mobile_gadgets_accessories_store_data_analysis.ipynb

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      "source": [
        "# This project is an analysis of a mobile gadgets and accessories store.\n",
        "\n",
        "\n",
        "\n",
        "The activities are going to work on are as follows;\n",
        "\n",
        "1. Data cleaning\n",
        "2. Data sorting\n",
        "3. Find best selling product.\n",
        "4. Find city with best sales performance.\n",
        "5. Find the peak time for orders.\n",
        "6. Find products that were bought together most as a pair of 2 and 3.\n",
        "\n",
        "Let's start the project."
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
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      "id": "rZmoucs1G2V_",
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ]
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      "source": [
        "# import relevant python modules for the analysis\n",
        "\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import os\n",
        "from itertools import combinations\n",
        "from collections import Counter\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "import pandas.util.testing as tm"
      ]
    },
    {
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      "id": "711e4690",
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          "output_type": "stream",
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          "text": [
            "  Order ID                     Product Quantity Ordered Price Each  \\\n",
            "0   176558        USB-C Charging Cable                2      11.95   \n",
            "1   176559  Bose SoundSport Headphones                1      99.99   \n",
            "\n",
            "       Order Date                   Purchase Address  \n",
            "0  4/19/2019 8:46       917 1st St, Dallas, TX 75001  \n",
            "1  4/7/2019 22:30  682 Chestnut St, Boston, MA 02215  \n"
          ]
        }
      ],
      "source": [
        "#merge 12 months data into 1 data file for analysis and read into jupyter notebooks\n",
        "\n",
        "file = './drive/My Drive/MLstart/mobile/All Sales Data.csv'\n",
        "df = pd.read_csv(file)\n",
        "print(df.head(2))"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "1d8b155c",
      "metadata": {
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      "source": [
        "#### Now let's perform some simple exploratory data analysis to further understand the data"
      ]
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              "  Order ID                     Product Quantity Ordered Price Each  \\\n",
              "0   176558        USB-C Charging Cable                2      11.95   \n",
              "1   176559  Bose SoundSport Headphones                1      99.99   \n",
              "2   176560                Google Phone                1        600   \n",
              "\n",
              "        Order Date                      Purchase Address  \n",
              "0   4/19/2019 8:46          917 1st St, Dallas, TX 75001  \n",
              "1   4/7/2019 22:30     682 Chestnut St, Boston, MA 02215  \n",
              "2  4/12/2019 14:38  669 Spruce St, Los Angeles, CA 90001  "
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              "      <th>Order ID</th>\n",
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              "      <th>Quantity Ordered</th>\n",
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              "      <td>176558</td>\n",
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              "      <td>917 1st St, Dallas, TX 75001</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>176559</td>\n",
              "      <td>Bose SoundSport Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>99.99</td>\n",
              "      <td>4/7/2019 22:30</td>\n",
              "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
              "    </tr>\n",
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              "      <td>1</td>\n",
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      "source": [
        "# data preview\n",
        "\n",
        "df.head(3)"
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            "text/plain": [
              "(185687, 6)"
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      "source": [
        "# check the shape of the data\n",
        "\n",
        "df.shape"
      ]
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            "text/plain": [
              "Order ID            object\n",
              "Product             object\n",
              "Quantity Ordered    object\n",
              "Price Each          object\n",
              "Order Date          object\n",
              "Purchase Address    object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ],
      "source": [
        "# data types\n",
        "\n",
        "df.dtypes"
      ]
    },
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          "data": {
            "text/plain": [
              "False"
            ]
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          "metadata": {},
          "execution_count": 8
        }
      ],
      "source": [
        "# do we have missing values?\n",
        "\n",
        "df.isna().values.any()"
      ]
    },
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      "id": "08e62cd7",
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        {
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          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ],
      "source": [
        "# do we have duplicates?\n",
        "\n",
        "df.duplicated().values.any()"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "94cecb15",
      "metadata": {
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      "source": [
        "#### Now we have to clean up the data to remove NAN values & duplicates and convert data types"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
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      "source": [
        "# drop missing values\n",
        "\n",
        "df.dropna(how='all', inplace= True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "id": "4f1582c4",
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      "source": [
        "# drop all duplicates\n",
        "\n",
        "df = df.drop_duplicates()"
      ]
    },
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        "execution": {
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          "iopub.status.busy": "2021-11-18T07:23:33.696855Z",
          "iopub.status.idle": "2021-11-18T07:23:33.821534Z",
          "shell.execute_reply": "2021-11-18T07:23:33.820923Z",
          "shell.execute_reply.started": "2021-11-18T06:15:23.092858Z"
        },
        "papermill": {
          "duration": 0.167142,
          "end_time": "2021-11-18T07:23:33.821698",
          "exception": false,
          "start_time": "2021-11-18T07:23:33.654556",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a1fc54cc",
        "outputId": "f559a92d-3116-436a-a9ba-50ea56493b74"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "# check\n",
        "\n",
        "df.isna().values.any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "id": "4d444add",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-18T07:23:33.966664Z",
          "iopub.status.busy": "2021-11-18T07:23:33.914448Z",
          "iopub.status.idle": "2021-11-18T07:23:34.157009Z",
          "shell.execute_reply": "2021-11-18T07:23:34.156473Z",
          "shell.execute_reply.started": "2021-11-18T06:15:23.220336Z"
        },
        "papermill": {
          "duration": 0.294776,
          "end_time": "2021-11-18T07:23:34.157170",
          "exception": false,
          "start_time": "2021-11-18T07:23:33.862394",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4d444add",
        "outputId": "cb0186af-9658-42ef-c3ab-7dd1b76e03c8"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "False"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ],
      "source": [
        "# check\n",
        "\n",
        "df.duplicated().values.any()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "id": "8e4186ef",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-18T07:23:34.243910Z",
          "iopub.status.busy": "2021-11-18T07:23:34.242958Z",
          "iopub.status.idle": "2021-11-18T07:23:34.246315Z",
          "shell.execute_reply": "2021-11-18T07:23:34.246860Z",
          "shell.execute_reply.started": "2021-11-18T06:15:23.446597Z"
        },
        "papermill": {
          "duration": 0.049644,
          "end_time": "2021-11-18T07:23:34.247035",
          "exception": false,
          "start_time": "2021-11-18T07:23:34.197391",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8e4186ef",
        "outputId": "f1b52963-76a6-4a6a-98ac-001ce6d078f6"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(185687, 6)"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ],
