assignment1.ipynb

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    "<a href=\"https://colab.research.google.com/github/rockett-m/CS0135/blob/main/assignment1.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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   "cell_type": "markdown",
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   "source": [
    "# Assignment 1\n",
    "Download auto_mpg.txt and store inside 'tabular' folder created inside of 'data' directory. Add this notebook to your python codebase.\n",
    "\n",
    "The **purpose** of this assignmnet is to become familiar with core Python (*Part 0*), numpy and Pandas basics (*Part 1*), and handling data (*Part 2*)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "NAME\n",
    "DATE"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6O_EXyhMzwHm"
   },
   "source": [
    "## Part 0\n",
    "\n",
    "The goal of Part 0 is to\n",
    "- Practice problems based on core Python\n",
    "- Gain better understanding of work-flows controlled by conditional statements"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jZFXAopHzwHm"
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   "source": [
    "### Resources for python\n",
    "\n",
    "Here are some of the best resources for Python on the web. \n",
    "\n",
    "#### Learning resources\n",
    "\n",
    "* [Interactive Python](http://interactivepython.org/) An online book that includes embedded live excercises. Fun!\n",
    "* [Dive Into Python](http://www.diveintopython.net/) An excellent, thorough book.\n",
    "* [tutorial point](http://www.tutorialspoint.com/python/index.htm) A resource that is useful when you want an explanation of one concept, rather than a whole chapter.\n",
    "\n",
    "\n",
    "#### Reference resources\n",
    "\n",
    "Typically, if you have a question about python, you can find an answer by using google. The following sites will usually have the best answer.\n",
    "\n",
    "* [Official python documentation](https://docs.python.org/3/library/)\n",
    "* [Quick Reference from Tutorial Point](http://www.tutorialspoint.com/python/python_quick_guide.htm)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "4SdCC2P3zwHm"
   },
   "source": [
    "**Sample.** Notice the printout below the solution. Notice it is self-documenting, including problem definition and solution (i.e., source code), along with result (i.e., printout)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
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     "height": 0
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   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Found value 2 in list\n",
      "Value 6 not found in list\n",
      "List items: [2, 3, 4, 5]\n"
     ]
    }
   ],
   "source": [
    "val = 2\n",
    "li = [2, 3, 4, 5]\n",
    "if val in li:\n",
    "    # print('Found value', val, 'in list')\n",
    "    print(f'Found value {val} in list')\n",
    "else:\n",
    "    print('Value', val, 'not found in list')\n",
    "if 6 in li:\n",
    "    print('Found value', 6, 'in list')\n",
    "else:\n",
    "    print('Value', 6, 'not found in list')\n",
    "print('List items:', li)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XPK-gdRezwHn"
   },
   "source": [
    "**0.1)** \n",
    "Describe the 4 core Python containers (note the keyword core, i.e., not numpy arrays or other container types that are included in Python Packages).  \n",
    "a) What are characteristics of each?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "jZu9ntQlzwHn"
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   "source": [
    "lists (li=[]), tuples (tu=()), strings (st=\"\"), dictionaries (dic={})-- each are containers with various characteristics.\n",
    "\n",
    "Lists and tuples can store any type, and can be made up of various different types. Both preserve order (as do strings), with the difference being lists are mutable, while tuples and strings are immutable.\n",
    "\n",
    "Strings are made up of sequences of characters.\n",
    "\n",
    "Dictionaries are key-value pairs, where values are accessed via indexing with key. Keys must be unique and are immutable, while values can be of any type and are mutable.\n",
    "\n",
    "Each container is accessed using square brackets, with indices for tuples, lists, and strings (i.e., ordered) and keys for dictionaries (i.e., unordered)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "b) Instantiate each with 0 elements (i.e., empty), and show adding a single element to each."
   ]
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   "source": [
    "# instantiate empty containers\n",
    "my_list = []\n",
    "my_tuple = ()\n",
    "my_string = ''\n",
    "my_dict = {}\n",
    "\n",
    "# add single elements\n",
    "elem = 10\n",
    "my_list.append(elem)\n",
    "my_tuple = ('value')\n",
    "my_string = 'value'\n",
    "my_dict.update({elem : 'value'})\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "RvUs0TDJzwHo"
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   "source": [
    "c) Provide 1 or more use cases for each.\n"
   ]
  },
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    "id": "iTi7wSglzwHo"
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   "source": [
    "List: This can be used to store the names of companies in the S&P500.  \n",
    "Tuple: This can be used to store different types of variables, for example, I am entering a stock order that includes the ticker (string), price (float), size (int), and other information. The tuple can store all these different types in one bundle.  \n",
    "String: This data struct can store a stock ticker or my name.  \n",
    "Dictionary: This can be used to have all the names of people in this class (first and last to avoid collisions), paired with their GPA's and or ages.  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "kgx7-K8YzwHo"
   },
   "source": [
    "**0.2)** Write  a program that takes in a positive number (in some variable, say `i`) and computes the sum of all the numbers\n",
    "between 0 and that number (inclusive). \n",
    "\n",
    "a) Do it using a for loop"
   ]
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   "source": [
    "i = 10"
   ]
  },
  {
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     "output_type": "stream",
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     "text": [
      "Sum using list using for loop 55\n"
     ]
    }
   ],
   "source": [
    "def sum_forloop(n):\n",
    "    \"\"\"\n",
    "    Function that returns the sum of all numbers in range [0, n].\n",
    "    :param n: upper limit of summation, which is positive integer greater than 0\n",
    "    :return: Sum from i=0 to i=n.\n",
    "    \"\"\"\n",
    "    if type(n) is not int or n < 0:\n",
    "        # check input meets conditions\n",
    "        return None\n",
    "    sum = 0\n",
    "    for i in range(n+1):\n",
    "        sum += i\n",
    "    return sum\n",
    "\n",
    "print('Sum using list using for loop', sum_forloop(i))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ihp3HG_czwHo"
   },
   "source": [
    "b) Do it in one line using the function `sum` and list comprehension."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
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     "height": 0
    },
    "id": "cHhJIk0VzwHo",
    "outputId": "97e861a1-fc2e-4856-ba04-c1df9fbcece6"
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   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "55"
      ]
     },
     "metadata": {},
     "execution_count": 5
    }
   ],
   "source": [
    "sum([x for x in range(i+1)])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "e6qBJlxGzwHo"
   },
   "source": [
    "**0.3)** Create a lookup table for your class schedule, with the CRN as keys and the name of class as the value. Loop over the dictionary and print out the CRN and course name (single line per class)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
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     "height": 0
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    "id": "uVPGu2u1zwHo",
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   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "CRN = 'CS135', class_name = 'Machine Learning'\n",
      "CRN = 'EE129', class_name = 'Computer Communication Networks'\n"
     ]
    }
   ],
   "source": [
    "my_dict = {'CS135': 'Machine Learning', 'EE129': 'Computer Communication Networks'}\n",
    "\n",
    "for CRN, class_name in my_dict.items():\n",
    "    print(f'{CRN = }, {class_name = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "gF47JuwwzwHo"
   },
   "source": [
    "**0.4)** Create an empty list. Then, copy the for-loop from previous excercise such that the program prompts you to input the time of the day (as type sting, and using military time would allow for AM and PM to be omitted). These times are to be stored in empty dictionay using the same keys (i.e., CRN->time class starts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
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    "id": "PvAS5PbUzwHo",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Enter the time of day in military format for class 'CS135': 10:30\n",
      "Enter the time of day in military format for class 'EE129': 10:30\n"
     ]
    }
   ],
   "source": [
    "import re\n",
    "\n",
    "my_list = []\n",
    "new_dict = {}\n",
    "\n",
    "for CRN, course_name in my_dict.items():\n",
    "    time_of_day = str(input(f\"Enter the time of day in military format for class '{CRN}': \"))\n",
    "    new_dict[CRN] = time_of_day\n",
    "\n",
    "for CRN, time in new_dict.items():\n",
    "    if not re.match(r'^([0-9]|[0-1][0-9]|[2][0-3]):([0-5][0-9])(:[0-5][0-9])?$', time):\n",
    "        print(f'invalid time {time} entered for class {CRN}')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "B_tauvu-zwHo"
   },
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZYdgZy1yzwHp"
   },
   "source": [
    "**0.5** Write a Python program to convert temperatures to and from Celsius, Fahrenheit.\n",
    "\n",
    "\n",
    "$$ \\frac{c}{5} = \\frac{f-32}{9},$$\n",
    "where $c$ is the temperature in Celsius and $f$ is the temperature in Fahrenheit.\n",
    "\n",
    "Test code:\n",
    "60°C is 140 in Fahrenheit\n",
    "45°F is 7 in Celsius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "id": "qFegBVPlzwHp",
    "outputId": "26b2e7a3-7ded-497d-f084-e733eb75c96e",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    }
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "45 deg. F is 7.222222222222222 deg. C\n",
      "60 deg. C is 15.555555555555555 deg. F\n"
     ]
    }
   ],
   "source": [
    "def fahrenheit2celsius(fahrenheit):\n",
    "    return (5 * (fahrenheit - 32)) / 9\n",
    "\n",
    "def celsius2fahrenheit(celsius):\n",
    "    return ((celsius * 9) / 5) + 32\n",
    "\n",
    "\n",
    "temp_c = 60\n",
    "temp_f = 45\n",
    "\n",
    "temp_c_out = fahrenheit2celsius(temp_f)\n",
    "temp_f_out = fahrenheit2celsius(temp_c)\n",
    "\n",
    "print(\"{} deg. F is {} deg. C\".format(temp_f, temp_c_out))\n",
    "print(\"{} deg. C is {} deg. F\".format(temp_c, temp_f_out))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "5Fa9k3hJzwHp"
   },
   "source": [
    "**0.6** Write a Python program to construct the following pattern, using a nested for loop.\n",
    "\n",
    "O\n",
    "\n",
    "O X\n",
    "\n",
    "O X O\n",
    "\n",
    "O X O X\n",
    "\n",
    "O X O X O\n",
    "\n",
    "O X O X\n",
    "\n",
    "O X O\n",
    "\n",
    "O X\n",
    "\n",
    "O"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
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    "id": "9zfVmbtazwHp",
    "outputId": "fd0ca33d-52b7-4759-ad75-29bf81efbf64"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "O\n",
      "OX\n",
      "OXO\n",
      "OXOX\n",
      "OXOXO\n",
      "OXOX\n",
      "OXO\n",
      "OX\n",
      "O\n"
     ]
    }
   ],
   "source": [
    "\n",
    "for i in [1,2,3,4,5,4,3,2,1]:\n",
    "    out = []\n",
    "    count = i\n",
    "    for j in range(count):\n",
    "        if count % 2 == 1:\n",
    "            out.append('O')\n",
    "        else:\n",
    "            out.append('X')\n",
    "        count -= 1\n",
    "    if i % 2 == 0:\n",
    "        out.reverse()\n",
    "    output = ''.join(out)\n",
    "    print(output)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "dGNt8klFzwHp"
   },
   "source": [
    "**0.7** Write a Python program that reads two integers representing a month and day and prints the season for that month and day. Go to the editor\n",
    "Expected Output:\n",
    "\n",
    "Input the month (e.g. January, February etc.): july                     \n",
    "Input the day: 31                                                       \n",
    "Season is summer  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
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     "base_uri": "https://localhost:8080/",
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    },
    "id": "H6Y8nsQuzwHp",
    "outputId": "b9c3f2fb-c49d-40b4-bc30-eca878463f0f"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Please enter the month: March\n",
      "Please enter the day: 30\n",
      "Season is spring\n"
     ]
    }
   ],
   "source": [
    "month = str(input('Please enter the month: ')).strip()\n",
    "day = int(input('Please enter the day: '))\n",
