Time Series Analysis PART 1.ipynb

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      "cell_type": "markdown",
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        "<a href=\"https://colab.research.google.com/github/AC8151/COG_INTERNSHIP_GN22CDBDS001/blob/main/Time%20Series%20Analysis%20PART%201.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
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        "id": "BgqejilCX_d7",
        "outputId": "41300750-f271-4fee-96d7-f7bc148c6a42"
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      "outputs": [
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          "text": [
            "--2022-04-04 12:37:23--  https://github.com/a-forty-two/diamler_17_jan/blob/main/Sample-Superstore.xls?raw=true\n",
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            "--2022-04-04 12:37:23--  https://github.com/a-forty-two/diamler_17_jan/raw/main/Sample-Superstore.xls\n",
            "Reusing existing connection to github.com:443.\n",
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            "Location: https://raw.githubusercontent.com/a-forty-two/diamler_17_jan/main/Sample-Superstore.xls [following]\n",
            "--2022-04-04 12:37:23--  https://raw.githubusercontent.com/a-forty-two/diamler_17_jan/main/Sample-Superstore.xls\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 3364352 (3.2M) [application/octet-stream]\n",
            "Saving to: ‘Sample-Superstore.xls?raw=true’\n",
            "\n",
            "Sample-Superstore.x 100%[===================>]   3.21M  --.-KB/s    in 0.07s   \n",
            "\n",
            "2022-04-04 12:37:23 (47.5 MB/s) - ‘Sample-Superstore.xls?raw=true’ saved [3364352/3364352]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "!wget https://github.com/a-forty-two/diamler_17_jan/blob/main/Sample-Superstore.xls?raw=true"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!cp Sample-Superstore.xls?raw=true superstore.xls"
      ],
      "metadata": {
        "id": "Htq3LECtYF06"
      },
      "execution_count": 2,
      "outputs": []
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    {
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      "source": [
        "!ls"
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      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            " sample_data  'Sample-Superstore.xls?raw=true'\t superstore.xls\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install pandas==1.2.0\n",
        "!pip install --upgrade xlrd"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4v4W9zukYhj7",
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      "execution_count": 4,
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          "text": [
            "Collecting pandas==1.2.0\n",
            "  Downloading pandas-1.2.0-cp37-cp37m-manylinux1_x86_64.whl (9.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 9.9 MB 5.2 MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.16.5 in /usr/local/lib/python3.7/dist-packages (from pandas==1.2.0) (1.21.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.2.0) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.3 in /usr/local/lib/python3.7/dist-packages (from pandas==1.2.0) (2018.9)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.7/dist-packages (from python-dateutil>=2.7.3->pandas==1.2.0) (1.15.0)\n",
            "Installing collected packages: pandas\n",
            "  Attempting uninstall: pandas\n",
            "    Found existing installation: pandas 1.3.5\n",
            "    Uninstalling pandas-1.3.5:\n",
            "      Successfully uninstalled pandas-1.3.5\n",
            "Successfully installed pandas-1.2.0\n",
            "Requirement already satisfied: xlrd in /usr/local/lib/python3.7/dist-packages (1.1.0)\n",
            "Collecting xlrd\n",
            "  Downloading xlrd-2.0.1-py2.py3-none-any.whl (96 kB)\n",
            "\u001b[K     |████████████████████████████████| 96 kB 2.9 MB/s \n",
            "\u001b[?25hInstalling collected packages: xlrd\n",
            "  Attempting uninstall: xlrd\n",
            "    Found existing installation: xlrd 1.1.0\n",
            "    Uninstalling xlrd-1.1.0:\n",
            "      Successfully uninstalled xlrd-1.1.0\n",
            "Successfully installed xlrd-2.0.1\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "cjtlYCIdLyyQ"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data = pd.read_excel('superstore.xls')\n",
        "pd.set_option('display.max_rows', 500)"
      ],
      "metadata": {
        "id": "LinBPxQrYTrt"
      },
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "data.head()"
      ],
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          "height": 629
        },
        "id": "L4PehZhmYehR",
        "outputId": "52f4d362-7de2-4ca6-bd5a-6bb653744d04"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Row ID        Order ID Order Date  Ship Date       Ship Mode Customer ID  \\\n",
              "0       1  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
              "1       2  CA-2016-152156 2016-11-08 2016-11-11    Second Class    CG-12520   \n",
              "2       3  CA-2016-138688 2016-06-12 2016-06-16    Second Class    DV-13045   \n",
              "3       4  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
              "4       5  US-2015-108966 2015-10-11 2015-10-18  Standard Class    SO-20335   \n",
              "\n",
              "     Customer Name    Segment        Country             City  ...  \\\n",
              "0      Claire Gute   Consumer  United States        Henderson  ...   \n",
              "1      Claire Gute   Consumer  United States        Henderson  ...   \n",
              "2  Darrin Van Huff  Corporate  United States      Los Angeles  ...   \n",
              "3   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
              "4   Sean O'Donnell   Consumer  United States  Fort Lauderdale  ...   \n",
              "\n",
              "  Postal Code  Region       Product ID         Category Sub-Category  \\\n",
              "0       42420   South  FUR-BO-10001798        Furniture    Bookcases   \n",
              "1       42420   South  FUR-CH-10000454        Furniture       Chairs   \n",
              "2       90036    West  OFF-LA-10000240  Office Supplies       Labels   \n",
              "3       33311   South  FUR-TA-10000577        Furniture       Tables   \n",
              "4       33311   South  OFF-ST-10000760  Office Supplies      Storage   \n",
