dmontenegro_Lab2.ipynb

{
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/dafmontenegro/MetNumUN2023I/blob/master/dmontenegro_Lab2.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -U fortran-magic"
      ],
      "metadata": {
        "id": "fWtlWcsfaFOj",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b459a99b-ae15-4941-b2ac-fc4f4ee3c5c0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Requirement already satisfied: fortran-magic in /usr/local/lib/python3.9/dist-packages (0.7)\n",
            "Requirement already satisfied: ipython in /usr/local/lib/python3.9/dist-packages (from fortran-magic) (7.9.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.9/dist-packages (from fortran-magic) (1.22.4)\n",
            "Requirement already satisfied: pygments in /usr/local/lib/python3.9/dist-packages (from ipython->fortran-magic) (2.6.1)\n",
            "Requirement already satisfied: backcall in /usr/local/lib/python3.9/dist-packages (from ipython->fortran-magic) (0.2.0)\n",
            "Requirement already satisfied: pexpect in /usr/local/lib/python3.9/dist-packages (from ipython->fortran-magic) (4.8.0)\n",
            "Requirement already satisfied: jedi>=0.10 in /usr/local/lib/python3.9/dist-packages (from ipython->fortran-magic) (0.18.2)\n",
            "Requirement already satisfied: prompt-toolkit<2.1.0,>=2.0.0 in /usr/local/lib/python3.9/dist-packages (from ipython->fortran-magic) (2.0.10)\n",
            "Requirement already satisfied: setuptools>=18.5 in /usr/local/lib/python3.9/dist-packages (from ipython->fortran-magic) (57.4.0)\n",
            "Requirement already satisfied: decorator in /usr/local/lib/python3.9/dist-packages (from ipython->fortran-magic) (4.4.2)\n",
            "Requirement already satisfied: traitlets>=4.2 in /usr/local/lib/python3.9/dist-packages (from ipython->fortran-magic) (5.7.1)\n",
            "Requirement already satisfied: pickleshare in /usr/local/lib/python3.9/dist-packages (from ipython->fortran-magic) (0.7.5)\n",
            "Requirement already satisfied: parso<0.9.0,>=0.8.0 in /usr/local/lib/python3.9/dist-packages (from jedi>=0.10->ipython->fortran-magic) (0.8.3)\n",
            "Requirement already satisfied: six>=1.9.0 in /usr/local/lib/python3.9/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->fortran-magic) (1.15.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.9/dist-packages (from prompt-toolkit<2.1.0,>=2.0.0->ipython->fortran-magic) (0.2.6)\n",
            "Requirement already satisfied: ptyprocess>=0.5 in /usr/local/lib/python3.9/dist-packages (from pexpect->ipython->fortran-magic) (0.7.0)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "autoscroll": false,
        "ein.tags": "worksheet-0",
        "id": "FrAt48DtiXb-",
        "outputId": "85166e93-31c7-4f80-c5bd-8a6357584d7c",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The fortranmagic extension is already loaded. To reload it, use:\n",
            "  %reload_ext fortranmagic\n"
          ]
        }
      ],
      "source": [
        "%matplotlib inline\n",
        "%load_ext fortranmagic\n",
        "\n",
        "import sys; sys.path.append('..')\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "mpl.rc('figure', figsize=(12, 7))\n",
        "\n",
        "ran_the_first_cell = True\n",
        "\n",
        "jan2017 = pd.to_datetime(['2017-01-03 00:00:00+00:00',\n",
        " '2017-01-04 00:00:00+00:00',\n",
        " '2017-01-05 00:00:00+00:00',\n",
        " '2017-01-06 00:00:00+00:00',\n",
        " '2017-01-09 00:00:00+00:00',\n",
        " '2017-01-10 00:00:00+00:00',\n",
        " '2017-01-11 00:00:00+00:00',\n",
        " '2017-01-12 00:00:00+00:00',\n",
        " '2017-01-13 00:00:00+00:00',\n",
        " '2017-01-17 00:00:00+00:00',\n",
        " '2017-01-18 00:00:00+00:00',\n",
        " '2017-01-19 00:00:00+00:00',\n",
        " '2017-01-20 00:00:00+00:00',\n",
        " '2017-01-23 00:00:00+00:00',\n",
        " '2017-01-24 00:00:00+00:00',\n",
        " '2017-01-25 00:00:00+00:00',\n",
        " '2017-01-26 00:00:00+00:00',\n",
        " '2017-01-27 00:00:00+00:00',\n",
        " '2017-01-30 00:00:00+00:00',\n",
        " '2017-01-31 00:00:00+00:00',\n",
        " '2017-02-01 00:00:00+00:00'])\n",
        "calendar = jan2017.values.astype('datetime64[D]')\n",
        "\n",
        "event_dates = pd.to_datetime(['2017-01-06 00:00:00+00:00',\n",
        "                             '2017-01-07 00:00:00+00:00',\n",
        "                             '2017-01-08 00:00:00+00:00']).values.astype('datetime64[D]')\n",
        "event_values = np.array([10, 15, 20])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "ein.tags": "worksheet-0",
        "id": "lHm410FBiXcC"
      },
      "source": [
        "<center>\n",
        "  <h1>The PyData Toolbox</h1>\n",
        "  <h3>Scott Sanderson (Twitter: @scottbsanderson, GitHub: ssanderson)</h3>\n",
        "  <h3><a href=\"https://github.com/ssanderson/pydata-toolbox\">https://github.com/ssanderson/pydata-toolbox</a></h3>\n",
        "</center>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "ein.tags": "worksheet-0",
        "id": "0CXHsFMBiXcD"
      },
      "source": [
        "# About Me:\n",
        "\n",
        "<img src=\"https://raw.githubusercontent.com/ssanderson/pydata-toolbox/master/notebooks/images/me.jpg\" alt=\"Drawing\" style=\"width: 300px;\"/>\n",
        "\n",
        "- Senior Engineer at [Quantopian](www.quantopian.com)\n",
        "- Background in Mathematics and Philosophy\n",
        "- **Twitter:** [@scottbsanderson](https://twitter.com/scottbsanderson)\n",
        "- **GitHub:** [ssanderson](github.com/ssanderson)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tcOO-3C8iXcE"
      },
      "source": [
        "## Outline\n",
        "\n",
        "- Built-in Data Structures\n",
        "- Numpy `array`\n",
        "- Pandas `Series`/`DataFrame`\n",
        "- Plotting and \"Real-World\" Analyses"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qGj-c9CliXcE"
      },
      "source": [
        "# Data Structures"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Nsn-QHmCiXcF"
      },
      "source": [
        "> Rule 5. Data dominates. If you've chosen the right data structures and organized things well, the algorithms\n",
        "will almost always be self-evident. Data structures, not algorithms, are central to programming.\n",
        "\n",
        "- *Notes on Programming in C*, by Rob Pike."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_yhFg_rXiXcF"
      },
      "source": [
        "# Lists"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "97QvmzbqiXcG"
      },
      "outputs": [],
      "source": [
        "assert ran_the_first_cell, \"Oh noes!\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nhIZgNXLiXcG",
        "outputId": "9799bb2e-b6bb-448c-a73a-088018ad8106",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 'two', 3.0, 4, 5.0, 'six']"
            ]
          },
          "metadata": {},
          "execution_count": 773
        }
      ],
      "source": [
        "l = [1, 'two', 3.0, 4, 5.0, \"six\"]\n",
        "l"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Cjj8bfDAiXcH",
        "outputId": "7734b158-ec4f-4779-8a3f-4ce20e4a3e5d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "first: 1\n",
            "second: two\n"
          ]
        }
      ],
      "source": [
        "# Lists can be indexed like C-style arrays.\n",
        "first = l[0]\n",
        "second = l[1]\n",
        "print(\"first:\", first)\n",
        "print(\"second:\", second)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cVozluPbiXcH",
        "outputId": "5578a445-b654-4fab-9775-1513a6c40810",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "last: six\n",
            "second to last: 5.0\n"
          ]
        }
      ],
      "source": [
        "# Negative indexing gives elements relative to the end of the list.\n",
        "last = l[-1]\n",
        "penultimate = l[-2]\n",
        "print(\"last:\", last)\n",
        "print(\"second to last:\", penultimate)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PTqqm8MeiXcI",
        "outputId": "795436ae-71cf-4a94-ec5c-c61461f18ae1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['two', 3.0]"
            ]
          },
          "metadata": {},
          "execution_count": 776
        }
      ],
      "source": [
        "# Lists can also be sliced, which makes a copy of elements between\n",
        "# start (inclusive) and stop (exclusive)\n",
        "sublist = l[1:3]\n",
        "sublist"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ugUUXXUViXcI",
        "outputId": "012aa646-47a6-4bd0-9a6f-09ae47ac1432",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, 'two', 3.0]"
            ]
          },
          "metadata": {},
          "execution_count": 777
        }
      ],
      "source": [
        "# l[:N] is equivalent to l[0:N].\n",
        "first_three = l[:3]\n",
        "first_three"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "otoRjbK7iXcI",
        "outputId": "09b0d786-6b26-4b01-d719-5cbb96eb4bcb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[4, 5.0, 'six']"
            ]
          },
          "metadata": {},
          "execution_count": 778
        }
      ],
      "source": [
        "# l[3:] is equivalent to l[3:len(l)].\n",
        "after_three = l[3:]\n",
        "after_three"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2ZndPPspiXcJ",
        "outputId": "e2c73a97-1044-4b4f-d9b1-10177c5c1ecb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['b', 'd', 'f']"
            ]
          },
          "metadata": {},
          "execution_count": 779
        }
      ],
      "source": [
        "# There's also a third parameter, \"step\", which gets every Nth element.\n",
        "l = ['a', 'b', 'c', 'd', 'e', 'f', 'g','h']\n",
        "l[1:7:2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "dt2OMhnZiXcJ",
        "outputId": "89357f02-87a9-46cc-ab74-1a59208e24a1",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['h', 'g', 'f', 'e', 'd', 'c', 'b', 'a']"
            ]
          },
          "metadata": {},
          "execution_count": 780
        }
      ],
      "source": [
        "# This is a cute way to reverse a list.\n",
        "l[::-1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uDIdawFwiXcJ",
        "outputId": "3ac65b97-01cb-4bbd-fe8c-dd6b87dc1684",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Before: [1, 2, 3, 4, 5]\n",
            "After: [1, 2, 3, 4, 5, 'six']\n"
          ]
        }
      ],
      "source": [
        "# Lists can be grown efficiently (in O(1) amortized time).\n",
        "l = [1, 2, 3, 4, 5]\n",
        "print(\"Before:\", l)\n",
        "l.append('six')\n",
        "print(\"After:\", l)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OIhO0xxUiXcK",
        "outputId": "ba8a417a-0c4e-4b39-b201-264ad111380f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[2, 4, 6, 8, 10]"
            ]
          },
          "metadata": {},
          "execution_count": 782
        }
      ],
      "source": [
        "# Comprehensions let us perform elementwise computations.\n",
        "l = [1, 2, 3, 4, 5]\n",
        "[x * 2 for x in l]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "ein.tags": "worksheet-0",
        "id": "gh1Uw4KtiXcK"
      },
      "source": [
        "## Review: Python Lists\n",
        "\n",
        "- Zero-indexed sequence of arbitrary Python values.\n",
        "- Slicing syntax: `l[start:stop:step]` copies elements at regular intervals from `start` to `stop`.\n",
        "- Efficient (`O(1)`) appends and removes from end.\n",
        "- Comprehension syntax: `[f(x) for x in l if cond(x)]`."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KPeFQQs5iXcK"
      },
      "source": [
        "# Dictionaries"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "55GAxN3AiXcL",
        "outputId": "68213bfe-5ff6-4a8f-ab5d-122ae8d7d52d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'David': 'Hume', 'Immanuel': 'Kant', 'Bertrand': 'Russell'}"
            ]
          },
          "metadata": {},
          "execution_count": 783
        }
      ],
      "source": [
        "# Dictionaries are key-value mappings.\n",
        "philosophers = {'David': 'Hume', 'Immanuel': 'Kant', 'Bertrand': 'Russell'}\n",
        "philosophers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "V3JS2kHSiXcL",
        "outputId": "19500547-498e-4df1-9f58-985cc8d16d8a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'David': 'Hume',\n",
              " 'Immanuel': 'Kant',\n",
              " 'Bertrand': 'Russell',\n",
              " 'Ludwig': 'Wittgenstein'}"
            ]
          },
          "metadata": {},
          "execution_count": 784
        }
      ],
      "source": [
        "# Like lists, dictionaries are size-mutable.\n",
        "philosophers['Ludwig'] = 'Wittgenstein'\n",
        "philosophers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YRGePTociXcL",
        "outputId": "df050ddb-8a9a-4e50-f900-0407030cc75a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'Immanuel': 'Kant', 'Bertrand': 'Russell', 'Ludwig': 'Wittgenstein'}"
            ]
          },
          "metadata": {},
          "execution_count": 785
        }
      ],
      "source": [
        "del philosophers['David']\n",
        "philosophers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "2E4f_6pQiXcM"
      },
      "outputs": [],
      "source": [
        "# No slicing.\n",
        "# ERROR(NameError: name 'philosophers' is not defined): philosophers['Bertrand':'Immanuel']"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "ein.tags": "worksheet-0",
        "id": "xvN8eqK2iXcM"
      },
      "source": [
        "## Review: Python Dictionaries\n",
        "\n",
        "- Unordered key-value mapping from (almost) arbitrary keys to arbitrary values.\n",
        "- Efficient (`O(1)`) lookup, insertion, and deletion.\n",
        "- No slicing (would require a notion of order)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3C9gwhYDiXcM"
      },
      "source": [
        "<center><img src=\"https://raw.githubusercontent.com/ssanderson/pydata-toolbox/master/notebooks/images/pacino.gif\" alt=\"Drawing\" style=\"width: 100%;\"/></center>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "eqFZgJyyiXcM"
      },
      "outputs": [],
      "source": [
        "# Suppose we have some matrices...\n",
        "a = [[1, 2, 3],\n",
        "     [2, 3, 4],\n",
        "     [5, 6, 7],\n",
        "     [1, 1, 1]]\n",
        "\n",
        "b = [[1, 2, 3, 4],\n",
        "     [2, 3, 4, 5]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "TfFqABAMiXcM"
      },
      "outputs": [],
      "source": [
        "def matmul(A, B):\n",
        "    if len(a) > 0 and len(b) > 0:\n",
        "        new_rows = len(a)\n",
        "        new_columns = len(b[0])\n",
        "        new_array = [[0 for column in range(new_columns)] for row in range(new_rows)]\n",
        "        for i in range(new_rows):\n",
        "            for j in range(new_columns):\n",
        "                for k in range(len(b)):\n",
        "                    new_array[i][j] = a[i][k] * b[k][j]\n",
        "        return new_array\n",
        "    else:\n",
        "        return \"Arrays are empty\""
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yuee_jmYiXcN"
      },
      "source": [
        "<center><img src=\"https://raw.githubusercontent.com/ssanderson/pydata-toolbox/master/notebooks/images/gross.gif\" alt=\"Drawing\" style=\"width: 50%;\"/></center>\n"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PerN-NDRiXcN",
        "outputId": "4cdbb288-25d3-483b-8cc9-a66f71c08b97",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 42 µs, sys: 1 µs, total: 43 µs\n",
            "Wall time: 47.7 µs\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[4, 6, 8, 10], [6, 9, 12, 15], [12, 18, 24, 30], [2, 3, 4, 5]]"
            ]
          },
          "metadata": {},
          "execution_count": 789
        }
      ],
      "source": [
        "%%time\n",
        "\n",
        "matmul(a, b)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own arrays**"
      ],
      "metadata": {
        "id": "SaBypNRfb5Ie"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_a = [[0, 1, 2],\n",
        "        [3, 4, 5],\n",
        "        [6, 7, 8],\n",
        "        [9, 0, 1]]\n",
        "\n",
        "my_b = [[0, 1, 2, 3],\n",
        "        [4, 5, 6, 7]]\n",
        "\n",
        "my_c = [[0, 1, 2, 3],\n",
        "        [4, 5, 5, 7],\n",
        "        [8, 9, 0, 1]]\n",
        "\n",
        "my_d = []"
      ],
      "metadata": {
        "id": "d0L9Sn1Ubzh1"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 0 - cpu info**"
      ],
      "metadata": {
        "id": "AAcF7qrOv8KF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!cat /proc/cpuinfo"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BQl1PQDJwDkc",
        "outputId": "b4e7d35c-6cc0-451c-b018-eca45bd2f776"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "processor\t: 0\n",
            "vendor_id\t: GenuineIntel\n",