    "\n",
    "season_dict = {}\n",
    "season_dict['winter'] = [['December', 21, 31],  ['January', 31], ['February', 29], ['March', 1, 19]]     # Dec 21 - Mar 19\n",
    "season_dict['spring'] = [['March', 20, 31],     ['April', 30],   ['May', 31],      ['June', 1, 20]]      # Mar 20 - Jun 20\n",
    "season_dict['summer'] = [['June', 21, 30],      ['July', 31],    ['August', 31],   ['September', 1, 22]] # Jun 21 - Sep 22\n",
    "season_dict['autumn'] = [['September', 23, 30], ['October', 31], ['November', 30], ['December', 1, 20]]  # Sep 23 - Dec 20\n",
    "\n",
    "mths = ['January', 'February', 'March', 'April', 'May', 'June', 'July', 'August', 'September', 'October', 'November', 'December']\n",
    "\n",
    "def bounds_check():\n",
    "    error = False\n",
    "\n",
    "    if day < 1 or day > 31:  # always true\n",
    "        print(f'Day {day} out of bounds for month {month}'); error = True\n",
    "    if month not in mths:\n",
    "        print(f'Month {month} not entered correctly'); error = True\n",
    "\n",
    "    # need to check days for overlap months\n",
    "    if not error:\n",
    "        for season, months in season_dict.items():\n",
    "            # 4th month in a season doesn't matter here as upper bound is lower\n",
    "            if month in months[0][0] and day > months[0][2]:\n",
    "                print(f'Day {day} out of bounds for month {month}'); error = True\n",
    "            elif (month in months[1][0] and day > months[1][1]) or (month in months[2][0] and day > months[2][1]):\n",
    "                print(f'Day {day} out of bounds for month {month}'); error = True\n",
    "\n",
    "    return error\n",
    "\n",
    "def main(error=False):\n",
    "    if not error:\n",
    "        for season, months in season_dict.items():\n",
    "            # easy middle months cases\n",
    "            if month in months[1][0] and day <= months[1][1]:\n",
    "                print(f'Season is {season}')\n",
    "            elif month in months[2][0] and day <= months[2][1]:\n",
    "                print(f'Season is {season}')\n",
    "\n",
    "            # overlap months\n",
    "            if month in months[0][0] and day >= months[0][1] and day <= months[0][2]:\n",
    "                print(f'Season is {season}')\n",
    "            elif month in months[3][0] and day <= months[3][2]:\n",
    "                print(f'Season is {season}')\n",
    "\n",
    "error = bounds_check()\n",
    "main(error)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "doh4NuzrzwHp"
   },
   "source": [
    "**0.8** Implement repeats(), as specified in doc-string. Then call on variables a and b below. Print True if repeated, else, print False."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "XbVgBDeczwHp",
    "outputId": "aaf846cd-9bed-4eae-8347-7b66813ece49"
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   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "list 'a' repeats 5: True\n",
      "list 'a' repeats 6: False\n",
      "list 'b' repeats 5: False\n"
     ]
    }
   ],
   "source": [
    "a = [1, 3, 1, 6, 3, 5, 5, 2]\n",
    "b = [1, 2, 3, 3, 4, 5, 6, 7, 8, 9]\n",
    "\n",
    "def repeated_val(xs, val=5):\n",
    "    \"\"\"Function to search whether 'val' is repeated in sequence.\n",
    "\n",
    "    :param xs:      List of items to search\n",
    "    :param val:     Val being searched (default = 5)\n",
    "\n",
    "    :return:        True if repeated 'val' and neighbors, i.e., [..., 'val', 'val', ...] = True; else, False\n",
    "    \"\"\"\n",
    "    prev = None\n",
    "    for i in range(len(xs)):\n",
    "        if i == 0: # first elem\n",
    "            prev = xs[i]\n",
    "            continue\n",
    "        else:\n",
    "            if (xs[i] == prev) and (xs[i] == val):  # 1) curr equal to prev; 2) curr equal to val\n",
    "                return True\n",
    "            prev = xs[i] # set prev to curr before next iteration\n",
    "\n",
    "    return False\n",
    "\n",
    "print(\"list 'a' repeats 5:\", repeated_val(a))\n",
    "print(\"list 'a' repeats 6:\", repeated_val(a, val=6))\n",
    "print(\"list 'b' repeats 5:\", repeated_val(b))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ONe9LETQzwHp"
   },
   "source": [
    "## Part 1\n",
    "\n",
    "The goal in this part is to\n",
    "- understand basic functionality of numpy and pandas\n",
    "- learn how to use numpy and pandas to solve common coding tasks\n",
    "- understand these packages to process real-world data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "F0GD4hPlzwHp"
   },
   "source": [
    "Import other libraries, such that numpy library is called by with np and pandas with pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "id": "rykrCaA4zwHp"
   },
   "outputs": [],
   "source": [
    "import os\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Ju_Ij2bkzwHp"
   },
   "source": [
    "### a) Numpy Basics\n",
    "*Make sure to leave random seeds in each cell so that the outputs match the expected answer."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QJwZQZdYzwHp"
   },
   "source": [
    "### 1)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "d55Nj1tZzwHp"
   },
   "source": [
    "Create a 10x10 array with random values and find the minimum and maximum values "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "MXHh37JKzwHp",
    "outputId": "847adb1a-11cf-4277-a4fd-e9e02ff5275c"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "arr = array([[0.69646919, 0.28613933, 0.22685145, 0.55131477, 0.71946897,\n",
      "        0.42310646, 0.9807642 , 0.68482974, 0.4809319 , 0.39211752],\n",
      "       [0.34317802, 0.72904971, 0.43857224, 0.0596779 , 0.39804426,\n",
      "        0.73799541, 0.18249173, 0.17545176, 0.53155137, 0.53182759],\n",
      "       [0.63440096, 0.84943179, 0.72445532, 0.61102351, 0.72244338,\n",
      "        0.32295891, 0.36178866, 0.22826323, 0.29371405, 0.63097612],\n",
      "       [0.09210494, 0.43370117, 0.43086276, 0.4936851 , 0.42583029,\n",
      "        0.31226122, 0.42635131, 0.89338916, 0.94416002, 0.50183668],\n",
      "       [0.62395295, 0.1156184 , 0.31728548, 0.41482621, 0.86630916,\n",
      "        0.25045537, 0.48303426, 0.98555979, 0.51948512, 0.61289453],\n",
      "       [0.12062867, 0.8263408 , 0.60306013, 0.54506801, 0.34276383,\n",
      "        0.30412079, 0.41702221, 0.68130077, 0.87545684, 0.51042234],\n",
      "       [0.66931378, 0.58593655, 0.6249035 , 0.67468905, 0.84234244,\n",
      "        0.08319499, 0.76368284, 0.24366637, 0.19422296, 0.57245696],\n",
      "       [0.09571252, 0.88532683, 0.62724897, 0.72341636, 0.01612921,\n",
      "        0.59443188, 0.55678519, 0.15895964, 0.15307052, 0.69552953],\n",
      "       [0.31876643, 0.6919703 , 0.55438325, 0.38895057, 0.92513249,\n",
      "        0.84167   , 0.35739757, 0.04359146, 0.30476807, 0.39818568],\n",
      "       [0.70495883, 0.99535848, 0.35591487, 0.76254781, 0.59317692,\n",
      "        0.6917018 , 0.15112745, 0.39887629, 0.2408559 , 0.34345601]])\n",
      "\n",
      "arr.max() = 0.9953584820340174\n",
      "arr.min() = 0.01612920669501683\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "arr = np.random.rand(10, 10)\n",
    "print(f'{arr = }\\n\\n{arr.max() = }\\n{arr.min() = }')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "cWh7BHGDzwHp"
   },
   "source": [
    "### 2)  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "p2WKpduPzwHp"
   },
   "source": [
    "Extract the integer part of array Z using 5 different numpy methods"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "id": "t8Ca1LgAzwHq",
    "outputId": "c78351c2-535a-4e58-fcaa-e29ced494744"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "Z = array([6.96469186, 2.86139335, 2.26851454, 5.51314769, 7.1946897 ,\n",
      "       4.2310646 , 9.80764198, 6.84829739, 4.80931901, 3.92117518])\n",
      "\n",
      "method = array([6, 2, 2, 5, 7, 4, 9, 6, 4, 3])\n",
      "method = array([6., 2., 2., 5., 7., 4., 9., 6., 4., 3.])\n",
      "method = array([6, 2, 2, 5, 7, 4, 9, 6, 4, 3])\n",
      "method = array([6., 2., 2., 5., 7., 4., 9., 6., 4., 3.])\n",
      "method = array([6., 2., 2., 5., 7., 4., 9., 6., 4., 3.])\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "Z = np.random.uniform(0, 10, 10)\n",
    "\n",
    "print(f'{Z = }\\n')\n",
    "\n",
    "m1 = np.array(Z, int)\n",
    "# m2 = [int(x) for x in Z]\n",
    "m2 = Z-Z%1\n",
    "m3 = Z.astype(int)\n",
    "m4 = np.floor(Z)\n",
    "m5 = np.trunc(Z)\n",
    "\n",
    "for method in [m1, m2, m3, m4, m5]:\n",
    "    print(f'{method = }')\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "CAwGhiSqzwHq"
   },
   "source": [
    "Create a vector of size 20 with values spanning (0, 1), i.e., 0 and 1 are excluded."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 167,
   "metadata": {
    "id": "Jf0SEuUNzwHq",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "3c9d4e14-f949-49c1-a729-a77d2f618a1a"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0.04761905 0.0952381  0.14285714 0.19047619 0.23809524 0.28571429\n",
      " 0.33333333 0.38095238 0.42857143 0.47619048 0.52380952 0.57142857\n",
      " 0.61904762 0.66666667 0.71428571 0.76190476 0.80952381 0.85714286\n",
      " 0.9047619  0.95238095]\n",
      "[0.04761905 0.0952381  0.14285714 0.19047619 0.23809524 0.28571429\n",
      " 0.33333333 0.38095238 0.42857143 0.47619048 0.52380952 0.57142857\n",
      " 0.61904762 0.66666667 0.71428571 0.76190476 0.80952381 0.85714286\n",
      " 0.9047619  0.95238095]\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# np.random.uniform(0, 1, size=20)\n",
    "# vect = np.linspace(start=0, stop=1, endpoint=False, num=20)\n",
    "\n",
    "# x0 = np.linspace(start=0, stop=1, num=20, endpoint=True) # has 0 and 1\n",
    "# x1 = np.linspace(start=0, stop=1, num=20, endpoint=False) # has 0\n",
    "# x2 = np.linspace(start=0, stop=1, num=20, endpoint=True)[1:-1] # only 18 values\n",
    "# x3 = np.linspace(start=0, stop=1, num=20, endpoint=False)[1:-1] # only 18 values\n",
    "x4 = np.linspace(start=0, stop=1, num=22, endpoint=True)[1:-1] # starts with 22 vals, snips off 0,1\n",
    "x5 = np.linspace(start=0, stop=1, num=21, endpoint=False)[1:] # starts with 21 vals, snips off 0\n",
    "\n",
    "# print(f'{x0}\\n{x1}\\n{x2}\\n{x3}\\n')\n",
    "print(f'{x4}\\n{x5}\\n')\n",
    "# https://www.w3resource.com/python-exercises/numpy/python-numpy-exercise-66.php"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bGiqf1fWzwHq"
   },
   "source": [
    "Create a random vector of size 15 and sort it\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "id": "CJUNM0ulzwHq",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "7ec8d8b6-c15f-43b2-a0ba-49fffb9ac729"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[0.69646919 0.28613933 0.22685145 0.55131477 0.71946897 0.42310646\n",
      " 0.9807642  0.68482974 0.4809319  0.39211752 0.34317802 0.72904971\n",
      " 0.43857224 0.0596779  0.39804426]\n",
      "[0.0596779  0.22685145 0.28613933 0.34317802 0.39211752 0.39804426\n",
      " 0.42310646 0.43857224 0.4809319  0.55131477 0.68482974 0.69646919\n",
      " 0.71946897 0.72904971 0.9807642 ]\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "vect = np.random.rand(15)\n",
    "print(vect)\n",
    "sorted = np.sort(vect)\n",
    "print(sorted)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "drGqXZ0mzwHq"
   },
   "source": [
    "Consider two random array A and B, check if they are equal"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "id": "BKnb42H8zwHq"
   },
   "outputs": [],
   "source": [
    "np.random.seed(123)\n",
    "A = np.random.randint(0, 2, 5)\n",
    "B = np.random.randint(0, 2, 5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "id": "cc8yVVDnzwHq",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "d64bc1a2-3218-42ba-c357-5e605f5b8531"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "metadata": {},
     "execution_count": 18
    }
   ],
   "source": [
    "# np.equal(A, B) # elem by elem\n",
    "np.array_equal(A, B) # entire array comparison\n",
    "# np.allclose(A, B) # another option"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "zcdHgRsnzwHq"
   },
   "source": [
    "matplotlib is the plotting library which pandas' plotting functionality is built upon, and it is usually aliased to plt.\n",
    "\n",
    "%matplotlib inline tells the notebook to show plots inline, instead of creating them in a separate window.\n",
    "\n",
    "plt.style.use('ggplot') is a style theme that most people find agreeable, based upon the styling of R's ggplot package.\n",
    "\n",
    "See the documentation <https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html> if you get stuck!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "KX1-u2ttzwHq"
   },
   "source": [
    "Make an array immutable (read-only)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "id": "C79qB6zazwHq"
   },
   "outputs": [],
   "source": [
    "Z = np.zeros(10)\n",
    "Z.flags.writeable = False"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "I02qvTaHzwHq"
   },
   "source": [
    "What if we want to plot multiple things? Pandas allows you to pass in a matplotlib Axis object for plots, and plots will also return an Axis object.\n",
    "\n",
    "Make a bar plot of monthly revenue with a line plot of monthly advertising spending (numbers in millions)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot') \n",
