numpy/10th_october_zscore.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/werowe/HypatiaAcademy/blob/master/numpy/10th_october_zscore.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "\n",
        "\n",
        "def calcZScore(row, a, b):\n",
        "    return (row - a) / b\n",
        "\n",
        "\n",
        "\n",
        "countries = [\n",
        "    \"Monaco\", \"Bermuda\", \"Norway\", \"Luxembourg\", \"Switzerland\", \"Ireland\",\n",
        "    \"United States\", \"Denmark\", \"Qatar\", \"Iceland\", \"Singapore\", \"Sweden\",\n",
        "    \"Australia\", \"Netherlands\", \"Austria\", \"Israel\", \"Hong Kong *\", \"Finland\",\n",
        "    \"Germany\", \"Canada\", \"United Arab Emirates\", \"United Kingdom\", \"Belgium\",\n",
        "    \"New Zealand\", \"France\", \"Macao\", \"Japan\", \"Italy\", \"South Korea\", \"Spain\",\n",
        "    \"Slovenia\", \"Estonia\", \"Saudi Arabia\", \"Czechia\", \"Portugal\", \"Lithuania\",\n",
        "    \"Slovakia\", \"Greece\", \"Latvia\", \"Hungary\", \"Poland\", \"Romania\", \"Chile\",\n",
        "    \"China\", \"Russia\", \"Malaysia\", \"Argentina\", \"Turkey\", \"Mexico\", \"Brazil\",\n",
        "    \"Thailand\", \"South Africa\", \"Colombia\", \"Ecuador\", \"Indonesia\", \"Ukraine\",\n",
        "    \"Egypt\", \"Vietnam\", \"Philippines\", \"Algeria\", \"Iran\", \"Morocco\",\n",
        "    \"Sri Lanka\", \"Bangladesh\", \"India\", \"Kenya\", \"Nigeria\", \"Cambodia\",\n",
        "    \"Cameroon\", \"Pakistan\", \"Nepal\", \"Myanmar\", \"Ethiopia\", \"Sudan\", \"Niger\",\n",
        "    \"Afghanistan\"\n",
        "]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "countries = [\n",
        "    \"Monaco\", \"Bermuda\", \"Norway\", \"Luxembourg\", \"Switzerland\", \"Ireland\",\n",
        "    \"United States\", \"Denmark\", \"Qatar\", \"Iceland\", \"Singapore\", \"Sweden\",\n",
        "    \"Australia\", \"Netherlands\", \"Austria\", \"Israel\", \"Hong Kong *\", \"Finland\",\n",
        "    \"Germany\", \"Canada\", \"United Arab Emirates\", \"United Kingdom\", \"Belgium\"\n",
        "]\n",
        "\n",
        "\n",
        "\n",
        "np.random.seed(0)  # For reproducibility\n",
        "\n",
        "\n",
        "# random income\n",
        "\n",
        "income = np.random.randint(500, 50000, len(countries))\n",
        "\n",
        "# we need to find the mean and the stand deviation for the data that you created\n",
        "\n",
        "\n",
        "# this says that the z-score is randomly created using the uniformation distribution\n",
        "\n",
        "# z-score is (observation - mean) / std\n",
        "\n",
        "\n",
        "#z_score = np.round(np.random.uniform(-1, 4.5, len(countries)), 2)\n",
        "\n",
        "# what is the average and stand of income\n",
        "\n",
        "mean = income.mean()\n",
        "std = income.std()\n",
        "\n",
        "\n",
        "# income is a Numpy array\n",
        "\n",
        "# is a scalar.  thats a single number.  so is std\n",
        "\n",
        "zScore = np.apply_along_axis(calcZScore, axis=0, arr=income, a=mean, b=std)\n",
        "\n",
        "zScore=np.around(zScore, 2)\n",
        "\n",
        "print(zScore)\n"
      ],
      "metadata": {
        "id": "wbCCT4XukA8I",
        "outputId": "e1fb4189-15f7-41f3-a4de-a56615f7b4b4",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[-2.09  1.11  1.03  1.29 -0.64  0.08  0.21  0.98 -0.68  1.37 -1.14 -1.1\n",
            "  1.5   0.79 -1.16 -0.97  0.22 -0.82  1.07 -0.41  0.49 -0.98 -0.15]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# plot histogram to see if data is normally distributed\n",
        "\n",
        "plt.hist(income)"
      ],
      "metadata": {
        "id": "rDLfhyJcC6q4",
        "outputId": "c222838b-7199-43f8-a3a0-f9790b5876cc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 501
        }
      },
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([1., 0., 3., 4., 2., 1., 3., 1., 5., 3.]),\n",