      "source": [
        "df.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 15,
      "id": "0a8ee084",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-18T07:23:34.332757Z",
          "iopub.status.busy": "2021-11-18T07:23:34.331690Z",
          "iopub.status.idle": "2021-11-18T07:23:34.343615Z",
          "shell.execute_reply": "2021-11-18T07:23:34.344178Z",
          "shell.execute_reply.started": "2021-11-18T06:15:23.453817Z"
        },
        "papermill": {
          "duration": 0.057,
          "end_time": "2021-11-18T07:23:34.344366",
          "exception": false,
          "start_time": "2021-11-18T07:23:34.287366",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "0a8ee084",
        "outputId": "4131c09a-4d76-474a-a80b-b6d4de0895ae"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product Quantity Ordered Price Each  \\\n",
              "0   176558        USB-C Charging Cable                2      11.95   \n",
              "1   176559  Bose SoundSport Headphones                1      99.99   \n",
              "2   176560                Google Phone                1        600   \n",
              "\n",
              "        Order Date                      Purchase Address  \n",
              "0   4/19/2019 8:46          917 1st St, Dallas, TX 75001  \n",
              "1   4/7/2019 22:30     682 Chestnut St, Boston, MA 02215  \n",
              "2  4/12/2019 14:38  669 Spruce St, Los Angeles, CA 90001  "
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Order ID</th>\n",
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              "      <th>Quantity Ordered</th>\n",
              "      <th>Price Each</th>\n",
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              "    </tr>\n",
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              "      <th>1</th>\n",
              "      <td>176559</td>\n",
              "      <td>Bose SoundSport Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>99.99</td>\n",
              "      <td>4/7/2019 22:30</td>\n",
              "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>176560</td>\n",
              "      <td>Google Phone</td>\n",
              "      <td>1</td>\n",
              "      <td>600</td>\n",
              "      <td>4/12/2019 14:38</td>\n",
              "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
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              "  </tbody>\n",
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              "      \n",
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              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
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              "    .colab-df-convert {\n",
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              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
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              "    .colab-df-convert:hover {\n",
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              "\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",
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              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b054203c-9dd6-4726-a677-7c1ebd1f2409 button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b054203c-9dd6-4726-a677-7c1ebd1f2409');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          await google.colab.output.renderOutput(dataTable, element);\n",
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          },
          "metadata": {},
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        }
      ],
      "source": [
        "df.head(3)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "id": "96088d87",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-18T07:23:34.431165Z",
          "iopub.status.busy": "2021-11-18T07:23:34.430142Z",
          "iopub.status.idle": "2021-11-18T07:23:34.460503Z",
          "shell.execute_reply": "2021-11-18T07:23:34.461034Z",
          "shell.execute_reply.started": "2021-11-18T06:15:23.475393Z"
        },
        "papermill": {
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          "end_time": "2021-11-18T07:23:34.461224",
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          "status": "completed"
        },
        "tags": [],
        "id": "96088d87"
      },
      "outputs": [],
      "source": [
        "df.drop(index=516, inplace=True)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "id": "f7bb7eb0",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-18T07:23:34.548614Z",
          "iopub.status.busy": "2021-11-18T07:23:34.547572Z",
          "iopub.status.idle": "2021-11-18T07:23:34.804939Z",
          "shell.execute_reply": "2021-11-18T07:23:34.804313Z",
          "shell.execute_reply.started": "2021-11-18T06:15:23.501112Z"
        },
        "papermill": {
          "duration": 0.302657,
          "end_time": "2021-11-18T07:23:34.805088",
          "exception": false,
          "start_time": "2021-11-18T07:23:34.502431",
          "status": "completed"
        },
        "tags": [],
        "id": "f7bb7eb0"
      },
      "outputs": [],
      "source": [
        "#let's change relevant columns to the correct data types\n",
        "\n",
        "df[['Quantity Ordered', 'Price Each']] = df[['Quantity Ordered', 'Price Each']].apply(pd.to_numeric)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "id": "01e42a97",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-18T07:23:34.895268Z",
          "iopub.status.busy": "2021-11-18T07:23:34.891004Z",
          "iopub.status.idle": "2021-11-18T07:23:34.898032Z",
          "shell.execute_reply": "2021-11-18T07:23:34.898673Z",
          "shell.execute_reply.started": "2021-11-18T06:15:23.755801Z"
        },
        "papermill": {
          "duration": 0.051806,
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          "exception": false,
          "start_time": "2021-11-18T07:23:34.847037",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "01e42a97",
        "outputId": "22e9f7da-afd1-4e37-e238-03fa89621b57"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Order ID             object\n",
              "Product              object\n",
              "Quantity Ordered      int64\n",
              "Price Each          float64\n",
              "Order Date           object\n",
              "Purchase Address     object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ],
      "source": [
        "# check to see if it worked\n",
        "df.dtypes"
      ]
    },
    {
      "cell_type": "markdown",
      "id": "2bfaeb13",
      "metadata": {
        "papermill": {
          "duration": 0.04105,
          "end_time": "2021-11-18T07:23:34.981335",
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          "status": "completed"
        },
        "tags": [],
        "id": "2bfaeb13"
      },
      "source": [
        "#### Next, we have to augment the data by adding relevant extra columns"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 19,
      "id": "c8b33eec",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-18T07:23:35.068553Z",
          "iopub.status.busy": "2021-11-18T07:23:35.067527Z",
          "iopub.status.idle": "2021-11-18T07:23:35.073634Z",
          "shell.execute_reply": "2021-11-18T07:23:35.074263Z",
          "shell.execute_reply.started": "2021-11-18T06:15:23.765895Z"
        },
        "papermill": {
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          "status": "completed"
        },
        "tags": [],
        "id": "c8b33eec"
      },
      "outputs": [],
      "source": [
        "df['Total'] = df['Quantity Ordered'] * df['Price Each']"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 20,
      "id": "9d7fd348",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-18T07:23:35.161747Z",
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          "shell.execute_reply": "2021-11-18T07:23:35.173926Z",
          "shell.execute_reply.started": "2021-11-18T06:15:23.775755Z"
        },
        "papermill": {
          "duration": 0.057615,
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          "exception": false,
          "start_time": "2021-11-18T07:23:35.116483",
          "status": "completed"
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          "height": 112
        },
        "id": "9d7fd348",
        "outputId": "7a44e71a-f8b0-4f3f-9073-1e5b9a202105"
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      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   176558        USB-C Charging Cable                 2       11.95   \n",
              "1   176559  Bose SoundSport Headphones                 1       99.99   \n",
              "\n",
              "       Order Date                   Purchase Address  Total  \n",
              "0  4/19/2019 8:46       917 1st St, Dallas, TX 75001  23.90  \n",
