pandas_stepic.ipynb

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        "<a href=\"https://colab.research.google.com/github/Sinrez/PythonProjects/blob/main/pandas_stepic.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "source": [
        "import pandas as pd\n",
        "df_o =pd.read_csv('/content/Orders.csv', sep = ';')\n",
        "df_od =  pd.read_csv('/content/Order_details.csv', sep = ';')\n",
        "df_p =  pd.read_csv('/content/Products.csv', sep = ';')\n",
        "df_c = pd.read_csv('/content/Customers.csv', sep = ';')\n",
        "df_e = pd.read_excel('/content/Employees.xlsx')"
      ],
      "metadata": {
        "id": "F7tdpXmu0yJ8"
      },
      "execution_count": 50,
      "outputs": []
    },
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      "source": [
        "df_od.info()"
      ],
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 2155 entries, 0 to 2154\n",
            "Data columns (total 5 columns):\n",
            " #   Column     Non-Null Count  Dtype  \n",
            "---  ------     --------------  -----  \n",
            " 0   OrderID    2155 non-null   int64  \n",
            " 1   ProductID  2155 non-null   int64  \n",
            " 2   UnitPrice  2155 non-null   float64\n",
            " 3   Quantity   2155 non-null   int64  \n",
            " 4   Discount   2155 non-null   float64\n",
            "dtypes: float64(2), int64(3)\n",
            "memory usage: 84.3 KB\n"
          ]
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df_p.info()"
      ],
      "metadata": {
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      "outputs": [
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 77 entries, 0 to 76\n",
            "Data columns (total 7 columns):\n",
            " #   Column                Non-Null Count  Dtype  \n",
            "---  ------                --------------  -----  \n",
            " 0   ProductID             77 non-null     int64  \n",
            " 1   ProductName           77 non-null     object \n",
            " 2   Product_Description   77 non-null     object \n",
            " 3   UnitPrice             77 non-null     float64\n",
            " 4   CategoryID            77 non-null     int64  \n",
            " 5   CategoryName          77 non-null     object \n",
            " 6   Category_Description  77 non-null     object \n",
            "dtypes: float64(1), int64(2), object(4)\n",
            "memory usage: 4.3+ KB\n"
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      "cell_type": "code",
      "source": [
        "df_o =pd.read_csv('/content/Orders.csv', sep = ';')\n",
        "df_o.describe()\n",
        "df_o.info()"
      ],
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      "execution_count": null,
      "outputs": [
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          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 830 entries, 0 to 829\n",
            "Data columns (total 7 columns):\n",
            " #   Column        Non-Null Count  Dtype  \n",
            "---  ------        --------------  -----  \n",
            " 0   OrderID       830 non-null    int64  \n",
            " 1   CustomerID    830 non-null    object \n",
            " 2   EmployeeID    830 non-null    int64  \n",
            " 3   OrderDate     830 non-null    object \n",
            " 4   RequiredDate  830 non-null    object \n",
            " 5   ShippedDate   809 non-null    object \n",
            " 6   Freight       830 non-null    float64\n",
            "dtypes: float64(1), int64(2), object(4)\n",
            "memory usage: 45.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_od =  pd.read_csv('/content/Order_details.csv', sep = ';')\n",
        "df_od.describe()"
      ],
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      "outputs": [
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          "data": {
            "text/plain": [
              "            OrderID    ProductID    UnitPrice     Quantity     Discount\n",
              "count   2155.000000  2155.000000  2155.000000  2155.000000  2155.000000\n",
              "mean   10659.375870    40.793039    26.218520    23.812993     0.056167\n",
              "std      241.378032    22.159019    29.827418    19.022047     0.083450\n",
              "min    10248.000000     1.000000     2.000000     1.000000     0.000000\n",
              "25%    10451.000000    22.000000    12.000000    10.000000     0.000000\n",
              "50%    10657.000000    41.000000    18.400000    20.000000     0.000000\n",
              "75%    10862.500000    60.000000    32.000000    30.000000     0.100000\n",
              "max    11077.000000    77.000000   263.500000   130.000000     0.250000"
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              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>10657.000000</td>\n",
              "      <td>41.000000</td>\n",
              "      <td>18.400000</td>\n",
              "      <td>20.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>10862.500000</td>\n",
              "      <td>60.000000</td>\n",
              "      <td>32.000000</td>\n",
              "      <td>30.000000</td>\n",
              "      <td>0.100000</td>\n",
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              "    <tr>\n",
              "      <th>max</th>\n",
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              "      <td>77.000000</td>\n",
              "      <td>263.500000</td>\n",
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    {
      "cell_type": "code",
      "source": [
        "df_p =  pd.read_csv('/content/Products.csv', sep = ';')\n",
        "# df_p.describe()\n",
        "df_p.shape[0]\n",
        "len(df_p)\n",
        "df_p.shape[1]\n",
        "df_p.shape"
      ],
      "metadata": {
        "colab": {
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        "outputId": "4d827d4e-6e84-42ee-b170-a3e2db72e1ee"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(77, 7)"
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          },
          "metadata": {},
          "execution_count": 4
        }
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    {
      "cell_type": "code",
      "source": [
        "df_c = pd.read_csv('/content/Customers.csv', sep = ';')\n",
        "df_c.head(25)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 833
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        "id": "WGFbiqPlz591",
        "outputId": "061edc78-c456-418b-cd7e-5ed4c0500026"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID         ContactName           ContactTitle           Phone  \\\n",
              "0       ALFKI        Maria Anders   Sales Representative     030-0074321   \n",
              "1       ANATR        Ana Trujillo                  Owner    (5) 555-4729   \n",
              "2       ANTON      Antonio Moreno                  Owner    (5) 555-3932   \n",
              "3       AROUT        Thomas Hardy   Sales Representative  (171) 555-7788   \n",
              "4       BERGS  Christina Berglund    Order Administrator   0921-12 34 65   \n",
              "5       BLAUS          Hanna Moos   Sales Representative      0621-08460   \n",
              "6       BLONP  Frederique Citeaux      Marketing Manager     88.60.15.31   \n",
              "7       BOLID       Martin Sommer                  Owner  (91) 555 22 82   \n",
              "8       BONAP    Laurence Lebihan                  Owner     91.24.45.40   \n",
              "9       BOTTM   Elizabeth Lincoln     Accounting Manager  (604) 555-4729   \n",
              "10      BSBEV   Victoria Ashworth   Sales Representative  (171) 555-1212   \n",
              "11      CACTU    Patricio Simpson            Sales Agent    (1) 135-5555   \n",
              "12      CENTC     Francisco Chang      Marketing Manager    (5) 555-3392   \n",
              "13      CHOPS           Yang Wang                  Owner     0452-076545   \n",
              "14      COMMI        Pedro Afonso        Sales Associate   (11) 555-7647   \n",
              "15      CONSH     Elizabeth Brown   Sales Representative  (171) 555-2282   \n",
              "16      DRACD        Sven Ottlieb    Order Administrator     0241-039123   \n",
              "17      DUMON      Janine Labrune                  Owner     40.67.88.88   \n",
              "18      EASTC           Ann Devon            Sales Agent  (171) 555-0297   \n",
              "19      ERNSH       Roland Mendel          Sales Manager       7675-3425   \n",
              "20      FAMIA           Aria Cruz    Marketing Assistant   (11) 555-9857   \n",
              "21      FISSA          Diego Roel     Accounting Manager  (91) 555 94 44   \n",
              "22      FOLIG       Martine Rance  Assistant Sales Agent     20.16.10.16   \n",
              "23      FOLKO       Maria Larsson                  Owner   0695-34 67 21   \n",
              "24      FRANK       Peter Franken      Marketing Manager     089-0877310   \n",
              "\n",
              "                          Address  \n",
              "0                   Obere Str. 57  \n",
              "1   Avda. de la Constitucion 2222  \n",
              "2                 Mataderos  2312  \n",
              "3                 120 Hanover Sq.  \n",
              "4                 Berguvsv?gen  8  \n",
              "5                  Forsterstr. 57  \n",
              "6                24, place Kleber  \n",
              "7                  C/ Araquil, 67  \n",
              "8            12, rue des Bouchers  \n",
              "9              23 Tsawassen Blvd.  \n",
              "10              Fauntleroy Circus  \n",
              "11                    Cerrito 333  \n",
              "12        Sierras de Granada 9993  \n",
              "13                   Hauptstr. 29  \n",
              "14           Av. dos Lusiadas, 23  \n",
              "15   Berkeley Gardens 12  Brewery  \n",
              "16                   Walserweg 21  \n",
              "17   67, rue des Cinquante Otages  \n",
              "18                 35 King George  \n",
              "19                   Kirchgasse 6  \n",
              "20                   Rua Oros, 92  \n",
              "21             C/ Moralzarzal, 86  \n",
              "22       184, chaussee de Tournai  \n",
              "23                   Akergatan 24  \n",
              "24              Berliner Platz 43  "
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              "      <th>CustomerID</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ALFKI</td>\n",
              "      <td>Maria Anders</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>030-0074321</td>\n",
              "      <td>Obere Str. 57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>ANATR</td>\n",
              "      <td>Ana Trujillo</td>\n",
              "      <td>Owner</td>\n",
              "      <td>(5) 555-4729</td>\n",
              "      <td>Avda. de la Constitucion 2222</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ANTON</td>\n",
              "      <td>Antonio Moreno</td>\n",
              "      <td>Owner</td>\n",
              "      <td>(5) 555-3932</td>\n",
              "      <td>Mataderos  2312</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AROUT</td>\n",
              "      <td>Thomas Hardy</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(171) 555-7788</td>\n",
              "      <td>120 Hanover Sq.</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BERGS</td>\n",
              "      <td>Christina Berglund</td>\n",
              "      <td>Order Administrator</td>\n",
              "      <td>0921-12 34 65</td>\n",
              "      <td>Berguvsv?gen  8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>BLAUS</td>\n",
              "      <td>Hanna Moos</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>0621-08460</td>\n",
              "      <td>Forsterstr. 57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>BLONP</td>\n",
              "      <td>Frederique Citeaux</td>\n",
              "      <td>Marketing Manager</td>\n",
              "      <td>88.60.15.31</td>\n",
              "      <td>24, place Kleber</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>BOLID</td>\n",
              "      <td>Martin Sommer</td>\n",
              "      <td>Owner</td>\n",
              "      <td>(91) 555 22 82</td>\n",
              "      <td>C/ Araquil, 67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>BONAP</td>\n",
              "      <td>Laurence Lebihan</td>\n",
              "      <td>Owner</td>\n",
              "      <td>91.24.45.40</td>\n",
              "      <td>12, rue des Bouchers</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>BOTTM</td>\n",
              "      <td>Elizabeth Lincoln</td>\n",
              "      <td>Accounting Manager</td>\n",
              "      <td>(604) 555-4729</td>\n",
              "      <td>23 Tsawassen Blvd.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>BSBEV</td>\n",
              "      <td>Victoria Ashworth</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(171) 555-1212</td>\n",
              "      <td>Fauntleroy Circus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>CACTU</td>\n",
              "      <td>Patricio Simpson</td>\n",
              "      <td>Sales Agent</td>\n",
              "      <td>(1) 135-5555</td>\n",
              "      <td>Cerrito 333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>CENTC</td>\n",
              "      <td>Francisco Chang</td>\n",
              "      <td>Marketing Manager</td>\n",
              "      <td>(5) 555-3392</td>\n",
              "      <td>Sierras de Granada 9993</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>CHOPS</td>\n",
              "      <td>Yang Wang</td>\n",
              "      <td>Owner</td>\n",
              "      <td>0452-076545</td>\n",
              "      <td>Hauptstr. 29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>COMMI</td>\n",
              "      <td>Pedro Afonso</td>\n",
              "      <td>Sales Associate</td>\n",
              "      <td>(11) 555-7647</td>\n",
              "      <td>Av. dos Lusiadas, 23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>CONSH</td>\n",
              "      <td>Elizabeth Brown</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(171) 555-2282</td>\n",
              "      <td>Berkeley Gardens 12  Brewery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>DRACD</td>\n",
              "      <td>Sven Ottlieb</td>\n",
              "      <td>Order Administrator</td>\n",
              "      <td>0241-039123</td>\n",
              "      <td>Walserweg 21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>DUMON</td>\n",
              "      <td>Janine Labrune</td>\n",
              "      <td>Owner</td>\n",
              "      <td>40.67.88.88</td>\n",
              "      <td>67, rue des Cinquante Otages</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>EASTC</td>\n",
              "      <td>Ann Devon</td>\n",
              "      <td>Sales Agent</td>\n",
              "      <td>(171) 555-0297</td>\n",
              "      <td>35 King George</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>ERNSH</td>\n",
              "      <td>Roland Mendel</td>\n",
              "      <td>Sales Manager</td>\n",
              "      <td>7675-3425</td>\n",
              "      <td>Kirchgasse 6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>FAMIA</td>\n",
              "      <td>Aria Cruz</td>\n",
              "      <td>Marketing Assistant</td>\n",
              "      <td>(11) 555-9857</td>\n",
              "      <td>Rua Oros, 92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>FISSA</td>\n",
              "      <td>Diego Roel</td>\n",
              "      <td>Accounting Manager</td>\n",
              "      <td>(91) 555 94 44</td>\n",
              "      <td>C/ Moralzarzal, 86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>FOLIG</td>\n",
              "      <td>Martine Rance</td>\n",
              "      <td>Assistant Sales Agent</td>\n",
              "      <td>20.16.10.16</td>\n",
              "      <td>184, chaussee de Tournai</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>FOLKO</td>\n",
              "      <td>Maria Larsson</td>\n",
              "      <td>Owner</td>\n",
              "      <td>0695-34 67 21</td>\n",
              "      <td>Akergatan 24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>FRANK</td>\n",
              "      <td>Peter Franken</td>\n",
              "      <td>Marketing Manager</td>\n",
              "      <td>089-0877310</td>\n",