            "cpu family\t: 6\n",
            "model\t\t: 79\n",
            "model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
            "stepping\t: 0\n",
            "microcode\t: 0xffffffff\n",
            "cpu MHz\t\t: 2199.998\n",
            "cache size\t: 56320 KB\n",
            "physical id\t: 0\n",
            "siblings\t: 2\n",
            "core id\t\t: 0\n",
            "cpu cores\t: 1\n",
            "apicid\t\t: 0\n",
            "initial apicid\t: 0\n",
            "fpu\t\t: yes\n",
            "fpu_exception\t: yes\n",
            "cpuid level\t: 13\n",
            "wp\t\t: yes\n",
            "flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n",
            "bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa mmio_stale_data retbleed\n",
            "bogomips\t: 4399.99\n",
            "clflush size\t: 64\n",
            "cache_alignment\t: 64\n",
            "address sizes\t: 46 bits physical, 48 bits virtual\n",
            "power management:\n",
            "\n",
            "processor\t: 1\n",
            "vendor_id\t: GenuineIntel\n",
            "cpu family\t: 6\n",
            "model\t\t: 79\n",
            "model name\t: Intel(R) Xeon(R) CPU @ 2.20GHz\n",
            "stepping\t: 0\n",
            "microcode\t: 0xffffffff\n",
            "cpu MHz\t\t: 2199.998\n",
            "cache size\t: 56320 KB\n",
            "physical id\t: 0\n",
            "siblings\t: 2\n",
            "core id\t\t: 0\n",
            "cpu cores\t: 1\n",
            "apicid\t\t: 1\n",
            "initial apicid\t: 1\n",
            "fpu\t\t: yes\n",
            "fpu_exception\t: yes\n",
            "cpuid level\t: 13\n",
            "wp\t\t: yes\n",
            "flags\t\t: fpu vme de pse tsc msr pae mce cx8 apic sep mtrr pge mca cmov pat pse36 clflush mmx fxsr sse sse2 ss ht syscall nx pdpe1gb rdtscp lm constant_tsc rep_good nopl xtopology nonstop_tsc cpuid tsc_known_freq pni pclmulqdq ssse3 fma cx16 pcid sse4_1 sse4_2 x2apic movbe popcnt aes xsave avx f16c rdrand hypervisor lahf_lm abm 3dnowprefetch invpcid_single ssbd ibrs ibpb stibp fsgsbase tsc_adjust bmi1 hle avx2 smep bmi2 erms invpcid rtm rdseed adx smap xsaveopt arat md_clear arch_capabilities\n",
            "bugs\t\t: cpu_meltdown spectre_v1 spectre_v2 spec_store_bypass l1tf mds swapgs taa mmio_stale_data retbleed\n",
            "bogomips\t: 4399.99\n",
            "clflush size\t: 64\n",
            "cache_alignment\t: 64\n",
            "address sizes\t: 46 bits physical, 48 bits virtual\n",
            "power management:\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 1 - Changing in matmul(A, B) Python len(B) (# of rows of B) for len(A[0]) (# of columns of A)**"
      ],
      "metadata": {
        "id": "EFlf_ZLgsUEo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def my_matmul(a, b):\n",
        "    if len(a) > 0 and len(b) > 0:\n",
        "        new_rows = len(a)\n",
        "        new_columns = len(b[0])\n",
        "        new_array = [[0 for column in range(new_columns)] for row in range(new_rows)]\n",
        "        for i in range(new_rows):\n",
        "            for j in range(new_columns):\n",
        "                for k in range(len(a[0])): # Changing in matmul(A, B) Python len(B) (# of rows of B) for len(A[0]) (# of columns of A)\n",
        "                    new_array[i][j] = a[i][k] * b[k][j]\n",
        "        return new_array\n",
        "    else:\n",
        "        return \"Arrays are empty\"\n",
        "\n",
        "# ERROR(IndexError: list index out of range): print(\"A x B (my_new_matmul):\", my_matmul(my_a, my_b))"
      ],
      "metadata": {
        "id": "T71BRNaG3fev"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 2 - Verifiying error with in matmul(A, B) Python with the original matrices when changing len(B) (# of rows of B) for len(A[0]) (# of colums of A)**"
      ],
      "metadata": {
        "id": "8sLiMfvlUJzp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "try:\n",
        "    result = my_matmul(my_a, my_b)\n",
        "except IndexError:\n",
        "    result = \"Arrays are not compatible\"\n",
        "print(\"A x B (my_new_matmul):\", result)"
      ],
      "metadata": {
        "id": "HjyOAFQW3gc3",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bdeb077c-eda1-47c7-f7d9-2ed0c9263bf7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A x B (my_new_matmul): Arrays are not compatible\n",
            "CPU times: user 1.38 ms, sys: 0 ns, total: 1.38 ms\n",
            "Wall time: 1.58 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 3 - Chekcing the matrix multiplication compatibility condition  len(A[0]) == len(B)**"
      ],
      "metadata": {
        "id": "AGztFKBNuQ_e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def my_matmul(a, b):\n",
        "    if len(a) > 0 and len(b) > 0:\n",
        "        b_rows = len(b)\n",
        "        a_columns = len(a[0])\n",
        "        if a_columns == b_rows:\n",
        "            new_rows = len(a)\n",
        "            new_columns = len(b[0])\n",
        "            new_array = [[0 for column in range(new_columns)] for row in range(new_rows)]\n",
        "            for i in range(new_rows):\n",
        "                for j in range(new_columns):\n",
        "                    for k in range(len(a[0])): # Changing in matmul(A, B) Python len(B) (# of rows of B) for len(A[0]) (# of columns of A)\n",
        "                        new_array[i][j] = a[i][k] * b[k][j]\n",
        "            return new_array\n",
        "        else:\n",
        "            return \"Arrays are not compatible\"\n",
        "    else:\n",
        "        return \"Arrays are empty\""
      ],
      "metadata": {
        "id": "4rgHkUNC3haX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "result = my_matmul(my_a, my_c)\n",
        "print(\"A x C (my_new_matmul):\", result)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cq2avlWyAO4f",
        "outputId": "172c0cd3-fdab-4517-a0b3-7c34c7a9a186"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A x C (my_new_matmul): [[16, 18, 0, 2], [40, 45, 0, 5], [64, 72, 0, 8], [8, 9, 0, 1]]\n",
            "CPU times: user 185 µs, sys: 4 µs, total: 189 µs\n",
            "Wall time: 151 µs\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 4 -  Verifiying error with in matmul(A, B) Python when checking the mtarix multiplication compatibility condition  len(A[0]) == len(B)**"
      ],
      "metadata": {
        "id": "A4SoDNPpuZpp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "result = my_matmul(my_a, my_b)\n",
        "print(\"A x B (my_new_matmul):\", result)"
      ],
      "metadata": {
        "id": "wpnd21c83jQI",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf8cbe13-9469-4296-97a2-8e22b72293d5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A x B (my_new_matmul): Arrays are not compatible\n",
            "CPU times: user 2.82 ms, sys: 0 ns, total: 2.82 ms\n",
            "Wall time: 6.28 ms\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 5 - Deifining A and B that are compatible for multiplcation**"
      ],
      "metadata": {
        "id": "MZTTYVu0ufCY"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import random\n",
        "\n",
        "def my_matrix(rows, columns):\n",
        "    return [[random.random() for column in range(columns)] for row in range(rows)]"
      ],
      "metadata": {
        "id": "q8nHrWQz3kby"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "my_random_a = my_matrix(3, 7)\n",
        "my_random_b = my_matrix(7, 3)\n",
        "print(\"A(3x7 random):\", my_random_a)\n",
        "print(\"B(7x3 random):\", my_random_b)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YItri7JdFpdi",
        "outputId": "be4e4c92-1750-4b56-9073-1c789db0d13e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A(3x7 random): [[0.3604701146912226, 0.3426440279501989, 0.8728408566903996, 0.3721237682047671, 0.930340031062782, 0.949989020823253, 0.5973415907269742], [0.8811026931424357, 0.8552589602381743, 0.1844576079944682, 0.47960028447411196, 0.640831475284565, 0.264681097671259, 0.47666429240865515], [0.4615370052274297, 0.6047953943696225, 0.9863780184926731, 0.8996470228946613, 0.06739465979765191, 0.7929719812567297, 0.017565077612833657]]\n",
            "B(7x3 random): [[0.6284300912044969, 0.9014408232846198, 0.26800986214672773], [0.5159900069734807, 0.08108355057628147, 0.11739921274154286], [0.689723295763967, 0.21423030470950744, 0.678056318600678], [0.5829180199240751, 0.7144036543857998, 0.565833220727921], [0.9073729219558568, 0.6123448202314705, 0.154443618693757], [0.2402727853967238, 0.8143090630455683, 0.17032759659072716], [0.852942741718499, 0.9362448986909415, 0.5446029027345571]]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 6 - Runinng the correct Python matrix multiplication code with the matrices with dimensions compatible for multiplication.**"
      ],
      "metadata": {
        "id": "GM5DAXUIuhEg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "my_matmul(my_random_a, my_random_b)"
      ],
      "metadata": {
        "id": "ZNgzN0YlP6Xi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a3e20ffc-6248-4ee9-e037-b0df1d4a64d4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 46 µs, sys: 0 ns, total: 46 µs\n",
            "Wall time: 48.6 µs\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[[0.5094981741371549, 0.5592580170940619, 0.325313964233988],\n",
              " [0.4065673484463467, 0.44627451215573066, 0.25959275727566733],\n",
              " [0.014982005457588567, 0.016445214310125973, 0.009565992254706996]]"
            ]
          },
          "metadata": {},
          "execution_count": 799
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 7 - Running 10 times matmul(randa, randb) with randa and randb a randon matrices of 600 x 100 and 100 x 600 and calulating the average execution time**"
      ],
      "metadata": {
        "id": "3-eUeNjHhHP7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "test = []\n",
        "\n",
        "for i in range(10):\n",
        "    my_random_a = my_matrix(600, 100)\n",
        "    my_random_b = my_matrix(100, 600)\n",
        "\n",
        "    start = time.perf_counter()\n",
        "    my_matmul(my_random_a, my_random_b)\n",
        "    end = time.perf_counter()\n",
        "\n",
        "    test.append(end-start)\n",
        "    print(f\"Case {i+1}: {test[-1]:.04f} seconds\")\n",
        "\n",
        "python_average = sum(test)/10\n",
        "print(f\"Python average: {python_average:.04f} seconds\")"
      ],
      "metadata": {
        "id": "uIC10D8mG0MQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ac68c39a-35b0-4490-f8e8-3098d1fe3804"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Case 1: 15.2470 seconds\n",
            "Case 2: 16.2940 seconds\n",
            "Case 3: 8.1125 seconds\n",
            "Case 4: 8.8858 seconds\n",
            "Case 5: 8.6616 seconds\n",
            "Case 6: 7.9559 seconds\n",
            "Case 7: 8.7474 seconds\n",
            "Case 8: 9.9356 seconds\n",
            "Case 9: 7.5786 seconds\n",
            "Case 10: 8.7925 seconds\n",
            "Python average: 10.0211 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 8 - Creating the average execution time data frame and adding Python's average execution time**"
      ],
      "metadata": {
        "id": "FXisJPNSN-Tf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_execution_time = pd.DataFrame({\"Language\": [],\"Average\": []})"
      ],
      "metadata": {
        "id": "ZJq3S28KZxHz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def add_execution_time(language, average):\n",
        "    global my_execution_time\n",
        "    my_execution_time = pd.concat([my_execution_time, pd.DataFrame({\"Language\": [language],\"Average\": [average]})], ignore_index=True)\n",
        "    return my_execution_time"
      ],
      "metadata": {
        "id": "2fK25g003-ww"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "add_execution_time(\"Python\", python_average)"
      ],
      "metadata": {
        "id": "unyAp6RJZ5RC",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "outputId": "3e90ab57-8ac1-4f8d-c962-71f45e03c591"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Language    Average\n",
              "0   Python  10.021088"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c798eeb8-48ec-4c6f-8b30-b6e2c9fb6b44\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Language</th>\n",
              "      <th>Average</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Python</td>\n",
              "      <td>10.021088</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c798eeb8-48ec-4c6f-8b30-b6e2c9fb6b44')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-c798eeb8-48ec-4c6f-8b30-b6e2c9fb6b44 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-c798eeb8-48ec-4c6f-8b30-b6e2c9fb6b44');\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": 803
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 9 - Running 10 times randa and randb mutiplicaction as NumPy arrays  adding NumPy's average execution time**"
      ],
      "metadata": {
        "id": "OiFYD3tghLcL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "test = []\n",
        "\n",
        "for i in range(10):\n",
        "    my_random_a = np.array(my_matrix(600, 100))\n",
        "    my_random_b = np.array(my_matrix(100, 600))\n",
        "\n",
        "    start = time.perf_counter()\n",
        "    my_random_a@my_random_b\n",
        "    end = time.perf_counter()\n",
        "\n",
        "    test.append(end-start)\n",
        "    print(f\"Case {i+1}: {test[-1]:.04f} seconds\")\n",
        "\n",
        "numpy_average = sum(test)/10\n",
        "print(f\"Numpy average: {numpy_average:.04f} seconds\")"
      ],
      "metadata": {
        "id": "bHX4icJx5KZo",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d1fe8b1b-cda5-405c-a7c7-63dd0af6cfed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Case 1: 0.0037 seconds\n",
            "Case 2: 0.0028 seconds\n",
            "Case 3: 0.0029 seconds\n",
            "Case 4: 0.0077 seconds\n",
            "Case 5: 0.0029 seconds\n",
            "Case 6: 0.0055 seconds\n",
            "Case 7: 0.0051 seconds\n",
            "Case 8: 0.0163 seconds\n",
            "Case 9: 0.0027 seconds\n",
            "Case 10: 0.0090 seconds\n",
            "Numpy average: 0.0059 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "add_execution_time(\"Numpy\", numpy_average)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 112
        },
        "id": "yuHD2wqhb2NP",
        "outputId": "4b651bd3-75d2-45fd-c7b6-0b77fb931080"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Language    Average\n",
              "0   Python  10.021088\n",
              "1    Numpy   0.005872"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3b00cbc8-d37a-46c5-9de1-7f9803ef4706\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Language</th>\n",
              "      <th>Average</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Python</td>\n",
              "      <td>10.021088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Numpy</td>\n",
              "      <td>0.005872</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b00cbc8-d37a-46c5-9de1-7f9803ef4706')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-3b00cbc8-d37a-46c5-9de1-7f9803ef4706 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-3b00cbc8-d37a-46c5-9de1-7f9803ef4706');\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": 805
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "hXy_q4S8iXcN"
      },
      "outputs": [],
      "source": [
        "# Maybe that's not that bad?  Let's try a simpler case.\n",
        "def python_dot_product(xs, ys):\n",
        "    return sum(x * y for x, y in zip(xs, ys))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "qylqVrbqiXcO"
      },
      "outputs": [],
      "source": [
        "%%fortran\n",
        "subroutine fortran_dot_product(xs, ys, result)\n",
        "    double precision, intent(in) :: xs(:)\n",
        "    double precision, intent(in) :: ys(:)\n",
        "    double precision, intent(out) :: result\n",
        "\n",
        "    result = sum(xs * ys)\n",
        "end"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "BGacVDX-iXcO"
      },
      "outputs": [],
      "source": [
        "list_data = [float(i) for i in range(100000)]\n",
        "array_data = np.array(list_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aaZPRE4aiXcO",
        "outputId": "9cce1e78-dbdc-4ab0-f721-d56287966d6d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 12.3 ms, sys: 0 ns, total: 12.3 ms\n",
            "Wall time: 12.7 ms\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "333328333350000.0"
            ]
          },
          "metadata": {},
          "execution_count": 809
        }
      ],
      "source": [
        "%%time\n",
        "python_dot_product(list_data, list_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "e9tNvTwLiXcO",
        "outputId": "367162b1-3ae9-46ff-abbc-52eb886ec70d",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 227 µs, sys: 5 µs, total: 232 µs\n",
