Lotteries_Analysis.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "## **Importing Data and Exploratory Data Analysis**"
      ],
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        "id": "m88uEz236KIB"
      }
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    {
      "cell_type": "code",
      "execution_count": 90,
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      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import datetime as dt\n",
        "import statistics"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DailyGrand = pd.read_csv(\"/content/drive/MyDrive/A Python Learning/DailyGrand/Final_DailyGrandPrize.csv\")\n",
        "Lotto649 = pd.read_csv(\"/content/drive/MyDrive/A Python Learning/Lotto649/Final_Lotto649_2012to2024.csv\")\n",
        "LottoMAX = pd.read_csv(\"/content/drive/MyDrive/A Python Learning/LottoMAX/Final_PrizeBreakdown_2013-23.csv\")\n",
        "LottoMAX_Tickets = pd.read_csv(\"/content/drive/MyDrive/A Python Learning/LottoMAX/Final_totalticketssold.csv\")\n",
        "Lotto649_jackpot = pd.read_csv(\"/content/drive/MyDrive/A Python Learning/Lotto649/Lotto649_20220917_JackpotNumbers.csv\")"
      ],
      "metadata": {
        "id": "vW4HHVHvYy09"
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      "execution_count": 91,
      "outputs": []
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      "cell_type": "code",
      "source": [
        "LottoMAX.fillna(value=0,inplace=True)\n",
        "LottoMAX.isna().sum()"
      ],
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        },
        "id": "-MTWny7BddrZ",
        "outputId": "c91845f4-25ad-4e20-9018-a72bea602922"
      },
      "execution_count": 92,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date                0\n",
              "Numbers Matched     0\n",
              "Prize Per Winner    0\n",
              "All Winners         0\n",
              "Prize Fund          0\n",
              "DatePY              0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 92
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "LottoMAX.info()"
      ],
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        "id": "rAAexk5EZ1Db",
        "outputId": "4cc8b6d8-1e27-4858-8190-6b4def667dbf"
      },
      "execution_count": 93,
      "outputs": [
        {
          "output_type": "stream",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 7274 entries, 0 to 7273\n",
            "Data columns (total 6 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   Date              7274 non-null   object \n",
            " 1   Numbers Matched   7274 non-null   object \n",
            " 2   Prize Per Winner  7274 non-null   object \n",
            " 3   All Winners       7274 non-null   float64\n",
            " 4   Prize Fund        7274 non-null   object \n",
            " 5   DatePY            7274 non-null   object \n",
            "dtypes: float64(1), object(5)\n",
            "memory usage: 341.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Number of Winners in each Lottery**"
      ],
      "metadata": {
        "id": "nbsPfaNBdVq3"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **LottoMax Number of Winners**"
      ],
      "metadata": {
        "id": "8-RR50kU8yiZ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LottoMAX['All Winners'] = LottoMAX['All Winners'].astype(float).astype(int)"
      ],
      "metadata": {
        "id": "tAFKjoC9ZqBe"
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      "execution_count": 94,
      "outputs": []
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      "execution_count": 95,
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          "data": {
            "text/plain": [
              "            Date     Numbers Matched  Prize Per Winner  All Winners  \\\n",
              "0     01-04-2013              Totals                 -       901364   \n",
              "1     01-04-2013             Match 3  Free Play Ticket       735767   \n",
              "2     01-04-2013  Match 3 plus Bonus            $20.00        77748   \n",
              "3     01-04-2013             Match 4            $20.00        84083   \n",
              "4     01-04-2013             Match 5           $121.30         3711   \n",
              "...          ...                 ...               ...          ...   \n",
              "7269  12-29-2023             Match 6         $4,041.50           42   \n",
              "7270  12-29-2023  Match 6 plus Bonus       $169,742.90            0   \n",
              "7271  12-29-2023             Match 7    $27,000,000.00            0   \n",
              "7272  12-29-2023             Match 3  Free Play Ticket       505659   \n",
              "7273  12-29-2023              Totals                 -       614558   \n",
              "\n",
              "          Prize Fund      DatePY  \n",
              "0     $24,407,128.00  2013-01-04  \n",
              "1                  -  2013-01-04  \n",
              "2      $1,554,960.00  2013-01-04  \n",
              "3      $1,681,660.00  2013-01-04  \n",
              "4        $450,144.30  2013-01-04  \n",
              "...              ...         ...  \n",
              "7269     $169,743.00  2023-12-29  \n",
              "7270           $0.00  2023-12-29  \n",
              "7271           $0.00  2023-12-29  \n",
              "7272               -  2023-12-29  \n",
              "7273   $2,745,171.10  2023-12-29  \n",
              "\n",
              "[7274 rows x 6 columns]"
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Numbers Matched</th>\n",
              "      <th>Prize Per Winner</th>\n",
              "      <th>All Winners</th>\n",
              "      <th>Prize Fund</th>\n",
              "      <th>DatePY</th>\n",
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              "      <th>0</th>\n",
              "      <td>01-04-2013</td>\n",
              "      <td>Totals</td>\n",
              "      <td>-</td>\n",
              "      <td>901364</td>\n",
              "      <td>$24,407,128.00</td>\n",
              "      <td>2013-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>01-04-2013</td>\n",
              "      <td>Match 3</td>\n",
              "      <td>Free Play Ticket</td>\n",
              "      <td>735767</td>\n",
              "      <td>-</td>\n",
              "      <td>2013-01-04</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>01-04-2013</td>\n",
              "      <td>Match 3 plus Bonus</td>\n",
              "      <td>$20.00</td>\n",
              "      <td>77748</td>\n",
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              "      <td>2013-01-04</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01-04-2013</td>\n",
              "      <td>Match 4</td>\n",
              "      <td>$20.00</td>\n",
              "      <td>84083</td>\n",
              "      <td>$1,681,660.00</td>\n",
              "      <td>2013-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>01-04-2013</td>\n",
              "      <td>Match 5</td>\n",
              "      <td>$121.30</td>\n",
              "      <td>3711</td>\n",
              "      <td>$450,144.30</td>\n",
              "      <td>2013-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>7269</th>\n",
              "      <td>12-29-2023</td>\n",
              "      <td>Match 6</td>\n",
              "      <td>$4,041.50</td>\n",
              "      <td>42</td>\n",
              "      <td>$169,743.00</td>\n",
              "      <td>2023-12-29</td>\n",
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              "      <th>7270</th>\n",
              "      <td>12-29-2023</td>\n",
              "      <td>Match 6 plus Bonus</td>\n",
              "      <td>$169,742.90</td>\n",
              "      <td>0</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>2023-12-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7271</th>\n",
              "      <td>12-29-2023</td>\n",
              "      <td>Match 7</td>\n",
              "      <td>$27,000,000.00</td>\n",
              "      <td>0</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>2023-12-29</td>\n",
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              "    <tr>\n",
              "      <th>7272</th>\n",
              "      <td>12-29-2023</td>\n",
              "      <td>Match 3</td>\n",
              "      <td>Free Play Ticket</td>\n",
              "      <td>505659</td>\n",
              "      <td>-</td>\n",
              "      <td>2023-12-29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7273</th>\n",
              "      <td>12-29-2023</td>\n",
              "      <td>Totals</td>\n",
              "      <td>-</td>\n",
              "      <td>614558</td>\n",
              "      <td>$2,745,171.10</td>\n",
              "      <td>2023-12-29</td>\n",
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              "<p>7274 rows × 6 columns</p>\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "LottoMAX",
              "summary": "{\n  \"name\": \"LottoMAX\",\n  \"rows\": 7274,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 792,\n        \"samples\": [\n          \"01-03-2023\",\n          \"05-28-2021\",\n          \"02-01-2022\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Numbers Matched\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 10,\n        \"samples\": [\n          \"Match 5 plus Bonus\",\n          \"Match 3\",\n          \"Match 6\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prize Per Winner\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2585,\n        \"samples\": [\n          \"$25,345,436.50\",\n          \"$5,034.60\",\n          \"$337,266.90\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"All Winners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 405968,\n        \"min\": 0,\n        \"max\": 3251053,\n        \"num_unique_values\": 4526,\n        \"samples\": [\n          346824,\n          595096,\n          3776\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prize Fund\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 5496,\n        \"samples\": [\n          \"$1,568,440.00\",\n          \"$2,915,160.00\",\n          \"$1,737,440.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DatePY\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 792,\n        \"samples\": [\n          \"2023-01-03\",\n          \"2021-05-28\",\n          \"2022-02-01\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 95
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LottoMAX_rows = LottoMAX[LottoMAX['Numbers Matched'] == 'Totals']\n",
        "LottoMAX_rows['Date'] = pd.to_datetime(LottoMAX_rows['Date'])\n",
        "LottoMAX_rows['Year'] = LottoMAX_rows['Date'].dt.year\n",
        "LottoMAXwinners = LottoMAX_rows.groupby('Year')['All Winners'].sum().reset_index()\n",
        "LottoMAXwinners.rename(columns={'All Winners': 'LottoMAXwinners'},inplace=True)\n",
        "LottoMAXwinners"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 602
        },
        "id": "CzFqf2o6aa54",
        "outputId": "fa17ce37-2977-4c01-b401-f3c7dd90722c"
      },
      "execution_count": 96,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-96-4046a186ddd4>:2: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  LottoMAX_rows['Date'] = pd.to_datetime(LottoMAX_rows['Date'])\n",
            "<ipython-input-96-4046a186ddd4>:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  LottoMAX_rows['Year'] = LottoMAX_rows['Date'].dt.year\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Year  LottoMAXwinners\n",
              "0   2013         48625279\n",
              "1   2014         48673609\n",
              "2   2015         66944849\n",
              "3   2016         67704508\n",
              "4   2017         57428823\n",
              "5   2018         79851510\n",
              "6   2019         61864421\n",
              "7   2020         78208370\n",
              "8   2021         94402821\n",
              "9   2022         92890436\n",
              "10  2023         65915089"
            ],
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              "type": "dataframe",
              "variable_name": "LottoMAXwinners",
              "summary": "{\n  \"name\": \"LottoMAXwinners\",\n  \"rows\": 11,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 2013,\n        \"max\": 2023,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          2018,\n          2013,\n          2022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LottoMAXwinners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15639707,\n        \"min\": 48625279,\n        \"max\": 94402821,\n        \"num_unique_values\": 11,\n        \"samples\": [\n          79851510,\n          48625279,\n          92890436\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 96
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=LottoMAXwinners,x='Year',y='LottoMAXwinners')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "pmnOq5Hsa1UN",
        "outputId": "7d2b5dbe-3bca-400c-df72-d3a650434771"
      },
      "execution_count": 97,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Year', ylabel='LottoMAXwinners'>"
            ]
          },
          "metadata": {},
          "execution_count": 97
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Lotto649 Number of Winners**"
      ],
      "metadata": {
