07_Algorithmic problem solving - solved.ipynb

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   "source": [
    "## Lecture 7 - Algorithmic problem solving"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c599074a-8b96-41ac-9404-2981b9a2f691",
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    "# Goals:\n",
    "- Algorithms are designed to solve computational problems.\n",
    "- Communicate the way of solving, searching for correct (prove) and efficient solutions.\n",
    "\n",
    "# Problem:\n",
    "- Set of inputs -> space of outputs (m -> n) mapping.\n",
    "- Binary relation between inputs and outputs, not necessarily strictly bijection (one-to-one).\n",
    "- Specify a predicate: Are there two people with the same birthday in the class today?\n",
    "- Mathematic problems - Scientific computing:\n",
    "    - What is the derivative of x^2 at x=1?\n",
    "- The algorithm should be general enough to apply to, say, C2 functions.\n",
    "    - Apply to arbitrarily large inputs.\n",
    "\n",
    "# Algorithm:\n",
    "- Procedure (function) generating outputs, ideally correct outputs.\n",
    "- m -> 1\n",
    "\n",
    "# Question:\n",
    "- Devise an algorithm that finds out if two people have the same birthday.\n",
    "\n",
    "\n",
    "# Efficiency, complexity:\n",
    "- Abstract sense, not seconds or hours\n",
    "- how many fundamental operations can algorithm do over real time\n",
    "- dont measure time, count ops (operations) -> asymptotic analysis\n",
    "- does not even need to be connected to the implementation - certain tasks has certain efficiency\n",
    "- exact performance depends on size of the input space (birthday problem for 10 or 1000 people in class)\n",
    "- most efficient O(1) access of item in dictionary\n",
    "- least efficient O(n!)\n",
    "    - Traveling Salesman Problem (**TSP**): The TSP involves finding the shortest possible route that visits a set of cities and returns to the origin city. A brute force approach, which tries every possible permutation to find the shortest tour, has a time complexity of O(n!).\n",
    "\n",
    "# For Us:\n",
    "- Breaking down complex problems into smaller, manageable parts.\n",
    "- Pieces should be clearly defined and well-tested (later in your coding life).\n",
    "- Efficient use of data structures.\n",
    "- We use only built-in libraries today.\n"
   ]
  },
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   "id": "a20dde5e-0202-40de-8ecf-3a14b98748aa",
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   "source": [
    "### warm up task\n",
    "\n",
    "- find a maximum value in a list\n",
    "- what steps would you take?"
   ]
  },
  {
   "cell_type": "code",
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    "\n",
    "\n",
    "def find_maximum(arr):\n",
    "    max_num = arr[0]  # Initialize max_num with the first element of the array\n",
    "    for num in arr:\n",
    "        # Compare each number with the current max_num\n",
    "        if num > max_num:\n",
    "            max_num = num  # Update max_num if a larger number is found\n",
    "    return max_num  # Return the largest number found in the array\n",
    "\n",
    "# Example usage\n",
    "arr = [3, 6, 2, 8, 4]\n",
    "print(f\"Maximum number in the array is: {find_maximum(arr)}\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "99926678-b66d-497c-aca8-4d9993246af2",
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   "source": [
    "# give an array of arbitrary length, find on what position lies a value\n",
    "# assume array is of integers\n",
    "# if value is not found, return None"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "533961bd-a01e-40a1-af09-af713065c766",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Element found at index: 1\n"
     ]
    }
   ],
   "source": [
    "def linear_search(arr, x):\n",
    "    # Iterate through each element in the array\n",
    "    for i in range(len(arr)):\n",
    "        # Check if the current element is equal to the target value x\n",
    "        if arr[i] == x:\n",
    "            return i  # Return the index of the element if found\n",
    "    return -1  # Return -1 if the element is not found in the array\n",
    "\n",
    "# Example usage\n",
    "arr = [3, 4, 1, 7, 9]\n",
    "x = 4\n",
    "print(f\"Element found at index: {linear_search(arr, x)}\")\n"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "## Linear Search\n",
