Udemy_Data_Structure_and_Algorithm.ipynb

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      "source": [
        "#  **# 1. Sorted Squared Array**\n",
        "You are given an array of Integers in which each subsequent value is not less than the previous value. Write a function that takes this array as an input and returns a new array with the squares of each number sorted in ascending order.\n",
        "\n",
        "  Ask for clarification.\n",
        "  *   Any negative integers in the array?\n",
        "  *   Any identical integers?\n",
        "  *   Is the array in order?\n",
        "  *   Is the array empty?\n",
        "\n",
        "  For Example:\n",
        "  *   [1,3,5] -> [1,9,25]\n",
        "  *   [2,3,3] -> [4,9,9]\n",
        "  *   [-4,-2,0,1,3] -> [0,1,4,9,16]"
      ],
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      "source": [
        "  # Method 1: Brute Force Method\n",
        "\n",
        "Given [-3,1,2,7]\n",
        "\n",
        "1.square each elelment and put them in a new array \n",
        "-> [9,1,4,49]\n",
        "\n",
        "(**T = O(n); S = O(n)**)\n",
        "\n",
        "2.sort this new array \n",
        "-> [1,4,9,49]\n",
        "\n",
        "(**T = O(nlogn); S = O(n)**)\n",
        "\n",
        "This method takes a time complexity of (n + nlogn), with n << nlogn, the actual time complexity is nlogn"
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      "source": [
        "# Time = O(nlogn)  Space = O(n)\n",
        "def sorted_squared(array):\n",
        "    new_array = [0]*len(array)\n",
        "    for i in range(len(array)):\n",
        "        new_array[i] = array[i]**2\n",
        "        # new_array[i] = array[i]*array[i]\n",
        "    new_array.sort()\n",
        "    return new_array\n",
        "\n",
        "\n",
        "print(sorted_squared([-5, 1, 2]))\n",
        "print(sorted_squared([0, 1, 2]))\n",
        "print(sorted_squared([]))"
      ],
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          "text": [
            "[1, 4, 25]\n",
            "[0, 1, 4]\n",
            "[]\n"
          ]
        }
      ]
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      "cell_type": "markdown",
      "source": [
        "# Method 2: Two Pointer\n",
        "Given: [-5, -4, -2, 1, 9, 12]\n",
        "\n",
        "1.create an new array that is the same length of given array ->new_array = [0] * len(array) \n",
        "\n",
        "2.setup two pointers from the beginning and the end of the given array -> i = 0; j = len(array)-1\n",
        "\n",
        "3.Instead of sorting, compare the squared values based on the pointers, and fill the new array in reverse order with the larger number -> **(T = O(n); S = O(n))**\n",
        "\n",
        "for k in reversed(range(len(array))), \n",
        "  \n",
        "  val_i = array[i]**2 \n",
        "  \n",
        "  i += 1\n",
        "  (remember to move the pointer if this is the larger value, keep the pointer if it is not)\n",
        "  \n",
        "  val_j = array[j]**2 \n",
        "  \n",
        "  j -= 1\n",
        "  (remember to move the pointer if this is the larger value, keep the pointer if it is not)\n",
        "  \n",
        "  new_array[k] = the larger number of (val_i, val_j)"
      ],
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      "cell_type": "code",
      "source": [
        "# Time = O(n) Space = O(n)\n",
        "def sorted_squared(array):\n",
        "    i = 0\n",
        "    j = len(array) - 1\n",
        "    new_array = [0] * len(array)\n",
        "    for k in reversed(range(len(array))):\n",
        "        sq_i = array[i]**2\n",
        "        sq_j = array[j]**2\n",
        "        if sq_i > sq_j:\n",
        "            new_array[k] = sq_i\n",
        "            i += 1\n",
        "        else:\n",
        "            new_array[k] = sq_j\n",
        "            j -= 1\n",
        "    return new_array\n",
        "\n",
        "print(sorted_squared([-5, 1, 2]))\n",
        "print(sorted_squared([0, 1, 2]))\n",
        "print(sorted_squared([]))\n",
        "print(sorted_squared([-5,-4,-2, 1, 9,12]))\n"
      ],
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          "text": [
            "[1, 4, 25]\n",
            "[0, 1, 4]\n",
            "[]\n",
            "[1, 4, 16, 25, 81, 144]\n"
          ]
        }
      ]
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      "cell_type": "markdown",
      "source": [
        "# **# 2. Monotonic Array**\n",
        "An array is monotonic if it is either monotone increasing or monotone decreasing. An array is monotone increasing if all its elements from left to right are non-decreasing. An array is monotone decreasing if all  its elements from left to right are non-increasing. Given an integer array return true if the given array is monotonic, or false otherwise. \n",
        "\n",
        "Ask for clarification.\n",
        "\n",
        "\n",
        "*   Is an empty array monotonic?\n",
        "*   Is an array with only 1 integer monotonic?\n",
        "\n",
        "For Example:\n",
        "\n",
        "\n",
        "*   [1,2,3] -> true\n",
        "*   [3,2,1] -> true\n",
        "*   [1,2,2] -> true\n",
        "*   [3,3,3] -> true\n",
        "*   [1,6,2] -> false\n",
        "*   [] -> true\n",
        "*   [0] -> true"
      ],
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      "cell_type": "markdown",
      "source": [
        "# Method: Head and Tail\n",
        "Given:\n",
        "1. [3, _, _, _, 7]\n",
        "2. [3, _, _, _, 3]\n",
        "3. [3, _, _, _, -5]\n",
        "\n",
        "Check the first and last element in this array to determine whether it is non increasing or non decreasing.\n",
        "\n",
        "And then verify the elements in between based on the three scenarios above. \n",
        "\n"
      ],
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      "source": [
        "def monotonic_array(array):\n",
        "    # consider the tendency of array when it is not empty\n",
        "    while len(array) > 0:\n",
        "      first = array[0]\n",
        "      last = array[len(array)-1]\n",
        "      if first > last:\n",
        "          for i in range(len(array)-1):\n",
        "              if array[i] < array[i+1]:\n",
        "                  return False\n",
        "      elif first == last:\n",
        "          for i in range(len(array)-1):\n",
        "              if array[i] != array[i+1]:\n",
        "                  return False\n",
        "      else:\n",
        "          for i in range(len(array)-1):\n",
        "              if array[i] > array[i+1]:\n",
        "                  return False\n",
        "      return True\n",
        "    # consider empty array as monotonic array\n",
        "    while len(array) == 0:\n",
        "      return True\n",
        "\n",
        "\n",
        "print(monotonic_array([1]))\n",
        "print(monotonic_array([3,1,4,2]))\n",
        "print(monotonic_array([]))\n",
        "print(monotonic_array([1,2,3,3,5]))"
      ],
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          "text": [
            "True\n",
            "False\n",
            "True\n",
            "True\n"
          ]
        }
      ]
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      "cell_type": "markdown",
      "source": [
        "# **# 3. Rotate Array**\n",
        "\n",
        "Given an array, rotate the array to the right by k steps, where k is non-negative.\n",
        "\n",
        "Ask for clarification.\n",
        "*   What happens if an empty array is passed? -> return an empty array\n",
        "*   If k = 0, no rotation is to happen? -> Yes\n",
        "\n",
        "For Example:\n",
        "*   [1,2] k=1 -> [2,1]\n",
        "*   [1,2] k=2 -> [2,1] -> [1,2]\n",
        "*   [1,2,3,4] k=3 -> [4,1,2,3] -> [3,4,1,2] -> [2,3,4,1]\n",
        "*   [1,2,3,4] k=0 -> [1,2,3,4]\n",
        "*   [] k=5 -> []"
      ],
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    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Brute Force Method (Not Valid on Leetcode)\n",
        "Given [1,2,3,4]， k=3\n",
        "\n",
        "1.check the remainder of array length divided by k, this determines how many of nums need to be rotate\n",
        "\n",
        "2.slicing the given array and reassemble as a new array, return new array\n",
        "\n",
        "**T = O(n x k); S = O(1)**\n"
      ],
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      "source": [
        "def rotate_array(k, array):\n",
        "  if k==0 or len(array)==0:\n",
        "    return array\n",
        "  else:\n",
        "    k = k%len(array)\n",
        "    subarraykeep = array[:-k]\n",
        "    subarrayrotate = array[-k:]\n",
        "    subarrayrotate.extend(subarraykeep)\n",
        "    return subarrayrotate\n",
        "\n",
        "print(rotate_array(3, [1,2,3,4]))\n",
        "print(rotate_array(2, [1,2]))\n",
        "print(rotate_array(2, [-1,-100,3,99]))\n",
        "print(rotate_array(3, [1,2,3,4,5,6,7]))"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[2, 3, 4, 1]\n",
            "[1, 2]\n",