            "Wall time: 194 µs\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "333328333350000.0"
            ]
          },
          "metadata": {},
          "execution_count": 810
        }
      ],
      "source": [
        "%%time\n",
        "fortran_dot_product(array_data, array_data)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qW-_Q7yuiXcO"
      },
      "source": [
        "<center><img src=\"https://raw.githubusercontent.com/ssanderson/pydata-toolbox/master/notebooks/images/sloth.gif\" alt=\"Drawing\" style=\"width: 1080px;\"/></center>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 10 - Deifining A (2x2)  and B (2x2)**"
      ],
      "metadata": {
        "id": "y37MLCQ4PQd3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "S = [[1568, 1999],\n",
        "     [7536, 1523]]\n",
        "U = [[7082, 3654],\n",
        "     [1973, 3791]]"
      ],
      "metadata": {
        "id": "7i0uJGD0v105"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 11 - Defining Fortran subroutine matmul(A,B) for 2x2 matrices**"
      ],
      "metadata": {
        "id": "nuV59KXQPipC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%fortran\n",
        "\n",
        "subroutine mat_mul(A, B, res)\n",
        "    real*8 :: A(2,2), B(2,2)\n",
        "    real*8,intent(out) :: res(2,2)\n",
        "    integer :: x, y, z\n",
        "\n",
        "    ! Initialize output matrix res to zero\n",
        "    res = 0.0\n",
        "\n",
        "    ! Perform matrix multiplication\n",
        "    do x = 1, 2\n",
        "        do y = 1, 2\n",
        "            do z = 1, 2\n",
        "                res(x,y) = res(x,y) + A(x,z) * B(z,y)\n",
        "            end do\n",
        "        end do\n",
        "    end do\n",
        "\n",
        "end subroutine mat_mul"
      ],
      "metadata": {
        "id": "faG3DgEHyyoP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 12 -Run Fortran subroutine matmul(A,B) with a and b 2x2 matrices**"
      ],
      "metadata": {
        "id": "zU1RmmstRPd4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "mat_1 = [[1,1],[2,2]]\n",
        "mat_2 = [[1,1],[2,2]]\n",
        "print(mat_1)\n",
        "print(mat_2)\n",
        "\n",
        "mat_mul(mat_1,mat_2)\n",
        "mat_mul(S,U)\n"
      ],
      "metadata": {
        "id": "LTcNJB4L6wwX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5750744d-d903-4ce9-b34d-c666052ebdc0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[1, 1], [2, 2]]\n",
            "[[1, 1], [2, 2]]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[15048603., 13307681.],\n",
              "       [56374831., 33310237.]])"
            ]
          },
          "metadata": {},
          "execution_count": 813
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 13 - Defining Fortran subroutine matmul(A,B) for 600x100 and 100x600 matrices**"
      ],
      "metadata": {
        "id": "_ra5WDYERX1e"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%fortran\n",
        "\n",
        "subroutine mat_mul_new(A, B, res)\n",
        "    real*8 :: A(600,100)\n",
        "    real*8 :: B(100,600)\n",
        "    real*8,intent(out) :: res(600,600)\n",
        "    do x = 1,600\n",
        "        do y = 1,600\n",
        "            do z = 1,100\n",
        "                res(x,y) = res(x,y) + A(x,z) * B(z,y)\n",
        "            end do\n",
        "        end do\n",
        "    end do\n",
        "\n",
        "end subroutine mat_mul_new"
      ],
      "metadata": {
        "id": "4mDURPVKkBDD"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 14 -Run Fortran subroutine matmul(A,B) with 600x100 and 100x600 matrices**"
      ],
      "metadata": {
        "id": "cf5NwQZQRjx-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "\n",
        "mat_new_1 = my_matrix(600, 100)\n",
        "mat_new_2 = my_matrix(100, 600)\n",
        "\n",
        "M_1 = np.array(mat_new_1)\n",
        "M_2 = np.array(mat_new_2)\n",
        "\n",
        "mat_mul_new(M_1,M_2)"
      ],
      "metadata": {
        "id": "S7xtJkrb6yuQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "be51de36-3e33-4242-c338-c5a55c9389c8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[27.97598054, 26.78436894, 26.77958131, ..., 24.90740001,\n",
              "        24.50087394, 25.16086464],\n",
              "       [26.86300635, 25.13357959, 26.17776014, ..., 25.57325077,\n",
              "        24.50179555, 23.68289567],\n",
              "       [28.59655364, 26.6663189 , 27.77566972, ..., 24.28178148,\n",
              "        24.72313362, 24.93778162],\n",
              "       ...,\n",
              "       [26.33506525, 24.81503049, 25.51831403, ..., 23.92585687,\n",
              "        21.92867732, 22.99016542],\n",
              "       [25.92278591, 25.01342195, 25.38310426, ..., 22.57546632,\n",
              "        22.41630358, 22.76865938],\n",
              "       [26.35746623, 24.68660812, 24.37946679, ..., 22.67812575,\n",
              "        23.04264001, 22.77239824]])"
            ]
          },
          "metadata": {},
          "execution_count": 815
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 15 - Running 10 times the  Fortran subroutine matmul(A,B) with 600x100 and 100x600 matrices and adding Fortran magic average execution time to the data frame**"
      ],
      "metadata": {
        "id": "5yjoH7W_R0E3"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "for i in range(10):\n",
        "    mat_new_3 = my_matrix(600, 100)\n",
        "    mat_new_4 = my_matrix(100, 600)\n",
        "\n",
        "    start = time.perf_counter()\n",
        "    mat_mul_new(mat_new_3, mat_new_4)\n",
        "    end = time.perf_counter()\n",
        "\n",
        "    test.append(end-start)\n",
        "    print(f\"Case {i+1}: {test[-1]:.04f} seconds\")\n",
        "\n",
        "fortran_average = sum(test)/10\n",
        "print(f\"Fortran average: {fortran_average:.04f} seconds\")\n"
      ],
      "metadata": {
        "id": "2-A0QOte603A",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "54721a7e-fbb6-4773-ece3-1246f7b9fc9c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Case 1: 0.1820 seconds\n",
            "Case 2: 0.1111 seconds\n",
            "Case 3: 0.1700 seconds\n",
            "Case 4: 0.1584 seconds\n",
            "Case 5: 0.1365 seconds\n",
            "Case 6: 0.1352 seconds\n",
            "Case 7: 0.1121 seconds\n",
            "Case 8: 0.2801 seconds\n",
            "Case 9: 0.3487 seconds\n",
            "Case 10: 0.3842 seconds\n",
            "Fortran average: 0.2077 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "add_execution_time(\"Fortran magic\",fortran_average)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "NHtrRdwtyPY5",
        "outputId": "ea5599fa-00ea-4158-fa5d-3a69c1c2a64b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "        Language    Average\n",
              "0         Python  10.021088\n",
              "1          Numpy   0.005872\n",
              "2  Fortran magic   0.207713"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1d11ac73-967a-4f8c-a097-5308474414d3\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Language</th>\n",
              "      <th>Average</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Python</td>\n",
              "      <td>10.021088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Numpy</td>\n",
              "      <td>0.005872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fortran magic</td>\n",
              "      <td>0.207713</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-1d11ac73-967a-4f8c-a097-5308474414d3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-1d11ac73-967a-4f8c-a097-5308474414d3 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-1d11ac73-967a-4f8c-a097-5308474414d3');\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": 817
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 16 - Creating a  Fortran program that mutiplies 10 times A(600x100) and  B (100x600) matrices**"
      ],
      "metadata": {
        "id": "w5hURiGOShpG"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import time\n",
        "\n",
        "test = []\n",
        "\n",
        "def fortran_10_times(a,b):\n",
        "  for i in range(10):\n",
        "      mat_new_p = my_matrix(a, b)\n",
        "      mat_new_r = my_matrix(b, a)\n",
        "\n",
        "      start = time.perf_counter()\n",
        "      mat_mul_new(mat_new_3, mat_new_4)\n",
        "      end = time.perf_counter()\n",
        "\n",
        "      test.append(end-start)\n",
        "      print(f\"Case {i+1}: {test[-1]:.04f} seconds\")\n",
        "\n",
        "  fortran_10_times_average = sum(test)/10\n",
        "  print(f\"Fortran 10 times average: {fortran_10_times_average:.04f} seconds\")\n"
      ],
      "metadata": {
        "id": "xCkI1njo62Hv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 17 - Running the Fortran program that mutiplies 10 times A(600x100) and  B (100x600) matrices**"
      ],
      "metadata": {
        "id": "LJOrD7hyYmTJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%time\n",
        "\n",
        "fortran_10_times(600,100)\n"
      ],
      "metadata": {
        "id": "hmBeCMGV63z6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "963b8ddf-f3cf-4c03-b947-f8d570591f7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Case 1: 0.2681 seconds\n",
            "Case 2: 0.1758 seconds\n",
            "Case 3: 0.2839 seconds\n",
            "Case 4: 0.3074 seconds\n",
            "Case 5: 0.3174 seconds\n",
            "Case 6: 0.2197 seconds\n",
            "Case 7: 0.2392 seconds\n",
            "Case 8: 0.1305 seconds\n",
            "Case 9: 0.1652 seconds\n",
            "Case 10: 0.1113 seconds\n",
            "Fortran 10 times average: 0.2219 seconds\n",
            "CPU times: user 1.44 s, sys: 11.4 ms, total: 1.45 s\n",
            "Wall time: 2.84 s\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "fortran_10_times_average = sum(test)/10\n",
        "print(fortran_10_times_average)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JE6v_lgC7DWJ",
        "outputId": "52eb14ff-3282-4f6f-cc16-390fbfa4f45a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.22185021550003511\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 18 - Adding Fortran average execution time to the data frame**"
      ],
      "metadata": {
        "id": "21wSmo50YfIo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "add_execution_time(\"Fortran 10 times average\",fortran_10_times_average)"
      ],
      "metadata": {
        "id": "UDMgoacS642X",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 175
        },
        "outputId": "f96118e7-5e49-43a4-a77a-05751bfcfa57"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Language    Average\n",
              "0                    Python  10.021088\n",
              "1                     Numpy   0.005872\n",
              "2             Fortran magic   0.207713\n",
              "3  Fortran 10 times average   0.221850"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-dc5ad65b-a99a-491a-8699-56d40de9b81d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Language</th>\n",
              "      <th>Average</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Python</td>\n",
              "      <td>10.021088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Numpy</td>\n",
              "      <td>0.005872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fortran magic</td>\n",
              "      <td>0.207713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fortran 10 times average</td>\n",
              "      <td>0.221850</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-dc5ad65b-a99a-491a-8699-56d40de9b81d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-dc5ad65b-a99a-491a-8699-56d40de9b81d 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-dc5ad65b-a99a-491a-8699-56d40de9b81d');\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": 821
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 19 - Creating a c program that mutiplies 10 times A(600x100) and  B (100x600) matrices**"
      ],
      "metadata": {
        "id": "8G8ZZ2hfZXYA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile my_matmul.c\n",
        "  #include <stdio.h>\n",
        "  #include <stdlib.h>\n",
        "  #include <time.h>\n",
        "\n",
        "\n",
        "  void fillMatrix(int **mat, int n, int m);\n",
        "  void matMul(int **matA, int **matB, int **result, int n, int m);\n",
        "  double multAverage(int **matA, int **matB, int **result, int n, int m, int rep);\n",
        "\n",
        "  int main(void) {\n",
        "    FILE *fileAvr;\n",
        "    int n = 600;\n",
        "    int m = 100;\n",
        "\n",
        "    int **pmatA;\n",
        "    int **pmatB;\n",
        "    int **presult;\n",
        "    unsigned int rep = 10;\n",
        "    double average = 0.0;\n",
        "\n",
        "    //matrix pointers\n",
        "    pmatA = (int **) malloc(n * sizeof(int *)); //pointers to each row\n",
        "    for(int i = 0; i < n; i++){\n",
        "      pmatA[i] = (int *) malloc(m * sizeof(int)); //pointers to each column\n",
        "    }\n",
        "\n",
        "    pmatB = (int **) malloc(m * sizeof(int *));\n",
        "    for(int i = 0; i < m; i++){\n",
        "      pmatB[i] = (int *) malloc(n * sizeof(int));\n",
        "    }\n",
        "\n",
        "    presult = (int **) malloc(n * sizeof(int *));\n",
        "    for(int i = 0; i < n; i++){\n",
        "      presult[i] = (int *) malloc(n * sizeof(int));\n",
        "    }\n",
        "\n",
        "    // fill matrix\n",
        "    fillMatrix(pmatA, n, m);\n",
        "    fillMatrix(pmatB, m, n);\n",
        "\n",
        "    //matrix multiplication repetitions\n",
        "    average = multAverage(pmatA, pmatB, presult, n, m, rep);\n",
        "    printf(\"Average in C: %f\\n seconds\", average);\n",
        "\n",
        "    //write average into a file\n",
        "    fileAvr = fopen(\"c_average.txt\", \"w\");\n",
        "    fprintf(fileAvr,\"%f\",average);\n",
        "    fclose(fileAvr);\n",
        "\n",
        "    return 0;\n",
        "  }\n",
        "\n",
        "  void fillMatrix(int **mat, int n, int m){\n",
        "    for(int i=0; i<n; i++){\n",
        "      for(int j=0; j<m; j++){\n",
        "        mat[i][j] = rand();\n",
        "      }\n",
        "    }\n",
        "    return;\n",
        "  }\n",
        "\n",
        "  double multAverage(int **matA, int **matB, int **result, int n, int m, int rep){\n",
        "    clock_t start, end;\n",
        "    double average = 0.0;\n",
        "    double execution = 0.0;\n",
        "\n",
        "    for(int i=0; i<rep; i++){\n",
        "      start = clock();\n",
        "      matMul(matA, matB, result, n, m);\n",
        "      end = clock();\n",
        "      execution = ((double)(end - start))/CLOCKS_PER_SEC;\n",
        "      average += execution;\n",
        "      printf(\"Case %d -> %f seconds\\n\",i+1, execution);\n",
        "    }\n",
        "    return average/((double) rep);\n",
        "  }\n",
        "\n",
        "  void matMul(int **matA, int **matB, int **result, int n, int m){\n",
        "\n",
        "    //Result matrix has:\n",
        "    //Rows = rows of matrix A\n",
        "    //Columns = columns of matrix B\n",
        "\n",
        "    for(int i=0; i<n; i++){\n",
        "      for(int j=0; j<n; j++){\n",
        "        for(int k=0; k<m; k++){\n",
        "          result[i][j] += matA[i][k] * matB[k][j];\n",
        "        }\n",
        "      }\n",
        "    }\n",
        "    return;\n",
        "  }"
      ],
      "metadata": {
        "id": "BknrT-KK65wP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b1bb19d4-dc59-4109-ae4f-82545271b00c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting my_matmul.c\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 20 - Running the c program that mutiplies 10 times A(600x100) and  B (100x600) matrices**"
      ],
      "metadata": {
        "id": "i-oPlTweZndB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!gcc  my_matmul.c -o  my_matmul\n",
        "!./my_matmul\n"
      ],
      "metadata": {
        "id": "CKFgaMTa67Ri",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "878f6bd5-0dcd-4e22-da99-3fad1a3a03f0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Case 1 -> 0.370689 seconds\n",
            "Case 2 -> 0.375458 seconds\n",
            "Case 3 -> 0.375932 seconds\n",
            "Case 4 -> 0.389430 seconds\n",
            "Case 5 -> 0.378274 seconds\n",
            "Case 6 -> 0.381063 seconds\n",
            "Case 7 -> 0.373553 seconds\n",
            "Case 8 -> 0.382746 seconds\n",
            "Case 9 -> 0.365836 seconds\n",
            "Case 10 -> 0.392319 seconds\n",
            "Average in C: 0.378530\n",
            " seconds"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 21 - Adding c average execution time to the data frame**"
      ],
