content/notebooks/chapter_01.ipynb

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   "source": [
    "# Stock Spans\n",
    "\n",
    "Chapter 1 of [Real-World Algorithms](https://mitpress.mit.edu/books/real-world-algorithms).\n",
    "\n",
    "---\n",
    "\n",
    "> Panos Louridas<br />\n",
    "> Athens University of Economics and Business"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "# Stacks in Python\n",
    "\n",
    "There is no special stack data structure in Python, as all the required functionality is provided by [lists](https://docs.python.org/3/tutorial/datastructures.html#more-on-lists).\n",
    "\n",
    "We push something on the stack by appending it to the end of the list, calling `append()` on the list.\n",
    "\n",
    "We pop something from the stack by calling `pop()` on the list."
   ]
  },
  {
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   "execution_count": 1,
   "metadata": {
    "slideshow": {
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4, 5, 6, 7]"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack = [3, 4, 5]\n",
    "stack.append(6)\n",
    "stack.append(7)\n",
    "stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
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    {
     "data": {
      "text/plain": [
       "7"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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    {
     "data": {
      "text/plain": [
       "[3, 4, 5, 6]"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "stack"
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  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "6"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack.pop()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4, 5]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack.pop()"
   ]
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   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[3, 4]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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    "jupyter": {
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    },
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   },
   "source": [
    "# The Stock Span Problem in Python\n",
    "\n",
    "We will work with data for the Dow Jones Industrial Average (DJIA) of the New York Stock Exchange.\n",
    "\n",
    "Before we start, let us see how the DJIA has evolved over time.\n",
    "\n",
    "The following lines require knowledge of special Python libraries; don't worry if you don't understand them.\n",
    "\n",
    "If you do want to run them, you have to install the [`pandas`](http://pandas.pydata.org/) and [`matplotlib`](https://matplotlib.org/) libraries, and enter them in a Jupyter notebook.\n",
    "\n",
    "The file [djia.csv](djia.csv) contains the DJIA from February 16, 1885 to February 29, 2024. The data was retrieved from MeasuringWorth (Samuel H. Williamson, \"Daily Closing Values of the DJA in the United States, 1885 to Present,\" MeasuringWorth, 2024\n",
    "URL: <https://www.measuringworth.com/datasets/DJA/>)."
   ]
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   "execution_count": 8,
   "metadata": {
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   "outputs": [
    {
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",
      "text/plain": [
       "<Figure size 1200x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "\n",
    "plt.style.use('ggplot')\n",
    "\n",
    "df = pd.read_csv(\"djia.csv\", comment=\"#\", parse_dates=[0], index_col=0, names=[\"date\", \"djia\"])\n",
    "plt = df.plot(figsize=(12, 6))\n",
    "plt.fmt_xdata = mdates.DateFormatter('%Y-%m-%d')\n",
    "min_x = df.index.min()\n",
    "max_x = df.index.max()\n",
    "plt.set_xlim([min_x, max_x])\n",
    "ticks = pd.date_range(start=min_x, end=max_x, freq='10A')\n",
    "ticks = ticks.append(pd.DatetimeIndex(['2024-02-29']))\n",
    "_ = plt.set_xticks(ticks)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Now back to basics.\n",
    "\n",
    "The following Python function implements the simple stock span algorithm.\n",
    "\n",
    "It takes as input a list `quotes` with the DJIA closing values, one per day.\n",
    "\n",
    "It returns a list `spans` with the stock spack for every day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def simple_stock_span(quotes: list[float]) -> list[int]:\n",
    "    spans = []\n",
    "    for i in range(len(quotes)):\n",
    "        k = 1\n",
    "        span_end = False\n",
    "        while i - k >= 0 and not span_end:\n",
    "            if quotes[i - k] <= quotes[i]:\n",
    "                k += 1\n",
    "            else:\n",
    "                span_end = True\n",
    "        spans.append(k)\n",
    "    return spans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "To use this function we must have constructed the `quotes` list.\n",
    "\n",
    "The following function takes a file that contains the DJIA data. The file has the following format:\n",
    "```\n",
    "2/27/2024,38972.41\n",
    "2/28/2024,38949.02\n",
    "2/29/2024,38996.39\n",
    "```\n",
    "\n",
    "Each quote is preceded by the corresponding date, in month-day-year format. Also, some lines in the file start with `#`. These are comments and we will ignore them.\n",
    "\n",