              " array([ 3232. ,  7818.8, 12405.6, 16992.4, 21579.2, 26166. , 30752.8,\n",
              "        35339.6, 39926.4, 44513.2, 49100. ]),\n",
              " <BarContainer object of 10 artists>)"
            ]
          },
          "metadata": {},
          "execution_count": 60
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "df = pd.DataFrame({\n",
        "    \"country\": countries,\n",
        "    \"income\": income,\n",
        "    \"zScore\": zScore\n",
        "})\n",
        "\n",
        "df\n"
      ],
      "metadata": {
        "id": "SmaHRkGTm8eR",
        "outputId": "c7fec4ea-5f6b-47c8-d238-7ea4ad47d9d1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 770
        }
      },
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                 country  income  zScore\n",
              "0                 Monaco    3232   -2.09\n",
              "1                Bermuda   44067    1.11\n",
              "2                 Norway   43113    1.03\n",
              "3             Luxembourg   46391    1.29\n",
              "4            Switzerland   21743   -0.64\n",
              "5                Ireland   30903    0.08\n",
              "6          United States   32603    0.21\n",
              "7                Denmark   42493    0.98\n",
              "8                  Qatar   21257   -0.68\n",
              "9                Iceland   47384    1.37\n",
              "10             Singapore   15435   -1.14\n",
              "11                Sweden   15930   -1.10\n",
              "12             Australia   49100    1.50\n",
              "13           Netherlands   40012    0.79\n",
              "14               Austria   15150   -1.16\n",
              "15                Israel   17589   -0.97\n",
              "16           Hong Kong *   32730    0.22\n",
              "17               Finland   19483   -0.82\n",
              "18               Germany   43595    1.07\n",
              "19                Canada   24652   -0.41\n",
              "20  United Arab Emirates   36165    0.49\n",
              "21        United Kingdom   17421   -0.98\n",
              "22               Belgium   27969   -0.15"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-7d1a8f51-4f69-40e5-b6c3-a956832945e2\" 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>country</th>\n",
              "      <th>income</th>\n",
              "      <th>zScore</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>Monaco</td>\n",
              "      <td>3232</td>\n",
              "      <td>-2.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>Bermuda</td>\n",
              "      <td>44067</td>\n",
              "      <td>1.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>Norway</td>\n",
              "      <td>43113</td>\n",
              "      <td>1.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>Luxembourg</td>\n",
              "      <td>46391</td>\n",
              "      <td>1.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>Switzerland</td>\n",
              "      <td>21743</td>\n",
              "      <td>-0.64</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>Ireland</td>\n",
              "      <td>30903</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>United States</td>\n",
              "      <td>32603</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>Denmark</td>\n",
              "      <td>42493</td>\n",
              "      <td>0.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>Qatar</td>\n",
              "      <td>21257</td>\n",
              "      <td>-0.68</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>Iceland</td>\n",
              "      <td>47384</td>\n",
              "      <td>1.37</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>Singapore</td>\n",
              "      <td>15435</td>\n",
              "      <td>-1.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>Sweden</td>\n",
              "      <td>15930</td>\n",
