-solve-problems-easily-and-efficiently

# string is traditionally a sequence of characters, either as a literal constant or as some kind of variable.
import string


def string_operations():
    s = 'abcab'

    # join
    ','.join(s)

    # split
    s.split(',')

    # replace
    s.replace()

    # index & find, index return -1 if not found
    s.index('a')
    s.find('a')
    s.rfind('a')

    # count
    s.count('a')

    # start & end with
    s.startswith('a')
    s.endswith('b')

    # translate
    intab = "aeiou"
    outtab = "12345"
    trantab = str.maketrans(intab, outtab)
    s.translate(trantab)

    # strip
    s.lstrip()
    s.rstrip()
    s.strip()

    # just
    s.ljust(10)
    s.rjust(10)
    s.center(10)
    s.zfill(10)

    # common string
    string.punctuation
    string.whitespace
    string.ascii_lowercase
    string.hexdigits
    string.printable

# stack common operation: push, pop, top
# stack is used in pairs, maintain extreme value
#
# FILO: First In, Last Out
# Time:  O(1)
# Space: O(n)


# sequence stack based on list
def stack_operations():
    # initialization
    stack = []

    # size of stack
    size = len(stack)

    # push
    stack.append(1)

    # check is empty
    if stack:
        # top
        top = stack[-1]

        # pop
        stack.pop()

# list is a collection which is ordered and changeable, with continuous space.
#
# Time:  O(1) to index or write or append, O(n) to insert or remove
# Space: O(n)


def list_operations():
    # initialization
    list1 = [3, 1, 2, 4]

    list2 = list(range(5))
    # [*X] equals to list(X)
    list2 = [*range(5)]

    # append
    list1.append(5)
    list1 += [5]
    list1 += 5,
    list1.extend([5, 6])

    # insert
    list1.insert(0)

    # index
    list1.index(3)

    # count
    list1.count(3)

    # remove
    list1.remove(3)

    # sort
    list1.sort(reverse=True)

    # reverse
    list1.reverse()

# binary search requires sorted iterator. Common variation is find left-most or right-most index.
#
# Time:  O(log(n))
# Space: O(1)


# [lo, hi] version
def binary_search(arr, target):
    lo, hi = 0, len(arr) - 1
    while lo <= hi:
        # no overflow problem in python, (lo + hi) // 2 is better.
        # mid = lo + (hi - lo) // 2
        mid = (lo + hi) // 2
        '''
        change to your comparison condition as you need
        '''
        # find the target
        if arr[mid] == target:
            break
        elif arr[mid] < target:
            lo = mid + 1
        else:
            hi = mid - 1


# [lo, hi) version
def binary_search2(arr, target):
    lo, hi = 0, len(arr)
    while lo < hi:
        mid = (lo + hi) // 2
        # find the target
        if arr[mid] == target:
            break
        elif arr[mid] < target:
            lo = mid + 1
        else:
            hi = mid


# result in [0, hi]
def bisect_left(arr, target, lo, hi):
    while lo < hi:
        mid = (lo + hi) // 2
        # left-most, so equal will be on the right side
        if arr[mid] < target:
            lo = mid + 1
        else:
            hi = mid
        '''
        # bisect right code
        if arr[mid] > target:
            hi = mid
        else:
            lo = mid + 1
        '''
    return lo