    "So our function will read the file line-by-line and return a list `dates` with the dates on which we have quotes and a list `quotes` that will contain the second item of each line (the DJIA value)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "To split the line, we use the [`split()`](https://docs.python.org/3/library/stdtypes.html#str.split) function, which splits a string into pieces, breaking the string at the places it finds the separator that we specify. The expression `parts = line.split(',')` breaks the line at the comma and returns the pieces in `parts`; `parts[0]` is the date and `parts[1]`, or equivalently `parts[-1]`, is the quote. We will ignore any lines that cannot be split into two parts (for example empty lines). \n",
    "\n",
    "As we noted, the dates are in month-day-year format. We will convert them to the year-month-day format. To do that, we need to split each date at `/`. The expression `month, day, year = parts[0].split('/')` will break the date in three parts and assign each one of them to a separate variable. Then, we assemble them in the required order by calling `'/'.join((year, month, day))`, and we append the resulting date into the `dates` list. We also append the quote value in the `quotes` list. As this is a string when we read it from the file, we convert it to a float with a call to `float()`.\n",
    "\n",
    "At the end, the function will return a list of dates and a list containing the corresponding quotes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def read_quotes(filename: str) -> tuple[list[str], list[float]]:\n",
    "    dates = []\n",
    "    quotes = []\n",
    "    with open(filename) as quotes_file:\n",
    "        for line in quotes_file:\n",
    "            if line.startswith('#'):\n",
    "                continue\n",
    "            parts = line.split(',')\n",
    "            if len(parts) != 2:\n",
    "                continue\n",
    "            month, day, year = parts[0].split('/')\n",
    "            date = '/'.join((year, month, day))\n",
    "            dates.append(date)\n",
    "            quotes.append(float(parts[-1]))\n",
    "    return dates, quotes"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Then for the file [djia.csv](djia.csv) we do the following:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "dates, quotes = read_quotes(\"djia.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Let's check how many lines we've read:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37963"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(quotes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We can call `simple_stock_span(quotes)` and print some stock spans."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3, 1, 1, 2, 37958, 37959, 1, 1, 1, 3]\n"
     ]
    }
   ],
   "source": [
    "spans_simple = simple_stock_span(quotes)\n",
    "print(spans_simple[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Out of curiosity, let's find the greatest stock span, using Python's `max()` function."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "37959"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_value = max(spans_simple)\n",
    "max_value"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Then we want to find the date when this happened. To do that, we need to get the index of the `max_value` in `spans`, as this will be the same with the index of the date in which it occurred in `dates`. To find the first index of an item in a list, we use the `index()` method."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2024/2/23'"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "max_value_indx = spans_simple.index(max_value)\n",
    "dates[max_value_indx]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Now we will examine how long `simple_stock_span(quotes)` takes to run.\n",
    "\n",
    "We will run it 10 times and report the average."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "iterations = 10\n",
    "start = time.time()\n",
    "for i in range(iterations):\n",
    "    simple_stock_span(quotes)\n",
    "end = time.time()\n",
    "    \n",
    "time_simple = (end - start) / iterations\n",
    "print(time_simple)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We will contrast that result with a stack-based implementation.\n",
    "\n",
    "The stack-based stock span algorithm in Python is as follows:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "def stack_stock_span(quotes: list[float]) -> list[int]:\n",
    "    spans = [1]\n",
    "    s = []\n",
    "    s.append(0)\n",
    "    for i in range(1, len(quotes)):\n",
    "        while len(s) != 0 and quotes[s[-1]] <= quotes[i]:\n",
    "            s.pop()\n",
    "        if len(s) == 0:\n",
    "            spans.append(i+1)\n",
    "        else:\n",
    "            spans.append(i - s[-1])\n",
    "        s.append(i)\n",
    "    return spans"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We call it in the same way:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "spans_stack = stack_stock_span(quotes)\n",
    "print(spans_stack[-10:])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We verify that the two results, `spans_simple` and `spans_stack` are the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "spans_simple == spans_stack"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "And we can measure the time it takes:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "start = time.time()\n",
    "for i in range(iterations):\n",
    "    stack_stock_span(quotes)\n",
    "end = time.time()\n",
    "    \n",
    "time_stack = (end - start) / iterations\n",
    "print(time_stack)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "This is a very substantial decrease! The difference, of course, is owed to the fact that the second algorithm executes much fewer steps than the first.\n",