              "      <td>-1.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>Australia</td>\n",
              "      <td>49100</td>\n",
              "      <td>1.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>Netherlands</td>\n",
              "      <td>40012</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>Austria</td>\n",
              "      <td>15150</td>\n",
              "      <td>-1.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>Israel</td>\n",
              "      <td>17589</td>\n",
              "      <td>-0.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>Hong Kong *</td>\n",
              "      <td>32730</td>\n",
              "      <td>0.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>Finland</td>\n",
              "      <td>19483</td>\n",
              "      <td>-0.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>Germany</td>\n",
              "      <td>43595</td>\n",
              "      <td>1.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>Canada</td>\n",
              "      <td>24652</td>\n",
              "      <td>-0.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>United Arab Emirates</td>\n",
              "      <td>36165</td>\n",
              "      <td>0.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>17421</td>\n",
              "      <td>-0.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>Belgium</td>\n",
              "      <td>27969</td>\n",
              "      <td>-0.15</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-7d1a8f51-4f69-40e5-b6c3-a956832945e2')\"\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 -960 960 960\">\n",
              "    <path d=\"M120-120v-720h720v720H120Zm60-500h600v-160H180v160Zm220 220h160v-160H400v160Zm0 220h160v-160H400v160ZM180-400h160v-160H180v160Zm440 0h160v-160H620v160ZM180-180h160v-160H180v160Zm440 0h160v-160H620v160Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\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",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\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-7d1a8f51-4f69-40e5-b6c3-a956832945e2 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-7d1a8f51-4f69-40e5-b6c3-a956832945e2');\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",
              "\n",
              "\n",
              "<div id=\"df-43ab7a6c-a282-4898-86fd-292fdfed129a\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-43ab7a6c-a282-4898-86fd-292fdfed129a')\"\n",
              "            title=\"Suggest charts.\"\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",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-43ab7a6c-a282-4898-86fd-292fdfed129a button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"mean\", mean)\n",
        "print(\"std\", std)"
      ],
      "metadata": {
        "id": "a2ProH0huvUe",
        "outputId": "0de8382c-ab7d-4bec-c04b-8a689a6fe86f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "mean 29931.17391304348\n",
            "std 12763.669683748525\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "\n",
        "cz=df[['country','zScore']]\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# answer is you roate the tick marks before you plot them\n",
        "\n",
        "plt.xticks(rotation=90)\n",
        "\n",
        "plt.plot(cz['country'], cz['zScore'])\n",
        "plt.xlabel('country')\n",
        "plt.ylabel('zScore')\n",
        "plt.title('County and zScore')\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n",
        "\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "qS6FBl2iu-bT",
        "outputId": "0b9f4c99-b447-41fa-b90e-9a264de3d815",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 606
        }
      },
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from scipy.stats import norm\n",
        "\n",
        "# Mean and standard deviation for the normal distribution\n",
        "mu = 0\n",
        "sigma = 1\n",
        "\n",
