stock analysis.ipynb

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      "source": [
        "#libraries\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data=pd.read_csv('/content/train.csv')"
      ],
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      "execution_count": 2,
      "outputs": []
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        "data.head()"
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      "outputs": [
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          "data": {
            "text/plain": [
              "   Row ID        Order ID  Order Date   Ship Date       Ship Mode Customer ID  \\\n",
              "0       1  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
              "1       2  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
              "2       3  CA-2017-138688  12/06/2017  16/06/2017    Second Class    DV-13045   \n",
              "3       4  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
              "4       5  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
              "\n",
              "     Customer Name    Segment        Country             City       State  \\\n",
              "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
              "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
              "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
              "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
              "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
              "\n",
              "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
              "0      42420.0  South  FUR-BO-10001798        Furniture    Bookcases   \n",
              "1      42420.0  South  FUR-CH-10000454        Furniture       Chairs   \n",
              "2      90036.0   West  OFF-LA-10000240  Office Supplies       Labels   \n",
              "3      33311.0  South  FUR-TA-10000577        Furniture       Tables   \n",
              "4      33311.0  South  OFF-ST-10000760  Office Supplies      Storage   \n",
              "\n",
              "                                        Product Name     Sales  \n",
              "0                  Bush Somerset Collection Bookcase  261.9600  \n",
              "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400  \n",
              "2  Self-Adhesive Address Labels for Typewriters b...   14.6200  \n",
              "3      Bretford CR4500 Series Slim Rectangular Table  957.5775  \n",
              "4                     Eldon Fold 'N Roll Cart System   22.3680  "
            ],
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>CA-2017-152156</td>\n",
              "      <td>08/11/2017</td>\n",
              "      <td>11/11/2017</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
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              "      <td>42420.0</td>\n",
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              "      <td>CA-2017-152156</td>\n",
              "      <td>08/11/2017</td>\n",
              "      <td>11/11/2017</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
              "      <td>Consumer</td>\n",
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              "      <td>Kentucky</td>\n",
              "      <td>42420.0</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-CH-10000454</td>\n",
              "      <td>Furniture</td>\n",
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              "      <td>731.9400</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>CA-2017-138688</td>\n",
              "      <td>12/06/2017</td>\n",
              "      <td>16/06/2017</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>DV-13045</td>\n",
              "      <td>Darrin Van Huff</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90036.0</td>\n",
              "      <td>West</td>\n",
              "      <td>OFF-LA-10000240</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Labels</td>\n",
              "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
              "      <td>14.6200</td>\n",
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              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>US-2016-108966</td>\n",
              "      <td>11/10/2016</td>\n",
              "      <td>18/10/2016</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>SO-20335</td>\n",
              "      <td>Sean O'Donnell</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Fort Lauderdale</td>\n",
              "      <td>Florida</td>\n",
              "      <td>33311.0</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-TA-10000577</td>\n",
              "      <td>Furniture</td>\n",
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              "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
              "      <td>957.5775</td>\n",
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              "      <td>Standard Class</td>\n",
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              "      <td>Sean O'Donnell</td>\n",
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              "      <td>United States</td>\n",
              "      <td>Fort Lauderdale</td>\n",
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              "      <td>33311.0</td>\n",
              "      <td>South</td>\n",
              "      <td>OFF-ST-10000760</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Storage</td>\n",
              "      <td>Eldon Fold 'N Roll Cart System</td>\n",
              "      <td>22.3680</td>\n",
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    \"08/07/2017\",\n          \"10/08/2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ship Date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1092,\n        \"samples\": [\n          \"26/11/2017\",\n          \"17/04/2015\",\n          \"13/07/2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ship Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Standard Class\",\n          \"Same Day\",\n          \"Second Class\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 753,\n        \"samples\": [\n          \"MF-17665\",\n          \"FH-14350\",\n          \"JH-15430\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 753,\n        \"samples\": [\n          \"Maureen Fritzler\",\n          \"Fred Harton\",\n          \"Jennifer Halladay\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Consumer\",\n          \"Corporate\",\n          \"Home Office\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 420,\n        \"samples\": [\n          \"Mount Vernon\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"New Mexico\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Postal Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31816.47621496938,\n        \"min\": 1040.0,\n        \"max\": 99207.0,\n        \"num_unique_values\": 509,\n        \"samples\": [\n          90604.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"West\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1703,\n        \"samples\": [\n          \"OFF-AR-10003504\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Furniture\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sub-Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Bookcases\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1692,\n        \"samples\": [\n          \"Surelock Post Binders\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 637.1944907933839,\n        \"min\": 0.444,\n        \"max\": 22638.48,\n        \"num_unique_values\": 3494,\n        \"samples\": [\n          36.51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "44D3aof_sm6N",
        "outputId": "12f296b7-83e6-4287-cab6-28b5e059d6ac"
      },
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4822 entries, 0 to 4821\n",
            "Data columns (total 18 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Row ID         4822 non-null   int64  \n",
            " 1   Order ID       4822 non-null   object \n",
            " 2   Order Date     4822 non-null   object \n",
            " 3   Ship Date      4822 non-null   object \n",
            " 4   Ship Mode      4822 non-null   object \n",
            " 5   Customer ID    4822 non-null   object \n",
            " 6   Customer Name  4822 non-null   object \n",
            " 7   Segment        4822 non-null   object \n",
            " 8   Country        4822 non-null   object \n",
            " 9   City           4822 non-null   object \n",
            " 10  State          4822 non-null   object \n",
            " 11  Postal Code    4821 non-null   float64\n",
            " 12  Region         4822 non-null   object \n",
            " 13  Product ID     4822 non-null   object \n",
            " 14  Category       4822 non-null   object \n",
            " 15  Sub-Category   4822 non-null   object \n",
            " 16  Product Name   4822 non-null   object \n",
            " 17  Sales          4822 non-null   float64\n",
            "dtypes: float64(2), int64(1), object(15)\n",
            "memory usage: 678.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHyolWTHs3uV",
        "outputId": "a844b0c4-36f4-4d0a-ae82-4d3db5a0797b"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4822, 18)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.duplicated().sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aoYV8urZtCfF",
        "outputId": "2a09079c-3f9f-4c00-b2b7-b4d0c1608813"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#filling  0 to the empty spaces\n",
        "data['Postal Code'].fillna(0,inplace=True)\n",
        "#changing from float to integer\n",
        "data['Postal Code']=data['Postal Code'].astype(int)\n",
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EebzlfMCtHI2",
        "outputId": "9feaafd8-9996-492c-fa95-bed372fce4cf"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 4822 entries, 0 to 4821\n",
            "Data columns (total 18 columns):\n",
            " #   Column         Non-Null Count  Dtype  \n",
            "---  ------         --------------  -----  \n",
            " 0   Row ID         4822 non-null   int64  \n",
            " 1   Order ID       4822 non-null   object \n",
            " 2   Order Date     4822 non-null   object \n",
            " 3   Ship Date      4822 non-null   object \n",
            " 4   Ship Mode      4822 non-null   object \n",
            " 5   Customer ID    4822 non-null   object \n",
            " 6   Customer Name  4822 non-null   object \n",
            " 7   Segment        4822 non-null   object \n",
            " 8   Country        4822 non-null   object \n",
            " 9   City           4822 non-null   object \n",
            " 10  State          4822 non-null   object \n",
            " 11  Postal Code    4822 non-null   int64  \n",