      "metadata": {
        "id": "U9Wf-9DPZ3R4"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matc = open('c_average.txt', 'r')\n",
        "avr_c = float(matc.readline())\n",
        "add_execution_time(\"C\", avr_c)"
      ],
      "metadata": {
        "id": "EfsTc5wN695I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "69945aaa-c828-4c10-ba46-286ff8f754f6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Language    Average\n",
              "0                    Python  10.021088\n",
              "1                     Numpy   0.005872\n",
              "2             Fortran magic   0.207713\n",
              "3  Fortran 10 times average   0.221850\n",
              "4                         C   0.378530"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-5d8a25e5-abb2-48a2-a1a2-6042a2fca051\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Language</th>\n",
              "      <th>Average</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Python</td>\n",
              "      <td>10.021088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Numpy</td>\n",
              "      <td>0.005872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fortran magic</td>\n",
              "      <td>0.207713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fortran 10 times average</td>\n",
              "      <td>0.221850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C</td>\n",
              "      <td>0.378530</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5d8a25e5-abb2-48a2-a1a2-6042a2fca051')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-5d8a25e5-abb2-48a2-a1a2-6042a2fca051 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-5d8a25e5-abb2-48a2-a1a2-6042a2fca051');\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": 824
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 22 - Creating a C++ program that mutiplies 10 times A(600x100) and  B (100x600) matrices**"
      ],
      "metadata": {
        "id": "XkgHEUB2aFI1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile my_matmul.cpp\n",
        "  #include <stdio.h>\n",
        "  #include <stdlib.h>\n",
        "  #include <time.h>\n",
        "\n",
        "\n",
        "  using namespace std;\n",
        "   void fillMatrix(int **mat, int n, int m);\n",
        "  void matMul(int **matA, int **matB, int **result, int n, int m);\n",
        "  double multAverage(int **matA, int **matB, int **result, int n, int m, int rep);\n",
        "\n",
        "  int main(void) {\n",
        "    FILE *fileAvr;\n",
        "    int n = 600;\n",
        "    int m = 100;\n",
        "\n",
        "    int **pmatA;\n",
        "    int **pmatB;\n",
        "    int **presult;\n",
        "    unsigned int rep = 10;\n",
        "    double average = 0.0;\n",
        "\n",
        "    //matrix pointers\n",
        "    pmatA = (int **) malloc(n * sizeof(int *)); //pointers to each row\n",
        "    for(int i = 0; i < n; i++){\n",
        "      pmatA[i] = (int *) malloc(m * sizeof(int)); //pointers to each column\n",
        "    }\n",
        "\n",
        "    pmatB = (int **) malloc(m * sizeof(int *));\n",
        "    for(int i = 0; i < m; i++){\n",
        "      pmatB[i] = (int *) malloc(n * sizeof(int));\n",
        "    }\n",
        "\n",
        "    presult = (int **) malloc(n * sizeof(int *));\n",
        "    for(int i = 0; i < n; i++){\n",
        "      presult[i] = (int *) malloc(n * sizeof(int));\n",
        "    }\n",
        "\n",
        "    // fill matrix\n",
        "    fillMatrix(pmatA, n, m);\n",
        "    fillMatrix(pmatB, m, n);\n",
        "\n",
        "    //matrix multiplication repetitions\n",
        "    average = multAverage(pmatA, pmatB, presult, n, m, rep);\n",
        "    printf(\"Average in C++: %f seconds\\n\",average);\n",
        "\n",
        "    //write average into a file\n",
        "    fileAvr = fopen(\"c_plus_average.txt\", \"w\");\n",
        "    fprintf(fileAvr,\"%f\",average);\n",
        "    fclose(fileAvr);\n",
        "\n",
        "    return 0;\n",
        "  }\n",
        "\n",
        "  void fillMatrix(int **mat, int n, int m){\n",
        "    for(int i=0; i<n; i++){\n",
        "      for(int j=0; j<m; j++){\n",
        "        mat[i][j] = rand();\n",
        "      }\n",
        "    }\n",
        "    return;\n",
        "  }\n",
        "\n",
        "  double multAverage(int **matA, int **matB, int **result, int n, int m, int rep){\n",
        "    clock_t start, end;\n",
        "    double average = 0.0;\n",
        "    double execution = 0.0;\n",
        "\n",
        "    for(int i=0; i<rep; i++){\n",
        "      start = clock();\n",
        "      matMul(matA, matB, result, n, m);\n",
        "      end = clock();\n",
        "      execution = ((double)(end - start))/CLOCKS_PER_SEC;\n",
        "      average += execution;\n",
        "      printf(\"Case %d -> %f seconds\\n\",i+1, execution);\n",
        "    }\n",
        "    return average/((double) rep);\n",
        "  }\n",
        "\n",
        "  void matMul(int **matA, int **matB, int **result, int n, int m){\n",
        "\n",
        "    //Result matrix has:\n",
        "    //Rows = rows of matrix A\n",
        "    //Columns = columns of matrix B\n",
        "\n",
        "    for(int i=0; i<n; i++){\n",
        "      for(int j=0; j<n; j++){\n",
        "        for(int k=0; k<m; k++){\n",
        "          result[i][j] += matA[i][k] * matB[k][j];\n",
        "        }\n",
        "      }\n",
        "    }\n",
        "    return;\n",
        "  }"
      ],
      "metadata": {
        "id": "U1CoUYEw6-9P",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29812bf1-d3e3-4984-c9fc-863673bf9846"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting my_matmul.cpp\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 23 - Running the C++ program that mutiplies 10 times A(600x100) and  B (100x600) matrices**"
      ],
      "metadata": {
        "id": "WFpb2F81aKJA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!g++ my_matmul.cpp -o my_matmul\n",
        "!./my_matmul"
      ],
      "metadata": {
        "id": "Q33PLFeG6_0r",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "26f54a5d-279f-48fe-f166-a9f1824218c1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Case 1 -> 0.389756 seconds\n",
            "Case 2 -> 0.387573 seconds\n",
            "Case 3 -> 0.388430 seconds\n",
            "Case 4 -> 0.394571 seconds\n",
            "Case 5 -> 0.373706 seconds\n",
            "Case 6 -> 0.381461 seconds\n",
            "Case 7 -> 0.381639 seconds\n",
            "Case 8 -> 0.384616 seconds\n",
            "Case 9 -> 0.390103 seconds\n",
            "Case 10 -> 0.374154 seconds\n",
            "Average in C++: 0.384601 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 24 - Adding C++ average execution time to the data frame**"
      ],
      "metadata": {
        "id": "GSEE2zDjaPmg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "matcpp = open('c_plus_average.txt', 'r')\n",
        "avrcpp = float(matcpp.readline())\n",
        "add_execution_time(\"C++\", avrcpp)"
      ],
      "metadata": {
        "id": "YL_YL8Qm7B0w",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "06b3255e-6207-4215-c598-07f4dea538ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Language    Average\n",
              "0                    Python  10.021088\n",
              "1                     Numpy   0.005872\n",
              "2             Fortran magic   0.207713\n",
              "3  Fortran 10 times average   0.221850\n",
              "4                         C   0.378530\n",
              "5                       C++   0.384601"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-15a59d73-b028-4fcd-9d56-a933b5d7e649\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Language</th>\n",
              "      <th>Average</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Python</td>\n",
              "      <td>10.021088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Numpy</td>\n",
              "      <td>0.005872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fortran magic</td>\n",
              "      <td>0.207713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fortran 10 times average</td>\n",
              "      <td>0.221850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C</td>\n",
              "      <td>0.378530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>C++</td>\n",
              "      <td>0.384601</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-15a59d73-b028-4fcd-9d56-a933b5d7e649')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-15a59d73-b028-4fcd-9d56-a933b5d7e649 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-15a59d73-b028-4fcd-9d56-a933b5d7e649');\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": 827
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 25 - Creating a Java program that mutiplies 10 times A(600x100) and  B (100x600) matrices**"
      ],
      "metadata": {
        "id": "OjOecKv-d-ML"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile MyMatMul.java\n",
        "\n",
        "    import java.lang.Math;\n",
        "    import java.io.FileWriter;\n",
        "    import java.io.IOException;\n",
        "\n",
        "    public class MyMatMul {\n",
        "\n",
        "        public static double[][] newMatrix(int rows, int columns){\n",
        "            double[][] myMatrix = new double[rows][columns];\n",
        "            for (int i = 0; i < rows; i++) {\n",
        "                for (int j = 0; j < columns; j++) {\n",
        "                    myMatrix[i][j] = Math.random();\n",
        "                }\n",
        "            }\n",
        "            return myMatrix;\n",
        "        }\n",
        "\n",
        "        public static double[][] matMul(double[][] matrixA, double[][] matrixB, int rows, int columns){\n",
        "            double[][] myMatrix = new double[rows][rows];\n",
        "            for (int i = 0; i < rows; i++) {\n",
        "                for (int j = 0; j < rows; j++) {\n",
        "                    for (int k = 0; k < columns; k++) {\n",
        "                        myMatrix[i][j] += matrixA[i][k] * matrixB[k][j];\n",
        "                    }\n",
        "                }\n",
        "            }\n",
        "            return myMatrix;\n",
        "        }\n",
        "\n",
        "        public static void main(String[] args) {\n",
        "            double total_time = 0;\n",
        "            for (int i = 0; i < 10; i++) {\n",
        "                double[][] myA = newMatrix(100, 600);\n",
        "                double[][] myB = newMatrix(600, 100);\n",
        "                double start = System.currentTimeMillis();\n",
        "                matMul(myA, myB, 100, 600);\n",
        "                double end = System.currentTimeMillis();\n",
        "                System.out.println(\"Case \" + i + \" : \" + (end - start)/1000 + \"seconds\");\n",
        "                total_time += end - start;\n",
        "            }\n",
        "            total_time /= 10000;\n",
        "            System.out.println(\"Average time: \" + total_time + \" seconds\");\n",
        "            try {\n",
        "                FileWriter newFile = new FileWriter(\"javaExecution.txt\");\n",
        "                newFile.write(Double.toString(total_time));\n",
        "                newFile.close();\n",
        "            } catch (IOException e) {\n",
        "                e.printStackTrace();\n",
        "            }\n",
        "        }\n",
        "\n",
        "    }"
      ],
      "metadata": {
        "id": "o8UPxOqs7C34",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8d75b340-4267-4c30-bd79-71ea170baefd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting MyMatMul.java\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 26 - Running the Java program that mutiplies 10 times A(600x100) and  B (100x600) matrices**"
      ],
      "metadata": {
        "id": "J75aKNvZeFHx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!javac MyMatMul.java"
      ],
      "metadata": {
        "id": "NP_QcZqA7EPn"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "!java MyMatMul"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JmVwvi97lEIM",
        "outputId": "7a53084a-6a31-461b-d53a-c6bf01a4e074"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Case 0 : 0.088seconds\n",
            "Case 1 : 0.076seconds\n",
            "Case 2 : 0.022seconds\n",
            "Case 3 : 0.03seconds\n",
            "Case 4 : 0.05seconds\n",
            "Case 5 : 0.046seconds\n",
            "Case 6 : 0.022seconds\n",
            "Case 7 : 0.019seconds\n",
            "Case 8 : 0.017seconds\n",
            "Case 9 : 0.016seconds\n",
            "Average time: 0.0386 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 27 - Adding Java average execution time to the data frame**"
      ],
      "metadata": {
        "id": "C75pEHRieNfo"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "java_execution_time = open(\"javaExecution.txt\", \"r\")\n",
        "java_time = float(java_execution_time.readline())\n",
        "add_execution_time(\"Java\", java_time)"
      ],
      "metadata": {
        "id": "5ZDLYWrY7Fhi",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "outputId": "a9bbf278-cd22-4318-f152-7a5bee877bd9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Language    Average\n",
              "0                    Python  10.021088\n",
              "1                     Numpy   0.005872\n",
              "2             Fortran magic   0.207713\n",
              "3  Fortran 10 times average   0.221850\n",
              "4                         C   0.378530\n",
              "5                       C++   0.384601\n",
              "6                      Java   0.038600"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a0309f87-1e06-46d0-b412-f20fbe785475\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Language</th>\n",
              "      <th>Average</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Python</td>\n",
              "      <td>10.021088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Numpy</td>\n",
              "      <td>0.005872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fortran magic</td>\n",
              "      <td>0.207713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fortran 10 times average</td>\n",
              "      <td>0.221850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C</td>\n",
              "      <td>0.378530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>C++</td>\n",
              "      <td>0.384601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Java</td>\n",
              "      <td>0.038600</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a0309f87-1e06-46d0-b412-f20fbe785475')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-a0309f87-1e06-46d0-b412-f20fbe785475 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-a0309f87-1e06-46d0-b412-f20fbe785475');\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": 831
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 28 - Creating a Javascript program that mutiplies 10 times A(600x100) and  B (100x600) matrices**"
      ],
      "metadata": {
        "id": "nHtevrHEeUiI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "%%writefile MyMatMul.js\n",
        "\n",
        "    const fs = require(\"fs\");\n",
        "\n",
        "    const myMatMul = (matrixA, matrixB, rows, columns) => {\n",
        "        const myMatrix = new Array(100).fill(new Array(100).fill(0));\n",
        "        for (let i = 0; i < rows; i++) {\n",
        "            for (let j = 0; j < rows; j++) {\n",
        "                for (let k = 0; k < columns; k++) {\n",
        "                    myMatrix[i][j] += matrixA[i][k] * matrixB[k][j];\n",
        "                }\n",
        "            }\n",
        "        }\n",
        "        return myMatrix;\n",
        "    }\n",
        "\n",
        "    let total_time = 0;\n",
        "    for (let i = 0; i < 10; i++) {\n",
        "        const matrixA = new Array(100).fill(new Array(600).fill(Math.random()));\n",
        "        const matrixB = new Array(600).fill(new Array(100).fill(Math.random()));\n",
        "        const start = Date.now();\n",
        "        myMatMul(matrixA, matrixB, 100, 600);\n",
        "        const end = Date.now();\n",
        "        console.log(`Case ${i}: ${(end - start)/1000} seconds`);\n",
        "        total_time += end - start;\n",
        "    }\n",
        "    total_time /= 10000;\n",
        "    console.log(`Average time: ${total_time} seconds`);\n",
        "    fs.writeFile(\"JavaScriptExecution.txt\", total_time.toString(), err => {\n",
        "        if (err) {\n",
        "            console.error(err);\n",
        "        }\n",
        "    });"
      ],
      "metadata": {