              "      <td>Berliner Platz 43</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d603fbae-f873-46a7-b2d8-06bf499d8d2a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-d603fbae-f873-46a7-b2d8-06bf499d8d2a 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-d603fbae-f873-46a7-b2d8-06bf499d8d2a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_o['Freight']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "e3ILgsVCO7q_",
        "outputId": "08670307-90c2-42f1-8e11-b56826f1e60c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      32.38\n",
              "1      11.61\n",
              "2      65.83\n",
              "3      41.34\n",
              "4      51.30\n",
              "       ...  \n",
              "825    24.95\n",
              "826    18.44\n",
              "827     6.19\n",
              "828    38.28\n",
              "829     8.53\n",
              "Name: Freight, Length: 830, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "s = df_o['Freight']\n",
        "s[1] #значение с индексом = 1\n",
        "s[1:5] #значения с индексами от 1 до 4\n",
        "s[1:-820] #значения с индексами от 1 до -820 с конца\n",
        "# s[[1,3,5]] #выбор значений с индексами 1,3,5"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yW1ZEbtRPULg",
        "outputId": "83d4907a-edc7-4055-dc6d-38da8ef645ea"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1     11.61\n",
              "2     65.83\n",
              "3     41.34\n",
              "4     51.30\n",
              "5     58.17\n",
              "6     22.98\n",
              "7    148.33\n",
              "8     13.97\n",
              "9     81.91\n",
              "Name: Freight, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_od[['OrderID', 'ProductID', 'UnitPrice']]"
      ],
      "metadata": {
        "id": "MZK4K0ajPtW9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "outputId": "2587820f-5bd0-4bc5-b921-0867f45e6cb8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      OrderID  ProductID  UnitPrice\n",
              "0       10248         11      14.00\n",
              "1       10248         42       9.80\n",
              "2       10248         72      34.80\n",
              "3       10249         14      18.60\n",
              "4       10249         51      42.40\n",
              "...       ...        ...        ...\n",
              "2150    11077         64      33.25\n",
              "2151    11077         66      17.00\n",
              "2152    11077         73      15.00\n",
              "2153    11077         75       7.75\n",
              "2154    11077         77      13.00\n",
              "\n",
              "[2155 rows x 3 columns]"
            ],
            "text/html": [
              "\n",
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              "      <th>OrderID</th>\n",
              "      <th>ProductID</th>\n",
              "      <th>UnitPrice</th>\n",
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              "      <th>0</th>\n",
              "      <td>10248</td>\n",
              "      <td>11</td>\n",
              "      <td>14.00</td>\n",
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              "    <tr>\n",
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              "      <td>10248</td>\n",
              "      <td>42</td>\n",
              "      <td>9.80</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10248</td>\n",
              "      <td>72</td>\n",
              "      <td>34.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10249</td>\n",
              "      <td>14</td>\n",
              "      <td>18.60</td>\n",
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              "      <th>4</th>\n",
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              "      <td>42.40</td>\n",
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              "      <th>2150</th>\n",
              "      <td>11077</td>\n",
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              "      <td>33.25</td>\n",
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              "    <tr>\n",
              "      <th>2151</th>\n",
              "      <td>11077</td>\n",
              "      <td>66</td>\n",
              "      <td>17.00</td>\n",
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              "      <th>2152</th>\n",
              "      <td>11077</td>\n",
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              "      <th>2153</th>\n",
              "      <td>11077</td>\n",
              "      <td>75</td>\n",
              "      <td>7.75</td>\n",
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              "      <th>2154</th>\n",
              "      <td>11077</td>\n",
              "      <td>77</td>\n",
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              "  </tbody>\n",
              "</table>\n",
              "<p>2155 rows × 3 columns</p>\n",
              "</div>\n",
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              "          const element = document.querySelector('#df-83883d8c-cda2-4397-a531-d94be8409c29');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
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          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_od[['OrderID', 'ProductID', 'UnitPrice']]\n",
        "\n",
        "# df_od.drop(columns = ['Quantity', 'Discount'])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "LccJva1CoBwG",
        "outputId": "6eb41c7c-b928-446c-b602-aba938b41109"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      OrderID  ProductID  UnitPrice\n",
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          "metadata": {},
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    {
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      "source": [
        "df_o['ShippedDate_flag']=df_o['ShippedDate'].isna()\n",
        "df_o['ShippedDate_flag']"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
        },
        "id": "3Z12ZgI7ovZK",
        "outputId": "00779fad-b427-436c-95e3-81d24489331a"
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      "execution_count": null,
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        {
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          "data": {
            "text/plain": [
              "0      False\n",
              "1      False\n",
              "2      False\n",
              "3      False\n",
              "4      False\n",
              "       ...  \n",
              "825     True\n",
              "826     True\n",
              "827     True\n",
              "828     True\n",
              "829     True\n",
              "Name: ShippedDate_flag, Length: 830, dtype: bool"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_p[~(df_p['CategoryName']=='Condiments')]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 572
        },
        "id": "d9cLE9rYo2Fn",
        "outputId": "909ad329-2795-4008-8159-157dbf7b4fb0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    ProductID                      ProductName Product_Description  UnitPrice  \\\n",
              "0           1                             Chai  10 boxes x 20 bags      18.00   \n",
              "1           2                            Chang  24 - 12 oz bottles      19.00   \n",
              "6           7  Uncle Bob's Organic Dried Pears     12 - 1 lb pkgs.      30.00   \n",
              "8           9                  Mishi Kobe Niku    18 - 500 g pkgs.      97.00   \n",
              "9          10                            Ikura    12 - 200 ml jars      31.00   \n",
              "..        ...                              ...                 ...        ...   \n",
              "71         72           Mozzarella di Giovanni    24 - 200 g pkgs.      34.80   \n",
              "72         73                       Rod Kaviar     24 - 150 g jars      15.00   \n",
              "73         74                    Longlife Tofu           5 kg pkg.      10.00   \n",
              "74         75             Rhonbrau Klosterbier  24 - 0.5 l bottles       7.75   \n",
              "75         76                     Lakkalikoori              500 ml      18.00   \n",
              "\n",
              "    CategoryID    CategoryName                         Category_Description  \n",
              "0            1       Beverages  Soft drinks. coffees. teas. beers. and ales  \n",
              "1            1       Beverages  Soft drinks. coffees. teas. beers. and ales  \n",
              "6            7         Produce                    Dried fruit and bean curd  \n",
              "8            6    Meat/Poultry                               Prepared meats  \n",
              "9            8         Seafood                             Seaweed and fish  \n",
              "..         ...             ...                                          ...  \n",
              "71           4  Dairy Products                                      Cheeses  \n",
              "72           8         Seafood                             Seaweed and fish  \n",
              "73           7         Produce                    Dried fruit and bean curd  \n",
              "74           1       Beverages  Soft drinks. coffees. teas. beers. and ales  \n",
              "75           1       Beverages  Soft drinks. coffees. teas. beers. and ales  \n",
              "\n",
              "[65 rows x 7 columns]"
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              "      <td>Rod Kaviar</td>\n",
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              "      <td>Rhonbrau Klosterbier</td>\n",
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              "      <td>7.75</td>\n",
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              "      <td>1</td>\n",
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              "      <td>Soft drinks. coffees. teas. beers. and ales</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_p[['CategoryID', 'CategoryName', 'Category_Description', 'ProductID',\n",
        "       'ProductName', 'Product_Description', 'UnitPrice']][[ 'ProductID',\n",
        "       'ProductName', 'Product_Description', 'UnitPrice']][[ \n",
        "       'ProductName', 'UnitPrice']]['ProductName']"
      ],
      "metadata": {
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              "0                                Chai\n",
              "1                               Chang\n",
              "2                       Aniseed Syrup\n",
              "3        Chef Anton's Cajun Seasoning\n",
              "4              Chef Anton's Gumbo Mix\n",
              "                   ...               \n",
              "72                         Rod Kaviar\n",
              "73                      Longlife Tofu\n",
              "74               Rhonbrau Klosterbier\n",
              "75                       Lakkalikoori\n",
              "76    Original Frankfurter gr?ne Sobe\n",
              "Name: ProductName, Length: 77, dtype: object"
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              "  CustomerID         ContactName          ContactTitle           Phone  \\\n",
              "0      ALFKI        Maria Anders  Sales Representative     030-0074321   \n",
              "1      ANATR        Ana Trujillo                 Owner    (5) 555-4729   \n",
              "2      ANTON      Antonio Moreno                 Owner    (5) 555-3932   \n",
              "3      AROUT        Thomas Hardy  Sales Representative  (171) 555-7788   \n",
              "4      BERGS  Christina Berglund   Order Administrator   0921-12 34 65   \n",
              "\n",
              "                         Address  \n",
              "0                  Obere Str. 57  \n",
              "1  Avda. de la Constitucion 2222  \n",
              "2                Mataderos  2312  \n",
              "3                120 Hanover Sq.  \n",
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            "text/plain": [
              "(17, 5)"
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          "metadata": {},
          "execution_count": 9
        }
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      "cell_type": "code",
      "source": [
        "df_o['OrderDate'] = df_o['OrderDate'].astype('datetime64')\n",
        "df_o[df_o['OrderDate'] > '1997-07-05'].OrderID.count()\n"
      ],
      "metadata": {
        "colab": {
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        "outputId": "07d872db-d74e-4b27-babd-04075a81b585"
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      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-17-cc6d4fa6ae6f>:1: FutureWarning: Passing unit-less datetime64 dtype to .astype is deprecated and will raise in a future version. Pass 'datetime64[ns]' instead\n",
            "  df_o['OrderDate'] = df_o['OrderDate'].astype('datetime64')\n"
          ]
        },
        {
          "output_type": "execute_result",
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            "text/plain": [
              "488"
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          "metadata": {},
          "execution_count": 17
        }
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    {
      "cell_type": "code",
      "source": [
        "df_o[df_o['Freight'] <= 10.98].OrderID.count()"
      ],
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        "colab": {
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        },
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        "outputId": "989de592-3d8f-4093-9bc2-cd655b4fb9ed"
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      "execution_count": null,
      "outputs": [
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          "output_type": "execute_result",
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            "text/plain": [
              "184"
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          "metadata": {},
          "execution_count": 21
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      "source": [
        "df_o[df_o['Freight'].between(10,20,inclusive=False)]"
      ],
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      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-22-3046f2e1f32a>:1: FutureWarning: Boolean inputs to the `inclusive` argument are deprecated in favour of `both` or `neither`.\n",
            "  df_o[df_o['Freight'].between(10,20,inclusive=False)]\n"
          ]
        },
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              "     OrderID CustomerID  EmployeeID  OrderDate             RequiredDate  \\\n",
              "1      10249      TOMSP           6 1996-07-05  1996-08-16 00:00:00.000   \n",
              "8      10256      WELLI           3 1996-07-15  1996-08-12 00:00:00.000   \n",
              "28     10276      TORTU           8 1996-08-08  1996-08-22 00:00:00.000   \n",
              "34     10282      ROMEY           4 1996-08-15  1996-09-12 00:00:00.000   \n",
              "39     10287      RICAR           8 1996-08-22  1996-09-19 00:00:00.000   \n",
              "..       ...        ...         ...        ...                      ...   \n",
              "812    11060      FRANS           2 1998-04-30  1998-05-28 00:00:00.000   \n",
              "813    11061      GREAL           4 1998-04-30  1998-06-11 00:00:00.000   \n",
              "817    11065      LILAS           8 1998-05-01  1998-05-29 00:00:00.000   \n",
              "821    11069      TORTU           1 1998-05-04  1998-06-01 00:00:00.000   \n",
              "826    11074      SIMOB           7 1998-05-06  1998-06-03 00:00:00.000   \n",
              "\n",
              "                 ShippedDate  Freight  \n",
              "1    1996-07-10 00:00:00.000    11.61  \n",
              "8    1996-07-17 00:00:00.000    13.97  \n",
              "28   1996-08-14 00:00:00.000    13.84  \n",
              "34   1996-08-21 00:00:00.000    12.69  \n",
              "39   1996-08-28 00:00:00.000    12.76  \n",
              "..                       ...      ...  \n",
              "812  1998-05-04 00:00:00.000    10.98  \n",
              "813                      NaN    14.01  \n",
              "817                      NaN    12.91  \n",
              "821  1998-05-06 00:00:00.000    15.67  \n",
              "826                      NaN    18.44  \n",
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              "      <td>1996-08-12 00:00:00.000</td>\n",
              "      <td>1996-07-17 00:00:00.000</td>\n",
              "      <td>13.97</td>\n",
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              "      <th>28</th>\n",
              "      <td>10276</td>\n",
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              "      <td>1996-08-28 00:00:00.000</td>\n",
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              "      <td>1998-05-04 00:00:00.000</td>\n",
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              "      <th>813</th>\n",