            "[3, 99, -1, -100]\n",
            "[5, 6, 7, 1, 2, 3, 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: Shift & Use extra array\n",
        "\n",
        "Store rotate elements in a temp array\n",
        "\n",
        "Shift the elements on the left to the right by k\n",
        "\n",
        "Assign the elements on the left from stored temp array\n",
        "\n",
        "**T = O(n); S = O(k)**\n",
        "\n"
      ],
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    {
      "cell_type": "code",
      "source": [
        "def rotate_array(k, array):\n",
        "    if len(array) == 0: return []\n",
        "    # get the actual rotate times by finding k remainder of array length\n",
        "    k=k%len(array)\n",
        "    # store the rotate array in a subarray\n",
        "    temp = array[-k:]\n",
        "    # shift the elements from left to right by adding k to its index\n",
        "    for i in reversed(range(0,len(array)-k)):\n",
        "        array[i+k]=array[i]\n",
        "    # assign the empty elements on the left from the stored subarray\n",
        "    for i in range(len(temp)):\n",
        "        array[i]=temp[i]\n",
        "    return array\n",
        "\n",
        "print(rotate_array(3, [1,2,3,4]))\n",
        "print(rotate_array(2, [1,2]))\n",
        "print(rotate_array(2, [-1,-100,3,99]))\n",
        "print(rotate_array(3, [1,2,3,4,5,6,7]))"
      ],
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          "base_uri": "https://localhost:8080/"
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      "outputs": [
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          "name": "stdout",
          "text": [
            "[2, 3, 4, 1]\n",
            "[1, 2]\n",
            "[3, 99, -1, -100]\n",
            "[5, 6, 7, 1, 2, 3, 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 3: Using Reverse\n",
        "Given: [1,2,3,4,5,6,7], k=3 -> [5,6,7,1,2,3,4]\n",
        "\n",
        "First reverse all elements of the array\n",
        "\n",
        "[7,6,5,4,3,2,1]\n",
        "\n",
        "Then reverse the first k elements\n",
        "[5,6,7,4,3,2,1]\n",
        "\n",
        "Last reverse the rest n-k elements\n",
        "[5,6,7,1,2,3,4]\n",
        "\n",
        "**T = O(n); S = O(1)**"
      ],
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      }
    },
    {
      "cell_type": "code",
      "source": [
        "def reverse(array,start,end):\n",
        "    while(start<end):\n",
        "        array[start],array[end] = array[end], array[start]\n",
        "        start +=1\n",
        "        end -=1\n",
        "    return array\n",
        "\n",
        "def rotate_array(k, array):\n",
        "    k= k % len(array)\n",
        "    reverse(array, 0, len(array)-1)\n",
        "    reverse(array, 0, k-1)\n",
        "    reverse(array, k, len(array)-1)\n",
        "    return array\n",
        "\n",
        "print(rotate_array(3, [1,2,3,4]))\n",
        "print(rotate_array(2, [1,2]))\n",
        "print(rotate_array(2, [-1,-100,3,99]))\n",
        "print(rotate_array(3, [1,2,3,4,5,6,7]))"
      ],
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          "text": [
            "[2, 3, 4, 1]\n",
            "[1, 2]\n",
            "[3, 99, -1, -100]\n",
            "[5, 6, 7, 1, 2, 3, 4]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 4. Container with most Water**\n",
        "\n",
        "You are given an integer array height of length n. There are n vertical lines drawn such that the two endpoints of the ith line are (i, 0) and (i, height[i]). Find two lines that together with the x-axis form a container, such that the container contains the most water(depth is constant across containers). Return the area(that the 2 lines and the X axis make) of container which can store the max amount of water. Notice that you may not slant the container.\n",
        "\n",
        "Ask for clarifications:\n",
        "*   Does Y axis count as a wall？\n",
        "*   Does a line inside the container affect area?\n",
        "*   Will the given height a single number?\n",
        "\n",
        "\n",
        "For Example:\n",
        "\n",
        "Input: height = [1,8,6,2,5,4,8,3,7]\n",
        "\n",
        "Output: 49\n",
        "\n",
        "Explanation: The above vertical lines are represented by array [1,8,6,2,5,4,8,3,7]. In this case, the max area of water (blue section) the container can contain is 49.\n",
        "\n",
        "Input: height = [1,1]\n",
        "\n",
        "Output: 1"
      ],
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Brute Force\n",
        "**T = O(n^2); S = O(1)**"
      ],
      "metadata": {
        "id": "aeGyjbsWIGWd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def maxArea(height):\n",
        "  maxArea = 0\n",
        "  for i in range(0,len(height)-1):\n",
        "    for j in range(i+1, len(height)):\n",
        "      area = min(height[i],height[j])*(j-i)\n",
        "      maxArea = max(area, maxArea)\n",
        "\n",
        "  return maxArea\n",
        "\n",
        "print(maxArea([1,8,6,2,5,4,8,3,7]))\n",
        "print(maxArea([1,1,2]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_6vr5Uo99yPa",
        "outputId": "a56f439f-c313-45d4-a473-ab273a952781"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "49\n",
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: Two Pointers\n",
        "\n",
        "**T = O(n)**\n",
        "\n",
        "**S = O(1)**"
      ],
      "metadata": {
        "id": "G2oglQP9Z8CW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def maxArea_optimum(height):\n",
        "  left = 0\n",
        "  right = len(height)-1\n",
        "  max_area = 0\n",
        "  while left < right:\n",
        "    area = min(height[left], height[right])*(right - left)\n",
        "    max_area = max(area, max_area)\n",
        "    if height[left]<height[right]:\n",
        "      left+=1\n",
        "    else:\n",
        "      right-=1\n",
        "  return max_area\n",
        "\n",
        "print(maxArea_optimum([1,8,6,2,5,4,8,3,7]))\n",
        "print(maxArea_optimum([1,1,2]))"
      ],
      "metadata": {
        "id": "CZMTjPGhHU-M",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cbca06b1-7e12-4aa6-c109-b36a238e48ed"
      },
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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "49\n",
            "2\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 5. Two Sums**\n",
        "\n",
        "Given an array of integers nums and an integer target, return indices of the two numbers such that they add up to target.\n",
        "\n",
        "You may assume that each input would have exactly one solution, and you may not use the same element twice.\n",
        "\n",
        "You can return the answer in any order.\n",
        "\n",
        "Example 1:\n",
        "\n",
        "Input: nums = [2,7,11,15], target = 9\n",
        "\n",
        "Output: [0,1]\n",
        "\n",
        "Explanation: Because nums[0] + nums[1] == 9, we return [0,1].\n",
        "\n",
        "Example 2:\n",
        "\n",
        "Input: nums = [3,2,4], target = 6\n",
        "\n",
        "Output: [1,2]\n",
        "\n",
        "Example 3:\n",
        "\n",
        "Input: nums = [3,3], target = 6\n",
        "\n",
        "Output: [0,1]"
      ],
      "metadata": {
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      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Brute Force\n",
        "**T = O(n^2); S = O(1)**"
      ],
      "metadata": {
        "id": "IeMhhBtNZnGV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Brute Force Solution\n",
        "def two_sum(array,target):\n",
        "    for i in range(len(array)-1):\n",
        "        for j in range(i+1,len(array)):\n",
        "            if array[i]+array[j] == target:\n",
        "                return [i,j]\n",
        "    return []\n",
        "\n",
        "print(two_sum([2,7,11,15],9))\n",
        "print(two_sum([3,2,4],6))\n",
        "print(two_sum([2,-1,5,3],4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nhfh6z1MYcxO",
        "outputId": "e029b2eb-de3b-4540-bbe3-d8d4c959d84e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1]\n",
            "[1, 2]\n",
            "[1, 2]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: One-pass Hash Table\n",
        "**T = O(n); S = O(n)**"
      ],
      "metadata": {
        "id": "J51q1wG5ZzuW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def two_sum_optimal(array,target):\n",
        "    num_available = {}\n",
        "    for i in range(len(array)):\n",
        "        needed_val = target - array[i]\n",
        "        if needed_val in num_available:\n",
        "            return [i,num_available[needed_val]]\n",
        "        else:\n",
        "            num_available[array[i]]=i\n",
        "    return []\n",
        "\n",
        "print(two_sum([2,7,11,15],9))\n",
        "print(two_sum([3,2,4],6))\n",
        "print(two_sum_optimal([2,-1,5,3],4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "blCdFnHlZWJm",
        "outputId": "aab6ab6e-dba1-4d8b-ad13-d486555c9575"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0, 1]\n",