        "# Generate a range of x values for the curve\n",
        "x = np.linspace(-3, 3, 1000)\n",
        "\n",
        "# Calculate the PDF for each x value using the normal distribution\n",
        "pdf = norm.pdf(x, loc=mu, scale=sigma)\n",
        "\n",
        "# Plot the normal curve\n",
        "plt.plot(x, pdf, label='Normal Distribution')\n",
        "\n",
        "# Values of x you want to plot on the normal curve\n",
        "values_to_plot = df[df['country'].isin([\"Monaco\", \"Bermuda\", \"Norway\", \"Luxembourg\", \"Switzerland\"])]\n",
        "\n",
        "# Plot the specific values on the curve\n",
        "\n",
        "toPlot=df[df['country'].isin([\"Monaco\", \"Bermuda\", \"Norway\", \"Luxembourg\", \"Switzerland\"])]\n",
        "\n",
        "colors=['green','purple','red','blue','orange']\n",
        "\n",
        "i=0\n",
        "for index, row in toPlot.iterrows():\n",
        "    plt.axvline(x=row['zScore'], color=colors[i], linestyle='--', label=row['country'])\n",
        "    i += 1\n",
        "\n",
        "# Label the axes and add a legend\n",
        "plt.xlabel('X')\n",
        "plt.ylabel('PDF')\n",
        "plt.legend()\n",
        "\n",
        "# Show the plot\n",
        "plt.show()\n"
      ],
      "metadata": {
        "id": "PQ2fVkGeFuP4",
        "outputId": "91c4f6ec-035d-4cda-d8dd-e167b02c9feb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 449
        }
      },
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": "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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x\n",
        "\n",
        "\n",
        ""
      ],
      "metadata": {
        "id": "k8ReyutFI4pm",
        "outputId": "cccb0ff1-d20f-41f4-dd81-20946d5cabd7",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([-3.        , -2.99399399, -2.98798799, -2.98198198, -2.97597598,\n",
              "       -2.96996997, -2.96396396, -2.95795796, -2.95195195, -2.94594595,\n",
              "       -2.93993994, -2.93393393, -2.92792793, -2.92192192, -2.91591592,\n",
              "       -2.90990991, -2.9039039 , -2.8978979 , -2.89189189, -2.88588589,\n",
              "       -2.87987988, -2.87387387, -2.86786787, -2.86186186, -2.85585586,\n",
              "       -2.84984985, -2.84384384, -2.83783784, -2.83183183, -2.82582583,\n",
              "       -2.81981982, -2.81381381, -2.80780781, -2.8018018 , -2.7957958 ,\n",
              "       -2.78978979, -2.78378378, -2.77777778, -2.77177177, -2.76576577,\n",
              "       -2.75975976, -2.75375375, -2.74774775, -2.74174174, -2.73573574,\n",
              "       -2.72972973, -2.72372372, -2.71771772, -2.71171171, -2.70570571,\n",
              "       -2.6996997 , -2.69369369, -2.68768769, -2.68168168, -2.67567568,\n",
              "       -2.66966967, -2.66366366, -2.65765766, -2.65165165, -2.64564565,\n",
              "       -2.63963964, -2.63363363, -2.62762763, -2.62162162, -2.61561562,\n",
              "       -2.60960961, -2.6036036 , -2.5975976 , -2.59159159, -2.58558559,\n",
              "       -2.57957958, -2.57357357, -2.56756757, -2.56156156, -2.55555556,\n",
              "       -2.54954955, -2.54354354, -2.53753754, -2.53153153, -2.52552553,\n",
              "       -2.51951952, -2.51351351, -2.50750751, -2.5015015 , -2.4954955 ,\n",
              "       -2.48948949, -2.48348348, -2.47747748, -2.47147147, -2.46546547,\n",
              "       -2.45945946, -2.45345345, -2.44744745, -2.44144144, -2.43543544,\n",
              "       -2.42942943, -2.42342342, -2.41741742, -2.41141141, -2.40540541,\n",
              "       -2.3993994 , -2.39339339, -2.38738739, -2.38138138, -2.37537538,\n",
              "       -2.36936937, -2.36336336, -2.35735736, -2.35135135, -2.34534535,\n",
              "       -2.33933934, -2.33333333, -2.32732733, -2.32132132, -2.31531532,\n",
              "       -2.30930931, -2.3033033 , -2.2972973 , -2.29129129, -2.28528529,\n",
              "       -2.27927928, -2.27327327, -2.26726727, -2.26126126, -2.25525526,\n",
