Python_Window_Functions.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/pthiagu2/DataMining/master/WA_Fn-UseC_-Accounts-Receivable.csv')"
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            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2466 entries, 0 to 2465\n",
            "Data columns (total 12 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   countryCode    2466 non-null   int64  \n",
            " 1   customerID     2466 non-null   object \n",
            " 2   PaperlessDate  2466 non-null   object \n",
            " 3   invoiceNumber  2466 non-null   int64  \n",
            " 4   InvoiceDate    2466 non-null   object \n",
            " 5   DueDate        2466 non-null   object \n",
            " 6   InvoiceAmount  2466 non-null   float64\n",
            " 7   Disputed       2466 non-null   object \n",
            " 8   SettledDate    2466 non-null   object \n",
            " 9   PaperlessBill  2466 non-null   object \n",
            " 10  DaysToSettle   2466 non-null   int64  \n",
            " 11  DaysLate       2466 non-null   int64  \n",
            "dtypes: float64(1), int64(4), object(7)\n",
            "memory usage: 231.3+ KB\n"
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        "df.columns"
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              "Index(['countryCode', 'customerID', 'PaperlessDate', 'invoiceNumber',\n",
              "       'InvoiceDate', 'DueDate', 'InvoiceAmount', 'Disputed', 'SettledDate',\n",
              "       'PaperlessBill', 'DaysToSettle', 'DaysLate'],\n",
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        "df.drop(['PaperlessDate','invoiceNumber', 'DueDate', 'Disputed', 'SettledDate','PaperlessBill','DaysToSettle'], axis = 1,inplace = True)"
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              "       countryCode  InvoiceAmount     DaysLate\n",
              "count  2466.000000    2466.000000  2466.000000\n",
              "mean    620.446067      59.895856     3.442417\n",
              "std     215.933610      20.435838     6.290607\n",
              "min     391.000000       5.260000     0.000000\n",
              "25%     406.000000      46.400000     0.000000\n",
              "50%     770.000000      60.560000     0.000000\n",
              "75%     818.000000      73.765000     5.000000\n",
              "max     897.000000     128.280000    45.000000"
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              "391    616\n",
              "406    561\n",
              "770    506\n",
              "897    396\n",
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              "Name: countryCode, dtype: int64"
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              "9149-MATVB    36\n",
              "8887-NCUZC    35\n",
              "4640-FGEJI    35\n",
              "0688-XNJRO    34\n",
              "9286-VLKMI    34\n",
              "              ..\n",
              "4632-QZOKX    17\n",
              "3676-CQAIF    17\n",
              "7245-CKNCN    17\n",
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              "2621-XCLEH    15\n",
              "Name: customerID, Length: 100, dtype: int64"
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              "count    100.000000\n",
              "mean       3.513581\n",
              "std        4.298480\n",
              "min        0.000000\n",
              "25%        0.198593\n",
              "50%        1.684113\n",
              "75%        5.346154\n",
              "max       19.600000\n",
              "Name: DaysLate, dtype: float64"
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            "text/plain": [
              "   countryCode  customerID InvoiceDate  InvoiceAmount  DaysLate\n",
              "0          391  0379-NEVHP    1/2/2013          55.94         0\n",
              "1          406  8976-AMJEO   1/26/2013          61.74         6\n",
              "2          391  2820-XGXSB    7/3/2013          65.88         0\n",
              "3          406  9322-YCTQO   2/10/2013         105.92         5\n",
              "4          818  6627-ELFBK  10/25/2012          72.27         4"
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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",
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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
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    {
      "cell_type": "markdown",
      "source": [
        "## Feature Creation"
      ],
      "metadata": {
        "id": "_EH4iFohYDz_"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Make sure data is in the right format\n",
        "df[\"InvoiceDate\"] = pd.to_datetime(df[\"InvoiceDate\"])"
      ],
      "metadata": {
        "id": "Mq-PTSA9ZvSk"
      },
      "execution_count": 10,
      "outputs": []
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    {
      "cell_type": "code",
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        "df.head()"
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        "outputId": "9253227c-af28-482d-fd53-5feffeb64749"
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      "execution_count": 11,
      "outputs": [
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              "   countryCode  customerID InvoiceDate  InvoiceAmount  DaysLate\n",