            "[1, 2]\n",
            "[2, 1]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 6 Three Sums**\n",
        "\n",
        "Given an integer array nums, return all the triplets [nums[i], nums[j], nums[k]] such that i != j, i != k, and j != k, and nums[i] + nums[j] + nums[k] == 0.\n",
        "\n",
        "Notice that the solution set must not contain duplicate triplets.\n",
        "\n",
        "Example 1:\n",
        "\n",
        "Input: nums = [-1,0,1,2,-1,-4]\n",
        "\n",
        "Output: [[-1,-1,2],[-1,0,1]]\n",
        "\n",
        "Example 2:\n",
        "\n",
        "Input: nums = [0,1,1]\n",
        "\n",
        "Output: []\n",
        "\n",
        "Example 3:\n",
        "\n",
        "Input: nums = [0,0,0]\n",
        "\n",
        "Output: [[0,0,0]]"
      ],
      "metadata": {
        "id": "Wdu_jtHh_0j7"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 0: No-Sort\n",
        "\n",
        "T = O(n^2)\n",
        "\n",
        "S = O(n)"
      ],
      "metadata": {
        "id": "2OvOeXlhxVZT"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def threeSum(nums):\n",
        "  res, dups = set(), set()\n",
        "  seen = {}\n",
        "  for i, val1 in enumerate(nums):\n",
        "    if val1 not in dups:\n",
        "      dups.add(val1)\n",
        "      for j, val2 in enumerate(nums[i+1:]):\n",
        "        complement = -val1 - val2\n",
        "        if complement in seen and seen[complement] == i:\n",
        "          res.add(tuple(sorted((val1, val2, complement))))\n",
        "        seen[val2] = i\n",
        "  return res\n",
        "\n",
        "nums1 = [-1,0,1,2,-1,-4]\n",
        "nums2 = [-2,-2,0,0,2,2]\n",
        "nums3 = [0,0,0]\n",
        "nums4 = [0,1,1]\n",
        "nums5 = []\n",
        "\n",
        "print(threeSum(nums1))\n",
        "print(threeSum(nums2))\n",
        "print(threeSum(nums3))\n",
        "print(threeSum(nums4))\n",
        "print(threeSum(nums5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oh1c6W_-xZJ7",
        "outputId": "b89b8ed8-2e77-4617-b77f-7166cd00ce11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "{(-1, 0, 1), (-1, -1, 2)}\n",
            "{(-2, 0, 2)}\n",
            "{(0, 0, 0)}\n",
            "set()\n",
            "set()\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Two pointers\n",
        "\n",
        "T = O (nlogn + n^2)\n",
        "\n",
        "S = O (logn) -> O(n)\n"
      ],
      "metadata": {
        "id": "wLa0VjXzBmBi"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def threeSum(nums):\n",
        "  res = []\n",
        "  nums.sort()\n",
        "  for i in range(len(nums)):\n",
        "    if nums[i] > 0:\n",
        "        break\n",
        "    if i == 0 or nums[i - 1] != nums[i]:\n",
        "        twoSumII(nums, i, res)\n",
        "  return res\n",
        "\n",
        "def twoSumII(nums, i, res):\n",
        "  lo, hi = i + 1, len(nums) - 1\n",
        "  while (lo < hi):\n",
        "    sum = nums[i] + nums[lo] + nums[hi]\n",
        "    if sum < 0:\n",
        "      lo += 1\n",
        "    elif sum > 0:\n",
        "      hi -= 1\n",
        "    else:\n",
        "      res.append([nums[i], nums[lo], nums[hi]])\n",
        "      lo += 1\n",
        "      hi -= 1\n",
        "      while lo < hi and nums[lo] == nums[lo - 1]:\n",
        "        lo += 1\n",
        "\n",
        "nums1 = [-1,0,1,2,-1,-4]\n",
        "nums2 = [-2,-2,0,0,2,2]\n",
        "nums3 = [0,0,0]\n",
        "nums4 = [0,1,1]\n",
        "nums5 = []\n",
        "\n",
        "print(threeSum(nums1))\n",
        "print(threeSum(nums2))\n",
        "print(threeSum(nums3))\n",
        "print(threeSum(nums4))\n",
        "print(threeSum(nums5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UCdtIL1YBHzi",
        "outputId": "ea309cd0-e78f-4f1b-d0d3-3bed8919e1aa"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-1, -1, 2], [-1, 0, 1]]\n",
            "[[-2, 0, 2]]\n",
            "[[0, 0, 0]]\n",
            "[]\n",
            "[]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: Hashset\n",
        "\n",
        "T = O(nlogn + n^2) -> O(n^2)\n",
        "\n",
        "S = O(n)\n"
      ],
      "metadata": {
        "id": "N_OBpJzsCeVC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def threeSum(nums):\n",
        "  res = []\n",
        "  nums.sort()\n",
        "  for i in range(len(nums)):\n",
        "    if nums[i] > 0:\n",
        "      break\n",
        "    if i == 0 or nums[i - 1] != nums[i]:\n",
        "      twoSum(nums, i, res)\n",
        "  return res\n",
        "\n",
        "def twoSum(nums, i, res):\n",
        "  seen = set()\n",
        "  j = i + 1\n",
        "  while j < len(nums):\n",
        "    complement = -nums[i] - nums[j]\n",
        "    if complement in seen:\n",
        "      res.append([nums[i], nums[j], complement])\n",
        "      while j + 1 < len(nums) and nums[j] == nums[j + 1]:\n",
        "        j += 1\n",
        "    seen.add(nums[j])\n",
        "    j += 1\n",
        "\n",
        "nums1 = [-1,0,1,2,-1,-4]\n",
        "nums2 = [-2,-2,0,0,2,2]\n",
        "nums3 = [0,0,0]\n",
        "nums4 = [0,1,1]\n",
        "nums5 = []\n",
        "\n",
        "print(threeSum(nums1))\n",
        "print(threeSum(nums2))\n",
        "print(threeSum(nums3))\n",
        "print(threeSum(nums4))\n",
        "print(threeSum(nums5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cE9VKjyqCvvo",
        "outputId": "869a3fcd-bfd4-46bd-a654-f73e93a1a663"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-1, 1, 0], [-1, 2, -1]]\n",
            "[[-2, 2, 0]]\n",
            "[[0, 0, 0]]\n",
            "[]\n",
            "[]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 3: Two Pointers Integrated\n",
        "\n",
        "T = O(n)\n",
        "\n",
        "S = O(n)"
      ],
      "metadata": {
        "id": "yyOTykNaDrXR"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def threeSum(nums):\n",
        "  result = []\n",
        "  #sort will expose any consecutive equal numbers\n",
        "  nums.sort()\n",
        "  #use enumerate for index and value, pass the condition where use the same value for the first number in triplet\n",
        "  for i, a in enumerate(nums):\n",
        "    if i > 0 and a == nums[i-1]:\n",
        "      continue\n",
        "    #use two pointers for the number on the right of first number\n",
        "    l, r = i+1, len(nums)-1\n",
        "    while l < r:\n",
        "      threeSum = a + nums[l] + nums[r]\n",
        "      # since this array is sorted and current sum larger than target, reduce the right indelx will reduce the right number\n",
        "      if threeSum > 0:\n",
        "        r -= 1\n",
        "      # similar reason to add left number index\n",
        "      elif threeSum < 0:\n",
        "        l += 1\n",
        "      # if threeSum == 0, append the triplet to result\n",
        "      else:\n",
        "        result.append([a, nums[l], nums[r]])\n",
        "        # notice for example [-2, -2, 0, 0, 2, 2]\n",
        "        # skip any repeating number for the same position\n",
        "        l += 1\n",
        "        while nums[l] == nums[l-1] and l < r:\n",
        "          l += 1\n",
        "  return result\n",
        "\n",
        "nums1 = [-1,0,1,2,-1,-4]\n",
        "nums2 = [-2,-2,0,0,2,2]\n",
        "nums3 = [0,0,0]\n",
        "nums4 = [0,1,1]\n",
        "nums5 = []\n",
        "\n",
        "print(threeSum(nums1))\n",
        "print(threeSum(nums2))\n",
        "print(threeSum(nums3))\n",
        "print(threeSum(nums4))\n",
        "print(threeSum(nums5))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "m4zngk8oAV3c",
        "outputId": "571a21d8-ccba-405b-9d5d-d95eb7028467"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-1, -1, 2], [-1, 0, 1]]\n",
            "[[-2, 0, 2]]\n",
            "[[0, 0, 0]]\n",
            "[]\n",
            "[]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 7. Valild Palindrome**\n",
        "\n",
        "A phrase is a palindrome if, after converting all uppercase letters into lowercase letters and removing all non-alphanumeric characters, it reads the same forward and backward. Alphanumeric characters include letters and numbers.\n",
        "\n",
        "Given a string s, return true if it is a palindrome, or false otherwise.\n",
        "\n",
        "Example 1:\n",
        "\n",
        "Input: s = \"A man, a plan, a canal: Panama\"\n",
        "\n",
        "Output: true\n",
        "\n",
        "Explanation: \"amanaplanacanalpanama\" is a palindrome.\n",
        "\n",
        "Example 2:\n",
        "\n",
        "Input: s = \"race a car\"\n",
        "\n",
        "Output: false\n",
        "\n",
        "Explanation: \"raceacar\" is not a palindrome.\n",
        "\n",
        "Example 3:\n",
        "\n",
        "Input: s = \" \"\n",
        "\n",
        "Output: true\n",
        "\n",
        "Explanation: s is an empty string \"\" after removing non-alphanumeric characters.\n",
        "Since an empty string reads the same forward and backward, it is a palindrome."