              "       -2.24924925, -2.24324324, -2.23723724, -2.23123123, -2.22522523,\n",
              "       -2.21921922, -2.21321321, -2.20720721, -2.2012012 , -2.1951952 ,\n",
              "       -2.18918919, -2.18318318, -2.17717718, -2.17117117, -2.16516517,\n",
              "       -2.15915916, -2.15315315, -2.14714715, -2.14114114, -2.13513514,\n",
              "       -2.12912913, -2.12312312, -2.11711712, -2.11111111, -2.10510511,\n",
              "       -2.0990991 , -2.09309309, -2.08708709, -2.08108108, -2.07507508,\n",
              "       -2.06906907, -2.06306306, -2.05705706, -2.05105105, -2.04504505,\n",
              "       -2.03903904, -2.03303303, -2.02702703, -2.02102102, -2.01501502,\n",
              "       -2.00900901, -2.003003  , -1.996997  , -1.99099099, -1.98498498,\n",
              "       -1.97897898, -1.97297297, -1.96696697, -1.96096096, -1.95495495,\n",
              "       -1.94894895, -1.94294294, -1.93693694, -1.93093093, -1.92492492,\n",
              "       -1.91891892, -1.91291291, -1.90690691, -1.9009009 , -1.89489489,\n",
              "       -1.88888889, -1.88288288, -1.87687688, -1.87087087, -1.86486486,\n",
              "       -1.85885886, -1.85285285, -1.84684685, -1.84084084, -1.83483483,\n",
              "       -1.82882883, -1.82282282, -1.81681682, -1.81081081, -1.8048048 ,\n",
              "       -1.7987988 , -1.79279279, -1.78678679, -1.78078078, -1.77477477,\n",
              "       -1.76876877, -1.76276276, -1.75675676, -1.75075075, -1.74474474,\n",
              "       -1.73873874, -1.73273273, -1.72672673, -1.72072072, -1.71471471,\n",
              "       -1.70870871, -1.7027027 , -1.6966967 , -1.69069069, -1.68468468,\n",
              "       -1.67867868, -1.67267267, -1.66666667, -1.66066066, -1.65465465,\n",
              "       -1.64864865, -1.64264264, -1.63663664, -1.63063063, -1.62462462,\n",
              "       -1.61861862, -1.61261261, -1.60660661, -1.6006006 , -1.59459459,\n",
              "       -1.58858859, -1.58258258, -1.57657658, -1.57057057, -1.56456456,\n",
              "       -1.55855856, -1.55255255, -1.54654655, -1.54054054, -1.53453453,\n",
              "       -1.52852853, -1.52252252, -1.51651652, -1.51051051, -1.5045045 ,\n",
              "       -1.4984985 , -1.49249249, -1.48648649, -1.48048048, -1.47447447,\n",
              "       -1.46846847, -1.46246246, -1.45645646, -1.45045045, -1.44444444,\n",
              "       -1.43843844, -1.43243243, -1.42642643, -1.42042042, -1.41441441,\n",
              "       -1.40840841, -1.4024024 , -1.3963964 , -1.39039039, -1.38438438,\n",
              "       -1.37837838, -1.37237237, -1.36636637, -1.36036036, -1.35435435,\n",
              "       -1.34834835, -1.34234234, -1.33633634, -1.33033033, -1.32432432,\n",
              "       -1.31831832, -1.31231231, -1.30630631, -1.3003003 , -1.29429429,\n",
              "       -1.28828829, -1.28228228, -1.27627628, -1.27027027, -1.26426426,\n",
              "       -1.25825826, -1.25225225, -1.24624625, -1.24024024, -1.23423423,\n",
              "       -1.22822823, -1.22222222, -1.21621622, -1.21021021, -1.2042042 ,\n",
              "       -1.1981982 , -1.19219219, -1.18618619, -1.18018018, -1.17417417,\n",
              "       -1.16816817, -1.16216216, -1.15615616, -1.15015015, -1.14414414,\n",
              "       -1.13813814, -1.13213213, -1.12612613, -1.12012012, -1.11411411,\n",
              "       -1.10810811, -1.1021021 , -1.0960961 , -1.09009009, -1.08408408,\n",
              "       -1.07807808, -1.07207207, -1.06606607, -1.06006006, -1.05405405,\n",
              "       -1.04804805, -1.04204204, -1.03603604, -1.03003003, -1.02402402,\n",
              "       -1.01801802, -1.01201201, -1.00600601, -1.        , -0.99399399,\n",
              "       -0.98798799, -0.98198198, -0.97597598, -0.96996997, -0.96396396,\n",
              "       -0.95795796, -0.95195195, -0.94594595, -0.93993994, -0.93393393,\n",