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              "      <td>1998-06-11 00:00:00.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>14.01</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>12.91</td>\n",
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              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td>11069</td>\n",
              "      <td>TORTU</td>\n",
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              "      <td>1998-05-04</td>\n",
              "      <td>1998-06-01 00:00:00.000</td>\n",
              "      <td>1998-05-06 00:00:00.000</td>\n",
              "      <td>15.67</td>\n",
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              "    <tr>\n",
              "      <th>826</th>\n",
              "      <td>11074</td>\n",
              "      <td>SIMOB</td>\n",
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              "      <td>1998-05-06</td>\n",
              "      <td>1998-06-03 00:00:00.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.44</td>\n",
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              "\n",
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              "        buttonEl.style.display =\n",
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              "\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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              "  </div>\n",
              "  "
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          },
          "metadata": {},
          "execution_count": 22
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_o[df_o['Freight'].between(10,20,inclusive=False)]"
      ],
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        "colab": {
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          "height": 479
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        "id": "1Ttg_G8jBiY9",
        "outputId": "3f2a290a-7014-4e6c-881a-a28bb12c911c"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-23-3046f2e1f32a>:1: FutureWarning: Boolean inputs to the `inclusive` argument are deprecated in favour of `both` or `neither`.\n",
            "  df_o[df_o['Freight'].between(10,20,inclusive=False)]\n"
          ]
        },
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          "output_type": "execute_result",
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              "     OrderID CustomerID  EmployeeID  OrderDate             RequiredDate  \\\n",
              "1      10249      TOMSP           6 1996-07-05  1996-08-16 00:00:00.000   \n",
              "8      10256      WELLI           3 1996-07-15  1996-08-12 00:00:00.000   \n",
              "28     10276      TORTU           8 1996-08-08  1996-08-22 00:00:00.000   \n",
              "34     10282      ROMEY           4 1996-08-15  1996-09-12 00:00:00.000   \n",
              "39     10287      RICAR           8 1996-08-22  1996-09-19 00:00:00.000   \n",
              "..       ...        ...         ...        ...                      ...   \n",
              "812    11060      FRANS           2 1998-04-30  1998-05-28 00:00:00.000   \n",
              "813    11061      GREAL           4 1998-04-30  1998-06-11 00:00:00.000   \n",
              "817    11065      LILAS           8 1998-05-01  1998-05-29 00:00:00.000   \n",
              "821    11069      TORTU           1 1998-05-04  1998-06-01 00:00:00.000   \n",
              "826    11074      SIMOB           7 1998-05-06  1998-06-03 00:00:00.000   \n",
              "\n",
              "                 ShippedDate  Freight  \n",
              "1    1996-07-10 00:00:00.000    11.61  \n",
              "8    1996-07-17 00:00:00.000    13.97  \n",
              "28   1996-08-14 00:00:00.000    13.84  \n",
              "34   1996-08-21 00:00:00.000    12.69  \n",
              "39   1996-08-28 00:00:00.000    12.76  \n",
              "..                       ...      ...  \n",
              "812  1998-05-04 00:00:00.000    10.98  \n",
              "813                      NaN    14.01  \n",
              "817                      NaN    12.91  \n",
              "821  1998-05-06 00:00:00.000    15.67  \n",
              "826                      NaN    18.44  \n",
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              "[91 rows x 7 columns]"
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              "      <td>10249</td>\n",
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              "      <td>1996-07-17 00:00:00.000</td>\n",
              "      <td>13.97</td>\n",
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              "      <td>12.69</td>\n",
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              "      <th>39</th>\n",
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              "      <td>RICAR</td>\n",
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              "      <td>1996-08-22</td>\n",
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              "      <td>1996-08-28 00:00:00.000</td>\n",
              "      <td>12.76</td>\n",
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              "      <td>1998-04-30</td>\n",
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              "      <td>1998-05-04 00:00:00.000</td>\n",
              "      <td>10.98</td>\n",
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              "      <th>813</th>\n",
              "      <td>11061</td>\n",
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              "      <td>1998-04-30</td>\n",
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              "      <td>NaN</td>\n",
              "      <td>14.01</td>\n",
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              "      <th>817</th>\n",
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              "      <td>1998-05-01</td>\n",
              "      <td>1998-05-29 00:00:00.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>12.91</td>\n",
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              "    <tr>\n",
              "      <th>821</th>\n",
              "      <td>11069</td>\n",
              "      <td>TORTU</td>\n",
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              "      <td>1998-05-04</td>\n",
              "      <td>1998-06-01 00:00:00.000</td>\n",
              "      <td>1998-05-06 00:00:00.000</td>\n",
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              "      <td>11074</td>\n",
              "      <td>SIMOB</td>\n",
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              "      <td>1998-05-06</td>\n",
              "      <td>1998-06-03 00:00:00.000</td>\n",
              "      <td>NaN</td>\n",
              "      <td>18.44</td>\n",
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              "<p>91 rows × 7 columns</p>\n",
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    {
      "cell_type": "code",
      "source": [
        "df_c[df_c['ContactTitle'].isin(['Sales Representative','Sales Agent','Sales Associate','Sales Manager','Assistant Sales Agent', 'Assistant Sales Representative'])].ContactTitle.count()"
      ],
      "metadata": {
        "colab": {
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          "data": {
            "text/plain": [
              "43"
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          },
          "metadata": {},
          "execution_count": 29
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        "df_p\n",
        "df_p[df_p['UnitPrice'].between(18,22,inclusive='both')].UnitPrice.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
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        "outputId": "9cf658b2-1bbc-432b-bbd0-a7129f543133"
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              "16"
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          "metadata": {},
          "execution_count": 34
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        "df_o[(df_o['OrderDate'].between('1997-01-01','1997-12-31',inclusive='both'))&(df_o['Freight']>500)]"
      ],
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        "colab": {
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          "height": 206
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        "id": "aLdQ6nbtTA0M",
        "outputId": "1f1c9e42-0ce4-4e60-a385-2067e1bc8a42"
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      "execution_count": null,
      "outputs": [
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              "     OrderID CustomerID  EmployeeID  OrderDate             RequiredDate  \\\n",
              "231    10479      RATTC           3 1997-03-19  1997-04-16 00:00:00.000   \n",
              "266    10514      ERNSH           3 1997-04-22  1997-05-20 00:00:00.000   \n",
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              "364    10612      SAVEA           1 1997-07-28  1997-08-25 00:00:00.000   \n",
              "443    10691      QUICK           2 1997-10-03  1997-11-14 00:00:00.000   \n",
              "\n",
              "                 ShippedDate  Freight  \n",
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            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_o['OrderDate']= df_o['OrderDate'].astype('datetime64')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V7FWW-sCYIv8",
        "outputId": "a68ed2e6-160f-4db1-e8fa-d4dc2bd8ba65"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-37-03041c909ad2>:1: FutureWarning: Passing unit-less datetime64 dtype to .astype is deprecated and will raise in a future version. Pass 'datetime64[ns]' instead\n",
            "  df_o['OrderDate']= df_o['OrderDate'].astype('datetime64')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_o[(df_o['OrderDate'].between('1997-01-01','1997-12-31',inclusive='both'))|(df_o['Freight']>500)].count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "M_esN2VpYKhx",
        "outputId": "c93daf56-e8c1-4e16-fba6-c72896d81164"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderID         416\n",
              "CustomerID      416\n",
              "EmployeeID      416\n",
              "OrderDate       416\n",
              "RequiredDate    416\n",
              "ShippedDate     416\n",
              "Freight         416\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#Скорее всего Sales будет иметь разный регистр взависимости от положения в тексте (в реальной жизни).\n",
        "#Предварительно можно перевести весть текст в нижний регистр.\n",
        "df_c[df_c['ContactTitle'].str.lower().str.contains('sales')]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "uqIdVGWVYgUQ",
        "outputId": "e9e11b1c-5755-477e-b838-87f6e70aa917"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID          ContactName                    ContactTitle  \\\n",
              "0       ALFKI         Maria Anders            Sales Representative   \n",
              "3       AROUT         Thomas Hardy            Sales Representative   \n",
              "5       BLAUS           Hanna Moos            Sales Representative   \n",
              "10      BSBEV    Victoria Ashworth            Sales Representative   \n",
              "11      CACTU     Patricio Simpson                     Sales Agent   \n",
              "14      COMMI         Pedro Afonso                 Sales Associate   \n",
              "15      CONSH      Elizabeth Brown            Sales Representative   \n",
              "18      EASTC            Ann Devon                     Sales Agent   \n",
              "19      ERNSH        Roland Mendel                   Sales Manager   \n",
              "22      FOLIG        Martine Rance           Assistant Sales Agent   \n",
              "26      FRANS        Paolo Accorti            Sales Representative   \n",
              "27      FURIB       Lino Rodriguez                   Sales Manager   \n",
              "29      GODOS    Jose Pedro Freyre                   Sales Manager   \n",
              "30      GOURL        Andre Fonseca                 Sales Associate   \n",
              "34      HILAA     Carlos Hernandez            Sales Representative   \n",
              "35      HUNGC        Yoshi Latimer            Sales Representative   \n",
              "36      HUNGO     Patricia McKenna                 Sales Associate   \n",
              "38      KOENE        Philip Cramer                 Sales Associate   \n",
              "39      LACOR        Daniel Tonini            Sales Representative   \n",
              "40      LAMAI       Annette Roulet                   Sales Manager   \n",
              "43      LEHMS       Renate Messner            Sales Representative   \n",
              "47      LONEP          Fran Wilson                   Sales Manager   \n",
              "49      MAISD      Catherine Dewey                     Sales Agent   \n",
              "52      NORTS       Simon Crowther                 Sales Associate   \n",
              "53      OCEAN       Yvonne Moncada                     Sales Agent   \n",
              "54      OLDWO        Rene Phillips            Sales Representative   \n",
              "57      PERIC  Guillermo Fernandez            Sales Representative   \n",
              "58      PICCO          Georg Pipps                   Sales Manager   \n",
              "59      PRINI     Isabel de Castro            Sales Representative   \n",
              "63      RANCH     Sergio Gutierrez            Sales Representative   \n",
              "64      RATTC         Paula Wilson  Assistant Sales Representative   \n",
              "65      REGGC      Maurizio Moroni                 Sales Associate   \n",
              "66      RICAR       Janete Limeira           Assistant Sales Agent   \n",
              "67      RICSU         Michael Holz                   Sales Manager   \n",
              "70      SAVEA       Jose Pavarotti            Sales Representative   \n",
              "71      SEVES           Hari Kumar                   Sales Manager   \n",
              "74      SPLIR   Art Braunschweiger                   Sales Manager   \n",
              "80      TRADH    Anabela Domingues            Sales Representative   \n",
              "81      TRAIH       Helvetius Nagy                 Sales Associate   \n",
              "82      VAFFE          Palle Ibsen                   Sales Manager   \n",
              "83      VICTE         Mary Saveley                     Sales Agent   \n",
              "85      WANDK          Rita Muller            Sales Representative   \n",
              "87      WELLI        Paula Parente                   Sales Manager   \n",
              "\n",
              "             Phone                                     Address  \n",
              "0      030-0074321                               Obere Str. 57  \n",
              "3   (171) 555-7788                             120 Hanover Sq.  \n",
              "5       0621-08460                              Forsterstr. 57  \n",
              "10  (171) 555-1212                           Fauntleroy Circus  \n",
              "11    (1) 135-5555                                 Cerrito 333  \n",
              "14   (11) 555-7647                        Av. dos Lusiadas, 23  \n",
              "15  (171) 555-2282                Berkeley Gardens 12  Brewery  \n",
              "18  (171) 555-0297                              35 King George  \n",
              "19       7675-3425                                Kirchgasse 6  \n",
              "22     20.16.10.16                    184, chaussee de Tournai  \n",
              "26     011-4988260                         Via Monte Bianco 34  \n",
              "27    (1) 354-2534                      Jardim das rosas n. 32  \n",
              "29  (95) 555 82 82                               C/ Romero, 33  \n",
              "30   (11) 555-9482                             Av. Brasil, 442  \n",
              "34    (5) 555-1340  Carrera 22 con Ave. Carlos Soublette #8-35  \n",
              "35  (503) 555-6874              City Center Plaza 516 Main St.  \n",
              "36       2 967 542                            8 Johnstown Road  \n",
              "38      0555-09876                               Maubelstr. 90  \n",
              "39     30.59.84.10                      67, avenue de l'Europe  \n",
              "40     61.77.61.10                       1 rue Alsace-Lorraine  \n",
              "43     069-0245984                                Magazinweg 7  \n",
              "47  (503) 555-9573                          89 Chiaroscuro Rd.  \n",
              "49  (02) 201 24 67                         Rue Joseph-Bens 532  \n",
              "52  (171) 555-7733                South House 300 Queensbridge  \n",
              "53    (1) 135-5333         Ing. Gustavo Moncada 8585 Piso 20-A  \n",