    "\n",
    "money = {'month' : ['J', 'F', 'M', 'A', 'M', 'J', 'J', 'A', 'S', 'O', 'N', 'D'],\n",
    "         'revenue' : [4, 6, 7, 11, 9, 13, 17, 24, 25, 28, 31, 36],\n",
    "         'advertising' : [1, 2, 3, 5, 5, 7, 9, 12, 14, 15, 18, 22]}\n",
    "\n",
    "df = pd.DataFrame(money); # print(df)\n",
    "# df.plot(x='revenue', y='advertising', xlabel='revenue (millions)', ylabel='advertising (millions)', color='green', legend=None, title='Advertising Spend vs Monthly Revenue')\n",
    "# plt.show()\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(30, 8))\n",
    "df.plot(ax=axes[0], x='month', y='revenue', kind='bar', color='green', xlabel='Month', ylabel='Revenue (millions of USD)', title='Revenue per Month')\n",
    "df.plot(ax=axes[1], x='month', y='advertising', kind='line', color='green', xlabel='Month', ylabel='Advertising (millions of USD)', title='Advertising Spend per Month')\n",
    "df.plot(ax=axes[2], x='advertising', y='revenue', kind='hexbin', xlabel='Advertising (millions of USD)', ylabel='Revenue (millions of USD)', title='Advertising Spend vs Monthly Revenue')\n"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 476
    },
    "id": "TVZojALZkfnj",
    "outputId": "4e366380-d234-461a-96ad-1b942acdaf49"
   },
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f238397a100>"
      ]
     },
     "metadata": {},
     "execution_count": 20
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 2160x576 with 4 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uH6bVCkAzwHq"
   },
   "source": [
    "Create a structured array representing a position (x,y) and a color (r,g,b). Instantiate structured array's values to be all zeros (though same method for other values as well)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "id": "g9cJ3O0nzwHq",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "d8169cc4-6b8a-4fd8-ba1a-aefe56e25e10"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[((0., 0.), (0., 0., 0.))]\n"
     ]
    }
   ],
   "source": [
    "# arr = [ ( 'position', [('x', float), ('y', float)] ),\n",
    "#         ( 'color',    [('r', float), ('g', float), ('b', float)] ) ]\n",
    "# zeroed = np.zeros(10, arr)\n",
    "# print(zeroed)\n",
    "\n",
    "arr_np = np.array([((0,0), (0,0,0))],\n",
    "         dtype = [ ( 'position', [('x', np.float16), ('y', np.float16)] ),\n",
    "                   ( 'color',    [('r', np.float16), ('g', np.float16), ('b', np.float16)] ) ] )\n",
    "\n",
    "print(arr_np)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ZtjhS-yszwHq"
   },
   "source": [
    "Considering a four dimensions array, how to get sum over the last two axis at once? "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "id": "JQUakdmnzwHq",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "78023cea-c59b-410b-b4c9-e2f5f228c948"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[40, 37, 56, 43],\n",
       "       [61, 44, 54, 70],\n",
       "       [48, 53, 64, 71]])"
      ]
     },
     "metadata": {},
     "execution_count": 22
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "A = np.random.randint(0, 10, (3, 4, 3, 4))\n",
    "sum = A.sum(axis=(-2,-1))\n",
    "sum"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NrmZipwwzwHq"
   },
   "source": [
    "Considering a (w,h,3) image of (dtype=ubyte), compute the number of unique colors\n",
    "\n",
    "Each color is represented by 3 bits, so the output should be a list of elements, each with 3 bits (all unique permutations of 0s and 1s).\n",
    "\n",
    "Can you convert colors? How about unique across a different axis?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "id": "kLp4hhkozwHq",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "69bbc1f1-1c6e-478d-dfc3-4abed77085c4"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "we have 8 unique colors in this image\n",
      "\n"
     ]
    }
   ],
   "source": [
    "np.random.seed(123)\n",
    "w, h = 16, 16\n",
    "I = np.random.randint(0, 2, (h, w, 3)).astype(np.ubyte)\n",
    "I.shape\n",
    "\n",
    "nums_list = []\n",
    "for matrix in I:\n",
    "    for bits3 in matrix:\n",
    "        decimal = bits3[2] * 4 + bits3[1] * 2 + bits3[0]\n",
    "        nums_list.append(decimal)\n",
    "\n",
    "print(f'we have {len(list(set(nums_list)))} unique colors in this image\\n')\n",
    "\n",
    "E = len(np.unique(nums_list)) # numpy version\n",
    "# E\n",
    "\n",
    "# 3 bits = 8 permutations -> will almost always be 8 but theoretically could be less based on random seed\n",
    "# 16 rows * 8 perms"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NQ3GueeLzwHq"
   },
   "source": [
    "How to accumulate elements of a vector (X) to an array (F) based on an index list (I)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "id": "VXndJwM6zwHr",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "ea7a664e-d43e-4e21-9e83-6b4114f3e764"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([0., 7., 0., 6., 5., 0., 0., 0., 0., 3.])"
      ]
     },
     "metadata": {},
     "execution_count": 24
    }
   ],
   "source": [
    "X = [1, 2, 3, 4, 5, 6]\n",
    "I = [1, 3, 9, 3, 4, 1]\n",
    "F = np.bincount(I, X)\n",
    "F"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0Gj9gx1SzwHr"
   },
   "source": [
    "How to read the following file?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "id": "L_3VGzQ5zwHr",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "865d1261-70d3-42da-f20d-26cc124355de"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "# File content:\n",
      "\n",
      "# -------------\n",
      "\n",
      "1,2,3,4,5\n",
      "\n",
      "6,,,7,8\n",
      "\n",
      ",,9,10,11\n",
      "\n",
      "# -------------\n"
     ]
    }
   ],
   "source": [
    "fpath = os.path.join(\"data\", \"tabular\", \"missing.dat\")\n",
    "with open(fpath, 'r') as fi:\n",
    "    # fi.readlines()\n",
    "    for line in fi:\n",
    "        print(line)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "OFylj7Y8zwHr"
   },
   "source": [
    "\n",
    "Convert a vector of ints into a matrix binary representation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "id": "0B2uRiR5zwHr",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "3fc04ccc-2635-4c1d-fb6c-7bc909ecb2f1"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[  0   1   2   3  15  16  32  64 128]\n",
      "[[0 0 0 0 0 0 0 0]\n",
      " [1 0 0 0 0 0 0 0]\n",
      " [0 1 0 0 0 0 0 0]\n",
      " [1 1 0 0 0 0 0 0]\n",
      " [1 1 1 1 0 0 0 0]\n",
      " [0 0 0 0 1 0 0 0]\n",
      " [0 0 0 0 0 1 0 0]\n",
      " [0 0 0 0 0 0 1 0]\n",
      " [0 0 0 0 0 0 0 1]]\n"
     ]
    }
   ],
   "source": [
    "I = np.array([0, 1, 2, 3, 15, 16, 32, 64, 128])\n",
    "B = ((I.reshape(-1,1) & (2**np.arange(8))) != 0).astype(int)\n",
    "\n",
    "print(f'{I}\\n{B}')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "VvlGfZltzwHr"
   },
   "source": [
    "Given a two dimensional array, how to extract unique rows?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "id": "MuXAfB61zwHr",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "0c02f06b-29b6-488f-9dba-f7d7ffd0a0fc"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[0 1 0]\n",
      " [0 0 0]\n",
      " [0 1 1]\n",
      " [0 0 0]\n",
      " [0 1 1]\n",
      " [0 1 0]]\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "array([[0, 0, 0],\n",
       "       [0, 1, 0],\n",
       "       [0, 1, 1]])"
      ]
     },
     "metadata": {},
     "execution_count": 27
    }
   ],
   "source": [
    "Z = np.random.randint(0, 2, (6, 3))\n",
    "print(Z)\n",
    "np.unique(Z, axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "QzjvsLTZzwHr"
   },
   "source": [
    "## Pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "tshcXaVOzwHr"
   },
   "source": [
    "Made-up data representing animals and trips to vet"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "id": "meDlCwzTzwHr"
   },
   "outputs": [],
   "source": [
    "data = {'animal': ['cat', 'cat', 'snake', 'dog', 'dog', 'cat', 'snake', 'cat', 'dog', 'dog'],\n",
    "        'age': [2.5, 3, 0.5, np.nan, 5, 2, 4.5, np.nan, 7, 3],\n",
    "        'visits': [1, 3, 2, 3, 2, 3, 1, 1, 2, 1],\n",
    "        'priority': ['yes', 'yes', 'no', 'yes', 'no', 'no', 'no', 'yes', 'no', 'no']}\n",
    "\n",
    "labels = ['a', 'b', 'c', 'd', 'e', 'f', 'g', 'h', 'i', 'j']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "w33FFw9TzwHr"
   },
   "source": [
    "Create a DataFrame df from this dictionary data which has the index labels."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "id": "yVH11-8-zwHr",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 363
    },
    "outputId": "4c771da2-e438-4744-bdc7-d4c23f49add0"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  animal  age  visits priority\n",
       "a    cat  2.5       1      yes\n",
       "b    cat  3.0       3      yes\n",
       "c  snake  0.5       2       no\n",
       "d    dog  NaN       3      yes\n",
       "e    dog  5.0       2       no\n",
       "f    cat  2.0       3       no\n",
       "g  snake  4.5       1       no\n",
       "h    cat  NaN       1      yes\n",
       "i    dog  7.0       2       no\n",
       "j    dog  3.0       1       no"
      ],
      "text/html": [
       "\n",
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       "\n",
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       "      <th>animal</th>\n",
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       "      <th>visits</th>\n",
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       "  </thead>\n",
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       "      <td>2.5</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>snake</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>d</th>\n",
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       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>e</th>\n",
       "      <td>dog</td>\n",
       "      <td>5.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>2.0</td>\n",
       "      <td>3</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>g</th>\n",
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       "      <td>4.5</td>\n",
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       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>h</th>\n",
       "      <td>cat</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>i</th>\n",
       "      <td>dog</td>\n",
       "      <td>7.0</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>j</th>\n",
       "      <td>dog</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1</td>\n",
       "      <td>no</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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       "  "
      ]
     },
     "metadata": {},
     "execution_count": 29
    }
   ],
   "source": [
    "df = pd.DataFrame(data=data, index=labels)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "uWINXVb5zwHr"
   },
   "source": [
    "Display a summary of the basic information about this DataFrame and its data."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "id": "Hk8fON_ezwHr",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "0a4c1f20-bed1-45ca-bc98-15b82bb7f9ae"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Index: 10 entries, a to j\n",
      "Data columns (total 4 columns):\n",
      " #   Column    Non-Null Count  Dtype  \n",
      "---  ------    --------------  -----  \n",
      " 0   animal    10 non-null     object \n",
      " 1   age       8 non-null      float64\n",
      " 2   visits    10 non-null     int64  \n",
      " 3   priority  10 non-null     object \n",
      "dtypes: float64(1), int64(1), object(2)\n",
      "memory usage: 400.0+ bytes\n",
      "            age     visits\n",
      "count  8.000000  10.000000\n",
      "mean   3.437500   1.900000\n",
      "std    2.007797   0.875595\n",
      "min    0.500000   1.000000\n",
      "25%    2.375000   1.000000\n",
      "50%    3.000000   2.000000\n",
      "75%    4.625000   2.750000\n",
      "max    7.000000   3.000000\n",
      "None\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f'{df.describe()}\\n{df.info()}\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vFKtq12ezwHr"
   },
   "source": [
    "Return the first 3 rows of the DataFrame df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "id": "iYXNFiNWzwHr",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 143
    },
    "outputId": "b207a5f2-56de-4d6a-d5ad-84867ae1fd3b"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "  animal  age  visits priority\n",
       "a    cat  2.5       1      yes\n",
       "b    cat  3.0       3      yes\n",
       "c  snake  0.5       2       no"
      ],
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       "      <td>2.5</td>\n",
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       "      <th>b</th>\n",
       "      <td>cat</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3</td>\n",
       "      <td>yes</td>\n",
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       "    <tr>\n",
       "      <th>c</th>\n",
       "      <td>snake</td>\n",
       "      <td>0.5</td>\n",
       "      <td>2</td>\n",
       "      <td>no</td>\n",
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   "source": [
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   ]
  },
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   "source": [
    "Select just the 'animal' and 'age' columns from the DataFrame df."