        "id": "45rEKg2j6R3k"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Lotto649['Date'] = pd.to_datetime(Lotto649['Date'])\n",
        "Lotto649['Year'] = Lotto649['Date'].dt.year\n",
        "mask = Lotto649['Year'] != 2024\n",
        "Lotto649 = Lotto649[mask]\n",
        "Lotto649winners = Lotto649.groupby('Year')['Winners'].sum().reset_index()\n",
        "Lotto649winners.rename(columns={'Winners': 'Lotto649winners'},inplace=True)\n",
        "Lotto649winners"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 425
        },
        "id": "GxVuFiD0hMX2",
        "outputId": "5c920b49-885a-4692-a409-497c56a066bc"
      },
      "execution_count": 98,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Year  Lotto649winners\n",
              "0   2012         23082065\n",
              "1   2013         47116494\n",
              "2   2014         86438281\n",
              "3   2015         97197683\n",
              "4   2016         83446358\n",
              "5   2017         81055839\n",
              "6   2018         83673406\n",
              "7   2019         76628308\n",
              "8   2020         68828105\n",
              "9   2021         73830013\n",
              "10  2022         73278384\n",
              "11  2023         87527136"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9ad9c158-58dd-412e-a35f-ec8b48e70c08\" 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",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Year</th>\n",
              "      <th>Lotto649winners</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2012</td>\n",
              "      <td>23082065</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2013</td>\n",
              "      <td>47116494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014</td>\n",
              "      <td>86438281</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015</td>\n",
              "      <td>97197683</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016</td>\n",
              "      <td>83446358</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2017</td>\n",
              "      <td>81055839</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2018</td>\n",
              "      <td>83673406</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>2019</td>\n",
              "      <td>76628308</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>2020</td>\n",
              "      <td>68828105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>2021</td>\n",
              "      <td>73830013</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>2022</td>\n",
              "      <td>73278384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>2023</td>\n",
              "      <td>87527136</td>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Lotto649winners",
              "summary": "{\n  \"name\": \"Lotto649winners\",\n  \"rows\": 12,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 2012,\n        \"max\": 2023,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          2022,\n          2021,\n          2012\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lotto649winners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20124806,\n        \"min\": 23082065,\n        \"max\": 97197683,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          73278384,\n          73830013,\n          23082065\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 98
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Daily Grand Number of Winners**"
      ],
      "metadata": {
        "id": "dgziBeA4i2Tv"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DailyGrand.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "ly6ggixji1qI",
        "outputId": "db389ced-7d7e-4243-d39c-379a965bdb7e"
      },
      "execution_count": 99,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date       Draw  Winners                                      Prize\n",
              "0  2016-10-20  5/5 + 1/1        0                              $7,000,000.00\n",
              "1  2016-10-20  5/5 + 0/1        1  $25,000.00 a Year for Life or $500,000.00\n",
              "2  2016-10-20  4/5 + 1/1        8                                  $1,000.00\n",
              "3  2016-10-20  4/5 + 0/1       77                                    $500.00\n",
              "4  2016-10-20  3/5 + 1/1      597                                    $100.00"
            ],
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              "        document.querySelector('#df-3cea1a47-460c-40bf-9747-d0dc64dcbbc7 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-3cea1a47-460c-40bf-9747-d0dc64dcbbc7');\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-50f32763-0ac1-4c4a-aca8-c54b9a182c37\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-50f32763-0ac1-4c4a-aca8-c54b9a182c37')\"\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-50f32763-0ac1-4c4a-aca8-c54b9a182c37 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "DailyGrand",
              "summary": "{\n  \"name\": \"DailyGrand\",\n  \"rows\": 8172,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 754,\n        \"samples\": [\n          \"2022-06-06\",\n          \"2019-04-15\",\n          \"2023-01-23\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Draw\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"1/5 + 1/1\",\n          \"5/5 + 0/1\",\n          \"3/5 + 0/1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Winners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18312,\n        \"min\": 0,\n        \"max\": 83258,\n        \"num_unique_values\": 3093,\n        \"samples\": [\n          2136,\n          30251,\n          49090\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prize\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"$7,000,000.00\",\n          \"$25,000.00 a Year for Life or $500,000.00\",\n          \"$20.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 99
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DailyGrand['Date'] = pd.to_datetime(DailyGrand['Date'])\n",
        "DailyGrand['Year'] = DailyGrand['Date'].dt.year\n",
        "mask = (DailyGrand['Year'] != 2016) & (DailyGrand['Year'] != 2024)\n",
        "DailyGrand = DailyGrand[mask]\n",
        "DailyGrandWinners = DailyGrand.groupby('Year')['Winners'].sum().reset_index()\n",
        "DailyGrandWinners.rename(columns={'Winners': 'DailyGrandWinners'},inplace=True)\n",
        "DailyGrandWinners"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "uWKpD1uAj6TL",
        "outputId": "5e2cd7bf-acab-4b8e-9c6a-ffba27ea54c4"
      },
      "execution_count": 100,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  DailyGrandWinners\n",
              "0  2017           14079412\n",
              "1  2018           15134845\n",
              "2  2019           13449231\n",
              "3  2020            9306989\n",
              "4  2021           10154278\n",
              "5  2022           10285216\n",
              "6  2023           10633925"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3c4415f5-da29-4c4b-a70a-ba9c3deb9cf1\" class=\"colab-df-container\">\n",
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              "  <thead>\n",
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              "      <th>Year</th>\n",
              "      <th>DailyGrandWinners</th>\n",
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              "      <th>1</th>\n",
              "      <td>2018</td>\n",
              "      <td>15134845</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019</td>\n",
              "      <td>13449231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020</td>\n",
              "      <td>9306989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021</td>\n",
              "      <td>10154278</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2022</td>\n",
              "      <td>10285216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2023</td>\n",
              "      <td>10633925</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c4415f5-da29-4c4b-a70a-ba9c3deb9cf1')\"\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",
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              "  </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-3c4415f5-da29-4c4b-a70a-ba9c3deb9cf1 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-3c4415f5-da29-4c4b-a70a-ba9c3deb9cf1');\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",
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              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-325837c3-3f2a-4ea8-aaa9-9f6a61ae35a9\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-325837c3-3f2a-4ea8-aaa9-9f6a61ae35a9')\"\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",
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              "    </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-325837c3-3f2a-4ea8-aaa9-9f6a61ae35a9 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_6764d601-28e6-4f88-938a-46a21b162dcf\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate:hover {\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "DailyGrandWinners",
              "summary": "{\n  \"name\": \"DailyGrandWinners\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2017,\n        \"max\": 2023,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2017,\n          2018,\n          2022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DailyGrandWinners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2294432,\n        \"min\": 9306989,\n        \"max\": 15134845,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          14079412,\n          15134845,\n          10285216\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 100
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Visualizations**"
      ],
      "metadata": {
        "id": "ELKMQgJDmKdQ"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "merged = pd.merge(LottoMAXwinners, Lotto649winners, on='Year')\n",
        "final_merged = pd.merge(merged, DailyGrandWinners, on='Year')\n",
        "final_merged"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "nrEyLwnFmM_O",
        "outputId": "e2dc51ff-0233-4013-dc73-0fd7d8097896"
      },
      "execution_count": 101,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  LottoMAXwinners  Lotto649winners  DailyGrandWinners\n",
              "0  2017         57428823         81055839           14079412\n",
              "1  2018         79851510         83673406           15134845\n",
              "2  2019         61864421         76628308           13449231\n",
              "3  2020         78208370         68828105            9306989\n",
              "4  2021         94402821         73830013           10154278\n",
              "5  2022         92890436         73278384           10285216\n",
              "6  2023         65915089         87527136           10633925"
            ],
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              "      <th>Year</th>\n",
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              "      <th>0</th>\n",
              "      <td>2017</td>\n",
              "      <td>57428823</td>\n",
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              "      <td>14079412</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018</td>\n",
              "      <td>79851510</td>\n",
              "      <td>83673406</td>\n",
              "      <td>15134845</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019</td>\n",
              "      <td>61864421</td>\n",
              "      <td>76628308</td>\n",
              "      <td>13449231</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020</td>\n",
              "      <td>78208370</td>\n",
              "      <td>68828105</td>\n",
              "      <td>9306989</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021</td>\n",
              "      <td>94402821</td>\n",
              "      <td>73830013</td>\n",
              "      <td>10154278</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2022</td>\n",
              "      <td>92890436</td>\n",
              "      <td>73278384</td>\n",
              "      <td>10285216</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2023</td>\n",
              "      <td>65915089</td>\n",
              "      <td>87527136</td>\n",
              "      <td>10633925</td>\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('final_merged');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "final_merged",
              "summary": "{\n  \"name\": \"final_merged\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2017,\n        \"max\": 2023,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2017,\n          2018,\n          2022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LottoMAXwinners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14661597,\n        \"min\": 57428823,\n        \"max\": 94402821,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          57428823,\n          79851510,\n          92890436\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lotto649winners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 6554656,\n        \"min\": 68828105,\n        \"max\": 87527136,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          81055839,\n          83673406,\n          73278384\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DailyGrandWinners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2294432,\n        \"min\": 9306989,\n        \"max\": 15134845,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          14079412,\n          15134845,\n          10285216\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 101