              "54  (907) 555-7584                             2743 Bering St.  \n",
              "57    (5) 552-3745                    Calle Dr. Jorge Cash 321  \n",
              "58       6562-9722                                 Geislweg 14  \n",
              "59    (1) 356-5634                      Estrada da sa?de n. 58  \n",
              "63    (1) 123-5555                      Av. del Libertador 900  \n",
              "64  (505) 555-5939                             2817 Milton Dr.  \n",
              "65     0522-556721                      Strada Provinciale 124  \n",
              "66   (21) 555-3412                         Av. Copacabana, 267  \n",
              "67     0897-034214                           Grenzacherweg 237  \n",
              "70  (208) 555-8097                             187 Suffolk Ln.  \n",
              "71  (171) 555-1717                             90 Wadhurst Rd.  \n",
              "74  (307) 555-4680                                P.O. Box 555  \n",
              "80   (11) 555-2167                     Av. Ines de Castro, 414  \n",
              "81  (206) 555-8257                           722 DaVinci Blvd.  \n",
              "82     86 21 32 43                               Smagsloget 45  \n",
              "83     78.32.54.86                          2, rue du Commerce  \n",
              "85     0711-020361                           Adenauerallee 900  \n",
              "87   (14) 555-8122                          Rua do Mercado, 12  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-20d1dc76-c57f-4515-9d14-be8ffe3c9522\">\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>CustomerID</th>\n",
              "      <th>ContactName</th>\n",
              "      <th>ContactTitle</th>\n",
              "      <th>Phone</th>\n",
              "      <th>Address</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ALFKI</td>\n",
              "      <td>Maria Anders</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>030-0074321</td>\n",
              "      <td>Obere Str. 57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AROUT</td>\n",
              "      <td>Thomas Hardy</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(171) 555-7788</td>\n",
              "      <td>120 Hanover Sq.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>BLAUS</td>\n",
              "      <td>Hanna Moos</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>0621-08460</td>\n",
              "      <td>Forsterstr. 57</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>BSBEV</td>\n",
              "      <td>Victoria Ashworth</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(171) 555-1212</td>\n",
              "      <td>Fauntleroy Circus</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>CACTU</td>\n",
              "      <td>Patricio Simpson</td>\n",
              "      <td>Sales Agent</td>\n",
              "      <td>(1) 135-5555</td>\n",
              "      <td>Cerrito 333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>COMMI</td>\n",
              "      <td>Pedro Afonso</td>\n",
              "      <td>Sales Associate</td>\n",
              "      <td>(11) 555-7647</td>\n",
              "      <td>Av. dos Lusiadas, 23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>CONSH</td>\n",
              "      <td>Elizabeth Brown</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(171) 555-2282</td>\n",
              "      <td>Berkeley Gardens 12  Brewery</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>EASTC</td>\n",
              "      <td>Ann Devon</td>\n",
              "      <td>Sales Agent</td>\n",
              "      <td>(171) 555-0297</td>\n",
              "      <td>35 King George</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>ERNSH</td>\n",
              "      <td>Roland Mendel</td>\n",
              "      <td>Sales Manager</td>\n",
              "      <td>7675-3425</td>\n",
              "      <td>Kirchgasse 6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>FOLIG</td>\n",
              "      <td>Martine Rance</td>\n",
              "      <td>Assistant Sales Agent</td>\n",
              "      <td>20.16.10.16</td>\n",
              "      <td>184, chaussee de Tournai</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>FRANS</td>\n",
              "      <td>Paolo Accorti</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>011-4988260</td>\n",
              "      <td>Via Monte Bianco 34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>FURIB</td>\n",
              "      <td>Lino Rodriguez</td>\n",
              "      <td>Sales Manager</td>\n",
              "      <td>(1) 354-2534</td>\n",
              "      <td>Jardim das rosas n. 32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>GODOS</td>\n",
              "      <td>Jose Pedro Freyre</td>\n",
              "      <td>Sales Manager</td>\n",
              "      <td>(95) 555 82 82</td>\n",
              "      <td>C/ Romero, 33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>GOURL</td>\n",
              "      <td>Andre Fonseca</td>\n",
              "      <td>Sales Associate</td>\n",
              "      <td>(11) 555-9482</td>\n",
              "      <td>Av. Brasil, 442</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>HILAA</td>\n",
              "      <td>Carlos Hernandez</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(5) 555-1340</td>\n",
              "      <td>Carrera 22 con Ave. Carlos Soublette #8-35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35</th>\n",
              "      <td>HUNGC</td>\n",
              "      <td>Yoshi Latimer</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(503) 555-6874</td>\n",
              "      <td>City Center Plaza 516 Main St.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>36</th>\n",
              "      <td>HUNGO</td>\n",
              "      <td>Patricia McKenna</td>\n",
              "      <td>Sales Associate</td>\n",
              "      <td>2 967 542</td>\n",
              "      <td>8 Johnstown Road</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>KOENE</td>\n",
              "      <td>Philip Cramer</td>\n",
              "      <td>Sales Associate</td>\n",
              "      <td>0555-09876</td>\n",
              "      <td>Maubelstr. 90</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>39</th>\n",
              "      <td>LACOR</td>\n",
              "      <td>Daniel Tonini</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>30.59.84.10</td>\n",
              "      <td>67, avenue de l'Europe</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>40</th>\n",
              "      <td>LAMAI</td>\n",
              "      <td>Annette Roulet</td>\n",
              "      <td>Sales Manager</td>\n",
              "      <td>61.77.61.10</td>\n",
              "      <td>1 rue Alsace-Lorraine</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>LEHMS</td>\n",
              "      <td>Renate Messner</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>069-0245984</td>\n",
              "      <td>Magazinweg 7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>47</th>\n",
              "      <td>LONEP</td>\n",
              "      <td>Fran Wilson</td>\n",
              "      <td>Sales Manager</td>\n",
              "      <td>(503) 555-9573</td>\n",
              "      <td>89 Chiaroscuro Rd.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>49</th>\n",
              "      <td>MAISD</td>\n",
              "      <td>Catherine Dewey</td>\n",
              "      <td>Sales Agent</td>\n",
              "      <td>(02) 201 24 67</td>\n",
              "      <td>Rue Joseph-Bens 532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>52</th>\n",
              "      <td>NORTS</td>\n",
              "      <td>Simon Crowther</td>\n",
              "      <td>Sales Associate</td>\n",
              "      <td>(171) 555-7733</td>\n",
              "      <td>South House 300 Queensbridge</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>53</th>\n",
              "      <td>OCEAN</td>\n",
              "      <td>Yvonne Moncada</td>\n",
              "      <td>Sales Agent</td>\n",
              "      <td>(1) 135-5333</td>\n",
              "      <td>Ing. Gustavo Moncada 8585 Piso 20-A</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>54</th>\n",
              "      <td>OLDWO</td>\n",
              "      <td>Rene Phillips</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(907) 555-7584</td>\n",
              "      <td>2743 Bering St.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>57</th>\n",
              "      <td>PERIC</td>\n",
              "      <td>Guillermo Fernandez</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(5) 552-3745</td>\n",
              "      <td>Calle Dr. Jorge Cash 321</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>58</th>\n",
              "      <td>PICCO</td>\n",
              "      <td>Georg Pipps</td>\n",
              "      <td>Sales Manager</td>\n",
              "      <td>6562-9722</td>\n",
              "      <td>Geislweg 14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>59</th>\n",
              "      <td>PRINI</td>\n",
              "      <td>Isabel de Castro</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(1) 356-5634</td>\n",
              "      <td>Estrada da sa?de n. 58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>RANCH</td>\n",
              "      <td>Sergio Gutierrez</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(1) 123-5555</td>\n",
              "      <td>Av. del Libertador 900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>RATTC</td>\n",
              "      <td>Paula Wilson</td>\n",
              "      <td>Assistant Sales Representative</td>\n",
              "      <td>(505) 555-5939</td>\n",
              "      <td>2817 Milton Dr.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>REGGC</td>\n",
              "      <td>Maurizio Moroni</td>\n",
              "      <td>Sales Associate</td>\n",
              "      <td>0522-556721</td>\n",
              "      <td>Strada Provinciale 124</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>RICAR</td>\n",
              "      <td>Janete Limeira</td>\n",
              "      <td>Assistant Sales Agent</td>\n",
              "      <td>(21) 555-3412</td>\n",
              "      <td>Av. Copacabana, 267</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>67</th>\n",
              "      <td>RICSU</td>\n",
              "      <td>Michael Holz</td>\n",
              "      <td>Sales Manager</td>\n",
              "      <td>0897-034214</td>\n",
              "      <td>Grenzacherweg 237</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>70</th>\n",
              "      <td>SAVEA</td>\n",
              "      <td>Jose Pavarotti</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(208) 555-8097</td>\n",
              "      <td>187 Suffolk Ln.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>71</th>\n",
              "      <td>SEVES</td>\n",
              "      <td>Hari Kumar</td>\n",
              "      <td>Sales Manager</td>\n",
              "      <td>(171) 555-1717</td>\n",
              "      <td>90 Wadhurst Rd.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>SPLIR</td>\n",
              "      <td>Art Braunschweiger</td>\n",
              "      <td>Sales Manager</td>\n",
              "      <td>(307) 555-4680</td>\n",
              "      <td>P.O. Box 555</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>80</th>\n",
              "      <td>TRADH</td>\n",
              "      <td>Anabela Domingues</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>(11) 555-2167</td>\n",
              "      <td>Av. Ines de Castro, 414</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>81</th>\n",
              "      <td>TRAIH</td>\n",
              "      <td>Helvetius Nagy</td>\n",
              "      <td>Sales Associate</td>\n",
              "      <td>(206) 555-8257</td>\n",
              "      <td>722 DaVinci Blvd.</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>82</th>\n",
              "      <td>VAFFE</td>\n",
              "      <td>Palle Ibsen</td>\n",
              "      <td>Sales Manager</td>\n",
              "      <td>86 21 32 43</td>\n",
              "      <td>Smagsloget 45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>83</th>\n",
              "      <td>VICTE</td>\n",
              "      <td>Mary Saveley</td>\n",
              "      <td>Sales Agent</td>\n",
              "      <td>78.32.54.86</td>\n",
              "      <td>2, rue du Commerce</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>85</th>\n",
              "      <td>WANDK</td>\n",
              "      <td>Rita Muller</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>0711-020361</td>\n",
              "      <td>Adenauerallee 900</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>87</th>\n",
              "      <td>WELLI</td>\n",
              "      <td>Paula Parente</td>\n",
              "      <td>Sales Manager</td>\n",
              "      <td>(14) 555-8122</td>\n",
              "      <td>Rua do Mercado, 12</td>\n",
              "    </tr>\n",
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    {
      "cell_type": "code",
      "source": [
        "df_c[df_c['ContactName'].str.startswith('A')].ContactName.count()\n",
        "\n"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "datXsOs8Y1kP",
        "outputId": "b9bba9ca-3351-4a7c-a042-45cf3b4c516a"
      },
      "execution_count": null,
      "outputs": [
        {
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          "data": {
            "text/plain": [
              "10"
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_c['Phone'].str.replace('-','').str.contains('5555').sum()"
      ],
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          "base_uri": "https://localhost:8080/"
        },
        "id": "yzX7NyDaaBbN",
        "outputId": "8b602943-54b9-416f-bd87-108637242292"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "df_c[df_c['Phone'].str.contains('5{4}') == True]  "
      ],
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          "height": 112
        },
        "id": "m7d9qiU2aQOt",
        "outputId": "bea8a94d-2edb-4bbf-b9bb-0443cd052457"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   CustomerID       ContactName          ContactTitle         Phone  \\\n",
              "11      CACTU  Patricio Simpson           Sales Agent  (1) 135-5555   \n",
              "63      RANCH  Sergio Gutierrez  Sales Representative  (1) 123-5555   \n",
              "\n",
              "                   Address  \n",
              "11             Cerrito 333  \n",
              "63  Av. del Libertador 900  "
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    {
      "cell_type": "code",
      "source": [
        "#сортировка по возрастанию по столбцу CategoryName\n",
        "df_p.sort_values('CategoryName').head()"
      ],
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        },
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      "execution_count": null,
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              "    ProductID                ProductName  Product_Description  UnitPrice  \\\n",
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              "\n",
              "    CategoryID CategoryName                         Category_Description  \n",
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              "66           1    Beverages  Soft drinks. coffees. teas. beers. and ales  \n",
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    {
      "cell_type": "code",
      "source": [
        "df_p.sort_values(['CategoryName','UnitPrice'], ascending = [True, False]).head(30)"
      ],
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          "data": {
            "text/plain": [
              "    ProductID                       ProductName  Product_Description  \\\n",
              "37         38                     C?te de Blaye   12 - 75 cl bottles   \n",
              "42         43                       Ipoh Coffee      16 - 500 g tins   \n",
              "1           2                             Chang   24 - 12 oz bottles   \n",
              "0           1                              Chai   10 boxes x 20 bags   \n",
              "34         35                    Steeleye Stout   24 - 12 oz bottles   \n",
              "38         39                  Chartreuse verte    750 cc per bottle   \n",
              "75         76                      Lakkalikoori               500 ml   \n",
              "69         70                     Outback Lager  24 - 355 ml bottles   \n",
              "33         34                     Sasquatch Ale   24 - 12 oz bottles   \n",