    "\n",
    "### Time Complexity\n",
    "- **O(n)**, where `n` is the number of elements in the list.\n",
    "\n",
    "### Performance Characteristics\n",
    "- **Worst-Case Scenario**: The element is at the end or not present, requiring `n` comparisons.\n",
    "- **Average-Case Scenario**: On average, `n/2` comparisons are made.\n",
    "\n",
    "### Efficiency\n",
    "- Linear search is less efficient for larger datasets, with performance degrading linearly with the size of the data.\n",
    "\n",
    "## Question:\n",
    "- What is the complexity of searching in `m x n` matrix?\n",
    "\n",
    "## Can we devise a better algorithm?"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "edebf315-f531-4776-b9db-d124526dd8a9",
   "metadata": {},
   "source": [
    "## Binary Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "7746271a-cab9-4199-98d3-9db30d254c1c",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Element found at index: 0\n"
     ]
    }
   ],
   "source": [
    "def binary_search(arr, x):\n",
    "    low = 0  # Starting index\n",
    "    high = len(arr) - 1  # Ending index\n",
    "\n",
    "    # Repeat until the two indices meet\n",
    "    while low <= high:\n",
    "        mid = (low + high) // 2  # Find the middle element\n",
    "\n",
    "        # If the middle element is less than x, ignore the left half\n",
    "        if arr[mid] < x:\n",
    "            low = mid + 1\n",
    "        # If the middle element is greater than x, ignore the right half\n",
    "        elif arr[mid] > x:\n",
    "            high = mid - 1\n",
    "        else:\n",
    "            return mid  # Element is found, return its index\n",
    "    return -1  # Element is not present in array\n",
    "\n",
    "# Example usage\n",
    "arr = [1, 3, 5, 7, 9]\n",
    "x = 1\n",
    "print(f\"Element found at index: {binary_search(arr, x)}\")\n"
   ]
  },
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   "cell_type": "markdown",
   "id": "08a296b4-c0d4-469b-af51-808c3f013314",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "### Time Complexity\n",
    "- **O(log n)**, assuming the data is sorted.\n",
    "   - How would we achieve this in real life?\n",
    "\n",
    "### Performance Characteristics\n",
    "- **Worst-Case and Average-Case Scenario**: The search space is halved with each step, significantly reducing the number of comparisons.\n",
    "\n",
    "### Efficiency\n",
    "- Significantly more efficient than linear search for large, sorted datasets. Efficiency increases as the dataset size grows.\n",
    "\n",
    "## Comparison\n",
    "\n",
    "- **Dataset Size Dependency**: Linear search's performance is proportional to the dataset size, while binary search's performance is logarithmically proportional.\n",
    "- **Precondition**: Binary search requires sorted data, unlike linear search.\n",
    "- **Practical Implications**: For small datasets, the speed difference might be negligible. However, for large datasets, especially if sorted, binary search is exponentially faster than linear search."
   ]
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   "id": "f79e199b-2f0d-43f7-bab4-65c26eb3f031",
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   "id": "e1f78c59-be1a-4559-8a3d-4e4b09460583",
   "metadata": {},
   "source": [
    "### Find sum of all subarrays of size k in an array"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "20389694-c8ba-4c11-a691-66bd2675baca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[13, 20, 24, 28]"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def double_iteration(arr:list[int], k:int)->list[int]:\n",
    "    results = []\n",
    "    for start in range(len(arr)-k+1):\n",
    "        current_sum = 0\n",
    "        for item in range(start, start+k):\n",
    "            current_sum += arr[item]\n",
    "        #append to results \n",
    "        results.append(current_sum)\n",
    "    return results\n",
    "arr = [1,5,7,8,9,11]\n",
    "double_iteration(arr, 3) #O(n*k)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "da86bace-7334-489c-a2a8-1dd386a928ac",
   "metadata": {},
   "source": [
    "**Performance Analysis:**\n",
    "\n",