   ]
  },
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   "execution_count": 32,
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    {
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     "data": {
      "text/plain": [
       "   age animal\n",
       "a  2.5    cat\n",
       "b  3.0    cat\n",
       "c  0.5  snake\n",
       "d  NaN    dog\n",
       "e  5.0    dog\n",
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   "source": [
    "df[['age', 'animal']]"
   ]
  },
  {
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    "id": "KvV2QUgVzwHr"
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   "source": [
    "Change the age in row 'f' to 1.5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "id": "tjaiC9OLzwHr",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 450
    },
    "outputId": "1e084bc4-fc9f-49be-9a24-28fc9a834aa5"
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   "outputs": [
    {
     "output_type": "stream",
     "name": "stderr",
     "text": [
      "<ipython-input-33-133ae7ba59e6>:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  df['age']['f'] = 1.5\n"
     ]
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      "text/plain": [
       "  animal  age  visits priority\n",
       "a    cat  2.5       1      yes\n",
       "b    cat  3.0       3      yes\n",
       "c  snake  0.5       2       no\n",
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   "source": [
    "df['age']['f'] = 1.5\n",
    "df"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "3LmIPR7czwHr"
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   "source": [
    "\n",
    "Calculate the mean age for each different animal in df."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "id": "szfFlEOOzwHs",
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     "height": 0
    },
    "outputId": "9b389651-a9ca-4ab9-b82b-7498abd152ae"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "animal\n",
       "cat      2.333333\n",
       "dog      5.000000\n",
       "snake    2.500000\n",
       "Name: age, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 34
    }
   ],
   "source": [
    "df.groupby('animal')['age'].mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SSt7TUW7zwHs"
   },
   "source": [
    " In the 'animal' column, change the 'snake' entries to 'python'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "id": "-8MPiF-tzwHs",
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     "height": 363
    },
    "outputId": "5ee1e454-8920-49e9-9114-6889b9615b53"
   },
   "outputs": [
    {
     "output_type": "execute_result",
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       "   animal  age  visits priority\n",
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   ],
   "source": [
    "df['animal'] = df['animal'].replace(to_replace='snake', value='python')\n",
    "df"
   ]
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  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lZonKEEazwHs"
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   "source": [
    "For each animal type and each number of visits, find the mean age. In other words, each row is an animal, each column is a number of visits and the values are the mean ages (hint: use a pivot table)."
   ]
  },
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      "text/plain": [
       "visits    1    2     3\n",
       "animal                \n",
       "cat     2.5  NaN  2.25\n",
       "dog     3.0  6.0   NaN\n",
       "python  4.5  0.5   NaN"
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     "metadata": {},
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   ],
   "source": [
    "# df.groupby(['animal', 'visits'])['age'].mean()\n",
    "df.pivot_table(index=\"animal\", columns=\"visits\", aggfunc=\"mean\")[\"age\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "BpojrRJPzwHs"
   },
   "source": [
    "Given a DataFrame, subtract the row mean from each element in the row?"
   ]
  },
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-10d36be2-34ad-4be7-a8aa-4ac864299967 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-10d36be2-34ad-4be7-a8aa-4ac864299967');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 37
    }
   ],
   "source": [
    "# a 5x3 frame of float values\n",
    "df_floats = pd.DataFrame(np.random.random(size=(5, 3)))\n",
    "print(df_floats)\n",
    "# df_floats - df_floats.mean()\n",
    "\n",
    "df_floats.sub(df_floats.mean(axis=1), axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fIDWcWRszwHs"
   },
   "source": [
    "### Series and Datetimeindex"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "ooIaqRZvzwHs"
   },
   "source": [
    "Create a DatetimeIndex that contains each business day of 2015 and use it to index a Series of random numbers. Let's call this Series s."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "id": "0hCADni5zwHs",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "8db5a5d2-ba3d-4bb4-ebc7-7921b31bba9f"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2015-01-01    0.444221\n",
       "2015-01-02    0.036323\n",
       "2015-01-05    0.040683\n",
       "2015-01-06    0.332754\n",
       "2015-01-07    0.947120\n",
       "                ...   \n",
       "2015-12-25    0.587616\n",
       "2015-12-28    0.967362\n",
       "2015-12-29    0.657667\n",
       "2015-12-30    0.584904\n",
       "2015-12-31    0.518773\n",
       "Freq: B, Length: 261, dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 38
    }
   ],
   "source": [
    "index = pd.bdate_range(start='2015-01-01', end='2015-12-31')\n",
    "# s = pd.DataFrame(np.random.rand(index.shape[0]), index=index)\n",
    "s = pd.Series(np.random.rand(index.shape[0]), index=index)\n",
    "s"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "vcN-78ggzwHs"
   },
   "source": [
    "Find the sum of the values in s for every Wednesday."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "id": "J3mzSe3KzwHs",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "7e4bbeb1-8ead-4d4b-f166-de0c68931e78"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "28.20463133931073"
      ]
     },
     "metadata": {},
     "execution_count": 39
    }
   ],
   "source": [
    "# np.sum(s[s.index.dayofweek == 2][0])\n",
    "s[index.weekday == 2].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "WhWsC6gtzwHs"
   },
   "source": [
    "For each calendar month in s, find the mean of values."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "id": "Iwf1U_CXzwHs",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "f1c0d5a8-38da-49ea-c037-dec2027b6cb7"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "1     0.514598\n",
       "2     0.411958\n",
       "3     0.482855\n",
       "4     0.465519\n",
       "5     0.586244\n",
       "6     0.459178\n",
       "7     0.499775\n",
       "8     0.579787\n",
       "9     0.509425\n",
       "10    0.545982\n",
       "11    0.496362\n",
       "12    0.549904\n",
       "dtype: float64"
      ]
     },
     "metadata": {},
     "execution_count": 40
    }
   ],
   "source": [
    "s.groupby(s.index.month).mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "trJKDud1zwHs"
   },
   "source": [
    "For each group of four consecutive calendar months in s, find the date on which the highest value occurred."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "id": "wZ9AM_3fzwHs",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "5a1b11b3-e68a-4f69-f695-bc92c5cdf5ff"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "2015-01-31   2015-01-27\n",
       "2015-05-31   2015-04-03\n",
       "2015-09-30   2015-06-10\n",
       "2016-01-31   2015-11-18\n",
       "Freq: 4M, dtype: datetime64[ns]"
      ]
     },
     "metadata": {},
     "execution_count": 41
    }
   ],
   "source": [
    "s.groupby(pd.Grouper(freq='4M')).idxmax()  # SHOULD NOT BE SPILLING INTO 2016 !!!!!!!!!!!!!!!!!!!!!!!!\n",
    "\n",
    "# idx = pd.bdate_range(start='2015-01-01', end='2015-09-30')\n",
    "# t = pd.DataFrame(np.random.rand(idx.shape[0]), index=idx)\n",
    "\n",
    "# t.groupby(pd.Grouper(freq='4M')).idxmax()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "Vv67eZZYzwHs"
   },
   "source": [
    "### Cleaning Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "axk5_lduzwHs"
   },
   "source": [
    "The DataFrame to use in the following puzzles:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "id": "E4TjFkNszwHs",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "outputId": "cf1c7b69-62c2-4ea4-f407-7036f02708da"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            From_To  FlightNumber  RecentDelays              Airline\n",
       "0      LoNDon_paris       10045.0      [23, 47]               KLM(!)\n",
       "1      MAdrid_miLAN           NaN            []    <Air France> (12)\n",
       "2  londON_StockhOlm       10065.0  [24, 43, 87]  (British Airways. )\n",
       "3    Budapest_PaRis           NaN          [13]       12. Air France\n",
       "4   Brussels_londOn       10085.0      [67, 32]          \"Swiss Air\""
      ],
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>From_To</th>\n",
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       "  <tbody>\n",
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       "      <th>0</th>\n",
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       "      <td>[23, 47]</td>\n",
       "      <td>KLM(!)</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>MAdrid_miLAN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[]</td>\n",
       "      <td>&lt;Air France&gt; (12)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>londON_StockhOlm</td>\n",
       "      <td>10065.0</td>\n",
       "      <td>[24, 43, 87]</td>\n",
       "      <td>(British Airways. )</td>\n",
       "    </tr>\n",
       "    <tr>\n",
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       "      <td>Budapest_PaRis</td>\n",
       "      <td>NaN</td>\n",
       "      <td>[13]</td>\n",
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       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brussels_londOn</td>\n",
       "      <td>10085.0</td>\n",
       "      <td>[67, 32]</td>\n",
       "      <td>\"Swiss Air\"</td>\n",
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       "                                                     [key], {});\n",
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       "\n",
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       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 42
    }
   ],
   "source": [
    "df = pd.DataFrame({'From_To': ['LoNDon_paris', 'MAdrid_miLAN', 'londON_StockhOlm', \n",
    "                               'Budapest_PaRis', 'Brussels_londOn'],\n",
    "              'FlightNumber': [10045, np.nan, 10065, np.nan, 10085],\n",
    "              'RecentDelays': [[23, 47], [], [24, 43, 87], [13], [67, 32]],\n",
    "                   'Airline': ['KLM(!)', '<Air France> (12)', '(British Airways. )', \n",
    "                               '12. Air France', '\"Swiss Air\"']})\n",
    "\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "J60JoXF0zwHs"
   },
   "source": [
    " Some values in the the FlightNumber column are missing. These numbers are meant to increase by 10 with each row so 10055 and 10075 need to be put in place. Fill in these missing numbers and make the column an integer column (instead of a float column)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "id": "uRPC5aJ8zwHs",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "outputId": "26a7fec5-4915-4748-b0f9-06c0afab4751"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "            From_To  FlightNumber  RecentDelays              Airline\n",
       "0      LoNDon_paris         10045      [23, 47]               KLM(!)\n",
       "1      MAdrid_miLAN         10055            []    <Air France> (12)\n",
       "2  londON_StockhOlm         10065  [24, 43, 87]  (British Airways. )\n",
       "3    Budapest_PaRis         10075          [13]       12. Air France\n",
       "4   Brussels_londOn         10085      [67, 32]          \"Swiss Air\""
      ],
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       "      <th>0</th>\n",
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       "      <td>&lt;Air France&gt; (12)</td>\n",
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       "          const element = document.querySelector('#df-61df5bbf-67a1-4dc9-b558-c7f752e9314c');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 43
    }
   ],
   "source": [
    "prev = ''\n",
    "arr = []\n",
    "\n",
    "for entry in df['FlightNumber']:\n",
    "    value = 0\n",
    "    if pd.isna(entry):\n",
    "        value = int(prev + 10)\n",
    "        arr.append(value)\n",
    "    else:\n",
    "        value = int(entry)\n",
    "        arr.append(value)\n",
    "    prev = int(value)\n",
    "\n",
    "df['FlightNumber'] = arr\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "aEayoMAqzwHs"
   },
   "source": [
    "The From_To column would be better as two separate columns! Split each string on the underscore delimiter _ to give a new temporary DataFrame with the correct values. Assign the correct column names to this temporary DataFrame."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
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    "id": "O5Tp5n_UzwHs",
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     "height": 206
    },
    "outputId": "825a0587-aa4b-4644-b35f-c779dc06f1b2"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       From         To\n",
       "0    LoNDon      paris\n",
       "1    MAdrid      miLAN\n",
       "2    londON  StockhOlm\n",
       "3  Budapest      PaRis\n",
       "4  Brussels     londOn"
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       "      <th>2</th>\n",
       "      <td>londON</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Budapest</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Brussels</td>\n",
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       "                                                     [key], {});\n",