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tickets = final_merged.copy()\n",
        "tickets['LottoMAX'] = tickets['LottoMAXwinners']/(1/7)\n",
        "tickets['Lotto649'] = tickets['Lotto649winners']/(1/6.6)\n",
        "tickets['DailyGrand'] = tickets['DailyGrandWinners']/(1/6.8)\n"
      ],
      "metadata": {
        "id": "2leFSfh8Qcl5"
      },
      "execution_count": 102,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Total Ticket Demand**"
      ],
      "metadata": {
        "id": "QuEt8wn4UZSw"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tickets = tickets.drop(columns=['LottoMAXwinners','Lotto649winners','DailyGrandWinners'])\n",
        "tickets.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "QkdHz9sFUU8I",
        "outputId": "27653754-05bf-4b48-bd7b-14c65a8e943a"
      },
      "execution_count": 103,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year     LottoMAX     Lotto649   DailyGrand\n",
              "0  2017  402001761.0  534968537.4   95740001.6\n",
              "1  2018  558960570.0  552244479.6  102916946.0\n",
              "2  2019  433050947.0  505746832.8   91454770.8\n",
              "3  2020  547458590.0  454265493.0   63287525.2\n",
              "4  2021  660819747.0  487278085.8   69049090.4"
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tickets",
              "summary": "{\n  \"name\": \"tickets\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2017,\n        \"max\": 2023,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2017,\n          2018,\n          2022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LottoMAX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102631184.22997424,\n        \"min\": 402001761.0,\n        \"max\": 660819747.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          402001761.0,\n          558960570.0,\n          650233052.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lotto649\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43260733.74863045,\n        \"min\": 454265493.0,\n        \"max\": 577679097.6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          534968537.4,\n          552244479.6,\n          483637334.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DailyGrand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15602142.796582898,\n        \"min\": 63287525.199999996,\n        \"max\": 102916946.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          95740001.6,\n          102916946.0,\n          69939468.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 103
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tickets.to_csv('/content/drive/MyDrive/A Python Learning/TicketSalesYearly.csv',index=False)"
      ],
      "metadata": {
        "id": "47yBYKULGoEC"
      },
      "execution_count": 104,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "melted_data = final_merged.melt('Year', var_name='Lottery', value_name='Winners')\n",
        "sns.lineplot(x='Year', y='Winners', hue='Lottery', data=melted_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "FW-fz7IMnOwP",
        "outputId": "d049064b-cba9-4423-c80a-45ee32901430"
      },
      "execution_count": 105,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Year', ylabel='Winners'>"
            ]
          },
          "metadata": {},
          "execution_count": 105
        },
        {
          "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": [
        "melted_data = tickets.melt('Year', var_name='Lottery', value_name='Total Tickets Sold')\n",
        "sns.lineplot(x='Year', y='Total Tickets Sold', hue='Lottery', data=melted_data)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "hcDGpIYAR9I0",
        "outputId": "43d223dd-2849-464f-f752-21e53f2c9fa0"
      },
      "execution_count": 106,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Year', ylabel='Total Tickets Sold'>"
            ]
          },
          "metadata": {},
          "execution_count": 106
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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THeFD28DNgdPh039rUNUVH71UHwAw7bcLuJKUaeGKyheLBpV3330XR48exfTp03HlyhWsW7cOP/30E8aOHWvJsoioAkrLUWHoigj8fPwGJAn4vGcDTO8bBFtOh09FMCy4JjrUqYxclRYTN0QiT621dEnlhkV/Alu3bo2tW7fi559/RlBQEL766ivMmTMHb775piXLIqIK5npyFvotPISDV+7BUWGDn95qheEda7GvARWZTCZhdv+mcHe0xblb6fh+7yVLl1RuWLQzbUkZ0xmHiKgwJ+JSMHL1SaRk5cHH1R7LQluhka+bpcuiMmr3uUSMWnMSkgSsG94W7WpXsnRJVqnMdKYlIrKkbZG3MGjJMaRk5aFxNTdsfyeYIYVK5KUgH7zRyh9CAO//EoW0bJWlSyrzGFSIqMIRQuC7P2IwcUMU8jRadGtUBRvebosqrvaWLo3KgS96NUSNSo64nZaLz7efQxm+cGEVGFSIqELJVWkw7udIzPv7CgBgVOfaWPRmSzgqLHpHESpHnOzk+P6NZrCRSfj19G1si7pl6ZLKNAYVIqow7mYoMXDJUew8kwC5TMI3rzbBpO6BnA6fTK55dQ9MeL4uAOCLbedxMyXbwhWVXQwqRFQhxCRmoM+CQ4i8kQo3B1usDnsG/VvzNhxUesY8WxutAjyQodTd1FKtKWNDltV5wN9fA6mWncSOQYWIyr19MUl4ddFh3ErNQY1Kjtg6pj1HY1Cpk9vI8P0bzeBsJ8eJ6/exaN9VS5dUdHdjgKXPA/9+A2wdDViwnw2DChGVa6uOxGFYeAQylWq0qemJrWOCUcvL2dJlUQXh7+mIqb0bAQDm/HUZUTdTLVvQ0wgBHF8CLO4EJJ4BHDyAZ94GLDinEIMKEZVLao0Wk3ecxxfbz0MrgNda+mFN2DPwcFJYujSqYPo2r4aXm1SFRiswcX0kspRqS5dUuIw7wNrXgd8/ANS5QO0uwOgjQMNXLFoWgwoRlTsZuSoMX3UC4YfjAAAfvVQf377WBAo5f+WR+UmShK/7NIavmz3ikrPx1c4Lli6poIu/AYvaAVf+BGzsgO7fAG9uBlyrWroyBhUiKl/i72fjtUVHsC/mLuxtZVj0ZguMebYOp8Mni3JztMXs/s0gScD6iJvYfS7R0iXpKDOBHeOA9YOA7GSgSmPg7f26yz0y64gI1lEFEZEJRN64jz4LDiPmTga8XOywYWQ7dG9s+b8IiQCgXe1KeLtTbQDApC1ncCc917IFxZ8AFncETq0CIAHBE4ARfwHeDSxb12MYVIioXPjtTAIG/HQU9zKVCPRxwfaxwWjq727psogMvNe1HoKquSI1W4UPNp6GVmuB0TQaNbBvFrDsRSDlGuBaDQjZAXSdCsjtzF/PUzCoEFGZJoTA/L8vY+y6U1CqtegS6I1No9vD193B0qURFaCQyzDnjeawt5XhwOV7WPGgH5XZpFwDVrwE7JsOCA0Q9Cow+hBQs5N56zACgwoRlVlKtQbvbzyN//vjEgBgWHBNLBnSCs52nA6frFcdb2d81rMhAGDW7ou4mJhe+jsVAji1GvixIxAfAdi5Av2WAq8t1w1BtmIMKkRUJqVk5eGtpcex5dQt2MgkTOsThC96NYQNp8OnMmDwM9XxfKA38tRaTPg5CrkqTentLDsF+OUtYMc7QF4mEBCsa0Vp8nrp7dOEGFSoRO5n5eHt1Scwa/dFaCxxrZUqpCtJmei78BCOx6XAxU6OFaGtMbhtgKXLIioySZIw67UmqOysQMydDMzafbF0dnTlL2BhOyD6V0BmC7wwBQj5FXCvXjr7KwUMKlRsSrUGb68+iT3n72DRvqsY/3Mk8tRl7F4WVOYcvnIP/RYewvXkbPh5OGDLmPboVM/L0mURGa2ysx2+fa0pAGDFoTj8e+mu6TauygF2fQys6QdkJgKV6wHD9wIdJgIyG9PtxwwYVKhYhBD4ZMtZHI9LgZPCBgobGX47m4CRq08gJ68UmzCpQlt//AaGLD+O9Fw1WlR3x7axwahbxcXSZREV23OB3hjSTtca+P7G00jJyiv5RhPOAD89Cxz7Ufe8zUhg5H7At1nJt20BDCpULAv+uaLvG7BoYBCWhbSEg60N9sXcRcjy48jIVVm6RCpHNFqB6b9HY9KWs1BrBV5p6ot1I9qisrP1DaUkMtanPRqgjrcz7mYoMWnzGYji3gBQqwUOzQWWdAHuXgScvIE3NwE9vgUUjqYt2owkUewzYnnp6elwc3NDWloaXF1dLV1O+aVRA+nxwP3rQOp1XL54Dheiz8JfSkID+/twyEsGXKoi0b8HJl6ojaO5AWji547woW3gyfuqUAll56kxYX0U/rxwBwAw8YW6mPB8Xc40S+XK+dtp6LPgEFQagZn9GmNAGyP7kKTFA1tHAXEHdM/r9wRemQc4VTZ9sSZgzOc3gwrphq1lJgH344DU6w8CSZw+mCDtlm68fRHdgA+2qdvitNsLmP7266jial9qpVP5lpiWi7CVETh/Ox0KuQzfvtYEvZtVs3RZRKXip3+vYvrvF+Fga4Pfxnco+l2+z24CfnsPyE0DbJ2A7jOB5m9Z9I7HT8OgQgXlpD4SQh7/egNQ5zz5/TZ2ULn64fh9F1xTV4a9V030e74DbDwDAFc/3bj8c5uAmF2AKlv/titSDVRuOwjubQYAHhyVQUV37lYawlZG4E66EpWcFPhpSEu0DPC0dFlEpUarFRi87BgOX01GUz83bBrdHrY2T+ihkZMK/P4hcPYX3fNqrYB+PwGVapul3pJgUKmIVLm6wJF6/ZGWkbiHgSQ37cnvl2S6aZTdA3SBwuBrDaTbeuLVRUdxOSkTDau6YuOodnAqbFKtvCwgZhdyTm2APPZv2OKR25n7tQEavwY06gs4e5vy6Kmc+fPCHYz/ORI5Kg3qejtjeWhr+HuW3WvsREWVkJaDl+YcQFqOCu88VwcfdKtf+IpxB3WXetJu6n5/d/oI6PQBYGNr3oKLiUGlPNJqgPRbhuHj0a+ZRbgTp2Nlg/BhEEhc/QB54f1JVBothoVH4MDle6jiaodtY4NR1e3p05PfvZOAteHz0Sbzb7S1iYYMD/6rSTKgZmddaAl8GXBwL/JpoPJNCIHlh+Iw7bcLEALoWLcyFrzZAq72ZeOXL5Ep/HYmAWPXnYJMAja83Q6tazzSkqjOA/75WtdpFkL3u7zfEsC/jaXKLRYGlbJICCDr7iPhI84wjKTFA1r1k7ehcNb9py2sVcS9OmBXxOudBmUJfLbtHNYduwEHWxtsHNUOQdXcivz++1l5CF1xHAnxcXjV7jjGekXB+d7phyvYKIC6L+ruN1HvpTLdM51KRq3RYsqvF7D66HUAwKBnqmPKK42e3PRNVE69/8tpbD4Vj2ruDtg1saMurN+NATYPBxLP6FZq/hbw0gzAruwN0WdQsVa56f/RTyROd9nmkb4dhbJRAG7+hVyaCQDcawCOnibvPLX0wDVM+y0akgQsHtwSLzbyMXobmUo1wsIjcCw2Bfa2MoT3roy22fuAs5uBu9EPV1Q4A/V76FpaancpM02YVHIZuSqM+zkS+2LuQpKAT7s3wPCONTmyhyqsjFwVes47iBsp2ejTtCrm1D4J/PE5oM4FHDx1I3oa9LJ0mcXGoGIpaiWQevPBiJm4goEk5/5TNiABrr4FQ0h+K4lLVUBmvr8u/7xwByNXn4AQwOc9G2B4x1rF3lauSoMxa0/h74tJsLWRMHdAc/RoXBW4c17XY/3cJl1Yy+fgATTsDQS9prsvhRmPm8zrdmoOhoVH4GJiBuxtdXeWfSnI+EBMVN6cvH4fYxbvwkybH/GczYOW6NrPA30WAi5l+2eEQaW0aDVA+u3/Hj2TkQDgKafTwdMwfDzaZ8TND5BbxwRW526l4fUfjyBHpcGgZ6rj6z5BJf7rVqXR4t0NUdh5JgEyCZj1ahO83spf96IQQPwJXWA5twXISnr4RpeqQKN+upYW3+ZWPeSOjHM2XjeyJylDCS8XOywd0gpN/d0tXRaRdbj4G7I3jYGjOhVK2CK785fw6Dy2XPzhxqBSUmnxwM1jBcNI6k1A+5QZV22d/uPSzIOvZeBaYmJaLnovOIg76Up0rFsZy0Nbm6yfgEYr8NnWs1gfcRMA8GWvhhgaXNNwJa1GN2nR2Y3AhV8B5SMjljxr6VpZGr8GeP1Hb3gqE/44n4gJ66OQo9KgfhUXLB/aGtXcn95Jm6jcU2YCez4BTq0CAMTKa2Fk1ih41GiCn0e0LRd3CGdQKalTq3W3wy6MTP4f/URq6B6Olcr0X/xZSjX6Lz6C87fTUdfbGZtGt4ebg2n7igihmw59yYFYAMB7XethXJc6hbfYqJXAlb26y0Mxuwzne6nSGGj8qq4jbhm6E2hFJ4TAsoOx+Pr3aAgBdKrnhQWDmsOFI3uIdC3LW0YAKdcASEDweFxvOhE95h9HVp4GH71UH2OerWPpKkuMQaWkbh4H/vhf4ZdoXH3L3J0ni0qjFXh79Unsjb6DSk4KbBsbXGpzVwgh8MPfV/Ddn5cAACM71cIn3QOffHlJmakLK+c26cLLo6Og/J/RtbQ06sM5WqyYWqPFlzvOY+0xXX+kNx+M7JFzZA9VdBo1cGA2sH+WbiZwVz+g749AzY4AgI0nbuLDTWcgl0nYMqY9mvi5W7beEmJQoWKZtvMClh6MhUIuw88j2qJlgEep73P5wVhM3XkBADCwTXVM6xNUtGbN7BQgeoeupSXuIMA5WqxeRq4KY9dF4t9LupE9n/VogLAOHNlDhJRrwJaRuhm+Ad0fXT1nG/z+EkJg7LpT+P1sImpVdsLO8R3gqChk0s0ygkGFjLbm6HV8vu0cAOCHgc3Rq6mv2fb9S8RNTNpyBloB9Grqi+/6NzWuT0x6AnB+q66l5dbJh8s5R4vVuJWag7AHI3scbG0wZ0AzdCvGUHeickUIIHINsHsSkJcJ2LnpAkqT1wtdPTU7Dy/NOYDE9FwMeqY6pvdtbOaCTYdBhYzy76W7GBoeAY1W4P2u9TDu+bpmr+G3MwmYuCESKo1Al0BvLHyzBexti3GJLeUacG6zrqXl7sWHy/VztLwO1H6Oc7SY0Zn4VIStPIG7D0b2LA9pjcZ+RZ80kKhcykoGdk4Aon/VPQ8I1l3qeUp/u0NX7uHNpccAAEuGtELXhlVKu9JSwaBCRXbpTgZeXXgYGUo1+jWvhtn9m1qsKf6fmCSMWn0SSrUWbWt5YmlIazgXdj+hohBCN0fLuU264GIwR4unbo6Wxq8B1duXi6F+1mr3uURM3BCJXJUWgT4uWBbKkT1EuPIXsG2M7tYnMlugy+dA+3FF7v/49W8XsORALDydFNg9sSO8XcreHeoZVKhI7mYo0WfBIdxKzUGbGp5YPbwN7OSW7Sh87FoywlaeQKZSjab+7lg5tDXcHQu/B1GRCaG79nt2k+4SkcEcLb5AUD/d5SHO0WIyQggsOXANM3ZdhBBA53pemM+RPVTRqXKAvZOBYz/qnleuD7y6BKja1KjNKNUa9FlwGNEJ6ehczwvhQ1uXub5eDCr0VLkqDQYuOYrIG6moUckRW8cEw8OphIHARM7EpyJk+XHcz1ahfhUXrA5rA29XE/3FoFHr5mg5t4lztJQS1YORPesejOx5q20AvuzVkCN7qGJLOKMbdpx/SbrNSKDrVMC2eC2Ml+9k4OUfDkKp1mJyr4YIfXw+KivHoEJPpNUKjF8fiZ1nEuDmYIstY9qjtpfxNywsTZfvZODNpceQlKFEjUqOWDP8Gfh5mLgzLOdoMbn0XBXGrj2FA5fvQZKAz3s2xLDgGmXurz0ik9FqgSM/AH99pZsw1LkK0HsBULdriTe98nAcvtxxHgq5DDvHdUC9KtY/oWg+BhV6otl/xOCHv69ALpOwOuwZtKtdydIlFepGcjbeXHYUN1NyUNXNHmuGP1N6geppc7Q0fh1o2Adw9iqd/ZcDN1OyEbYyApfuZMLB1gbzBjYvsx39iEwiLR7YOkrXigsA9XvqbiboVNkkmxdCYGh4BPbF3EWgjwu2vxNs8cv3RcWgQv9p88l4vL9Rd3Orb15rgv7599qxUolpuRi87BiuJGWikpMCq8LaoJFvKY8YyU4BLmzXdcI1mKPFBqjVWXd5qMHLgD1HruSLupmK4SsjcC8zD1Vc7bAspDWCqvH8UAV2dhOw8z3d5WVbJ6D7TKD5WybvB5eUkYuX5hxASlYeRnSsic96NjTp9ksLgwoV6ti1ZAxedgwqjcCYZ2vjo5cCLV1SkaRk5SFk+XGcvZUGF3s5woe2RssAT/PsPP22rgPu2U3A7VMPl9vY6ZpuG7+mm6OlmNeZy4NdZxMwcUMUlGotGlR1xfLQVqjqVnHPB1VwOanA7x8CZ3/RPa/WCuj3E1Cpdqntcu+FOxi+6gQAYE3YM+hQ1zQtNqWJQYUKiL2Xhb4LDyE1W4UejX0wf2ALyMrQja3Sc1UIC49ARNx9ONja4KchLdGxrpkvwyRf1d3Z+Vwhc7QE9tS1tFSgOVqEEFj87zXM3KU7F10CvTFvYPPiDyknKuviDuou9aTd1LXAdvpQ97Ap/Z+Jz7aexdpjN1DF1Q67J3SymsER/4VBhQykZueh78LDiL2Xhab+7tgwsm3xJlOzsJw8DUatOYn9l+5CYSPDD4OaW2Z200fnaDm7GUireHO0qDRa/G/bOf1dsEPb18DnPRtwZA9VTOo84J+vgUNzAQjAo6auFcW/jdlKyMnToOcPB3Dtbha6B/lg4ZstrLoTO4MK6eWptRiy/BiOXktBNXcHbB3bvkxODpQvT63FxA2R+P1sImxkEr59rQn6tfCzXEH6OVo2Ppij5e7D1/LnaGn8GlC1WbmZoyUtRzey5+CVe5BJwP9eboihZWxoJJHJ3I0BNg8HEs/onjd/C3hpBmBn/hE4Z+PT0HfhIai1wur7IDKoEABd0/xHm85g48l4ONvJsWl0OwT6lP3zpNZoMWnLWWw6GQ8A+Kp3I7zVroZliwIezNHyr66VJfrxOVpq6wJL0GuAVz3L1VhCN1OyMSw8ApeTMuGosMEPA5vj+QYc2UMVkBBAxFLgj88Bda6uNfWVeUCDXhYta+G+K/hmdwycFDb4fUJHBFRysmg9/8WYz2+LttNOnjwZkiQZPAIDy0YHz7Jg0f6r2HgyHjIJmD+oebkIKQAgt5Hhm1ebILR9DQDA/7afx4J/rli2KEB3Hbp2F6DPAuDDy8Aba4FGfQG5A5ByVXf79gWtgcWdgONLgJz7lq7YKJE37qPvwkO4nJSJKq52+OXtdgwpVDFl3AHWvgb8/oEupNR+HhhzxOIhBQDe7lQbbWp6IitPgwnro6DSaC1dUolZvNdbo0aNsHfvXv1zudziJZULv59NwDe7YwAAU15phGfre1u4ItOSySR82ashXO3lmPf3FXy7JwYZuWp8/FJ967guK7fTDWFu8DKgzNDN0XJ2E3D1LyDhtO6x5zPdL7YWbwE1Oll1f5bfzybg3QcjexpWdcXy0NbwcSu7lxCJiu3ib8COcUB2MiC3180u22ak1VzatZFJ+P6NZnhpzr+IupmKH/6+gve6lt1WXMAKgopcLoePD2/3bkpRN1Px7oYoAMDQ4BrWcVmkFEiShPderA8Xe1t8/Xs0ftx/FZlKFaa+EmRdI5rsXIAm/XWPrGRdf5bI1cCdcw9umrhJN/tts8FAs0GAu/VcVxZCYNH+q/rQ+/yDkT1OHNlDFY0yE9jzCXBqle65T2Og3xLAu4Fl6ypENXcHfN23Mcb/HIn5f19G53qVzTelQymw+J9wly9fhq+vL2rVqoU333wTN27c+M91lUol0tPTDR5kKP5+NoavPAGlWosugd74vIxM/lMSIzrVwvS+jSFJwJqjN/DeL1bc3OlUCWg7Chh1EBjxD9AqDLBz093ded90YE5jYHVf3TBotdKipao0WkzafFYfUkLb18BPQ1oxpFDFE38CWNzxQUiRgOAJwPC/rDKk5HulqS/6Nq8GrQAmbohCRq7K0iUVm0U70+7atQuZmZmoX78+EhISMGXKFNy6dQvnzp2Di0vBHtOTJ0/GlClTCixnZ1qdjFwVXlt0BDF3MtCgqis2jmpXoea02B51C+//chpqrUDXhlXww8DmZWMYdl42cHGn7pdg/lTbAODgATR5A2g+WPfXmxml5agwes1JHL6aDJkEfNmrEUIe9AkiqjA0auDAbF3/MqEBXP2Avj8CNTtaurIiSc9VofucA7iVmoNXW/hhdn/j7tJcmsrsqJ/U1FQEBATgu+++Q1hYWIHXlUollMqHf2Wmp6fD39+fQQW6kTBhK09g/6W78Haxw7axwfB1r3izg/4VfQej155CnlqL4DqV8NNbZawFIOUaELUOiFwLZNx+uLxqM11flqDXAAf3Ui3hZko2hoZH4EpSJpwUNvhhUHN0CWSnWapgUq4BW0bqph8AdD97PWeX+s+fqUXEpeCNxUegFcCCQS3Qs0lVS5cEoAwHFQBo3bo1XnjhBcyYMeOp63J4so4QAl9sP4/VR6/DwdYGv7zdDo39Ku59Vg5fvYcRK08gK0+DFtXdsSK0Ddwcy9hssVoNcPVvXV+Wi7/r7roK6DrvNXhFF1oCOpi8A+7J6/cxctUJJGflwcfVHstDW6Ohb8X92aIKSAggcg2wexKQl6m7NNtzNtDkdUtXVmz5N6J1tZdj98ROVvFHbJkZnvy4zMxMXL16FVWrWkfiKyvCD8dh9dHrkCRgzoBmFTqkAED72pWxZvgzcHOwxakbqRiw5CjuZli2v4fRZDa6ewn1XwW8fxHoNgPwbqgbCnn2F2BlL+CH5sD+b3V3aDWBnWduY+CSo0jOykMjX1dsGxvMkEIVS1Yy8MtbwI53dCEloAMw+mCZDikAMP75umjq7470XDXe/+U0tFqrap94Kou2qHzwwQfo1asXAgICcPv2bXz55ZeIiorChQsX4OX19Pu4sEVFd6ljxKoT0Arg0x6BGNmp9G58VdZcTEzH4KXHcS9TiVqVnbB6+DOoZgV/SRSbELobI55arbuzs/JBZ3JJppu/pflbQP3uuqHRRm1WYOG+q/h2j67T7AsNqmDugGZl65IZUUld2QtsGwtkJgIyW6DL50D7cbo/GsqB2HtZ6DnvALLzNPikeyDe7mzZz4oyc+lnwIAB+Pfff5GcnAwvLy906