    "*Time Complexity*: As in the above approach, There are two loops, where first loop runs (N – K) times and second loop runs for K times. Hence the Time Complexity will be O(N*K).\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "71036d24-e149-4b27-becd-4f28a529664e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[13, 20, 24, 28]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def fixed_sliding_window(arr:list[int], k:int)->list[int]:\n",
    "    #Sum up the first subarray and add to the result\n",
    "    curr_subarray = sum(arr[:k])\n",
    "    results = [curr_subarray]\n",
    "    #now iterate from the first item until lenght-k+1 (note the -k +1)\n",
    "    for idx in range(1, len(arr)-k+1):\n",
    "        curr_subarray = curr_subarray - arr[idx-1]\n",
    "        curr_subarray = curr_subarray + arr[idx+k-1]\n",
    "        results.append(curr_subarray)\n",
    "    return results\n",
    "\n",
    "arr = [1,5,7,8,9,11] \n",
    "fixed_sliding_window(arr, 3)#O(n)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8047baa5-6d2d-4f0d-b7a4-5b96b694c948",
   "metadata": {},
   "source": [
    "**Performance Analysis**:\n",
    "What do you guys think?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "2d0ffb86-4760-4464-a88c-41599bfad16e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets compare the algorithms\n",
    "import random\n",
    "\n",
    "arr = [random.randint(0, 10000) for _ in range(1000)]\n",
    "k = 120"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "273e4662-8129-46fa-9e8c-7d03c0c656e5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "21.4 ms ± 2.77 ms per loop (mean ± std. dev. of 10 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r 10 -n 10 double_iteration(arr, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fd06c4e2-52fc-4877-a9d0-54723b6887d5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "608 µs ± 60.7 µs per loop (mean ± std. dev. of 10 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit -r 10 -n 10 fixed_sliding_window(arr, k)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "92e15508-e51d-4dc0-8801-a823910c9bf8",
   "metadata": {},
   "outputs": [],
   "source": [
    "## imagine sliding windows with more complicated functions (mean, meadian) \n",
    "# create a structure which adds -> removes elements from the end and start of the window\n",
    "# idea is to always keep one pass through the data "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "42a6c6ea-e586-4002-b817-3866ee8c0836",
   "metadata": {},
   "source": [
    "# lets get back to data processing level a bit\n",
    "## Lets try to make a small project of putting together a dataset of Tarantino movies from wikipedia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "1abb1df0-1e3a-4b54-a944-3bcac8164888",
   "metadata": {},
   "outputs": [],
   "source": [
    "#lets prepare step by step what we will do"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "f4881107-7ef0-406b-83c3-25f326187d9c",
   "metadata": {},
   "outputs": [],
   "source": [
    "import requests\n",
    "from bs4 import BeautifulSoup\n",
    "root_url = \"https://en.wikipedia.org\"\n",
    "\n",
    "def get_soup_for_link(lnk):\n",
    "    r = requests.get(lnk)\n",
    "    return BeautifulSoup(r.text,'html')\n",
    "soup = get_soup_for_link(f\"{root_url}/wiki/Quentin_Tarantino#Filmography\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "29a9a449-4409-4f3a-8bd7-f6f7e006f1c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[['/wiki/Reservoir_Dogs', 'Reservoir Dogs'],\n",
       " ['/wiki/Miramax', 'Miramax'],\n",
       " ['/wiki/Pulp_Fiction', 'Pulp Fiction'],\n",
       " ['/wiki/Jackie_Brown', 'Jackie Brown'],\n",
       " ['/wiki/Kill_Bill:_Volume_1', 'Kill Bill: Volume 1'],\n",
       " ['/wiki/Kill_Bill:_Volume_2', 'Kill Bill: Volume 2'],\n",
       " ['/wiki/Death_Proof', 'Death Proof'],\n",
       " ['/wiki/Dimension_Films', 'Dimension Films'],\n",
       " ['/wiki/Inglourious_Basterds', 'Inglourious Basterds'],\n",
       " ['/wiki/The_Weinstein_Company', 'The Weinstein Company'],\n",
       " ['/wiki/Universal_Pictures', 'Universal Pictures'],\n",
       " ['/wiki/Django_Unchained', 'Django Unchained'],\n",
       " ['/wiki/Sony_Pictures_Releasing', 'Sony Pictures Releasing'],\n",
       " ['/wiki/The_Hateful_Eight', 'The Hateful Eight'],\n",
       " ['/wiki/Once_Upon_a_Time_in_Hollywood', 'Once Upon a Time in Hollywood']]"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#find table element\n",
    "table = soup.findAll('caption')[0].parent\n",