        "id": "6t1-WhmW7GhX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b3c84a82-54a1-4517-f546-a2738bdfc910"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Overwriting MyMatMul.js\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 29 - Running the Javascript program that mutiplies 10 times A(600x100) and  B (100x600) matrices**"
      ],
      "metadata": {
        "id": "87XpOlCteUZJ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!node MyMatMul.js"
      ],
      "metadata": {
        "id": "QEm52oVe7HyF",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "677bb106-544b-4712-ff8c-bd306777197c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Case 0: 0.135 seconds\n",
            "Case 1: 0.176 seconds\n",
            "Case 2: 0.127 seconds\n",
            "Case 3: 0.071 seconds\n",
            "Case 4: 0.082 seconds\n",
            "Case 5: 0.077 seconds\n",
            "Case 6: 0.14 seconds\n",
            "Case 7: 0.067 seconds\n",
            "Case 8: 0.043 seconds\n",
            "Case 9: 0.036 seconds\n",
            "Average time: 0.0954 seconds\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 30 - Adding Javascript average execution time to the data frame**"
      ],
      "metadata": {
        "id": "LvxaACodc9-K"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "javascript_execution_time = open(\"JavaScriptExecution.txt\", \"r\")\n",
        "javascript_time = float(javascript_execution_time.readline())\n",
        "add_execution_time(\"JavaScript\", javascript_time)"
      ],
      "metadata": {
        "id": "BP_cdxR17J50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "291c3c5f-d07d-4dc3-e382-ff1ed5731d84"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Language    Average\n",
              "0                    Python  10.021088\n",
              "1                     Numpy   0.005872\n",
              "2             Fortran magic   0.207713\n",
              "3  Fortran 10 times average   0.221850\n",
              "4                         C   0.378530\n",
              "5                       C++   0.384601\n",
              "6                      Java   0.038600\n",
              "7                JavaScript   0.095400"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3b9c6832-e7a1-42d9-a099-99936de97806\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Language</th>\n",
              "      <th>Average</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Python</td>\n",
              "      <td>10.021088</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Numpy</td>\n",
              "      <td>0.005872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fortran magic</td>\n",
              "      <td>0.207713</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fortran 10 times average</td>\n",
              "      <td>0.221850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C</td>\n",
              "      <td>0.378530</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>C++</td>\n",
              "      <td>0.384601</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Java</td>\n",
              "      <td>0.038600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>JavaScript</td>\n",
              "      <td>0.095400</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3b9c6832-e7a1-42d9-a099-99936de97806')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-3b9c6832-e7a1-42d9-a099-99936de97806 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-3b9c6832-e7a1-42d9-a099-99936de97806');\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": 834
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 31 - Finding the minimun average esecuiton time in the data frame**"
      ],
      "metadata": {
        "id": "XPP0Ir1dezN6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "min_execution_time = my_execution_time ['Average'].min()\n",
        "min_execution_time"
      ],
      "metadata": {
        "id": "y0rA3N9v7KyM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "acd6f49e-6f99-4da0-e13d-67df21d76123"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.005872088499927486"
            ]
          },
          "metadata": {},
          "execution_count": 835
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 32 - Adding the Speed factor columne to the data frame**"
      ],
      "metadata": {
        "id": "_IjH2K6ldJAQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "speed = []\n",
        "for i in range (len(my_execution_time)):\n",
        "  speed.append(my_execution_time['Average'].iloc[i]/min_execution_time)\n",
        "my_execution_time['Speed']= speed"
      ],
      "metadata": {
        "id": "w3pxN_bbcg8l"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**My own example 33 - Sorting the the data frame by average execution time**"
      ],
      "metadata": {
        "id": "CjiN21fvdRch"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "my_execution_time.sort_values(by=['Average'],inplace = True)\n",
        "my_execution_time"
      ],
      "metadata": {
        "id": "d8eiDHB0GpBF",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "outputId": "cdd45c2d-3207-4dfb-955c-44241d0cc43e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                   Language    Average        Speed\n",
              "1                     Numpy   0.005872     1.000000\n",
              "6                      Java   0.038600     6.573470\n",
              "7                JavaScript   0.095400    16.246349\n",
              "2             Fortran magic   0.207713    35.372900\n",
              "3  Fortran 10 times average   0.221850    37.780462\n",
              "4                         C   0.378530    64.462584\n",
              "5                       C++   0.384601    65.496458\n",
              "0                    Python  10.021088  1706.562896"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-383946f6-a865-4c91-bf69-de3c7fac9f2e\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Language</th>\n",
              "      <th>Average</th>\n",
              "      <th>Speed</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Numpy</td>\n",
              "      <td>0.005872</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>Java</td>\n",
              "      <td>0.038600</td>\n",
              "      <td>6.573470</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>JavaScript</td>\n",
              "      <td>0.095400</td>\n",
              "      <td>16.246349</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Fortran magic</td>\n",
              "      <td>0.207713</td>\n",
              "      <td>35.372900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Fortran 10 times average</td>\n",
              "      <td>0.221850</td>\n",
              "      <td>37.780462</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>C</td>\n",
              "      <td>0.378530</td>\n",
              "      <td>64.462584</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>C++</td>\n",
              "      <td>0.384601</td>\n",
              "      <td>65.496458</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Python</td>\n",
              "      <td>10.021088</td>\n",
              "      <td>1706.562896</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-383946f6-a865-4c91-bf69-de3c7fac9f2e')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-383946f6-a865-4c91-bf69-de3c7fac9f2e 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-383946f6-a865-4c91-bf69-de3c7fac9f2e');\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": 837
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "5RlZ-kCGiXcO"
      },
      "source": [
        "## Why is the Python Version so Much Slower?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rWOUuY4QiXcP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bc55ee9-cc72-4909-8b0f-23a16a6eea19"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[1, (4+0j), 9.0, 'fourfourfourfour']"
            ]
          },
          "metadata": {},
          "execution_count": 838
        }
      ],
      "source": [
        "# Dynamic typing.\n",
        "def mul_elemwise(xs, ys):\n",
        "    return [x * y for x, y in zip(xs, ys)]\n",
        "\n",
        "mul_elemwise([1, 2, 3, 4], [1, 2 + 0j, 3.0, 'four'])\n",
        "#[type(x) for x in _]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "cW0Jexk6iXcP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9201f9fd-adce-4a0d-e6c5-43bcbbb993bd"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  1           0 LOAD_NAME                0 (a)\n",
            "              2 LOAD_NAME                1 (b)\n",
            "              4 LOAD_NAME                2 (c)\n",
            "              6 BINARY_MULTIPLY\n",
            "              8 BINARY_ADD\n",
            "             10 RETURN_VALUE\n"
          ]
        }
      ],
      "source": [
        "# Interpretation overhead.\n",
        "source_code = 'a + b * c'\n",
        "bytecode = compile(source_code, '', 'eval')\n",
        "import dis; dis.dis(bytecode)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BdvCAketiXcP"
      },
      "source": [
        "## Why is the Python Version so Slow?\n",
        "- Dynamic typing means that every single operation requires dispatching on the input type.\n",
        "- Having an interpreter means that every instruction is fetched and dispatched at runtime.\n",
        "- Other overheads:\n",
        "  - Arbitrary-size integers.\n",
        "  - Reference-counted garbage collection."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zvFKYtobiXcP"
      },
      "source": [
        "> This is the paradox that we have to work with when we're doing scientific or numerically-intensive Python. What makes Python fast for development -- this high-level, interpreted, and dynamically-typed aspect of the language -- is exactly what makes it slow for code execution.\n",
        "\n",
        "- Jake VanderPlas, [*Losing Your Loops: Fast Numerical Computing with NumPy*](https://www.youtube.com/watch?v=EEUXKG97YRw)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UqE431pkiXcP"
      },
      "source": [
        "# What Do We Do?"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "4uh7h3HyiXcP"
      },
      "source": [
        "<center><img src=\"https://raw.githubusercontent.com/ssanderson/pydata-toolbox/master/notebooks/images/runaway.gif\" alt=\"Drawing\" style=\"width: 50%;\"/></center>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-9z62YT1iXcP"
      },
      "source": [
        "<center><img src=\"https://raw.githubusercontent.com/ssanderson/pydata-toolbox/master/notebooks/images/thisisfine.gif\" alt=\"Drawing\" style=\"width: 1080px;\"/></center>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Moc6UrkiXcQ"
      },
      "source": [
        "- Python is slow for numerical computation because it performs dynamic dispatch on every operation we perform..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "collapsed": true,
        "id": "U4gN8tp2iXcQ"
      },
      "source": [
        "- ...but often, we just want to do the same thing over and over in a loop!"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Yu0ySyEhiXcQ"
      },
      "source": [
        "- If we don't need Python's dynamicism, we don't want to pay (much) for it."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KUwG9isRiXcQ"
      },
      "source": [
        "- **Idea:** Dispatch **once per operation** instead of **once per element**."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "62itImahiXcQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8a8caddb-7013-48b8-ab8b-b9c663ebd519"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([1, 2, 3, 4])"
            ]
          },
          "metadata": {},
          "execution_count": 840
        }
      ],
      "source": [
        "import numpy as np\n",
        "\n",
        "data = np.array([1, 2, 3, 4])\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "q6qikr_tiXcQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "91df70dc-0081-4b1c-a3ad-5bf677b68586"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([2, 4, 6, 8])"
            ]
          },
          "metadata": {},
          "execution_count": 841
        }
      ],
      "source": [
        "data + data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "26QlWUw8iXcQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cf61caa0-9188-46bd-b662-ff7ca024f3fe"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 459 µs, sys: 11 µs, total: 470 µs\n",
            "Wall time: 478 µs\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "333328333350000.0"
            ]
          },
          "metadata": {},
          "execution_count": 842
        }
      ],
      "source": [
        "%%time\n",
        "# Naive dot product\n",
        "(array_data * array_data).sum()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "xoigDrpEiXcQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f8e7a459-bc27-470a-977a-0ca456d3985a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 1.31 ms, sys: 981 µs, total: 2.29 ms\n",
            "Wall time: 1.43 ms\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "333328333350000.0"
            ]
          },
          "metadata": {},
          "execution_count": 843
        }
      ],
      "source": [
        "%%time\n",
        "# Built-in dot product.\n",
        "array_data.dot(array_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tcewetQ0iXcR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e3fcbdbe-3f64-4082-a9a6-a75986a3804a"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "CPU times: user 281 µs, sys: 7 µs, total: 288 µs\n",
            "Wall time: 253 µs\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "333328333350000.0"
            ]
          },
          "metadata": {},
          "execution_count": 844
        }
      ],
      "source": [
        "%%time\n",
        "fortran_dot_product(array_data, array_data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Whkcgo1xiXcR"
      },
      "outputs": [],
      "source": [
        "# Numpy won't allow us to write a string into an int array.\n",
        "# ERROR(ValueError: invalid literal for int() with base 10: 'foo'): data[0] = \"foo\""
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "OM_YUwF0iXcR"
      },
      "outputs": [],
      "source": [
        "# We also can't grow an array once it's created.\n",
        "# ERROR(AttributeError: 'numpy.ndarray' object has no attribute 'append'): data.append(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "_ByTInO5iXcR",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "38e626db-10a6-49ab-a83e-ed3ecaa89a0c"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[1, 2],\n",
              "       [3, 4]])"
            ]
          },
          "metadata": {},
          "execution_count": 847
        }
      ],
      "source": [
        "# We **can** reshape an array though.\n",
        "two_by_two = data.reshape(2, 2)\n",
        "two_by_two"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rqUM9z90iXcR"
      },
      "source": [
        "Numpy arrays are:\n",
        "\n",
        "- Fixed-type"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xI5M1gzviXcR"
      },
      "source": [
        "- Size-immutable"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Xb7Dkl2tiXcS"
      },
      "source": [
        "- Multi-dimensional"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r0oMRqQXiXcS"
      },
      "source": [
        "- Fast\\*"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kz1MurDBiXcS"
      },
      "source": [
        "\\* If you use them correctly."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qivn_IZOiXcS"
      },
      "source": [
        "# What's in an Array?"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vAuc9ZoYiXcS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "aa541031-dc3f-4f0a-cb01-098cf992eb50"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Array:\n",
            "[[1 2 3]\n",
            " [4 5 6]]\n",
            "===========\n",
            "DType: int16\n",
            "Shape: (2, 3)\n",
            "Strides: (6, 2)\n",
            "Data: b'\\x01\\x00\\x02\\x00\\x03\\x00\\x04\\x00\\x05\\x00\\x06\\x00'\n"
          ]
        }
      ],
      "source": [
        "arr = np.array([1, 2, 3, 4, 5, 6], dtype='int16').reshape(2, 3)\n",
        "print(\"Array:\\n\", arr, sep='')\n",
        "print(\"===========\")\n",
        "print(\"DType:\", arr.dtype)\n",
        "print(\"Shape:\", arr.shape)\n",
        "print(\"Strides:\", arr.strides)\n",
        "print(\"Data:\", arr.data.tobytes())"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "K81LXWsCiXcS"
      },
      "source": [
        "# Core Operations\n",
        "\n",
        "- Vectorized **ufuncs** for elementwise operations.\n",
        "- Fancy indexing and masking for selection and filtering.\n",
        "- Aggregations across axes.\n",
        "- Broadcasting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "kwVm_T0UiXcS"
      },
      "source": [
        "# UFuncs\n",
        "\n",
        "UFuncs (universal functions) are functions that operate elementwise on one or more arrays."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "bltDIE9jiXcS",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a993d12d-ae01-4970-e16c-d381e240dd8f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3,  4],\n",