            " 12  Region         4822 non-null   object \n",
            " 13  Product ID     4822 non-null   object \n",
            " 14  Category       4822 non-null   object \n",
            " 15  Sub-Category   4822 non-null   object \n",
            " 16  Product Name   4822 non-null   object \n",
            " 17  Sales          4822 non-null   float64\n",
            "dtypes: float64(1), int64(2), object(15)\n",
            "memory usage: 678.2+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "1owUeOhbtnVS",
        "outputId": "2a67cfad-69c4-4821-f13e-e2bc122f7f59"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "            Row ID   Postal Code         Sales\n",
              "count  4822.000000   4822.000000   4822.000000\n",
              "mean   2411.500000  54896.480091    229.168572\n",
              "std    1392.135829  31823.001384    637.194491\n",
              "min       1.000000      0.000000      0.444000\n",
              "25%    1206.250000  22304.000000     16.768000\n",
              "50%    2411.500000  55122.000000     52.596000\n",
              "75%    3616.750000  90004.000000    212.033500\n",
              "max    4822.000000  99207.000000  22638.480000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-75d988c5-6637-45cf-a65b-03a25df7d4a0\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
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              "      <th></th>\n",
              "      <th>Row ID</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>4822.000000</td>\n",
              "      <td>4822.000000</td>\n",
              "      <td>4822.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>2411.500000</td>\n",
              "      <td>54896.480091</td>\n",
              "      <td>229.168572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>1392.135829</td>\n",
              "      <td>31823.001384</td>\n",
              "      <td>637.194491</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.444000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>1206.250000</td>\n",
              "      <td>22304.000000</td>\n",
              "      <td>16.768000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
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              "      <td>55122.000000</td>\n",
              "      <td>52.596000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>3616.750000</td>\n",
              "      <td>90004.000000</td>\n",
              "      <td>212.033500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>4822.000000</td>\n",
              "      <td>99207.000000</td>\n",
              "      <td>22638.480000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
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              "\n",
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              "    async function quickchart(key) {\n",
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              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"Row ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1737.5673399504176,\n        \"min\": 1.0,\n        \"max\": 4822.0,\n        \"num_unique_values\": 6,\n        \"samples\": [\n          4822.0,\n          2411.5,\n          3616.75\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Postal Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 36823.42561566669,\n        \"min\": 0.0,\n        \"max\": 99207.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          54896.48009124844,\n          55122.0,\n          4822.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7873.57150419383,\n        \"min\": 0.444,\n        \"max\": 22638.48,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          229.16857169224392,\n          52.596000000000004,\n          4822.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# ***DATA CLEANING***"
      ],
      "metadata": {
        "id": "lkRQpvCrv4CQ"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**checking for duplicates**"
      ],
      "metadata": {
        "id": "l-5Kz0qXOIhm"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "if data.duplicated().sum()>0:\n",
        "  print(\"There are duplicates\")\n",
        "else:\n",
        "  print(\"There are no duplicates\")"
      ],
      "metadata": {
        "id": "ZHxACHs3v1pU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "37a44820-0100-475a-fbbb-3262d2cd68c6"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are no duplicates\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.duplicated(keep=False).sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YOkrBVreOYIf",
        "outputId": "d9bddde9-03d8-47ad-9858-f8ca32d70d3c"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **EXPLORATORY DATA ANALYSIS**"
      ],
      "metadata": {
        "id": "oN4muLGyPwJT"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**CUSTOMER ANALYSIS**"
      ],
      "metadata": {
        "id": "V064slH4QDWI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "K49Q9BLJPk1V",
        "outputId": "e07ff22d-a495-4fa7-d1b5-b0d25207c377"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Row ID        Order ID  Order Date   Ship Date     Ship Mode Customer ID  \\\n",
              "0       1  CA-2017-152156  08/11/2017  11/11/2017  Second Class    CG-12520   \n",
              "1       2  CA-2017-152156  08/11/2017  11/11/2017  Second Class    CG-12520   \n",
              "2       3  CA-2017-138688  12/06/2017  16/06/2017  Second Class    DV-13045   \n",
              "\n",
              "     Customer Name    Segment        Country         City       State  \\\n",
              "0      Claire Gute   Consumer  United States    Henderson    Kentucky   \n",
              "1      Claire Gute   Consumer  United States    Henderson    Kentucky   \n",
              "2  Darrin Van Huff  Corporate  United States  Los Angeles  California   \n",
              "\n",
              "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
              "0        42420  South  FUR-BO-10001798        Furniture    Bookcases   \n",
              "1        42420  South  FUR-CH-10000454        Furniture       Chairs   \n",
              "2        90036   West  OFF-LA-10000240  Office Supplies       Labels   \n",
              "\n",
              "                                        Product Name   Sales  \n",
              "0                  Bush Somerset Collection Bookcase  261.96  \n",
              "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.94  \n",
              "2  Self-Adhesive Address Labels for Typewriters b...   14.62  "
            ],
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              "  <div id=\"df-03114e9c-4bda-4a56-b22c-0ef5fb946740\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
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              "    .dataframe thead th {\n",
              "        text-align: right;\n",
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>CA-2017-152156</td>\n",
              "      <td>08/11/2017</td>\n",
              "      <td>11/11/2017</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
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              "      <td>Henderson</td>\n",
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              "      <td>261.96</td>\n",
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              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>CA-2017-152156</td>\n",
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              "      <td>11/11/2017</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Henderson</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>42420</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-CH-10000454</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
              "      <td>731.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>CA-2017-138688</td>\n",
              "      <td>12/06/2017</td>\n",
              "      <td>16/06/2017</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>DV-13045</td>\n",
              "      <td>Darrin Van Huff</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90036</td>\n",
              "      <td>West</td>\n",
              "      <td>OFF-LA-10000240</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Labels</td>\n",
              "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
              "      <td>14.62</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-03114e9c-4bda-4a56-b22c-0ef5fb946740')\"\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",
              "      display:flex;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-03114e9c-4bda-4a56-b22c-0ef5fb946740 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-03114e9c-4bda-4a56-b22c-0ef5fb946740');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-b8c043ff-bb67-476b-bc52-1bfa5acf9190\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b8c043ff-bb67-476b-bc52-1bfa5acf9190')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
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              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
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              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
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              "    20% {\n",