    "lnks = []\n",
    "for item in table.findAll('a'):\n",
    "   lnks.append([item['href'], item['title']])\n",
    "lnks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "a8816607-1b61-4bc0-8520-37d0098cc7dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "error getting https://en.wikipedia.org//wiki/Miramax\n",
      "error getting https://en.wikipedia.org//wiki/Dimension_Films\n",
      "error getting https://en.wikipedia.org//wiki/The_Weinstein_Company\n",
      "error getting https://en.wikipedia.org//wiki/Universal_Pictures\n",
      "error getting https://en.wikipedia.org//wiki/Sony_Pictures_Releasing\n"
     ]
    }
   ],
   "source": [
    "def get_movie_df(link):\n",
    "    movie_soup = get_soup_for_link(link)\n",
    "    \n",
    "    try:\n",
    "        movie_table = movie_soup.findAll('table',{'class':'infobox vevent'})[0]\n",
    "    except IndexError:\n",
    "        print(f\"error getting {link}\")\n",
    "        return pd.DataFrame()\n",
    "    # Extract data from the table\n",
    "    data = {}\n",
    "    for row in movie_table.find_all('tr'):\n",
    "        if row.th and row.td:\n",
    "            key = row.th.text.strip()\n",
    "            value = row.td.text.strip()\n",
    "            data[key] = value\n",
    "    \n",
    "    # Convert dictionary to Pandas DataFrame\n",
    "    df = pd.DataFrame(list(data.items()), columns=['Attribute', 'Value'])\n",
    "    df.set_index('Attribute',inplace=True)\n",
    "    return df\n",
    "\n",
    "\n",
    "movie_dfs = []\n",
    "for movei_link, movie in lnks:\n",
    "    mov_df = get_movie_df(f\"{root_url}/{movei_link}\")\n",
    "    if not mov_df.empty:\n",
    "        mov_df.loc['title']=movie\n",
    "        movie_dfs.append(mov_df)\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "997876c3-8bf0-44b6-900b-59086d486d32",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Attribute</th>\n",
       "      <th>Directed by</th>\n",
       "      <th>Written by</th>\n",
       "      <th>Produced by</th>\n",
       "      <th>Starring</th>\n",
       "      <th>Cinematography</th>\n",
       "      <th>Edited by</th>\n",
       "      <th>Productioncompanies</th>\n",
       "      <th>Distributed by</th>\n",
       "      <th>Release dates</th>\n",
       "      <th>Running time</th>\n",
       "      <th>...</th>\n",
       "      <th>Box office</th>\n",
       "      <th>title</th>\n",
       "      <th>Story by</th>\n",
       "      <th>Screenplay by</th>\n",
       "      <th>Based on</th>\n",
       "      <th>Productioncompany</th>\n",
       "      <th>Music by</th>\n",
       "      <th>Release date</th>\n",
       "      <th>Languages</th>\n",
       "      <th>Countries</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Value</th>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Lawrence Bender</td>\n",
       "      <td>Harvey Keitel\\nTim Roth\\nChris Penn\\nSteve Bus...</td>\n",
       "      <td>Andrzej Sekuła</td>\n",
       "      <td>Sally Menke</td>\n",
       "      <td>Live America Inc.\\nDog Eat Dog Productions</td>\n",
       "      <td>Miramax Films</td>\n",
       "      <td>January 21, 1992 (1992-01-21) (Sundance)\\nOcto...</td>\n",
       "      <td>99 minutes[1]</td>\n",
       "      <td>...</td>\n",
       "      <td>$2.9 million[1]</td>\n",
       "      <td>Reservoir Dogs</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Value</th>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Lawrence Bender</td>\n",
       "      <td>John Travolta\\nSamuel L. Jackson\\nUma Thurman\\...</td>\n",
       "      <td>Andrzej Sekuła</td>\n",
       "      <td>Sally Menke</td>\n",
       "      <td>A Band Apart\\nJersey Films</td>\n",
       "      <td>Miramax Films</td>\n",
       "      <td>May 21, 1994 (1994-05-21) (Cannes)\\nOctober 14...</td>\n",
       "      <td>154 minutes[1]</td>\n",
       "      <td>...</td>\n",
       "      <td>$213.9 million[2]</td>\n",
       "      <td>Pulp Fiction</td>\n",
       "      <td>Quentin Tarantino\\nRoger Avary</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Value</th>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Lawrence Bender</td>\n",
       "      <td>Pam Grier\\nSamuel L. Jackson\\nRobert Forster\\n...</td>\n",
       "      <td>Guillermo Navarro</td>\n",
       "      <td>Sally Menke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miramax Films</td>\n",