              "       [ 5,  6,  7,  8,  9],\n",
              "       [10, 11, 12, 13, 14]])"
            ]
          },
          "metadata": {},
          "execution_count": 849
        }
      ],
      "source": [
        "data = np.arange(15).reshape(3, 5)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "STwE0VCpiXcT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fefcdcab-15ae-4d13-ada0-16b5542d3cc7"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  0,   1,   4,   9,  16],\n",
              "       [ 25,  36,  49,  64,  81],\n",
              "       [100, 121, 144, 169, 196]])"
            ]
          },
          "metadata": {},
          "execution_count": 850
        }
      ],
      "source": [
        "# Binary operators.\n",
        "data * data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "N__jqMifiXcT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fcd4f85f-896b-445f-8ce6-0a84ff14d683"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.        , 1.        , 1.41421356, 1.73205081, 2.        ],\n",
              "       [2.23606798, 2.44948974, 2.64575131, 2.82842712, 3.        ],\n",
              "       [3.16227766, 3.31662479, 3.46410162, 3.60555128, 3.74165739]])"
            ]
          },
          "metadata": {},
          "execution_count": 851
        }
      ],
      "source": [
        "# Unary functions.\n",
        "np.sqrt(data)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j-2fPm3LiXcT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "13948822-5c1c-4c7c-822d-02994c1c95eb"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ True, False, False,  True, False],\n",
              "       [False,  True, False, False,  True],\n",
              "       [False, False,  True, False, False]])"
            ]
          },
          "metadata": {},
          "execution_count": 852
        }
      ],
      "source": [
        "# Comparison operations\n",
        "(data % 3) == 0"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iDUXVIuhiXcT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cec59d33-7d5a-4339-9292-52f92b828182"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ True, False, False, False, False],\n",
              "       [False,  True, False, False, False],\n",
              "       [False, False,  True, False, False]])"
            ]
          },
          "metadata": {},
          "execution_count": 853
        }
      ],
      "source": [
        "# Boolean combinators.\n",
        "((data % 2) == 0) & ((data % 3) == 0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f2FWSuiJiXcT",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "50724485-050b-4ae0-9056-85e841200da6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 30,  80, 130],\n",
              "       [ 80, 255, 430],\n",
              "       [130, 430, 730]])"
            ]
          },
          "metadata": {},
          "execution_count": 854
        }
      ],
      "source": [
        "# as of python 3.5, @ is matrix-multiply\n",
        "data @ data.T"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9EsiKbSCiXcT"
      },
      "source": [
        "# UFuncs Review\n",
        "\n",
        "- UFuncs provide efficient elementwise operations applied across one or more arrays.\n",
        "- Arithmetic Operators (`+`, `*`, `/`)\n",
        "- Comparisons (`==`, `>`, `!=`)\n",
        "- Boolean Operators (`&`, `|`, `^`)\n",
        "- Trigonometric Functions (`sin`, `cos`)\n",
        "- Transcendental Functions (`exp`, `log`)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GGLCgF20iXcU"
      },
      "source": [
        "# Selections"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3SAH9am8iXcU"
      },
      "source": [
        "We often want to perform an operation on just a subset of our data."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "AWmtLHYwiXcU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "101208e9-3f74-4e61-d926-c8d559768338"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.34185385, 0.64251645, 0.86575984, 0.98468459,\n",
              "       0.98496101, 0.8665558 , 0.64373604, 0.34335012, 0.00159265])"
            ]
          },
          "metadata": {},
          "execution_count": 855
        }
      ],
      "source": [
        "sines = np.sin(np.linspace(0, 3.14, 10))\n",
        "cosines = np.cos(np.linspace(0, 3.14, 10))\n",
        "sines"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "u8LHY0j9iXcU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "1c1b20a6-df64-4613-ddf9-e0cd2467e374"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.0"
            ]
          },
          "metadata": {},
          "execution_count": 856
        }
      ],
      "source": [
        "# Slicing works with the same semantics as Python lists.\n",
        "sines[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "hh6dTObciXcU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "34ae0b69-8839-4078-beea-5699b113f31f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.34185385, 0.64251645])"
            ]
          },
          "metadata": {},
          "execution_count": 857
        }
      ],
      "source": [
        "sines[:3]  # First three elements"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rqB_uVABiXcU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d542ae3e-1faa-47e2-9b9b-09a512acaf99"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.98496101, 0.8665558 , 0.64373604, 0.34335012, 0.00159265])"
            ]
          },
          "metadata": {},
          "execution_count": 858
        }
      ],
      "source": [
        "sines[5:]  # Elements from 5 on."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "j2KDBhZaiXcU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7336f5ae-3f3d-406c-ad1c-ffad92bb03a0"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.64251645, 0.98468459, 0.8665558 , 0.34335012])"
            ]
          },
          "metadata": {},
          "execution_count": 859
        }
      ],
      "source": [
        "sines[::2]  # Every other element."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "lR7JjjhjiXcV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8673471b-f5d0-4175-eb10-4f7cf97bf427"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "sines:\n",
            " [0.         0.34185385 0.64251645 0.86575984 0.98468459 0.98496101\n",
            " 0.8665558  0.64373604 0.34335012 0.00159265]\n",
            "sines > 0.5:\n",
            " [False False  True  True  True  True  True  True False False]\n",
            "sines[sines > 0.5]:\n",
            " [0.64251645 0.86575984 0.98468459 0.98496101 0.8665558  0.64373604]\n"
          ]
        }
      ],
      "source": [
        "# More interesting: we can index with boolean arrays to filter by a predicate.\n",
        "print(\"sines:\\n\", sines)\n",
        "print(\"sines > 0.5:\\n\", sines > 0.5)\n",
        "print(\"sines[sines > 0.5]:\\n\", sines[sines > 0.5])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "gORuDL3RiXcV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "fa0222ad-8e68-4bd2-a5ad-52f9d496f675"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0.         0.34185385 0.64251645 0.86575984 0.98468459 0.98496101\n",
            " 0.8665558  0.64373604 0.34335012 0.00159265]\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.        , 0.98468459, 0.64373604])"
            ]
          },
          "metadata": {},
          "execution_count": 861
        }
      ],
      "source": [
        "# We index with lists/arrays of integers to select values at those indices.\n",
        "print(sines)\n",
        "sines[[0, 4, 7]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "Gu2vDHNgiXcV"
      },
      "outputs": [],
      "source": [
        "# Index arrays are often used for sorting one or more arrays.\n",
        "unsorted_data = np.array([1, 3, 2, 12, -1, 5, 2])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uroIWxu1iXcV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "77c71eee-73e0-4344-f0ce-198ccb25c5d1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([4, 0, 2, 6, 1, 5, 3])"
            ]
          },
          "metadata": {},
          "execution_count": 863
        }
      ],
      "source": [
        "sort_indices = np.argsort(unsorted_data)\n",
        "sort_indices"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "igwLM_b1iXcV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "79bc4d3e-95d4-4600-c87d-230931f2554a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-1,  1,  2,  2,  3,  5, 12])"
            ]
          },
          "metadata": {},
          "execution_count": 864
        }
      ],
      "source": [
        "unsorted_data[sort_indices]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "7IRVEVvdiXcV"
      },
      "outputs": [],
      "source": [
        "market_caps = np.array([12, 6, 10, 5, 6])  # Presumably in dollars?\n",
        "assets = np.array(['A', 'B', 'C', 'D', 'E'])"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jE8KVrn9iXcV",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "9fa941b3-0ffc-45d6-9955-86338bc99104"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['D', 'B', 'E', 'C', 'A'], dtype='<U1')"
            ]
          },
          "metadata": {},
          "execution_count": 866
        }
      ],
      "source": [
        "# Sort assets by market cap by using the permutation that would sort market caps on ``assets``.\n",
        "sort_by_mcap = np.argsort(market_caps)\n",
        "assets[sort_by_mcap]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "scrolled": true,
        "id": "jaGW-7fTiXcW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2cefab60-e5b7-4fb2-92f8-058661f472cf"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dates:\n",
            " array(['2017-01-06', '2017-01-07', '2017-01-08'], dtype='datetime64[D]')\n",
            "Values:\n",
            " array([10, 15, 20])\n",
            "Calendar:\n",
            " array(['2017-01-03', '2017-01-04', '2017-01-05', '2017-01-06',\n",
            "       '2017-01-09', '2017-01-10', '2017-01-11', '2017-01-12',\n",
            "       '2017-01-13', '2017-01-17', '2017-01-18', '2017-01-19',\n",
            "       '2017-01-20', '2017-01-23', '2017-01-24', '2017-01-25',\n",
            "       '2017-01-26', '2017-01-27', '2017-01-30', '2017-01-31',\n",
            "       '2017-02-01'], dtype='datetime64[D]')\n"
          ]
        }
      ],
      "source": [
        "# Indexers are also useful for aligning data.\n",
        "print(\"Dates:\\n\", repr(event_dates))\n",
        "print(\"Values:\\n\", repr(event_values))\n",
        "print(\"Calendar:\\n\", repr(calendar))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Uo28AVcTiXcW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bfe76973-a576-44ce-e2da-f114341b58d5"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Raw Dates: ['2017-01-06' '2017-01-07' '2017-01-08']\n",
            "Indices: [3 4 4]\n",
            "Forward-Filled Dates: ['2017-01-06' '2017-01-09' '2017-01-09']\n"
          ]
        }
      ],
      "source": [
        "print(\"Raw Dates:\", event_dates)\n",
        "print(\"Indices:\", calendar.searchsorted(event_dates))\n",
        "print(\"Forward-Filled Dates:\", calendar[calendar.searchsorted(event_dates)])"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "oDEVEEOriXcW"
      },
      "source": [
        "On multi-dimensional arrays, we can slice along each axis independently."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "prPSgZhniXcW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c88fba5d-49bd-413b-c94b-03383135ed52"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3,  4],\n",
              "       [ 5,  6,  7,  8,  9],\n",
              "       [10, 11, 12, 13, 14],\n",
              "       [15, 16, 17, 18, 19],\n",
              "       [20, 21, 22, 23, 24]])"
            ]
          },
          "metadata": {},
          "execution_count": 869
        }
      ],
      "source": [
        "data = np.arange(25).reshape(5, 5)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "KAulAQ2yiXcW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0c302cc6-e7a7-4d6b-ab5d-a9b94e9da192"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 1],\n",
              "       [5, 6]])"
            ]
          },
          "metadata": {},
          "execution_count": 870
        }
      ],
      "source": [
        "data[:2, :2]  # First two rows and first two columns."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "f9_S0RcdiXcW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5bcb1305-3d6d-4375-ef2d-e891e770694a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0, 4],\n",
              "       [5, 9]])"
            ]
          },
          "metadata": {},
          "execution_count": 871
        }
      ],
      "source": [
        "data[:2, [0, -1]]  # First two rows, first and last columns."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rre5hGs9iXcW",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7919c4fa-01fc-4c5f-98ff-c8169fdfbd86"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3,  4],\n",
              "       [10, 11, 12, 13, 14],\n",
              "       [20, 21, 22, 23, 24]])"
            ]
          },
          "metadata": {},
          "execution_count": 872
        }
      ],
      "source": [
        "data[(data[:, 0] % 2) == 0]  # Rows where the first column is divisible by two."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zl1zYEQgiXcX"
      },
      "source": [
        "# Selections Review\n",
        "\n",
        "- Indexing with an integer removes a dimension.\n",
        "- Slicing operations work on Numpy arrays the same way they do on lists.\n",
        "- Indexing with a boolean array filters to True locations.\n",
        "- Indexing with an integer array selects indices along an axis.\n",
        "- Multidimensional arrays can apply selections independently along different axes."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0tDspA_KiXcX"
      },
      "source": [
        "## Reductions\n",
        "\n",
        "Functions that reduce an array to a scalar."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "1s42E1qCiXcX"
      },
      "source": [
        "$Var(X) = \\frac{1}{N}\\sqrt{\\sum_{i=1}^N (x_i - \\bar{x})^2}$"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "pkjccK94iXcX"
      },
      "outputs": [],
      "source": [
        "def variance(x):\n",
        "    return ((x - x.mean()) ** 2).sum() / len(x)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "jqiZV_FMiXcX",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "c1e79339-bf51-4238-a64d-3a1ea97cdcb5"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1.048126887101788"
            ]
          },
          "metadata": {},
          "execution_count": 874
        }
      ],
      "source": [
        "variance(np.random.standard_normal(1000))"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WHVBON1qiXcX"
      },
      "source": [
        "- `sum()` and `mean()` are both **reductions**."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w7gh4_vmiXcX"
      },
      "source": [
        "- In the simplest case, we use these to reduce an entire array into a single value..."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Hxr-Py7ciXcY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7be1730e-8f77-4a96-a774-16e1f413a9cc"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.5"
            ]
          },
          "metadata": {},
          "execution_count": 875
        }
      ],
      "source": [
        "data = np.arange(30)\n",
        "data.mean()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M1mqf-RfiXcY"
      },
      "source": [
        "- ...but we can do more interesting things with multi-dimensional arrays."
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "okW92XNriXcY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ad82ca7b-4110-431c-d3bf-17a2c98a797a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9],\n",
              "       [10, 11, 12, 13, 14, 15, 16, 17, 18, 19],\n",
              "       [20, 21, 22, 23, 24, 25, 26, 27, 28, 29]])"
            ]
          },
          "metadata": {},
          "execution_count": 876
        }
      ],
      "source": [
        "data = np.arange(30).reshape(3, 10)\n",
        "data"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "_DwCYNHQiXcY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a1a62993-685f-48d9-cb46-71063cff5067"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "14.5"
            ]
          },
          "metadata": {},
          "execution_count": 877
        }
      ],
      "source": [
        "data.mean()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z7S6mpm3iXcY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f999c57-9a97-41e1-c53e-f86117aff769"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([10., 11., 12., 13., 14., 15., 16., 17., 18., 19.])"