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              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b8c043ff-bb67-476b-bc52-1bfa5acf9190 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4822,\n  \"fields\": [\n    {\n      \"column\": \"Row ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1392,\n        \"min\": 1,\n        \"max\": 4822,\n        \"num_unique_values\": 4822,\n        \"samples\": [\n          4345,\n          1222,\n          1903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Order ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2387,\n        \"samples\": [\n          \"CA-2016-153416\",\n          \"US-2017-100839\",\n          \"CA-2016-130995\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Order Date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1004,\n        \"samples\": [\n          \"29/01/2018\",\n          \"08/07/2017\",\n          \"10/08/2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ship Date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1092,\n        \"samples\": [\n          \"26/11/2017\",\n          \"17/04/2015\",\n          \"13/07/2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ship Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Standard Class\",\n          \"Same Day\",\n          \"Second Class\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 753,\n        \"samples\": [\n          \"MF-17665\",\n          \"FH-14350\",\n          \"JH-15430\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 753,\n        \"samples\": [\n          \"Maureen Fritzler\",\n          \"Fred Harton\",\n          \"Jennifer Halladay\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Consumer\",\n          \"Corporate\",\n          \"Home Office\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 420,\n        \"samples\": [\n          \"Mount Vernon\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"New Mexico\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Postal Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31823,\n        \"min\": 0,\n        \"max\": 99207,\n        \"num_unique_values\": 510,\n        \"samples\": [\n          2920\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"West\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1703,\n        \"samples\": [\n          \"OFF-AR-10003504\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Furniture\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sub-Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Bookcases\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1692,\n        \"samples\": [\n          \"Surelock Post Binders\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 637.1944907933839,\n        \"min\": 0.444,\n        \"max\": 22638.48,\n        \"num_unique_values\": 3494,\n        \"samples\": [\n          36.51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#types of customers\n",
        "types_of_customers= data['Segment'].unique()\n",
        "print(types_of_customers)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "xxqK-ZZfQViW",
        "outputId": "b6060f5f-958f-4772-d32f-9e302b4c3f97"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['Consumer' 'Corporate' 'Home Office']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#number of customers in each segment\n",
        "number_of_customers=data['Segment'].value_counts().reset_index()\n",
        "#renaming the columns\n",
        "number_of_customers.columns=['Segment','total sales']\n",
        "#number_of_customers = number_of_customers.rename(columns={'index': 'Segment', 'Segment': 'Number of Customers'})\n",
        "number_of_customers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 143
        },
        "id": "TenWxOwkQ7Of",
        "outputId": "1b0cc035-504a-4fc7-f4ea-64591f266541"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Segment  total sales\n",
              "0     Consumer         2571\n",
              "1    Corporate         1360\n",
              "2  Home Office          891"
            ],
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              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "\n",
              "  <div id=\"id_5cc9039f-1139-47db-9908-7540379d770b\">\n",
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              "        height: 32px;\n",
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              "\n",
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              "        box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "        fill: #FFFFFF;\n",
              "      }\n",
              "    </style>\n",
              "    <button class=\"colab-df-generate\" onclick=\"generateWithVariable('number_of_customers')\"\n",
              "            title=\"Generate code using this dataframe.\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "    </button>\n",
              "    <script>\n",
              "      (() => {\n",
              "      const buttonEl =\n",
              "        document.querySelector('#id_5cc9039f-1139-47db-9908-7540379d770b button.colab-df-generate');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      buttonEl.onclick = () => {\n",
              "        google.colab.notebook.generateWithVariable('number_of_customers');\n",
              "      }\n",
              "      })();\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "number_of_customers",
              "summary": "{\n  \"name\": \"number_of_customers\",\n  \"rows\": 3,\n  \"fields\": [\n    {\n      \"column\": \"Segment\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Consumer\",\n          \"Corporate\",\n          \"Home Office\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"total sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 866,\n        \"min\": 891,\n        \"max\": 2571,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          2571,\n          1360,\n          891\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#plotting a pie chart\n",
        "plt.pie(number_of_customers['total sales'],labels=number_of_customers['Segment'],autopct='%1.1f%%',shadow=True)\n",
        "plt.title('Number of Customers in each Segment')\n",
        "plt.show()\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 428
        },
        "id": "xcqrd8ciRwSS",
        "outputId": "35dc0270-ddc7-445e-f088-aee25b53ddf4"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **sales per costomers segment**"
      ],
      "metadata": {
        "id": "pZGrV4NoUTRN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sales_per_category= data.groupby(\"Segment\")[\"Sales\"].sum().reset_index()\n",
        "sales_per_category = sales_per_category.rename(columns={\"customer type\": \"segment\", \"Total sales\": 'sales'})\n",
        "print(sales_per_category)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GKH77mLIUZoP",
        "outputId": "cebadd49-842a-41fd-f600-45c0e2c4a67c"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "       Segment        Sales\n",
            "0     Consumer  560511.2539\n",
            "1    Corporate  314387.0533\n",
            "2  Home Office  230152.5455\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.bar(sales_per_category['Segment'],sales_per_category['Sales'])\n",
        "plt.xlabel('Segment')\n",
        "plt.ylabel('Sales')\n",
        "plt.title('Sales per Customer Segment')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "S0jx6sY8W1od",
        "outputId": "dc917457-7f50-4420-a04d-fa63f6834341"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Costomer layalty**"
      ],
      "metadata": {
        "id": "-2Kf70jUaxnb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 389
        },
        "id": "sFkEAupvbQtY",
        "outputId": "25bc4379-4c00-40b4-9f42-4384092ead3f"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Row ID        Order ID  Order Date   Ship Date     Ship Mode Customer ID  \\\n",
              "0       1  CA-2017-152156  08/11/2017  11/11/2017  Second Class    CG-12520   \n",
              "1       2  CA-2017-152156  08/11/2017  11/11/2017  Second Class    CG-12520   \n",
              "2       3  CA-2017-138688  12/06/2017  16/06/2017  Second Class    DV-13045   \n",
              "\n",
              "     Customer Name    Segment        Country         City       State  \\\n",
              "0      Claire Gute   Consumer  United States    Henderson    Kentucky   \n",
              "1      Claire Gute   Consumer  United States    Henderson    Kentucky   \n",
              "2  Darrin Van Huff  Corporate  United States  Los Angeles  California   \n",
              "\n",
              "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
              "0        42420  South  FUR-BO-10001798        Furniture    Bookcases   \n",
              "1        42420  South  FUR-CH-10000454        Furniture       Chairs   \n",
              "2        90036   West  OFF-LA-10000240  Office Supplies       Labels   \n",
              "\n",
              "                                        Product Name   Sales  \n",
              "0                  Bush Somerset Collection Bookcase  261.96  \n",
              "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.94  \n",
              "2  Self-Adhesive Address Labels for Typewriters b...   14.62  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-de2d1794-c06d-4c79-a631-4a2c02372d22\" class=\"colab-df-container\">\n",
              "    <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Row ID</th>\n",
              "      <th>Order ID</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Ship Date</th>\n",
              "      <th>Ship Mode</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Customer Name</th>\n",
              "      <th>Segment</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>State</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Region</th>\n",
              "      <th>Product ID</th>\n",
              "      <th>Category</th>\n",
              "      <th>Sub-Category</th>\n",
              "      <th>Product Name</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>CA-2017-152156</td>\n",
              "      <td>08/11/2017</td>\n",
              "      <td>11/11/2017</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Henderson</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>42420</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-BO-10001798</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Bookcases</td>\n",
              "      <td>Bush Somerset Collection Bookcase</td>\n",
              "      <td>261.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>CA-2017-152156</td>\n",
              "      <td>08/11/2017</td>\n",
              "      <td>11/11/2017</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Henderson</td>\n",
              "      <td>Kentucky</td>\n",
              "      <td>42420</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-CH-10000454</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Chairs</td>\n",
              "      <td>Hon Deluxe Fabric Upholstered Stacking Chairs,...</td>\n",
              "      <td>731.94</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>CA-2017-138688</td>\n",
              "      <td>12/06/2017</td>\n",
              "      <td>16/06/2017</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>DV-13045</td>\n",
              "      <td>Darrin Van Huff</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
              "      <td>California</td>\n",
              "      <td>90036</td>\n",
              "      <td>West</td>\n",
              "      <td>OFF-LA-10000240</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Labels</td>\n",
              "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
              "      <td>14.62</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "\n",
              "  .colab-df-quickchart {\n",
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              "\n",