      ],
      "metadata": {
        "id": "zeK8G4fxdWK2"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Brute Force\n",
        "\n",
        "**T = O(n); S = O(n)**"
      ],
      "metadata": {
        "id": "ThmlxD9oeMzV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import re\n",
        "\n",
        "def isPalindrome(s):\n",
        "  data = re.sub(r'[^0-9a-zA-Z]', '', s)   \n",
        "  # ignor all non-alphanumeric characters\n",
        "\n",
        "  return data.lower() == data[::-1].lower()\n",
        "\n",
        "s1 = \"A man, a plan, a canal: Panama\"\n",
        "s2 = \"race a car\"\n",
        "s3 = \" \"\n",
        "print(isPalindrome(s1))\n",
        "print(isPalindrome(s2))\n",
        "print(isPalindrome(s3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1iif6pk_eXJ1",
        "outputId": "d2f1edf5-fa71-469a-d905-a1113cbeddb6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "False\n",
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: Two Pointers\n",
        "\n",
        "**T = O(n); S = O(1)**"
      ],
      "metadata": {
        "id": "XqEg8XEkgUot"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def isPalindrome(s):\n",
        "  s= s.strip()\n",
        "  if len(s) == 0 or len(s) == 1: return True\n",
        "  list_s = [x.lower() for x in s if x.isalnum()]\n",
        "  l = 0\n",
        "  r = len(list_s)-1\n",
        "  #for i in range(len(list_s)):\n",
        "  while l<r:\n",
        "    if list_s[l] != list_s[r]:\n",
        "        return False\n",
        "    l+=1\n",
        "    r-=1\n",
        "  return True\n",
        "\n",
        "s1 = \"A man, a plan, a canal: Panama\"\n",
        "s2 = \"race a car\"\n",
        "s3 = \" \"\n",
        "print(isPalindrome(s1))\n",
        "print(isPalindrome(s2))\n",
        "print(isPalindrome(s3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fJOqN-Kcf6Vt",
        "outputId": "ad7097d0-56d2-44aa-889d-1d57a15be12b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "False\n",
            "True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 8. Contains Duplicates**\n",
        "Given an integer array nums, return true if any value appears at least twice in the array, and return false if every element is distinct.\n",
        "\n",
        "Example 1:\n",
        "\n",
        "Input: nums = [1,2,3,1]\n",
        "\n",
        "Output: true\n",
        "\n",
        "Example 2:\n",
        "\n",
        "Input: nums = [1,2,3,4]\n",
        "\n",
        "Output: false"
      ],
      "metadata": {
        "id": "_t8IFPzVhI-t"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: List to Set, Compare length\n",
        "\n",
        "**T = O(n); S = O(1)**"
      ],
      "metadata": {
        "id": "fc5IGdlOiaY9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def containsDuplicate(nums):\n",
        "  set_nums = set(nums)\n",
        "  unique_number = len(set_nums)\n",
        "  if unique_number == len(nums):\n",
        "      return False\n",
        "  else:\n",
        "      return True\n",
        "\n",
        "print(containsDuplicate([1,2,3,1]))\n",
        "print(containsDuplicate([1,2,3,4]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YQiaepRyhi1V",
        "outputId": "89b4d0e0-bc19-4aba-fc6a-41b3ca5af0d3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: Single Set check One Pass\n",
        "\n",
        "T = O(n); S = O(n)"
      ],
      "metadata": {
        "id": "DA09dNjfihx0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def containsDuplicate(nums):\n",
        "  hashset = set()\n",
        "  for n in nums:\n",
        "      if n in hashset:\n",
        "          return True\n",
        "      hashset.add(n)\n",
        "  return False\n",
        "\n",
        "print(containsDuplicate([1,2,3,1]))\n",
        "print(containsDuplicate([1,2,3,4]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xXN4Rai1iJn0",
        "outputId": "103aaa89-ff1d-40f9-94d5-8f710dc1eedd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 3: Check Equal Fragment\n",
        "**T = O(n log n); S = O(1)**"
      ],
      "metadata": {
        "id": "1SYnAzAYkN6U"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def containsDuplicate(nums):\n",
        "  sorted_nums = sorted(nums)\n",
        "  for i in range(len(sorted_nums)-1):\n",
        "    if (sorted_nums[i] == sorted_nums[i+1]):\n",
        "      return True\n",
        "  return False\n",
        "\n",
        "print(containsDuplicate([1,2,3,1]))\n",
        "print(containsDuplicate([1,2,3,4]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HjoOUMrRjhIt",
        "outputId": "33f4bdd6-793d-4da7-af56-afafcffe3cef"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 9. Valid Anagram**\n",
        "\n",
        "Given two strings s and t, return true if t is an anagram of s, and false otherwise.\n",
        "\n",
        "An Anagram is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all the original letters exactly once.\n",
        "\n",
        "Example 1:\n",
        "\n",
        "Input: s = \"anagram\", t = \"nagaram\"\n",
        "\n",
        "Output: true\n",
        "\n",
        "Example 2:\n",
        "\n",
        "Input: s = \"rat\", t = \"car\"\n",
        "\n",
        "Output: false"
      ],
      "metadata": {
        "id": "FI5dO59Jk5-U"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Sort & Compare\n",
        "**T = O(n log n); S = O(1)**"
      ],
      "metadata": {
        "id": "K_wGxiUYlwJ0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def isAnagram(s, t):\n",
        "  t_list = sorted(list(t))\n",
        "  s_list = sorted(list(s))\n",
        "  if t_list == s_list:\n",
        "      return True\n",
        "  else:\n",
        "      return False\n",
        "\n",
        "s = \"anagram\"\n",
        "t = \"nagaram\"\n",
        "print(isAnagram(s,t))\n",
        "s = \"rat\"\n",
        "t = \"car\"\n",
        "print(isAnagram(s,t))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1c_-LmbalTSz",
        "outputId": "2841e715-f252-4f4e-9f55-3c18a9a14a75"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2：Hash Table & count frequency\n",
        "**T = O(n); S = O(n)**"
      ],
      "metadata": {
        "id": "hukBjQ0Xy18D"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def isAnagram(s, t):\n",
        "  if len(s) != len(t):\n",
        "    return False\n",
        "  table_t = {}\n",
        "  table_s = {}\n",
        "  t_list = list(t)\n",
        "  s_list = list(s)\n",
        "  # use a hashtable to count frequency of each char in string s\n",
        "  for i in range(0, len(t_list)-1):\n",
        "    if t_list[i] in table_t.keys():\n",
        "      table_t[t_list[i]] += 1\n",
        "    table_t[t_list[i]] = 1\n",
        "  # use a hashtable to count frequency of each char in string t\n",
        "  for j in range(0, len(s_list)-1):\n",
        "    if s_list[j] in table_s.keys():\n",
        "      table_s[s_list[j]] += 1\n",
        "    table_s[s_list[j]] = 1\n",
        "  # compare the value of each key between the two tables\n",
        "  for k in table_t.keys():\n",
        "    if not (k in table_s.keys() and table_s[k] == table_t[k]):\n",
        "      return False\n",
        "  return True\n",
        "\n",
        "s = \"anagram\"\n",
        "t = \"nagaram\"\n",
        "print(isAnagram(s,t))\n",
        "s = \"rat\"\n",
        "t = \"car\"\n",
        "print(isAnagram(s,t))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "9mt_Fcw4y6bZ",
        "outputId": "2f1c2e98-cc35-4337-c086-4b035d6e8be0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 10. Group Anagram**\n",
        "Given an array of strings strs, group the anagrams together. You can return the answer in any order.\n",
        "\n",
        "An Anagram is a word or phrase formed by rearranging the letters of a different word or phrase, typically using all the original letters exactly once.\n",
        "\n",
        "Example 1:\n",
        "\n",
        "Input: strs = [\"eat\",\"tea\",\"tan\",\"ate\",\"nat\",\"bat\"]\n",
        "\n",
        "Output: [[\"bat\"],[\"nat\",\"tan\"],[\"ate\",\"eat\",\"tea\"]]\n",
        "\n",
        "Example 2:\n",
        "\n",
        "Input: strs = [\"\"]\n",
        "\n",
        "Output: [[\"\"]]\n",
        "\n",
        "Example 3:\n",
        "\n",
        "Input: strs = [\"a\"]\n",
        "\n",
        "Output: [[\"a\"]]\n"
      ],
      "metadata": {
        "id": "8D0SY7ji4SW6"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Sorting Tuple\n",
        "**T = O(nlogn); S = O(n)**\n",
        "\n",
        "1.   create dictionary\n",
        "2.   for each string, sort it and convert as tuple, use the tuple as the key and use the string as the value\n",
        "3.   return all values\n",