            ]
          },
          "metadata": {},
          "execution_count": 878
        }
      ],
      "source": [
        "data.mean(axis=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "g-DOKtM4iXcY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00dea3b7-6b35-443e-c64c-ad378f9b6d34"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([ 4.5, 14.5, 24.5])"
            ]
          },
          "metadata": {},
          "execution_count": 879
        }
      ],
      "source": [
        "data.mean(axis=1)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zsIEVqkgiXcY"
      },
      "source": [
        "## Reductions Review\n",
        "\n",
        "- Reductions allow us to perform efficient aggregations over arrays.\n",
        "- We can do aggregations over a single axis to collapse a single dimension.\n",
        "- Many built-in reductions (`mean`, `sum`, `min`, `max`, `median`, ...)."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hvqRiy2qiXcZ"
      },
      "source": [
        "# Broadcasting"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "OyIDakdiiXcZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "98d65a3b-a7cd-4dbe-9c40-1ff80146c610"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Row:\n",
            "[1 2 3 4]\n",
            "Column:\n",
            "[[1]\n",
            " [2]\n",
            " [3]]\n"
          ]
        }
      ],
      "source": [
        "row = np.array([1, 2, 3, 4])\n",
        "column = np.array([[1], [2], [3]])\n",
        "print(\"Row:\\n\", row, sep='')\n",
        "print(\"Column:\\n\", column, sep='')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Dej4wsX-iXcZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8336f63c-c817-4ad8-aa88-fda202534755"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[2, 3, 4, 5],\n",
              "       [3, 4, 5, 6],\n",
              "       [4, 5, 6, 7]])"
            ]
          },
          "metadata": {},
          "execution_count": 881
        }
      ],
      "source": [
        "row + column"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tMdSsqXfiXcZ"
      },
      "source": [
        "<center><img src=\"https://raw.githubusercontent.com/ssanderson/pydata-toolbox/master/notebooks/images/broadcasting.png\" alt=\"Drawing\" style=\"width: 60%;\"/></center>\n",
        "\n",
        "<h5>Source: http://www.scipy-lectures.org/_images/numpy_broadcasting.png</h5>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JVLNvazIiXcZ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a0ba142-4282-4910-fcdd-a459935b744f"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Data:\n",
            "[[ 0  1  2  3  4  5  6  7  8  9]\n",
            " [10 11 12 13 14 15 16 17 18 19]\n",
            " [20 21 22 23 24 25 26 27 28 29]]\n",
            "Mean:\n",
            "[10. 11. 12. 13. 14. 15. 16. 17. 18. 19.]\n",
            "Data - Mean:\n",
            "[[-10. -10. -10. -10. -10. -10. -10. -10. -10. -10.]\n",
            " [  0.   0.   0.   0.   0.   0.   0.   0.   0.   0.]\n",
            " [ 10.  10.  10.  10.  10.  10.  10.  10.  10.  10.]]\n"
          ]
        }
      ],
      "source": [
        "# Broadcasting is particularly useful in conjunction with reductions.\n",
        "print(\"Data:\\n\", data, sep='')\n",
        "print(\"Mean:\\n\", data.mean(axis=0), sep='')\n",
        "print(\"Data - Mean:\\n\", data - data.mean(axis=0), sep='')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JHmGF_mGiXcZ"
      },
      "source": [
        "# Broadcasting Review\n",
        "\n",
        "- Numpy operations can work on arrays of different dimensions as long as the arrays' shapes are still \"compatible\".\n",
        "- Broadcasting works by \"tiling\" the smaller array along the missing dimension.\n",
        "- The result of a broadcasted operation is always at least as large in each dimension as the largest array in that dimension."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Tmy_pKVniXca"
      },
      "source": [
        "# Numpy Review"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TiKskIDqiXca"
      },
      "source": [
        "- Numerical algorithms are slow in pure Python because the overhead dynamic dispatch dominates our runtime."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WRex_YahiXca"
      },
      "source": [
        "- Numpy solves this problem by:\n",
        "  1. Imposing additional restrictions on the contents of arrays.\n",
        "  2. Moving the inner loops of our algorithms into compiled C code."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HHcs1cowiXca"
      },
      "source": [
        "- Using Numpy effectively often requires reworking an algorithms to use vectorized operations instead of for-loops, but the resulting operations are usually simpler, clearer, and faster than the pure Python equivalent."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GoPdOSfSiXca"
      },
      "source": [
        "<center><img src=\"https://raw.githubusercontent.com/ssanderson/pydata-toolbox/master/notebooks/images/unicorn.jpg\" alt=\"Drawing\" style=\"width: 75%;\"/></center>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GqyHpT0DiXca"
      },
      "source": [
        "Numpy is great for many things, but..."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t2pSdZyeiXca"
      },
      "source": [
        "- Sometimes our data is equipped with a natural set of **labels**:\n",
        "  - Dates/Times\n",
        "  - Stock Tickers\n",
        "  - Field Names (e.g. Open/High/Low/Close)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "eQkbld3YiXca"
      },
      "source": [
        "- Sometimes we have **more than one type of data** that we want to keep grouped together.\n",
        "  - Tables with a mix of real-valued and categorical data."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "FUUCEShmiXcb"
      },
      "source": [
        "- Sometimes we have **missing** data, which we need to ignore, fill, or otherwise work around."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7BErgdXjiXcb"
      },
      "source": [
        "<center><img src=\"https://raw.githubusercontent.com/ssanderson/pydata-toolbox/master/notebooks/images/panda-wrangling.gif\" alt=\"Drawing\" style=\"width: 75%;\"/></center>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B-LuFQ2BiXcb"
      },
      "source": [
        "<center><img src=\"https://raw.githubusercontent.com/ssanderson/pydata-toolbox/master/notebooks/images/pandas_logo.png\" alt=\"Drawing\" style=\"width: 75%;\"/></center>\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "B8Hv2pY4iXcb"
      },
      "source": [
        "Pandas extends Numpy with more complex data structures:\n",
        "\n",
        "- `Series`: 1-dimensional, homogenously-typed, labelled array.\n",
        "- `DataFrame`: 2-dimensional, semi-homogenous, labelled table."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xYDFME14iXcb"
      },
      "source": [
        "Pandas also provides many utilities for:\n",
        "- Input/Output\n",
        "- Data Cleaning\n",
        "- Rolling Algorithms\n",
        "- Plotting"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "uOvK2ZfeiXcb"
      },
      "source": [
        "# Selection in Pandas"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "G6ploxoOiXcb",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "7ecd3687-b775-41a5-b1e7-a364f70e55ea"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    1\n",
              "b    2\n",
              "c    3\n",
              "d    4\n",
              "e    5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 883
        }
      ],
      "source": [
        "s = pd.Series(index=['a', 'b', 'c', 'd', 'e'], data=[1, 2, 3, 4, 5])\n",
        "s"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "PU-7iKpqiXcc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b45f1c32-7f3b-463e-fcff-ee7cd23b13a2"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The index is: Index(['a', 'b', 'c', 'd', 'e'], dtype='object')\n",
            "The values are: [1 2 3 4 5]\n"
          ]
        }
      ],
      "source": [
        "# There are two pieces to a Series: the index and the values.\n",
        "print(\"The index is:\", s.index)\n",
        "print(\"The values are:\", s.values)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pUnHyGr5iXcc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0ee47054-1ea1-40d2-e6f9-1aac1dad30ce"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 885
        }
      ],
      "source": [
        "# We can look up values out of a Series by position...\n",
        "s.iloc[0]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "IyM3qqygiXcc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "69a22874-ca3f-4a11-a82b-edf3abd33174"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1"
            ]
          },
          "metadata": {},
          "execution_count": 886
        }
      ],
      "source": [
        "# ... or by label.\n",
        "s.loc['a']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JrCItQtiiXcc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "68c9f45f-3913-4667-ba47-f9af3219bc7f"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    1\n",
              "b    2\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 887
        }
      ],
      "source": [
        "# Slicing works as expected...\n",
        "s.iloc[:2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tzTbbk9YiXcc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "f1adb599-b595-4bd6-ee2e-72ee0df5938a"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    1\n",
              "b    2\n",
              "c    3\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 888
        }
      ],
      "source": [
        "# ...but it works with labels too!\n",
        "s.loc[:'c']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "qrVQ0b0ziXcd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "bcfd4d16-d58e-466e-81fb-58e782c5aba1"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    1\n",
              "e    5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 889
        }
      ],
      "source": [
        "# Fancy indexing works the same as in numpy.\n",
        "s.iloc[[0, -1]]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "THswhsDLiXcd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "3f256d1c-67c4-427f-ec58-92e5fd94db29"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "c    3\n",
              "d    4\n",
              "e    5\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 890
        }
      ],
      "source": [
        "# As does boolean masking.\n",
        "s.loc[s > 2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nYGjpHaIiXcd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "10853b54-19c5-41a9-ccdb-f7dfd1abe795"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    10.0\n",
              "c    20.0\n",
              "d    30.0\n",
              "z    40.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 891
        }
      ],
      "source": [
        "# Element-wise operations are aligned by index.\n",
        "other_s = pd.Series({'a': 10.0, 'c': 20.0, 'd': 30.0, 'z': 40.0})\n",
        "other_s"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "nkK07CuEiXcd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b9fb3cda-56eb-4226-a5ea-02477b3e328b"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    11.0\n",
              "b     NaN\n",
              "c    23.0\n",
              "d    34.0\n",
              "e     NaN\n",
              "z     NaN\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 892
        }
      ],
      "source": [
        "s + other_s"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "pePOoBl3iXcd",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "27395400-5a71-4f67-a106-ad172bf66dc9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "a    11.0\n",
              "b     0.0\n",
              "c    23.0\n",
              "d    34.0\n",
              "e     0.0\n",
              "z     0.0\n",
              "dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 893
        }
      ],
      "source": [
        "# We can fill in missing values with fillna().\n",
        "(s + other_s).fillna(0.0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8LYqk1oDiXcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 237
        },
        "outputId": "16afda6a-4532-4ae9-cb10-c86feb344278"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Adj Close      Close       High        Low       Open       Volume\n",
              "Date                                                                          \n",
              "2010-01-04  27.613066  30.572857  30.642857  30.340000  30.490000  123432400.0\n",
              "2010-01-05  27.660807  30.625713  30.798571  30.464285  30.657143  150476200.0\n",
              "2010-01-06  27.220825  30.138571  30.747143  30.107143  30.625713  138040000.0\n",
              "2010-01-07  27.170504  30.082857  30.285715  29.864286  30.250000  119282800.0\n",
              "2010-01-08  27.351143  30.282858  30.285715  29.865715  30.042856  111902700.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3a977704-0737-42f9-a8d9-c3e66aeb69d9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Close</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Open</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-04</th>\n",
              "      <td>27.613066</td>\n",
              "      <td>30.572857</td>\n",
              "      <td>30.642857</td>\n",
              "      <td>30.340000</td>\n",
              "      <td>30.490000</td>\n",
              "      <td>123432400.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-05</th>\n",
              "      <td>27.660807</td>\n",
              "      <td>30.625713</td>\n",
              "      <td>30.798571</td>\n",
              "      <td>30.464285</td>\n",
              "      <td>30.657143</td>\n",
              "      <td>150476200.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-06</th>\n",
              "      <td>27.220825</td>\n",
              "      <td>30.138571</td>\n",
              "      <td>30.747143</td>\n",
              "      <td>30.107143</td>\n",
              "      <td>30.625713</td>\n",
              "      <td>138040000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-07</th>\n",
              "      <td>27.170504</td>\n",
              "      <td>30.082857</td>\n",
              "      <td>30.285715</td>\n",
              "      <td>29.864286</td>\n",
              "      <td>30.250000</td>\n",
              "      <td>119282800.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-08</th>\n",
              "      <td>27.351143</td>\n",
              "      <td>30.282858</td>\n",
              "      <td>30.285715</td>\n",
              "      <td>29.865715</td>\n",
              "      <td>30.042856</td>\n",
              "      <td>111902700.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3a977704-0737-42f9-a8d9-c3e66aeb69d9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-3a977704-0737-42f9-a8d9-c3e66aeb69d9 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-3a977704-0737-42f9-a8d9-c3e66aeb69d9');\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": 894
        }
      ],
      "source": [
        "# Most real datasets are read in from an external file format.\n",
        "url = 'https://raw.githubusercontent.com/ssanderson/pydata-toolbox/master/notebooks/AAPL.csv'\n",
        "aapl = pd.read_csv(url, parse_dates=['Date'], index_col='Date')\n",
        "aapl.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "JasJjximiXcd",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "outputId": "30f61f7a-a80b-4731-c023-468c30d3feca"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Adj Close      Close\n",
              "Date                            \n",
              "2010-01-04  27.613066  30.572857\n",
              "2010-01-05  27.660807  30.625713"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-8ee739b6-874b-4c38-bbb4-52622065bf8d\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adj Close</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-01-04</th>\n",
              "      <td>27.613066</td>\n",
              "      <td>30.572857</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-01-05</th>\n",
              "      <td>27.660807</td>\n",
              "      <td>30.625713</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ee739b6-874b-4c38-bbb4-52622065bf8d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-8ee739b6-874b-4c38-bbb4-52622065bf8d 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-8ee739b6-874b-4c38-bbb4-52622065bf8d');\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": 895
        }
      ],
      "source": [
        "# Slicing generalizes to two dimensions as you'd expect:\n",
        "aapl.iloc[:2, :2]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "aAnXEGHMiXce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "7afad058-95e5-4454-8b53-c77aaa4f3041"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                Close       Volume\n",
              "Date                              \n",
              "2010-02-01  27.818571  187469100.0\n",
              "2010-02-02  27.980000  174585600.0\n",
              "2010-02-03  28.461428  153832000.0\n",
              "2010-02-04  27.435715  189413000.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-c986b9a8-f469-4e17-8617-530960a611f9\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Close</th>\n",
              "      <th>Volume</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Date</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2010-02-01</th>\n",
              "      <td>27.818571</td>\n",
              "      <td>187469100.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-02</th>\n",
              "      <td>27.980000</td>\n",
              "      <td>174585600.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-03</th>\n",
              "      <td>28.461428</td>\n",
              "      <td>153832000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2010-02-04</th>\n",
              "      <td>27.435715</td>\n",
              "      <td>189413000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c986b9a8-f469-4e17-8617-530960a611f9')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-c986b9a8-f469-4e17-8617-530960a611f9 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-c986b9a8-f469-4e17-8617-530960a611f9');\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": 896
        }
      ],
      "source": [
        "aapl.loc[pd.Timestamp('2010-02-01'):pd.Timestamp('2010-02-04'), ['Close', 'Volume']]"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Up935r8GiXce"
      },
      "source": [
        "# Rolling Operations"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "28dFZSZmiXce"
      },
      "source": [
        "<center><img src=\"https://raw.githubusercontent.com/ssanderson/pydata-toolbox/master/notebooks/images/rolling.gif\" alt=\"Drawing\" style=\"width: 75%;\"/></center>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-YvkXxL0iXce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "outputId": "58fb8ccb-7e33-4ee2-b093-18035e14f09c"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "aapl.rolling(5)[['Close', 'Adj Close']].mean().plot();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "HKynE6AMiXce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 442