              "66         67         Laughing Lumberjack Lager   24 - 12 oz bottles   \n",
              "74         75              Rhonbrau Klosterbier   24 - 0.5 l bottles   \n",
              "23         24                Guaran? Fant?stica     12 - 355 ml cans   \n",
              "62         63                      Vegie-spread      15 - 625 g jars   \n",
              "7           8        Northwoods Cranberry Sauce      12 - 12 oz jars   \n",
              "60         61                    Sirop d'erable  24 - 500 ml bottles   \n",
              "5           6      Grandma's Boysenberry Spread       12 - 8 oz jars   \n",
              "3           4      Chef Anton's Cajun Seasoning       48 - 6 oz jars   \n",
              "4           5            Chef Anton's Gumbo Mix             36 boxes   \n",
              "64         65  Louisiana Fiery Hot Pepper Sauce    32 - 8 oz bottles   \n",
              "43         44                      Gula Malacca       20 - 2 kg bags   \n",
              "65         66         Louisiana Hot Spiced Okra       24 - 8 oz jars   \n",
              "14         15                      Genen Shouyu  24 - 250 ml bottles   \n",
              "76         77   Original Frankfurter gr?ne Sobe             12 boxes   \n",
              "2           3                     Aniseed Syrup  12 - 550 ml bottles   \n",
              "19         20            Sir Rodney's Marmalade        30 gift boxes   \n",
              "61         62                    Tarte au sucre              48 pies   \n",
              "26         27                Schoggi Schokolade   100 - 100 g pieces   \n",
              "25         26               Gumbar Gummibarchen     100 - 250 g bags   \n",
              "48         49                          Maxilaku      24 - 50 g pkgs.   \n",
              "15         16                           Pavlova     32 - 500 g boxes   \n",
              "\n",
              "    UnitPrice  CategoryID CategoryName  \\\n",
              "37     263.50           1    Beverages   \n",
              "42      46.00           1    Beverages   \n",
              "1       19.00           1    Beverages   \n",
              "0       18.00           1    Beverages   \n",
              "34      18.00           1    Beverages   \n",
              "38      18.00           1    Beverages   \n",
              "75      18.00           1    Beverages   \n",
              "69      15.00           1    Beverages   \n",
              "33      14.00           1    Beverages   \n",
              "66      14.00           1    Beverages   \n",
              "74       7.75           1    Beverages   \n",
              "23       4.50           1    Beverages   \n",
              "62      43.90           2   Condiments   \n",
              "7       40.00           2   Condiments   \n",
              "60      28.50           2   Condiments   \n",
              "5       25.00           2   Condiments   \n",
              "3       22.00           2   Condiments   \n",
              "4       21.35           2   Condiments   \n",
              "64      21.05           2   Condiments   \n",
              "43      19.45           2   Condiments   \n",
              "65      17.00           2   Condiments   \n",
              "14      15.50           2   Condiments   \n",
              "76      13.00           2   Condiments   \n",
              "2       10.00           2   Condiments   \n",
              "19      81.00           3  Confections   \n",
              "61      49.30           3  Confections   \n",
              "26      43.90           3  Confections   \n",
              "25      31.23           3  Confections   \n",
              "48      20.00           3  Confections   \n",
              "15      17.45           3  Confections   \n",
              "\n",
              "                                 Category_Description  \n",
              "37        Soft drinks. coffees. teas. beers. and ales  \n",
              "42        Soft drinks. coffees. teas. beers. and ales  \n",
              "1         Soft drinks. coffees. teas. beers. and ales  \n",
              "0         Soft drinks. coffees. teas. beers. and ales  \n",
              "34        Soft drinks. coffees. teas. beers. and ales  \n",
              "38        Soft drinks. coffees. teas. beers. and ales  \n",
              "75        Soft drinks. coffees. teas. beers. and ales  \n",
              "69        Soft drinks. coffees. teas. beers. and ales  \n",
              "33        Soft drinks. coffees. teas. beers. and ales  \n",
              "66        Soft drinks. coffees. teas. beers. and ales  \n",
              "74        Soft drinks. coffees. teas. beers. and ales  \n",
              "23        Soft drinks. coffees. teas. beers. and ales  \n",
              "62  Sweet and savory sauces. relishes. spreads. an...  \n",
              "7   Sweet and savory sauces. relishes. spreads. an...  \n",
              "60  Sweet and savory sauces. relishes. spreads. an...  \n",
              "5   Sweet and savory sauces. relishes. spreads. an...  \n",
              "3   Sweet and savory sauces. relishes. spreads. an...  \n",
              "4   Sweet and savory sauces. relishes. spreads. an...  \n",
              "64  Sweet and savory sauces. relishes. spreads. an...  \n",
              "43  Sweet and savory sauces. relishes. spreads. an...  \n",
              "65  Sweet and savory sauces. relishes. spreads. an...  \n",
              "14  Sweet and savory sauces. relishes. spreads. an...  \n",
              "76  Sweet and savory sauces. relishes. spreads. an...  \n",
              "2   Sweet and savory sauces. relishes. spreads. an...  \n",
              "19                Desserts. candies. and sweet breads  \n",
              "61                Desserts. candies. and sweet breads  \n",
              "26                Desserts. candies. and sweet breads  \n",
              "25                Desserts. candies. and sweet breads  \n",
              "48                Desserts. candies. and sweet breads  \n",
              "15                Desserts. candies. and sweet breads  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ProductID</th>\n",
              "      <th>ProductName</th>\n",
              "      <th>Product_Description</th>\n",
              "      <th>UnitPrice</th>\n",
              "      <th>CategoryID</th>\n",
              "      <th>CategoryName</th>\n",
              "      <th>Category_Description</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>37</th>\n",
              "      <td>38</td>\n",
              "      <td>C?te de Blaye</td>\n",
              "      <td>12 - 75 cl bottles</td>\n",
              "      <td>263.50</td>\n",
              "      <td>1</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Soft drinks. coffees. teas. beers. and ales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>42</th>\n",
              "      <td>43</td>\n",
              "      <td>Ipoh Coffee</td>\n",
              "      <td>16 - 500 g tins</td>\n",
              "      <td>46.00</td>\n",
              "      <td>1</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Soft drinks. coffees. teas. beers. and ales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>Chang</td>\n",
              "      <td>24 - 12 oz bottles</td>\n",
              "      <td>19.00</td>\n",
              "      <td>1</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Soft drinks. coffees. teas. beers. and ales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>Chai</td>\n",
              "      <td>10 boxes x 20 bags</td>\n",
              "      <td>18.00</td>\n",
              "      <td>1</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Soft drinks. coffees. teas. beers. and ales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>34</th>\n",
              "      <td>35</td>\n",
              "      <td>Steeleye Stout</td>\n",
              "      <td>24 - 12 oz bottles</td>\n",
              "      <td>18.00</td>\n",
              "      <td>1</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Soft drinks. coffees. teas. beers. and ales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>38</th>\n",
              "      <td>39</td>\n",
              "      <td>Chartreuse verte</td>\n",
              "      <td>750 cc per bottle</td>\n",
              "      <td>18.00</td>\n",
              "      <td>1</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Soft drinks. coffees. teas. beers. and ales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75</th>\n",
              "      <td>76</td>\n",
              "      <td>Lakkalikoori</td>\n",
              "      <td>500 ml</td>\n",
              "      <td>18.00</td>\n",
              "      <td>1</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Soft drinks. coffees. teas. beers. and ales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>69</th>\n",
              "      <td>70</td>\n",
              "      <td>Outback Lager</td>\n",
              "      <td>24 - 355 ml bottles</td>\n",
              "      <td>15.00</td>\n",
              "      <td>1</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Soft drinks. coffees. teas. beers. and ales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>33</th>\n",
              "      <td>34</td>\n",
              "      <td>Sasquatch Ale</td>\n",
              "      <td>24 - 12 oz bottles</td>\n",
              "      <td>14.00</td>\n",
              "      <td>1</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Soft drinks. coffees. teas. beers. and ales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>66</th>\n",
              "      <td>67</td>\n",
              "      <td>Laughing Lumberjack Lager</td>\n",
              "      <td>24 - 12 oz bottles</td>\n",
              "      <td>14.00</td>\n",
              "      <td>1</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Soft drinks. coffees. teas. beers. and ales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>74</th>\n",
              "      <td>75</td>\n",
              "      <td>Rhonbrau Klosterbier</td>\n",
              "      <td>24 - 0.5 l bottles</td>\n",
              "      <td>7.75</td>\n",
              "      <td>1</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Soft drinks. coffees. teas. beers. and ales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>24</td>\n",
              "      <td>Guaran? Fant?stica</td>\n",
              "      <td>12 - 355 ml cans</td>\n",
              "      <td>4.50</td>\n",
              "      <td>1</td>\n",
              "      <td>Beverages</td>\n",
              "      <td>Soft drinks. coffees. teas. beers. and ales</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>63</td>\n",
              "      <td>Vegie-spread</td>\n",
              "      <td>15 - 625 g jars</td>\n",
              "      <td>43.90</td>\n",
              "      <td>2</td>\n",
              "      <td>Condiments</td>\n",
              "      <td>Sweet and savory sauces. relishes. spreads. an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Northwoods Cranberry Sauce</td>\n",
              "      <td>12 - 12 oz jars</td>\n",
              "      <td>40.00</td>\n",
              "      <td>2</td>\n",
              "      <td>Condiments</td>\n",
              "      <td>Sweet and savory sauces. relishes. spreads. an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>60</th>\n",
              "      <td>61</td>\n",
              "      <td>Sirop d'erable</td>\n",
              "      <td>24 - 500 ml bottles</td>\n",
              "      <td>28.50</td>\n",
              "      <td>2</td>\n",
              "      <td>Condiments</td>\n",
              "      <td>Sweet and savory sauces. relishes. spreads. an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Grandma's Boysenberry Spread</td>\n",
              "      <td>12 - 8 oz jars</td>\n",
              "      <td>25.00</td>\n",
              "      <td>2</td>\n",
              "      <td>Condiments</td>\n",
              "      <td>Sweet and savory sauces. relishes. spreads. an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Chef Anton's Cajun Seasoning</td>\n",
              "      <td>48 - 6 oz jars</td>\n",
              "      <td>22.00</td>\n",
              "      <td>2</td>\n",
              "      <td>Condiments</td>\n",
              "      <td>Sweet and savory sauces. relishes. spreads. an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Chef Anton's Gumbo Mix</td>\n",
              "      <td>36 boxes</td>\n",
              "      <td>21.35</td>\n",
              "      <td>2</td>\n",
              "      <td>Condiments</td>\n",
              "      <td>Sweet and savory sauces. relishes. spreads. an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>65</td>\n",
              "      <td>Louisiana Fiery Hot Pepper Sauce</td>\n",
              "      <td>32 - 8 oz bottles</td>\n",
              "      <td>21.05</td>\n",
              "      <td>2</td>\n",
              "      <td>Condiments</td>\n",
              "      <td>Sweet and savory sauces. relishes. spreads. an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>43</th>\n",
              "      <td>44</td>\n",
              "      <td>Gula Malacca</td>\n",
              "      <td>20 - 2 kg bags</td>\n",
              "      <td>19.45</td>\n",
              "      <td>2</td>\n",
              "      <td>Condiments</td>\n",
              "      <td>Sweet and savory sauces. relishes. spreads. an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>66</td>\n",
              "      <td>Louisiana Hot Spiced Okra</td>\n",
              "      <td>24 - 8 oz jars</td>\n",
              "      <td>17.00</td>\n",
              "      <td>2</td>\n",
              "      <td>Condiments</td>\n",
              "      <td>Sweet and savory sauces. relishes. spreads. an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>15</td>\n",
              "      <td>Genen Shouyu</td>\n",
              "      <td>24 - 250 ml bottles</td>\n",
              "      <td>15.50</td>\n",
              "      <td>2</td>\n",
              "      <td>Condiments</td>\n",
              "      <td>Sweet and savory sauces. relishes. spreads. an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>76</th>\n",
              "      <td>77</td>\n",
              "      <td>Original Frankfurter gr?ne Sobe</td>\n",
              "      <td>12 boxes</td>\n",
              "      <td>13.00</td>\n",
              "      <td>2</td>\n",
              "      <td>Condiments</td>\n",
              "      <td>Sweet and savory sauces. relishes. spreads. an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>Aniseed Syrup</td>\n",
              "      <td>12 - 550 ml bottles</td>\n",
              "      <td>10.00</td>\n",
              "      <td>2</td>\n",
              "      <td>Condiments</td>\n",
              "      <td>Sweet and savory sauces. relishes. spreads. an...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>20</td>\n",
              "      <td>Sir Rodney's Marmalade</td>\n",
              "      <td>30 gift boxes</td>\n",
              "      <td>81.00</td>\n",
              "      <td>3</td>\n",
              "      <td>Confections</td>\n",
              "      <td>Desserts. candies. and sweet breads</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>62</td>\n",
              "      <td>Tarte au sucre</td>\n",
              "      <td>48 pies</td>\n",
              "      <td>49.30</td>\n",
              "      <td>3</td>\n",
              "      <td>Confections</td>\n",
              "      <td>Desserts. candies. and sweet breads</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>27</td>\n",
              "      <td>Schoggi Schokolade</td>\n",
              "      <td>100 - 100 g pieces</td>\n",
              "      <td>43.90</td>\n",
              "      <td>3</td>\n",
              "      <td>Confections</td>\n",
              "      <td>Desserts. candies. and sweet breads</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>26</td>\n",
              "      <td>Gumbar Gummibarchen</td>\n",
              "      <td>100 - 250 g bags</td>\n",
              "      <td>31.23</td>\n",
              "      <td>3</td>\n",
              "      <td>Confections</td>\n",
              "      <td>Desserts. candies. and sweet breads</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>48</th>\n",
              "      <td>49</td>\n",
              "      <td>Maxilaku</td>\n",
              "      <td>24 - 50 g pkgs.</td>\n",
              "      <td>20.00</td>\n",
              "      <td>3</td>\n",
              "      <td>Confections</td>\n",
              "      <td>Desserts. candies. and sweet breads</td>\n",
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              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>16</td>\n",
              "      <td>Pavlova</td>\n",