        "\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "pxgD5mLFkMO8"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import collections\n",
        "\n",
        "def groupAnagrams(strs):\n",
        "  ans = collections.defaultdict(list)\n",
        "  for s in strs:\n",
        "      ans[tuple(sorted(s))].append(s)\n",
        "  return ans.values()\n",
        "\n",
        "print(groupAnagrams([\"eat\",\"tea\",\"tan\",\"ate\",\"nat\",\"bat\"]))\n",
        "print(groupAnagrams([\"\"]))\n",
        "print(groupAnagrams([\"a\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GjETgynwkB-t",
        "outputId": "37abbb98-d585-4acd-cdd1-77c00e752fa6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_values([['eat', 'tea', 'ate'], ['tan', 'nat'], ['bat']])\n",
            "dict_values([['']])\n",
            "dict_values([['a']])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: Sorting String\n",
        "**T = O(n); S = O(n)**\n",
        "1.   create dictionary\n",
        "2.   for each string, sort it and convert as string, use the sorted string as the key and use the string as the value\n",
        "3.   return all values\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "9Sax1Z-ElG1E"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import collections\n",
        "\n",
        "def groupAnagrams(strs):\n",
        "  ans = {}\n",
        "  for s in strs:\n",
        "    key = ''.join(sorted(s))\n",
        "    if key in ans.keys():\n",
        "      ans[key].append(s)\n",
        "    else:\n",
        "      ans[key] = [s]\n",
        "  return ans.values()\n",
        "\n",
        "print(groupAnagrams([\"eat\",\"tea\",\"tan\",\"ate\",\"nat\",\"bat\"]))\n",
        "print(groupAnagrams([\"\"]))\n",
        "print(groupAnagrams([\"a\"]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a394879LlWnE",
        "outputId": "5817c0bb-c642-4604-f516-c36e71bc9638"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "dict_values([['eat', 'tea', 'ate'], ['tan', 'nat'], ['bat']])\n",
            "dict_values([['']])\n",
            "dict_values([['a']])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 11. Isomorphic Strings**\n",
        "\n",
        "Given two strings s and t, determine if they are isomorphic.\n",
        "\n",
        "Two strings s and t are isomorphic if the characters in s can be replaced to get t.\n",
        "\n",
        "All occurrences of a character must be replaced with another character while preserving the order of characters. No two characters may map to the same character, but a character may map to itself.\n",
        "\n",
        "Input: s = \"egg\", t = \"add\"\n",
        "\n",
        "Output: true\n",
        "\n",
        "Input: s = \"paper\", t = \"title\"\n",
        "\n",
        "Output: true\n",
        "\n",
        "Input: s = \"foo\", t = \"bar\"\n",
        "\n",
        "Output: false\n",
        "\n",
        "Input: s = \"bbbaaaba\", t = \"aaabbbba\"\n",
        "\n",
        "Output: false"
      ],
      "metadata": {
        "id": "QwEyymTsHyko"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: zip & set\n",
        "\n",
        "zip is a function that pairs two objects, it needs to be convert to either list or set to use."
      ],
      "metadata": {
        "id": "zHq_UWypguFc"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def isIsomorphic(s, t):\n",
        "  return len(set(zip(s, t))) == len(set(s)) == len(set(t))\n",
        "\n",
        "print(isIsomorphic(\"egg\", \"add\"))\n",
        "print(isIsomorphic(\"foo\", \"bar\"))\n",
        "print(isIsomorphic(\"paper\", \"title\"))\n",
        "print(isIsomorphic(\"bbbaaaba\", \"aaabbbba\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OH-ZN6WngzCr",
        "outputId": "444f11d2-03cf-42b3-88f9-6a20ab14cbe6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "False\n",
            "True\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: Find func"
      ],
      "metadata": {
        "id": "FT1ONPwVI_bv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def isIsomorphic(s, t): \n",
        "  return [s.find(i) for i in s] == [t.find(j) for j in t]\n",
        "\n",
        "print(isIsomorphic(\"egg\", \"add\"))\n",
        "print(isIsomorphic(\"foo\", \"bar\"))\n",
        "print(isIsomorphic(\"paper\", \"title\"))\n",
        "print(isIsomorphic(\"bbbaaaba\", \"aaabbbba\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sAFE48lgJD1v",
        "outputId": "6aeb851f-359b-4fdf-c8f3-d316ad48ce90"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "False\n",
            "True\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 3: Two dictionary Pair\n",
        "1.   Create two dictionaries for string s and t\n",
        "2.   Since the isomorphic string should have chars from two strings as pairs, they will have the same length\n",
        "3.   for s dictionary, use s char as key and t char as value, same for t dictionary\n",
        "4.   for s dictionary, if s[i] already in it, check whether its value equals t[i]"
      ],
      "metadata": {
        "id": "iMP7j9eTKD83"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def isIsomorphic(s, t):\n",
        "  s2t, t2s = {}, {}\n",
        "  for i in range(len(s)):\n",
        "      if s[i] in s2t and s2t[s[i]] != t[i]:\n",
        "          return False\n",
        "      if t[i] in t2s and t2s[t[i]] != s[i]:\n",
        "          return False\n",
        "      s2t[s[i]] = t[i]\n",
        "      t2s[t[i]] = s[i]\n",
        "  return True\n",
        "\n",
        "print(isIsomorphic(\"egg\", \"add\"))\n",
        "print(isIsomorphic(\"foo\", \"bar\"))\n",
        "print(isIsomorphic(\"paper\", \"title\"))\n",
        "print(isIsomorphic(\"bbbaaaba\", \"aaabbbba\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "me9AKgK6KIBH",
        "outputId": "08763908-6432-4084-f6ee-cb396f7351fd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "True\n",
            "False\n",
            "True\n",
            "False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 12. Length of Last Word**\n",
        "\n",
        "Given a string s consisting of words and spaces, return the length of the last word in the string.\n",
        "\n",
        "A word is a maximal substring consisting of non-space characters only.\n",
        "\n",
        "Example 1:\n",
        "\n",
        "Input: s = \"Hello World\"\n",
        "\n",
        "Output: 5\n",
        "\n",
        "Example 2:\n",
        "\n",
        "Input: s = \"   fly me   to   the moon  \"\n",
        "\n",
        "Output: 4\n",
        "\n",
        "Example 3：\n",
        "\n",
        "Input: s = \"luffy is still joyboy\"\n",
        "\n",
        "Output: 6"
      ],
      "metadata": {
        "id": "Tzm2q5mCFLl3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1：Brute Force\n",
        "\n",
        "T = O(n)\n",
        "\n",
        "S = O(n)"
      ],
      "metadata": {
        "id": "-seVcNriFsk_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def lengthOfLastWord(s):\n",
        "  list_s = s.split()\n",
        "  list_s_reverse = list_s[::-1]\n",
        "  length = len(list_s_reverse[0])\n",
        "  return length\n",
        "\n",
        "s1 = \"Hello World\"\n",
        "s2 = \" fly me to the moon \"\n",
        "s3 = \"luffy is still joyboy\"\n",
        "\n",
        "print(lengthOfLastWord(s1))\n",
        "print(lengthOfLastWord(s2))\n",
        "print(lengthOfLastWord(s3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cOKYVAT6Fmb2",
        "outputId": "f1534668-99aa-43b8-808e-b3025599f871"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n",
            "4\n",
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: String Index Manipulation\n",
        "\n",
        "T = O(n)\n",
        "\n",
        "S = O(1)"
      ],
      "metadata": {
        "id": "DoMRwxf7GN0v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def lengthOfLastWord(s):\n",
        "  # trim the trailing spaces\n",
        "  p = len(s) - 1\n",
        "  while p >= 0 and s[p] == ' ':\n",
        "    p -= 1\n",
        "\n",
        "  # compute the length of last word\n",
        "  length = 0\n",
        "  while p >= 0 and s[p] != ' ':\n",
        "    p -= 1\n",
        "    length += 1\n",
        "  return length\n",
        "\n",
        "s1 = \"Hello World\"\n",
        "s2 = \" fly me to the moon \"\n",
        "s3 = \"luffy is still joyboy\"\n",
        "\n",
        "print(lengthOfLastWord(s1))\n",
        "print(lengthOfLastWord(s2))\n",
        "print(lengthOfLastWord(s3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mTP22d2DGb0P",
        "outputId": "f91029fc-d817-4205-a7cd-f105f56b687a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n",
            "4\n",
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 3: One Loop Iteration\n",
        "\n",