       "      <td>December 8, 1997 (1997-12-08) (Ziegfeld Theatr...</td>\n",
       "      <td>154 minutes[1]</td>\n",
       "      <td>...</td>\n",
       "      <td>$74.7 million[2]</td>\n",
       "      <td>Jackie Brown</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Rum Punchby Elmore Leonard</td>\n",
       "      <td>A Band Apart</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Value</th>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Lawrence Bender</td>\n",
       "      <td>Uma Thurman\\nLucy Liu\\nVivica A. Fox\\nMichael ...</td>\n",
       "      <td>Robert Richardson</td>\n",
       "      <td>Sally Menke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miramax Films[1]</td>\n",
       "      <td>NaN</td>\n",
       "      <td>111 minutes</td>\n",
       "      <td>...</td>\n",
       "      <td>$180.9 million[2]</td>\n",
       "      <td>Kill Bill: Volume 1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A Band Apart[1]</td>\n",
       "      <td>RZA</td>\n",
       "      <td>October 10, 2003 (2003-10-10)</td>\n",
       "      <td>EnglishChineseJapanese</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Value</th>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Lawrence Bender</td>\n",
       "      <td>Uma Thurman\\nDavid Carradine\\nMichael Madsen\\n...</td>\n",
       "      <td>Robert Richardson</td>\n",
       "      <td>Sally Menke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Miramax Films</td>\n",
       "      <td>April 8, 2004 (2004-04-08) (Cinerama Dome)\\nAp...</td>\n",
       "      <td>137 minutes</td>\n",
       "      <td>...</td>\n",
       "      <td>$152.2 million[3]</td>\n",
       "      <td>Kill Bill: Volume 2</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>A Band Apart[1]</td>\n",
       "      <td>RZA\\nRobert Rodriguez</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Value</th>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Quentin Tarantino\\nRobert Rodriguez\\nElizabeth...</td>\n",
       "      <td>Kurt Russell\\nRosario Dawson\\nVanessa Ferlito\\...</td>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Sally Menke</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Dimension Films</td>\n",
       "      <td>NaN</td>\n",
       "      <td>113 minutes</td>\n",
       "      <td>...</td>\n",
       "      <td>$31.1 million[1]</td>\n",
       "      <td>Death Proof</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Troublemaker Studios</td>\n",
       "      <td>NaN</td>\n",
       "      <td>April 6, 2007 (2007-04-06)\\n(released as part ...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Value</th>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Lawrence Bender</td>\n",
       "      <td>Brad Pitt\\nChristoph Waltz\\nMichael Fassbender...</td>\n",
       "      <td>Robert Richardson</td>\n",
       "      <td>Sally Menke</td>\n",
       "      <td>The Weinstein Company[1]\\nUniversal Pictures[1...</td>\n",
       "      <td>The Weinstein Company (United States)\\nUnivers...</td>\n",
       "      <td>May 20, 2009 (2009-05-20) (Cannes)\\nAugust 20,...</td>\n",
       "      <td>153 minutes[2]</td>\n",
       "      <td>...</td>\n",
       "      <td>$321.5 million[7]</td>\n",
       "      <td>Inglourious Basterds</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>English\\nGerman\\nFrench</td>\n",
       "      <td>United States[3][4][5]\\nGermany[3][4]</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Value</th>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Stacey Sher\\nReginald Hudlin\\nPilar Savone</td>\n",
       "      <td>Jamie Foxx\\nChristoph Waltz\\nLeonardo DiCaprio...</td>\n",
       "      <td>Robert Richardson</td>\n",
       "      <td>Fred Raskin</td>\n",
       "      <td>A Band Apart[1]\\nColumbia Pictures[1]</td>\n",
       "      <td>The Weinstein Company[1] (United States)[2]\\nC...</td>\n",
       "      <td>December 11, 2012 (2012-12-11) (Ziegfeld Theat...</td>\n",
       "      <td>165 minutes[4]</td>\n",
       "      <td>...</td>\n",
       "      <td>$426 million[3]</td>\n",
       "      <td>Django Unchained</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Value</th>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Richard N. Gladstein\\nStacey Sher\\nShannon McI...</td>\n",
       "      <td>Samuel L. Jackson\\nKurt Russell\\nJennifer Jaso...</td>\n",