              "1  4/7/2019 22:30  682 Chestnut St, Boston, MA 02215  99.99  "
            ],
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              "\n",
              "  <div id=\"df-d5395d11-cb1f-4b38-9973-fb280dc570e8\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "      <th>Order ID</th>\n",
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              "      <th>Quantity Ordered</th>\n",
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              "      <th>Order Date</th>\n",
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              "      <th>0</th>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>176559</td>\n",
              "      <td>Bose SoundSport Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>99.99</td>\n",
              "      <td>4/7/2019 22:30</td>\n",
              "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
              "      <td>99.99</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-d5395d11-cb1f-4b38-9973-fb280dc570e8')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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        "df['Month'] = df['Order Date'].str[0:1].apply(pd.to_numeric)"
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              "Order ID             object\n",
              "Product              object\n",
              "Quantity Ordered      int64\n",
              "Price Each          float64\n",
              "Order Date           object\n",
              "Purchase Address     object\n",
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              "dtype: object"
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        "def city(address):\n",
        "    return address.split(\",\")[1]\n",
        "    \n",
        "def state(address):\n",
        "    return address.split(\",\")[2].split(\" \")[1]\n",
        "\n",
        "def zipcode(address):\n",
        "    return address.split(\",\")[2].split(\" \")[3]\n",
        "    \n",
        "df['City'] = df['Purchase Address'].apply(lambda x: city(x) + ', ' + state(x))"
      ]
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              "      <td>99.99</td>\n",
              "      <td>4/7/2019 22:30</td>\n",
              "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
              "      <td>99.99</td>\n",
              "      <td>4</td>\n",
              "      <td>Boston, MA</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-ed93d310-7f10-4986-bd38-5b7c5316cfac')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-ed93d310-7f10-4986-bd38-5b7c5316cfac 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-ed93d310-7f10-4986-bd38-5b7c5316cfac');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "df.head(2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 28,
      "id": "6cdca0bc",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-18T07:23:41.508531Z",
          "iopub.status.busy": "2021-11-18T07:23:41.505100Z",
          "iopub.status.idle": "2021-11-18T07:23:41.684261Z",
          "shell.execute_reply": "2021-11-18T07:23:41.684909Z",
          "shell.execute_reply.started": "2021-11-18T06:15:29.509252Z"
        },
        "papermill": {
          "duration": 0.229336,
          "end_time": "2021-11-18T07:23:41.685090",
          "exception": false,
          "start_time": "2021-11-18T07:23:41.455754",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6cdca0bc",
        "outputId": "2b1f6fcf-fabd-4ed4-9f13-e4e8db8e7289"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product\n",
              "USB-C Charging Cable          21859\n",
              "Lightning Charging Cable      21610\n",
              "AAA Batteries (4-pack)        20612\n",
              "AA Batteries (4-pack)         20558\n",
              "Wired Headphones              18849\n",
              "Apple Airpods Headphones      15525\n",
              "Bose SoundSport Headphones    13298\n",
              "27in FHD Monitor               7498\n",
              "iPhone                         6840\n",
              "27in 4K Gaming Monitor         6225\n",
              "34in Ultrawide Monitor         6174\n",
              "Google Phone                   5522\n",
              "Flatscreen TV                  4794\n",
              "Macbook Pro Laptop             4721\n",
              "ThinkPad Laptop                4126\n",
              "20in Monitor                   4098\n",
              "Vareebadd Phone                2065\n",
              "LG Washing Machine              666\n",
              "LG Dryer                        646\n",
              "Name: Quantity Ordered, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "df.groupby('Product').count()['Quantity Ordered'].sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 29,
      "id": "a2e78f99",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-18T07:23:41.779334Z",
          "iopub.status.busy": "2021-11-18T07:23:41.778354Z",
          "iopub.status.idle": "2021-11-18T07:23:41.803627Z",
          "shell.execute_reply": "2021-11-18T07:23:41.804250Z",
          "shell.execute_reply.started": "2021-11-18T06:15:29.695994Z"
        },
        "papermill": {
          "duration": 0.074936,
          "end_time": "2021-11-18T07:23:41.804435",
          "exception": false,
          "start_time": "2021-11-18T07:23:41.729499",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "a2e78f99",
        "outputId": "702f542d-db67-4262-840c-fd457fbdf198"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "# How many unique products are there?\n",
        "\n",
        "df['Product'].nunique()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 30,
      "id": "dde6be04",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-18T07:23:41.900985Z",
          "iopub.status.busy": "2021-11-18T07:23:41.900168Z",
          "iopub.status.idle": "2021-11-18T07:23:42.036127Z",
          "shell.execute_reply": "2021-11-18T07:23:42.035407Z",
          "shell.execute_reply.started": "2021-11-18T06:15:29.723969Z"
        },
        "papermill": {
          "duration": 0.186095,
          "end_time": "2021-11-18T07:23:42.036312",
          "exception": false,
          "start_time": "2021-11-18T07:23:41.850217",
          "status": "completed"
        },
        "tags": [],
        "id": "dde6be04"
      },
      "outputs": [],
      "source": [
        "df_viz = df.groupby('Product').count()['Quantity Ordered'].sort_values()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 31,
      "id": "fef60003",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-18T07:23:42.136811Z",
          "iopub.status.busy": "2021-11-18T07:23:42.136028Z",
          "iopub.status.idle": "2021-11-18T07:23:42.598712Z",
          "shell.execute_reply": "2021-11-18T07:23:42.598026Z",
          "shell.execute_reply.started": "2021-11-18T06:15:29.863352Z"
        },
        "papermill": {
          "duration": 0.513734,
          "end_time": "2021-11-18T07:23:42.598887",
          "exception": false,
          "start_time": "2021-11-18T07:23:42.085153",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "fef60003",
        "outputId": "dd12deb2-a991-44d4-bdcb-6d5333dcdbb9"
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "source": [
        "# Best selling product by volume\n",
        "fig, ax = plt.subplots(figsize=(6,8))\n",
        "df_viz.plot(kind = 'barh',ax= ax)\n",
        "ax.set(title = 'Sales Count By Product')\n",
        "ax.title.set(y=1.07)\n",
        "ax.set_xlabel('Total Sold',size=12)\n",
        "ax.set_ylabel('Products',size=12)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 32,
      "id": "5e8eaeb7",
      "metadata": {
        "execution": {
          "iopub.execute_input": "2021-11-18T07:23:42.695954Z",
          "iopub.status.busy": "2021-11-18T07:23:42.695172Z",
          "iopub.status.idle": "2021-11-18T07:23:42.749770Z",
          "shell.execute_reply": "2021-11-18T07:23:42.750391Z",
          "shell.execute_reply.started": "2021-11-18T06:15:30.183552Z"
        },
        "papermill": {
          "duration": 0.104756,
          "end_time": "2021-11-18T07:23:42.750589",
          "exception": false,
          "start_time": "2021-11-18T07:23:42.645833",
          "status": "completed"
        },
        "tags": [],
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5e8eaeb7",
        "outputId": "d6443bbf-61e1-4d21-e130-860450206c28"
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product\n",
              "Macbook Pro Laptop            8032500.00\n",
              "iPhone                        4792900.00\n",
              "ThinkPad Laptop               4127958.72\n",
              "Google Phone                  3317400.00\n",
              "27in 4K Gaming Monitor        2433147.61\n",
              "34in Ultrawide Monitor        2352898.08\n",
              "Apple Airpods Headphones      2345550.00\n",
              "Flatscreen TV                 1443900.00\n",
              "Bose SoundSport Headphones    1342865.70\n",