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              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4822,\n  \"fields\": [\n    {\n      \"column\": \"Row ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1392,\n        \"min\": 1,\n        \"max\": 4822,\n        \"num_unique_values\": 4822,\n        \"samples\": [\n          4345,\n          1222,\n          1903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Order ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2387,\n        \"samples\": [\n          \"CA-2016-153416\",\n          \"US-2017-100839\",\n          \"CA-2016-130995\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Order Date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1004,\n        \"samples\": [\n          \"29/01/2018\",\n          \"08/07/2017\",\n          \"10/08/2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ship Date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1092,\n        \"samples\": [\n          \"26/11/2017\",\n          \"17/04/2015\",\n          \"13/07/2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ship Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Standard Class\",\n          \"Same Day\",\n          \"Second Class\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 753,\n        \"samples\": [\n          \"MF-17665\",\n          \"FH-14350\",\n          \"JH-15430\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 753,\n        \"samples\": [\n          \"Maureen Fritzler\",\n          \"Fred Harton\",\n          \"Jennifer Halladay\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Consumer\",\n          \"Corporate\",\n          \"Home Office\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 420,\n        \"samples\": [\n          \"Mount Vernon\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"New Mexico\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Postal Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31823,\n        \"min\": 0,\n        \"max\": 99207,\n        \"num_unique_values\": 510,\n        \"samples\": [\n          2920\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"West\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1703,\n        \"samples\": [\n          \"OFF-AR-10003504\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Furniture\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sub-Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Bookcases\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1692,\n        \"samples\": [\n          \"Surelock Post Binders\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 637.1944907933839,\n        \"min\": 0.444,\n        \"max\": 22638.48,\n        \"num_unique_values\": 3494,\n        \"samples\": [\n          36.51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "customer_order_freq=data.groupby(['Customer ID','Customer Name','Segment'])['Order ID'].count().reset_index()\n",
        "customer_order_freq.rename(columns={'Order ID': 'Total Orders'}, inplace=True)\n",
        "repeat_costomers=customer_order_freq[customer_order_freq['Total Orders']>=1]\n",
        "sorted_repeat_customers=repeat_costomers.sort_values(by=\"Total Orders\",ascending=False)\n",
        "print(sorted_repeat_customers.head(10).reset_index(drop=True))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "W6-HNTdXZwbn",
        "outputId": "00f24cef-7858-4780-e75c-587619bbc4be"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  Customer ID     Customer Name      Segment  Total Orders\n",
            "0    AP-10915    Arthur Prichep     Consumer            23\n",
            "1    BM-11650        Brian Moss    Corporate            23\n",
            "2    KL-16645      Ken Lonsdale     Consumer            22\n",
            "3    JE-15745        Joel Eaton     Consumer            21\n",
            "4    EP-13915        Emily Phan     Consumer            21\n",
            "5    ZC-21910  Zuschuss Carroll     Consumer            21\n",
            "6    PP-18955        Paul Prost  Home Office            21\n",
            "7    IM-15070      Irene Maddox     Consumer            20\n",
            "8    SP-20620  Stefania Perrino    Corporate            20\n",
            "9    SC-20095      Sanjit Chand     Consumer            19\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Ranking Costomers according to sales**"
      ],
      "metadata": {
        "id": "GgKtAl_Am4Hp"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "customer_sales=data.groupby(['Customer ID','Customer Name','Segment'])['Sales'].sum().reset_index()\n",
        "customer_sales"
      ],
      "metadata": {
        "id": "fYnUOg6HiFbV",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 423
        },
        "outputId": "f98a9256-e756-4602-b2cd-f50877583d7d"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Customer ID       Customer Name      Segment     Sales\n",
              "0      AA-10315          Alex Avila     Consumer  1127.988\n",
              "1      AA-10375        Allen Armold     Consumer   764.698\n",
              "2      AA-10480        Andrew Allen     Consumer   632.532\n",
              "3      AA-10645       Anna Andreadi     Consumer  2483.215\n",
              "4      AB-10060     Adam Bellavance  Home Office  4821.552\n",
              "..          ...                 ...          ...       ...\n",
              "748    XP-21865        Xylona Preis     Consumer   689.872\n",
              "749    YC-21895      Yoseph Carroll    Corporate  4332.166\n",
              "750    YS-21880       Yana Sorensen    Corporate  1009.572\n",
              "751    ZC-21910    Zuschuss Carroll     Consumer  5854.869\n",
              "752    ZD-21925  Zuschuss Donatelli     Consumer  1101.424\n",
              "\n",
              "[753 rows x 4 columns]"
            ],
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>AA-10315</td>\n",
              "      <td>Alex Avila</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>1127.988</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>AA-10375</td>\n",
              "      <td>Allen Armold</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>764.698</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>AA-10480</td>\n",
              "      <td>Andrew Allen</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>632.532</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>AA-10645</td>\n",
              "      <td>Anna Andreadi</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>2483.215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>AB-10060</td>\n",
              "      <td>Adam Bellavance</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>4821.552</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>748</th>\n",
              "      <td>XP-21865</td>\n",
              "      <td>Xylona Preis</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>689.872</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>749</th>\n",
              "      <td>YC-21895</td>\n",
              "      <td>Yoseph Carroll</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>4332.166</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>750</th>\n",
              "      <td>YS-21880</td>\n",
              "      <td>Yana Sorensen</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>1009.572</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>751</th>\n",
              "      <td>ZC-21910</td>\n",
              "      <td>Zuschuss Carroll</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>5854.869</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>752</th>\n",
              "      <td>ZD-21925</td>\n",
              "      <td>Zuschuss Donatelli</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>1101.424</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>753 rows × 4 columns</p>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "customer_sales",
              "summary": "{\n  \"name\": \"customer_sales\",\n  \"rows\": 753,\n  \"fields\": [\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 753,\n        \"samples\": [\n          \"TM-21490\",\n          \"SF-20065\",\n          \"JM-15535\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Name\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 753,\n        \"samples\": [\n          \"Tony Molinari\",\n          \"Sandra Flanagan\",\n          \"Jessica Myrick\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Consumer\",\n          \"Home Office\",\n          \"Corporate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1907.0111231793599,\n        \"min\": 4.616,\n        \"max\": 24195.646,\n        \"num_unique_values\": 753,\n        \"samples\": [\n          325.86,\n          84.492,\n          728.877\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **top sales**"
      ],
      "metadata": {
        "id": "yuPCEp6fwRi1"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.sort_values(by='Sales', ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 894
        },
        "id": "O5mSRp3iwQUP",
        "outputId": "fc1c5a59-b6a8-411c-aaad-0fc698f9ad43"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Row ID        Order ID  Order Date   Ship Date       Ship Mode  \\\n",
              "2697    2698  CA-2015-145317  18/03/2015  23/03/2015  Standard Class   \n",
              "2623    2624  CA-2018-127180  22/10/2018  24/10/2018     First Class   \n",
              "4190    4191  CA-2018-166709  17/11/2018  22/11/2018  Standard Class   \n",
              "4098    4099  CA-2015-116904  23/09/2015  28/09/2015  Standard Class   \n",
              "4277    4278  US-2017-107440  16/04/2017  20/04/2017  Standard Class   \n",
              "...      ...             ...         ...         ...             ...   \n",
              "976      977  US-2018-100209  09/07/2018  15/07/2018  Standard Class   \n",
              "2761    2762  CA-2018-126536  12/10/2018  14/10/2018     First Class   \n",
              "2106    2107  US-2015-152723  26/09/2015  26/09/2015        Same Day   \n",
              "4711    4712  CA-2015-112403  31/03/2015  31/03/2015        Same Day   \n",
              "4101    4102  US-2018-102288  19/06/2018  23/06/2018  Standard Class   \n",
              "\n",
              "     Customer ID     Customer Name      Segment        Country           City  \\\n",
              "2697    SM-20320       Sean Miller  Home Office  United States   Jacksonville   \n",
              "2623    TA-21385      Tom Ashbrook  Home Office  United States  New York City   \n",
              "4190    HL-15040      Hunter Lopez     Consumer  United States         Newark   \n",
              "4098    SC-20095      Sanjit Chand     Consumer  United States    Minneapolis   \n",
              "4277    BS-11365      Bill Shonely    Corporate  United States       Lakewood   \n",
              "...          ...               ...          ...            ...            ...   \n",
              "976     TD-20995     Tamara Dahlen     Consumer  United States       Portland   \n",
              "2761    NK-18490      Neil Knudson  Home Office  United States  San Francisco   \n",