       "      <td>Robert Richardson</td>\n",
       "      <td>Fred Raskin</td>\n",
       "      <td>The Weinstein Company[1]\\nShiny Penny[2]\\nFilm...</td>\n",
       "      <td>The Weinstein Company[2]</td>\n",
       "      <td>December 7, 2015 (2015-12-07) (Cinerama Dome)\\...</td>\n",
       "      <td>187 minutes (Roadshow)\\n168 minutes (General)\\...</td>\n",
       "      <td>...</td>\n",
       "      <td>$156.5 million[6]</td>\n",
       "      <td>The Hateful Eight</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Ennio Morricone</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Value</th>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>Quentin Tarantino</td>\n",
       "      <td>David Heyman\\nShannon McIntosh\\nQuentin Tarantino</td>\n",
       "      <td>Leonardo DiCaprio\\nBrad Pitt\\nMargot Robbie\\nE...</td>\n",
       "      <td>Robert Richardson</td>\n",
       "      <td>Fred Raskin</td>\n",
       "      <td>Columbia Pictures\\nBona Film Group\\nHeyday Fil...</td>\n",
       "      <td>Sony Pictures Releasing</td>\n",
       "      <td>May 21, 2019 (2019-05-21) (Cannes)\\nJuly 26, 2...</td>\n",
       "      <td>161 minutes[1]</td>\n",
       "      <td>...</td>\n",
       "      <td>$377.6 million[4]</td>\n",
       "      <td>Once Upon a Time in Hollywood</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>United States\\nUnited Kingdom\\nChina[2]</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10 rows × 23 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Attribute        Directed by         Written by  \\\n",
       "Value      Quentin Tarantino  Quentin Tarantino   \n",
       "Value      Quentin Tarantino  Quentin Tarantino   \n",
       "Value      Quentin Tarantino                NaN   \n",
       "Value      Quentin Tarantino  Quentin Tarantino   \n",
       "Value      Quentin Tarantino  Quentin Tarantino   \n",
       "Value      Quentin Tarantino  Quentin Tarantino   \n",
       "Value      Quentin Tarantino  Quentin Tarantino   \n",
       "Value      Quentin Tarantino  Quentin Tarantino   \n",
       "Value      Quentin Tarantino  Quentin Tarantino   \n",
       "Value      Quentin Tarantino  Quentin Tarantino   \n",
       "\n",
       "Attribute                                        Produced by  \\\n",
       "Value                                        Lawrence Bender   \n",
       "Value                                        Lawrence Bender   \n",
       "Value                                        Lawrence Bender   \n",
       "Value                                        Lawrence Bender   \n",
       "Value                                        Lawrence Bender   \n",
       "Value      Quentin Tarantino\\nRobert Rodriguez\\nElizabeth...   \n",
       "Value                                        Lawrence Bender   \n",
       "Value             Stacey Sher\\nReginald Hudlin\\nPilar Savone   \n",
       "Value      Richard N. Gladstein\\nStacey Sher\\nShannon McI...   \n",
       "Value      David Heyman\\nShannon McIntosh\\nQuentin Tarantino   \n",
       "\n",
       "Attribute                                           Starring  \\\n",
       "Value      Harvey Keitel\\nTim Roth\\nChris Penn\\nSteve Bus...   \n",
       "Value      John Travolta\\nSamuel L. Jackson\\nUma Thurman\\...   \n",
       "Value      Pam Grier\\nSamuel L. Jackson\\nRobert Forster\\n...   \n",
       "Value      Uma Thurman\\nLucy Liu\\nVivica A. Fox\\nMichael ...   \n",
       "Value      Uma Thurman\\nDavid Carradine\\nMichael Madsen\\n...   \n",
       "Value      Kurt Russell\\nRosario Dawson\\nVanessa Ferlito\\...   \n",
       "Value      Brad Pitt\\nChristoph Waltz\\nMichael Fassbender...   \n",
       "Value      Jamie Foxx\\nChristoph Waltz\\nLeonardo DiCaprio...   \n",
       "Value      Samuel L. Jackson\\nKurt Russell\\nJennifer Jaso...   \n",
       "Value      Leonardo DiCaprio\\nBrad Pitt\\nMargot Robbie\\nE...   \n",
       "\n",
       "Attribute     Cinematography    Edited by  \\\n",
       "Value         Andrzej Sekuła  Sally Menke   \n",
       "Value         Andrzej Sekuła  Sally Menke   \n",
       "Value      Guillermo Navarro  Sally Menke   \n",
       "Value      Robert Richardson  Sally Menke   \n",
       "Value      Robert Richardson  Sally Menke   \n",
       "Value      Quentin Tarantino  Sally Menke   \n",
       "Value      Robert Richardson  Sally Menke   \n",