NABX3/9NWrXLtoJrOhB5cLtdLz242Fk52kwsI3/g5EvVjQs1wrE3cvCm0uP4VZqDqq5O2DN8GdQs7J1ztRolLxsIHqHLrRcP/hwuYMn0HSArgNulUZP34xai8+2nsXGB7P8hnWoiU97NICNNQ3vJipNqhxg72Tg2I+655XrA68uAapaT8dTU1l//AYmbTkLWxsJW8cEI6ia5Vrfy0xQKamKHFTupOeiz4JDSEjLRXCdSggf2ga2vCFcoW6n5mDw0mO4di8LlZ3tsDqsDRpULUf/X5KvAlFrdZ1wMxIeLvdt8aAD7quAfcFfSGnZKoxacxJHrulG9kzpHYS32gaYsXAyifvXgeuHgHuXdC0BcgVgY6drWbNRPPbV7pHXn7KeGe74a3EJZ4AtIx7eDb3NSN0EbrZluOX1CYQQeHv1Sfxx4Q7qeDvj13c6wEFhmRYjBpVyLjtPjTcWH8XZW2mo7eWELWOC4eZQxjqLmtm9TCWGLDuOCwnpcLWXY+WwNmhe3cPSZZmWRv2gA+4q3Uy4WrVuudxBd0fn5oOBGh0AScKN5GwMDT+Oq3ez4KSwwfw3W+C5cjZ7cbkkBHA/Fog7pAsncYcM795tSpKsCIHm0WBjW/xQVNT1TNV5XKsFjvwA/PWVrqO6cxWg9wJdv7ByLiUrDy/N+RdJGUoMaReAqb2DLFIHg0o5ptUKjFqjS8SeTgpsGxOM6pUcLV1WmZCWo8Kw8AicvH4fjgobLA1phfa1K1u6rNKReRc4s0E3aij/r0UA8KiJWzVfxbCouojJdoGvmz2WhbYuXy1M5YkQuhaz6wd1oSTuoOGwdQCQyQHf5roh7BC6iQI1KkCjBNR5D74qAU3ef3x9ZD1hpRMl5pPJjQg2TwhCN44+vGQa+DLQay7gVE5/FxTiwOW7eGvZcQDA8tBWFpl+gEGlHJvxezQW/3sNCrkMP494pkxPi2wJ2XlqvL36JA5cvgeFXIaFg1rghYbleI4QIXSzakau1s12m5cBANAICacULVGv22i4NXtF9wucLE8I3SWcuIMPW0wyEw3XkdkC1VoCNYJ1LWR+bQA7Z9PsX6N+QrB5NPg8KQAVsp4mr5D3FjFElRZbJ6D7TF0n9ArYt2/qrxew/FAsKjsrsHtiJ1R2Nq4TfkkxqJRTPx+/gU+2nAUAzB3QDL2bVbNwRWWTUq3BuHWR+OPCHdjIJHzXv2m5P5dCCPz451lc2bcW/eX78IzskVYWx8oPO+Ba8ZTg5ZJWq2vxun7oYTjJumu4jo0d4NcKCMgPJq0BRQVpRRWiiK1DRoYouQJoORSoVHFHSeaqNOiz4BAuJmagS6A3loW0MutgDAaVcujg5XsIXXEcaq3Auy/Uw4QX6lq6pDJNrdHio01nsCXyFiQJ+LpPYwx6prqlyyoVeWotPtlyFptP6Ub2jOhYE5Pa2MLm9Fog6mfDv9irtdJ1wG3UD7Av3z9TFqHVAknnH/QxeXA5JyfFcB25vS6M1OioazWp1gqw5Z2qyfQuJqbjlfmHkKfW4qs+5u1Mz6BSzlxJykDfhYeRkatG3+bV8F3/phyGbAJarcCXO3Qz+gKwirkFTC01Ow9vrz6JY7EpsJFJmPJKIwx+9JeRRq2bPyJyNXBp98MOuLaOQMM+ulaWgPYVsmncJLQaIPHsIy0mh4HcVMN1bB0B/za61pKADkC1FkbPhUNUXMsPxmLqzguwt5Vh57gOqONd8D57pYFBpRxJzlSiz8JDuJmSg9Y1PLBm+DOwk5ePCYisgRAC3+6JwcJ9VwEA7zxXB++/WK9cBMG4e1kYFh6Ba/ey4Gwnx4I3W6BzvSdMpJiZBJxerwst9y49XO5ZWxdYmg4EXDlr9BNp1EDi6YcdX28cBZRphusonAH/Z3TBpEYHXSdY9hEiC9FqBUJWHMeBy/fQyNcVW8cEQyEv/akuGFTKiVyVBm8uPYaT1++juqcjto0NhqcTf6GVhoX7ruCb3bqZWUPb18AXLzeErAxPehYRl4KRq07gfrYK1dwdsCy0FQJ9ivgzIgRw87gusJzfqptKHNANV637oi601HtJNxy1otOogNtRDy7jHARuHNN3WNazcwWqt33YYlK1acWYo4TKjKT0XHSb8y/uZ6vwduda+KR76fdVY1ApB4QQmLA+CjtO34aLvRxbxwSjjreJevZToVYfvY4vtp+DEMCrLfww69XGkJfBSfS2R93ChxvPIE+jRRM/NywNaQVvl2L2cVBmAhe26WbAvXn04XInrwcdcN8CvOqbpO4yQZ2nu41B3INgcvM4oMoyXMfeDaje/kGLSTDg06TcTMNO5dee84l4e/VJSBKwbnhbtKtdqVT3x6BSDnz/5yXM/esy5DIJK4e1QXCdijPG35K2Rsbjg41noNEKdA/ywZwBzcrMpTYhBOb9dQXf79VdtunWqArmvNHcdDNP3rusa2WJ+hnISnq43K+NrpUlqB9gZ57r22ajygVunXzQv+QgcDMCUOcYruPg8XBETkCw7tYFDCZUBk3afAbrI26iqps9dk/oVKp3nWdQKeO2Rd7CxA1RAICZ/RpjQJvyORrFWv1xPhHvrItEnkaLjnUrY/FbLeGosO6meqVag082n8WWyFsAgJGdamHSS4Glc/lKowIu//mgA+4eQGh0y20dgUZ9da0s1duWzQ64qhwgPuJBi8kh3fePz+XhWFnXUhLwoMXEq4HpZkwlsqAspRov/3BQdwPDJlUxf2DzUuuvx6BShkXEpeDNJceQp9Ga7VohFXTw8j2MWHUCOSoNWgV4YPnQ1nC1t84+Gfez8vD2mpM4/mBkz1e9g8w31Doj8WEH3OQrD5dXqvOgA+4gwMWKJ9TLy9Jdvsmfw+TWSd2cG49y8n54GSegg+5SV1kMYURFcPpmKl5ddBhqrcDs15vi1ZZ+pbIfBpUy6npyFvosOIT72Sq81MgHC99sUaY7dJZ1J6/fx9AVx5Geq0YjX1esGtYGlcw8e+PTxD4Y2RN7LwsuD0b2dHrSyJ7SIgRw85iuL8v5rQ/7bUg2QL1uutBS90XLd8BVZug6vObPYXL71MMh2flcqj68jFOjgy50MZhQBTL/78v4vz8uwdlOjl0TOsLf0/QTDJo8qMybN6/IOx8/fnyR1y2p8hRU0rJV6LvoEK7dzUITPzdsGNnOYne1pIfO307DkGXHkZyVh9peTlg7vC183Kxj8q3jsSkYufoEUh+M7Fke2hr1faygj4gyQxdWTq0G4o8/XO7kDTQbCDQbDHjVM08tuWm6IcL5LSa3ox5eqsrn6vdIi0kw4FmLwYQqNI1WYMBPRxARdx8tAzywYWRbkw8sMHlQqVmzpsHzu3fvIjs7G+7u7gCA1NRUODo6wtvbG9euXSt+5UYqL0ElT61FyPLjOHItGVXd7LF9bDC8Xa3jw5CAq3czMXjpMSSk5cLPwwFrhz+DgEpOFq1pa2Q8Pt50FnkaLZr6u2PJkJbFH9lTmpIuAlFrdJeHHp0a3r+trpWlUV/T3acGAHLuA9ePPJxgLfFMwRvtuVfXzfoaEKwLJ+4BDCZEj7mZko0ecw8gQ6nGe13rYfzzpp0NvVQv/axbtw4LFy7EsmXLUL++blhiTEwMRowYgbfffhtvvvlm8Ss3UnkIKkIITNp8FhtO3ISTwgYbR7VHQ9+yeSzlWfz9bAxeegxxydnwdrHDmuHPoF4V87deCCEwZ+9lzP3rMgCge5APvuvfzPpb3zQqXcfbyNXA5T8ehgdbJyCoL9B8iG52VmMDQ3bKw5v3XT8IJJ4D8NivNI+aDydXCwgG3P1NckhE5d3WyHi8u+E0ujWqgh8HtzRpx9pSDSq1a9fGpk2b0Lx5c4PlJ0+exGuvvYbY2FjjKy6m8hBUftx/FTN3XYRMApaGWOZ221Q0SRm5GLLsOC4mZsDd0RYrh7ZBU393s+1fqdZg0uaz2PpgZM+ozrXxUbf6Za8fU3oCcPpnIHINkHL14fLK9R7OgOvsXfh7M+/qgkl+OEk6X3CdSnUNR+W4+pbOcRCVc0IIHLh8Dx3rVjb56J9SDSqOjo7Yv38/WrdubbD8+PHjePbZZ5GdnW18xcVU1oPK7nMJGLXmFABgcq+GCA2u+ZR3kKWlZuchdEUEom6mwtlOjqUhrdC2VulOjAQ8GNmz+iSOx6VALpMwrU9Q2R+2LgRw44iuL8uFbYDqwe8OmRyo2013c8SqTXXrxD0IJ3cvFtyOV+DDyzgBwYCLj1kPg4iMV6pBpVevXrh16xaWLl2KFi1aANC1powcORLVqlXDjh07il+5kcpyUDkTn4r+i48gV6VFSLsATOkdZOmSqIgylWqMWHkCR64lw04uw4+DW+K5wP9oATCBa3czMSw8AnHJ2XCxl2PRmy3RoW45mwAwNx04v0UXWm6dePK63o0ehpKAYMDZAqOciKhESjWo3L17FyEhIdi9ezdsbXVDDdVqNbp164bw8HB4e5feL+zHldWgcjs1B70XHMLdDCWere+FpUNalcmp2iuyXJUG76w7hb3RSZDLJMwZ0AwvNzH9JYZj15Lx9pqTSM1Wwc/DAStCW6OuBfrGmFVStO6y0OmfdX1QfIIeXsap3h5wKv0WLCIqXWaZR+XSpUu4eFHXDBsYGIh69cw03PARZTGoZCrVeG3RYVxMzECgjws2jmoHFyudSIyeTKXR4v1fTmPH6duQScCMfo3xRmvTXY7ZcioeH28+A5VGoJm/O5YMaQUvF+uax6VUadS6WWEVlh1hRUSmZ8znd7HnBa9Xr55FwklZptZoMW7dKVxMzEBlZzssC23NkFKG2drI8P0bzeBkJ8fPx2/g481nkZGrxvCOtUq0XSEEvt97GfMejOzp2bgqZvdvCntbKx/ZY2o2ct5lmIiKFlTee++9Im/wu+++K3Yx5d2036LxT8xd2NvKsCykFaq5O1i6JCohG5mE6X2D4Govx+J/r2Hab9HIyFVj4gt1i9VLPlelwcebz2B71G0AwJhna+ODF8vgyB4iIhMpUlCJjIws0sZK6+ZF5cHKw3EIPxwHAPi+fzOzDmul0iVJEiZ1D4SLvRz/94furteZSjU+79nAqJ+JlKw8jFx1Aieu34dcJmF638bo35pzfhBRxVakoPLPP/+Udh3l2j8XkzDlV918Dx+/FIjujatauCIyNUmS8E6XunC2k2Pyrxew7GAsMnPVmN6vMWyK0Bpy9cHInusPRvb8OLglguuUs5E9RETFUKILwPHx8QAAP7/SubtieRCdkI531p2CVgD9W/lhVOeS9V8g6xYaXBNOdnJ8vPkMNpy4icw8Nb7v3wwK+X+P6jpyNRmj1pxEWo4K/p66kT11vMv5yB4ioiIyOqhotVpMmzYNs2fPRmZmJgDAxcUF77//Pj777DPIZBxmmy8pIxdh4RHIytOgXa1KmNanMS+PVQCvt/KHs50c49dH4rczCchSqrHozZaFTnO/6WQ8PtmiG9nToro7fhrSCpWt7A7NRKag0WigUqksXQaZia2tLWxsTDMAwOig8tlnn2HZsmWYOXMmgoODAQAHDx7E5MmTkZubi6+//tokhZV1OXkajFh5ArfTclHLywk/Dm75xL+qqXzp3rgqltrJ8fbqE9gXcxchK45jWUgr/SgvrVbguz8vYf4/VwAALzepiv97vQKO7KFyTwiBxMREpKamWroUMjN3d3f4+PiU+A90o+dR8fX1xY8//ohXXnnFYPn27dsxZswY3Lp1q0QFGcNa51HRagXGrjuFXecS4eFoi61jglGjMueCqIiOx6YgLDwCGUo1mvi5YeXQNnBQ2OCDjaex80wCAGDsc7XxfleO7KHyKSEhAampqfD29oajoyNblSsAIQSys7ORlJQEd3d3VK1asF9mqc6jkpKSgsDAwALLAwMDkZKSYuzmyqVv/4jBrnOJUNjIsPitVgwpFVibmp74eWRbDFl+HGfi09B/8RG4OtjiZP7Inn6N0b8VR/ZQ+aTRaPQhpVIlzihckTg46KbfSEpKgre3d4kuAxl9LaJp06aYP39+geXz589H06ZNi11IefFLxE0s2qe7I+ys1xqjTU1PC1dElhZUzQ2/vN0WVVztcDkpEyev34ervRyrwtowpFC5lt8nxdHR0cKVkCXk/7uXtG+S0S0q33zzDXr27Im9e/eiXbt2AIAjR47g5s2b+P3330tUTFl3+Oo9fLr1LABg/PN10bc5R0ORTh1vF2wa1R7DV56AgMDCN1uijrezpcsiMgte7qmYTPXvbnRQ6dy5My5duoQFCxbo7/XTr18/jBkzBr6+pr8pW1lx9W4mRq0+CbVW4JWmvnj3hbqWLomsjL+nI3ZP7AiAv7iJiIqqWPOo+Pr6cnTPI1Ky8jAsPALpuWq0qO6Ob15rwg8iKhT/XxARGafIfVTu3buH69evGyw7f/48hg4div79+2PdunUmL64sUKo1eHv1CVxPzoa/pwOWDGnFIaZERGVIaGgo+vTpU6z3Pvvss5g4caLBsn379kGSJA7JNpEiB5Vx48Zh3rx5+udJSUno2LEjIiIioFQqERoaitWrV5dKkdZKCIFJm88iIu4+XOzlWB7SGpU4WRcREZmAEAJqtdrSZVhckYPK0aNHDeZOWbVqFTw9PREVFYXt27dj+vTpWLBgQakUaa1++PsKtkbego1MwqI3W6JuFU57TkRUnuzfvx9t2rSBnZ0dqlatikmTJunDQ2hoKPbv34+5c+dCkiRIkoS4uDg899xzAAAPDw9IkoTQ0FAAupndZ8yYgZo1a8LBwQFNmzbFpk2b9PvKb4nZtWsXWrZsCTs7O6xZswYymQwnTpwwqGvOnDkICAiAVqs1z4mwoCIHlcTERNSoUUP//O+//0a/fv0gl+u6ubzyyiu4fPmyyQu0VtujbuG7Py8BAKb1CUKHuryBHBFReXLr1i306NEDrVu3xunTp7Fo0SIsW7YM06ZNAwDMnTsX7dq1w4gRI5CQkICEhAT4+/tj8+bNAICYmBgkJCRg7ty5AIAZM2Zg1apV+PHHH3H+/Hm8++67GDx4MPbv32+w30mTJmHmzJmIjo7GK6+8ghdeeAErVqwwWGfFihUIDQ2tELetKXJnWldXV6SmpiIgIAAAcPz4cYSFhelflyQJSqXS9BVaoZPXU/DhpjMAgJGdamFgm+oWroiIiExt4cKF8Pf3x/z58yFJEgIDA3H79m18/PHH+OKLL+Dm5gaFQgFHR0f4+Pjo3+fpqZs/y9vbG+7u7gAApVKJ6dOnG0ztUatWLRw8eBCLFy9G586d9e+fOnUqunbtqn8+fPhwjBo1Ct999x3s7Oxw6tQpnD17Ftu3bzfDWbC8Ikextm3bYt68edBqtdi0aRMyMjLQpUsX/euXLl2Cv3/5n7zqRnI2Rq46iTy1Fi82rIKPXyo4Sy8REZV90dHRaNeuncFoveDgYGRmZiI+Pt6obV25cgXZ2dno2rUrnJ2d9Y9Vq1bh6tWrBuu2atXK4HmfPn1gY2ODrVu3AgDCw8Px3HPPGVzlKM+K3KLy1Vdf4fnnn8eaNWugVqvx6aefwsPDQ//6+vXrDRJheZSWo8KwlRFIzspDUDVXzBnQDDa8PwsRET1FZmYmAOC3335DtWrVDF6zszMchOHkZHjbFYVCgSFDhmDFihXo168f1q1bp7+cVBEUOag0adIE0dHROHToEHx8fPDMM88YvD5gwAA0bNjQ5AVaC5VGi7FrT+FKUiZ8XO2xLKQ1HBXFmoaGiIjKgAYNGmDz5s0QQuhbVQ4dOgQXFxf4+elmHlcoFNBoNAbvUygUAGCwvGHDhrCzs8ONGzeK9Uf98OHDERQUhIULF0KtVqNfv37FPawyx6hP2sqVK6N3796FvtazZ0+TFGSNhBD4Yvs5HLxyD44KGywLbYUqrvaWLouIiEwkLS0NUVFRBstGjhyJOXPmYNy4cXjnnXcQExODL7/8Eu+9956+E2uNGjVw7NgxxMXFwdnZGZ6enggICIAkSdi5cyd69OgBBwcHuLi44IMPPsC7774LrVaLDh06IC0tDYcOHYKrqytCQkKeWF+DBg3Qtm1bfPzxxxg2bJj+pn8VgijD0tLSBACRlpZWqvv5af9VEfDxTlFz0k6x90Jiqe6LiKi8yMnJERcuXBA5OTmWLuWJQkJCBIACj7CwMLFv3z7RunVroVAohI+Pj/j444+FSqXSvzcmJka0bdtWODg4CAAiNjZWCCHE1KlThY+Pj5AkSYSEhAghhNBqtWLOnDmifv36wtbWVnh5eYlu3bqJ/fv3CyGE+OeffwQAcf/+/ULrXLZsmQAgjh8/Xpqnw2Se9O9vzOe3JIQQlgpJJZWeng43NzekpaXB1dW1VPax53wiRq05CSGAL15uiGEdapbKfoiIypvc3FzExsaiZs2asLdnK3RJffXVV9i4cSPOnDlj6VKK5En//sZ8flvNAOyZM2dCkqQCUxFb0tn4NExcHwUhgLfaBmBocA1Ll0RERBVMZmYmzp07h/nz52PcuHGWLsfsrCKoREREYPHixWjSpImlS9FLSMtB2MoI5Kg06FzPC1/2asgbyhERkdm98847aNmyJZ599lkMGzbM0uWYndFBJX+imXzbt29Hnz598OmnnyIvL8/oAjIzM/Hmm29iyZIlBsOdLSlLqUZY+AkkZShRv4oL5g9qDrmNVWQ6IiKqYMLDw6FUKrFhwwbY2FS8m94a/en79ttv49Il3dTx165dw4ABA+Do6IiNGzfio48+MrqAsWPHomfPnnjhhReMfm9pWfDPFVxISEdlZwWWhbaCi72tpUsiIiKqkIyeCOTSpUto1qwZAGDjxo3o1KkT1q1bh0OHDmHAgAGYM2dOkbe1fv16nDp1ChEREUVaX6lUGkzTn56ebkzpRTb++bq4k67E4LbV4efhWCr7ICIioqczukVFCKG/W+PevXvRo0cPAIC/vz/u3btX5O3cvHkTEyZMwNq1a4vcG3zGjBlwc3PTP0pryn57WxvM7t8Uzatbx6UoIiKiisrooNKqVStMmzYNq1evxv79+/UTvcXGxqJKlSpF3s7JkyeRlJSEFi1aQC6XQy6XY//+/Zg3bx7kcnmBmf4A4JNPPkFaWpr+cfPmTWPLJyIiojLE6Es/33//PQYPHoxt27bhs88+Q506dQAAmzZtQvv27Yu8neeff96gUy4ADB06FIGBgfj4448L7TBkZ2dX4J4IREREVH4ZHVSaNm1aIGAAwLfffgu5vOibc3FxQVBQkMEyJycnVKpUqcByIiIiqpiMvvRTq1YtJCcnF1iem5uLevXqmaQoIiIiSwoNDUWfPn2K9d5nn322wOSl+/btgyRJSE1NNWpbcXFxkCQJNjY2uHXrlsFrCQkJkMvlkCQJcXFxBd7brVs32NjYFBiwotFo0L59+wI3NkxLS4O/vz8+++wzo2osbUYHlbi4uEL7jyiVSsTHx5eomH379hk1aoiIiKgiqFatGlatWmWwbOXKlahWrVqh69+4cQOHDx/GO++8g+XLlxu8ZmNjg/DwcOzevRtr167VLx83bhw8PT3x5Zdfmv4ASqDIQWXHjh3YsWMHAGDPnj365zt27MDWrVvx1VdfoWZN3geHiIjKt/3796NNmzaws7ND1apVMWnSJKjVagC6lpj9+/dj7ty5kCRJ39rx3HPPAQA8PDwgSRJCQ0MB6P7IHz9+PLy9vWFvb48OHToUOmVHSEgIVqxYYbBsxYoV/3nX5RUrVuDll1/G6NGj8fPPPyMnJ8fg9Xr16mHmzJkYN24cEhISsH37dqxfvx6rVq2CQqEo6SkyraLeBVGSJCFJkpDJZPrv8x8KhULUq1dP/Prrr0bcV7HkzHX3ZCIiMl5ZuXtyYUJCQkTv3r0LLI+PjxeOjo5izJgxIjo6WmzdulVUrlxZfPnll0IIIVJTU0W7du3EiBEjREJCgkhISBBqtVps3rxZABAxMTEiISFBpKamCiGEGD9+vPD19RW///67OH/+vAgJCREeHh4iOTlZCCFEbGys/o7JlStXFgcOHBBCCHHgwAHh5eUljh8/bnDXZiF0d2kOCAgQO3fuFEII0bJlS7Fq1aoCx6LVasWzzz4rnn/+eeHt7S2++uorE55B0909uci9X/PnTqlZsyYiIiJQuXLl0shNRERUjgkhkKMq2H2gtDnY2pjkfm0LFy6Ev78/5s+fD0mSEBgYiNu3b+Pjjz/GF198ATc3NygUCjg6OsLHx0f/Pk9PTwCAt7c33N3dAQBZWVlYtGgRwsPD0b17dwDAkiVL8Oeff2LZsmX48MMP9e+3tbXF4MGDsXz5cnTo0AHLly/H4MGDYWtbcOb0vXv3Ijs7G926dQMADB48GMuWLcNbb71lsJ4kSVi0aBEaNGiAxo0bY9KkSSU+P6XB6FE/sbGx+u9zc3N5624iIiqyHJUGDb/YY/b9XpjaDY4Koz/yCoiOjka7du0MQk9wcDAyMzMRHx+P6tWrF3lbV69ehUqlQnBwsH6Zra0t2rRpg+jo6ALrDxs2DO3bt8f06dOxceNGHDlyRH/J6VHLly/HG2+8oR+JO3DgQHz44Ye4evUqateuXWBdR0dHxMbGIj4+HjVq1Chy/eZidGdarVaLr776CtWqVYOzszOuXbsGAPjf//6HZcuWmbxAIiIiAho3bozAwEAMHDgQDRo0KHQqj5SUFGzduhULFy7UT6ZarVo1qNXqAp1qDx8+jO+//x47d+5EmzZtEBYWBiGEuQ6nyIyOl9OmTcPKlSvxzTffYMSIEfrlQUFBmDNnDsLCwkxaIBERlR8Otja4MLWbRfZrCg0aNMDmzZshhNC3qhw6dAguLi7w8/MDACgUigKjY/M7qD66vHbt2lAoFDh06BACAgIAACqVChEREQWGN+cbNmwYxowZg0WLFhX6+tq1a+Hn54dt27YZLP/jjz8we/ZsTJ06FTY2NsjOzkZoaChGjx6N5557DjVr1kTjxo3x448/YvTo0Uafl1JlbOeY2rVri7179wohhHB2dhZXr14VQggRHR0t3N3djd1cibAzLRGR9SrrnWmfffZZERkZafCIi4sTjo6OYuzYsSI6Olps27bNoDOtEEKMGDFCtG7dWsTGxoq7d+8KjUYj4uPjhSRJIjw8XCQlJYmMjAwhhBATJkwQvr6+YteuXQadaVNSUoQQDzvTRkZGCiGEUKlU4u7du0KlUgkhhIiMjDToTNu0aVPx8ccfFzie1NRUoVAo9B1sx48fL+rUqSOysrL06/z444/C2dnZoGNuSZiqM63RQcXe3l7ExcUJIQyDyvnz54WTk5OxmysRBhUiIutV1oMKgAKPsLAwsW/fPtG6dWuhUCiEj4+P+Pjjj/XBQQghYmJiRNu2bYWDg4NBiJg6darw8fERkiSJkJAQIYTuHI0bN05UrlxZ2NnZieDgYHH8+HH9th4PKo97NKicOHFCP0KoMN27dxd9+/YV+/btEzY2NvoRRI968cUXRZcuXYRWqy3eiXuEqYKKJIRxF6RatmyJd999F4MHD4aLiwtOnz6NWrVqYerUqfjzzz9x4MABk7X2PE16ejrc3NyQlpYGV1dXs+2XiIieLjc3F7GxsahZsyYHXlRAT/r3N+bz2+g+Kl988QVCQkJw69YtaLVabNmyBTExMVi1ahV27txp7OaIiIiI/pPRo3569+6NX3/9FXv37oWTkxO++OILREdH49dff0XXrl1Lo0YiIiKqoIxuUYmPj0fHjh3x559/Fnjt6NGjaNu2rUkKIyIiIjK6ReXFF19ESkpKgeWHDh3CSy+9ZJKiiIiIiIBiBJW2bdvixRdfREZGhn7Zv//+ix49eljdHReJiIiobDM6qCxduhTVq1dHr169oFQq8c8//6Bnz56YOnUq3n333dKokYiIiCooo4OKTCbD+vXrYWtriy5duuCVV17BjBkzMGHChNKoj4iIiCqwInWmPXPmTIFlkydPxsCBAzF48GB06tRJv06TJk1MWyERERFVWEUKKs2aNYMkSQY3K8p/vnjxYvz000/6+x48fn8DIiIiouIqUlCJjY0t7TqIiIiICihSH5WAgIAiP4iIiMq60NBQ9OnTp1jvffbZZwvc/Xjfvn2QJAmpqanF2mZ4eDiaNGkCe3t7eHt7Y+zYsYWud+XKFbi4uMDd3d1guUqlwtSpU1G7dm3Y29ujadOm2L17d7FqMTejO9POmDEDy5cvL7B8+fLlmDVrlkmKIiIiIp3vvvsOn332GSZNmoTz589j79696NatW4H1VCoVBg4ciI4dOxZ47fPPP8fixYvxww8/4MKFCxg1ahT69u2LyMhIcxxCiRgdVBYvXozAwMACyxs1aoQff/zRJEURERFZq/3796NNmzaws7ND1apVMWnSJKjVagC6lpj9+/dj7ty5kCQJkiQhLi4Ozz33HADAw8MDkiQhNDQUAKBUKjF+/Hh4e3vD3t4eHTp0QEREhH5f9+/fx+eff45Vq1Zh0KBBqF27Npo0aYJXXnmlQF2ff/45AgMD0b9//wKvrV69Gp9++il69OiBWrVqYfTo0ejRowdmz55dCmfItIwOKomJiahatWqB5V5eXkhISDBJUURERNbo1q1b6NGjB1q3bo3Tp09j0aJFWLZsGaZNmwYAmDt3Ltq1a4cRI0YgISEBCQkJ8Pf3x+bNmwEAMTExSEhIwNy5cwEAH330ETZv3oyVK1fi1KlTqFOnDrp166afAf7PP/+EVqvFrVu30KBBA/j5+aF///64efOmQV1///03Nm7ciAULFhRat1KpLHAHYwcHBxw8eNCk56c0GB1U/P39cejQoQLLDx06BF9fX5MURURE5ZQQQF6W+R+PjFotiYULF8Lf3x/z589HYGAg+vTpgylTpmD27NnQarVwc3ODQqGAo6MjfHx84OPjAxsbG3h6egIAvL294ePjAzc3N2RlZWHRokX49ttv0b17dzRs2BBLliyBg4MDli1bBgC4du0atFotpk+fjjlz5mDTpk1ISUlB165dkZeXBwBITk5GaGgowsPD4erqWmjd3bp1w3fffYfLly9Dq9Xizz//xJYtW8pEA4PRNyUcMWIEJk6cCJVKhS5dugAA/vrrL3z00Ud4//33TV4gERGVI6psYLoF/qj99DagcCrxZqKjo9GuXTtIkqRfFhwcjMzMTMTHx6N69epF3tbVq1ehUqkQHBysX2Zra4s2bdogOjoaAKDVaqFSqTBv3jy8+OKLAICff/4ZPj4++Oeff9CtWzeMGDECgwYNQqdOnf5zX3PnzsWIESMQGBgISZJQu3ZtDB06tNA+p9bG6KDy4YcfIjk5GWPGjNGnOXt7e3z88cf45JNPTF4gERFRRZXf1aJhw4b6ZV5eXqhcuTJu3LgBQHfZZ8eOHfi///s/AIAQAlqtFnK5HD/99BOGDRsGLy8vbNu2Dbm5uUhOToavry8mTZqEWrVqmf+gjGR0UJEkCbNmzcL//vc/REdHw8HBAXXr1oWdnV1p1EdEROWJraOudcMS+zWBBg0aYPPmzfpJTgFd1wcXFxf4+fkBABQKRYHJTxUKBQAYLK9duzYUCgUOHTqkn95DpVIhIiJCP7w5v7UlJiZGv/2UlBTcu3dP/54jR44YbHf79u2YNWsWDh8+jGrVqhnUYW9vj2rVqkGlUmHz5s2Fdry1NkYHlXzOzs5o3bq1KWshIqLyTpJMcgnGHNLS0hAVFWWwbOTIkZgzZw7GjRuHd955BzExMfjyyy/x3nvvQSbTdfusUaMGjh07hri4ODg7O8PT0xMBAQGQJAk7d+5Ejx494ODgAGdnZ4wePRoffvghPD09Ub16dXzzzTfIzs5GWFgYAKBevXro3bs3JkyYgJ9++gmurq745JNPEBgYqB9J1KBBA4MaT5w4AZlMhqCgIP2yY8eO4datW2jWrBlu3bqFyZMnQ6vV4qOPPirFM2gaRQoq/fr103fS6dev3xPX3bJli0kKIyIisqR9+/ahefPmBsvCwsLw+++/48MPP0TTpk3h6emJsLAwfP755/p1PvjgA4SEhKBhw4bIyclBbGwsatSogSlTpmDSpEkYOnQohgwZgvDwcMycORNarRZvvfUWMjIy0KpVK+zZswceHh767a1atQrvvvsuevbsCZlMhs6dO2P37t2wtbUt8rHk5ubi888/x7Vr1+Ds7IwePXpg9erVBSaGs0aSEE/vCj106FDMmzcPLi4uGDp06BPXXbFihcmKe5r09HS4ubkhLS3tP3s6ExGRZeTm5iI2NhY1a9YsMDSWyr8n/fsb8/ldpBaVFStWYOrUqfjggw/MGkSIiIioYivyPCpTpkxBZmZmadZCREREZKDIQaUIV4iIiIiITMqomWkfneCGiIiIqLQZNTy5Xr16Tw0r+fcnICIiIiopo4LKlClT4ObmVlq1EBERERkwKqgMGDAA3t7epVULERERkYEi91Fh/xQiIiIyN476ISIiIqtV5KCi1Wp52YeIiMgI4eHhBtPUT548Gc2aNbNYPaZkrmMxangyERFRRRAaGgpJkiBJEmxtbVGlShV07doVy5cvh1arLfJ23njjDVy6dKlEtQghsGTJErRr1w6urq5wdnZGo0aNMGHCBFy5cqVE2y4LGFSIiIgK8dJLLyEhIQFxcXHYtWsXnnvuOUyYMAEvv/wy1Gp1kbbh4OBQoqsRQggMGjQI48ePR48ePfDHH3/gwoULWLZsGezt7TFt2rT/fG9eXl6x92tNGFSIiIgKYWdnBx8fH1SrVg0tWrTAp59+iu3bt2PXrl0IDw8HAHz33Xdo3LgxnJyc4O/vjzFjxhjcbubxSz+P+vfff2Fra4vExESD5RMnTkTHjh0BABs2bMD69euxYcMG/O9//0Pbtm1RvXp1tG3bFrNmzTK4/15oaCj69OmDr7/+Gr6+vqhfvz4AYPXq1WjVqhVcXFzg4+ODQYMGISkpSf++ffv2QZIk/PXXX2jVqhUcHR3Rvn17xMTEGNQ1c+ZMVKlSBS4uLggLC0Nubm6xz60xGFSIiMhshBDIVmWb/WGqASFdunRB06ZNsWXLFgCATCbDvHnzcP78eaxcuRJ///03PvrooyJtq1OnTqhVqxZWr16tX6ZSqbB27VoMGzYMAPDzzz+jfv36eOWVVwrdxuMjcv/66y/ExMTgzz//xM6dO/Xb/Oqrr3D69Gls27YNcXFxCA0NLbCtzz77DLNnz8aJEycgl8v1NQDAL7/8gsmTJ2P69Ok4ceIEqlatioULFxbpOEuqSPOo7Nixo8gb/K+TSURElKPOwTPrnjH7fo8NOgZHW0eTbCswMBBnzpwBoGv9yFejRg1MmzYNo0aNKvKHeFhYGFasWIEPP/wQAPDrr78iNzcX/fv3BwBcunRJ3zKSb+LEiVi6dCkAwN3dHfHx8frXnJycsHTpUigUCv2yRwNHrVq1MG/ePLRu3RqZmZlwdnbWv/b111+jc+fOAIBJkyahZ8+eyM3Nhb29PebMmYOwsDCEhYUBAKZNm4a9e/eapVWlSEGlT58+RdqYJEnQaDRF3vmiRYuwaNEixMXFAQAaNWqEL774At27dy/yNoiIiMxJCKFvydi7dy9mzJiBixcvIj09HWq1Grm5ucjOzoaj49ODUWhoKD7//HMcPXoUbdu2RXh4OPr37w8nJ6f/fM9nn32Gd955B1u2bMH06dMNXmvcuLFBSAGAkydPYvLkyTh9+jTu37+v7wx848YNNGzYUL9ekyZN9N9XrVoVAJCUlITq1asjOjoao0aNMthuu3bt8M8//zz1GEuqSEHFmB7OxvDz88PMmTNRt25dCCGwcuVK9O7dG5GRkWjUqFGp7JOIiCzHQe6AY4OOWWS/phIdHY2aNWsiLi4OL7/8MkaPHo2vv/4anp6eOHjwIMLCwpCXl1ekoOLt7Y1evXphxYoVqFmzJnbt2oV9+/bpX69bt26BviJeXl7w8vIqtJPu4wEnKysL3bp1Q7du3bB27Vp4eXnhxo0b6NatW4HOtra2tvrv84NYaX3+G8OoKfRNrVevXgbPv/76ayxatAhHjx5lUCEiKockSTLZJRhL+Pvvv3H27Fm8++67OHnyJLRaLWbPng2ZTNfl85dffjF6m8OHD8fAgQPh5+eH2rVrIzg4WP/awIEDMWjQIGzfvh29e/c2etsXL15EcnIyZs6cCX9/fwDAiRMnjN5OgwYNcOzYMQwZMkS/7OjRo0ZvpziKFVSysrKwf/9+3Lhxo0AiGz9+fLEK0Wg02LhxI7KystCuXbtibYOIiMhUlEolEhMTodFocOfOHezevRszZszAyy+/jCFDhuDcuXNQqVT44Ycf0KtXLxw6dAg//vij0fvp1q0bXF1dMW3aNEydOtXgtQEDBmDLli0YMGAAPvnkE3Tr1g1VqlTB9evXsWHDBtjY2Dxx29WrV4dCocAPP/yAUaNG4dy5c/jqq6+MrnHChAkIDQ1Fq1atEBwcjLVr1+L8+fOoVauW0dsyltFBJTIyEj169EB2djaysrLg6emJe/fuwdHREd7e3kYHlbNnz6Jdu3bIzc2Fs7Mztm7danDN7FFKpRJKpVL/PD093djyiYiIimT37t2oWrUq5HI5PDw80LRpU8ybNw8hISGQyWRo2rQpvvvuO8yaNQuffPIJOnXqhBkzZhi0OhSFTCZDaGgopk+fXuC9kiRhw4YNWLJkCVasWIFvvvkGKpUKfn5+eP755/Hdd989cdteXl4IDw/Hp59+innz5qFFixb4v//7P6MHvrzxxhu4evUqPvroI+Tm5uLVV1/F6NGjsWfPHqO2UxySMHLM1rPPPot69erhxx9/hJubG06fPg1bW1sMHjwYEyZMQL9+/YwqIC8vDzdu3EBaWho2bdqEpUuXYv/+/YWGlcmTJ2PKlCkFlqelpcHV1dWo/RIRUenKzc1FbGwsatasCXt7e0uXY9XCwsJw9+5do0bZWrsn/funp6fDzc2tSJ/fRgcVd3d3HDt2DPXr14e7uzuOHDmiv3YVEhKCixcvGn80j3jhhRdQu3ZtLF68uMBrhbWo+Pv7M6gQEVkhBpWnS0tLw9mzZ9G1a1fs2LEDXbt2tXRJJmOqoGL0pR9bW1t9pyFvb2/cuHEDDRo0gJubG27evGns5grQarUGYeRRdnZ2sLOzK/E+iIiIrEHv3r1x/PhxjBo1qlyFFFMyOqg0b94cERERqFu3Ljp37owvvvgC9+7dw+rVqxEUFGTUtj755BN0794d1atXR0ZGBtatW4d9+/aZ5ZoXERGRpT06FJkKZ/QU+tOnT9dPBPP111/Dw8MDo0ePxt27dwu9XPMkSUlJGDJkCOrXr4/nn38eERER2LNnD1MlERERAShGi0qrVq3033t7e2P37t3F3vmyZcuK/V4iIiIq/4xuUenSpQtSU1MLLE9PT0eXLl1MURMREZUjprohIJUtpvp3Nzqo7Nu3r8Akb4Cud++BAwdMUhQREZV9+VOyZ2dnW7gSsoT8f/dHp+YvjiJf+sm/UyQAXLhwAYmJifrnGo0Gu3fvRrVq1UpUDBERlR82NjZwd3dHUlISAMDR0VF/Dxkqv4QQyM7ORlJSEtzd3Z86e+7TFDmoNGvWDJIkQZKkQi/xODg44IcffihRMUREVL74+PgAgD6sUMXh7u6u//cviSIHldjYWAghUKtWLRw/fhxeXl761xQKBby9vUucmoiIqHyRJAlVq1aFt7c3VCqVpcshM7G1tTVZJihyUAkICABgHbd8JiKissXGxoZ/zFKxFOvuyVevXsWcOXMQHR0NAGjYsCEmTJiA2rVrm7Q4IiIiqtiMHvWzZ88eNGzYEMePH0eTJk3QpEkTHDt2DI0aNcKff/5ZGjUSERFRBWX0TQmbN2+Obt26YebMmQbLJ02ahD/++AOnTp0yaYFPYsxNjYiIiMg6GPP5bXSLSnR0NMLCwgosHzZsGC5cuGDs5oiIiIj+k9FBxcvLC1FRUQWWR0VFwdvb2xQ1EREREQEwojPt1KlT8cEHH2DEiBEYOXIkrl27hvbt2wMADh06hFmzZuG9994rtUKJiIio4ilyHxUbGxskJCTAy8sLc+bMwezZs3H79m0AgK+vLz788EOMHz/erLMOso8KERFR2WPM53eRg4pMJkNiYqLB5Z2MjAwAgIuLSwnKLT4GFSIiorLHmM9vo+ZReby1xFIBhYiIiCoGo4JKvXr1nnppJyUlpUQFEREREeUzKqhMmTIFbm5upVULERERkQGjgsqAAQM4BJmIiIjMpsjzqJhzNA8RERERYERQMXKmfSIiIqISK/KlH61WW5p1EBERERVg9BT6RERERObCoEJERERWi0GFiIiIrBaDChEREVktBhUiIiKyWgwqREREZLUYVIiIiMhqMagQERGR1WJQISIiIqvFoEJERERWi0GFiIiIrBaDChEREVktBhUiIiKyWgwqREREZLUYVIiIiMhqMagQERGR1WJQISIiIqvFoEJERERWi0GFiIiIrBaDChEREVktBhUiIiKyWgwqREREZLUYVIiIiMhqMagQERGR1WJQISIiIqtl0aAyY8YMtG7dGi4uLvD29kafPn0QExNjyZKIiIjIilg0qOzfvx9jx47F0aNH8eeff0KlUuHFF19EVlaWJcsiIiIiKyEJIYSli8h39+5deHt7Y//+/ejUqdNT109PT4ebmxvS0tLg6upqhgqJiIiopIz5/JabqaYiSUtLAwB4enoW+rpSqYRSqdQ/T09PN0tdREREZBlW05lWq9Vi4sSJCA4ORlBQUKHrzJgxA25ubvqHv7+/maskIiIic7KaSz+jR4/Grl27cPDgQfj5+RW6TmEtKv7+/rz0Q0REVIaUuUs/77zzDnbu3Il///33P0MKANjZ2cHOzs6MlREREZElWTSoCCEwbtw4bN26Ffv27UPNmjUtWQ4RERFZGYsGlbFjx2LdunXYvn07XFxckJiYCABwc3ODg4ODJUsjIiIiK2DRPiqSJBW6fMWKFQgNDX3q+zk8mYiIqOwpM31UrKQfLxEREVkpqxmeTERERPQ4BhUiIiKyWgwqREREZLUYVIiIiMhqMagQERGR1WJQISIiIqvFoEJERERWi0GFiIiIrBaDChEREVktBhUiIiKyWgwqREREZLUYVIiIiMhqMagQERGR1WJQISIiIqvFoEJERERWi0GFiIiIrBaDChEREVktBhUiIiKyWgwqREREZLUYVIiIiMhqMagQERGR1WJQISIiIqvFoEJERERWi0GFiIiIrBaDChEREVktBhUiIiKyWgwqREREZLUYVIiIiMhqMagQERGR1WJQISIiIqvFoEJERERWi0GFiIiIrBaDChEREVktBhUiIiKyWgwqREREZLUYVIiIiMhqMagQERGR1WJQISIiIqvFoEJERERWi0GFiIiIrBaDChEREVktBhUiIiKyWgwqREREZLUYVIiIiMhqMagQERGR1bJoUPn333/Rq1cv+Pr6QpIkbNu2zZLlEBERkZWRW3LnWVlZaNq0KYYNG4Z+/fpZshR6CrVWjSxVFrJUWchUZSJblY1MVab+ezsbO3jae8LT3hOVHCrB3c4dcplF/3sREVE5YNFPku7du6N79+6WLKFcE0IgR52jDxf6r3lZyFJnITMv0+C1x7/PUj1cJ1eTa/T+3e3c9eHFw95DF2LsK+mWOXjqX/O094SrwhWSJJXCWSAiorKsTP3Jq1QqoVQq9c/T09MtWE3pUWlU+taKx0PDo60ZhYaMByEk/6tWaE1am0KmgLPCGU62TgYPpVqJ5NxkpOSmIFWZCq3QIlWZilRlKq6lXXvqduUyOTztCgaYR1tpHg09DnIHkx4XERFZpzIVVGbMmIEpU6ZYuoxCabQaZKuzC4SG/2qt0AeNvExkq7MNWjdUWpVJa5NJMn2gcLZ11n91tHV8+FzhDCe5E5wUD9d5fH0nWyfY2tgW6Vyk5aUhJScFKbm6R36ISclNMViekpuCTFUm1Fo1knKSkJSTVKRjcpQ7PmyZeUrA4WUoIqKySxJCCEsXAQCSJGHr1q3o06fPf65TWIuKv78/0tLS4OrqarJa4jPiEZkUadia8UiwyG+xeDR4ZKuzTbb/fA5yh6eHi8cChcH6Cmc4yh3hIHew6ssqSo0S93PvG4SX/DBjEHByU5Cck1ysIPfoZSj9w+GRS1GPLHOxdbHq80VEVNalp6fDzc2tSJ/fZerPTDs7O9jZ2ZX6fqLuRuHTg58W671ymbxAK4T+e4Vh2Hj0dYP1FU5wlDtWmFYAOxs7+Dj5wMfJ56nrCiGQpcoq2FLzWCtN/uN+7n0ICOMvQxXSOlPYZShPe0/Yy+1NcRqIiKgQFeOT0Ei+Tr5oV7Vdgb4Y/9Vq8eglE4WNwtLll2uSJMFZ4QxnhTOqu1Z/6vrFvgyVnYSk7GJchrIvvJUm/3tehiIiMo5Ff2NmZmbiypUr+uexsbGIioqCp6cnqld/+odQaWlRpQV+evEni+2fTMdGZqMPCUWRfxkqOTe50Faax5ertCpkq7ORnZmN+Mz4p25fggQfJx8EVQ5C48qNEVQ5CI0qNYKjrWNJD5WIqFyyaB+Vffv24bnnniuwPCQkBOHh4U99vzHXuIhMrbiXoR4nk2So5VZLH1waV26MOh51YCt7esdlIqKyyJjPb6vpTFscDCpUlmi0GtxX3se11Gs4e+8szt07h7P3zuJO9p0C69rZ2KGBZwODlhd/F3928iWicoFBhagMScpOwrl75wweGaqMAuu52bkhqFKQQXip5FDJAhUTEZUMgwpRGaYVWtxIv6FvdTl37xyiU6ILHZbt6+RrEFwaVmrI/i5EZPUYVIjKGZVGhUv3L+HsvbP6ABObFlugz4tMkqG2e22D/i613WuzvwsRWRUGFaIKICMvAxeSLxj0dylsSLW9jT0aVDLs7+Ln7Mf+LkRkMQwqRBXUnaw7OJds2N8lU5VZYD13O3cEVTbs71LUIdxERCXFoEJEAHT9Xa6nX9e3uJy7dw4XUy4W2t+lmnM1g+DSwLMB+7sQUalgUCGi/5SnydP3d8kPMLFpsQXWk0ky1HGvU6C/C2fWJaKSYlAhIqNk5GXgfPJ5XXC5q+uwezfnboH17G3s0bBSQ4OWl2rO1djfhagMEUJAIzRQa9X6r/kPg+dC99VF4QJ/F3+T1sCgQkQldifrjsElo/PJ5wvt7+Jh52EQXIIqB8HD3sMCFROZhhDC4IP6iR/oD9bRaDVFfs/jy9Wi4Ps1Wk2h29JoNVAJlcE+jaklf7vG6FmrJ2Z2nGnSc1xu755MROZTxakKqjhVwfMBzwPQ9XeJS4/Tt7qcu3cOF+9fxH3lfRy4dQAHbh3Qv7eaczWDS0YNKjWAg9zBUodS5gkhoNKqkKPOQY46B9nqbN33qhz9MqVGCa3QQkBACAGt0EILLYR48Dz/e+hee/T7x9+n//7Be/KX/ed6j2+jOPt67P1P2xcA/TFCwPB4Uch6j33/pHCQv25FI5fkkMvksJHZ6L5Kuq8uti4WrYstKkRUbHmaPMSkxBj0d4lLjyuwno1kgzrudQxaXspbfxchBJQapT445KhzkK3KNnhuEDIef6gKWfbIQyM0lj7ECk2CBLlM90Fe2Ae6rcxW/8Gevzx/vUc/9POXPx4GHl9usJ9H13nkuY3MBraSrcF7nlbL49vKX2Yj2Zj1Ei4v/RCRxaTnpeP8vfP64HL23lncy7lXYD0HuQMaeDbQBRcvXYDxdfIt1V+WWqFFrjq38LDwlKBQlPcUdtNJU7OV2cJB7lDgYS+3hyRJkEEGmSSDBEn3XNI9B3QdpGWQAdLD7yVJgoRC1nvse/32HmwfEh5+/+h6/7W9x/b1pP0+WvuT9vukfT3tGG1lDz7gHwkdBT7gHwka+e8j02BQISKrIYTAneyC/V2yVFkF1vW09zSY38XfxR+56tzCg8JTgkVhLRu5mlyzHLOdjV2hYaLAw1b31VHu+NR1HW0dYS+35yzDVC4wqBCRVdMKLeLS4gxuCRBzPwZqrXGd/ErC3sYejrZPDwiFBQt9ePiPgGEjszHbcRCVRQwqRFTmKDXKAv1d7mbfhb3c3qBVoShB4b+CRf577OX2bMonsiCO+iGiMsfOxg5NvJqgiVcTS5dCRFaEf1IQERGR1WJQISIiIqvFoEJERERWi0GFiIiIrBaDChEREVktBhUiIiKyWgwqREREZLUYVIiIiMhqMagQERGR1WJQISIiIqvFoEJERERWi0GFiIiIrBaDChEREVktBhUiIiKyWnJLF1ASQggAQHp6uoUrISIioqLK/9zO/xx/kjIdVDIyMgAA/v7+Fq6EiIiIjJWRkQE3N7cnriOJosQZK6XVanH79m24uLhAkiSTbjs9PR3+/v64efMmXF1dTbrt8obnquh4roqO56roeK6KjufKOKV1voQQyMjIgK+vL2SyJ/dCKdMtKjKZDH5+fqW6D1dXV/5nLiKeq6LjuSo6nqui47kqOp4r45TG+XpaS0o+dqYlIiIiq8WgQkRERFaLQeU/2NnZ4csvv4SdnZ2lS7F6PFdFx3NVdDxXRcdzVXQ8V8axhvNVpjvTEhERUfnGFhUiIiKyWgwqREREZLUYVIiIiMhqldugMmPGDLRu3RouLi7w9vZGnz59EBMTY7BObm4uxo4di0qVKsHZ2Rmvvvoq7ty5Y7DO+PHj0bJlS9jZ2aFZs2YF9jN58mRIklTg4eTkVJqHZ1LmOlcAsGfPHrRt2xYuLi7w8vLCq6++iri4uFI6MtMz57n65Zdf0KxZMzg6OiIgIADffvttaR1WqTHF+Tp9+jQGDhwIf39/ODg4oEGDBpg7d26Bfe3btw8tWrSAnZ0d6tSpg/Dw8NI+PJMy17lKSEjAoEGDUK9ePchkMkycONEch2dS5jpXW7ZsQdeuXeHl5QVXV1e0a9cOe/bsMcsxmoq5ztXBgwcRHByMSpUqwcHBAYGBgfj+++9NcxCinOrWrZtYsWKFOHfunIiKihI9evQQ1atXF5mZmfp1Ro0aJfz9/cVff/0lTpw4Idq2bSvat29vsJ1x48aJ+fPni7feeks0bdq0wH4yMjJEQkKCwaNhw4YiJCSklI/QdMx1rq5duybs7OzEJ598Iq5cuSJOnjwpOnXqJJo3b17ah2gy5jpXv//+u5DL5WLRokXi6tWrYufOnaJq1arihx9+KO1DNClTnK9ly5aJ8ePHi3379omrV6+K1atXCwcHB4Nzce3aNeHo6Cjee+89ceHCBfHDDz8IGxsbsXv3brMeb0mY61zFxsaK8ePHi5UrV4pmzZqJCRMmmPMwTcJc52rChAli1qxZ4vjx4+LSpUvik08+Eba2tuLUqVNmPd6SMNe5OnXqlFi3bp04d+6ciI2NFatXrxaOjo5i8eLFJT6GchtUHpeUlCQAiP379wshhEhNTRW2trZi48aN+nWio6MFAHHkyJEC7//yyy8L/UB5XFRUlAAg/v33X5PVbm6lda42btwo5HK50Gg0+mU7duwQkiSJvLw80x+IGZTWuRo4cKB47bXXDJbNmzdP+Pn5Ca1Wa9qDMKOSnq98Y8aMEc8995z++UcffSQaNWpksM4bb7whunXrZuIjMJ/SOleP6ty5c5kMKo8zx7nK17BhQzFlyhTTFG4B5jxXffv2FYMHDy5xzeX20s/j0tLSAACenp4AgJMnT0KlUuGFF17QrxMYGIjq1avjyJEjxd7P0qVLUa9ePXTs2LFkBVtQaZ2rli1bQiaTYcWKFdBoNEhLS8Pq1avxwgsvwNbW1rQHYSalda6USiXs7e0Nljk4OCA+Ph7Xr183QeWWYarzlZaWpt8GABw5csRgGwDQrVu3Ev0sW1ppnavyyFznSqvVIiMjo0yfT3Odq8jISBw+fBidO3cucc0VIqhotVpMnDgRwcHBCAoKAgAkJiZCoVDA3d3dYN0qVaogMTGxWPvJzc3F2rVrERYWVtKSLaY0z1XNmjXxxx9/4NNPP4WdnR3c3d0RHx+PX375xZSHYDalea66deuGLVu24K+//oJWq8WlS5cwe/ZsALo+BmWRqc7X4cOHsWHDBowcOVK/LDExEVWqVCmwjfT0dOTk5Jj2QMygNM9VeWPOc/V///d/yMzMRP/+/U1WvzmZ41z5+fnBzs4OrVq1wtixYzF8+PAS112mb0pYVGPHjsW5c+dw8ODBUt3P1q1bkZGRgZCQkFLdT2kqzXOVmJiIESNGICQkBAMHDkRGRga++OILvPbaa/jzzz9Nfgfs0laa52rEiBG4evUqXn75ZahUKri6umLChAmYPHnyU+80aq1Mcb7OnTuH3r1748svv8SLL75owuqsC89V0ZnrXK1btw5TpkzB9u3b4e3tXex9WZI5ztWBAweQmZmJo0ePYtKkSahTpw4GDhxYkrLLf4vKO++8g507d+Kff/4xuNOyj48P8vLykJqaarD+nTt34OPjU6x9LV26FC+//HKBv+zKitI+VwsWLICbmxu++eYbNG/eHJ06dcKaNWvw119/4dixY6Y6DLMo7XMlSRJmzZqFzMxMXL9+HYmJiWjTpg0AoFatWiY5BnMyxfm6cOECnn/+eYwcORKff/65wWs+Pj4FRlbduXMHrq6ucHBwMO3BlLLSPlflibnO1fr16zF8+HD88ssvBS4xlhXmOlc1a9ZE48aNMWLECLz77ruYPHlyyYsvcS8XK6XVasXYsWOFr6+vuHTpUoHX8zsQbdq0Sb/s4sWLxe5Me+3aNSFJkvj1119NUr85metcvffee6JNmzYGy27fvi0AiEOHDpX8QMzA3P+vHvXWW2+Jdu3aFbt2SzDV+Tp37pzw9vYWH374YaH7+eijj0RQUJDBsoEDB5apzrTmOlePKqudac15rtatWyfs7e3Ftm3bTHsQZmKJ/1f5pkyZIgICAkpUvxDleNTP6NGjhZubm9i3b5/B0OHs7Gz9OqNGjRLVq1cXf//9tzhx4oRo165dgQ+Cy5cvi8jISPH222+LevXqicjISBEZGSmUSqXBep9//rnw9fUVarXaLMdnSuY6V3/99ZeQJElMmTJFXLp0SZw8eVJ069ZNBAQEGOzLmpnrXN29e1csWrRIREdHi8jISDF+/Hhhb28vjh07ZtbjLSlTnK+zZ88KLy8vMXjwYINtJCUl6dfJH5784YcfiujoaLFgwYIyNzzZXOdKCKH//9ayZUsxaNAgERkZKc6fP2+2Yy0pc52rtWvXCrlcLhYsWGCwTmpqqlmPtyTMda7mz58vduzYIS5duiQuXbokli5dKlxcXMRnn31W4mMot0EFQKGPFStW6NfJyckRY8aMER4eHsLR0VH07dtXJCQkGGync+fOhW4nNjZWv45GoxF+fn7i008/NdPRmZY5z9XPP/8smjdvLpycnISXl5d45ZVXRHR0tJmOtOTMda7u3r0r2rZtK5ycnISjo6N4/vnnxdGjR814pKZhivP15ZdfFrqNx/9S++eff0SzZs2EQqEQtWrVMthHWWDOc1WUdayZuc7Vf/2clqV5ssx1rubNmycaNWokHB0dhaurq2jevLlYuHChwXQUxcW7JxMREZHVKvedaYmIiKjsYlAhIiIiq8WgQkRERFaLQYWIiIisFoMKERERWS0GFSIiIrJaDCpERERktRhUiIiIyGoxqBAREZHVYlAholIlhMALL7yAbt26FXht4cKFcHd3R3x8vAUqI6KygEGFiEqVJElYsWIFjh07hsWLF+uXx8bG4qOPPsIPP/xgcNt5U1CpVCbdHhFZDoMKEZU6f39/zJ07Fx988AFiY2MhhEBYWBhefPFFNG/eHN27d4ezszOqVKmCt956C/fu3dO/d/fu3ejQoQPc3d1RqVIlvPzyy7h69ar+9bi4OEiShA0bNqBz586wt7fH2rVrLXGYRFQKeFNCIjKbPn36IC0tDf369cNXX32F8+fPo1GjRhg+fDiGDBmCnJwcfPzxx1Cr1fj7778BAJs3b4YkSWjSpAkyMzPxxRdfIC4uDlFRUZDJZIiLi0PNmjVRo0YNzJ49G82bN4e9vT2qVq1q4aMlIlNgUCEis0lKSkKjRo2QkpKCzZs349y5czhw4AD27NmjXyc+Ph7+/v6IiYlBvXr1Cmzj3r178PLywtmzZxEUFKQPKnPmzMGECRPMeThEZAa89ENEZuPt7Y23334bDRo0QJ8+fXD69Gn8888/cHZ21j8CAwMBQH955/Llyxg4cCBq1aoFV1dX1KhRAwBw48YNg223atXKrMdCROYht3QBRFSxyOVyyOW6Xz2ZmZno1asXZs2aVWC9/Es3vXr1QkBAAJYsWQJfX19otVoEBQUhLy/PYH0nJ6fSL56IzI5BhYgspkWLFti8eTNq1KihDy+PSk5ORkxMDJYsWYKOHTsCAA4ePGjuMonIgnjph4gsZuzYsUhJScHAgQMRERGBq1evYs+ePRg6dCg0Gg08PDxQqVIl/PTTT7hy5Qr+/vtvvPfee5Yum4jMiEGFiCzG19cXhw4dgkajwYsvvojGjRtj4sSJcHd3h0wmg0wmw/r163Hy5EkEBQXh3XffxbfffmvpsonIjDjqh4iIiKwWW1SIiIjIajGoEBERkdViUCEiIiKrxaBCREREVotBhYiIiKwWgwoRERFZLQYVIiIisloMKkRERGS1GFSIiIjIajGoEBERkdViUCEiIiKrxaBCREREVuv/AaXwvAIroJsrAAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Calculating Expected Value**"
      ],
      "metadata": {
        "id": "38njjNQidgAx"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **DailyGrand Expected Value**\n",
        "Daily Grand Expected Value is much simpler than the other lotteries because the prize pool stays consistent, without change in the value of the jackpot. It is always a thousand dollars per day for life (or 7 million)."