              "2106    HG-14965     Henry Goldwyn    Corporate  United States       Mesquite   \n",
              "4711    JO-15280     Jas O'Carroll     Consumer  United States   Philadelphia   \n",
              "4101    ZC-21910  Zuschuss Carroll     Consumer  United States        Houston   \n",
              "\n",
              "             State  Postal Code   Region       Product ID         Category  \\\n",
              "2697       Florida        32216    South  TEC-MA-10002412       Technology   \n",
              "2623      New York        10024     East  TEC-CO-10004722       Technology   \n",
              "4190      Delaware        19711     East  TEC-CO-10004722       Technology   \n",
              "4098     Minnesota        55407  Central  OFF-BI-10001120  Office Supplies   \n",
              "4277    New Jersey         8701     East  TEC-MA-10001047       Technology   \n",
              "...            ...          ...      ...              ...              ...   \n",
              "976         Oregon        97206     West  OFF-BI-10002012  Office Supplies   \n",
              "2761    California        94110     West  TEC-AC-10003709       Technology   \n",
              "2106         Texas        75150  Central  OFF-BI-10003460  Office Supplies   \n",
              "4711  Pennsylvania        19120     East  OFF-BI-10003529  Office Supplies   \n",
              "4101         Texas        77095  Central  OFF-AP-10002906  Office Supplies   \n",
              "\n",
              "     Sub-Category                                       Product Name  \\\n",
              "2697     Machines  Cisco TelePresence System EX90 Videoconferenci...   \n",
              "2623      Copiers              Canon imageCLASS 2200 Advanced Copier   \n",
              "4190      Copiers              Canon imageCLASS 2200 Advanced Copier   \n",
              "4098      Binders               Ibico EPK-21 Electric Binding System   \n",
              "4277     Machines   3D Systems Cube Printer, 2nd Generation, Magenta   \n",
              "...           ...                                                ...   \n",
              "976       Binders            Wilson Jones Easy Flow II Sheet Lifters   \n",
              "2761  Accessories                          Maxell 4.7GB DVD-R 5/Pack   \n",
              "2106      Binders                                  Acco 3-Hole Punch   \n",
              "4711      Binders                      Avery Round Ring Poly Binders   \n",
              "4101   Appliances  Hoover Replacement Belt for Commercial Guardsm...   \n",
              "\n",
              "          Sales  \n",
              "2697  22638.480  \n",
              "2623  11199.968  \n",
              "4190  10499.970  \n",
              "4098   9449.950  \n",
              "4277   9099.930  \n",
              "...         ...  \n",
              "976       1.080  \n",
              "2761      0.990  \n",
              "2106      0.876  \n",
              "4711      0.852  \n",
              "4101      0.444  \n",
              "\n",
              "[4822 rows x 18 columns]"
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              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Row ID</th>\n",
              "      <th>Order ID</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Ship Date</th>\n",
              "      <th>Ship Mode</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Customer Name</th>\n",
              "      <th>Segment</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>State</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Region</th>\n",
              "      <th>Product ID</th>\n",
              "      <th>Category</th>\n",
              "      <th>Sub-Category</th>\n",
              "      <th>Product Name</th>\n",
              "      <th>Sales</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2697</th>\n",
              "      <td>2698</td>\n",
              "      <td>CA-2015-145317</td>\n",
              "      <td>18/03/2015</td>\n",
              "      <td>23/03/2015</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>SM-20320</td>\n",
              "      <td>Sean Miller</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>United States</td>\n",
              "      <td>Jacksonville</td>\n",
              "      <td>Florida</td>\n",
              "      <td>32216</td>\n",
              "      <td>South</td>\n",
              "      <td>TEC-MA-10002412</td>\n",
              "      <td>Technology</td>\n",
              "      <td>Machines</td>\n",
              "      <td>Cisco TelePresence System EX90 Videoconferenci...</td>\n",
              "      <td>22638.480</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2623</th>\n",
              "      <td>2624</td>\n",
              "      <td>CA-2018-127180</td>\n",
              "      <td>22/10/2018</td>\n",
              "      <td>24/10/2018</td>\n",
              "      <td>First Class</td>\n",
              "      <td>TA-21385</td>\n",
              "      <td>Tom Ashbrook</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>United States</td>\n",
              "      <td>New York City</td>\n",
              "      <td>New York</td>\n",
              "      <td>10024</td>\n",
              "      <td>East</td>\n",
              "      <td>TEC-CO-10004722</td>\n",
              "      <td>Technology</td>\n",
              "      <td>Copiers</td>\n",
              "      <td>Canon imageCLASS 2200 Advanced Copier</td>\n",
              "      <td>11199.968</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4190</th>\n",
              "      <td>4191</td>\n",
              "      <td>CA-2018-166709</td>\n",
              "      <td>17/11/2018</td>\n",
              "      <td>22/11/2018</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>HL-15040</td>\n",
              "      <td>Hunter Lopez</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Newark</td>\n",
              "      <td>Delaware</td>\n",
              "      <td>19711</td>\n",
              "      <td>East</td>\n",
              "      <td>TEC-CO-10004722</td>\n",
              "      <td>Technology</td>\n",
              "      <td>Copiers</td>\n",
              "      <td>Canon imageCLASS 2200 Advanced Copier</td>\n",
              "      <td>10499.970</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4098</th>\n",
              "      <td>4099</td>\n",
              "      <td>CA-2015-116904</td>\n",
              "      <td>23/09/2015</td>\n",
              "      <td>28/09/2015</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>SC-20095</td>\n",
              "      <td>Sanjit Chand</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Minneapolis</td>\n",
              "      <td>Minnesota</td>\n",
              "      <td>55407</td>\n",
              "      <td>Central</td>\n",
              "      <td>OFF-BI-10001120</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Binders</td>\n",
              "      <td>Ibico EPK-21 Electric Binding System</td>\n",
              "      <td>9449.950</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4277</th>\n",
              "      <td>4278</td>\n",
              "      <td>US-2017-107440</td>\n",
              "      <td>16/04/2017</td>\n",
              "      <td>20/04/2017</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>BS-11365</td>\n",
              "      <td>Bill Shonely</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Lakewood</td>\n",
              "      <td>New Jersey</td>\n",
              "      <td>8701</td>\n",
              "      <td>East</td>\n",
              "      <td>TEC-MA-10001047</td>\n",
              "      <td>Technology</td>\n",
              "      <td>Machines</td>\n",
              "      <td>3D Systems Cube Printer, 2nd Generation, Magenta</td>\n",
              "      <td>9099.930</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",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>976</th>\n",
              "      <td>977</td>\n",
              "      <td>US-2018-100209</td>\n",
              "      <td>09/07/2018</td>\n",
              "      <td>15/07/2018</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>TD-20995</td>\n",
              "      <td>Tamara Dahlen</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Portland</td>\n",
              "      <td>Oregon</td>\n",
              "      <td>97206</td>\n",
              "      <td>West</td>\n",
              "      <td>OFF-BI-10002012</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Binders</td>\n",
              "      <td>Wilson Jones Easy Flow II Sheet Lifters</td>\n",
              "      <td>1.080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2761</th>\n",
              "      <td>2762</td>\n",
              "      <td>CA-2018-126536</td>\n",
              "      <td>12/10/2018</td>\n",
              "      <td>14/10/2018</td>\n",
              "      <td>First Class</td>\n",
              "      <td>NK-18490</td>\n",
              "      <td>Neil Knudson</td>\n",
              "      <td>Home Office</td>\n",
              "      <td>United States</td>\n",
              "      <td>San Francisco</td>\n",
              "      <td>California</td>\n",
              "      <td>94110</td>\n",
              "      <td>West</td>\n",
              "      <td>TEC-AC-10003709</td>\n",
              "      <td>Technology</td>\n",
              "      <td>Accessories</td>\n",
              "      <td>Maxell 4.7GB DVD-R 5/Pack</td>\n",
              "      <td>0.990</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2106</th>\n",
              "      <td>2107</td>\n",
              "      <td>US-2015-152723</td>\n",
              "      <td>26/09/2015</td>\n",
              "      <td>26/09/2015</td>\n",
              "      <td>Same Day</td>\n",
              "      <td>HG-14965</td>\n",
              "      <td>Henry Goldwyn</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Mesquite</td>\n",
              "      <td>Texas</td>\n",
              "      <td>75150</td>\n",
              "      <td>Central</td>\n",
              "      <td>OFF-BI-10003460</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Binders</td>\n",
              "      <td>Acco 3-Hole Punch</td>\n",
              "      <td>0.876</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4711</th>\n",
              "      <td>4712</td>\n",
              "      <td>CA-2015-112403</td>\n",
              "      <td>31/03/2015</td>\n",
              "      <td>31/03/2015</td>\n",
              "      <td>Same Day</td>\n",
              "      <td>JO-15280</td>\n",
              "      <td>Jas O'Carroll</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Philadelphia</td>\n",
              "      <td>Pennsylvania</td>\n",