              "0          391  0379-NEVHP  2013-01-02          55.94         0\n",
              "1          406  8976-AMJEO  2013-01-26          61.74         6\n",
              "2          391  2820-XGXSB  2013-07-03          65.88         0\n",
              "3          406  9322-YCTQO  2013-02-10         105.92         5\n",
              "4          818  6627-ELFBK  2012-10-25          72.27         4"
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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    {
      "cell_type": "code",
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        "df = df.sort_values('InvoiceDate')\n",
        "# Global Days Late Last 30 Days \n",
        "df[\"GlobalDaysLateLast30days\"] = df.rolling(window = '30d', on = \"InvoiceDate\").DaysLate.mean()"
      ],
      "metadata": {
        "id": "v3b-h21nLg8J"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Global Days Late Last 3 Invoices (min_periods = 1 to avoid NaN) \n",
        "df[\"GlobalDaysLateLast3Inv\"] = df.rolling(window = 3, on = \"InvoiceDate\").DaysLate.mean()"
      ],
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      "outputs": []
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      "execution_count": 14,
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              "      countryCode  customerID InvoiceDate  InvoiceAmount  DaysLate  \\\n",
              "543           391  7209-MDWKR  2013-12-02          67.69         0   \n",
              "2326          897  6391-GBFQJ  2013-12-02          18.05         0   \n",
              "2425          897  6391-GBFQJ  2013-12-02           8.38         0   \n",
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              "2046          770  9149-MATVB  2013-12-02          38.50         0   \n",
              "\n",
              "      GlobalDaysLateLast30days  GlobalDaysLateLast3Inv  \n",
              "543                   1.859813                1.333333  \n",
              "2326                  1.842593                1.333333  \n",
              "2425                  1.825688                0.000000  \n",
              "599                   1.863636                2.000000  \n",
              "2046                  1.846847                2.000000  "
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              "      countryCode  customerID InvoiceDate  InvoiceAmount  DaysLate  \\\n",
              "1279          391  6708-DPYTF  2012-01-03          55.37        14   \n",
              "75            770  3993-QUNVJ  2012-01-03          50.39         0   \n",
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              "1494          818  8887-NCUZC  2012-01-03          15.99        13   \n",
              "\n",
              "      GlobalDaysLateLast30days  GlobalDaysLateLast3Inv  \n",
              "1279                 14.000000                     NaN  \n",
              "75                    7.000000                     NaN  \n",
              "1584                  4.666667                4.666667  \n",
              "1460                  9.250000                7.666667  \n",
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              "    <tr>\n",
              "      <th>1584</th>\n",
              "      <td>406</td>\n",
              "      <td>5164-VMYWJ</td>\n",
              "      <td>2012-01-03</td>\n",
              "      <td>71.33</td>\n",
              "      <td>0</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>4.666667</td>\n",
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              "    <tr>\n",
              "      <th>1460</th>\n",
              "      <td>818</td>\n",
              "      <td>1604-LIFKX</td>\n",
              "      <td>2012-01-03</td>\n",
              "      <td>97.60</td>\n",
              "      <td>23</td>\n",
              "      <td>9.250000</td>\n",
              "      <td>7.666667</td>\n",
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              "    <tr>\n",
              "      <th>1494</th>\n",
              "      <td>818</td>\n",
              "      <td>8887-NCUZC</td>\n",
              "      <td>2012-01-03</td>\n",
              "      <td>15.99</td>\n",
              "      <td>13</td>\n",
              "      <td>10.000000</td>\n",
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          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Customer last 3 Invoices average days late\n",
        "df[\"AvLateLast3\"] = df.groupby(\"customerID\").rolling(window = 3,min_periods = 1).\\\n",
        "                              DaysLate.mean().reset_index().set_index(\"level_1\").\\\n",
        "                              sort_index()[\"DaysLate\"]"
      ],
      "metadata": {
        "id": "v7sEPdJfP47Z"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Customer Average Days Late Last 30 days   \n",
        "mean_30d = (\n",
        "    df\n",
        "    .set_index('InvoiceDate') # !important\n",
        "    .sort_index()\n",
        "    .groupby('customerID')\n",
        "    .rolling('30d')['DaysLate'].mean()\n",
        "    .reset_index(name='CustomerDaysLate_30days')\n",
        ")\n",
        "\n",
        "# merge the rolling mean back to original dataframe\n",
        "df = df.merge(mean_30d) \n"
      ],
      "metadata": {
        "id": "Ti97T9E3pl3-"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.query('customerID == \"6708-DPYTF\"')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "MNZ7Pd7Yom70",