        "T = O(n)\n",
        "\n",
        "S = O(1)"
      ],
      "metadata": {
        "id": "B0pbu9EqGyos"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def lengthOfLastWord(s):\n",
        "  p, length = len(s), 0\n",
        "\n",
        "  while p > 0:\n",
        "    p -= 1\n",
        "    # we're in the middle of the last word\n",
        "    if s[p] != ' ':\n",
        "      length += 1\n",
        "    # here is the end of last word\n",
        "    elif length > 0:\n",
        "      return length\n",
        "\n",
        "  return length\n",
        "\n",
        "s1 = \"Hello World\"\n",
        "s2 = \" fly me to the moon \"\n",
        "s3 = \"luffy is still joyboy\"\n",
        "\n",
        "print(lengthOfLastWord(s1))\n",
        "print(lengthOfLastWord(s2))\n",
        "print(lengthOfLastWord(s3))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wp6VZIjEG_ke",
        "outputId": "a9100781-1ba5-4c67-82b5-d4c1588025d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "5\n",
            "4\n",
            "6\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 13. Top K Frequent Elements**\n",
        "\n",
        "Given an integer array nums and an integer k, return the k most frequent elements. You may return the answer in any order.\n",
        "\n",
        "Example 1:\n",
        "\n",
        "Input: nums = [1,1,1,2,2,3], k = 2\n",
        "\n",
        "Output: [1,2]\n",
        "\n",
        "Example 2:\n",
        "\n",
        "Input: nums = [1], k = 1\n",
        "\n",
        "Output: [1]"
      ],
      "metadata": {
        "id": "8O8-SIySKQQ-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Brute Force (Hashset)\n",
        "\n",
        "**T = O (n)**\n",
        "\n",
        "**S = O (n)**\n"
      ],
      "metadata": {
        "id": "G92sf5wvKpwf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def topKFrequent(nums, k):\n",
        "  res = []\n",
        "  # create a hashset to count each number's frequence\n",
        "  hashset = {}\n",
        "  for n in nums:\n",
        "    if n in hashset:\n",
        "      hashset[n] += 1\n",
        "    else:\n",
        "      hashset[n] = 1\n",
        "  # get the list of each number's count\n",
        "  freq_count = []\n",
        "  for freq in hashset.values():\n",
        "    freq_count.append(freq)\n",
        "  # when the unique number is more than 1:\n",
        "  # sort & reversse to get the top k frequent from the hashset\n",
        "  if len(freq_count) >1:\n",
        "    freq_count.sort()\n",
        "    freq_count.reverse()\n",
        "    top_k = freq_count[0:k]\n",
        "    for key in hashset.keys():\n",
        "      if hashset[key] in top_k:\n",
        "        res.append(key)\n",
        "    return res\n",
        "  # when the unique number is only 1 or the array is empty:\n",
        "  # just get the key from the hashset and return as result\n",
        "  else:\n",
        "    for key in hashset.keys():\n",
        "      res.append(key)\n",
        "    return res\n",
        "\n",
        "print(topKFrequent([1,1,1,2,2,3], 2))\n",
        "print(topKFrequent([1,1,1,2,2,3,4,4,4,4,5,5,5,5,5], 4))\n",
        "print(topKFrequent([1], 1))\n",
        "print(topKFrequent([], 1))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-cLQnSr_KtSj",
        "outputId": "c9b635c5-de76-4888-ddb3-742c5ae1c55b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 2]\n",
            "[1, 2, 4, 5]\n",
            "[1]\n",
            "[]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: Heap & Counter\n",
        "\n",
        "**T = O (Nlogk)**\n",
        "\n",
        "**S = O (N + k)**"
      ],
      "metadata": {
        "id": "5DoW6VY1pIx1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from collections import Counter\n",
        "import heapq\n",
        "\n",
        "def topKFrequent(nums, k):\n",
        "  # O(1) time \n",
        "  if k == len(nums):\n",
        "    return nums\n",
        "\n",
        "  # 1. build hash map : character and how often it appears\n",
        "  # O(N) time\n",
        "  count = Counter(nums)\n",
        "  # 2-3. build heap of top k frequent elements and\n",
        "  # convert it into an output array\n",
        "  # O(N log k) time\n",
        "  return heapq.nlargest(k, count.keys(), key=count.get)\n",
        "\n",
        "print(topKFrequent([1,1,1,2,2,3], 2))\n",
        "print(topKFrequent([1,1,1,2,2,3,4,4,4,4,5,5,5,5,5], 4))\n",
        "print(topKFrequent([1], 1))\n",
        "print(topKFrequent([], 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJzHQyRWpi_U",
        "outputId": "2c4b2fb3-3ffc-4c76-814b-3fd4cbb05bc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 2]\n",
            "[5, 4, 1, 2]\n",
            "[1]\n",
            "[]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 3: Bucket Sort\n",
        "\n",
        "**T = O(n)**\n",
        "\n",
        "**S = O(n)**"
      ],
      "metadata": {
        "id": "nVrlxGNg9lm9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def topKFrequent(nums, k):\n",
        "  bucket = [[] for _ in range(len(nums) + 1)]\n",
        "  Count = Counter(nums).items()  \n",
        "  for num, freq in Count: \n",
        "    bucket[freq].append(num) \n",
        "  flat_list = [item for sublist in bucket for item in sublist]\n",
        "  return flat_list[::-1][:k]\n",
        "\n",
        "print(topKFrequent([1,1,1,2,2,3], 2))\n",
        "print(topKFrequent([1,1,1,2,2,3,4,4,4,4,5,5,5,5,5], 4))\n",
        "print(topKFrequent([1], 1))\n",
        "print(topKFrequent([], 1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OdF_-YUr22wE",
        "outputId": "b54b7e69-e3db-4e4a-fe1a-bcf81859fc7c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 2]\n",
            "[5, 4, 1, 2]\n",
            "[1]\n",
            "[]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 14. Fibonacci Number**\n",
        "\n",
        "The Fibonacci numbers, commonly denoted F(n) form a sequence, called the Fibonacci sequence, such that each number is the sum of the two preceding ones, starting from 0 and 1. That is,\n",
        "\n",
        "F(0) = 0, F(1) = 1\n",
        "\n",
        "F(n) = F(n - 1) + F(n - 2), for n > 1.\n",
        "\n",
        "Given n, calculate F(n)"
      ],
      "metadata": {
        "id": "9RSBaGFGAeFU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Brute Force (Recursion)\n",
        "\n",
        "**T = O (2^n)**\n",
        "\n",
        "**S = O (n)**\n",
        "\n"
      ],
      "metadata": {
        "id": "JAVl9FKGCPUD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def fib(n):\n",
        "  if n == 0 or n == 1:\n",
        "    return n\n",
        "  else:\n",
        "    res = fib(n-1) + fib(n-2)\n",
        "    return res\n",
        "\n",
        "print(fib(0))\n",
        "print(fib(1))\n",
        "print(fib(2))\n",
        "print(fib(3))\n",
        "print(fib(4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KwdE-a9SCVTb",
        "outputId": "88994c93-b175-471e-cb1d-a47c1879ed2f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "1\n",
            "1\n",
            "2\n",
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: Iterative Bottom-Up\n",
        "\n",
        "**T = O (n)**\n",
        "\n",
        "**S = O (1)**"
      ],
      "metadata": {
        "id": "QvV05JpaFCmC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def fib(n):\n",
        "  a,b=0,1\n",
        "  for _ in range(n):\n",
        "    a,b=b,a+b\n",
        "  return a\n",
        "\n",
        "print(fib(0))\n",
        "print(fib(1))\n",
        "print(fib(2))\n",
        "print(fib(3))\n",
        "print(fib(4))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0EFHYrfAFOgR",
        "outputId": "b645193d-d029-497a-e12f-4d292e025b8b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0\n",
            "1\n",
            "1\n",
            "2\n",
            "3\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 15. Power Sum of Peculiar Array**\n",
        "\n",
        "A peculiar type of array in which each element is either an integer or another peculiar array. Assume that a peculiar array is never empty.\n",
        "\n",
        "Write a function that will take a peculiar array as its input and find the sum of its elements. Equivalent value of an array is the sum of its elements raised to the number which represents how far nested it is.\n",
        "\n",
        "For Example:\n",
        "\n",
        "Input: [2,3,[4, 1, 2]] = 2 + 3 + (4 + 1 + 2)^2 = 54\n",
        "\n",
        "Input: [1,2,[7,[3,4],2]] = 1 + 2 + (7 + (3 + 4)^3 + 2)^2 = 123,907\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "S3mdO0ljfU-i"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Brute Force (Recursion)\n",
        "\n",
        "T = O(n)\n",