              "       -0.92792793, -0.92192192, -0.91591592, -0.90990991, -0.9039039 ,\n",
              "       -0.8978979 , -0.89189189, -0.88588589, -0.87987988, -0.87387387,\n",
              "       -0.86786787, -0.86186186, -0.85585586, -0.84984985, -0.84384384,\n",
              "       -0.83783784, -0.83183183, -0.82582583, -0.81981982, -0.81381381,\n",
              "       -0.80780781, -0.8018018 , -0.7957958 , -0.78978979, -0.78378378,\n",
              "       -0.77777778, -0.77177177, -0.76576577, -0.75975976, -0.75375375,\n",
              "       -0.74774775, -0.74174174, -0.73573574, -0.72972973, -0.72372372,\n",
              "       -0.71771772, -0.71171171, -0.70570571, -0.6996997 , -0.69369369,\n",
              "       -0.68768769, -0.68168168, -0.67567568, -0.66966967, -0.66366366,\n",
              "       -0.65765766, -0.65165165, -0.64564565, -0.63963964, -0.63363363,\n",
              "       -0.62762763, -0.62162162, -0.61561562, -0.60960961, -0.6036036 ,\n",
              "       -0.5975976 , -0.59159159, -0.58558559, -0.57957958, -0.57357357,\n",
              "       -0.56756757, -0.56156156, -0.55555556, -0.54954955, -0.54354354,\n",
              "       -0.53753754, -0.53153153, -0.52552553, -0.51951952, -0.51351351,\n",
              "       -0.50750751, -0.5015015 , -0.4954955 , -0.48948949, -0.48348348,\n",
              "       -0.47747748, -0.47147147, -0.46546547, -0.45945946, -0.45345345,\n",
              "       -0.44744745, -0.44144144, -0.43543544, -0.42942943, -0.42342342,\n",
              "       -0.41741742, -0.41141141, -0.40540541, -0.3993994 , -0.39339339,\n",
              "       -0.38738739, -0.38138138, -0.37537538, -0.36936937, -0.36336336,\n",
              "       -0.35735736, -0.35135135, -0.34534535, -0.33933934, -0.33333333,\n",
              "       -0.32732733, -0.32132132, -0.31531532, -0.30930931, -0.3033033 ,\n",
              "       -0.2972973 , -0.29129129, -0.28528529, -0.27927928, -0.27327327,\n",
              "       -0.26726727, -0.26126126, -0.25525526, -0.24924925, -0.24324324,\n",
              "       -0.23723724, -0.23123123, -0.22522523, -0.21921922, -0.21321321,\n",
              "       -0.20720721, -0.2012012 , -0.1951952 , -0.18918919, -0.18318318,\n",
              "       -0.17717718, -0.17117117, -0.16516517, -0.15915916, -0.15315315,\n",
              "       -0.14714715, -0.14114114, -0.13513514, -0.12912913, -0.12312312,\n",
              "       -0.11711712, -0.11111111, -0.10510511, -0.0990991 , -0.09309309,\n",
              "       -0.08708709, -0.08108108, -0.07507508, -0.06906907, -0.06306306,\n",
              "       -0.05705706, -0.05105105, -0.04504505, -0.03903904, -0.03303303,\n",
              "       -0.02702703, -0.02102102, -0.01501502, -0.00900901, -0.003003  ,\n",
              "        0.003003  ,  0.00900901,  0.01501502,  0.02102102,  0.02702703,\n",
              "        0.03303303,  0.03903904,  0.04504505,  0.05105105,  0.05705706,\n",
              "        0.06306306,  0.06906907,  0.07507508,  0.08108108,  0.08708709,\n",
              "        0.09309309,  0.0990991 ,  0.10510511,  0.11111111,  0.11711712,\n",
              "        0.12312312,  0.12912913,  0.13513514,  0.14114114,  0.14714715,\n",
              "        0.15315315,  0.15915916,  0.16516517,  0.17117117,  0.17717718,\n",
              "        0.18318318,  0.18918919,  0.1951952 ,  0.2012012 ,  0.20720721,\n",
              "        0.21321321,  0.21921922,  0.22522523,  0.23123123,  0.23723724,\n",
              "        0.24324324,  0.24924925,  0.25525526,  0.26126126,  0.26726727,\n",
              "        0.27327327,  0.27927928,  0.28528529,  0.29129129,  0.2972973 ,\n",
              "        0.3033033 ,  0.30930931,  0.31531532,  0.32132132,  0.32732733,\n",
              "        0.33333333,  0.33933934,  0.34534535,  0.35135135,  0.35735736,\n",
              "        0.36336336,  0.36936937,  0.37537538,  0.38138138,  0.38738739,\n",