        "outputId": "ce25baa7-4cc6-44e3-c90d-1ef04b690a78"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      countryCode  customerID InvoiceDate  InvoiceAmount  DaysLate  \\\n",
              "0             391  6708-DPYTF  2012-01-03          55.37        14   \n",
              "57            391  6708-DPYTF  2012-01-21          92.19         0   \n",
              "90            391  6708-DPYTF  2012-02-01          80.31        12   \n",
              "132           391  6708-DPYTF  2012-02-13          86.74        16   \n",
              "280           391  6708-DPYTF  2012-03-24          92.76        12   \n",
              "360           391  6708-DPYTF  2012-04-15          58.73        13   \n",
              "491           391  6708-DPYTF  2012-05-23          79.59        13   \n",
              "646           391  6708-DPYTF  2012-07-04          76.81        13   \n",
              "674           391  6708-DPYTF  2012-07-11          73.60        14   \n",
              "675           391  6708-DPYTF  2012-07-11          73.60        14   \n",
              "676           391  6708-DPYTF  2012-07-11          69.00         3   \n",
              "677           391  6708-DPYTF  2012-07-11          69.00         3   \n",
              "745           391  6708-DPYTF  2012-08-01          57.69         8   \n",
              "754           391  6708-DPYTF  2012-08-02          74.65        17   \n",
              "759           391  6708-DPYTF  2012-08-04          46.29         1   \n",
              "866           391  6708-DPYTF  2012-09-03          27.42         3   \n",
              "1125          391  6708-DPYTF  2012-11-11          94.19         4   \n",
              "1500          391  6708-DPYTF  2013-02-17          56.99         0   \n",
              "1521          391  6708-DPYTF  2013-02-24          64.64        11   \n",
              "1594          391  6708-DPYTF  2013-03-19          87.99        10   \n",
              "1599          391  6708-DPYTF  2013-03-20          81.59         9   \n",
              "1739          391  6708-DPYTF  2013-04-24          89.07         4   \n",
              "1760          391  6708-DPYTF  2013-04-29          82.04         0   \n",
              "1779          391  6708-DPYTF  2013-05-05          76.49         0   \n",
              "2239          391  6708-DPYTF  2013-09-08          64.19         2   \n",
              "2249          391  6708-DPYTF  2013-09-11          79.52         0   \n",
              "2340          391  6708-DPYTF  2013-10-03          99.22        15   \n",
              "2341          391  6708-DPYTF  2013-10-03          99.22        15   \n",
              "2342          391  6708-DPYTF  2013-10-03         105.25         1   \n",
              "2343          391  6708-DPYTF  2013-10-03         105.25         1   \n",
              "2386          391  6708-DPYTF  2013-10-18          79.61        14   \n",
              "2409          391  6708-DPYTF  2013-10-24          93.24        10   \n",
              "2456          391  6708-DPYTF  2013-11-07          66.92         8   \n",
              "2517          391  6708-DPYTF  2013-11-25          76.18         6   \n",
              "\n",
              "      GlobalDaysLateLast30days  GlobalDaysLateLast3Inv  AvLateLast3  \\\n",
              "0                    14.000000                     NaN    14.000000   \n",
              "57                    4.655172                0.000000     7.000000   \n",
              "90                    5.560440                4.000000     8.666667   \n",
              "132                   5.336842               10.333333     9.333333   \n",
              "280                   4.268519               12.333333    13.333333   \n",
              "360                   3.769912                4.333333    13.666667   \n",
              "491                   4.020000               17.333333    12.666667   \n",
              "646                   4.186275                5.000000    13.000000   \n",
              "674                   4.601942                4.666667    13.333333   \n",
              "675                   4.601942                4.666667    13.333333   \n",
              "676                   4.586538                5.666667    10.000000   \n",
              "677                   4.586538                5.666667    10.000000   \n",
              "745                   4.685714                4.000000     8.333333   \n",
              "754                   4.926606                8.333333     9.333333   \n",
              "759                   4.519231                3.666667     8.666667   \n",
              "866                   2.938776                5.666667     7.000000   \n",
              "1125                  2.678899                5.666667     2.666667   \n",
              "1500                  3.118812                4.666667     2.333333   \n",
              "1521                  2.504854                4.000000     5.000000   \n",
              "1594                  4.755556               11.333333     7.000000   \n",
              "1599                  5.054945                3.000000    10.000000   \n",
              "1739                  3.401786                2.666667     7.666667   \n",
              "1760                  3.421053                0.000000     4.333333   \n",