        },
        "outputId": "60a219d7-df7e-4f55-9722-e53e1bca45c7"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Drop `Volume`, since it's way bigger than everything else.\n",
        "aapl.drop('Volume', axis=1).resample('2W').max().plot();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ayoPHapZiXce",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 416
        },
        "outputId": "a38f5d48-2428-441b-bbd6-8243ecee4990"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# 30-day rolling exponentially-weighted stddev of returns.\n",
        "aapl['Close'].pct_change().ewm(span=30).std().plot();"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "npT_ZgpYiXce"
      },
      "source": [
        "# \"Real World\" Data"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from io import BytesIO\n",
        "import os\n",
        "from urllib.parse import urlencode\n",
        "\n",
        "import requests\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "\n",
        "def read_avocadata(start_date, end_date, cache_loc='avocadata.html'):\n",
        "    \"\"\"Download avocado data to a dataframe.\n",
        "    Parameters\n",
        "    ----------\n",
        "    \"\"\"\n",
        "    start_date = pd.Timestamp(start_date)\n",
        "    end_date = pd.Timestamp(end_date)\n",
        "    base_url = 'https://www.marketnews.usda.gov/mnp/fv-report-retail'\n",
        "    query_params = {\n",
        "        'class': ['FRUITS'],\n",
        "        'commodity': ['AVOCADOS'],\n",
        "        'compareLy': ['No'],\n",
        "        'endDate': [end_date.strftime(\"%m/%d/%Y\")],\n",
        "        'format': ['excel'],\n",
        "        'organic': ['ALL'],\n",
        "        'portal': ['fv'],\n",
        "        'region': ['ALL'],\n",
        "        'repDate': [start_date.strftime(\"%m/%d/%Y\")],\n",
        "        'type': ['retail'],\n",
        "    }\n",
        "\n",
        "    url = base_url + '?' + urlencode(query_params, doseq=1)\n",
        "\n",
        "    if not os.path.exists(cache_loc):\n",
        "        resp = requests.get(url, stream=True)\n",
        "        resp.raise_for_status()\n",
        "\n",
        "        with open(cache_loc, 'wb') as f:\n",
        "            for block in resp.iter_content(chunk_size=4096):\n",
        "                f.write(block)\n",
        "        f.close()\n",
        "\n",
        "    with open(cache_loc, 'rb') as f:\n",
        "        frame = pd.read_html(f, header=0)[0]\n",
        "\n",
        "    # Cleanup\n",
        "    frame = frame[frame['Unit'] == 'each']\n",
        "    frame['Organic'] = (frame['Organic'] == 'Y')\n",
        "    frame['Variety'].replace(\n",
        "        {'VARIOUS GREENSKIN VARIETIES': 'GREENSKIN'},\n",
        "        inplace=True,\n",
        "    )\n",
        "    frame['Date'] = pd.to_datetime(frame['Date'].values, utc=True)\n",
        "\n",
        "    frame['Region'] = frame['Region'].str.replace(' U.S.', '')\n",
        "    frame['Region'] = frame['Region'].str.replace(' ', '_')\n",
        "\n",
        "    # Drop useless columns.\n",
        "    return frame.drop(\n",
        "        ['Class', 'Commodity', 'Environment', 'Unit', '% Marked Local'],\n",
        "        axis=1,\n",
        "    )"
      ],
      "metadata": {
        "id": "2rH2NXFDH-le"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "XcYC7BGAiXcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 241
        },
        "outputId": "6aed0776-8101-4a74-a59b-8859bf233d59"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-900-74fdf899ad9d>:54: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  frame['Region'] = frame['Region'].str.replace(' U.S.', '')\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                       Date     Region Variety  Organic  Number of Stores  \\\n",
              "0 2014-01-03 00:00:00+00:00   NATIONAL    HASS    False              9184   \n",
              "1 2014-01-03 00:00:00+00:00   NATIONAL    HASS     True               872   \n",
              "2 2014-01-03 00:00:00+00:00  NORTHEAST    HASS    False              1449   \n",
              "3 2014-01-03 00:00:00+00:00  NORTHEAST    HASS     True                66   \n",
              "4 2014-01-03 00:00:00+00:00  SOUTHEAST    HASS    False              2286   \n",
              "\n",
              "   Weighted Avg Price  Low Price  High Price  \n",
              "0                0.93        NaN         NaN  \n",
              "1                1.44        NaN         NaN  \n",
              "2                1.08        0.5        1.67  \n",
              "3                1.54        1.5        2.00  \n",
              "4                0.98        0.5        1.99  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-64ce2115-ec1e-4c33-830f-9bbf4bddf8b7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Region</th>\n",
              "      <th>Variety</th>\n",
              "      <th>Organic</th>\n",
              "      <th>Number of Stores</th>\n",
              "      <th>Weighted Avg Price</th>\n",
              "      <th>Low Price</th>\n",
              "      <th>High Price</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2014-01-03 00:00:00+00:00</td>\n",
              "      <td>NATIONAL</td>\n",
              "      <td>HASS</td>\n",
              "      <td>False</td>\n",
              "      <td>9184</td>\n",
              "      <td>0.93</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2014-01-03 00:00:00+00:00</td>\n",
              "      <td>NATIONAL</td>\n",
              "      <td>HASS</td>\n",
              "      <td>True</td>\n",
              "      <td>872</td>\n",
              "      <td>1.44</td>\n",
              "      <td>NaN</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-01-03 00:00:00+00:00</td>\n",
              "      <td>NORTHEAST</td>\n",
              "      <td>HASS</td>\n",
              "      <td>False</td>\n",
              "      <td>1449</td>\n",
              "      <td>1.08</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-01-03 00:00:00+00:00</td>\n",
              "      <td>NORTHEAST</td>\n",
              "      <td>HASS</td>\n",
              "      <td>True</td>\n",
              "      <td>66</td>\n",
              "      <td>1.54</td>\n",
              "      <td>1.5</td>\n",
              "      <td>2.00</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2014-01-03 00:00:00+00:00</td>\n",
              "      <td>SOUTHEAST</td>\n",
              "      <td>HASS</td>\n",
              "      <td>False</td>\n",
              "      <td>2286</td>\n",
              "      <td>0.98</td>\n",
              "      <td>0.5</td>\n",
              "      <td>1.99</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-64ce2115-ec1e-4c33-830f-9bbf4bddf8b7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-64ce2115-ec1e-4c33-830f-9bbf4bddf8b7 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-64ce2115-ec1e-4c33-830f-9bbf4bddf8b7');\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": 901
        }
      ],
      "source": [
        "avocados = read_avocadata('2014', '2016')\n",
        "avocados.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "tjk0EsEsiXcf",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ffebffe4-83c4-4639-cbc9-02e0fa40de46"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                  datetime64[ns, UTC]\n",
              "Region                             object\n",
              "Variety                            object\n",
              "Organic                              bool\n",
              "Number of Stores                    int64\n",
              "Weighted Avg Price                float64\n",
              "Low Price                         float64\n",
              "High Price                        float64\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 902
        }
      ],
      "source": [
        "# Unlike numpy arrays, pandas DataFrames can have a different dtype for each column.\n",
        "avocados.dtypes"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "YcpqjQCdiXcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "358d030d-f336-434b-9b2d-50f855396b4e"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# What's the regional average price of a HASS avocado every day?\n",
        "hass = avocados[avocados.Variety == 'HASS']\n",
        "hass.groupby(['Date', 'Region'])['Weighted Avg Price'].mean().unstack().ffill().plot();"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "collapsed": true,
        "id": "k1Zdnr4jiXcf"
      },
      "outputs": [],
      "source": [
        "def _organic_spread(group):\n",
        "\n",
        "    if len(group.columns) != 2:\n",
        "        return pd.Series(index=group.index, data=0.0)\n",
        "\n",
        "    is_organic = group.columns.get_level_values('Organic').values.astype(bool)\n",
        "    organics = group.loc[:, is_organic].squeeze()\n",
        "    non_organics = group.loc[:, ~is_organic].squeeze()\n",
        "    diff = organics - non_organics\n",
        "    return diff\n",
        "\n",
        "def organic_spread_by_region(df):\n",
        "    \"\"\"What's the difference between the price of an organic\n",
        "    and non-organic avocado within each region?\n",
        "    \"\"\"\n",
        "    return (\n",
        "        df\n",
        "        .set_index(['Date', 'Region', 'Organic'])\n",
        "         ['Weighted Avg Price']\n",
        "        .unstack(level=['Region', 'Organic'])\n",
        "        .ffill()\n",
        "        .groupby(level='Region', axis=1)\n",
        "        .apply(_organic_spread)\n",
        "    )"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iU-Th92BiXcf",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 440
        },
        "outputId": "4fcbcc0e-ba69-4cfb-d2c5-755b245a8649"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x504 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "organic_spread_by_region(hass).plot();\n",
        "plt.gca().set_title(\"Daily Regional Organic Spread\");\n",
        "plt.legend(bbox_to_anchor=(1, 1));"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vjR02HpEiXcg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "c35efe03-9978-4912-d4ee-4a38d31371b9"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Region           ALASKA    HAWAII   MIDWEST  NATIONAL  NORTHEAST  NORTHWEST  \\\n",
              "Region                                                                        \n",
              "ALASKA         1.000000  0.202723  0.175251  0.007844   0.051049   0.087575   \n",
              "HAWAII         0.202723  1.000000 -0.021116  0.373914   0.247171   0.341155   \n",
              "MIDWEST        0.175251 -0.021116  1.000000  0.062595  -0.010213  -0.043783   \n",
              "NATIONAL       0.007844  0.373914  0.062595  1.000000   0.502035   0.579102   \n",
              "NORTHEAST      0.051049  0.247171 -0.010213  0.502035   1.000000   0.242039   \n",
              "NORTHWEST      0.087575  0.341155 -0.043783  0.579102   0.242039   1.000000   \n",
              "SOUTHEAST      0.129079  0.019388  0.047437 -0.040539  -0.236225  -0.032306   \n",
              "SOUTHWEST     -0.070868  0.159192 -0.059128  0.635006   0.360389   0.165992   \n",
              "SOUTH_CENTRAL  0.161624  0.092632  0.068902  0.486524   0.149881   0.349935   \n",
              "\n",
              "Region         SOUTHEAST  SOUTHWEST  SOUTH_CENTRAL  \n",
              "Region                                              \n",
              "ALASKA          0.129079  -0.070868       0.161624  \n",
              "HAWAII          0.019388   0.159192       0.092632  \n",
              "MIDWEST         0.047437  -0.059128       0.068902  \n",
              "NATIONAL       -0.040539   0.635006       0.486524  \n",
              "NORTHEAST      -0.236225   0.360389       0.149881  \n",
              "NORTHWEST      -0.032306   0.165992       0.349935  \n",
              "SOUTHEAST       1.000000  -0.167631      -0.021176  \n",
              "SOUTHWEST      -0.167631   1.000000       0.254504  \n",
              "SOUTH_CENTRAL  -0.021176   0.254504       1.000000  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-6f276d9b-f004-490d-88a5-01d2a56c30b8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th>Region</th>\n",
              "      <th>ALASKA</th>\n",
              "      <th>HAWAII</th>\n",
              "      <th>MIDWEST</th>\n",
              "      <th>NATIONAL</th>\n",
              "      <th>NORTHEAST</th>\n",
              "      <th>NORTHWEST</th>\n",
              "      <th>SOUTHEAST</th>\n",
              "      <th>SOUTHWEST</th>\n",
              "      <th>SOUTH_CENTRAL</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Region</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ALASKA</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.202723</td>\n",
              "      <td>0.175251</td>\n",
              "      <td>0.007844</td>\n",
              "      <td>0.051049</td>\n",
              "      <td>0.087575</td>\n",
              "      <td>0.129079</td>\n",
              "      <td>-0.070868</td>\n",
              "      <td>0.161624</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HAWAII</th>\n",
              "      <td>0.202723</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.021116</td>\n",
              "      <td>0.373914</td>\n",
              "      <td>0.247171</td>\n",
              "      <td>0.341155</td>\n",
              "      <td>0.019388</td>\n",
              "      <td>0.159192</td>\n",
              "      <td>0.092632</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MIDWEST</th>\n",
              "      <td>0.175251</td>\n",
              "      <td>-0.021116</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.062595</td>\n",
              "      <td>-0.010213</td>\n",
              "      <td>-0.043783</td>\n",
              "      <td>0.047437</td>\n",
              "      <td>-0.059128</td>\n",
              "      <td>0.068902</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NATIONAL</th>\n",
              "      <td>0.007844</td>\n",
              "      <td>0.373914</td>\n",
              "      <td>0.062595</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.502035</td>\n",
              "      <td>0.579102</td>\n",
              "      <td>-0.040539</td>\n",
              "      <td>0.635006</td>\n",
              "      <td>0.486524</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NORTHEAST</th>\n",
              "      <td>0.051049</td>\n",
              "      <td>0.247171</td>\n",
              "      <td>-0.010213</td>\n",
              "      <td>0.502035</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.242039</td>\n",
              "      <td>-0.236225</td>\n",
              "      <td>0.360389</td>\n",
              "      <td>0.149881</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NORTHWEST</th>\n",
              "      <td>0.087575</td>\n",
              "      <td>0.341155</td>\n",
              "      <td>-0.043783</td>\n",
              "      <td>0.579102</td>\n",
              "      <td>0.242039</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.032306</td>\n",
              "      <td>0.165992</td>\n",
              "      <td>0.349935</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SOUTHEAST</th>\n",
              "      <td>0.129079</td>\n",
              "      <td>0.019388</td>\n",
              "      <td>0.047437</td>\n",
              "      <td>-0.040539</td>\n",
              "      <td>-0.236225</td>\n",
              "      <td>-0.032306</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>-0.167631</td>\n",
              "      <td>-0.021176</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SOUTHWEST</th>\n",
              "      <td>-0.070868</td>\n",
              "      <td>0.159192</td>\n",
              "      <td>-0.059128</td>\n",
              "      <td>0.635006</td>\n",
              "      <td>0.360389</td>\n",
              "      <td>0.165992</td>\n",
              "      <td>-0.167631</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.254504</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SOUTH_CENTRAL</th>\n",
              "      <td>0.161624</td>\n",
              "      <td>0.092632</td>\n",
              "      <td>0.068902</td>\n",
              "      <td>0.486524</td>\n",
              "      <td>0.149881</td>\n",
              "      <td>0.349935</td>\n",
              "      <td>-0.021176</td>\n",
              "      <td>0.254504</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6f276d9b-f004-490d-88a5-01d2a56c30b8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <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",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\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-6f276d9b-f004-490d-88a5-01d2a56c30b8 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-6f276d9b-f004-490d-88a5-01d2a56c30b8');\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": 906
        }
      ],
      "source": [
        "spread_correlation = organic_spread_by_region(hass).corr()\n",
        "spread_correlation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-fbKjqw6iXcg",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 712
        },
        "outputId": "aba8a2dc-f30f-4659-f97f-b8e655155aa3"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 4 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "import seaborn as sns\n",
        "grid = sns.clustermap(spread_correlation, annot=True)\n",
        "fig = grid.fig\n",
        "axes = fig.axes\n",
        "ax = axes[2]\n",
        "ax.set_xticklabels(ax.get_xticklabels(), rotation=45);"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-6jsBAhQiXcg"
      },
      "source": [
        "# Pandas Review\n",
        "\n",
        "- Pandas extends numpy with more complex datastructures and algorithms.\n",
        "- If you understand numpy, you understand 90% of pandas.\n",
        "- `groupby`, `set_index`, and `unstack` are powerful tools for working with categorical data.\n",
        "- Avocado prices are surprisingly interesting :)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "_u_fG58UiXcg"
      },
      "source": [
        "# Thanks!"
      ]
    }
  ],
  "metadata": {
    "celltoolbar": "Slideshow",
    "kernelspec": {
      "display_name": "Python 3",
      "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": "3.5.2"
    },
    "livereveal": {
      "start_slideshow_at": "selected"
    },
    "name": "Main.ipynb",
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "gh1Uw4KtiXcK",
        "K81LXWsCiXcS",
        "9EsiKbSCiXcT",
        "zsIEVqkgiXcY",
        "JHmGF_mGiXcZ",
        "-6jsBAhQiXcg",
        "_u_fG58UiXcg"
      ],
      "include_colab_link": true
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}