              "27in FHD Monitor              1131074.59\n",
              "Vareebadd Phone                827200.00\n",
              "20in Monitor                   453818.74\n",
              "LG Washing Machine             399600.00\n",
              "LG Dryer                       387600.00\n",
              "Lightning Charging Cable       346376.55\n",
              "USB-C Charging Cable           285975.45\n",
              "Wired Headphones               246082.76\n",
              "AA Batteries (4-pack)          106041.60\n",
              "AAA Batteries (4-pack)          92648.14\n",
              "Name: Total, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ],
      "source": [
        "# Best selling product by revenue\n",
        "\n",
        "df.groupby('Product').sum()['Total'].sort_values(ascending=False)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 33,
      "id": "1ccf069d",
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      "source": [
        "salesByProduct = df.groupby('Product').sum()['Total'].sort_values()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Total'] = df['Quantity Ordered'] * df['Price Each']"
      ],
      "metadata": {
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      },
      "id": "xh_X0MbKIahF",
      "execution_count": 34,
      "outputs": []
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        "df.reset_index(drop=True, inplace = True)"
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      "metadata": {
        "id": "Q2v8NsuAIg4r"
      },
      "id": "Q2v8NsuAIg4r",
      "execution_count": 35,
      "outputs": []
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    {
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        "df.shape"
      ],
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        "outputId": "928f80d9-6577-4a11-8860-f68aedd8b336"
      },
      "id": "Q-56lSZrIlPS",
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(185686, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Month'] = df['Order Date'].str[0:1].apply(pd.to_numeric)"
      ],
      "metadata": {
        "id": "REvJFkFBIlMb"
      },
      "id": "REvJFkFBIlMb",
      "execution_count": 37,
      "outputs": []
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      "id": "Po7bSW_vIlDL",
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            "text/plain": [
              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   176558        USB-C Charging Cable                 2       11.95   \n",
              "1   176559  Bose SoundSport Headphones                 1       99.99   \n",
              "\n",
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              "Order ID             object\n",
              "Product              object\n",
              "Quantity Ordered      int64\n",
              "Price Each          float64\n",
              "Order Date           object\n",
              "Purchase Address     object\n",
              "Total               float64\n",
              "Month                 int64\n",
              "City                 object\n",
              "dtype: object"
            ]
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        }
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        "def city(address):\n",
        "    return address.split(\",\")[1]\n",
        "    \n",
        "def state(address):\n",
        "    return address.split(\",\")[2].split(\" \")[1]\n",
        "\n",
        "def zipcode(address):\n",
        "    return address.split(\",\")[2].split(\" \")[3]\n",
        "    \n",
        "df['City'] = df['Purchase Address'].apply(lambda x: city(x) + ', ' + state(x))"
      ],
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        "id": "O2nKyAi5IzwL"
      },
      "id": "O2nKyAi5IzwL",
      "execution_count": 40,
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      "execution_count": 41,
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              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   176558        USB-C Charging Cable                 2       11.95   \n",
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              "\n",
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              "                                                     [key], {});\n",
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        "df.groupby('Product').count()['Quantity Ordered'].sort_values(ascending=False)"
      ],
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      "id": "hGxkxPXOKTZG",
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product\n",
              "USB-C Charging Cable          21859\n",
              "Lightning Charging Cable      21610\n",
              "AAA Batteries (4-pack)        20612\n",
              "AA Batteries (4-pack)         20558\n",
              "Wired Headphones              18849\n",
              "Apple Airpods Headphones      15525\n",
              "Bose SoundSport Headphones    13298\n",
              "27in FHD Monitor               7498\n",
              "iPhone                         6840\n",
              "27in 4K Gaming Monitor         6225\n",
              "34in Ultrawide Monitor         6174\n",
              "Google Phone                   5522\n",
              "Flatscreen TV                  4794\n",
              "Macbook Pro Laptop             4721\n",
              "ThinkPad Laptop                4126\n",
              "20in Monitor                   4098\n",
              "Vareebadd Phone                2065\n",
              "LG Washing Machine              666\n",
              "LG Dryer                        646\n",
              "Name: Quantity Ordered, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# How many unique products are there?\n",
        "\n",
        "df['Product'].nunique()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gdgeZJIXKTQh",
        "outputId": "4be74f75-be66-4def-bb60-31c7f4530700"
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      "id": "gdgeZJIXKTQh",
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "19"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df_viz = df.groupby('Product').count()['Quantity Ordered'].sort_values()"
      ],
      "metadata": {
        "id": "ogQfqVmtKTOF"
      },
      "id": "ogQfqVmtKTOF",
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Best selling product by volume\n",
        "fig, ax = plt.subplots(figsize=(6,8))\n",
        "df_viz.plot(kind = 'barh',ax= ax)\n",
        "ax.set(title = 'Sales Count By Product')\n",
        "ax.title.set(y=1.07)\n",
        "ax.set_xlabel('Total Sold',size=12)\n",
        "ax.set_ylabel('Products',size=12)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 546
        },
        "id": "WAb5jYhEKTLo",
        "outputId": "9e665686-00f6-405e-88ac-15c8664cfa66"
      },
      "id": "WAb5jYhEKTLo",
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Best selling product by revenue\n",
        "\n",
        "df.groupby('Product').sum()['Total'].sort_values(ascending=False)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ehwMRYypKTJS",
        "outputId": "da2b4a46-4d94-4f05-cb15-c9750b228c7c"
      },
      "id": "ehwMRYypKTJS",
      "execution_count": 46,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Product\n",
              "Macbook Pro Laptop            8032500.00\n",
              "iPhone                        4792900.00\n",
              "ThinkPad Laptop               4127958.72\n",
              "Google Phone                  3317400.00\n",
              "27in 4K Gaming Monitor        2433147.61\n",
              "34in Ultrawide Monitor        2352898.08\n",
              "Apple Airpods Headphones      2345550.00\n",
              "Flatscreen TV                 1443900.00\n",
              "Bose SoundSport Headphones    1342865.70\n",
              "27in FHD Monitor              1131074.59\n",
              "Vareebadd Phone                827200.00\n",
              "20in Monitor                   453818.74\n",
              "LG Washing Machine             399600.00\n",
              "LG Dryer                       387600.00\n",
              "Lightning Charging Cable       346376.55\n",
              "USB-C Charging Cable           285975.45\n",
              "Wired Headphones               246082.76\n",
              "AA Batteries (4-pack)          106041.60\n",
              "AAA Batteries (4-pack)          92648.14\n",
              "Name: Total, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "salesByProduct = df.groupby('Product').sum()['Total'].sort_values()"
      ],
      "metadata": {
        "id": "I3SWN_lqKTGY"
      },
      "id": "I3SWN_lqKTGY",
      "execution_count": 50,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib.ticker import FuncFormatter"
      ],
      "metadata": {
        "id": "5fapWO_TuJ5c"
      },
      "id": "5fapWO_TuJ5c",
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "fig, ax = plt.subplots(figsize=(6,8))\n",