              "1779                  3.459459                0.000000     1.333333   \n",
              "2239                  2.504425                0.666667     0.666667   \n",
              "2249                  2.318182                0.000000     0.666667   \n",
              "2340                  2.623853                5.000000     5.666667   \n",
              "2341                  2.623853                5.000000     5.666667   \n",
              "2342                  2.585586                5.333333     5.333333   \n",
              "2343                  2.585586                5.333333     5.333333   \n",
              "2386                  2.237113                8.000000    10.000000   \n",
              "2409                  2.011111                8.000000     8.333333   \n",
              "2456                  1.565217                4.333333    10.666667   \n",
              "2517                  1.686275                4.666667     8.000000   \n",
              "\n",
              "      CustomerDaysLate_30days  \n",
              "0                   14.000000  \n",
              "57                   7.000000  \n",
              "90                   8.666667  \n",
              "132                  9.333333  \n",
              "280                 12.000000  \n",
              "360                 12.500000  \n",
              "491                 13.000000  \n",
              "646                 13.000000  \n",
              "674                 13.500000  \n",
              "675                 10.000000  \n",
              "676                 13.500000  \n",
              "677                 10.000000  \n",
              "745                  9.500000  \n",
              "754                 11.000000  \n",
              "759                  8.600000  \n",
              "866                  3.000000  \n",
              "1125                 4.000000  \n",
              "1500                 0.000000  \n",
              "1521                 5.500000  \n",
              "1594                10.500000  \n",
              "1599                10.000000  \n",
              "1739                 4.000000  \n",
              "1760                 2.000000  \n",
              "1779                 1.333333  \n",
              "2239                 2.000000  \n",
              "2249                 1.000000  \n",
              "2340                 5.666667  \n",
              "2341                 4.500000  \n",
              "2342                 5.666667  \n",
              "2343                 4.500000  \n",
              "2386                10.000000  \n",
              "2409                10.000000  \n",
              "2456                10.666667  \n",
              "2517                 7.000000  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>countryCode</th>\n",
              "      <th>customerID</th>\n",
              "      <th>InvoiceDate</th>\n",
              "      <th>InvoiceAmount</th>\n",
              "      <th>DaysLate</th>\n",
              "      <th>GlobalDaysLateLast30days</th>\n",
              "      <th>GlobalDaysLateLast3Inv</th>\n",
              "      <th>AvLateLast3</th>\n",
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              "      <th>0</th>\n",
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              "      <th>57</th>\n",
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              "      <td>7.000000</td>\n",
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              "      <th>132</th>\n",
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              "      <td>6708-DPYTF</td>\n",
              "      <td>2012-02-13</td>\n",
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              "      <td>9.333333</td>\n",
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              "      <td>13.666667</td>\n",
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              "      <th>491</th>\n",
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              "    <tr>\n",
              "      <th>759</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2012-08-04</td>\n",
              "      <td>46.29</td>\n",
              "      <td>1</td>\n",
              "      <td>4.519231</td>\n",
              "      <td>3.666667</td>\n",
              "      <td>8.666667</td>\n",
              "      <td>8.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>866</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2012-09-03</td>\n",
              "      <td>27.42</td>\n",
              "      <td>3</td>\n",
              "      <td>2.938776</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>3.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1125</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2012-11-11</td>\n",
              "      <td>94.19</td>\n",
              "      <td>4</td>\n",
              "      <td>2.678899</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1500</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-02-17</td>\n",
              "      <td>56.99</td>\n",
              "      <td>0</td>\n",
              "      <td>3.118812</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>2.333333</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1521</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-02-24</td>\n",
              "      <td>64.64</td>\n",