       "Value      Robert Richardson  Fred Raskin   \n",
       "Value      Robert Richardson  Fred Raskin   \n",
       "Value      Robert Richardson  Fred Raskin   \n",
       "\n",
       "Attribute                                Productioncompanies  \\\n",
       "Value             Live America Inc.\\nDog Eat Dog Productions   \n",
       "Value                             A Band Apart\\nJersey Films   \n",
       "Value                                                    NaN   \n",
       "Value                                                    NaN   \n",
       "Value                                                    NaN   \n",
       "Value                                                    NaN   \n",
       "Value      The Weinstein Company[1]\\nUniversal Pictures[1...   \n",
       "Value                  A Band Apart[1]\\nColumbia Pictures[1]   \n",
       "Value      The Weinstein Company[1]\\nShiny Penny[2]\\nFilm...   \n",
       "Value      Columbia Pictures\\nBona Film Group\\nHeyday Fil...   \n",
       "\n",
       "Attribute                                     Distributed by  \\\n",
       "Value                                          Miramax Films   \n",
       "Value                                          Miramax Films   \n",
       "Value                                          Miramax Films   \n",
       "Value                                       Miramax Films[1]   \n",
       "Value                                          Miramax Films   \n",
       "Value                                        Dimension Films   \n",
       "Value      The Weinstein Company (United States)\\nUnivers...   \n",
       "Value      The Weinstein Company[1] (United States)[2]\\nC...   \n",
       "Value                               The Weinstein Company[2]   \n",
       "Value                                Sony Pictures Releasing   \n",
       "\n",
       "Attribute                                      Release dates  \\\n",
       "Value      January 21, 1992 (1992-01-21) (Sundance)\\nOcto...   \n",
       "Value      May 21, 1994 (1994-05-21) (Cannes)\\nOctober 14...   \n",
       "Value      December 8, 1997 (1997-12-08) (Ziegfeld Theatr...   \n",
       "Value                                                    NaN   \n",
       "Value      April 8, 2004 (2004-04-08) (Cinerama Dome)\\nAp...   \n",
       "Value                                                    NaN   \n",
       "Value      May 20, 2009 (2009-05-20) (Cannes)\\nAugust 20,...   \n",
       "Value      December 11, 2012 (2012-12-11) (Ziegfeld Theat...   \n",
       "Value      December 7, 2015 (2015-12-07) (Cinerama Dome)\\...   \n",
       "Value      May 21, 2019 (2019-05-21) (Cannes)\\nJuly 26, 2...   \n",
       "\n",
       "Attribute                                       Running time  ...  \\\n",
       "Value                                          99 minutes[1]  ...   \n",
       "Value                                         154 minutes[1]  ...   \n",
       "Value                                         154 minutes[1]  ...   \n",
       "Value                                            111 minutes  ...   \n",
       "Value                                            137 minutes  ...   \n",
       "Value                                            113 minutes  ...   \n",
       "Value                                         153 minutes[2]  ...   \n",
       "Value                                         165 minutes[4]  ...   \n",
       "Value      187 minutes (Roadshow)\\n168 minutes (General)\\...  ...   \n",
       "Value                                         161 minutes[1]  ...   \n",
       "\n",
       "Attribute         Box office                          title  \\\n",
       "Value        $2.9 million[1]                 Reservoir Dogs   \n",
       "Value      $213.9 million[2]                   Pulp Fiction   \n",
       "Value       $74.7 million[2]                   Jackie Brown   \n",
       "Value      $180.9 million[2]            Kill Bill: Volume 1   \n",
       "Value      $152.2 million[3]            Kill Bill: Volume 2   \n",
       "Value       $31.1 million[1]                    Death Proof   \n",
       "Value      $321.5 million[7]           Inglourious Basterds   \n",
       "Value        $426 million[3]               Django Unchained   \n",
       "Value      $156.5 million[6]              The Hateful Eight   \n",
       "Value      $377.6 million[4]  Once Upon a Time in Hollywood   \n",