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       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
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       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 44
    }
   ],
   "source": [
    "from_col = []\n",
    "to_col = []\n",
    "for entry in df['From_To']:\n",
    "    frm, to = entry.split('_')[0], entry.split('_')[1]\n",
    "    from_col.append(frm); to_col.append(to)\n",
    "\n",
    "df_temp = pd.DataFrame()\n",
    "df_temp['From'] = from_col\n",
    "df_temp['To'] = to_col\n",
    "\n",
    "df_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "6fXN_EQGzwHs"
   },
   "source": [
    "Notice how the capitalisation of the city names is all mixed up in this temporary DataFrame. Standardise the strings so that only the first letter is uppercase (e.g. \"londON\" should become \"London\".)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "id": "x3vDZu3tzwHs",
    "colab": {
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     "height": 206
    },
    "outputId": "05de45d5-c257-40a1-d863-420ac9886f6c"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "       From         To\n",
       "0    London      Paris\n",
       "1    Madrid      Milan\n",
       "2    London  Stockholm\n",
       "3  Budapest      Paris\n",
       "4  Brussels     London"
      ],
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       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-3c8d59d9-284c-48a9-befd-725b42af5697');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
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       "  "
      ]
     },
     "metadata": {},
     "execution_count": 45
    }
   ],
   "source": [
    "df_temp['From'] = df_temp['From'].str.title()\n",
    "df_temp['To'] = df_temp['To'].str.title()\n",
    "# df_temp.columns = ['From', 'To']\n",
    "df_temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "fdsqMI2QzwHt"
   },
   "source": [
    "Delete the From_To column from df and attach the temporary DataFrame from the previous questions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "id": "4YDqOkMFzwHt",
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     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "outputId": "b8dfc642-1fda-43c4-dd98-a28c2d7c47f8"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   FlightNumber  RecentDelays              Airline      From         To\n",
       "0         10045      [23, 47]               KLM(!)    London      Paris\n",
       "1         10055            []    <Air France> (12)    Madrid      Milan\n",
       "2         10065  [24, 43, 87]  (British Airways. )    London  Stockholm\n",
       "3         10075          [13]       12. Air France  Budapest      Paris\n",
       "4         10085      [67, 32]          \"Swiss Air\"  Brussels     London"
      ],
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       "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
       "  </svg>\n",
       "      </button>\n",
       "      \n",
       "  <style>\n",
       "    .colab-df-container {\n",
       "      display:flex;\n",
       "      flex-wrap:wrap;\n",
       "      gap: 12px;\n",
       "    }\n",
       "\n",
       "    .colab-df-convert {\n",
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       "\n",
       "    .colab-df-convert:hover {\n",
       "      background-color: #E2EBFA;\n",
       "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "\n",
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       "        buttonEl.style.display =\n",
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       "          const element = document.querySelector('#df-c6e49cf5-28ed-4109-bf17-29e8f61ec287');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 46
    }
   ],
   "source": [
    "try:\n",
    "    df = df.drop('From_To', axis=1)\n",
    "    df = df.join(df_temp)\n",
    "except:\n",
    "    pass\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lEH5TtSCzwHt"
   },
   "source": [
    "### Plotting\n",
    "Pandas is integrated with the plotting library matplotlib, and makes plotting DataFrames very user-friendly! Plotting in a notebook environment usually makes use of the following boilerplate:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "9BdQ-zhezwHt"
   },
   "source": [
    "matplotlib is the plotting library which pandas' plotting functionality is built upon, and it is usually aliased to plt.\n",
    "\n",
    "%matplotlib inline tells the notebook to show plots inline, instead of creating them in a separate window.\n",
    "\n",
    "plt.style.use('ggplot') is a style theme that most people find agreeable, based upon the styling of R's ggplot package.\n",
    "\n",
    "See the documentation <https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.plot.html> if you get stuck!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "id": "iPTBZQ46zwHt"
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "id": "yRAgHtPXzwHt",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "outputId": "83ede43f-bd44-428f-d78e-3ab22f0a23d3"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   xs  ys\n",
       "0   1   4\n",
       "1   5   2\n",
       "2   2   1\n",
       "3   8   9\n",
       "4   1   6"
      ],
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       "      <th>3</th>\n",
       "      <td>8</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "      fill: #FFFFFF;\n",
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       "  </style>\n",
       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-19fedec8-96c8-4a53-91e1-f0f182966392 button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-19fedec8-96c8-4a53-91e1-f0f182966392');\n",
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       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
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       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 48
    }
   ],
   "source": [
    "df = pd.DataFrame({\"xs\":[1,5,2,8,1], \"ys\":[4,2,1,9,6]})\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "rbR428KYzwHt"
   },
   "source": [
    "### 1.31)\n",
    "For starters, make a scatter plot of this random data, but use black X's instead of the default markers. Add title \"Scatter Plot\" to the plot. Use df from previous cell.\n",
    "\n",
    "*NOTE: Don't forget to add [any] title and axes labels*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "scrolled": true,
    "id": "leJfGRm-zwHt",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "outputId": "99035431-4f01-4a7c-dd5a-0f5818ae83ca"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": "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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "df.plot(x='xs', y='ys', kind='scatter', s=100, title='Scatter Plot', color='black', marker='x')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "mdISv_bwzwHt"
   },
   "source": [
    "Columns in your DataFrame can also be used to modify colors and sizes. Bill has been keeping track of his performance at work over time, as well as how good he was feeling that day, and whether he had a cup of coffee in the morning. Make a plot which incorporates all four features of this DataFrame.\n",
    "\n",
    "(Hint: If you're having trouble seeing the plot, try multiplying the Series which you choose to represent size by 10 or more)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {
    "id": "hjJG6OkuzwHt",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 299
    },
    "outputId": "bdcf488f-d983-49b5-f40f-cf59126614a1"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "df2 = pd.DataFrame({\"productivity\":[5,2,3,1,4,5,6,7,8,3,4,8,9],\n",
    "                   \"hours_in\"    :[1,9,6,5,3,9,2,9,1,7,4,2,2],\n",
    "                   \"happiness\"   :[2,1,3,2,3,1,2,3,1,2,2,1,3],\n",
    "                   \"caffienated\" :[0,0,1,1,0,0,0,0,1,1,0,1,0]})\n",
    "\n",
    "\n",
    "df2.plot(x='productivity', y='hours_in', s=df2.happiness * 100, c=df2.caffienated, kind='scatter', title=\"Bill's work performance\") # s = size of dots, c = color of dots\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "IKaukBlizwHt"
   },
   "source": [
    "### 1.33)\n",
    "What if we want to plot multiple things? Pandas allows you to pass in a matplotlib Axis object for plots, and plots will also return an Axis object.\n",
    "\n",
    "Make a bar plot of monthly revenue with a line plot of monthly advertising spending (numbers in millions)\n",
    "- Two plots should be in one figure\n",
    "- Make sure that the y-axis scales of 2 plots are different\n",
    "- Be sure to include legend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "id": "csGwQ12hzwHt",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 461
    },
    "outputId": "1424d767-3596-4ddb-a26a-c3bc5bb165e7"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 2160x576 with 4 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "df_to_plot = pd.DataFrame({\"revenue\":[57,68,63,71,72,90,80,62,59,51,47,52],\n",
    "                   \"advertising\":[2.1,1.9,2.7,3.0,3.6,3.2,2.7,2.4,1.8,1.6,1.3,1.9],\n",
    "                   \"month\":range(12)\n",
    "                  })\n",
    "\n",
    "fig, axes = plt.subplots(1, 3, figsize=(30, 8))\n",
    "df_to_plot.plot(ax=axes[0], x='month', y='revenue', kind='bar', color='green', xlabel='Month', ylabel='Revenue (millions of USD)', title='Revenue per Month')\n",
    "df_to_plot.plot(ax=axes[1], x='month', y='advertising', kind='line', color='green', xlabel='Month', ylabel='Advertising (millions of USD)', title='Advertising Spend per Month')\n",
    "df_to_plot.plot(ax=axes[2], x='advertising', y='revenue', kind='hexbin', xlabel='Advertising (millions of USD)', ylabel='Revenue (millions of USD)', title='Advertising Spend vs Monthly Revenue')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0OhTyB12zwHt"
   },
   "source": [
    "### 1.33)\n",
    "What if we want to put the x-axis in a different scale? Create two line plots with xs as x-axis and ys as y-axis. First plot uses log scaling on x-axis, and the second plot uses default scaling on x-axis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "id": "-Pmw8QeYzwHt",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 454
    },
    "outputId": "6260ae78-0a00-4504-81f9-61bb5b78fb88"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 2160x576 with 2 Axes>"
      ],
      "image/png": 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     },
     "metadata": {}
    }
   ],
   "source": [
    "import numpy as np\n",
    "df3 = pd.DataFrame({\"xs\":np.logspace(-2, 2, base=10, num=12),\n",
    "                   \"ys\":range(12)\n",
    "                  })\n",
    "\n",
    "fig, axes = plt.subplots(1, 2, figsize=(30, 8))\n",
    "df3.plot(ax=axes[0], x='xs', y='ys', xlabel='xs', ylabel='ys', kind='line', color='green', logx=True, title='Logarithmic x-axis: plot of xs vs ys')\n",
    "df3.plot(ax=axes[1], x='xs', y='ys', xlabel='xs', ylabel='ys', kind='line', color='blue', title='Standard x-axis plot of xs vs ys')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lGdhhyJlzwHt"
   },
   "source": [
    "##Matrix Manipulations\n",
    "Lets first create a matrix and perform some manipulations of it.\n",
    "\n",
    "Using numpy's matrix data structure, define the following matricies:\n",
    "\n",
    "$$A=\\left[ \\begin{array}{ccc} 3 & 5 & 9 \\\\ 3 & 3 & 4 \\\\ 5 & 9 & 17 \\end{array} \\right]$$\n",
    "\n",
    "$$B=\\left[ \\begin{array}{c} 2 \\\\ 1 \\\\ 4 \\end{array} \\right]$$\n",
    "\n",
    "After this solve the matrix equation:\n",
    "$$Ax = B$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "metadata": {
    "id": "0_I6thhgzwHt",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "3e34168b-43e5-45ab-fe55-0c49eada11e8"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[[ 3  5  9]\n",
      " [ 3  3  4]\n",
      " [ 5  9 17]]\n",
      "[[2]\n",
      " [1]\n",
      " [4]]\n",
      "ans = array([[ 1.],\n",
      "       [-2.],\n",
      "       [ 1.]])\n"
     ]
    }
   ],
   "source": [
    "A = np.array([3, 5, 9, 3, 3, 4, 5, 9, 17]).reshape(3,3)\n",
    "B = np.array([2, 1, 4]).reshape(3,1)\n",
    "ans = np.linalg.solve(A, B)\n",
    "print(f'{A}\\n{B}\\n{ans = }')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "nnjPnX-uzwHt"
   },
   "source": [
    "Now write three functions for matrix multiply $C=AB$ in each of the following styles:\n",
    "\n",
    "1. By using nested for loops to impliment the naive algorithm ($C_{ij}=\\sum_{k=0}^{m-1}A_{ik}B_{kj}$)\n",
    "2. Using numpy's built in martrix multiplication  \n",
    "\n",
    "Both methods should have the same answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "id": "y87qaLGfzwHt",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "cda192a9-6233-4321-af4b-ec3ccb5332d6"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "C1 = array([[47],\n",
      "       [25],\n",
      "       [87]])\n",
      "C2 = array([[47],\n",
      "       [25],\n",
      "       [87]])\n",
      "C3 = array([[47],\n",
      "       [25],\n",
      "       [87]])\n",
      "C4 = array([[47],\n",
      "       [25],\n",
      "       [87]])\n",
      "\n"
     ]
    }
   ],
   "source": [
    "temp_arr = []\n",
    "count = 0; row_sum = 0\n",
    "\n",
    "for idxi, i in np.ndenumerate(A): # tuple indexing (0,0) ... (2,2)\n",
    "    count += 1\n",
    "    for idxj, j in np.ndenumerate(B): # tuple indexing (0,0) ... (2,0)\n",
    "        if idxi[1] == idxj[0]:\n",
    "            row_sum += i*j\n",
    "\n",
    "    if count % len(B) == 0:\n",
    "        temp_arr.append(row_sum); row_sum = 0\n",
    "\n",
    "'''\n",
    "3*2 + 5*1 + 9*4  = 47\n",
    "3*2 + 3*1 + 4*4  = 25\n",
    "5*2 + 9*1 + 4*17 = 87\n",
    "'''\n",
    "C1 = np.array(temp_arr).reshape(3,1)\n",
    "# C2 =  np.multiply(A, B)\n",
    "C2 =  np.dot(A, B)\n",
    "C3 =  np.matmul(A, B)\n",
    "C4 = A@B\n",
    "print(f'{C1 = }\\n{C2 = }\\n{C3 = }\\n{C4 = }\\n')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "g8_D_2M9zwHt"
   },
   "source": [
    "## Part 2\n",
    "Getting used to the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "metadata": {