        "salesByProduct.plot(kind = 'barh', ax=ax)\n",
        "ax.set(title= 'Sales By Product')\n",
        "ax.title.set(y=1.10)\n",
        "ax.set_xlabel('Total Sales', size=12)\n",
        "ax.set_ylabel('Products', size=12)\n",
        "ax.ticklabel_format(useOffset=False,style='plain',axis='x')\n",
        "labels = ax.get_xticklabels()\n",
        "\n",
        "# define a function to convert scientific notation to shorter value\n",
        "def totalsales(x, pos):\n",
        "    if x >=1e6:\n",
        "        s = '${:1.1f}M'.format(x*1e-6) \n",
        "    else:\n",
        "        s = '${:1.0f}K'.format(x*1e-3) \n",
        "    return s\n",
        "formatter = FuncFormatter(totalsales)\n",
        "\n",
        "ax.xaxis.set_major_formatter(formatter)\n",
        "plt.setp(labels, rotation =0)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 559
        },
        "id": "T1vS8htsOpLw",
        "outputId": "60408992-3b6d-4805-ea11-38a0f80746e1"
      },
      "id": "T1vS8htsOpLw",
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x576 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# City with the highest sales\n",
        "df.groupby('City').sum()['Total'].round()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6HgjALgVLIVV",
        "outputId": "54268191-848d-434b-ad28-ebebbf3d8b41"
      },
      "id": "6HgjALgVLIVV",
      "execution_count": 56,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "City\n",
              " Atlanta, GA          2794199.0\n",
              " Austin, TX           1818044.0\n",
              " Boston, MA           3658628.0\n",
              " Dallas, TX           2765374.0\n",
              " Los Angeles, CA      5448304.0\n",
              " New York City, NY    4661867.0\n",
              " Portland, ME          449321.0\n",
              " Portland, OR         1870011.0\n",
              " San Francisco, CA    8254744.0\n",
              " Seattle, WA          2745046.0\n",
              "Name: Total, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "citysales = df.groupby('City').sum()['Total'].round()\n",
        "fig, ax = plt.subplots(figsize=(6,4))\n",
        "citysales.plot(kind='bar', ax=ax)\n",
        "ax.set(title= 'Sales By City')\n",
        "ax.title.set(y=1.10)\n",
        "ax.ticklabel_format(useOffset=False,style='plain',axis='y')\n",
        "labels = ax.get_xticklabels()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 399
        },
        "id": "DGVc2V0rLIS5",
        "outputId": "ef14a572-69c6-46f6-e3a6-63050406206e"
      },
      "id": "DGVc2V0rLIS5",
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df['Order Date'] = pd.to_datetime(df['Order Date'])"
      ],
      "metadata": {
        "id": "E2MKLEOULIQO"
      },
      "id": "E2MKLEOULIQO",
      "execution_count": 59,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Now we have converted the order date column to datetime data type\n",
        "df.dtypes\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uQheHvcZLINe",
        "outputId": "7d66bff4-e1f9-48e5-bbb6-a7571ebc90aa"
      },
      "id": "uQheHvcZLINe",
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Order ID                    object\n",
              "Product                     object\n",
              "Quantity Ordered             int64\n",
              "Price Each                 float64\n",
              "Order Date          datetime64[ns]\n",
              "Purchase Address            object\n",
              "Total                      float64\n",
              "Month                        int64\n",
              "City                        object\n",
              "dtype: object"
            ]
          },
          "metadata": {},
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's see the dataframe again\n",
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "VYbkzddOLIKq",
        "outputId": "29ab67a3-c21e-4c6a-8435-4dcd7ed653f9"
      },
      "id": "VYbkzddOLIKq",
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   176558        USB-C Charging Cable                 2       11.95   \n",
              "1   176559  Bose SoundSport Headphones                 1       99.99   \n",
              "2   176560                Google Phone                 1      600.00   \n",
              "\n",
              "           Order Date                      Purchase Address   Total  Month  \\\n",
              "0 2019-04-19 08:46:00          917 1st St, Dallas, TX 75001   23.90      4   \n",
              "1 2019-04-07 22:30:00     682 Chestnut St, Boston, MA 02215   99.99      4   \n",
              "2 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001  600.00      4   \n",
              "\n",
              "               City  \n",
              "0        Dallas, TX  \n",
              "1        Boston, MA  \n",
              "2   Los Angeles, CA  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-3e5b3ba1-9f3d-4331-a20d-f1b7bc4bf877\">\n",
              "    <div 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>Order ID</th>\n",
              "      <th>Product</th>\n",
              "      <th>Quantity Ordered</th>\n",
              "      <th>Price Each</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Purchase Address</th>\n",
              "      <th>Total</th>\n",
              "      <th>Month</th>\n",
              "      <th>City</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>176558</td>\n",
              "      <td>USB-C Charging Cable</td>\n",
              "      <td>2</td>\n",
              "      <td>11.95</td>\n",
              "      <td>2019-04-19 08:46:00</td>\n",
              "      <td>917 1st St, Dallas, TX 75001</td>\n",
              "      <td>23.90</td>\n",
              "      <td>4</td>\n",
              "      <td>Dallas, TX</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>176559</td>\n",
              "      <td>Bose SoundSport Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>99.99</td>\n",
              "      <td>2019-04-07 22:30:00</td>\n",
              "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
              "      <td>99.99</td>\n",
              "      <td>4</td>\n",
              "      <td>Boston, MA</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>176560</td>\n",
              "      <td>Google Phone</td>\n",
              "      <td>1</td>\n",
              "      <td>600.00</td>\n",
              "      <td>2019-04-12 14:38:00</td>\n",
              "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
              "      <td>600.00</td>\n",
              "      <td>4</td>\n",
              "      <td>Los Angeles, CA</td>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          },
          "metadata": {},
          "execution_count": 61
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Augment relevant columns to have hours and minutes element\n",
        "\n",
        "df['Daytime Hour'] = df['Order Date'].dt.hour\n",
        "df['Daytime Minute'] = df['Order Date'].dt.minute\n",
        "df.head(3)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 144
        },
        "id": "KpU_XmG1LIIM",
        "outputId": "0fbbfa9e-e57b-4e3f-a4e7-8954215746fd"
      },
      "id": "KpU_XmG1LIIM",
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   176558        USB-C Charging Cable                 2       11.95   \n",
              "1   176559  Bose SoundSport Headphones                 1       99.99   \n",
              "2   176560                Google Phone                 1      600.00   \n",
              "\n",
              "           Order Date                      Purchase Address   Total  Month  \\\n",
              "0 2019-04-19 08:46:00          917 1st St, Dallas, TX 75001   23.90      4   \n",
              "1 2019-04-07 22:30:00     682 Chestnut St, Boston, MA 02215   99.99      4   \n",
              "2 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001  600.00      4   \n",
              "\n",
              "               City  Daytime Hour  Daytime Minute  \n",
              "0        Dallas, TX             8              46  \n",
              "1        Boston, MA            22              30  \n",
              "2   Los Angeles, CA            14              38  "
            ],
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              "      <th>Order ID</th>\n",
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              "      <td>2019-04-19 08:46:00</td>\n",
              "      <td>917 1st St, Dallas, TX 75001</td>\n",
              "      <td>23.90</td>\n",
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              "      <td>Dallas, TX</td>\n",
              "      <td>8</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>176559</td>\n",
              "      <td>Bose SoundSport Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>99.99</td>\n",
              "      <td>2019-04-07 22:30:00</td>\n",