              "      <td>32 - 500 g boxes</td>\n",
              "      <td>17.45</td>\n",
              "      <td>3</td>\n",
              "      <td>Confections</td>\n",
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        "df_p[df_p['CategoryName']=='Condiments'].sort_values('UnitPrice').head()"
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              "    ProductID                      ProductName  Product_Description  \\\n",
              "2           3                    Aniseed Syrup  12 - 550 ml bottles   \n",
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              "14         15                     Genen Shouyu  24 - 250 ml bottles   \n",
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              "    UnitPrice  CategoryID CategoryName  \\\n",
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        "df_o[(df_o['OrderDate'].between('1997-01-01','1997-12-31',inclusive='both'))].sort_values('Freight',ascending = False)"
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              "     OrderID CustomerID  EmployeeID  OrderDate             RequiredDate  \\\n",
              "292    10540      QUICK           3 1997-05-19  1997-06-16 00:00:00.000   \n",
              "443    10691      QUICK           2 1997-10-03  1997-11-14 00:00:00.000   \n",
              "266    10514      ERNSH           3 1997-04-22  1997-05-20 00:00:00.000   \n",
              "231    10479      RATTC           3 1997-03-19  1997-04-16 00:00:00.000   \n",
              "364    10612      SAVEA           1 1997-07-28  1997-08-25 00:00:00.000   \n",
              "..       ...        ...         ...        ...                      ...   \n",
              "451    10699      MORGK           3 1997-10-09  1997-11-06 00:00:00.000   \n",
              "338    10586      REGGC           9 1997-07-02  1997-07-30 00:00:00.000   \n",
              "167    10415      HUNGC           3 1997-01-15  1997-02-12 00:00:00.000   \n",
              "261    10509      BLAUS           4 1997-04-17  1997-05-15 00:00:00.000   \n",
              "396    10644      WELLI           3 1997-08-25  1997-09-22 00:00:00.000   \n",
              "\n",
              "                 ShippedDate  Freight  \n",
              "292  1997-06-13 00:00:00.000  1007.64  \n",
              "443  1997-10-22 00:00:00.000   810.05  \n",
              "266  1997-05-16 00:00:00.000   789.95  \n",
              "231  1997-03-21 00:00:00.000   708.95  \n",
              "364  1997-08-01 00:00:00.000   544.08  \n",
              "..                       ...      ...  \n",
              "451  1997-10-13 00:00:00.000     0.58  \n",
              "338  1997-07-09 00:00:00.000     0.48  \n",
              "167  1997-01-24 00:00:00.000     0.20  \n",
              "261  1997-04-29 00:00:00.000     0.15  \n",
              "396  1997-09-01 00:00:00.000     0.14  \n",
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              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>451</th>\n",
              "      <td>10699</td>\n",
              "      <td>MORGK</td>\n",
              "      <td>3</td>\n",
              "      <td>1997-10-09</td>\n",
              "      <td>1997-11-06 00:00:00.000</td>\n",
              "      <td>1997-10-13 00:00:00.000</td>\n",
              "      <td>0.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>338</th>\n",
              "      <td>10586</td>\n",
              "      <td>REGGC</td>\n",
              "      <td>9</td>\n",
              "      <td>1997-07-02</td>\n",
              "      <td>1997-07-30 00:00:00.000</td>\n",
              "      <td>1997-07-09 00:00:00.000</td>\n",
              "      <td>0.48</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>167</th>\n",
              "      <td>10415</td>\n",
              "      <td>HUNGC</td>\n",
              "      <td>3</td>\n",
              "      <td>1997-01-15</td>\n",
              "      <td>1997-02-12 00:00:00.000</td>\n",
              "      <td>1997-01-24 00:00:00.000</td>\n",
              "      <td>0.20</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>261</th>\n",
              "      <td>10509</td>\n",
              "      <td>BLAUS</td>\n",
              "      <td>4</td>\n",
              "      <td>1997-04-17</td>\n",
              "      <td>1997-05-15 00:00:00.000</td>\n",
              "      <td>1997-04-29 00:00:00.000</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>396</th>\n",
              "      <td>10644</td>\n",
              "      <td>WELLI</td>\n",
              "      <td>3</td>\n",
              "      <td>1997-08-25</td>\n",
              "      <td>1997-09-22 00:00:00.000</td>\n",
              "      <td>1997-09-01 00:00:00.000</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>408 rows × 7 columns</p>\n",
              "</div>\n",
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    {
      "cell_type": "code",
      "source": [
        "df_o[(df_o['OrderDate'].between('1997-01-01','1997-12-31',inclusive='both')) & (df_o['CustomerID'] == 'ANTON')].sort_values(['OrderID', 'Freight'])"
      ],
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      "execution_count": null,
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            "text/plain": [
              "     OrderID CustomerID  EmployeeID  OrderDate             RequiredDate  \\\n",
              "259    10507      ANTON           7 1997-04-15  1997-05-13 00:00:00.000   \n",
              "287    10535      ANTON           4 1997-05-13  1997-06-10 00:00:00.000   \n",
              "325    10573      ANTON           7 1997-06-19  1997-07-17 00:00:00.000   \n",
              "429    10677      ANTON           1 1997-09-22  1997-10-20 00:00:00.000   \n",
              "434    10682      ANTON           3 1997-09-25  1997-10-23 00:00:00.000   \n",
              "\n",
              "                 ShippedDate  Freight  \n",
              "259  1997-04-22 00:00:00.000    47.45  \n",
              "287  1997-05-21 00:00:00.000    15.64  \n",
              "325  1997-06-20 00:00:00.000    84.84  \n",
              "429  1997-09-26 00:00:00.000     4.03  \n",
              "434  1997-10-01 00:00:00.000    36.13  "
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    },
    {
      "cell_type": "code",
      "source": [
        "df_c[(df_c['ContactTitle'] == 'Owner')| (df_c['ContactTitle'] == 'Sales Representative')].ContactTitle.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
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        "outputId": "d61b66df-0bb8-4f84-cfeb-bb2a28242cc8"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "34"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_o.Freight.max() - df_o.Freight.min()"
      ],
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      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1007.62"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_o['OrderDate']= df_o['OrderDate'].astype('datetime64')"
      ],
      "metadata": {
        "id": "0ibsecTOhK_K"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_o[df_o['OrderDate'] == '1998-02-26'].Freight.sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4CgpmXquhMJT",
        "outputId": "6f69b8ff-1de0-4644-ebf7-ab0c4f9f3f1d"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "776.27"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_p.groupby('CategoryName').ProductID.count()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/"
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        "id": "9tMLsNg2b5iN",
        "outputId": "85c4f328-cb1b-4dfb-8fed-d5c9641d44a0"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CategoryName\n",
              "Beverages         12\n",
              "Condiments        12\n",
              "Confections       13\n",
              "Dairy Products    10\n",
              "Grains/Cereals     7\n",
              "Meat/Poultry       6\n",
              "Produce            5\n",
              "Seafood           12\n",
              "Name: ProductID, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_p.groupby('CategoryName').UnitPrice.min()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dC6Tg-56cCQ0",
        "outputId": "9e92e963-2d48-460c-87fc-f830397cf625"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CategoryName\n",
              "Beverages          4.50\n",
              "Condiments        10.00\n",
              "Confections        9.20\n",
              "Dairy Products     2.50\n",
              "Grains/Cereals     7.00\n",
              "Meat/Poultry       7.45\n",
              "Produce           10.00\n",
              "Seafood            6.00\n",
              "Name: UnitPrice, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_c.groupby('ContactTitle').ContactTitle.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0roJe84ecKma",
        "outputId": "e518c805-fa4b-4a42-9fbd-c720c6821d77"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "ContactTitle\n",
              "Accounting Manager                10\n",
              "Assistant Sales Agent              2\n",
              "Assistant Sales Representative     1\n",
              "Marketing Assistant                6\n",
              "Marketing Manager                 12\n",
              "Order Administrator                2\n",
              "Owner                             17\n",
              "Owner/Marketing Assistant          1\n",
              "Sales Agent                        5\n",
              "Sales Associate                    7\n",
              "Sales Manager                     11\n",
              "Sales Representative              17\n",
              "Name: ContactTitle, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_c.groupby('ContactTitle',as_index=False).ContactTitle.count()"
      ],
      "metadata": {
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          "height": 426
        },
        "id": "2iqkZKEq6_7f",
        "outputId": "18ffd01f-ae2d-41be-bd08-7f4c2fa1756a"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    ContactTitle\n",
              "0             10\n",
              "1              2\n",
              "2              1\n",
              "3              6\n",
              "4             12\n",
              "5              2\n",
              "6             17\n",
              "7              1\n",
              "8              5\n",
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              "  "
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          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_o.groupby('EmployeeID').OrderID.count()\n",
        "# df_o"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "foLTVESy7e0B",
        "outputId": "ca85e1ce-da6a-4230-b5a3-0abfb77b4be0"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "EmployeeID\n",
              "1    123\n",
              "2     96\n",
              "3    127\n",
              "4    156\n",
              "5     42\n",
              "6     67\n",
              "7     72\n",
              "8    104\n",
              "9     43\n",
              "Name: OrderID, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_o['OrderDate']= df_o['OrderDate'].astype('datetime64')"
      ],
      "metadata": {
        "id": "to7zh6OJ8DP-"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_o.groupby('OrderDate', as_index=False).count().sort_values('OrderID', ascending=False)"
      ],
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        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "t-gVmiSS8IQC",
        "outputId": "dcb6b032-06b8-4076-cbd7-66362c3d1138"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     OrderDate  OrderID  CustomerID  EmployeeID  RequiredDate  ShippedDate  \\\n",
              "430 1998-02-26        6           6           6             6            6   \n",
              "479 1998-05-06        4           4           4             4            0   \n",
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              "..         ...      ...         ...         ...           ...          ...   \n",
              "167 1997-02-24        1           1           1             1            1   \n",
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              "162 1997-02-17        1           1           1             1            1   \n",
              "240 1997-06-05        1           1           1             1            1   \n",
              "\n",
              "     Freight  \n",
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              "479        4  \n",
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              "..       ...  \n",
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              "165        1  \n",
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              "\n",
              "[480 rows x 7 columns]"
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-424b3d27-8610-4058-a782-68833e4eecb6');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
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              "  "
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_o.groupby(['EmployeeID','CustomerID'],as_index=False).OrderID.count().sort_values('OrderID', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "PoBBYs5tu_wS",
        "outputId": "1c3a4d39-4d88-4f13-9023-eda5c327526c"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     EmployeeID CustomerID  OrderID\n",
              "128           3      BERGS        6\n",
              "103           2      QUICK        6\n",
              "50            1      SAVEA        6\n",
              "391           8      FOLKO        6\n",
              "150           3      HUNGO        5\n",
              "..          ...        ...      ...\n",
              "188           4      ANATR        1\n",
              "186           3      WILMK        1\n",
              "183           3      WARTH        1\n",
              "182           3      VINET        1\n",
              "463           9      WELLI        1\n",
              "\n",
              "[464 rows x 3 columns]"
            ],
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              "      <th></th>\n",
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              "      <th>50</th>\n",
              "      <td>1</td>\n",
              "      <td>SAVEA</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>391</th>\n",
              "      <td>8</td>\n",
              "      <td>FOLKO</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>150</th>\n",
              "      <td>3</td>\n",
              "      <td>HUNGO</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>188</th>\n",
              "      <td>4</td>\n",
              "      <td>ANATR</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>186</th>\n",
              "      <td>3</td>\n",
              "      <td>WILMK</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>183</th>\n",
              "      <td>3</td>\n",
              "      <td>WARTH</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>182</th>\n",
              "      <td>3</td>\n",
              "      <td>VINET</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>463</th>\n",
              "      <td>9</td>\n",
              "      <td>WELLI</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>464 rows × 3 columns</p>\n",
              "</div>\n",
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        "df_od['Discount_fact'] = df_od['UnitPrice'] * df_od['Quantity'] * df_od['Discount']\n",
        "df_od['Revenue'] = df_od['UnitPrice'] * df_od['Quantity'] - df_od['Discount_fact']\n",
        "df_od.groupby(['OrderID','ProductID']).Discount_fact.sum()"
      ],
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        "id": "zIbw9ZpOv1Wg",
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      "execution_count": 34,
      "outputs": [
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          "data": {
            "text/plain": [
              "OrderID  ProductID\n",
              "10248    11           0.000\n",
              "         42           0.000\n",
              "         72           0.000\n",
              "10249    14           0.000\n",
              "         51           0.000\n",
              "                      ...  \n",
              "11077    64           1.995\n",
              "         66           0.000\n",
              "         73           0.300\n",
              "         75           0.000\n",
              "         77           0.000\n",
              "Name: Discount_fact, Length: 2155, dtype: float64"
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          "execution_count": 34
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      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_c= df_c.rename(columns = {'CustomerID':'CustID'})\n",
        "\n",
        "df_c.head()"