      ],
      "metadata": {
        "id": "39pFLWBI6klX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from itertools import cycle\n",
        "sequence = [1/13348188, 1/2224698, 1/60674, 1/10112, 1/1411, 1/235.2, 1/100.8, 1/19.7, 1/12.3]\n",
        "num_repetitions = len(DailyGrand) // len(sequence)\n",
        "repeated_sequence = cycle(sequence)\n",
        "repeated_values = [next(repeated_sequence) for _ in range(num_repetitions * len(sequence))]\n",
        "DailyGrand['Probability'] = repeated_values"
      ],
      "metadata": {
        "id": "fBaTKbOse2F7"
      },
      "execution_count": 107,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DailyGrand['Prize'] = DailyGrand['Prize'].replace(\"FREE PLAY\", \"$3.00\")\n"
      ],
      "metadata": {
        "id": "UmrWQLd3f2y-"
      },
      "execution_count": 108,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "DailyGrand.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "-rybGWa3dfXf",
        "outputId": "ae501168-7830-4ac4-b07d-9d6c18d04e2a"
      },
      "execution_count": 109,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Date       Draw  Winners          Prize  Year   Probability\n",
              "189 2017-01-02  5/5 + 1/1        0  $7,000,000.00  2017  7.491654e-08\n",
              "190 2017-01-02  5/5 + 0/1        0    $500,000.00  2017  4.494992e-07\n",
              "191 2017-01-02  4/5 + 1/1       11      $1,000.00  2017  1.648152e-05\n",
              "192 2017-01-02  4/5 + 0/1       63        $500.00  2017  9.889241e-05\n",
              "193 2017-01-02  3/5 + 1/1      457        $100.00  2017  7.087172e-04\n",
              "194 2017-01-02  3/5 + 0/1     2862         $20.00  2017  4.251701e-03\n",
              "195 2017-01-02  2/5 + 1/1     6943         $10.00  2017  9.920635e-03\n",
              "196 2017-01-02  1/5 + 1/1    37116          $4.00  2017  5.076142e-02\n",
              "197 2017-01-02  0/5 + 1/1    61223          $3.00  2017  8.130081e-02\n",
              "198 2017-01-05  5/5 + 1/1        0  $7,000,000.00  2017  7.491654e-08"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ccd58c29-12ee-413d-b146-310cb918988c\" 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",
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Draw</th>\n",
              "      <th>Winners</th>\n",
              "      <th>Prize</th>\n",
              "      <th>Year</th>\n",
              "      <th>Probability</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>189</th>\n",
              "      <td>2017-01-02</td>\n",
              "      <td>5/5 + 1/1</td>\n",
              "      <td>0</td>\n",
              "      <td>$7,000,000.00</td>\n",
              "      <td>2017</td>\n",
              "      <td>7.491654e-08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>190</th>\n",
              "      <td>2017-01-02</td>\n",
              "      <td>5/5 + 0/1</td>\n",
              "      <td>0</td>\n",
              "      <td>$500,000.00</td>\n",
              "      <td>2017</td>\n",
              "      <td>4.494992e-07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>191</th>\n",
              "      <td>2017-01-02</td>\n",
              "      <td>4/5 + 1/1</td>\n",
              "      <td>11</td>\n",
              "      <td>$1,000.00</td>\n",
              "      <td>2017</td>\n",
              "      <td>1.648152e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>192</th>\n",
              "      <td>2017-01-02</td>\n",
              "      <td>4/5 + 0/1</td>\n",
              "      <td>63</td>\n",
              "      <td>$500.00</td>\n",
              "      <td>2017</td>\n",
              "      <td>9.889241e-05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>193</th>\n",
              "      <td>2017-01-02</td>\n",
              "      <td>3/5 + 1/1</td>\n",
              "      <td>457</td>\n",
              "      <td>$100.00</td>\n",
              "      <td>2017</td>\n",
              "      <td>7.087172e-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>194</th>\n",
              "      <td>2017-01-02</td>\n",
              "      <td>3/5 + 0/1</td>\n",
              "      <td>2862</td>\n",
              "      <td>$20.00</td>\n",
              "      <td>2017</td>\n",
              "      <td>4.251701e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>195</th>\n",
              "      <td>2017-01-02</td>\n",
              "      <td>2/5 + 1/1</td>\n",
              "      <td>6943</td>\n",
              "      <td>$10.00</td>\n",
              "      <td>2017</td>\n",
              "      <td>9.920635e-03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>196</th>\n",
              "      <td>2017-01-02</td>\n",
              "      <td>1/5 + 1/1</td>\n",
              "      <td>37116</td>\n",
              "      <td>$4.00</td>\n",
              "      <td>2017</td>\n",
              "      <td>5.076142e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>197</th>\n",
              "      <td>2017-01-02</td>\n",
              "      <td>0/5 + 1/1</td>\n",
              "      <td>61223</td>\n",
              "      <td>$3.00</td>\n",
              "      <td>2017</td>\n",
              "      <td>8.130081e-02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>198</th>\n",
              "      <td>2017-01-05</td>\n",
              "      <td>5/5 + 1/1</td>\n",
              "      <td>0</td>\n",
              "      <td>$7,000,000.00</td>\n",
              "      <td>2017</td>\n",
              "      <td>7.491654e-08</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "    .colab-df-convert {\n",
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              "    .colab-df-convert:hover {\n",
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              "        document.querySelector('#df-ccd58c29-12ee-413d-b146-310cb918988c 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-ccd58c29-12ee-413d-b146-310cb918988c');\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "      --bg-color: #E8F0FE;\n",
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              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
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              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "  .colab-df-quickchart-complete:disabled,\n",
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              "    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",
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              "    20% {\n",
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              "  }\n",
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              "\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",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "DailyGrand",
              "summary": "{\n  \"name\": \"DailyGrand\",\n  \"rows\": 7956,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2017-01-02 00:00:00\",\n        \"max\": \"2023-12-28 00:00:00\",\n        \"num_unique_values\": 730,\n        \"samples\": [\n          \"2021-06-28 00:00:00\",\n          \"2018-06-04 00:00:00\",\n          \"2019-11-25 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Draw\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"1/5 + 1/1\",\n          \"5/5 + 0/1\",\n          \"3/5 + 0/1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Winners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18161,\n        \"min\": 0,\n        \"max\": 83258,\n        \"num_unique_values\": 2988,\n        \"samples\": [\n          26037,\n          33799,\n          28745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prize\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 14,\n        \"samples\": [\n          \"$25,000.00 A Year for Life or $500,000.00\",\n          \"$1,000.00 A Day for Life or $7,000,000.00\",\n          \"$7,000,000.00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2017,\n        \"max\": 2023,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2017,\n          2018,\n          2022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.027691727710725533,\n        \"min\": 7.491653548781303e-08,\n        \"max\": 0.08130081300813008,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.050761421319796954,\n          4.494992129268782e-07,\n          0.004251700680272109\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 109
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TailyGrand = DailyGrand.copy()\n",
        "TailyGrand = TailyGrand.drop_duplicates()\n",
        "TailyGrand['Prize'] = TailyGrand['Prize'].str.extract(r'\\$([0-9,]+\\.[0-9]{2})', expand=False)\n",
        "TailyGrand['Prize'] = TailyGrand['Prize'].replace(\"25,000.00\", \"500,000.00\")\n",
        "TailyGrand['Prize'] = TailyGrand['Prize'].str.replace(r'[,]', '')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cXR7WuMog6DB",
        "outputId": "43181dfd-217c-49a8-a5cd-e3cd22a0dd45"
      },
      "execution_count": 110,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-110-cc26859cd98f>:5: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  TailyGrand['Prize'] = TailyGrand['Prize'].str.replace(r'[,]', '')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TailyGrand.loc[TailyGrand['Draw'] == '5/5 + 1/1', 'Prize'] = 7000000.0\n",
        "TailyGrand.loc[TailyGrand['Draw'] == '5/5 + 0/1', 'Prize'] = 500000.0\n",
        "TailyGrand.loc[TailyGrand['Draw'] == '0/5 + 1/1', 'Prize'] = 3.0"
      ],
      "metadata": {
        "id": "uFuqSDE-wS_l"
      },
      "execution_count": 111,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TailyGrand['Prize'] = TailyGrand['Prize'].astype(float)"
      ],
      "metadata": {
        "id": "H3S3XdNFhuoS"
      },
      "execution_count": 112,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "expected_value = TailyGrand.groupby('Date').apply(lambda x: (x['Prize'] * x['Probability']).sum())\n",
        "print(\"Expected Value of Daily Grand:\", expected_value[1])\n",
        "TailyGrand['Expected Value'] = 1.5171532909193532"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "s6XFnRbBu3zV",
        "outputId": "d2d01a97-3bdb-4a9f-e4db-8e35c3d31249"
      },
      "execution_count": 113,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Expected Value of Daily Grand: 1.5171532909193532\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TailyGrand['Year']=pd.to_datetime(TailyGrand['Date']).dt.year"
      ],
      "metadata": {
        "id": "EfEpRjJLGVjA"
      },
      "execution_count": 114,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "TailyGrand.head()"
      ],
      "metadata": {
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      "execution_count": 115,
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          "data": {
            "text/plain": [
              "          Date       Draw  Winners      Prize  Year   Probability  \\\n",
              "189 2017-01-02  5/5 + 1/1        0  7000000.0  2017  7.491654e-08   \n",
              "190 2017-01-02  5/5 + 0/1        0   500000.0  2017  4.494992e-07   \n",
              "191 2017-01-02  4/5 + 1/1       11     1000.0  2017  1.648152e-05   \n",
              "192 2017-01-02  4/5 + 0/1       63      500.0  2017  9.889241e-05   \n",
              "193 2017-01-02  3/5 + 1/1      457      100.0  2017  7.087172e-04   \n",
              "\n",
              "     Expected Value  \n",
              "189        1.517153  \n",
              "190        1.517153  \n",
              "191        1.517153  \n",
              "192        1.517153  \n",
              "193        1.517153  "
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "TailyGrand",
              "summary": "{\n  \"name\": \"TailyGrand\",\n  \"rows\": 6570,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2017-01-02 00:00:00\",\n        \"max\": \"2023-12-28 00:00:00\",\n        \"num_unique_values\": 730,\n        \"samples\": [\n          \"2021-06-28 00:00:00\",\n          \"2018-06-04 00:00:00\",\n          \"2019-11-25 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Draw\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"1/5 + 1/1\",\n          \"5/5 + 0/1\",\n          \"3/5 + 0/1\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Winners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17148,\n        \"min\": 0,\n        \"max\": 83258,\n        \"num_unique_values\": 2988,\n        \"samples\": [\n          26037,\n          33799,\n          28745\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2185909.8820411996,\n        \"min\": 3.0,\n        \"max\": 7000000.0,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          4.0,\n          500000.0,\n          20.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1,\n        \"min\": 2017,\n        \"max\": 2023,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2017,\n          2018,\n          2022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Probability\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.027692094896943183,\n        \"min\": 7.491653548781303e-08,\n        \"max\": 0.08130081300813008,\n        \"num_unique_values\": 9,\n        \"samples\": [\n          0.050761421319796954,\n          4.494992129268782e-07,\n          0.004251700680272109\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Expected Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.2206150522607376e-16,\n        \"min\": 1.5171532909193532,\n        \"max\": 1.5171532909193532,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          1.5171532909193532\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 115
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "TailyGrand.to_csv('/content/drive/MyDrive/A Python Learning/ExpectedValueDailyGrand.csv',index=False)"
      ],
      "metadata": {
        "id": "EbmTcF6nGI6y"
      },
      "execution_count": 116,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Expected Value of \"1000 per Day\" at different life expectancies**"
      ],
      "metadata": {
        "id": "ieKG6oGR70Aa"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating the maximum expected value, we have to see the \"Thousand Dollar per Day\" for different life expectancies, I put 10 to 80 years of staying alive to see the present value of the lottery. Ultimately, it is a better deal to accept 7 million at a ~4% discount rate, even if an individual lived 80 years, they would have a future value of ~6million."
      ],
      "metadata": {
        "id": "ti9Bkyxt66sA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "DailyGrandLotteryJackpotLTvalue = {\n",
        "    'Years': [10, 20, 30, 40, 50, 60, 70, 80],\n",
        "    'Days': [3650, 7300, 10950, 14600, 18250, 21900, 25550, 29200],\n",
        "    'Future Value': [3650000, 7300000, 10950000, 14600000, 18250000, 21900000, 25550000, 29200000],\n",
        "    'Present Value': [1831464, 2497804, 3155369, 3802391, 4436074, 5054578, 5656014, 6238458]\n",
        "}\n",
        "\n",
        "DailyGrandLotteryJackpotLTvaluedf = pd.DataFrame(DailyGrandLotteryJackpotLTvalue)\n",
        "DailyGrandLotteryJackpotLTvaluedf.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "viRqNtNZ5SEG",
        "outputId": "2579074a-cde3-484c-f163-0314994db285"
      },
      "execution_count": 117,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "   Years   Days  Future Value  Present Value\n",
              "0     10   3650       3650000        1831464\n",
              "1     20   7300       7300000        2497804\n",
              "2     30  10950      10950000        3155369\n",
              "3     40  14600      14600000        3802391\n",
              "4     50  18250      18250000        4436074\n",
              "5     60  21900      21900000        5054578\n",
              "6     70  25550      25550000        5656014\n",
              "7     80  29200      29200000        6238458"
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              "  <div class=\"colab-df-container\">\n",
              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8ff5a3ae-54ae-4f99-b58f-fd6e2d6e81c7')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "  </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-8ff5a3ae-54ae-4f99-b58f-fd6e2d6e81c7 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-8ff5a3ae-54ae-4f99-b58f-fd6e2d6e81c7');\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-908e934a-6061-48f4-9842-020c018a177b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-908e934a-6061-48f4-9842-020c018a177b')\"\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-908e934a-6061-48f4-9842-020c018a177b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "DailyGrandLotteryJackpotLTvaluedf",
              "summary": "{\n  \"name\": \"DailyGrandLotteryJackpotLTvaluedf\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Years\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 24,\n        \"min\": 10,\n        \"max\": 80,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          20,\n          60,\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Days\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8940,\n        \"min\": 3650,\n        \"max\": 29200,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7300,\n          21900,\n          3650\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Future Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 8940637,\n        \"min\": 3650000,\n        \"max\": 29200000,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          7300000,\n          21900000,\n          3650000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Present Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1545060,\n        \"min\": 1831464,\n        \"max\": 6238458,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          2497804,\n          5054578,\n          1831464\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 117
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "DailyGrandLotteryJackpotLTvaluedf.to_csv('/content/drive/MyDrive/A Python Learning/DailyGrandPVFV.csv',index=False)"
      ],
      "metadata": {
        "id": "QoDTW4ekGcjM"
      },
      "execution_count": 118,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Number of Lotteries per year"
      ],
      "metadata": {
        "id": "pMGYO4wo8VRx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "grouped_data = TailyGrand[TailyGrand['Prize'] == 7000000.0].groupby(TailyGrand['Year']).size()\n",
        "grouped_data"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "URdLpUdQ6UOQ",
        "outputId": "f33000c9-5bd7-435f-9642-e3d9efcfa490"
      },
      "execution_count": 119,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Year\n",
              "2017    104\n",
              "2018    105\n",
              "2019    104\n",
              "2020    105\n",
              "2021    104\n",
              "2022    104\n",
              "2023    104\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 119
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Prizepayout per year for dailygrand"
      ],
      "metadata": {
        "id": "CkJzT_Lj8YfV"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prizepayout = TailyGrand.groupby(pd.to_datetime(TailyGrand['Date']).dt.year).apply(lambda x: (x['Prize'] * x['Winners']).sum())\n",
        "prizepayout = pd.DataFrame(prizepayout, columns=['prize']).reset_index()\n",
        "prizepayout.columns = ['date', 'prize']\n",
        "prizepayout\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "EyR9rkguxWin",
        "outputId": "955f9c30-2760-4bc9-d745-b49833801b7f"
      },
      "execution_count": 120,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   date        prize\n",
              "0  2017  121679078.0\n",
              "1  2018  136901567.0\n",
              "2  2019  127477115.0\n",
              "3  2020  107273823.0\n",
              "4  2021  176701272.0\n",
              "5  2022  113294147.0\n",
              "6  2023  110552526.0"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-ceb70fb0-2210-40f8-a926-ccde997f1797\" class=\"colab-df-container\">\n",
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              "      <th></th>\n",
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              "      <th>prize</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2017</td>\n",
              "      <td>121679078.0</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2018</td>\n",
              "      <td>136901567.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2019</td>\n",
              "      <td>127477115.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2020</td>\n",
              "      <td>107273823.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2021</td>\n",
              "      <td>176701272.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>2022</td>\n",
              "      <td>113294147.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>2023</td>\n",
              "      <td>110552526.0</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "      buttonEl.style.display =\n",
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              "        const element = document.querySelector('#df-ceb70fb0-2210-40f8-a926-ccde997f1797');\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
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              "  }\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-9619e172-f7fc-4b94-91f2-280755135ee4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "  <div id=\"id_e405be09-ab93-4083-99de-e2de41756991\">\n",
              "    <style>\n",
              "      .colab-df-generate {\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-generate: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-generate {\n",
              "        background-color: #3B4455;\n",
              "        fill: #D2E3FC;\n",
              "      }\n",
              "\n",
              "      [theme=dark] .colab-df-generate: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",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('prizepayout')\"\n",
              "            title=\"Generate code using this dataframe.\"\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=\"M7,19H8.4L18.45,9,17,7.55,7,17.6ZM5,21V16.75L18.45,3.32a2,2,0,0,1,2.83,0l1.4,1.43a1.91,1.91,0,0,1,.58,1.4,1.91,1.91,0,0,1-.58,1.4L9.25,21ZM18.45,9,17,7.55Zm-12,3A5.31,5.31,0,0,0,4.9,8.1,5.31,5.31,0,0,0,1,6.5,5.31,5.31,0,0,0,4.9,4.9,5.31,5.31,0,0,0,6.5,1,5.31,5.31,0,0,0,8.1,4.9,5.31,5.31,0,0,0,12,6.5,5.46,5.46,0,0,0,6.5,12Z\"/>\n",
              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_e405be09-ab93-4083-99de-e2de41756991 button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('prizepayout');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "prizepayout",
              "summary": "{\n  \"name\": \"prizepayout\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"date\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2017,\n        \"max\": 2023,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2017,\n          2018,\n          2022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"prize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 23947469.541089896,\n        \"min\": 107273823.0,\n        \"max\": 176701272.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          121679078.0,\n          136901567.0,\n          113294147.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 120
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.barplot(data=prizepayout,x='date',y='prize')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 482
        },
        "id": "3FiqHo-dyGFk",
        "outputId": "f0690890-76bf-45a7-dec6-971d55dfe852"
      },
      "execution_count": 121,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='date', ylabel='prize'>"
            ]
          },
          "metadata": {},
          "execution_count": 121
        },
        {
          "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": [
        "filtered_data = TailyGrand[(TailyGrand['Draw'] == '5/5 + 1/1') & (TailyGrand['Winners'] > 0)]\n",
        "filtered_data.groupby('Year')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jQEEf9EMg9_A",
        "outputId": "a2148234-28cc-4625-c851-0f9edf8d7641"
      },
      "execution_count": 122,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x7c4148d97b20>"
            ]
          },
          "metadata": {},
          "execution_count": 122
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Expected Value for LottoMax**"
      ],
      "metadata": {
        "id": "Xiqgwq8ilCUL"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LottoMAX.loc[LottoMAX['Numbers Matched'] == 'Match 3', 'Prize Per Winner'] = '$5.00'\n",
        "LottoMAX.loc[LottoMAX['Numbers Matched'] == 'Totals', 'Prize Per Winner'] = '0.0'\n",
        "LottoMAX['Prize Per Winner'] = LottoMAX['Prize Per Winner'].str.extract(r'\\$([0-9,]+\\.[0-9]{2})', expand=False)\n",
        "LottoMAX['Prize Per Winner'] = LottoMAX['Prize Per Winner'].str.replace(r'[,]', '')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hiwvjFxUGPgf",
        "outputId": "1363b58a-14b2-4738-f8be-f62b2f8811f7"
      },
      "execution_count": 123,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-123-f3a20214a07e>:4: FutureWarning: The default value of regex will change from True to False in a future version.\n",
            "  LottoMAX['Prize Per Winner'] = LottoMAX['Prize Per Winner'].str.replace(r'[,]', '')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LottoMAX['Prize Per Winner'] = LottoMAX['Prize Per Winner'].astype(float)"
      ],
      "metadata": {
        "id": "F5TWke68IU69"
      },
      "execution_count": 124,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "LottoMAX1 = LottoMAX.copy()"
      ],
      "metadata": {
        "id": "r-Gybao4LXDB"
      },
      "execution_count": 125,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "LottoMAX = LottoMAX.dropna()"
      ],
      "metadata": {
        "id": "NYb0_aWIl6jt"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "4fqC2f3bta3L"
      },
      "execution_count": 126,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Using 2019 to 2023, as the lottery was altered to include Match 5 plus Bonus and Match 4 plus Bonus in 2019.**"
      ],
      "metadata": {
        "id": "ELCiQl4OtbB-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "LottoMAX['Date'] = pd.to_datetime(LottoMAX['Date'])\n",
        "mask = (LottoMAX['Date'].dt.year >= 2019) & (LottoMAX['Date'].dt.year <= 2023)\n",
        "LottoMAX = LottoMAX[mask]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MZFsfp9fIDaL",
        "outputId": "26633039-ae6e-48f2-b377-e45af3ef95db"
      },
      "execution_count": 127,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-127-1399f95fd593>:1: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  LottoMAX['Date'] = pd.to_datetime(LottoMAX['Date'])\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "LottoMAX.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "3COMbX0QqeOd",
        "outputId": "871eb316-ba26-40fb-8187-9170d9589905"
      },
      "execution_count": 128,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "           Date     Numbers Matched  Prize Per Winner  All Winners  \\\n",
              "2432 2019-01-04             Match 7        10000000.0            0   \n",
              "2433 2019-01-04  Match 6 plus Bonus           78329.2            4   \n",
              "2434 2019-01-04             Match 6            4973.3           67   \n",
              "2436 2019-01-04             Match 4              20.0        88132   \n",
              "2437 2019-01-04  Match 3 plus Bonus              20.0        84820   \n",
              "\n",
              "         Prize Fund      DatePY  \n",
              "2432          $0.00  2019-01-04  \n",
              "2433    $313,316.80  2019-01-04  \n",
              "2434    $333,211.10  2019-01-04  \n",
              "2436  $1,762,640.00  2019-01-04  \n",
              "2437  $1,696,400.00  2019-01-04  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-2e053032-ed2c-4e5b-8026-fdc175114ddd\" 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>Numbers Matched</th>\n",