              "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
              "      <td>99.99</td>\n",
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              "      <td>30</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>176560</td>\n",
              "      <td>Google Phone</td>\n",
              "      <td>1</td>\n",
              "      <td>600.00</td>\n",
              "      <td>2019-04-12 14:38:00</td>\n",
              "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
              "      <td>600.00</td>\n",
              "      <td>4</td>\n",
              "      <td>Los Angeles, CA</td>\n",
              "      <td>14</td>\n",
              "      <td>38</td>\n",
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              "\n",
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              "\n",
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              "\n",
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              "          const element = document.querySelector('#df-7c11b2cf-c8e7-4da5-b151-44cf381f1b38');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
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            ]
          },
          "metadata": {},
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      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's visualize what times the orders came in on average.\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(8,5.2))\n",
        "df.groupby('Daytime Hour').count()['Quantity Ordered'].plot()\n",
        "ax.set(title = 'Timeline of Orders')\n",
        "ax.title.set(y=1.01)\n",
        "ax.set_xlabel('Daytime Hour', size=12)\n",
        "ax.set_ylabel('Quantity Ordered', size=12)\n",
        "labels = df['Daytime Hour'].unique()\n",
        "ax.set_xticks(labels)\n",
        "ax.grid(axis='x')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 366
        },
        "id": "Js0ie05ZLIFr",
        "outputId": "2ab0c6b0-47b5-4e1d-d8d9-02d62fa184e9"
      },
      "id": "Js0ie05ZLIFr",
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 576x374.4 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Let's then see which products were ordered together most often\n",
        "# How do I know that products were ordered together? \n",
        "#If you see the dataframe below, you can see that some ORDER ID are repeated for different products. \n",
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "Y3aOb8p3LICZ",
        "outputId": "6aed4520-4e8f-45ec-85b7-cd9c4010d151"
      },
      "id": "Y3aOb8p3LICZ",
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   176558        USB-C Charging Cable                 2       11.95   \n",
              "1   176559  Bose SoundSport Headphones                 1       99.99   \n",
              "2   176560                Google Phone                 1      600.00   \n",
              "3   176560            Wired Headphones                 1       11.99   \n",
              "4   176561            Wired Headphones                 1       11.99   \n",
              "\n",
              "           Order Date                      Purchase Address   Total  Month  \\\n",
              "0 2019-04-19 08:46:00          917 1st St, Dallas, TX 75001   23.90      4   \n",
              "1 2019-04-07 22:30:00     682 Chestnut St, Boston, MA 02215   99.99      4   \n",
              "2 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001  600.00      4   \n",
              "3 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001   11.99      4   \n",
              "4 2019-04-30 09:27:00     333 8th St, Los Angeles, CA 90001   11.99      4   \n",
              "\n",
              "               City  Daytime Hour  Daytime Minute  \n",
              "0        Dallas, TX             8              46  \n",
              "1        Boston, MA            22              30  \n",
              "2   Los Angeles, CA            14              38  \n",
              "3   Los Angeles, CA            14              38  \n",
              "4   Los Angeles, CA             9              27  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-45d05405-ad45-44ec-836c-704a22e9c1d6\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "    .dataframe tbody tr th:only-of-type {\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Order ID</th>\n",
              "      <th>Product</th>\n",
              "      <th>Quantity Ordered</th>\n",
              "      <th>Price Each</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Purchase Address</th>\n",
              "      <th>Total</th>\n",
              "      <th>Month</th>\n",
              "      <th>City</th>\n",
              "      <th>Daytime Hour</th>\n",
              "      <th>Daytime Minute</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>176558</td>\n",
              "      <td>USB-C Charging Cable</td>\n",
              "      <td>2</td>\n",
              "      <td>11.95</td>\n",
              "      <td>2019-04-19 08:46:00</td>\n",
              "      <td>917 1st St, Dallas, TX 75001</td>\n",
              "      <td>23.90</td>\n",
              "      <td>4</td>\n",
              "      <td>Dallas, TX</td>\n",
              "      <td>8</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>176559</td>\n",
              "      <td>Bose SoundSport Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>99.99</td>\n",
              "      <td>2019-04-07 22:30:00</td>\n",
              "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
              "      <td>99.99</td>\n",
              "      <td>4</td>\n",
              "      <td>Boston, MA</td>\n",
              "      <td>22</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>176560</td>\n",
              "      <td>Google Phone</td>\n",
              "      <td>1</td>\n",
              "      <td>600.00</td>\n",
              "      <td>2019-04-12 14:38:00</td>\n",
              "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
              "      <td>600.00</td>\n",
              "      <td>4</td>\n",
              "      <td>Los Angeles, CA</td>\n",
              "      <td>14</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>176560</td>\n",
              "      <td>Wired Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>11.99</td>\n",
              "      <td>2019-04-12 14:38:00</td>\n",
              "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
              "      <td>11.99</td>\n",
              "      <td>4</td>\n",
              "      <td>Los Angeles, CA</td>\n",
              "      <td>14</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>176561</td>\n",
              "      <td>Wired Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>11.99</td>\n",
              "      <td>2019-04-30 09:27:00</td>\n",
              "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
              "      <td>11.99</td>\n",
              "      <td>4</td>\n",
              "      <td>Los Angeles, CA</td>\n",
              "      <td>9</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\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",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-45d05405-ad45-44ec-836c-704a22e9c1d6 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-45d05405-ad45-44ec-836c-704a22e9c1d6');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# What products were bought together most?\n",
        "\n",
        "repeated_orders = df[df['Order ID'].duplicated(keep=False)]\n",
        "repeated_orders['Bought Together'] = repeated_orders.groupby('Order ID')['Product'].transform(lambda x: \",\".join(x))\n",
        "#repeated_orders = repeated_orders[['Order ID', 'Bought Together']]\n",
        "repeated_orders = repeated_orders[['Order ID', 'Bought Together']].drop_duplicates()\n",
        "repeated_orders.head(10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "id": "vMY1e3D3LH_P",
        "outputId": "affacd7a-0e5d-423e-9dc9-213b1e9e2dbd"
      },
      "id": "vMY1e3D3LH_P",
      "execution_count": 65,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "    Order ID                                    Bought Together\n",
              "2     176560                      Google Phone,Wired Headphones\n",
              "17    176574                  Google Phone,USB-C Charging Cable\n",
              "30    176586                AAA Batteries (4-pack),Google Phone\n",
              "117   176672      Lightning Charging Cable,USB-C Charging Cable\n",
              "127   176681           Apple Airpods Headphones,ThinkPad Laptop\n",
              "136   176689  Bose SoundSport Headphones,AAA Batteries (4-pack)\n",
              "187   176739                34in Ultrawide Monitor,Google Phone\n",
              "223   176774      Lightning Charging Cable,USB-C Charging Cable\n",
              "231   176781                    iPhone,Lightning Charging Cable\n",
              "248   176797  Google Phone,Bose SoundSport Headphones,Wired ..."