              "        0.39339339,  0.3993994 ,  0.40540541,  0.41141141,  0.41741742,\n",
              "        0.42342342,  0.42942943,  0.43543544,  0.44144144,  0.44744745,\n",
              "        0.45345345,  0.45945946,  0.46546547,  0.47147147,  0.47747748,\n",
              "        0.48348348,  0.48948949,  0.4954955 ,  0.5015015 ,  0.50750751,\n",
              "        0.51351351,  0.51951952,  0.52552553,  0.53153153,  0.53753754,\n",
              "        0.54354354,  0.54954955,  0.55555556,  0.56156156,  0.56756757,\n",
              "        0.57357357,  0.57957958,  0.58558559,  0.59159159,  0.5975976 ,\n",
              "        0.6036036 ,  0.60960961,  0.61561562,  0.62162162,  0.62762763,\n",
              "        0.63363363,  0.63963964,  0.64564565,  0.65165165,  0.65765766,\n",
              "        0.66366366,  0.66966967,  0.67567568,  0.68168168,  0.68768769,\n",
              "        0.69369369,  0.6996997 ,  0.70570571,  0.71171171,  0.71771772,\n",
              "        0.72372372,  0.72972973,  0.73573574,  0.74174174,  0.74774775,\n",
              "        0.75375375,  0.75975976,  0.76576577,  0.77177177,  0.77777778,\n",
              "        0.78378378,  0.78978979,  0.7957958 ,  0.8018018 ,  0.80780781,\n",
              "        0.81381381,  0.81981982,  0.82582583,  0.83183183,  0.83783784,\n",
              "        0.84384384,  0.84984985,  0.85585586,  0.86186186,  0.86786787,\n",
              "        0.87387387,  0.87987988,  0.88588589,  0.89189189,  0.8978979 ,\n",
              "        0.9039039 ,  0.90990991,  0.91591592,  0.92192192,  0.92792793,\n",
              "        0.93393393,  0.93993994,  0.94594595,  0.95195195,  0.95795796,\n",
              "        0.96396396,  0.96996997,  0.97597598,  0.98198198,  0.98798799,\n",
              "        0.99399399,  1.        ,  1.00600601,  1.01201201,  1.01801802,\n",
              "        1.02402402,  1.03003003,  1.03603604,  1.04204204,  1.04804805,\n",
              "        1.05405405,  1.06006006,  1.06606607,  1.07207207,  1.07807808,\n",
              "        1.08408408,  1.09009009,  1.0960961 ,  1.1021021 ,  1.10810811,\n",
              "        1.11411411,  1.12012012,  1.12612613,  1.13213213,  1.13813814,\n",
              "        1.14414414,  1.15015015,  1.15615616,  1.16216216,  1.16816817,\n",
              "        1.17417417,  1.18018018,  1.18618619,  1.19219219,  1.1981982 ,\n",
              "        1.2042042 ,  1.21021021,  1.21621622,  1.22222222,  1.22822823,\n",
              "        1.23423423,  1.24024024,  1.24624625,  1.25225225,  1.25825826,\n",
              "        1.26426426,  1.27027027,  1.27627628,  1.28228228,  1.28828829,\n",
              "        1.29429429,  1.3003003 ,  1.30630631,  1.31231231,  1.31831832,\n",
              "        1.32432432,  1.33033033,  1.33633634,  1.34234234,  1.34834835,\n",
              "        1.35435435,  1.36036036,  1.36636637,  1.37237237,  1.37837838,\n",
              "        1.38438438,  1.39039039,  1.3963964 ,  1.4024024 ,  1.40840841,\n",
              "        1.41441441,  1.42042042,  1.42642643,  1.43243243,  1.43843844,\n",
              "        1.44444444,  1.45045045,  1.45645646,  1.46246246,  1.46846847,\n",
              "        1.47447447,  1.48048048,  1.48648649,  1.49249249,  1.4984985 ,\n",
              "        1.5045045 ,  1.51051051,  1.51651652,  1.52252252,  1.52852853,\n",
              "        1.53453453,  1.54054054,  1.54654655,  1.55255255,  1.55855856,\n",
              "        1.56456456,  1.57057057,  1.57657658,  1.58258258,  1.58858859,\n",
              "        1.59459459,  1.6006006 ,  1.60660661,  1.61261261,  1.61861862,\n",
              "        1.62462462,  1.63063063,  1.63663664,  1.64264264,  1.64864865,\n",
              "        1.65465465,  1.66066066,  1.66666667,  1.67267267,  1.67867868,\n",
              "        1.68468468,  1.69069069,  1.6966967 ,  1.7027027 ,  1.70870871,\n",