              "      <th>Prize Per Winner</th>\n",
              "      <th>All Winners</th>\n",
              "      <th>Prize Fund</th>\n",
              "      <th>DatePY</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2432</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>Match 7</td>\n",
              "      <td>10000000.0</td>\n",
              "      <td>0</td>\n",
              "      <td>$0.00</td>\n",
              "      <td>2019-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2433</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>Match 6 plus Bonus</td>\n",
              "      <td>78329.2</td>\n",
              "      <td>4</td>\n",
              "      <td>$313,316.80</td>\n",
              "      <td>2019-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2434</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>Match 6</td>\n",
              "      <td>4973.3</td>\n",
              "      <td>67</td>\n",
              "      <td>$333,211.10</td>\n",
              "      <td>2019-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2436</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>Match 4</td>\n",
              "      <td>20.0</td>\n",
              "      <td>88132</td>\n",
              "      <td>$1,762,640.00</td>\n",
              "      <td>2019-01-04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2437</th>\n",
              "      <td>2019-01-04</td>\n",
              "      <td>Match 3 plus Bonus</td>\n",
              "      <td>20.0</td>\n",
              "      <td>84820</td>\n",
              "      <td>$1,696,400.00</td>\n",
              "      <td>2019-01-04</td>\n",
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              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-2e053032-ed2c-4e5b-8026-fdc175114ddd');\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",
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              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "      --bg-color: #E8F0FE;\n",
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              "  [theme=dark] .colab-df-quickchart {\n",
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              "\n",
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              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
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              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "LottoMAX",
              "repr_error": "0"
            }
          },
          "metadata": {},
          "execution_count": 128
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Building the dataframe"
      ],
      "metadata": {
        "id": "Ly_F3Pk3tiA6"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prizes = [0,10000000,20000000,30000000,40000000,50000000,60000000]\n",
        "LottoMAX_combined_df = pd.DataFrame()\n",
        "dfs =[]\n",
        "for prize in prizes:\n",
        "  matching_rows = LottoMAX[(LottoMAX['Prize Per Winner'] >= prize) & (LottoMAX['Prize Per Winner'] < (prize+10000000))]\n",
        "  dates = matching_rows['Date'].unique()\n",
        "  Higherpower = ['Match 6 plus Bonus','Match 6','Match 5 plus Bonus','Match 5','Match 4 plus Bonus']\n",
        "  b=[]\n",
        "  for power in Higherpower:\n",
        "    a=[]\n",
        "    f = []\n",
        "    g=[]\n",
        "    h = []\n",
        "    for date in dates:\n",
        "      cc = LottoMAX[(LottoMAX['Date'] == date) & (LottoMAX['Numbers Matched'] == power)]\n",
        "      mean_prize = cc['Prize Per Winner'].mean()\n",
        "      if pd.notnull(mean_prize):\n",
        "        a.append(mean_prize)\n",
        "        mean = statistics.mean(a)\n",
        "    b.append(mean)\n",
        "  c= int(b[0])\n",
        "  d = int(b[1])\n",
        "  e = int(b[2])\n",
        "  k = int(b[3])\n",
        "  l = int(b[4])\n",
        "  df = pd.DataFrame({'Jackpot': [prize+5000000],'Match 6 plus Bonus': [c], 'Match 6': [d], 'Match 5 plus Bonus': [e],'Match 5':[k],'Match 4 plus Bonus':[l],'Match 4':[20],'Match 3 plus Bonus':[20],'Match 3':[5]})\n",
        "  dfs.append(df)\n",
        "LottoMAX_combined_df = pd.concat(dfs, ignore_index=True)\n",
        "LottoMAX_combined_df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 269
        },
        "id": "SALzb2zqE3ts",
        "outputId": "4248dfce-08c4-470a-bdf9-cedaf479ecd7"
      },
      "execution_count": 129,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Jackpot  Match 6 plus Bonus  Match 6  Match 5 plus Bonus  Match 5  \\\n",
              "0   5000000              219973     4817                 928      105   \n",
              "1  15000000              143772     4818                 913      105   \n",
              "2  25000000              164435     5003                 953      106   \n",
              "3  35000000              200671     4747                 945      106   \n",
              "4  45000000              166388     4662                 934      104   \n",
              "5  55000000              259897     4771                 913      105   \n",
              "6  65000000              305529     4909                 917      105   \n",
              "\n",
              "   Match 4 plus Bonus  Match 4  Match 3 plus Bonus  Match 3  \n",
              "0                  49       20                  20        5  \n",
              "1                  49       20                  20        5  \n",
              "2                  50       20                  20        5  \n",
              "3                  50       20                  20        5  \n",
              "4                  49       20                  20        5  \n",
              "5                  49       20                  20        5  \n",
              "6                  49       20                  20        5  "
            ],
            "text/html": [
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              "      <td>945</td>\n",
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              "      <td>5</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>45000000</td>\n",
              "      <td>166388</td>\n",
              "      <td>4662</td>\n",
              "      <td>934</td>\n",
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              "      <td>5</td>\n",
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              "      <td>55000000</td>\n",
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              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "  </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",
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              "      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-0b04399c-21b2-4783-bfc0-e410b4ffea68 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "LottoMAX_combined_df",
              "summary": "{\n  \"name\": \"LottoMAX_combined_df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Jackpot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21602468,\n        \"min\": 5000000,\n        \"max\": 65000000,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5000000,\n          15000000,\n          55000000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 6 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57956,\n        \"min\": 143772,\n        \"max\": 305529,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          219973,\n          143772,\n          259897\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111,\n        \"min\": 4662,\n        \"max\": 5003,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4817,\n          4818,\n          4771\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 5 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 913,\n        \"max\": 953,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          928,\n          913,\n          917\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 104,\n        \"max\": 106,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          105,\n          106,\n          104\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 4 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 49,\n        \"max\": 50,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          50,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 20,\n        \"max\": 20,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 3 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 20,\n        \"max\": 20,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 129
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "prizes = [0,10000000,20000000,30000000,40000000,50000000,60000000]\n",
        "f=[]\n",
        "g=[]\n",
        "h=[]\n",
        "dfs=[]\n",
        "for prize in prizes:\n",
        "  matching_rows = LottoMAX1[(LottoMAX1['Prize Per Winner'] >= prize) & (LottoMAX1['Prize Per Winner'] < (prize+10000000))]\n",
        "  dates = matching_rows['Date'].unique()\n",
        "  f=[]\n",
        "  h=[]\n",
        "  for date in dates:\n",
        "    cc = LottoMAX1[(LottoMAX1['Date'] == date) & (LottoMAX1['Numbers Matched'] == 'Totals')]\n",
        "    f.append(cc['All Winners'].mean())\n",
        "  print(date, f)\n",
        "  fsum = sum(f)\n",
        "  count=len(f)\n",
        "  df = pd.DataFrame({'Jackpot': [prize+5000000],'Tickets Sold':[int((fsum/count)/(1/7))]})\n",
        "  dfs.append(df)\n",
        "pepe = pd.concat(dfs, ignore_index=True)\n",
        "pepe.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "62G5pWtFua0J",
        "outputId": "7596c797-dd5d-4c95-e5d2-b9cdb193d219"
      },
      "execution_count": 130,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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              "    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-649dea08-87c5-423c-9522-81c679a92caa button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pepe",
              "summary": "{\n  \"name\": \"pepe\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Jackpot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21602468,\n        \"min\": 5000000,\n        \"max\": 65000000,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5000000,\n          15000000,\n          55000000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tickets Sold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2096638,\n        \"min\": 4810872,\n        \"max\": 10860820,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6739353,\n          4810872,\n          8064934\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 130
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_dfs = pd.merge(LottoMAX_combined_df, pepe, on='Jackpot')"
      ],
      "metadata": {
        "id": "P23CAU6GxLtp"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "ODQXLyOhxuUa"
      },
      "execution_count": 131,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_dfs['Jackpot Amount'] = ['1-10m','10-20m','20-30m','30-40m','40-50m','50-60m','60-68m']\n",
        "merged_dfs['MaxMillions'] = [0,0,0,0,0,4,8]\n",
        "column_to_move = merged_dfs.pop('Jackpot Amount')\n",
        "merged_dfs.insert(0, 'Jackpot Amount', column_to_move)"
      ],
      "metadata": {
        "id": "HZ7SGeVUxnHu"
      },
      "execution_count": 132,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_dfs"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "UQ9d37NaxQD0",
        "outputId": "c38cabe0-f4f5-4973-baa3-746c7398c450"
      },
      "execution_count": 133,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Jackpot Amount   Jackpot  Match 6 plus Bonus  Match 6  Match 5 plus Bonus  \\\n",
              "0          1-10m   5000000              219973     4817                 928   \n",
              "1         10-20m  15000000              143772     4818                 913   \n",
              "2         20-30m  25000000              164435     5003                 953   \n",
              "3         30-40m  35000000              200671     4747                 945   \n",
              "4         40-50m  45000000              166388     4662                 934   \n",
              "5         50-60m  55000000              259897     4771                 913   \n",
              "6         60-68m  65000000              305529     4909                 917   \n",
              "\n",
              "   Match 5  Match 4 plus Bonus  Match 4  Match 3 plus Bonus  Match 3  \\\n",
              "0      105                  49       20                  20        5   \n",
              "1      105                  49       20                  20        5   \n",
              "2      106                  50       20                  20        5   \n",
              "3      106                  50       20                  20        5   \n",
              "4      104                  49       20                  20        5   \n",
              "5      105                  49       20                  20        5   \n",
              "6      105                  49       20                  20        5   \n",
              "\n",
              "   Tickets Sold  MaxMillions  \n",
              "0       6739353            0  \n",
              "1       4810872            0  \n",
              "2       5014211            0  \n",
              "3       6191832            0  \n",
              "4       5924889            0  \n",
              "5       8064934            4  \n",
              "6      10860820            8  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-d41fb813-0893-43e6-990b-dc614aff7d46\" 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>Jackpot Amount</th>\n",
              "      <th>Jackpot</th>\n",
              "      <th>Match 6 plus Bonus</th>\n",
              "      <th>Match 6</th>\n",
              "      <th>Match 5 plus Bonus</th>\n",
              "      <th>Match 5</th>\n",
              "      <th>Match 4 plus Bonus</th>\n",
              "      <th>Match 4</th>\n",
              "      <th>Match 3 plus Bonus</th>\n",
              "      <th>Match 3</th>\n",
              "      <th>Tickets Sold</th>\n",
              "      <th>MaxMillions</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1-10m</td>\n",
              "      <td>5000000</td>\n",
              "      <td>219973</td>\n",
              "      <td>4817</td>\n",
              "      <td>928</td>\n",
              "      <td>105</td>\n",
              "      <td>49</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>6739353</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10-20m</td>\n",
              "      <td>15000000</td>\n",
              "      <td>143772</td>\n",
              "      <td>4818</td>\n",
              "      <td>913</td>\n",
              "      <td>105</td>\n",
              "      <td>49</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>4810872</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20-30m</td>\n",
              "      <td>25000000</td>\n",
              "      <td>164435</td>\n",
              "      <td>5003</td>\n",
              "      <td>953</td>\n",
              "      <td>106</td>\n",
              "      <td>50</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>5014211</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30-40m</td>\n",
              "      <td>35000000</td>\n",
              "      <td>200671</td>\n",
              "      <td>4747</td>\n",
              "      <td>945</td>\n",
              "      <td>106</td>\n",
              "      <td>50</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>6191832</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40-50m</td>\n",
              "      <td>45000000</td>\n",
              "      <td>166388</td>\n",
              "      <td>4662</td>\n",
              "      <td>934</td>\n",
              "      <td>104</td>\n",
              "      <td>49</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>5924889</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>50-60m</td>\n",
              "      <td>55000000</td>\n",
              "      <td>259897</td>\n",
              "      <td>4771</td>\n",
              "      <td>913</td>\n",
              "      <td>105</td>\n",
              "      <td>49</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>8064934</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>60-68m</td>\n",
              "      <td>65000000</td>\n",
              "      <td>305529</td>\n",
              "      <td>4909</td>\n",
              "      <td>917</td>\n",
              "      <td>105</td>\n",
              "      <td>49</td>\n",
              "      <td>20</td>\n",
              "      <td>20</td>\n",
              "      <td>5</td>\n",
              "      <td>10860820</td>\n",
              "      <td>8</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-d41fb813-0893-43e6-990b-dc614aff7d46')\"\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-d41fb813-0893-43e6-990b-dc614aff7d46 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-d41fb813-0893-43e6-990b-dc614aff7d46');\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-6d2f5cb4-5bf0-4fd4-a103-39cc094e944b\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6d2f5cb4-5bf0-4fd4-a103-39cc094e944b')\"\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",
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              "    </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-6d2f5cb4-5bf0-4fd4-a103-39cc094e944b button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "merged_dfs",
              "summary": "{\n  \"name\": \"merged_dfs\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Jackpot Amount\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"1-10m\",\n          \"10-20m\",\n          \"50-60m\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jackpot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21602468,\n        \"min\": 5000000,\n        \"max\": 65000000,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5000000,\n          15000000,\n          55000000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 6 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57956,\n        \"min\": 143772,\n        \"max\": 305529,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          219973,\n          143772,\n          259897\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111,\n        \"min\": 4662,\n        \"max\": 5003,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4817,\n          4818,\n          4771\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 5 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 913,\n        \"max\": 953,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          928,\n          913,\n          917\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 104,\n        \"max\": 106,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          105,\n          106,\n          104\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 4 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 49,\n        \"max\": 50,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          50,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 20,\n        \"max\": 20,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 3 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 20,\n        \"max\": 20,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tickets Sold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2096638,\n        \"min\": 4810872,\n        \"max\": 10860820,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6739353\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaxMillions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 133
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Expected Value for draws\n",
        "merged_dfs['Expected Value'] = (merged_dfs['Jackpot'] * (1/33294800) +\n",
        "                               merged_dfs['Match 6 plus Bonus'] * (1/4756400)+\n",
        "                               merged_dfs['Match 6'] * (1/113248)+\n",
        "                               merged_dfs['Match 5 plus Bonus'] * (1/37749)+\n",
        "                               merged_dfs['Match 5'] * (1/1841)+\n",
        "                               merged_dfs['Match 4 plus Bonus'] * (1/1105)+\n",
        "                               merged_dfs['Match 4'] * (1/82.9)+\n",
        "                               merged_dfs['Match 3 plus Bonus'] * (1/82.9)+\n",
        "                               merged_dfs['Match 3'] * (1/8.5)+\n",
        "                               merged_dfs['MaxMillions']*(1/33294800))"
      ],
      "metadata": {
        "id": "D5NJZyUayS1J"
      },
      "execution_count": 134,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_dfs.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "ybxopeQW1WnN",
        "outputId": "e8fdb713-bdcf-4565-ed44-72b493b660d8"
      },
      "execution_count": 135,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Jackpot Amount   Jackpot  Match 6 plus Bonus  Match 6  Match 5 plus Bonus  \\\n",
              "0          1-10m   5000000              219973     4817                 928   \n",
              "1         10-20m  15000000              143772     4818                 913   \n",
              "2         20-30m  25000000              164435     5003                 953   \n",
              "3         30-40m  35000000              200671     4747                 945   \n",
              "4         40-50m  45000000              166388     4662                 934   \n",
              "5         50-60m  55000000              259897     4771                 913   \n",
              "6         60-68m  65000000              305529     4909                 917   \n",
              "\n",
              "   Match 5  Match 4 plus Bonus  Match 4  Match 3 plus Bonus  Match 3  \\\n",
              "0      105                  49       20                  20        5   \n",
              "1      105                  49       20                  20        5   \n",
              "2      106                  50       20                  20        5   \n",
              "3      106                  50       20                  20        5   \n",
              "4      104                  49       20                  20        5   \n",
              "5      105                  49       20                  20        5   \n",
              "6      105                  49       20                  20        5   \n",
              "\n",
              "   Tickets Sold  MaxMillions  Expected Value  \n",
              "0       6739353            0        1.435662  \n",
              "1       4810872            0        1.719600  \n",
              "2       5014211            0        2.028433  \n",
              "3       6191832            0        2.333926  \n",
              "4       5924889            0        2.624032  \n",
              "5       8064934            4        2.944989  \n",
              "6      10860820            8        3.256254  "
            ],
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              "      <th>0</th>\n",
              "      <td>1-10m</td>\n",
              "      <td>5000000</td>\n",
              "      <td>219973</td>\n",
              "      <td>4817</td>\n",
              "      <td>928</td>\n",
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              "      <td>10-20m</td>\n",
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              "      <td>35000000</td>\n",
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              "      <td>945</td>\n",
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              "      <td>2.333926</td>\n",
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              "      <th>4</th>\n",
              "      <td>40-50m</td>\n",
              "      <td>45000000</td>\n",
              "      <td>166388</td>\n",
              "      <td>4662</td>\n",
              "      <td>934</td>\n",
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              "      <td>2.624032</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>50-60m</td>\n",
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              "      <td>4771</td>\n",
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              "      <td>2.944989</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
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              "      <td>4909</td>\n",
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              "  .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-cbb54d2c-57d9-4113-806e-34cde075570e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "merged_dfs",
              "summary": "{\n  \"name\": \"merged_dfs\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Jackpot Amount\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"1-10m\",\n          \"10-20m\",\n          \"50-60m\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jackpot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21602468,\n        \"min\": 5000000,\n        \"max\": 65000000,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5000000,\n          15000000,\n          55000000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 6 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 57956,\n        \"min\": 143772,\n        \"max\": 305529,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          219973,\n          143772,\n          259897\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 111,\n        \"min\": 4662,\n        \"max\": 5003,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4817,\n          4818,\n          4771\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 5 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15,\n        \"min\": 913,\n        \"max\": 953,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          928,\n          913,\n          917\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 104,\n        \"max\": 106,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          105,\n          106,\n          104\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 4 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 49,\n        \"max\": 50,\n        \"num_unique_values\": 2,\n        \"samples\": [\n          50,\n          49\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 20,\n        \"max\": 20,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 3 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 20,\n        \"max\": 20,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          20\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tickets Sold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2096638,\n        \"min\": 4810872,\n        \"max\": 10860820,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          6739353\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"MaxMillions\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3,\n        \"min\": 0,\n        \"max\": 8,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Expected Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.6564861275253436,\n        \"min\": 1.4356622464064304,\n        \"max\": 3.256254226414699,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.4356622464064304\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 135
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_dfs.to_csv('/content/drive/MyDrive/A Python Learning/ExpectedValueLottoMAX.csv',index=False)"
      ],
      "metadata": {
        "id": "75c5jzssF8yg"
      },
      "execution_count": 136,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **Expected Value for Lotto649**"
      ],
      "metadata": {
        "id": "RUEibI-GFFDm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Lotto649 is tricky because the prize amounts vary based on a percentage of the prize payout (48% of Lotto649 Sales). Therefore, rather than having inconsistency in percentage-based approach, I opted for an average of the prize amount for each Jackpot amount from 2012 to 2023, therefore this includes lower Jackpot amounts of 4 million in the past."