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-abeccb8a-bb37-4774-b765-49fa4eb3dc09\">\n",
              "    <div 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",
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              "\n",
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              "        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>Order ID</th>\n",
              "      <th>Bought Together</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>176560</td>\n",
              "      <td>Google Phone,Wired Headphones</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>176574</td>\n",
              "      <td>Google Phone,USB-C Charging Cable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>176586</td>\n",
              "      <td>AAA Batteries (4-pack),Google Phone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>117</th>\n",
              "      <td>176672</td>\n",
              "      <td>Lightning Charging Cable,USB-C Charging Cable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>127</th>\n",
              "      <td>176681</td>\n",
              "      <td>Apple Airpods Headphones,ThinkPad Laptop</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>136</th>\n",
              "      <td>176689</td>\n",
              "      <td>Bose SoundSport Headphones,AAA Batteries (4-pack)</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>187</th>\n",
              "      <td>176739</td>\n",
              "      <td>34in Ultrawide Monitor,Google Phone</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>223</th>\n",
              "      <td>176774</td>\n",
              "      <td>Lightning Charging Cable,USB-C Charging Cable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>231</th>\n",
              "      <td>176781</td>\n",
              "      <td>iPhone,Lightning Charging Cable</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>248</th>\n",
              "      <td>176797</td>\n",
              "      <td>Google Phone,Bose SoundSport Headphones,Wired ...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-abeccb8a-bb37-4774-b765-49fa4eb3dc09')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
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              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
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              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
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              "    }\n",
              "\n",
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              "    }\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-abeccb8a-bb37-4774-b765-49fa4eb3dc09 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-abeccb8a-bb37-4774-b765-49fa4eb3dc09');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 65
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# continuation of the operation from last cell\n",
        "\n",
        "count = Counter()\n",
        "for row in repeated_orders['Bought Together']:\n",
        "    row_list = row.split(',')\n",
        "    count.update(Counter(combinations(row_list, 2)))\n",
        "    \n",
        "for most_common, number_sold in count.most_common(10):\n",
        "    print(most_common, number_sold)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cTXiXOW8LH8W",
        "outputId": "6e8f8516-eef1-4d75-c8fd-8c1e9121e3cc"
      },
      "id": "cTXiXOW8LH8W",
      "execution_count": 66,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('iPhone', 'Lightning Charging Cable') 1002\n",
            "('Google Phone', 'USB-C Charging Cable') 985\n",
            "('iPhone', 'Wired Headphones') 447\n",
            "('Google Phone', 'Wired Headphones') 413\n",
            "('Vareebadd Phone', 'USB-C Charging Cable') 361\n",
            "('iPhone', 'Apple Airpods Headphones') 360\n",
            "('Google Phone', 'Bose SoundSport Headphones') 220\n",
            "('USB-C Charging Cable', 'Wired Headphones') 159\n",
            "('Vareebadd Phone', 'Wired Headphones') 143\n",
            "('Lightning Charging Cable', 'Wired Headphones') 92\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "count = Counter()\n",
        "for row in repeated_orders['Bought Together']:\n",
        "    row_list = row.split(',')\n",
        "    count.update(Counter(combinations(row_list, 3)))\n",
        "    \n",
        "for most_common, number_sold in count.most_common(10):\n",
        "    print(most_common, number_sold)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jPUzs3YaMLfK",
        "outputId": "c83e9ae6-cf3b-4702-f879-6573b3395a33"
      },
      "id": "jPUzs3YaMLfK",
      "execution_count": 67,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "('Google Phone', 'USB-C Charging Cable', 'Wired Headphones') 86\n",
            "('iPhone', 'Lightning Charging Cable', 'Wired Headphones') 62\n",
            "('iPhone', 'Lightning Charging Cable', 'Apple Airpods Headphones') 47\n",
            "('Google Phone', 'USB-C Charging Cable', 'Bose SoundSport Headphones') 35\n",
            "('Vareebadd Phone', 'USB-C Charging Cable', 'Wired Headphones') 33\n",
            "('iPhone', 'Apple Airpods Headphones', 'Wired Headphones') 27\n",
            "('Google Phone', 'Bose SoundSport Headphones', 'Wired Headphones') 24\n",
            "('Vareebadd Phone', 'USB-C Charging Cable', 'Bose SoundSport Headphones') 16\n",
            "('USB-C Charging Cable', 'Bose SoundSport Headphones', 'Wired Headphones') 5\n",
            "('Vareebadd Phone', 'Bose SoundSport Headphones', 'Wired Headphones') 5\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "GH-3HTJQXIt4",
        "outputId": "1a8b0c7b-9114-4019-d4a8-16b19894a408"
      },
      "id": "GH-3HTJQXIt4",
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Order ID                     Product  Quantity Ordered  Price Each  \\\n",
              "0   176558        USB-C Charging Cable                 2       11.95   \n",
              "1   176559  Bose SoundSport Headphones                 1       99.99   \n",
              "2   176560                Google Phone                 1      600.00   \n",
              "3   176560            Wired Headphones                 1       11.99   \n",
              "4   176561            Wired Headphones                 1       11.99   \n",
              "\n",
              "           Order Date                      Purchase Address   Total  Month  \\\n",
              "0 2019-04-19 08:46:00          917 1st St, Dallas, TX 75001   23.90      4   \n",
              "1 2019-04-07 22:30:00     682 Chestnut St, Boston, MA 02215   99.99      4   \n",
              "2 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001  600.00      4   \n",
              "3 2019-04-12 14:38:00  669 Spruce St, Los Angeles, CA 90001   11.99      4   \n",
              "4 2019-04-30 09:27:00     333 8th St, Los Angeles, CA 90001   11.99      4   \n",
              "\n",
              "               City  Daytime Hour  Daytime Minute  \n",
              "0        Dallas, TX             8              46  \n",
              "1        Boston, MA            22              30  \n",
              "2   Los Angeles, CA            14              38  \n",
              "3   Los Angeles, CA            14              38  \n",
              "4   Los Angeles, CA             9              27  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-a7aaa1bf-2aca-431d-bf78-a399af9c9bd1\">\n",
              "    <div 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>Order ID</th>\n",
              "      <th>Product</th>\n",
              "      <th>Quantity Ordered</th>\n",
              "      <th>Price Each</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Purchase Address</th>\n",
              "      <th>Total</th>\n",
              "      <th>Month</th>\n",
              "      <th>City</th>\n",
              "      <th>Daytime Hour</th>\n",
              "      <th>Daytime Minute</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>176558</td>\n",
              "      <td>USB-C Charging Cable</td>\n",
              "      <td>2</td>\n",
              "      <td>11.95</td>\n",
              "      <td>2019-04-19 08:46:00</td>\n",