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      "execution_count": 35,
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            "text/plain": [
              "  CustID         ContactName          ContactTitle           Phone  \\\n",
              "0  ALFKI        Maria Anders  Sales Representative     030-0074321   \n",
              "1  ANATR        Ana Trujillo                 Owner    (5) 555-4729   \n",
              "2  ANTON      Antonio Moreno                 Owner    (5) 555-3932   \n",
              "3  AROUT        Thomas Hardy  Sales Representative  (171) 555-7788   \n",
              "4  BERGS  Christina Berglund   Order Administrator   0921-12 34 65   \n",
              "\n",
              "                         Address  \n",
              "0                  Obere Str. 57  \n",
              "1  Avda. de la Constitucion 2222  \n",
              "2                Mataderos  2312  \n",
              "3                120 Hanover Sq.  \n",
              "4                Berguvsv?gen  8  "
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      "execution_count": 36,
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            "text/plain": [
              "    CustID              ContactName          ContactTitle          Phone  \\\n",
              "0    ALFKI             Maria Anders  Sales Representative    030-0074321   \n",
              "1    ALFKI             Maria Anders  Sales Representative    030-0074321   \n",
              "2    ALFKI             Maria Anders  Sales Representative    030-0074321   \n",
              "3    ALFKI             Maria Anders  Sales Representative    030-0074321   \n",
              "4    ALFKI             Maria Anders  Sales Representative    030-0074321   \n",
              "..     ...                      ...                   ...            ...   \n",
              "825  WOLZA  Zbyszek Piestrzeniewicz                 Owner  (26) 642-7012   \n",
              "826  WOLZA  Zbyszek Piestrzeniewicz                 Owner  (26) 642-7012   \n",
              "827  WOLZA  Zbyszek Piestrzeniewicz                 Owner  (26) 642-7012   \n",
              "828  WOLZA  Zbyszek Piestrzeniewicz                 Owner  (26) 642-7012   \n",
              "829  WOLZA  Zbyszek Piestrzeniewicz                 Owner  (26) 642-7012   \n",
              "\n",
              "             Address  OrderID CustomerID  EmployeeID  OrderDate  \\\n",
              "0      Obere Str. 57    10643      ALFKI           6 1997-08-25   \n",
              "1      Obere Str. 57    10692      ALFKI           4 1997-10-03   \n",
              "2      Obere Str. 57    10702      ALFKI           4 1997-10-13   \n",
              "3      Obere Str. 57    10835      ALFKI           1 1998-01-15   \n",
              "4      Obere Str. 57    10952      ALFKI           1 1998-03-16   \n",
              "..               ...      ...        ...         ...        ...   \n",
              "825  ul. Filtrowa 68    10792      WOLZA           1 1997-12-23   \n",
              "826  ul. Filtrowa 68    10870      WOLZA           5 1998-02-04   \n",
              "827  ul. Filtrowa 68    10906      WOLZA           4 1998-02-25   \n",
              "828  ul. Filtrowa 68    10998      WOLZA           8 1998-04-03   \n",
              "829  ul. Filtrowa 68    11044      WOLZA           4 1998-04-23   \n",
              "\n",
              "                RequiredDate              ShippedDate  Freight  \n",
              "0    1997-09-22 00:00:00.000  1997-09-02 00:00:00.000    29.46  \n",
              "1    1997-10-31 00:00:00.000  1997-10-13 00:00:00.000    61.02  \n",
              "2    1997-11-24 00:00:00.000  1997-10-21 00:00:00.000    23.94  \n",
              "3    1998-02-12 00:00:00.000  1998-01-21 00:00:00.000    69.53  \n",
              "4    1998-04-27 00:00:00.000  1998-03-24 00:00:00.000    40.42  \n",
              "..                       ...                      ...      ...  \n",
              "825  1998-01-20 00:00:00.000  1997-12-31 00:00:00.000    23.79  \n",
              "826  1998-03-04 00:00:00.000  1998-02-13 00:00:00.000    12.04  \n",
              "827  1998-03-11 00:00:00.000  1998-03-03 00:00:00.000    26.29  \n",
              "828  1998-04-17 00:00:00.000  1998-04-17 00:00:00.000    20.31  \n",
              "829  1998-05-21 00:00:00.000  1998-05-01 00:00:00.000     8.72  \n",
              "\n",
              "[830 rows x 12 columns]"
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              "      <td>ALFKI</td>\n",
              "      <td>4</td>\n",
              "      <td>1997-10-03</td>\n",
              "      <td>1997-10-31 00:00:00.000</td>\n",
              "      <td>1997-10-13 00:00:00.000</td>\n",
              "      <td>61.02</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>ALFKI</td>\n",
              "      <td>Maria Anders</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>030-0074321</td>\n",
              "      <td>Obere Str. 57</td>\n",
              "      <td>10702</td>\n",
              "      <td>ALFKI</td>\n",
              "      <td>4</td>\n",
              "      <td>1997-10-13</td>\n",
              "      <td>1997-11-24 00:00:00.000</td>\n",
              "      <td>1997-10-21 00:00:00.000</td>\n",
              "      <td>23.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>ALFKI</td>\n",
              "      <td>Maria Anders</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>030-0074321</td>\n",
              "      <td>Obere Str. 57</td>\n",
              "      <td>10835</td>\n",
              "      <td>ALFKI</td>\n",
              "      <td>1</td>\n",
              "      <td>1998-01-15</td>\n",
              "      <td>1998-02-12 00:00:00.000</td>\n",
              "      <td>1998-01-21 00:00:00.000</td>\n",
              "      <td>69.53</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>ALFKI</td>\n",
              "      <td>Maria Anders</td>\n",
              "      <td>Sales Representative</td>\n",
              "      <td>030-0074321</td>\n",
              "      <td>Obere Str. 57</td>\n",
              "      <td>10952</td>\n",
              "      <td>ALFKI</td>\n",
              "      <td>1</td>\n",
              "      <td>1998-03-16</td>\n",
              "      <td>1998-04-27 00:00:00.000</td>\n",
              "      <td>1998-03-24 00:00:00.000</td>\n",
              "      <td>40.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>825</th>\n",
              "      <td>WOLZA</td>\n",
              "      <td>Zbyszek Piestrzeniewicz</td>\n",
              "      <td>Owner</td>\n",
              "      <td>(26) 642-7012</td>\n",
              "      <td>ul. Filtrowa 68</td>\n",
              "      <td>10792</td>\n",
              "      <td>WOLZA</td>\n",
              "      <td>1</td>\n",
              "      <td>1997-12-23</td>\n",
              "      <td>1998-01-20 00:00:00.000</td>\n",
              "      <td>1997-12-31 00:00:00.000</td>\n",
              "      <td>23.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>826</th>\n",
              "      <td>WOLZA</td>\n",
              "      <td>Zbyszek Piestrzeniewicz</td>\n",
              "      <td>Owner</td>\n",
              "      <td>(26) 642-7012</td>\n",
              "      <td>ul. Filtrowa 68</td>\n",
              "      <td>10870</td>\n",
              "      <td>WOLZA</td>\n",
              "      <td>5</td>\n",
              "      <td>1998-02-04</td>\n",
              "      <td>1998-03-04 00:00:00.000</td>\n",
              "      <td>1998-02-13 00:00:00.000</td>\n",
              "      <td>12.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>827</th>\n",
              "      <td>WOLZA</td>\n",
              "      <td>Zbyszek Piestrzeniewicz</td>\n",
              "      <td>Owner</td>\n",
              "      <td>(26) 642-7012</td>\n",
              "      <td>ul. Filtrowa 68</td>\n",
              "      <td>10906</td>\n",
              "      <td>WOLZA</td>\n",
              "      <td>4</td>\n",
              "      <td>1998-02-25</td>\n",
              "      <td>1998-03-11 00:00:00.000</td>\n",
              "      <td>1998-03-03 00:00:00.000</td>\n",
              "      <td>26.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>828</th>\n",
              "      <td>WOLZA</td>\n",
              "      <td>Zbyszek Piestrzeniewicz</td>\n",
              "      <td>Owner</td>\n",
              "      <td>(26) 642-7012</td>\n",
              "      <td>ul. Filtrowa 68</td>\n",
              "      <td>10998</td>\n",
              "      <td>WOLZA</td>\n",
              "      <td>8</td>\n",
              "      <td>1998-04-03</td>\n",
              "      <td>1998-04-17 00:00:00.000</td>\n",
              "      <td>1998-04-17 00:00:00.000</td>\n",
              "      <td>20.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>829</th>\n",
              "      <td>WOLZA</td>\n",
              "      <td>Zbyszek Piestrzeniewicz</td>\n",
              "      <td>Owner</td>\n",
              "      <td>(26) 642-7012</td>\n",
              "      <td>ul. Filtrowa 68</td>\n",
              "      <td>11044</td>\n",
              "      <td>WOLZA</td>\n",
              "      <td>4</td>\n",
              "      <td>1998-04-23</td>\n",
              "      <td>1998-05-21 00:00:00.000</td>\n",
              "      <td>1998-05-01 00:00:00.000</td>\n",
              "      <td>8.72</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>830 rows × 12 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3c6e24ea-a422-452c-ba04-03e76b7fa06b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
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              "\n",
              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
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              "\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-3c6e24ea-a422-452c-ba04-03e76b7fa06b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "a = df_o2.merge(df_od)\n",
        "# a\n",
        "a['Discountf'] = a['Quantity'] * a['UnitPrice'] * a['Discount']\n",
        "a['Revenue'] = a['Quantity'] * a['UnitPrice'] - a['Discountf']\n",
        "a2 = a.groupby('OrderID').agg({'Discountf':'sum', 'Revenue':'sum'}).reset_index()\n",
        "\n",
        "a2\n",
        "# b = a2.merge(df_p, left_on='ProductID', right_on='ProductID')\n",
        "# print(a2.groupby('CategoryName').Revenue.sum())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "-5QzpI0J1CWG",
        "outputId": "77f2481b-e3af-462f-8614-90a4e9839b6d"
      },
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     OrderID  Discountf   Revenue\n",
              "0      10266     18.240   346.560\n",
              "1      10271      0.000    48.000\n",
              "2      10279    117.000   351.000\n",
              "3      10292      0.000  1296.000\n",
              "4      10295      0.000   121.600\n",
              "..       ...        ...       ...\n",
              "132    11057      0.000    45.000\n",
              "133    11061      0.000   510.000\n",
              "134    11067      0.000    86.850\n",
              "135    11069      0.000   360.000\n",
              "136    11074     12.215   232.085\n",
              "\n",
              "[137 rows x 3 columns]"
            ],
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              "\n",
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              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
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              "\n",
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              "        vertical-align: top;\n",
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              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>OrderID</th>\n",
              "      <th>Discountf</th>\n",
              "      <th>Revenue</th>\n",
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              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>10266</td>\n",
              "      <td>18.240</td>\n",
              "      <td>346.560</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>10271</td>\n",
              "      <td>0.000</td>\n",
              "      <td>48.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>10279</td>\n",
              "      <td>117.000</td>\n",
              "      <td>351.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>10292</td>\n",
              "      <td>0.000</td>\n",
              "      <td>1296.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>10295</td>\n",
              "      <td>0.000</td>\n",
              "      <td>121.600</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
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              "    <tr>\n",
              "      <th>132</th>\n",
              "      <td>11057</td>\n",
              "      <td>0.000</td>\n",
              "      <td>45.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>133</th>\n",
              "      <td>11061</td>\n",
              "      <td>0.000</td>\n",
              "      <td>510.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>134</th>\n",
              "      <td>11067</td>\n",
              "      <td>0.000</td>\n",
              "      <td>86.850</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>135</th>\n",
              "      <td>11069</td>\n",
              "      <td>0.000</td>\n",
              "      <td>360.000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>11074</td>\n",
              "      <td>12.215</td>\n",
              "      <td>232.085</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>137 rows × 3 columns</p>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-97c0dae7-cac4-434b-925d-9a976edc3741')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
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              "\n",
              "    .colab-df-convert {\n",
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              "\n",
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              "      background-color: #E2EBFA;\n",
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              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
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              "\n",
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "\n",
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              "        const buttonEl =\n",
              "          document.querySelector('#df-97c0dae7-cac4-434b-925d-9a976edc3741 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-97c0dae7-cac4-434b-925d-9a976edc3741');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
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              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_od['Discount_fact'] = df_od['UnitPrice']*df_od['Quantity']*df_od['Discount']\n",
        "df_od['Clean_revenue'] = df_od['UnitPrice']*df_od['Quantity']*(1-df_od['Discount'])\n",
        "df_od.merge(df_p, left_on = 'ProductID', right_on = 'ProductID') \\\n",
        "    .groupby('CategoryName') \\\n",
        "    .Clean_revenue \\\n",
        "    .sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UPGKOB_n4Ahs",
        "outputId": "d75ef24c-068a-405e-d639-4635034c1612"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "CategoryName\n",
              "Beverages         267868.1800\n",
              "Condiments        106047.0850\n",
              "Confections       167357.2250\n",
              "Dairy Products    234507.2850\n",
              "Grains/Cereals     95744.5875\n",
              "Meat/Poultry      163022.3595\n",
              "Produce            99984.5800\n",
              "Seafood           131261.7375\n",
              "Name: Clean_revenue, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 47
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_e['Title']"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "edC0uajH4xXT",
        "outputId": "2de8e373-1bcc-43d4-f61d-da71a3262a31"
      },
      "execution_count": 51,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0        Sales Representative\n",
              "1       Vice President, Sales\n",
              "2        Sales Representative\n",
              "3        Sales Representative\n",
              "4               Sales Manager\n",
              "5        Sales Representative\n",
              "6        Sales Representative\n",
              "7    Inside Sales Coordinator\n",
              "8        Sales Representative\n",
              "Name: Title, dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 51
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        "a.head()"
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              "                   Title\n",