    "id": "IjhYNtVszwHu",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "outputId": "f359d63c-5f10-4bd5-aa06-7486e298a11c"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "    mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0  18.0          8         307.0         130    3504          12.0   \n",
       "1  15.0          8         350.0         165    3693          11.5   \n",
       "2  18.0          8         318.0         150    3436          11.0   \n",
       "3  16.0          8         304.0         150    3433          12.0   \n",
       "4  17.0          8         302.0         140    3449          10.5   \n",
       "\n",
       "   model_year  origin                   car_name  \n",
       "0          70       1  chevrolet chevelle malibu  \n",
       "1          70       1          buick skylark 320  \n",
       "2          70       1         plymouth satellite  \n",
       "3          70       1              amc rebel sst  \n",
       "4          70       1                ford torino  "
      ],
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       "  "
      ]
     },
     "metadata": {},
     "execution_count": 55
    }
   ],
   "source": [
    "# Reads text file and uses '|' as separator\n",
    "auto = pd.read_table('data/tabular/auto_mpg.txt', sep='|')\n",
    "auto.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "y3egCbGRzwHu"
   },
   "source": [
    "Answer the following questions about the data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "1HpyzRLUzwHu"
   },
   "source": [
    "a) What is the shape of the data?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "id": "cqOr1vv5zwHu",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "fd17850f-c22f-4feb-b493-79c883d87928"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "(392, 9)"
      ]
     },
     "metadata": {},
     "execution_count": 56
    }
   ],
   "source": [
    "auto.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "bI0D_nfwzwHu"
   },
   "source": [
    "b) How many rows and columns are there?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "id": "YrY1KUZezwHu",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "6856d267-2485-46a8-9116-6f8e6b6bd08f"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "392 rows and 9 columns\n"
     ]
    }
   ],
   "source": [
    "print(f'{auto.shape[0]} rows and {auto.shape[1]} columns')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "XBhV_2C7zwHu"
   },
   "source": [
    "c) What variables are available?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "id": "3sHf5pjPzwHu",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "0364863c-953c-460d-f1ca-f6f48a4eda1a"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "Index(['mpg', 'cylinders', 'displacement', 'horsepower', 'weight',\n",
       "       'acceleration', 'model_year', 'origin', 'car_name'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "execution_count": 58
    }
   ],
   "source": [
    "auto.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "LqFAwDQazwHu"
   },
   "source": [
    "d) What are the ranges for the values in each numeric column?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "id": "8rlTp8_TzwHu",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 0
    },
    "outputId": "c19cd5ab-7933-4fd4-db92-725dfbe76c82"
   },
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "mins:\n",
      "mpg                9.0\n",
      "cylinders          3.0\n",
      "displacement      68.0\n",
      "horsepower        46.0\n",
      "weight          1613.0\n",
      "acceleration       8.0\n",
      "model_year        70.0\n",
      "origin             1.0\n",
      "dtype: float64\n",
      "\n",
      "maxes:\n",
      "mpg               46.6\n",
      "cylinders          8.0\n",
      "displacement     455.0\n",
      "horsepower       230.0\n",
      "weight          5140.0\n",
      "acceleration      24.8\n",
      "model_year        82.0\n",
      "origin             3.0\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "maxes = auto.select_dtypes('number').max()\n",
    "mins = auto.select_dtypes('number').min()\n",
    "print(f'mins:\\n{mins}\\n\\nmaxes:\\n{maxes}')\n",
    "# auto.select_dtypes('number').max() - auto.select_dtypes('number').min()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "81ewPMjTzwHu"
   },
   "source": [
    "e) What is the average value for each column?  Does that differ significantly\n",
    "  from the median?"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "In absolute terms, the displacement and weight values differ a decent amount between the average and the median, with horsepower varying as well.\n",
    "The other categories have very small numerical differences of <= 2 in absolute value.  \n",
    "If we look at percent change between averages and medians; however, cylinders, displacement, and origin are all significantly different \n",
    "at roughly 30% and above.  \n"
   ],
   "metadata": {
    "id": "OcCji1-ozb1K"
   }
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "id": "hVXW3nwvzwHu",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 238
    },
    "outputId": "4de5a9c1-14bf-438d-ad46-490f726e9409"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "                                 mpg  cylinders  displacement  horsepower  \\\n",
       "mins                        9.000000   3.000000      68.00000   46.000000   \n",
       "maxes                      46.600000   8.000000     455.00000  230.000000   \n",
       "averages                   23.445918   5.471939     194.41199  104.469388   \n",
       "medians                    22.750000   4.000000     151.00000   93.500000   \n",
       "abs difference (avg, med)   0.695918   1.471939      43.41199   10.969388   \n",
       "pct change (avg, med)       3.060000  36.800000      28.75000   11.730000   \n",
       "\n",
       "                                weight  acceleration  model_year     origin  \n",
       "mins                       1613.000000      8.000000   70.000000   1.000000  \n",
       "maxes                      5140.000000     24.800000   82.000000   3.000000  \n",
       "averages                   2977.584184     15.541327   75.979592   1.576531  \n",
       "medians                    2803.500000     15.500000   76.000000   1.000000  \n",
       "abs difference (avg, med)   174.084184      0.041327    0.020408   0.576531  \n",
       "pct change (avg, med)         6.210000      0.270000    0.030000  57.650000  "
      ],
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       "\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
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       "      <th>mins</th>\n",
       "      <td>9.000000</td>\n",
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       "      <td>68.00000</td>\n",
       "      <td>46.000000</td>\n",
       "      <td>1613.000000</td>\n",
       "      <td>8.000000</td>\n",
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       "      <td>1.000000</td>\n",
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       "      <th>maxes</th>\n",
       "      <td>46.600000</td>\n",
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       "      <td>82.000000</td>\n",
       "      <td>3.000000</td>\n",
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       "    <tr>\n",
       "      <th>averages</th>\n",
       "      <td>23.445918</td>\n",
       "      <td>5.471939</td>\n",
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       "      <td>1.576531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>medians</th>\n",
       "      <td>22.750000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>151.00000</td>\n",
       "      <td>93.500000</td>\n",
       "      <td>2803.500000</td>\n",
       "      <td>15.500000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>abs difference (avg, med)</th>\n",
       "      <td>0.695918</td>\n",
       "      <td>1.471939</td>\n",
       "      <td>43.41199</td>\n",
       "      <td>10.969388</td>\n",
       "      <td>174.084184</td>\n",
       "      <td>0.041327</td>\n",
       "      <td>0.020408</td>\n",
       "      <td>0.576531</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>pct change (avg, med)</th>\n",
       "      <td>3.060000</td>\n",
       "      <td>36.800000</td>\n",
       "      <td>28.75000</td>\n",
       "      <td>11.730000</td>\n",
       "      <td>6.210000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.030000</td>\n",
       "      <td>57.650000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>\n",
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       "              title=\"Convert this dataframe to an interactive table.\"\n",
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       "  </svg>\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
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       "    }\n",
       "  </style>\n",
       "\n",
       "      <script>\n",
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       "          document.querySelector('#df-e7eca8c0-ff83-47f7-81fc-66f3285d3e6f button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-e7eca8c0-ff83-47f7-81fc-66f3285d3e6f');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 60
    }
   ],
   "source": [
    "import warnings # to suppress pesky warnings from interfering from reading the analysis \\/ \n",
    "warnings.simplefilter(action='ignore', category=FutureWarning)\n",
    "''' example:\n",
    "<ipython-input-325-60fbf316ac7b>:1: FutureWarning: Dropping of nuisance columns in DataFrame reductions\n",
    " (with 'numeric_only=None') is deprecated; in a future version this will raise TypeError.  \n",
    " Select only valid columns before calling the reduction.\n",
    "'''\n",
    "averages = auto.mean()\n",
    "medians = auto.median()\n",
    "diffs = abs(averages - medians)\n",
    "\n",
    "pct_change = []\n",
    "for i, j in zip(averages, medians):\n",
    "    ans = abs(round( ((j-i)/abs(j) * 100), 2))\n",
    "    pct_change.append(ans)\n",
    "\n",
    "temp_df = pd.DataFrame(mins).T # index 0\n",
    "temp_df.loc[1] = maxes\n",
    "temp_df.loc[2] = averages\n",
    "temp_df.loc[3] = medians\n",
    "temp_df.loc[4] = diffs\n",
    "temp_df.loc[5] = pct_change\n",
    "\n",
    "temp_df.index = ['mins', 'maxes', 'averages', 'medians', 'abs difference (avg, med)', 'pct change (avg, med)']\n",
    "temp_df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NQwxged-zwHu"
   },
   "source": [
    "Answer the following questions about the data:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "SYLz3YOnzwHu"
   },
   "source": [
    "a) Which 5 cars get the best gas mileage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "id": "yCVWZ0UVzwHu",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "outputId": "bc5d92e4-9da6-4487-f57a-ca4eab260024"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "320  46.6          4          86.0          65    2110          17.9   \n",
       "327  44.6          4          91.0          67    1850          13.8   \n",
       "323  44.3          4          90.0          48    2085          21.7   \n",
       "388  44.0          4          97.0          52    2130          24.6   \n",
       "324  43.4          4          90.0          48    2335          23.7   \n",
       "\n",
       "     model_year  origin              car_name  \n",
       "320          80       3             mazda glc  \n",
       "327          80       3   honda civic 1500 gl  \n",
       "323          80       2  vw rabbit c (diesel)  \n",
       "388          82       2             vw pickup  \n",
       "324          80       2    vw dasher (diesel)  "
      ],
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       "      <td>mazda glc</td>\n",
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       "      <td>vw rabbit c (diesel)</td>\n",
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       "      <td>82</td>\n",
       "      <td>2</td>\n",
       "      <td>vw pickup</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>324</th>\n",
       "      <td>43.4</td>\n",
       "      <td>4</td>\n",
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       "      <script>\n",
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       "          document.querySelector('#df-3aea9b9f-6b82-4245-9139-19a2acf2a41f button.colab-df-convert');\n",
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       "          const element = document.querySelector('#df-3aea9b9f-6b82-4245-9139-19a2acf2a41f');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 61
    }
   ],
   "source": [
    "auto.sort_values('mpg', ascending=False).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "0CqUw-_dzwHu"
   },
   "source": [
    "b) Which 5 cars with more than 4 cylinders get the best gas mileage?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "id": "gwgbsLSYzwHu",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "outputId": "60376e2f-e49f-40ba-9a74-9e1abb94eeed"
   },
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "381  38.0          6         262.0          85    3015          17.0   \n",
       "325  36.4          5         121.0          67    2950          19.9   \n",
       "330  32.7          6         168.0         132    2910          11.4   \n",
       "355  30.7          6         145.0          76    3160          19.6   \n",
       "304  28.8          6         173.0         115    2595          11.3   \n",
       "\n",
       "     model_year  origin                           car_name  \n",
       "381          82       1  oldsmobile cutlass ciera (diesel)  \n",
       "325          80       2                audi 5000s (diesel)  \n",
       "330          80       3                      datsun 280-zx  \n",
       "355          81       2                       volvo diesel  \n",
       "304          79       1                 chevrolet citation  "
      ],
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    "auto[auto.cylinders > 4].sort_values(by=['mpg'], ascending=False).head(5)"
   ]
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    "c) Which 5 cars get the worst gas mileage?"