              "      <td>19120</td>\n",
              "      <td>East</td>\n",
              "      <td>OFF-BI-10003529</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Binders</td>\n",
              "      <td>Avery Round Ring Poly Binders</td>\n",
              "      <td>0.852</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4101</th>\n",
              "      <td>4102</td>\n",
              "      <td>US-2018-102288</td>\n",
              "      <td>19/06/2018</td>\n",
              "      <td>23/06/2018</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>ZC-21910</td>\n",
              "      <td>Zuschuss Carroll</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Houston</td>\n",
              "      <td>Texas</td>\n",
              "      <td>77095</td>\n",
              "      <td>Central</td>\n",
              "      <td>OFF-AP-10002906</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Appliances</td>\n",
              "      <td>Hoover Replacement Belt for Commercial Guardsm...</td>\n",
              "      <td>0.444</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>4822 rows × 18 columns</p>\n",
              "</div>\n",
              "    <div class=\"colab-df-buttons\">\n",
              "\n",
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              "\n",
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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4822,\n  \"fields\": [\n    {\n      \"column\": \"Row ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1392,\n        \"min\": 1,\n        \"max\": 4822,\n        \"num_unique_values\": 4822,\n        \"samples\": [\n          3918,\n          4753,\n          4621\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Order ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2387,\n        \"samples\": [\n          \"US-2017-150035\",\n          \"CA-2018-132122\",\n          \"CA-2015-121664\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Order Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1004,\n        \"samples\": [\n          \"28/04/2016\",\n          \"25/04/2016\",\n          \"24/09/2017\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ship Date\",\n      \"properties\": {\n        \"dtype\": \"object\",\n        \"num_unique_values\": 1092,\n        \"samples\": [\n          \"29/11/2018\",\n          \"07/11/2016\",\n          \"07/12/2016\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ship Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"First Class\",\n          \"Same Day\",\n          \"Standard Class\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 753,\n        \"samples\": [\n          \"BB-10990\",\n          \"DH-13675\",\n          \"SN-20710\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 753,\n        \"samples\": [\n          \"Barry Blumstein\",\n          \"Duane Huffman\",\n          \"Steve Nguyen\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Home Office\",\n          \"Consumer\",\n          \"Corporate\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 420,\n        \"samples\": [\n          \"Toledo\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"Tennessee\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Postal Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31823,\n        \"min\": 0,\n        \"max\": 99207,\n        \"num_unique_values\": 510,\n        \"samples\": [\n          33161\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"East\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1703,\n        \"samples\": [\n          \"OFF-PA-10000466\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Technology\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sub-Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Machines\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1692,\n        \"samples\": [\n          \"GBC Personal VeloBind Strips\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 637.1944907933839,\n        \"min\": 0.444,\n        \"max\": 22638.48,\n        \"num_unique_values\": 3494,\n        \"samples\": [\n          55.36\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.pivot_table(values='Sales', index='Region', columns='Category', aggfunc='sum')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "2lW-V3k1w3rY",
        "outputId": "33648cd4-e7b5-46ad-fb2c-679a6e2aa285"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Category    Furniture  Office Supplies  Technology\n",
              "Region                                            \n",
              "Central    81994.8352        72086.767   85623.006\n",
              "East       89148.3350       101400.611  126870.574\n",
              "South      48752.0220        66476.932   86381.032\n",
              "West      122313.1175       101275.832  122727.789"
            ],
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              "\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4,\n  \"fields\": [\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"East\",\n          \"West\",\n          \"Central\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Furniture\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 30172.864119226375,\n        \"min\": 48752.022,\n        \"max\": 122313.1175,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          89148.335,\n          122313.1175,\n          81994.8352\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Office Supplies\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 18648.984736131675,\n        \"min\": 66476.932,\n        \"max\": 101400.611,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          101400.611,\n          101275.832,\n          72086.767\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Technology\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 22465.443489485926,\n        \"min\": 85623.00600000001,\n        \"max\": 126870.57400000001,\n        \"num_unique_values\": 4,\n        \"samples\": [\n          126870.57400000001,\n          122727.789,\n          85623.00600000001\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Aggregate Data**"
      ],
      "metadata": {
        "id": "lZdbcL46xWFt"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**total sales**"
      ],
      "metadata": {
        "id": "wu-ZbZASxecF"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Sales'].sum()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i4yo4_MoxX-W",
        "outputId": "1064722c-ebbd-4882-9ca5-373a9565ab47"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1105050.8527000002"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**average sales**"
      ],
      "metadata": {
        "id": "QHpY1pxmxygb"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data['Sales'].mean()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dH7fg_yex1wY",
        "outputId": "b962b8fe-b847-4812-e2f4-f4608a7a6219"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "229.16857169224392"
            ]
          },
          "metadata": {},
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**sales per region**"
      ],
      "metadata": {
        "id": "ldUSEuEVyXmX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "sales_per_region=data.groupby('Region')['Sales'].sum()\n",
        "sales_per_region"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "baiZAQ7zybVP",
        "outputId": "d4ecf939-d963-4f59-f3e5-10065b29bbdd"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Region\n",
              "Central    239704.6082\n",
              "East       317419.5200\n",
              "South      201609.9860\n",
              "West       346316.7385\n",
              "Name: Sales, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "trends over the years"
      ],
      "metadata": {
        "id": "rT7_RVRAy7wf"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('Order Date')['Sales'].sum().plot()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 466
        },
        "id": "kypefGiay0JL",
        "outputId": "39049305-3811-4caa-9a93-831ee6c3d05d"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Order Date'>"
            ]
          },
          "metadata": {},
          "execution_count": 30
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.groupby('Category')['Sales'].sum().plot(kind='bar')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "CQQrmxQazY_6",
        "outputId": "e90d2861-6f1e-4506-8466-29df51678392"
      },
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Category'>"
            ]
          },
          "metadata": {},
          "execution_count": 31
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert 'Order Date' column to datetime\n",
        "data['Order Date'] = pd.to_datetime(data['Order Date'])\n",
        "\n",
        "# Now you can extract the year\n",
        "data['Order Date'].dt.year"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "HeZxnpQWzaPQ",
        "outputId": "246590df-f0c9-44d1-c309-2e303a2521ca"
      },
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "time data \"15/04/2018\" doesn't match format \"%m/%d/%Y\", at position 4. You might want to try:\n    - passing `format` if your strings have a consistent format;\n    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this.",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-34-4affff381cf2>\u001b[0m in \u001b[0;36m<cell line: 2>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Convert 'Order Date' column to datetime\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order Date'\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_datetime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      3\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[0;31m# Now you can extract the year\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Order Date'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0myear\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36mto_datetime\u001b[0;34m(arg, errors, dayfirst, yearfirst, utc, format, exact, unit, infer_datetime_format, origin, cache)\u001b[0m\n\u001b[1;32m   1044\u001b[0m                 \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtz_localize\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utc\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1045\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mABCSeries\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1046\u001b[0;31m         \u001b[0mcache_array\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_maybe_cache\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcache\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1047\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0mcache_array\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mempty\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1048\u001b[0m             \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmap\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcache_array\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36m_maybe_cache\u001b[0;34m(arg, format, cache, convert_listlike)\u001b[0m\n\u001b[1;32m    248\u001b[0m         \u001b[0munique_dates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0munique\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    249\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munique_dates\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 250\u001b[0;31m             \u001b[0mcache_dates\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mconvert_listlike\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0munique_dates\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    251\u001b[0m             \u001b[0;31m# GH#45319\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    252\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36m_convert_listlike_datetimes\u001b[0;34m(arg, format, name, utc, unit, errors, dayfirst, yearfirst, exact)\u001b[0m\n\u001b[1;32m    451\u001b[0m     \u001b[0;31m# `format` could be inferred, or user didn't ask for mixed-format parsing.