        "\n",
        "S = O(1)"
      ],
      "metadata": {
        "id": "RAwlTUzfiMVA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def power_sum(array,power=1):\n",
        "  sum = 0\n",
        "  for i in array:\n",
        "    if type(i) == list:\n",
        "      sum += power_sum(i, power+1)\n",
        "    else:\n",
        "      sum += i\n",
        "  return sum**power\n",
        "\n",
        "\n",
        "print(power_sum([1,2,[3,4],[[2]]]))\n",
        "print(power_sum([2,3,[4, 1, 2]]))\n",
        "print(power_sum([1,2,[7,[3,4],2]]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "quEwuoI8hFyZ",
        "outputId": "34f71008-b975-492b-c207-3fd2423abfce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "116\n",
            "54\n",
            "123907\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 16. Valid Parentheses**\n",
        "\n",
        "Given a string s containing just the characters '(', ')', '{', '}', '[' and ']', determine if the input string is valid.\n",
        "\n",
        "An input string is valid if:\n",
        "\n",
        "Open brackets must be closed by the same type of brackets.\n",
        "Open brackets must be closed in the correct order.\n",
        "Every close bracket has a corresponding open bracket of the same type.\n",
        "\n",
        "Example:\n",
        "\n",
        "Input: s = \"()\"\n",
        "\n",
        "Output: true\n",
        "\n",
        "Input: s = \"()[]{}\"\n",
        "\n",
        "Output: true\n",
        "\n",
        "Input: s = \"(]\"\n",
        "\n",
        "Output: false\n"
      ],
      "metadata": {
        "id": "_t7D0uwC-dXF"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Stack & Match\n",
        "\n",
        "**T = O (n)**\n",
        "\n",
        "**S = O (1)**"
      ],
      "metadata": {
        "id": "Mmk199oO7kOq"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def validParentheses(s):\n",
        "  stack = []\n",
        "  closeToOpen = {')':'(', ']':'[', '}':'{'}\n",
        "\n",
        "  for c in s:\n",
        "    if c in closeToOpen:\n",
        "      if stack and stack[-1] == closeToOpen[c]:\n",
        "        stack.pop()\n",
        "      else:\n",
        "        return False\n",
        "    else:\n",
        "      stack.append(c)\n",
        "  \n",
        "  if not stack:\n",
        "    return True\n",
        "  else:\n",
        "    return False\n",
        "\n",
        "print(\"()\", validParentheses(\"()\"))\n",
        "print(\"()[]{}\", validParentheses(\"()[]{}\"))\n",
        "print(\"(]\", validParentheses(\"(]\"))\n",
        "print(\"([)]\", validParentheses(\"([)]\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FjxJzMnp18Z9",
        "outputId": "d7ca9ffb-8be6-4db9-de03-e25dfc750f4f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "() True\n",
            "()[]{} True\n",
            "(] False\n",
            "([)] False\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 17. Generate Parentheses**\n",
        "\n",
        "Given n pairs of parentheses, write a function to generate all combinations of well-formed parentheses.\n",
        "\n",
        "For Example:\n",
        "\n",
        "Input: n = 3\n",
        "\n",
        "Output: [\"((()))\",\"(()())\",\"(())()\",\"()(())\",\"()()()\"]\n",
        "\n",
        "Input: n = 1\n",
        "\n",
        "Output: [\"()\"]"
      ],
      "metadata": {
        "id": "Pgge5cl1A2Yu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Stack & Backtracking\n",
        "\n",
        "**T = O(4^n/n^(1/2))**\n",
        "\n",
        "**S = O(4^n/n^(1/2))**\n",
        "\n",
        "Cannot add close parenthese in the beginning\n",
        "\n",
        "-> add open parenthese first, but no more than n.\n",
        "\n",
        "-> Only add closed parenthese when there is more open parentheses. \n",
        "\n",
        "-> When both the number of open and closed parenthese is equal to n, add the result to the output list.\n",
        "\n",
        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        "\n",
        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)\n",
        "\n",
        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)\n"
      ],
      "metadata": {
        "id": "JoUb5a1DJJ5J"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generateParentheses(n):\n",
        "  # Only add open parentheses if open < n\n",
        "  # Only add a closing parenthese if closed < open\n",
        "  # Valid if open == closed == n\n",
        "  stack = []\n",
        "  res = []\n",
        "\n",
        "  def backtrack(openN, closedN):\n",
        "    if openN == closedN == n:\n",
        "      res.append(\"\".join(stack))\n",
        "      return\n",
        "    \n",
        "    if openN < n:\n",
        "      stack.append(\"(\")\n",
        "      backtrack(openN + 1, closedN)\n",
        "      stack.pop()\n",
        "    \n",
        "    if closedN < openN:\n",
        "      stack.append(\")\")\n",
        "      backtrack(openN, closedN + 1)\n",
        "      stack.pop()\n",
        "  \n",
        "  backtrack(0,0)\n",
        "  return res\n",
        "\n",
        "print(generateParentheses(4))\n",
        "print(generateParentheses(3))\n",
        "print(generateParentheses(2))\n",
        "print(generateParentheses(1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rRLxD2zIElBz",
        "outputId": "62555d0b-0c99-42e9-c7f2-1ccfd8d8c237"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['(((())))', '((()()))', '((())())', '((()))()', '(()(()))', '(()()())', '(()())()', '(())(())', '(())()()', '()((()))', '()(()())', '()(())()', '()()(())', '()()()()']\n",
            "['((()))', '(()())', '(())()', '()(())', '()()()']\n",
            "['(())', '()()']\n",
            "['()']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: Closure Number\n",
        "\n",
        "**T = O(4^n / n^(1/2))**\n",
        "\n",
        "**S = O(4^n / n^(1/2))**\n",
        "\n",
        "Consider the closure number of a valid parentheses sequence S: the least index >= 0 so that S[0], S[1], ..., S[2*index+1] is valid. Clearly, every parentheses sequence has a unique closure number. We can try to enumerate them individually.\n",
        "\n",
        "For each closure number c, we know the starting and ending brackets must be at index 0 and 2*c + 1. Then, the 2*c elements between must be a valid sequence, plus the rest of the elements must be a valid sequence."
      ],
      "metadata": {
        "id": "IziPGEUTNccm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def generateParentheses(N):\n",
        "  if N == 0: \n",
        "    return ['']\n",
        "  ans = []\n",
        "  for c in range(N):\n",
        "    for left in generateParentheses(c):\n",
        "      for right in generateParentheses(N-1-c):\n",
        "        ans.append('({}){}'.format(left, right))\n",
        "  return ans\n",
        "\n",
        "print(generateParentheses(4))\n",
        "print(generateParentheses(3))\n",
        "print(generateParentheses(2))\n",
        "print(generateParentheses(1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hcSHEiR2NqKM",
        "outputId": "a3c2b27c-1ed3-4a2d-9692-2ab5f2f801d1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['()()()()', '()()(())', '()(())()', '()(()())', '()((()))', '(())()()', '(())(())', '(()())()', '((()))()', '(()()())', '(()(()))', '((())())', '((()()))', '(((())))']\n",
            "['()()()', '()(())', '(())()', '(()())', '((()))']\n",
            "['()()', '(())']\n",
            "['()']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 18. Permutation**\n",
        "\n",
        "Given an array nums of distinct integers, return all the possible permutations. You can return the answer in any order.\n",
        "\n",
        "For Example:\n",
        "\n",
        "Input: nums = [1,2,3]\n",
        "\n",
        "Output: [[1,2,3],[1,3,2],[2,1,3],[2,3,1],[3,1,2],[3,2,1]]\n",
        "\n",
        "Input: nums = [0,1]\n",
        "\n",
        "Output: [[0,1],[1,0]]\n",
        "\n",
        "Input: nums = [1]\n",
        "\n",
        "Output: [[1]]"
      ],
      "metadata": {
        "id": "dNj3cr0SQ-BT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Swap & Recursion\n",
        "\n",
        "**T = O(n^2)**\n",
        "\n",
        "**S = O(n! x n)**\n",
        "\n",
        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        "\n",
        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        "\n",
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      ],
      "metadata": {
        "id": "WCpZAhjOPMBO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def all_permutations(nums):\n",
        "  permutations = []\n",
        "  \n",
        "  if len(nums)==0: return [[]]\n",