              "        1.71471471,  1.72072072,  1.72672673,  1.73273273,  1.73873874,\n",
              "        1.74474474,  1.75075075,  1.75675676,  1.76276276,  1.76876877,\n",
              "        1.77477477,  1.78078078,  1.78678679,  1.79279279,  1.7987988 ,\n",
              "        1.8048048 ,  1.81081081,  1.81681682,  1.82282282,  1.82882883,\n",
              "        1.83483483,  1.84084084,  1.84684685,  1.85285285,  1.85885886,\n",
              "        1.86486486,  1.87087087,  1.87687688,  1.88288288,  1.88888889,\n",
              "        1.89489489,  1.9009009 ,  1.90690691,  1.91291291,  1.91891892,\n",
              "        1.92492492,  1.93093093,  1.93693694,  1.94294294,  1.94894895,\n",
              "        1.95495495,  1.96096096,  1.96696697,  1.97297297,  1.97897898,\n",
              "        1.98498498,  1.99099099,  1.996997  ,  2.003003  ,  2.00900901,\n",
              "        2.01501502,  2.02102102,  2.02702703,  2.03303303,  2.03903904,\n",
              "        2.04504505,  2.05105105,  2.05705706,  2.06306306,  2.06906907,\n",
              "        2.07507508,  2.08108108,  2.08708709,  2.09309309,  2.0990991 ,\n",
              "        2.10510511,  2.11111111,  2.11711712,  2.12312312,  2.12912913,\n",
              "        2.13513514,  2.14114114,  2.14714715,  2.15315315,  2.15915916,\n",
              "        2.16516517,  2.17117117,  2.17717718,  2.18318318,  2.18918919,\n",
              "        2.1951952 ,  2.2012012 ,  2.20720721,  2.21321321,  2.21921922,\n",
              "        2.22522523,  2.23123123,  2.23723724,  2.24324324,  2.24924925,\n",
              "        2.25525526,  2.26126126,  2.26726727,  2.27327327,  2.27927928,\n",
              "        2.28528529,  2.29129129,  2.2972973 ,  2.3033033 ,  2.30930931,\n",
              "        2.31531532,  2.32132132,  2.32732733,  2.33333333,  2.33933934,\n",
              "        2.34534535,  2.35135135,  2.35735736,  2.36336336,  2.36936937,\n",
              "        2.37537538,  2.38138138,  2.38738739,  2.39339339,  2.3993994 ,\n",
              "        2.40540541,  2.41141141,  2.41741742,  2.42342342,  2.42942943,\n",
              "        2.43543544,  2.44144144,  2.44744745,  2.45345345,  2.45945946,\n",
              "        2.46546547,  2.47147147,  2.47747748,  2.48348348,  2.48948949,\n",
              "        2.4954955 ,  2.5015015 ,  2.50750751,  2.51351351,  2.51951952,\n",
              "        2.52552553,  2.53153153,  2.53753754,  2.54354354,  2.54954955,\n",
              "        2.55555556,  2.56156156,  2.56756757,  2.57357357,  2.57957958,\n",
              "        2.58558559,  2.59159159,  2.5975976 ,  2.6036036 ,  2.60960961,\n",
              "        2.61561562,  2.62162162,  2.62762763,  2.63363363,  2.63963964,\n",
              "        2.64564565,  2.65165165,  2.65765766,  2.66366366,  2.66966967,\n",
              "        2.67567568,  2.68168168,  2.68768769,  2.69369369,  2.6996997 ,\n",
              "        2.70570571,  2.71171171,  2.71771772,  2.72372372,  2.72972973,\n",
              "        2.73573574,  2.74174174,  2.74774775,  2.75375375,  2.75975976,\n",
              "        2.76576577,  2.77177177,  2.77777778,  2.78378378,  2.78978979,\n",
              "        2.7957958 ,  2.8018018 ,  2.80780781,  2.81381381,  2.81981982,\n",
              "        2.82582583,  2.83183183,  2.83783784,  2.84384384,  2.84984985,\n",
              "        2.85585586,  2.86186186,  2.86786787,  2.87387387,  2.87987988,\n",
              "        2.88588589,  2.89189189,  2.8978979 ,  2.9039039 ,  2.90990991,\n",
              "        2.91591592,  2.92192192,  2.92792793,  2.93393393,  2.93993994,\n",
              "        2.94594595,  2.95195195,  2.95795796,  2.96396396,  2.96996997,\n",
              "        2.97597598,  2.98198198,  2.98798799,  2.99399399,  3.        ])"
            ]
          },
          "metadata": {},
          "execution_count": 68
        }
      ]
    }
  ]
}