              "      <td>917 1st St, Dallas, TX 75001</td>\n",
              "      <td>23.90</td>\n",
              "      <td>4</td>\n",
              "      <td>Dallas, TX</td>\n",
              "      <td>8</td>\n",
              "      <td>46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>176559</td>\n",
              "      <td>Bose SoundSport Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>99.99</td>\n",
              "      <td>2019-04-07 22:30:00</td>\n",
              "      <td>682 Chestnut St, Boston, MA 02215</td>\n",
              "      <td>99.99</td>\n",
              "      <td>4</td>\n",
              "      <td>Boston, MA</td>\n",
              "      <td>22</td>\n",
              "      <td>30</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>176560</td>\n",
              "      <td>Google Phone</td>\n",
              "      <td>1</td>\n",
              "      <td>600.00</td>\n",
              "      <td>2019-04-12 14:38:00</td>\n",
              "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
              "      <td>600.00</td>\n",
              "      <td>4</td>\n",
              "      <td>Los Angeles, CA</td>\n",
              "      <td>14</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>176560</td>\n",
              "      <td>Wired Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>11.99</td>\n",
              "      <td>2019-04-12 14:38:00</td>\n",
              "      <td>669 Spruce St, Los Angeles, CA 90001</td>\n",
              "      <td>11.99</td>\n",
              "      <td>4</td>\n",
              "      <td>Los Angeles, CA</td>\n",
              "      <td>14</td>\n",
              "      <td>38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>176561</td>\n",
              "      <td>Wired Headphones</td>\n",
              "      <td>1</td>\n",
              "      <td>11.99</td>\n",
              "      <td>2019-04-30 09:27:00</td>\n",
              "      <td>333 8th St, Los Angeles, CA 90001</td>\n",
              "      <td>11.99</td>\n",
              "      <td>4</td>\n",
              "      <td>Los Angeles, CA</td>\n",
              "      <td>9</td>\n",
              "      <td>27</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-a7aaa1bf-2aca-431d-bf78-a399af9c9bd1')\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
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              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-a7aaa1bf-2aca-431d-bf78-a399af9c9bd1 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-a7aaa1bf-2aca-431d-bf78-a399af9c9bd1');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create submission DataFrame\n",
        "df.to_csv(\"./drive/My Drive/MLstart/mobile/data.csv\", index = False)"
      ],
      "metadata": {
        "id": "gVE0f6mJXFJd"
      },
      "id": "gVE0f6mJXFJd",
      "execution_count": 78,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import files"
      ],
      "metadata": {
        "id": "2MH67I7ywUmF"
      },
      "id": "2MH67I7ywUmF",
      "execution_count": 80,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create submission csv file csv file\n",
        "df.to_csv(\"./drive/My Drive/MLstart/mobile/data.csv\", index = False)\n",
        "files.download('./drive/My Drive/MLstart/mobile/data.csv') "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 17
        },
        "id": "L44CWXUbWoo_",
        "outputId": "aed9646f-8c48-482c-d290-01f1434f8cd4"
      },
      "id": "L44CWXUbWoo_",
      "execution_count": 81,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "\n",
              "    async function download(id, filename, size) {\n",
              "      if (!google.colab.kernel.accessAllowed) {\n",
              "        return;\n",
              "      }\n",
              "      const div = document.createElement('div');\n",
              "      const label = document.createElement('label');\n",
              "      label.textContent = `Downloading \"${filename}\": `;\n",
              "      div.appendChild(label);\n",
              "      const progress = document.createElement('progress');\n",
              "      progress.max = size;\n",
              "      div.appendChild(progress);\n",
              "      document.body.appendChild(div);\n",
              "\n",
              "      const buffers = [];\n",
              "      let downloaded = 0;\n",
              "\n",
              "      const channel = await google.colab.kernel.comms.open(id);\n",
              "      // Send a message to notify the kernel that we're ready.\n",
              "      channel.send({})\n",
              "\n",
              "      for await (const message of channel.messages) {\n",
              "        // Send a message to notify the kernel that we're ready.\n",
              "        channel.send({})\n",
              "        if (message.buffers) {\n",
              "          for (const buffer of message.buffers) {\n",
              "            buffers.push(buffer);\n",
              "            downloaded += buffer.byteLength;\n",
              "            progress.value = downloaded;\n",
              "          }\n",
              "        }\n",
              "      }\n",
              "      const blob = new Blob(buffers, {type: 'application/binary'});\n",
              "      const a = document.createElement('a');\n",
              "      a.href = window.URL.createObjectURL(blob);\n",
              "      a.download = filename;\n",
              "      div.appendChild(a);\n",
              "      a.click();\n",
              "      div.remove();\n",
              "    }\n",
              "  "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Javascript object>"
            ],
            "application/javascript": [
              "download(\"download_be611a88-d620-42e9-bc68-2912d1e396ca\", \"data.csv\", 23004658)"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "id": "56d38f0f",
      "metadata": {
        "papermill": {
          "duration": 0.053766,
          "end_time": "2021-11-18T07:24:05.137524",
          "exception": false,
          "start_time": "2021-11-18T07:24:05.083758",
          "status": "completed"
        },
        "tags": [],
        "id": "56d38f0f"
      },
      "source": [
        "## Conclusion\n",
        "\n",
        "What can we do with the details of this analysis?\n",
        "\n",
        "1. We can decide to drop products that are not performing well in volume & revenue.\n",
        "2. If the company is struggling with runnig cost, store in Portland, ME can be closed.\n",
        "3. Peak time for orders can help in two ways; for focused advertising & also in terms of staff allocation/rotation.\n",
        "4. Products bought together can be offered as one package with discount, to drive sales.\n",
        "5. The Mac Book Pro was the most popular product.\n",
        "6. While the iPhone & its charger were the pair most frequently purchased.\n",
        "7. The outlet in San Francisco, CA was the best performing.\n",
        "\n",
        "\n",
        "Thank you for your time. Your comments, and constructive criticisms (if any) are welcome. "
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
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    "language_info": {
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        "version": 3
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      "mimetype": "text/x-python",
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      "pygments_lexer": "ipython3",
      "version": "3.7.10"
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    "papermill": {
      "default_parameters": {},
      "duration": 45.356116,
      "end_time": "2021-11-18T07:24:05.901178",
      "environment_variables": {},
      "exception": null,
      "input_path": "__notebook__.ipynb",
      "output_path": "__notebook__.ipynb",
      "parameters": {},
      "start_time": "2021-11-18T07:23:20.545062",
      "version": "2.3.3"
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    "colab": {
      "name": "mobile-gadgets-accessories-store-data-analysis.ipynb",
      "provenance": [],
      "collapsed_sections": []
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}