      ],
      "metadata": {
        "id": "_B4vsf-J9H9L"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "Lotto649_jackpot['Date'] = pd.to_datetime(Lotto649_jackpot['Date'])"
      ],
      "metadata": {
        "id": "TWklXcqZGep_"
      },
      "execution_count": 137,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Lotto649_merged = pd.merge(Lotto649,Lotto649_jackpot, on='Date', how='outer')"
      ],
      "metadata": {
        "id": "7W-FhSHrFLQg"
      },
      "execution_count": 138,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Lotto649_merged.dropna(subset=['Prize'], inplace=True)"
      ],
      "metadata": {
        "id": "6TQifbp8Of9B"
      },
      "execution_count": 139,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Lotto649_merged"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "id": "nq_XOs5uI3aG",
        "outputId": "49e270ab-b10d-4de5-f519-9e38a3231b89"
      },
      "execution_count": 140,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Date                Draw      Prize   Winners    Year     Jackpot\n",
              "0     2012-01-04             Match 6  3052345.0       0.0  2012.0         NaN\n",
              "1     2012-01-04  Match 5 plus Bonus    72674.0       3.0  2012.0         NaN\n",
              "2     2012-01-04             Match 5     1765.0     102.0  2012.0         NaN\n",
              "3     2012-01-04             Match 4       50.0    6787.0  2012.0         NaN\n",
              "4     2012-01-04             Match 3       10.0  110913.0  2012.0         NaN\n",
              "...          ...                 ...        ...       ...     ...         ...\n",
              "10011 2023-12-30             Match 4       61.0    5572.0  2023.0  20000000.0\n",
              "10012 2023-12-30             Match 3       10.0   98098.0  2023.0  20000000.0\n",
              "10013 2023-12-30  Match 2 plus Bonus        5.0   60853.0  2023.0  20000000.0\n",
              "10014 2023-12-30             Match 2        0.0  657477.0  2023.0  20000000.0\n",
              "10015 2023-12-30   Gold Ball Jackpot  1000000.0       1.0  2023.0  20000000.0\n",
              "\n",
              "[10016 rows x 6 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0458ad54-fec2-4faa-a8f6-812f38275a42\" 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>Draw</th>\n",
              "      <th>Prize</th>\n",
              "      <th>Winners</th>\n",
              "      <th>Year</th>\n",
              "      <th>Jackpot</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2012-01-04</td>\n",
              "      <td>Match 6</td>\n",
              "      <td>3052345.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2012-01-04</td>\n",
              "      <td>Match 5 plus Bonus</td>\n",
              "      <td>72674.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2012-01-04</td>\n",
              "      <td>Match 5</td>\n",
              "      <td>1765.0</td>\n",
              "      <td>102.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2012-01-04</td>\n",
              "      <td>Match 4</td>\n",
              "      <td>50.0</td>\n",
              "      <td>6787.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2012-01-04</td>\n",
              "      <td>Match 3</td>\n",
              "      <td>10.0</td>\n",
              "      <td>110913.0</td>\n",
              "      <td>2012.0</td>\n",
              "      <td>NaN</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10011</th>\n",
              "      <td>2023-12-30</td>\n",
              "      <td>Match 4</td>\n",
              "      <td>61.0</td>\n",
              "      <td>5572.0</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>20000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10012</th>\n",
              "      <td>2023-12-30</td>\n",
              "      <td>Match 3</td>\n",
              "      <td>10.0</td>\n",
              "      <td>98098.0</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>20000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10013</th>\n",
              "      <td>2023-12-30</td>\n",
              "      <td>Match 2 plus Bonus</td>\n",
              "      <td>5.0</td>\n",
              "      <td>60853.0</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>20000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10014</th>\n",
              "      <td>2023-12-30</td>\n",
              "      <td>Match 2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>657477.0</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>20000000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10015</th>\n",
              "      <td>2023-12-30</td>\n",
              "      <td>Gold Ball Jackpot</td>\n",
              "      <td>1000000.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2023.0</td>\n",
              "      <td>20000000.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>10016 rows × 6 columns</p>\n",
              "</div>\n",
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              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0458ad54-fec2-4faa-a8f6-812f38275a42')\"\n",
              "            title=\"Convert this dataframe to an interactive table.\"\n",
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              "        const dataTable =\n",
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              "        if (!dataTable) return;\n",
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              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
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              "        await google.colab.output.renderOutput(dataTable, element);\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "Lotto649_merged",
              "summary": "{\n  \"name\": \"Lotto649_merged\",\n  \"rows\": 10016,\n  \"fields\": [\n    {\n      \"column\": \"Date\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2012-01-04 00:00:00\",\n        \"max\": \"2023-12-30 00:00:00\",\n        \"num_unique_values\": 1252,\n        \"samples\": [\n          \"2018-12-01 00:00:00\",\n          \"2023-11-18 00:00:00\",\n          \"2022-03-26 00:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Draw\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 9,\n        \"samples\": [\n          \"Guaranteed Prize Draw\",\n          \"Match 5 plus Bonus\",\n          \"Match 2 plus Bonus\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Prize\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4183514.406370364,\n        \"min\": 0.0,\n        \"max\": 68000000.0,\n        \"num_unique_values\": 2961,\n        \"samples\": [\n          681.0,\n          2745.0,\n          18724.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Winners\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 194773.2889436069,\n        \"min\": 0.0,\n        \"max\": 2493441.0,\n        \"num_unique_values\": 4802,\n        \"samples\": [\n          67696.0,\n          145326.0,\n          79025.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 3.45145312167394,\n        \"min\": 2012.0,\n        \"max\": 2023.0,\n        \"num_unique_values\": 12,\n        \"samples\": [\n          2022.0,\n          2021.0,\n          2012.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Jackpot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 14927277.116095573,\n        \"min\": 10000000.0,\n        \"max\": 68000000.0,\n        \"num_unique_values\": 30,\n        \"samples\": [\n          64000000.0,\n          40000000.0,\n          56000000.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 140
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Test"
      ],
      "metadata": {
        "id": "Khk3SUvF9vzA"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prize = 20000000\n",
        "matching_rows = Lotto649_merged[((Lotto649_merged['Prize'] > prize) & (Lotto649_merged['Prize'] < (prize+10000000)))| ((Lotto649_merged['Jackpot'] > prize)& (Lotto649_merged['Jackpot']< (prize+10000000)))]\n",
        "dates = matching_rows['Date'].unique()\n",
        "Higherpower = ['Match 5 plus Bonus','Match 5','Match 4']\n",
        "a=[]\n",
        "f=[]\n",
        "g =[]\n",
        "h=[]\n",
        "for power in Higherpower:\n",
        "  f = []\n",
        "  for date in dates:\n",
        "    cc = Lotto649_merged[(Lotto649_merged['Date'] == date) & (Lotto649_merged['Draw'] == power)]\n",
        "    a.append(cc['Prize'].mean())\n",
        "    f.append(cc['Winners'].mean())\n",
        "  count = len(f)\n",
        "  fsum = sum(f)\n",
        "  g.append(fsum)\n",
        "  gsum = sum(g)\n",
        "  print(gsum)\n",
        "hsum = (gsum/(1/6.6))/count\n",
        "print(hsum)\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tecpjtkpdqm0",
        "outputId": "77890443-4cdc-47d0-b020-18dca377532a"
      },
      "execution_count": 141,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "249.0\n",
            "11516.0\n",
            "617415.0\n",
            "41161.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Creating a pandas dataframe on average Lottery Prize Amounts at different prize levels."
      ],
      "metadata": {
        "id": "L7a4gUUl9w3I"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prizes = [0,10000000,20000000,30000000,40000000,50000000,60000000]\n",
        "combined_df = pd.DataFrame()\n",
        "dfs =[]\n",
        "for prize in prizes:\n",
        "  matching_rows = Lotto649_merged[((Lotto649_merged['Prize'] >= prize) & (Lotto649_merged['Prize'] < (prize+10000000)))| ((Lotto649_merged['Jackpot'] >= prize)& (Lotto649_merged['Jackpot']< (prize+10000000)))]\n",
        "  dates = matching_rows['Date'].unique()\n",
        "  Higherpower = ['Match 5 plus Bonus','Match 5','Match 4']\n",
        "  b=[]\n",
        "  for power in Higherpower:\n",
        "    a=[]\n",
        "    f = []\n",
        "    g=[]\n",
        "    h = []\n",
        "    for date in dates:\n",
        "      cc = Lotto649_merged[(Lotto649_merged['Date'] == date) & (Lotto649_merged['Draw'] == power)]\n",
        "      a.append(cc['Prize'].mean())\n",
        "      f.append(cc['Winners'].mean())\n",
        "          # Create a new DataFrame for the current date and 'Prize' values\n",
        "      mean = statistics.mean(a)\n",
        "    b.append(mean)\n",
        "  print(len(a))\n",
        "  c= int(b[0])\n",
        "  d = int(b[1])\n",
        "  e = int(b[2])\n",
        "  df = pd.DataFrame({'Match 6': [prize+5000000],'Match 5 plus Bonus': [c], 'Match 5': [d], 'Match 4': [e],'Match 3':[10],'Match 2 plus Bonus':[5],'Match 2':[3]})\n",
        "  dfs.append(df)\n",
        "combined_df = pd.concat(dfs, ignore_index=True)\n",
        "combined_df.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 390
        },
        "id": "A46yu445V1cM",
        "outputId": "7cc59866-5469-425f-86dd-29f76f314c0e"
      },
      "execution_count": 142,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1252\n",
            "311\n",
            "116\n",
            "55\n",
            "29\n",
            "12\n",
            "9\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Match 6  Match 5 plus Bonus  Match 5  Match 4  Match 3  \\\n",
              "0   5000000              147174     2172       77       10   \n",
              "1  15000000              152384     2171       78       10   \n",
              "2  25000000              147930     1951       77       10   \n",
              "3  35000000              162689     1682       82       10   \n",
              "4  45000000              152723     1225       69       10   \n",
              "5  55000000              111364     1062       70       10   \n",
              "6  65000000              161118     1094       78       10   \n",
              "\n",
              "   Match 2 plus Bonus  Match 2  \n",
              "0                   5        3  \n",
              "1                   5        3  \n",
              "2                   5        3  \n",
              "3                   5        3  \n",
              "4                   5        3  \n",
              "5                   5        3  \n",
              "6                   5        3  "
            ],
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              "  .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-ccfa3903-0252-4f57-a27a-369e3a9b233e button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "combined_df",
              "summary": "{\n  \"name\": \"combined_df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Match 6\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21602468,\n        \"min\": 5000000,\n        \"max\": 65000000,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5000000,\n          15000000,\n          55000000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 5 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17186,\n        \"min\": 111364,\n        \"max\": 162689,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          147174,\n          152384,\n          111364\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 494,\n        \"min\": 1062,\n        \"max\": 2172,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2172,\n          2171,\n          1062\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 69,\n        \"max\": 82,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          78,\n          70,\n          82\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 10,\n        \"max\": 10,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 2 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 142
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Calculating Total tickets sold per Jackpot Value"
      ],
      "metadata": {
        "id": "LbCCyLGF-EZB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "prizes = [0,10000000,20000000,30000000,40000000,50000000,60000000]\n",
        "f=[]\n",
        "g=[]\n",
        "h=[]\n",
        "dfs=[]\n",
        "for prize in prizes:\n",
        "  matching_rows = Lotto649_merged[((Lotto649_merged['Prize'] > prize) & (Lotto649_merged['Prize'] < (prize+10000000)))| ((Lotto649_merged['Jackpot'] > prize)& (Lotto649_merged['Jackpot']< (prize+10000000)))]\n",
        "  dates = matching_rows['Date'].unique()\n",
        "  f=[]\n",
        "  h=[]\n",
        "  for date in dates:\n",
        "    cc = Lotto649_merged[(Lotto649_merged['Date'] == date) & (Lotto649_merged['Draw'] == 'Match 2')]\n",
        "    f.append(cc['Winners'].mean())\n",
        "  print(date, f)\n",
        "  fsum = sum(f)\n",
        "  count=len(f)\n",
        "  df = pd.DataFrame({'Gold Jackpot': [prize+5000000],'Tickets Sold':[int((fsum/count)/(1/8.3))]})\n",
        "  dfs.append(df)\n",
        "pepe = pd.concat(dfs, ignore_index=True)\n",
        "pepe.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "Cr8tonIaklvO",
        "outputId": "0802bcd3-197e-4e19-9737-06d8d3bd8837"
      },
      "execution_count": 143,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
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              "   Gold Jackpot  Tickets Sold\n",
              "0       5000000       4436560\n",
              "1      15000000       4400234\n",
              "2      25000000       5309584\n",
              "3      35000000       5344866\n",
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              "        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",
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              "\n",
              "\n",
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              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-d7f355b7-d312-4f4f-be52-176aa58278d4')\"\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",
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              "\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",
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              "    20% {\n",
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              "      border-left-color: var(--fill-color);\n",
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              "    30% {\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      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-d7f355b7-d312-4f4f-be52-176aa58278d4 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "pepe",
              "summary": "{\n  \"name\": \"pepe\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Gold Jackpot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21602468,\n        \"min\": 5000000,\n        \"max\": 65000000,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5000000,\n          15000000,\n          55000000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tickets Sold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2233028,\n        \"min\": 4400234,\n        \"max\": 10827314,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4436560,\n          4400234,\n          6881200\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 143
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "combined_df['Match 6 5mil'] = combined_df['Match 6']"
      ],
      "metadata": {
        "id": "FChsP1bTYRIg"
      },
      "execution_count": 144,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "combined_df['Match 6 5mil'] = 5000000\n",
        "column_to_move = combined_df.pop('Match 6 5mil')\n",
        "combined_df.insert(1, 'Match 6 5mil', column_to_move)\n",
        "\n",
        "\n",
        "combined_df = combined_df.rename(columns={'Match 6' : 'Gold Jackpot'})"
      ],
      "metadata": {
        "id": "tu8t4RhrPxz0"
      },
      "execution_count": 145,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df = pd.merge(combined_df, pepe, on='Gold Jackpot')"
      ],
      "metadata": {
        "id": "bxruqCXErcEx"
      },
      "execution_count": 146,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df['Gold Jackpot Amount'] = ['1-10m','10-20','20-30m','30-40m','40-50m','50-60m','60-68m']\n",
        "merged_df['Balls'] = [30,27.5,22.5,17.5,12.5,7.5,2.5]"
      ],
      "metadata": {
        "id": "KGYkM95tsdUM"
      },
      "execution_count": 147,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Expected Value for draws\n",
        "merged_df['Expected Value'] = ((merged_df['Gold Jackpot'] * (1/merged_df['Tickets Sold'])*(1/merged_df['Balls']))+\n",
        "                               merged_df['Match 6 5mil'] * (1/13983816 )+\n",
        "                               merged_df['Match 5 plus Bonus'] * (1/2330636 )+\n",
        "                               merged_df['Match 5'] * (1/55492)+\n",
        "                               merged_df['Match 4'] * (1/1033)+\n",
        "                               merged_df['Match 3'] * (1/56.7 )+\n",
        "                               merged_df['Match 2 plus Bonus'] * (1/81.2 )+\n",
        "                               merged_df['Match 2'] * (1/8.3)+\n",
        "                               1000000*(1/merged_df['Tickets Sold']))\n",
        "print((merged_df['Gold Jackpot'] * (1/merged_df['Tickets Sold'])*(1/merged_df['Balls'])))"
      ],
      "metadata": {
        "id": "4YhnyBA4Ye0v",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "822b76d2-b815-41f0-ed5d-46ab097c299f"
      },
      "execution_count": 148,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0    0.037567\n",
            "1    0.123960\n",
            "2    0.209265\n",
            "3    0.374191\n",
            "4    0.625508\n",
            "5    1.065706\n",
            "6    2.401334\n",
            "dtype: float64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "column_to_move = merged_df.pop('Gold Jackpot Amount')\n",
        "merged_df.insert(0, 'Gold Jackpot Amount', column_to_move)"
      ],
      "metadata": {
        "id": "SAe5futBjzIW"
      },
      "execution_count": 149,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Final Result for Lotto649 Expected Value"
      ],
      "metadata": {
        "id": "wvVyN2Xt-S3T"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 286
        },
        "id": "-WxdI9brrw-B",
        "outputId": "9e30a1be-1065-4189-e505-753646b20244"
      },
      "execution_count": 150,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Gold Jackpot Amount  Gold Jackpot  Match 6 5mil  Match 5 plus Bonus  \\\n",
              "0               1-10m       5000000       5000000              147174   \n",
              "1               10-20      15000000       5000000              152384   \n",
              "2              20-30m      25000000       5000000              147930   \n",
              "3              30-40m      35000000       5000000              162689   \n",
              "4              40-50m      45000000       5000000              152723   \n",
              "5              50-60m      55000000       5000000              111364   \n",
              "6              60-68m      65000000       5000000              161118   \n",
              "\n",
              "   Match 5  Match 4  Match 3  Match 2 plus Bonus  Match 2  Tickets Sold  \\\n",
              "0     2172       77       10                   5        3       4436560   \n",
              "1     2171       78       10                   5        3       4400234   \n",
              "2     1951       77       10                   5        3       5309584   \n",
              "3     1682       82       10                   5        3       5344866   \n",
              "4     1225       69       10                   5        3       5755326   \n",
              "5     1062       70       10                   5        3       6881200   \n",
              "6     1094       78       10                   5        3      10827314   \n",
              "\n",
              "   Balls  Expected Value  \n",
              "0   30.0        1.396740  \n",
              "1   27.5        1.488180  \n",
              "2   22.5        1.527719  \n",
              "3   17.5        1.697727  \n",
              "4   12.5        1.910604  \n",
              "5    7.5        2.302659  \n",
              "6    2.5        3.614992  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-1b1e482a-ed9b-4646-b378-7e444786e83a\" 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>Gold Jackpot Amount</th>\n",
              "      <th>Gold Jackpot</th>\n",
              "      <th>Match 6 5mil</th>\n",
              "      <th>Match 5 plus Bonus</th>\n",
              "      <th>Match 5</th>\n",
              "      <th>Match 4</th>\n",
              "      <th>Match 3</th>\n",
              "      <th>Match 2 plus Bonus</th>\n",
              "      <th>Match 2</th>\n",
              "      <th>Tickets Sold</th>\n",
              "      <th>Balls</th>\n",
              "      <th>Expected Value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1-10m</td>\n",
              "      <td>5000000</td>\n",
              "      <td>5000000</td>\n",
              "      <td>147174</td>\n",
              "      <td>2172</td>\n",
              "      <td>77</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4436560</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1.396740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10-20</td>\n",
              "      <td>15000000</td>\n",
              "      <td>5000000</td>\n",
              "      <td>152384</td>\n",
              "      <td>2171</td>\n",
              "      <td>78</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>4400234</td>\n",
              "      <td>27.5</td>\n",
              "      <td>1.488180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>20-30m</td>\n",
              "      <td>25000000</td>\n",
              "      <td>5000000</td>\n",
              "      <td>147930</td>\n",
              "      <td>1951</td>\n",
              "      <td>77</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5309584</td>\n",
              "      <td>22.5</td>\n",
              "      <td>1.527719</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>30-40m</td>\n",
              "      <td>35000000</td>\n",
              "      <td>5000000</td>\n",
              "      <td>162689</td>\n",
              "      <td>1682</td>\n",
              "      <td>82</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5344866</td>\n",
              "      <td>17.5</td>\n",
              "      <td>1.697727</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>40-50m</td>\n",
              "      <td>45000000</td>\n",
              "      <td>5000000</td>\n",
              "      <td>152723</td>\n",
              "      <td>1225</td>\n",
              "      <td>69</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>5755326</td>\n",
              "      <td>12.5</td>\n",
              "      <td>1.910604</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>50-60m</td>\n",
              "      <td>55000000</td>\n",
              "      <td>5000000</td>\n",
              "      <td>111364</td>\n",
              "      <td>1062</td>\n",
              "      <td>70</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>6881200</td>\n",
              "      <td>7.5</td>\n",
              "      <td>2.302659</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>60-68m</td>\n",
              "      <td>65000000</td>\n",
              "      <td>5000000</td>\n",
              "      <td>161118</td>\n",
              "      <td>1094</td>\n",
              "      <td>78</td>\n",
              "      <td>10</td>\n",
              "      <td>5</td>\n",
              "      <td>3</td>\n",
              "      <td>10827314</td>\n",
              "      <td>2.5</td>\n",
              "      <td>3.614992</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
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              "            title=\"Convert this dataframe to an interactive table.\"\n",
              "            style=\"display:none;\">\n",
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              "\n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
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              "\n",
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              "      margin-bottom: 4px;\n",
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              "\n",
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              "      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-1b1e482a-ed9b-4646-b378-7e444786e83a 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-1b1e482a-ed9b-4646-b378-7e444786e83a');\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",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "merged_df",
              "summary": "{\n  \"name\": \"merged_df\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Gold Jackpot Amount\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 7,\n        \"samples\": [\n          \"1-10m\",\n          \"10-20\",\n          \"50-60m\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Gold Jackpot\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 21602468,\n        \"min\": 5000000,\n        \"max\": 65000000,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          5000000,\n          15000000,\n          55000000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 6 5mil\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 5000000,\n        \"max\": 5000000,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5000000\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 5 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 17186,\n        \"min\": 111364,\n        \"max\": 162689,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          147174\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 5\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 494,\n        \"min\": 1062,\n        \"max\": 2172,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2172\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 4\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 4,\n        \"min\": 69,\n        \"max\": 82,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          78\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 3\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 10,\n        \"max\": 10,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          10\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 2 plus Bonus\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 5,\n        \"max\": 5,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Match 2\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0,\n        \"min\": 3,\n        \"max\": 3,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          3\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Tickets Sold\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2233028,\n        \"min\": 4400234,\n        \"max\": 10827314,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          4436560\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Balls\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 10.249854818948599,\n        \"min\": 2.5,\n        \"max\": 30.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          30.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Expected Value\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.7796958443535569,\n        \"min\": 1.3967401968405535,\n        \"max\": 3.6149917061421677,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          1.3967401968405535\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 150
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "merged_df.to_csv('/content/drive/MyDrive/A Python Learning/ExpectedValueLotto649.csv',index=False)"
      ],
      "metadata": {
        "id": "SLq1-59CkH3-"
      },
      "execution_count": 151,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "Total Tickets Sold"
      ],
      "metadata": {
        "id": "VXunFHZwZRn-"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "tickets.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "OJwbITCrZR4N",
        "outputId": "3bb2fcc0-9228-4071-f3e6-ca6a49f9b1be"
      },
      "execution_count": 152,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year     LottoMAX     Lotto649   DailyGrand\n",
              "0  2017  402001761.0  534968537.4   95740001.6\n",
              "1  2018  558960570.0  552244479.6  102916946.0\n",
              "2  2019  433050947.0  505746832.8   91454770.8\n",
              "3  2020  547458590.0  454265493.0   63287525.2\n",
              "4  2021  660819747.0  487278085.8   69049090.4"
            ],
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              "    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-209a5f6d-c13c-43e4-b83a-ad2295ddb724 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tickets",
              "summary": "{\n  \"name\": \"tickets\",\n  \"rows\": 7,\n  \"fields\": [\n    {\n      \"column\": \"Year\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2,\n        \"min\": 2017,\n        \"max\": 2023,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          2017,\n          2018,\n          2022\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"LottoMAX\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 102631184.22997424,\n        \"min\": 402001761.0,\n        \"max\": 660819747.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          402001761.0,\n          558960570.0,\n          650233052.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Lotto649\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 43260733.74863045,\n        \"min\": 454265493.0,\n        \"max\": 577679097.6,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          534968537.4,\n          552244479.6,\n          483637334.4\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"DailyGrand\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 15602142.796582898,\n        \"min\": 63287525.199999996,\n        \"max\": 102916946.0,\n        \"num_unique_values\": 7,\n        \"samples\": [\n          95740001.6,\n          102916946.0,\n          69939468.8\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 152
        }
      ]
    }
  ]
}