       "\n",
       "Attribute                        Story by      Screenplay by  \\\n",
       "Value                                 NaN                NaN   \n",
       "Value      Quentin Tarantino\\nRoger Avary                NaN   \n",
       "Value                                 NaN  Quentin Tarantino   \n",
       "Value                                 NaN                NaN   \n",
       "Value                                 NaN                NaN   \n",
       "Value                                 NaN                NaN   \n",
       "Value                                 NaN                NaN   \n",
       "Value                                 NaN                NaN   \n",
       "Value                                 NaN                NaN   \n",
       "Value                                 NaN                NaN   \n",
       "\n",
       "Attribute                    Based on     Productioncompany  \\\n",
       "Value                             NaN                   NaN   \n",
       "Value                             NaN                   NaN   \n",
       "Value      Rum Punchby Elmore Leonard          A Band Apart   \n",
       "Value                             NaN       A Band Apart[1]   \n",
       "Value                             NaN       A Band Apart[1]   \n",
       "Value                             NaN  Troublemaker Studios   \n",
       "Value                             NaN                   NaN   \n",
       "Value                             NaN                   NaN   \n",
       "Value                             NaN                   NaN   \n",
       "Value                             NaN                   NaN   \n",
       "\n",
       "Attribute               Music by  \\\n",
       "Value                        NaN   \n",
       "Value                        NaN   \n",
       "Value                        NaN   \n",
       "Value                        RZA   \n",
       "Value      RZA\\nRobert Rodriguez   \n",
       "Value                        NaN   \n",
       "Value                        NaN   \n",
       "Value                        NaN   \n",
       "Value            Ennio Morricone   \n",
       "Value                        NaN   \n",
       "\n",
       "Attribute                                       Release date  \\\n",
       "Value                                                    NaN   \n",
       "Value                                                    NaN   \n",
       "Value                                                    NaN   \n",
       "Value                          October 10, 2003 (2003-10-10)   \n",
       "Value                                                    NaN   \n",
       "Value      April 6, 2007 (2007-04-06)\\n(released as part ...   \n",
       "Value                                                    NaN   \n",
       "Value                                                    NaN   \n",
       "Value                                                    NaN   \n",
       "Value                                                    NaN   \n",
       "\n",
       "Attribute                Languages                                Countries  \n",
       "Value                          NaN                                      NaN  \n",
       "Value                          NaN                                      NaN  \n",
       "Value                          NaN                                      NaN  \n",
       "Value       EnglishChineseJapanese                                      NaN  \n",
       "Value                          NaN                                      NaN  \n",
       "Value                          NaN                                      NaN  \n",
       "Value      English\\nGerman\\nFrench    United States[3][4][5]\\nGermany[3][4]  \n",
       "Value                          NaN                                      NaN  \n",
       "Value                          NaN                                      NaN  \n",
       "Value                          NaN  United States\\nUnited Kingdom\\nChina[2]  \n",
       "\n",
       "[10 rows x 23 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat(movie_dfs,axis=1).transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "a46008bf-e03d-42eb-87df-f75aa5c021f2",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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