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    452\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mformat\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mformat\u001b[0m \u001b[0;34m!=\u001b[0m \u001b[0;34m\"mixed\"\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 453\u001b[0;31m         \u001b[0;32mreturn\u001b[0m \u001b[0m_array_strptime_with_fallback\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mutc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mformat\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexact\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    454\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    455\u001b[0m     result, tz_parsed = objects_to_datetime64ns(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/tools/datetimes.py\u001b[0m in \u001b[0;36m_array_strptime_with_fallback\u001b[0;34m(arg, name, utc, fmt, exact, errors)\u001b[0m\n\u001b[1;32m    482\u001b[0m     \u001b[0mCall\u001b[0m \u001b[0marray_strptime\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;32mwith\u001b[0m \u001b[0mfallback\u001b[0m \u001b[0mbehavior\u001b[0m \u001b[0mdepending\u001b[0m \u001b[0mon\u001b[0m \u001b[0;34m'errors'\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    483\u001b[0m     \"\"\"\n\u001b[0;32m--> 484\u001b[0;31m     \u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimezones\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marray_strptime\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mfmt\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mexact\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mexact\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0merrors\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mutc\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mutc\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    485\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0many\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtz\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mtz\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mtimezones\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    486\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0m_return_parsed_timezone_results\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtimezones\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mutc\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/tslibs/strptime.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/tslibs/strptime.pyx\u001b[0m in \u001b[0;36mpandas._libs.tslibs.strptime.array_strptime\u001b[0;34m()\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: time data \"15/04/2018\" doesn't match format \"%m/%d/%Y\", at position 4. You might want to try:\n    - passing `format` if your strings have a consistent format;\n    - passing `format='ISO8601'` if your strings are all ISO8601 but not necessarily in exactly the same format;\n    - passing `format='mixed'`, and the format will be inferred for each element individually. You might want to use `dayfirst` alongside this."
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## **sales predictions**"
      ],
      "metadata": {
        "id": "xmxZvAnq1snb"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "**split data**"
      ],
      "metadata": {
        "id": "nUAA61hj3Wga"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = data.drop(columns=['Sales'])\n",
        "y = data['Sales']\n"
      ],
      "metadata": {
        "id": "QJ4amnGVz7fO"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**Train-Test Split**"
      ],
      "metadata": {
        "id": "34BFnBvo3jic"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n"
      ],
      "metadata": {
        "id": "xAObZkw-3red"
      },
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "**model selection**"
      ],
      "metadata": {
        "id": "RJW9Ieuf4RLD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "model = LinearRegression()\n",
        "model.fit(X_train, y_train)\n"
      ],
      "metadata": {
        "id": "FCto7gDc4UcA",
        "outputId": "75a0c7e8-1fbd-4462-f1ba-623f867cabbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        }
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "error",
          "ename": "ValueError",
          "evalue": "could not convert string to float: 'CA-2018-125199'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-37-cae5216284c4>\u001b[0m in \u001b[0;36m<cell line: 4>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m \u001b[0mmodel\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mLinearRegression\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmodel\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfit\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_base.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, X, y, sample_weight)\u001b[0m\n\u001b[1;32m    646\u001b[0m         \u001b[0maccept_sparse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mFalse\u001b[0m \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mpositive\u001b[0m \u001b[0;32melse\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m\"csr\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"csc\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"coo\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    647\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 648\u001b[0;31m         X, y = self._validate_data(\n\u001b[0m\u001b[1;32m    649\u001b[0m             \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my_numeric\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mmulti_output\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mTrue\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    650\u001b[0m         )\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/base.py\u001b[0m in \u001b[0;36m_validate_data\u001b[0;34m(self, X, y, reset, validate_separately, **check_params)\u001b[0m\n\u001b[1;32m    582\u001b[0m                 \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_array\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0minput_name\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"y\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_y_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    583\u001b[0m             \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 584\u001b[0;31m                 \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcheck_X_y\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mcheck_params\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    585\u001b[0m             \u001b[0mout\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    586\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_X_y\u001b[0;34m(X, y, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, multi_output, ensure_min_samples, ensure_min_features, y_numeric, estimator)\u001b[0m\n\u001b[1;32m   1104\u001b[0m         )\n\u001b[1;32m   1105\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1106\u001b[0;31m     X = check_array(\n\u001b[0m\u001b[1;32m   1107\u001b[0m         \u001b[0mX\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1108\u001b[0m         \u001b[0maccept_sparse\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0maccept_sparse\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/validation.py\u001b[0m in \u001b[0;36mcheck_array\u001b[0;34m(array, accept_sparse, accept_large_sparse, dtype, order, copy, force_all_finite, ensure_2d, allow_nd, ensure_min_samples, ensure_min_features, estimator, input_name)\u001b[0m\n\u001b[1;32m    877\u001b[0m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mastype\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;32mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    878\u001b[0m                 \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 879\u001b[0;31m                     \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_asarray_with_order\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mxp\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    880\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mComplexWarning\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mcomplex_warning\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    881\u001b[0m                 raise ValueError(\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/sklearn/utils/_array_api.py\u001b[0m in \u001b[0;36m_asarray_with_order\u001b[0;34m(array, dtype, order, copy, xp)\u001b[0m\n\u001b[1;32m    183\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__name__\u001b[0m \u001b[0;32min\u001b[0m \u001b[0;34m{\u001b[0m\u001b[0;34m\"numpy\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"numpy.array_api\"\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    184\u001b[0m         \u001b[0;31m# Use NumPy API to support order\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 185\u001b[0;31m         \u001b[0marray\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    186\u001b[0m         \u001b[0;32mreturn\u001b[0m \u001b[0mxp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mcopy\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    187\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__array__\u001b[0;34m(self, dtype)\u001b[0m\n\u001b[1;32m   1996\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__array__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mnpt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDTypeLike\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1997\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1998\u001b[0;31m         \u001b[0marr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masarray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1999\u001b[0m         if (\n\u001b[1;32m   2000\u001b[0m             \u001b[0mastype_is_view\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvalues\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marr\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mValueError\u001b[0m: could not convert string to float: 'CA-2018-125199'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Convert the 'y_train' Series to numeric, handling non-numerical values\n",