        "  \n",
        "  def helper(nums,i):\n",
        "    # when i iterate through the entire array\n",
        "    if i == len(nums) -1:\n",
        "      # append the original array to result in the end\n",
        "      permutations.append(nums.copy())\n",
        "      # finish and return\n",
        "      return\n",
        "    # the initial i value will start from 0 to len(nums)-1\n",
        "    # each time i increase by 1 until reaches the last index of array\n",
        "    # for each number within this range, exchange nums[i] with nums[j]\n",
        "    for j in range(i,len(nums)):\n",
        "      nums[i],nums[j] = nums[j],nums[i]\n",
        "      helper(nums,i+1)\n",
        "      nums[i],nums[j] = nums[j],nums[i]\n",
        "  \n",
        "  helper(nums,0)\n",
        "  return permutations\n",
        "\n",
        "\n",
        "print(all_permutations([0,1]))\n",
        "print(all_permutations([1,2,3]))\n",
        "print(all_permutations([1,2,3,4]))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SRlOLWm0ERhG",
        "outputId": "6465e4c9-1fb7-4186-aaad-760ce68dd975"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[0, 1], [1, 0]]\n",
            "[[1, 2, 3], [1, 3, 2], [2, 1, 3], [2, 3, 1], [3, 2, 1], [3, 1, 2]]\n",
            "[[1, 2, 3, 4], [1, 2, 4, 3], [1, 3, 2, 4], [1, 3, 4, 2], [1, 4, 3, 2], [1, 4, 2, 3], [2, 1, 3, 4], [2, 1, 4, 3], [2, 3, 1, 4], [2, 3, 4, 1], [2, 4, 3, 1], [2, 4, 1, 3], [3, 2, 1, 4], [3, 2, 4, 1], [3, 1, 2, 4], [3, 1, 4, 2], [3, 4, 1, 2], [3, 4, 2, 1], [4, 2, 3, 1], [4, 2, 1, 3], [4, 3, 2, 1], [4, 3, 1, 2], [4, 1, 3, 2], [4, 1, 2, 3]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 19. Subsets**\n",
        "Given an integer array nums of unique elements, return all possible  subsets (the power set).\n",
        "\n",
        "The solution set must not contain duplicate subsets. Return the solution in any order.\n",
        "\n",
        "For Example:\n",
        "\n",
        "Input: nums = [1,2,3]\n",
        "\n",
        "Output: [[],[1],[2],[1,2],[3],[1,3],[2,3],[1,2,3]]\n",
        "\n",
        "Input: nums = [0]\n",
        "\n",
        "Output: [[],[0]]\n"
      ],
      "metadata": {
        "id": "PggvkMN-RAvu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Brute Force\n",
        "\n",
        "T = O(n * 2^n)\n",
        "\n",
        "S = O(1)"
      ],
      "metadata": {
        "id": "L3wtdT8tdze9"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def subsets(nums):\n",
        "    n = len(nums)\n",
        "    result = [[]]\n",
        "\n",
        "    for i in range(1, 2**n):\n",
        "        subset = []\n",
        "        for j in range(n):\n",
        "            if i & (1 << j):\n",
        "                subset.append(nums[j])\n",
        "        result.append(subset)\n",
        "\n",
        "    return result\n",
        "\n",
        "print(len(subsets([0])), subsets([0]))\n",
        "print(len(subsets([1,8,7])), subsets([1,8,7]))\n",
        "print(len(subsets([1,2,3,4])), subsets([1,2,3,4]))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7FZC-BcBdl9-",
        "outputId": "478e4d66-d6eb-4eb2-f34a-2e545d995918"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 [[], [0]]\n",
            "8 [[], [1], [8], [1, 8], [7], [1, 7], [8, 7], [1, 8, 7]]\n",
            "16 [[], [1], [2], [1, 2], [3], [1, 3], [2, 3], [1, 2, 3], [4], [1, 4], [2, 4], [1, 2, 4], [3, 4], [1, 3, 4], [2, 3, 4], [1, 2, 3, 4]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: Backtrack\n",
        "\n",
        "T = O(n)\n",
        "\n",
        "S = O(1)"
      ],
      "metadata": {
        "id": "N1iUd0_HeC7d"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def all_subsets(nums):\n",
        "  all_subsets = []\n",
        "\n",
        "  if len(nums) == 1:\n",
        "    return [[], nums]\n",
        "  \n",
        "  def helper(nums, i, subset):\n",
        "    if i == len(nums):\n",
        "      all_subsets.append(subset.copy())\n",
        "      return\n",
        "    \n",
        "    helper(nums, i+1, subset)\n",
        "    subset.append(nums[i])\n",
        "    helper(nums, i+1, subset)\n",
        "    subset.pop()\n",
        "\n",
        "  helper(nums, 0, [])\n",
        "  return all_subsets\n",
        "\n",
        "print(len(all_subsets([0])), all_subsets([0]))\n",
        "print(len(all_subsets([1,8,7])), all_subsets([1,8,7]))\n",
        "print(len(all_subsets([1,2,3,4])), all_subsets([1,2,3,4]))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iVqvSbIm1aFQ",
        "outputId": "32534947-d67e-4b86-c134-6fbb2325dc0a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "2 [[], [0]]\n",
            "8 [[], [7], [8], [8, 7], [1], [1, 7], [1, 8], [1, 8, 7]]\n",
            "16 [[], [4], [3], [3, 4], [2], [2, 4], [2, 3], [2, 3, 4], [1], [1, 4], [1, 3], [1, 3, 4], [1, 2], [1, 2, 4], [1, 2, 3], [1, 2, 3, 4]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **# 20. Subsets II**\n",
        "\n",
        "Given an integer array nums that may contain duplicates, return all possible \n",
        "subsets (the power set).\n",
        "\n",
        "The solution set must not contain duplicate subsets. Return the solution in any order.\n",
        "\n",
        "For Example:\n",
        "\n",
        "Input: nums = [1,2,2]\n",
        "\n",
        "Output: [[],[1],[1,2],[1,2,2],[2],[2,2]]\n",
        "\n",
        "Input: nums = [0]\n",
        "\n",
        "Output: [[],[0]]"
      ],
      "metadata": {
        "id": "ajQPYLQauzqp"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 1: Brute Force\n",
        "\n",
        "T = O(n^2)\n",
        "\n",
        "S = O(1)"
      ],
      "metadata": {
        "id": "q3T7xu76k30F"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def subsetsWithDup(nums):\n",
        "    nums.sort()  # Sort the input array to handle duplicates\n",
        "    \n",
        "    result = [[]]  # Initialize with an empty subset\n",
        "    \n",
        "    prev_count = 0\n",
        "    \n",
        "    for i in range(len(nums)):\n",
        "        start = prev_count if i > 0 and nums[i] == nums[i-1] else 0\n",
        "        count = len(result)\n",
        "        \n",
        "        for j in range(start, count):\n",
        "            subset = result[j] + [nums[i]]\n",
        "            result.append(subset)\n",
        "        \n",
        "        prev_count = count\n",
        "    \n",
        "    return result\n",
        "\n",
        "# Example 1\n",
        "nums = [1, 2, 2]\n",
        "print(subsetsWithDup(nums))\n",
        "# Output: [[], [1], [1, 2], [1, 2, 2], [2], [2, 2]]\n",
        "\n",
        "# Example 2\n",
        "nums = [0]\n",
        "print(subsetsWithDup(nums))\n",
        "# Output: [[], [0]]\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "saG9LZWllDDb",
        "outputId": "27891ec6-ea0f-46f4-9785-2648f2ba0711"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[], [1], [2], [1, 2], [2, 2], [1, 2, 2]]\n",
            "[[], [0]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Method 2: Backtrack\n",
        "\n",
        "T = O(n)\n",
        "\n",
        "S = O(1)"
      ],
      "metadata": {
        "id": "DtFLem48mgAk"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def subsetsWithDup(nums):\n",
        "    # Sort the input array to handle duplicates\n",
        "    nums.sort()\n",
        "    \n",
        "    def backtrack(start, subset):\n",
        "        # Add the current subset to the result\n",
        "        result.append(subset[:])\n",
        "        \n",
        "        for i in range(start, len(nums)):\n",
        "            # Skip duplicates\n",
        "            if i > start and nums[i] == nums[i-1]:\n",
        "                continue\n",
        "            # Add the current element to the subset\n",
        "            subset.append(nums[i])\n",
        "            # Recursively generate subsets\n",
        "            backtrack(i+1, subset)\n",
        "            # Remove the current element from the subset for the next iteration\n",
        "            subset.pop()\n",
        "    \n",
        "    result = []\n",
        "    backtrack(0, [])\n",
        "    return result\n",
        "\n",
        "# Example 1\n",
        "nums = [1, 2, 2]\n",
        "print(subsetsWithDup(nums))\n",
        "# Output: [[], [1], [1, 2], [1, 2, 2], [2], [2, 2]]\n",
        "\n",
        "# Example 2\n",
        "nums = [0]\n",
        "print(subsetsWithDup(nums))\n",
        "# Output: [[], [0]]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NlR3R-lTmq6N",
        "outputId": "459c4e24-c2eb-4093-daa7-84680f9b4fc6"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[], [1], [1, 2], [1, 2, 2], [2], [2, 2]]\n",
            "[[], [0]]\n"
          ]
        }
      ]
    }
  ]
}