   ]
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       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
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    "auto.sort_values('mpg', ascending=True).head(5)"
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    "d) Which 5 cars with 4 or fewer cylinders get the worst gas mileage?"
   ]
  },
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       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "110  18.0          3          70.0          90    2124          13.5   \n",
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       "70   19.0          3          70.0          97    2330          13.5   \n",
       "111  19.0          4         122.0          85    2310          18.5   \n",
       "\n",
       "     model_year  origin         car_name  \n",
       "110          73       3        maxda rx3  \n",
       "75           72       2  volvo 145e (sw)  \n",
       "119          73       2      volvo 144ea  \n",
       "70           72       3  mazda rx2 coupe  \n",
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       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
       "          dataTable['output_type'] = 'display_data';\n",
       "          await google.colab.output.renderOutput(dataTable, element);\n",
       "          const docLink = document.createElement('div');\n",
       "          docLink.innerHTML = docLinkHtml;\n",
       "          element.appendChild(docLink);\n",
       "        }\n",
       "      </script>\n",
       "    </div>\n",
       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 64
    }
   ],
   "source": [
    "auto[auto.cylinders <= 4].sort_values(by=['mpg'], ascending=True).head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "NkwJ4jG0zwHu"
   },
   "source": [
    "##Part 3\n",
    "Use groupby and aggregations to explore the relationships \n",
    "between mpg and the other variables.  Which variables seem to have the greatest\n",
    "effect on mpg?\n",
    "Some examples of things you might want to look at are:\n",
    "- What is the mean mpg for cars for each number of cylinders?  \n",
    "(i.e. 3 cylinders, 4 cylinders, 5 cylinders, etc)?"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# mean mpg for each amount of cylinders\n",
    "dataf = pd.DataFrame(auto.groupby('cylinders')['mpg'].mean())\n",
    "dataf.reset_index(inplace=True)\n",
    "dataf.columns = ['cylinders', 'mpg (mean)']\n",
    "dataf"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 206
    },
    "id": "jzgZ_4g2MynY",
    "outputId": "cf7653ca-faeb-4862-9581-ab1ab7d82aff"
   },
   "execution_count": 65,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "   cylinders  mpg (mean)\n",
       "0          3   20.550000\n",
       "1          4   29.283920\n",
       "2          5   27.366667\n",
       "3          6   19.973494\n",
       "4          8   14.963107"
      ],
      "text/html": [
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       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th></th>\n",
       "      <th>cylinders</th>\n",
       "      <th>mpg (mean)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>20.550000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>29.283920</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>27.366667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>19.973494</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>14.963107</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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       "    .colab-df-convert:hover {\n",
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       "\n",
       "    [theme=dark] .colab-df-convert {\n",
       "      background-color: #3B4455;\n",
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       "\n",
       "    [theme=dark] .colab-df-convert:hover {\n",
       "      background-color: #434B5C;\n",
       "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
       "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
       "      fill: #FFFFFF;\n",
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       "\n",
       "      <script>\n",
       "        const buttonEl =\n",
       "          document.querySelector('#df-a6daebce-bc39-4928-96d3-08944a56d0fe button.colab-df-convert');\n",
       "        buttonEl.style.display =\n",
       "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
       "\n",
       "        async function convertToInteractive(key) {\n",
       "          const element = document.querySelector('#df-a6daebce-bc39-4928-96d3-08944a56d0fe');\n",
       "          const dataTable =\n",
       "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
       "                                                     [key], {});\n",
       "          if (!dataTable) return;\n",
       "\n",
       "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
       "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
       "            + ' to learn more about interactive tables.';\n",
       "          element.innerHTML = '';\n",
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       "          await google.colab.output.renderOutput(dataTable, element);\n",
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       "          docLink.innerHTML = docLinkHtml;\n",
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       "  </div>\n",
       "  "
      ]
     },
     "metadata": {},
     "execution_count": 65
    }
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "id": "iT8iawTNzwHu",
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 642
    },
    "outputId": "b0687c6c-a1eb-4814-fc59-187f97e7ba19"
   },
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 2160x864 with 6 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# auto.info()\n",
    "# tell us the number of distinct values and their means per category vs 'mpg'\n",
    "for count, col in enumerate(auto.columns[1:7]): \n",
    "    pass # print(col, count)\n",
    "    # print(auto.groupby(col)['mpg'].mean())\n",
    "\n",
    "# ['cylinders', 'displacement', 'horsepower', 'weight', 'acceleration', 'model_year'] # categories we care about vs 'mpg' for graphing\n",
    "\n",
    "# will want to bin data with many rows ['displacement:81', 'horsepower:93', 'weight:346', 'acceleration:95',  ]\n",
    "# no need to bin 'cylinders:5' and 'model_year:13' \n",
    "\n",
    "# mpg_cyl = pd.DataFrame(data=auto.groupby('cylinders')['mpg'].mean(), columns=['cylinders', 'mpg'])\n",
    "mpg_cyl = pd.DataFrame(data=auto.groupby('cylinders')['mpg'].mean())\n",
    "mpg_dpl = pd.DataFrame(data=auto.groupby('displacement')['mpg'].mean())\n",
    "mpg_hpw = pd.DataFrame(data=auto.groupby('horsepower')['mpg'].mean())\n",
    "mpg_wgt = pd.DataFrame(data=auto.groupby('weight')['mpg'].mean())\n",
    "mpg_acc = pd.DataFrame(data=auto.groupby('acceleration')['mpg'].mean())\n",
    "mpg_myr = pd.DataFrame(data=auto.groupby('model_year')['mpg'].mean())\n",
    "\n",
    "for datafr in [mpg_cyl, mpg_dpl, mpg_hpw, mpg_wgt, mpg_acc, mpg_myr]:\n",
    "    datafr.reset_index(inplace=True)\n",
    "\n",
    "%matplotlib inline\n",
    "plt.style.use('ggplot') \n",
    "\n",
    "fig, axes = plt.subplots(2, 3, figsize=(30, 12))\n",
    "mpg_cyl.plot(ax=axes[0, 0], x='cylinders',    y='mpg', kind='bar',  xlabel='cylinders',    ylabel='mpg', title='cylinders vs mpg (avg)',    color='green')\n",
    "mpg_dpl.plot(ax=axes[0, 1], x='displacement', y='mpg', kind='line', xlabel='displacement', ylabel='mpg', title='displacement vs mpg', color='blue')\n",
    "mpg_hpw.plot(ax=axes[0, 2], x='horsepower',   y='mpg', kind='line', xlabel='horsepower',   ylabel='mpg', title='horsepower vs mpg',   color='purple')\n",
    "mpg_wgt.plot(ax=axes[1, 0], x='weight',       y='mpg', kind='line', xlabel='weight',       ylabel='mpg', title='weight vs mpg',       color='orange')\n",
    "mpg_acc.plot(ax=axes[1, 1], x='acceleration', y='mpg', kind='line', xlabel='acceleration', ylabel='mpg', title='acceleration vs mpg', color='red')\n",
    "mpg_myr.plot(ax=axes[1, 2], x='model_year',   y='mpg', kind='bar',  xlabel='model_year',   ylabel='mpg', title='model_year vs mpg (avg)',   color='teal')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "\n",
    "How has mpg trended over the years?  \n",
    "Over the years, average mpg has trended upwarded (increased). See plot 6 of 6. "
   ],
   "metadata": {
    "id": "LTcBobwuNPHp"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "auto.sort_values('model_year', inplace=True)\n",
    "auto # https://datatofish.com/sort-pandas-dataframe/"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 424
    },
    "id": "U5lGvfQBHAoN",
    "outputId": "c0e9f861-bf33-4bc2-cd77-bc2a4968132e"
   },
   "execution_count": 142,
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": [
       "      mpg  cylinders  displacement  horsepower  weight  acceleration  \\\n",
       "0    18.0          8         307.0         130    3504          12.0   \n",
       "7    14.0          8         440.0         215    4312           8.5   \n",
       "12   15.0          8         400.0         150    3761           9.5   \n",
       "11   14.0          8         340.0         160    3609           8.0   \n",
       "10   15.0          8         383.0         170    3563          10.0   \n",
       "..    ...        ...           ...         ...     ...           ...   \n",
       "379  38.0          4          91.0          67    1995          16.2   \n",
       "378  32.0          4          91.0          67    1965          15.7   \n",
       "382  26.0          4         156.0          92    2585          14.5   \n",
       "376  34.0          4         108.0          70    2245          16.9   \n",
       "391  31.0          4         119.0          82    2720          19.4   \n",
       "\n",
       "     model_year  origin                    car_name  \n",
       "0            70       1   chevrolet chevelle malibu  \n",
       "7            70       1           plymouth fury iii  \n",
       "12           70       1       chevrolet monte carlo  \n",
       "11           70       1          plymouth 'cuda 340  \n",
       "10           70       1         dodge challenger se  \n",
       "..          ...     ...                         ...  \n",
       "379          82       3               datsun 310 gx  \n",
       "378          82       3          honda civic (auto)  \n",
       "382          82       1  chrysler lebaron medallion  \n",
       "376          82       3              toyota corolla  \n",
       "391          82       1                  chevy s-10  \n",
       "\n",
       "[392 rows x 9 columns]"
      ],
      "text/html": [
       "\n",
       "  <div id=\"df-2408fe6b-a96a-4093-9067-61abfa55f8f5\">\n",
       "    <div class=\"colab-df-container\">\n",
       "      <div>\n",
       "<style scoped>\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>mpg</th>\n",
       "      <th>cylinders</th>\n",
       "      <th>displacement</th>\n",