        "y_train = pd.to_numeric(y_train, errors='coerce')\n",
        "X_train = pd.to_numeric(X_train, errors='coerce')\n",
        "# Drop rows with missing values after conversion\n",
        "y_train = y_train.dropna()\n",
        "\n",
        "# Re-fit the model\n",
        "model.fit(X_train, y_train)"
      ],
      "metadata": {
        "id": "CTVSuRSb4nTx",
        "outputId": "a4e7e18e-55c3-460c-d025-a5aed79ac5d8",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 349
        }
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "error",
          "ename": "TypeError",
          "evalue": "arg must be a list, tuple, 1-d array, or Series",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-41-7bc16e69ece4>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Convert the 'y_train' Series to numeric, handling non-numerical values\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0my_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'coerce'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mX_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mto_numeric\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mX_train\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0merrors\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'coerce'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;31m# Drop rows with missing values after conversion\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0my_train\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0my_train\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdropna\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/tools/numeric.py\u001b[0m in \u001b[0;36mto_numeric\u001b[0;34m(arg, errors, downcast, dtype_backend)\u001b[0m\n\u001b[1;32m    189\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0marray\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"O\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    190\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0marg\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m\"ndim\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 191\u001b[0;31m         \u001b[0;32mraise\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"arg must be a list, tuple, 1-d array, or Series\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    192\u001b[0m     \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    193\u001b[0m         \u001b[0mvalues\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0marg\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mTypeError\u001b[0m: arg must be a list, tuple, 1-d array, or Series"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
      "metadata": {
        "id": "6rORrGvM4cHB",
        "outputId": "c33fcc67-fbbd-463c-cd02-21f97aae45d6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        }
      },
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Row ID        Order ID  Order Date   Ship Date       Ship Mode Customer ID  \\\n",
              "0       1  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
              "1       2  CA-2017-152156  08/11/2017  11/11/2017    Second Class    CG-12520   \n",
              "2       3  CA-2017-138688  12/06/2017  16/06/2017    Second Class    DV-13045   \n",
              "3       4  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
              "4       5  US-2016-108966  11/10/2016  18/10/2016  Standard Class    SO-20335   \n",
              "\n",
              "     Customer Name    Segment        Country             City       State  \\\n",
              "0      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
              "1      Claire Gute   Consumer  United States        Henderson    Kentucky   \n",
              "2  Darrin Van Huff  Corporate  United States      Los Angeles  California   \n",
              "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
              "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale     Florida   \n",
              "\n",
              "   Postal Code Region       Product ID         Category Sub-Category  \\\n",
              "0        42420  South  FUR-BO-10001798        Furniture    Bookcases   \n",
              "1        42420  South  FUR-CH-10000454        Furniture       Chairs   \n",
              "2        90036   West  OFF-LA-10000240  Office Supplies       Labels   \n",
              "3        33311  South  FUR-TA-10000577        Furniture       Tables   \n",
              "4        33311  South  OFF-ST-10000760  Office Supplies      Storage   \n",
              "\n",
              "                                        Product Name     Sales  \n",
              "0                  Bush Somerset Collection Bookcase  261.9600  \n",
              "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400  \n",
              "2  Self-Adhesive Address Labels for Typewriters b...   14.6200  \n",
              "3      Bretford CR4500 Series Slim Rectangular Table  957.5775  \n",
              "4                     Eldon Fold 'N Roll Cart System   22.3680  "
            ],
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              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>CA-2017-152156</td>\n",
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              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>CA-2017-138688</td>\n",
              "      <td>12/06/2017</td>\n",
              "      <td>16/06/2017</td>\n",
              "      <td>Second Class</td>\n",
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              "      <td>11/10/2016</td>\n",
              "      <td>18/10/2016</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>SO-20335</td>\n",
              "      <td>Sean O'Donnell</td>\n",
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              "      <td>Florida</td>\n",
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              "      <td>957.5775</td>\n",
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              "      <td>18/10/2016</td>\n",
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              "      <td>SO-20335</td>\n",
              "      <td>Sean O'Donnell</td>\n",
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              "      <td>OFF-ST-10000760</td>\n",
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              "      <td>Storage</td>\n",
              "      <td>Eldon Fold 'N Roll Cart System</td>\n",
              "      <td>22.3680</td>\n",
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              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-380c8a8a-63ef-465c-a080-e5fd7473f877 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "data",
              "summary": "{\n  \"name\": \"data\",\n  \"rows\": 4822,\n  \"fields\": [\n    {\n      \"column\": \"Row ID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1392,\n        \"min\": 1,\n        \"max\": 4822,\n        \"num_unique_values\": 4822,\n        \"samples\": [\n          4345,\n          1222,\n          1903\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Order ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 2387,\n        \"samples\": [\n          \"CA-2016-153416\",\n          \"US-2017-100839\",\n          \"CA-2016-130995\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Order Date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1004,\n        \"samples\": [\n          \"29/01/2018\",\n          \"08/07/2017\",\n          \"10/08/2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ship Date\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1092,\n        \"samples\": [\n          \"26/11/2017\",\n          \"17/04/2015\",\n          \"13/07/2018\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Ship Mode\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"Standard Class\",\n          \"Same Day\",\n          \"Second Class\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 753,\n        \"samples\": [\n          \"MF-17665\",\n          \"FH-14350\",\n          \"JH-15430\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Customer Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 753,\n        \"samples\": [\n          \"Maureen Fritzler\",\n          \"Fred Harton\",\n          \"Jennifer Halladay\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Segment\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Consumer\",\n          \"Corporate\",\n          \"Home Office\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Country\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"United States\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"City\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 420,\n        \"samples\": [\n          \"Mount Vernon\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"State\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 47,\n        \"samples\": [\n          \"New Mexico\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Postal Code\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 31823,\n        \"min\": 0,\n        \"max\": 99207,\n        \"num_unique_values\": 510,\n        \"samples\": [\n          2920\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Region\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"West\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product ID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1703,\n        \"samples\": [\n          \"OFF-AR-10003504\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 3,\n        \"samples\": [\n          \"Furniture\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sub-Category\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 17,\n        \"samples\": [\n          \"Bookcases\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Product Name\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1692,\n        \"samples\": [\n          \"Surelock Post Binders\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Sales\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 637.1944907933839,\n        \"min\": 0.444,\n        \"max\": 22638.48,\n        \"num_unique_values\": 3494,\n        \"samples\": [\n          36.51\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "8XYSZ5ND4e3q"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}