              "      <td>11</td>\n",
              "      <td>2.504854</td>\n",
              "      <td>4.000000</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1594</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-03-19</td>\n",
              "      <td>87.99</td>\n",
              "      <td>10</td>\n",
              "      <td>4.755556</td>\n",
              "      <td>11.333333</td>\n",
              "      <td>7.000000</td>\n",
              "      <td>10.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1599</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-03-20</td>\n",
              "      <td>81.59</td>\n",
              "      <td>9</td>\n",
              "      <td>5.054945</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1739</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-04-24</td>\n",
              "      <td>89.07</td>\n",
              "      <td>4</td>\n",
              "      <td>3.401786</td>\n",
              "      <td>2.666667</td>\n",
              "      <td>7.666667</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1760</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-04-29</td>\n",
              "      <td>82.04</td>\n",
              "      <td>0</td>\n",
              "      <td>3.421053</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1779</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-05-05</td>\n",
              "      <td>76.49</td>\n",
              "      <td>0</td>\n",
              "      <td>3.459459</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.333333</td>\n",
              "      <td>1.333333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2239</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-09-08</td>\n",
              "      <td>64.19</td>\n",
              "      <td>2</td>\n",
              "      <td>2.504425</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>2.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2249</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-09-11</td>\n",
              "      <td>79.52</td>\n",
              "      <td>0</td>\n",
              "      <td>2.318182</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>1.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2340</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-10-03</td>\n",
              "      <td>99.22</td>\n",
              "      <td>15</td>\n",
              "      <td>2.623853</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>5.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2341</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-10-03</td>\n",
              "      <td>99.22</td>\n",
              "      <td>15</td>\n",
              "      <td>2.623853</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>5.666667</td>\n",
              "      <td>4.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2342</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-10-03</td>\n",
              "      <td>105.25</td>\n",
              "      <td>1</td>\n",
              "      <td>2.585586</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>5.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2343</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-10-03</td>\n",
              "      <td>105.25</td>\n",
              "      <td>1</td>\n",
              "      <td>2.585586</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>5.333333</td>\n",
              "      <td>4.500000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2386</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-10-18</td>\n",
              "      <td>79.61</td>\n",
              "      <td>14</td>\n",
              "      <td>2.237113</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>10.000000</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2409</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-10-24</td>\n",
              "      <td>93.24</td>\n",
              "      <td>10</td>\n",
              "      <td>2.011111</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>8.333333</td>\n",
              "      <td>10.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2456</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-11-07</td>\n",
              "      <td>66.92</td>\n",
              "      <td>8</td>\n",
              "      <td>1.565217</td>\n",
              "      <td>4.333333</td>\n",
              "      <td>10.666667</td>\n",
              "      <td>10.666667</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2517</th>\n",
              "      <td>391</td>\n",
              "      <td>6708-DPYTF</td>\n",
              "      <td>2013-11-25</td>\n",
              "      <td>76.18</td>\n",
              "      <td>6</td>\n",
              "      <td>1.686275</td>\n",
              "      <td>4.666667</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>7.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-31fb5fa0-5fa1-49a3-8367-6058d7abe4f3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
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              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-31fb5fa0-5fa1-49a3-8367-6058d7abe4f3 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-31fb5fa0-5fa1-49a3-8367-6058d7abe4f3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "KmVcFkUN45zw"
      },
      "execution_count": 18,
      "outputs": []
    }
  ]
}