       "      <th>horsepower</th>\n",
       "      <th>weight</th>\n",
       "      <th>acceleration</th>\n",
       "      <th>model_year</th>\n",
       "      <th>origin</th>\n",
       "      <th>car_name</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>18.0</td>\n",
       "      <td>8</td>\n",
       "      <td>307.0</td>\n",
       "      <td>130</td>\n",
       "      <td>3504</td>\n",
       "      <td>12.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet chevelle malibu</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>440.0</td>\n",
       "      <td>215</td>\n",
       "      <td>4312</td>\n",
       "      <td>8.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth fury iii</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>400.0</td>\n",
       "      <td>150</td>\n",
       "      <td>3761</td>\n",
       "      <td>9.5</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>chevrolet monte carlo</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>14.0</td>\n",
       "      <td>8</td>\n",
       "      <td>340.0</td>\n",
       "      <td>160</td>\n",
       "      <td>3609</td>\n",
       "      <td>8.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>plymouth 'cuda 340</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>15.0</td>\n",
       "      <td>8</td>\n",
       "      <td>383.0</td>\n",
       "      <td>170</td>\n",
       "      <td>3563</td>\n",
       "      <td>10.0</td>\n",
       "      <td>70</td>\n",
       "      <td>1</td>\n",
       "      <td>dodge challenger se</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>379</th>\n",
       "      <td>38.0</td>\n",
       "      <td>4</td>\n",
       "      <td>91.0</td>\n",
       "      <td>67</td>\n",
       "      <td>1995</td>\n",
       "      <td>16.2</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>datsun 310 gx</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>378</th>\n",
       "      <td>32.0</td>\n",
       "      <td>4</td>\n",
       "      <td>91.0</td>\n",
       "      <td>67</td>\n",
       "      <td>1965</td>\n",
       "      <td>15.7</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>honda civic (auto)</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>382</th>\n",
       "      <td>26.0</td>\n",
       "      <td>4</td>\n",
       "      <td>156.0</td>\n",
       "      <td>92</td>\n",
       "      <td>2585</td>\n",
       "      <td>14.5</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chrysler lebaron medallion</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>376</th>\n",
       "      <td>34.0</td>\n",
       "      <td>4</td>\n",
       "      <td>108.0</td>\n",
       "      <td>70</td>\n",
       "      <td>2245</td>\n",
       "      <td>16.9</td>\n",
       "      <td>82</td>\n",
       "      <td>3</td>\n",
       "      <td>toyota corolla</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>391</th>\n",
       "      <td>31.0</td>\n",
       "      <td>4</td>\n",
       "      <td>119.0</td>\n",
       "      <td>82</td>\n",
       "      <td>2720</td>\n",
       "      <td>19.4</td>\n",
       "      <td>82</td>\n",
       "      <td>1</td>\n",
       "      <td>chevy s-10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>392 rows × 9 columns</p>\n",
       "</div>\n",
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     },
     "metadata": {},
     "execution_count": 142
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "What is the mpg for the group of lighter cars vs the group of heavier cars?  \n",
    "Note: Be creative in the ways in which you divide up the data.  You are trying\n",
    "to create segments of the data using logical filters and comparing the mpg\n",
    "for each segment of the data.  "
   ],
   "metadata": {
    "id": "NYDEpm7Mb1_o"
   }
  },
  {
   "cell_type": "markdown",
   "source": [
    "The mpg for the lighter cars is typically much higher than for heavier cars, as we would expect."
   ],
   "metadata": {
    "id": "Sf4enbEtLec-"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "# From chart above:\n",
    "# Car weight range: (1613 - 5140 pounds)\n",
    "# Car weight average (2977.58 pounds)\n",
    "# Car weight median: (2803.5 pounds)\n",
    "\n",
    "# https://stackoverflow.com/questions/17411940/matplotlib-scatter-plot-legend\n",
    "\n",
    "light = pd.DataFrame(auto[auto.weight <= auto['weight'].mean()].sort_values(by=['model_year'], ascending=True), columns=['model_year', 'weight', 'mpg'])\n",
    "heavy = pd.DataFrame(auto[auto.weight > auto['weight'].mean()].sort_values(by=['model_year'], ascending=True), columns=['model_year', 'weight', 'mpg'])\n",
    "# print(f'{light}\\n{heavy}\\n')\n",
    "\n",
    "g0 = plt.scatter(x=light['mpg'], y=light['weight'])\n",
    "g1 = plt.scatter(x=heavy['mpg'], y=heavy['weight'])\n",
    "plt.legend((g0, g1), ('light weight cars', 'heavy weight cars'),\n",
    "            scatterpoints=1, loc='upper right', ncol=3, fontsize=10)\n",
    "plt.gca().update(dict(xlabel='efficiency (mpg)', ylabel='weight (lbs)', title='heavy and light cars with their mpg'))\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
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     "height": 626
    },
    "id": "L6QzpsuSNxE4",
    "outputId": "169d52fa-5306-41bd-fbc9-84f8da553ee8"
   },
   "execution_count": 147,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "S5ZxvSxEzwHu"
   },
   "source": [
    "Let's now look how MPG has changed over time, while also considering\n",
    "how specific groups have changed-- look at low, mid, and high power\n",
    "cars based upon their horsepower and see how these groups have changed over time.\n",
    "\n",
    "Defines low power as below 100 horsepower  \n",
    "Defines mid power as between 100 and 150 (inclusive) horsepower  \n",
    "Defines high power as above 150 horsepower\n",
    "\n",
    "In his data, he called the original dataset 'auto'.\n",
    "\n",
    "**Now to look at how efficency has changed over time based on power and weight classes, two things that we know play a large role in gas mileage.  First, we create a table of efficiency by power class and year.**"
   ]
  },
  {
   "cell_type": "code",
   "source": [
    "# MY, HP, MPG in that priority (low to high) sort...power levels defined above\n",
    "low_power = auto[auto[\"horsepower\"] < 100].sort_values(by=['model_year', 'horsepower', 'mpg'], axis=0, ascending=[True, True, True])\n",
    "med_power = auto[(auto[\"horsepower\"] >= 100) & (auto[\"horsepower\"] < 150)].sort_values(by=['model_year', 'horsepower', 'mpg'], axis=0, ascending=[True, True, True])\n",
    "high_power = auto[auto[\"horsepower\"] > 150].sort_values(by=['model_year', 'horsepower', 'mpg'], axis=0, ascending=[True, True, True])\n",
    "\n",
    "# high_power"
   ],
   "metadata": {
    "id": "cuy12YC9N9v3"
   },
   "execution_count": 148,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "p1 = plt.scatter(x=low_power['model_year'], y=low_power['mpg'], color='green')\n",
    "p2 = plt.scatter(x=med_power['model_year'], y=med_power['mpg'], color='blue')\n",
    "p3 = plt.scatter(x=high_power['model_year'], y=high_power['mpg'], color='red')\n",
    "\n",
    "plt.legend((p1, p2, p3), ('low_power', 'med_power', 'high_power'), \n",
    "           scatterpoints=1, loc='upper left', ncol=3, fontsize=10)\n",
    "plt.gca().update(dict(xlabel='model year', ylabel='efficiency (mpg)', title='Car power levels and efficiency (mpg) from model years 1970-1982'))\n",
    "plt.rcParams[\"figure.figsize\"] = (10, 10)\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 626
    },
    "id": "L7qdXO4gAnIt",
    "outputId": "c6c313df-edfc-4948-971d-f1a836bfa3c6"
   },
   "execution_count": 149,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "We see here that efficiency trends upwards based on model year in the low horsepower category.  \n",
    "There are no cars at the most recent years (1980-1982) so it is harder to draw a conclusion of mpg trends. It appears as if the worst mpg cars for the high HP category only existing from 1970-1973 with overall mpg averages improving in the 1970s for the cars in the dataset.  \n",
    "The medium HP cars improved in the 1980s, but there are less of them in that time period than the 1970s for each year. The peaks of mpg's absolutely improved for medium HP cars from 1979-1983."
   ],
   "metadata": {
    "id": "v5ssxR-RGxop"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "light_weight = auto[auto.weight <= auto['weight'].mean()].sort_values(by=['model_year', 'weight'], ascending=[True, True])\n",
    "heavy_weight = auto[auto.weight > auto['weight'].mean()].sort_values(by=['model_year', 'weight'], ascending=[True, True])\n",
    "\n",
    "# heavy_weight"
   ],
   "metadata": {
    "id": "G87mMe3NEkT3"
   },
   "execution_count": 171,
   "outputs": []
  },
  {
   "cell_type": "code",
   "source": [
    "k0 = plt.scatter(x=light['model_year'], y=light['weight'])\n",
    "k1 = plt.scatter(x=heavy['model_year'], y=heavy['weight'])\n",
    "plt.legend((k0, k1), ('light weight cars', 'heavy weight cars'),\n",
    "            scatterpoints=1, loc='upper right', ncol=3, fontsize=10)\n",
    "plt.gca().update(dict(xlabel='model year', ylabel='weight (lbs)', title='heavy and light from model years 1970-1982'))\n",
    "plt.show()"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 626
    },
    "id": "WXcNGHsTPVTM",
    "outputId": "32fae935-fe53-416c-afce-7e9d884e1a51"
   },
   "execution_count": 172,
   "outputs": [
    {
     "output_type": "display_data",
     "data": {
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ],
      "image/png": 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\n"
     },
     "metadata": {}
    }
   ]
  },
  {
   "cell_type": "markdown",
   "source": [
    "As the years progress from 1970 to 1982, the amount of light cars (below the mean of ~2977.58 pounds) is very consistent; however, the amount of heavy cars drops significantly.  \n",
    "The peak weight per model year shows a consistent downtrend as well."
   ],
   "metadata": {
    "id": "ubmHBm2fP8tQ"
   }
  },
  {
   "cell_type": "code",
   "source": [
    "%%shell\n",
    "jupyter nbconvert --to html ///content/assignment1.ipynb"
   ],
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/"
    },
    "id": "kFkZazQZWGeb",
    "outputId": "43b687c4-fd1a-47a0-91ec-135be081690c"
   },
   "execution_count": 2,
   "outputs": [
    {
     "output_type": "stream",
     "name": "stdout",
     "text": [
      "[NbConvertApp] Converting notebook ///content/assignment1.ipynb to html\n",
      "[NbConvertApp] Writing 1202503 bytes to ///content/assignment1.html\n"
     ]
    },
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": []
     },
     "metadata": {},
     "execution_count": 2
    }
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  },
  "colab": {
   "provenance": [],
   "collapsed_sections": [
    "Ju_Ij2bkzwHp"
   ],
   "include_colab_link": true
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}