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        "b = pd.DataFrame({'Title':df_c['ContactTitle']})"
      ],
      "metadata": {
        "id": "lF6Qa5nt5F0A"
      },
      "execution_count": 54,
      "outputs": []
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    {
      "cell_type": "code",
      "source": [
        "pd.DataFrame({'Title':pd.concat([df_e['Title'],df_c['ContactTitle']])})"
      ],
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      "execution_count": 55,
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              "                        Title\n",
              "0        Sales Representative\n",
              "1       Vice President, Sales\n",
              "2        Sales Representative\n",
              "3        Sales Representative\n",
              "4               Sales Manager\n",
              "..                        ...\n",
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              "87              Sales Manager\n",
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              "[100 rows x 1 columns]"
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        "pd2 = pd.DataFrame({'Title':pd.concat([df_e['Title'],df_c['ContactTitle']])})\n",
        "pd2[pd2['Title'] == 'Sales Representative'].count()"
      ],
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      "execution_count": 59,
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          "data": {
            "text/plain": [
              "Title    23\n",
              "dtype: int64"
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          "metadata": {},
          "execution_count": 59
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    {
      "cell_type": "code",
      "source": [
        "df_o['OrderDate'] = df_o['OrderDate'].astype('datetime64')\n",
        "df_o['quarter'] = df_o['OrderDate'].dt.quarter\n",
        "df_o['year']=df_o['OrderDate'].dt.year\n",
        "df_o['week']=df_o['OrderDate'].dt.isocalendar().week"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_T4B2P-87Ks0",
        "outputId": "68ef81e5-baa0-400b-c018-b39d4e5ee8e5"
      },
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-70-a95e9af2aa06>:1: FutureWarning: Passing unit-less datetime64 dtype to .astype is deprecated and will raise in a future version. Pass 'datetime64[ns]' instead\n",
            "  df_o['OrderDate'] = df_o['OrderDate'].astype('datetime64')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_o['month']=df_o['OrderDate'].dt.month\n",
        "df_o.groupby(['year','month']).agg({'OrderID':'count'}).reset_index().sort_values('OrderID', ascending=False)"
      ],
      "metadata": {
        "colab": {
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        "id": "eeHyJbQx7PPN",
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      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    year  month  OrderID\n",
              "21  1998      4       74\n",
              "20  1998      3       73\n",
              "18  1998      1       55\n",
              "19  1998      2       54\n",
              "17  1997     12       48\n",
              "15  1997     10       38\n",
              "14  1997      9       37\n",
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              "12  1997      7       33\n",
              "6   1997      1       33\n",
              "13  1997      8       33\n",
              "10  1997      5       32\n",
              "9   1997      4       31\n",
              "5   1996     12       31\n",
              "8   1997      3       30\n",
              "11  1997      6       30\n",
              "7   1997      2       29\n",
              "3   1996     10       26\n",
              "1   1996      8       25\n",
              "4   1996     11       25\n",
              "2   1996      9       23\n",
              "0   1996      7       22\n",
              "22  1998      5       14"
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              "      <td>1996</td>\n",
              "      <td>12</td>\n",
              "      <td>31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1997</td>\n",
              "      <td>3</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1997</td>\n",
              "      <td>6</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>1997</td>\n",
              "      <td>2</td>\n",
              "      <td>29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1996</td>\n",
              "      <td>10</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1996</td>\n",
              "      <td>8</td>\n",
              "      <td>25</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
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              "      <td>11</td>\n",
              "      <td>25</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1996</td>\n",
              "      <td>9</td>\n",
              "      <td>23</td>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1996</td>\n",
              "      <td>7</td>\n",
              "      <td>22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1998</td>\n",
              "      <td>5</td>\n",
              "      <td>14</td>\n",
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          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_o[(df_o['week'] == 43) & (df_o['year'] == 1996)].OrderID.count()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZsrcHrEr7-w8",
        "outputId": "caa12bb2-e865-49a7-8abc-0ff3a7b6879b"
      },
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5"
            ]
          },
          "metadata": {},
          "execution_count": 81
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#изменяем тип данных дат на datetime64\n",
        "df_o['OrderDate'] = df_o['OrderDate'].astype('datetime64')\n",
        "df_o['RequiredDate'] = df_o['RequiredDate'].astype('datetime64')\n",
        "df_o['ShippedDate'] = df_o['ShippedDate'].astype('datetime64')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R57jx8gR75ix",
        "outputId": "651972d5-0240-425b-8d70-26206ac9b07a"
      },
      "execution_count": 82,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-82-17678791769d>:2: FutureWarning: Passing unit-less datetime64 dtype to .astype is deprecated and will raise in a future version. Pass 'datetime64[ns]' instead\n",
            "  df_o['OrderDate'] = df_o['OrderDate'].astype('datetime64')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#вычленяем год, месяц, день месяца, квартал и неделю с даты заказа\n",
        "df_o['Year'] = df_o['OrderDate'].dt.year\n",
        "df_o['Month'] = df_o['OrderDate'].dt.month\n",
        "df_o['Day'] = df_o['OrderDate'].dt.day\n",
        "df_o['quarter'] = df_o['OrderDate'].dt.quarter\n",
        "df_o['Week_ISO'] = df_o['OrderDate'].dt.isocalendar().week #dt.week -устарели"
      ],
      "metadata": {
        "id": "FnCKVKE6EEdT"
      },
      "execution_count": 83,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df_o.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 270
        },
        "id": "DLBh5mrBEH5W",
        "outputId": "11244416-2ff0-43d8-9385-90e7845327b7"
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      "execution_count": 84,
      "outputs": [
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          "data": {
            "text/plain": [
              "   OrderID CustomerID  EmployeeID  OrderDate RequiredDate ShippedDate  \\\n",
              "0    10248      VINET           5 1996-07-04   1996-08-01  1996-07-16   \n",
              "1    10249      TOMSP           6 1996-07-05   1996-08-16  1996-07-10   \n",
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              "4    10252      SUPRD           4 1996-07-09   1996-08-06  1996-07-11   \n",
              "\n",
              "   Freight  quarter  month  year  week  Year  Month  Day  Week_ISO  \n",
              "0    32.38        3      7  1996    27  1996      7    4        27  \n",
              "1    11.61        3      7  1996    27  1996      7    5        27  \n",
              "2    65.83        3      7  1996    28  1996      7    8        28  \n",
              "3    41.34        3      7  1996    28  1996      7    8        28  \n",
              "4    51.30        3      7  1996    28  1996      7    9        28  "
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#сначала сгруппируем кол-во заказов по месяцу и году\n",
        "df_for_pivoting = df_o.groupby(['Year','Month']).OrderID.count().reset_index()\n",
        "#проверяем\n",
        "df_for_pivoting.head()"
      ],
      "metadata": {
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          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "sC-8-CdmEMRc",
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      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Year  Month  OrderID\n",
              "0  1996      7       22\n",
              "1  1996      8       25\n",
              "2  1996      9       23\n",
              "3  1996     10       26\n",
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              "      <td>1996</td>\n",
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              "      <td>23</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1996</td>\n",
              "      <td>10</td>\n",
              "      <td>26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1996</td>\n",
              "      <td>11</td>\n",
              "      <td>25</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 86
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#теперь строим сводную таблицу\n",
        "df_for_pivoting.pivot_table(index = 'Year', columns = 'Month', values = 'OrderID').reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "f9DfTOGMEO_P",
        "outputId": "2513f70c-9ced-4af2-9128-f66a314ba74c"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Month  Year     1     2     3     4     5     6     7     8     9    10    11  \\\n",
              "0      1996   NaN   NaN   NaN   NaN   NaN   NaN  22.0  25.0  23.0  26.0  25.0   \n",
              "1      1997  33.0  29.0  30.0  31.0  32.0  30.0  33.0  33.0  37.0  38.0  34.0   \n",
              "2      1998  55.0  54.0  73.0  74.0  14.0   NaN   NaN   NaN   NaN   NaN   NaN   \n",
              "\n",
              "Month    12  \n",
              "0      31.0  \n",
              "1      48.0  \n",
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              "                                                     [key], {});\n",
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    {
      "cell_type": "code",
      "source": [
        "df_for_pivoting.pivot_table(index = 'Year', columns = 'Month', values = 'OrderID', fill_value= 0).reset_index()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
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        },
        "id": "lGtuHwy5Eft1",
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      },
      "execution_count": 89,
      "outputs": [
        {
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            "text/plain": [
              "Month  Year   1   2   3   4   5   6   7   8   9  10  11  12\n",
              "0      1996   0   0   0   0   0   0  22  25  23  26  25  31\n",
              "1      1997  33  29  30  31  32  30  33  33  37  38  34  48\n",
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              "      <th>0</th>\n",
              "      <td>1996</td>\n",
              "      <td>0</td>\n",
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              "      <td>0</td>\n",
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              "          const element = document.querySelector('#df-4d737019-8c2f-48df-9b37-c91f29db210c');\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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              "  "
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        "#можно было сделать группировку и агрегацию непосредственно в методе .pivot_table(). \n",
        "#для этого добавляем aggfunc='count'\n",
        "df_o.pivot_table(index = 'Year', columns = 'Month', values = 'OrderID', fill_value= 0, aggfunc='count').reset_index()"
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              "Month  Year   1   2   3   4   5   6   7   8   9  10  11  12\n",
              "0      1996   0   0   0   0   0   0  22  25  23  26  25  31\n",
              "1      1997  33  29  30  31  32  30  33  33  37  38  34  48\n",
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        "df_o.pivot_table(index = 'Year', columns = 'quarter', values = 'OrderID', fill_value= 0, aggfunc='count').reset_index()"
      ],
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            "text/plain": [
              "quarter  Year    1   2    3    4\n",
              "0        1996    0   0   70   82\n",
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    {
      "cell_type": "code",
      "source": [
        "df_sex = pd.DataFrame({'TitleOfCourtesy':['Ms.', 'Dr.', 'Mrs.', 'Mr.'],'Sex':['female','male','female','male']})\n",
        "a = df_e.merge(df_sex).merge(df_o[['OrderID','EmployeeID']])\n",
        "df_sex_OrderID = a[['Sex','OrderID']]\n",
        "b = df_sex_OrderID.groupby('Sex').OrderID.count().reset_index()\n",
        "b['OrderID'].max() - b['OrderID'].min()"
      ],
      "metadata": {
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      "execution_count": 92,
      "outputs": [
        {
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            "text/plain": [
              "276"
            ]
          },
          "metadata": {},
          "execution_count": 92
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        "df_o['EmployeeID'].hist()"
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      "execution_count": 93,
      "outputs": [
        {
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            "text/plain": [
              "<Axes: >"
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          "metadata": {},
          "execution_count": 93
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              "<Figure size 640x480 with 1 Axes>"
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            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# создание новых столбцов с номером недели\n",
        "df_o['OrderDate'] = pd.to_datetime(df_o['OrderDate'])\n",
        "df_o['Year'] = df_o['OrderDate'].dt.year\n",
        "df_o['Week'] = df_o['OrderDate'].dt.week\n",
        "\n",
        "df_od = df_od.merge(df_o[['OrderID', 'year', 'week']], on='OrderID')\n",
        "\n",
        "# оставление заказов только за 1997 и 1998 годы\n",
        "df_o = df_o.loc[(df_o['year'] == 1997) | (df_o['year'] == 1998)]\n",
        "df_od = df_od.loc[(df_od['year'] == 1997) | (df_od['year'] == 1998)]\n",
        "\n",
        "# рассчет чистой выручки по неделям\n",
        "revenue_97 = df_o.loc[df_o['Year'] == 1997].merge(df_od, on='OrderID').groupby('Week')['UnitPrice'].sum()\n",
        "revenue_98 = df_o.loc[df_o['Year'] == 1998].merge(df_od, on='OrderID').groupby('Week')['UnitPrice'].sum()\n",
        "\n",
        "# объединение данных по чистой выручке в один датафрейм\n",
        "revenue_df = pd.concat([revenue_97, revenue_98], axis=1)\n",
        "revenue_df.columns = ['Revenue_97', 'Revenue_98']\n",
        "\n",
        "# построение столбчатой диаграммы\n",
        "revenue_df.plot(kind='bar')\n",
        "\n",
        "# поиск номеров недель, на которых выручка в 1997 году была выше, чем в 1998\n",
        "weeks = list(revenue_df[revenue_df['Revenue_97'] > revenue_df['Revenue_98']].index)\n",
        "print(weeks)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 598
        },
        "id": "L0IX83RVIlTD",
        "outputId": "4dc1e76a-712c-46b9-d17e-45642c31626c"
      },
      "execution_count": 102,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "<ipython-input-102-2631c8b1f58a>:4: FutureWarning: Series.dt.weekofyear and Series.dt.week have been deprecated. Please use Series.dt.isocalendar().week instead.\n",
            "  df_o['Week'] = df_o['OrderDate'].dt.week\n",
            "<ipython-input-102-2631c8b1f58a>:13: FutureWarning: Passing 'suffixes' which cause duplicate columns {'week_y', 'year_y'} in the result is deprecated and will raise a MergeError in a future version.\n",
            "  revenue_97 = df_o.loc[df_o['Year'] == 1997].merge(df_od, on='OrderID').groupby('Week')['UnitPrice'].sum()\n",
            "<ipython-input-102-2631c8b1f58a>:14: FutureWarning: Passing 'suffixes' which cause duplicate columns {'week_y', 'year_y'} in the result is deprecated and will raise a MergeError in a future version.\n",
            "  revenue_98 = df_o.loc[df_o['Year'] == 1998].merge(df_od, on='OrderID').groupby('Week')['UnitPrice'].sum()\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[1, 17]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}