    "\n",
    "In fact, we can have the computer count exactly the number of steps it executes when executing the code. First, we put all the code we want to count in one file; below we have copied the functions we've written and we call `simple_stock_span()` and `stack_stock_span()` once each."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "import time\n",
    "\n",
    "def simple_stock_span(quotes: list[float]) -> list[int]:\n",
    "    spans = []\n",
    "    for i in range(len(quotes)):\n",
    "        k = 1\n",
    "        span_end = False\n",
    "        while i - k >= 0 and not span_end:\n",
    "            if quotes[i - k] <= quotes[i]:\n",
    "                k += 1\n",
    "            else:\n",
    "                span_end = True\n",
    "        spans.append(k)\n",
    "    return spans\n",
    "\n",
    "def stack_stock_span(quotes: list[float]) -> list[int]:\n",
    "    spans = [1]\n",
    "    s = []\n",
    "    s.append(0)\n",
    "    for i in range(1, len(quotes)):\n",
    "        while len(s) != 0 and quotes[s[-1]] <= quotes[i]:\n",
    "            s.pop()\n",
    "        if len(s) == 0:\n",
    "            spans.append(i+1)\n",
    "        else:\n",
    "            spans.append(i - s[-1])\n",
    "        s.append(i)\n",
    "    return spans\n",
    "\n",
    "def read_quotes(filename: str) -> tuple[list[str], list[float]]:\n",
    "    dates = []\n",
    "    quotes = []\n",
    "    with open(filename) as quotes_file:\n",
    "        for line in quotes_file:\n",
    "            if line.startswith('#'):\n",
    "                continue\n",
    "            parts = line.split(',')\n",
    "            if len(parts) != 2:\n",
    "                continue\n",
    "            month, day, year = parts[0].split('/')\n",
    "            date = '/'.join((year, month, day))\n",
    "            dates.append(date)\n",
    "            quotes.append(float(parts[-1]))\n",
    "    return dates, quotes\n",
    "\n",
    "_, quotes = read_quotes(\"djia.csv\") # we use _ for a variable that we\n",
    "                                    # don't care to use\n",
    "\n",
    "spans_simple = simple_stock_span(quotes)\n",
    "\n",
    "spans_stack = stack_stock_span(quotes)\n",
    "\n",
    "print('spans_simple == spans_stack:', spans_simple == spans_stack)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "We save the code in a file, say [`stock_spans.py`](stock_spans.py). Then we execute the code with:\n",
    "\n",
    "```\n",
    "python -m trace --count -C . stock_spans.py\n",
    "```\n",
    "\n",
    "This will trace the execution of the program and count the number of times each line is executed---so it will run much longer than normal, don't worry if you see it taking too much time. When it finishes, you will see a file called `stock_span.cover` that contains the counts (you can ignore any other file you may see whose filename ends in `.cover`). If you open the file, you can verify the dramatic difference between the two algorithms:\n",
    "\n",
    "```\n",
    "    1: import time\n",
    "       \n",
    "    1: def simple_stock_span(quotes: list[float]) -> list[int]:\n",
    "    1:     spans = []\n",
    "37963:     for i in range(len(quotes)):\n",
    "37962:         k = 1\n",
    "37962:         span_end = False\n",
    "40650173:         while i - k >= 0 and not span_end:\n",
    "40612211:             if quotes[i - k] <= quotes[i]:\n",
    "40575832:                 k += 1\n",
    "                   else:\n",
    "36379:                 span_end = True\n",
    "37962:         spans.append(k)\n",
    "    1:     return spans\n",
    "       \n",
    "    1: def stack_stock_span(quotes: list[float]) -> list[int]:\n",
    "    1:     spans = [1]\n",
    "    1:     s = []\n",
    "    1:     s.append(0)\n",
    "37962:     for i in range(1, len(quotes)):\n",
    "75919:         while len(s) != 0 and quotes[s[-1]] <= quotes[i]:\n",
    "37958:             s.pop()\n",
    "37961:         if len(s) == 0:\n",
    " 1582:             spans.append(i+1)\n",
    "               else:\n",
    "36379:             spans.append(i - s[-1])\n",
    "37961:         s.append(i)\n",
    "    1:     return spans\n",
    "       \n",
    "    1: def read_quotes(filename: str) -> tuple[list[str], list[float]]:\n",
    "    1:     dates = []\n",
    "    1:     quotes = []\n",
    "    2:     with open(filename) as quotes_file:\n",
    "37974:         for line in quotes_file:\n",
    "37973:             if line.startswith('#'):\n",
    "    9:                 continue\n",
    "37964:             parts = line.split(',')\n",
    "37964:             if len(parts) != 2:\n",
    "    2:                 continue\n",
    "37962:             month, day, year = parts[0].split('/')\n",
    "37962:             date = '/'.join((year, month, day))\n",
    "37962:             dates.append(date)\n",
    "37962:             quotes.append(float(parts[-1]))\n",
    "    1:     return dates, quotes\n",
    "       \n",
    "    1: _, quotes = read_quotes(\"djia.csv\")  # we use _ for a variable that we\n",
    "       # don't care to use\n",
    "       \n",
    "       \n",
    "    1: spans_simple = simple_stock_span(quotes)\n",
    "       \n",
    "    1: spans_stack = stack_stock_span(quotes)\n",
    "       \n",
    "    1: print('spans_simple == spans_stack:', spans_simple == spans_stack)\n",
    "\n",
    "```"
   ]
  }
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