              "\n",
              "                                        Product Name     Sales  Quantity  \\\n",
              "0                  Bush Somerset Collection Bookcase  261.9600         2   \n",
              "1  Hon Deluxe Fabric Upholstered Stacking Chairs,...  731.9400         3   \n",
              "2  Self-Adhesive Address Labels for Typewriters b...   14.6200         2   \n",
              "3      Bretford CR4500 Series Slim Rectangular Table  957.5775         5   \n",
              "4                     Eldon Fold 'N Roll Cart System   22.3680         2   \n",
              "\n",
              "   Discount    Profit  \n",
              "0      0.00   41.9136  \n",
              "1      0.00  219.5820  \n",
              "2      0.00    6.8714  \n",
              "3      0.45 -383.0310  \n",
              "4      0.20    2.5164  \n",
              "\n",
              "[5 rows x 21 columns]"
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              "      <td>Claire Gute</td>\n",
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              "      <td>2016-11-11</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CG-12520</td>\n",
              "      <td>Claire Gute</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Henderson</td>\n",
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              "      <td>2016-06-16</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>DV-13045</td>\n",
              "      <td>Darrin Van Huff</td>\n",
              "      <td>Corporate</td>\n",
              "      <td>United States</td>\n",
              "      <td>Los Angeles</td>\n",
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              "      <td>90036</td>\n",
              "      <td>West</td>\n",
              "      <td>OFF-LA-10000240</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Labels</td>\n",
              "      <td>Self-Adhesive Address Labels for Typewriters b...</td>\n",
              "      <td>14.6200</td>\n",
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              "      <td>US-2015-108966</td>\n",
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              "      <td>2015-10-18</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>SO-20335</td>\n",
              "      <td>Sean O'Donnell</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Fort Lauderdale</td>\n",
              "      <td>...</td>\n",
              "      <td>33311</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-TA-10000577</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Tables</td>\n",
              "      <td>Bretford CR4500 Series Slim Rectangular Table</td>\n",
              "      <td>957.5775</td>\n",
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              "      <td>0.45</td>\n",
              "      <td>-383.0310</td>\n",
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              "      <td>US-2015-108966</td>\n",
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              "      <td>2015-10-18</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>SO-20335</td>\n",
              "      <td>Sean O'Donnell</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Fort Lauderdale</td>\n",
              "      <td>...</td>\n",
              "      <td>33311</td>\n",
              "      <td>South</td>\n",
              "      <td>OFF-ST-10000760</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Storage</td>\n",
              "      <td>Eldon Fold 'N Roll Cart System</td>\n",
              "      <td>22.3680</td>\n",
              "      <td>2</td>\n",
              "      <td>0.20</td>\n",
              "      <td>2.5164</td>\n",
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          "metadata": {},
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    },
    {
      "cell_type": "code",
      "source": [
        "data.info()\n",
        "# 9994  total\n",
        "# no null values"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aKgW-i9hZ8m4",
        "outputId": "81d9f3fd-7308-48a8-c48f-f098ea39a598"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9994 entries, 0 to 9993\n",
            "Data columns (total 21 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   Row ID         9994 non-null   int64         \n",
            " 1   Order ID       9994 non-null   object        \n",
            " 2   Order Date     9994 non-null   datetime64[ns]\n",
            " 3   Ship Date      9994 non-null   datetime64[ns]\n",
            " 4   Ship Mode      9994 non-null   object        \n",
            " 5   Customer ID    9994 non-null   object        \n",
            " 6   Customer Name  9994 non-null   object        \n",
            " 7   Segment        9994 non-null   object        \n",
            " 8   Country        9994 non-null   object        \n",
            " 9   City           9994 non-null   object        \n",
            " 10  State          9994 non-null   object        \n",
            " 11  Postal Code    9994 non-null   int64         \n",
            " 12  Region         9994 non-null   object        \n",
            " 13  Product ID     9994 non-null   object        \n",
            " 14  Category       9994 non-null   object        \n",
            " 15  Sub-Category   9994 non-null   object        \n",
            " 16  Product Name   9994 non-null   object        \n",
            " 17  Sales          9994 non-null   float64       \n",
            " 18  Quantity       9994 non-null   int64         \n",
            " 19  Discount       9994 non-null   float64       \n",
            " 20  Profit         9994 non-null   float64       \n",
            "dtypes: datetime64[ns](2), float64(3), int64(3), object(13)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# unique values exist per column! (CARDINALITY)\n",
        "for name in data.columns:\n",
        "  print(name , end=\" \")\n",
        "  print(data[name].value_counts())\n",
        "# too bulky to analyze, we just need to know per column \n",
        "# a COUNT of unique values!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qDsQ_HIXaBkh",
        "outputId": "8d4da5a5-e9ce-44a7-9b5b-89e9d5968264"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Row ID 2049    1\n",
            "8865    1\n",
            "6806    1\n",
            "4759    1\n",
            "8857    1\n",
            "       ..\n",
            "5432    1\n",
            "7481    1\n",
            "1338    1\n",
            "3387    1\n",
            "2047    1\n",
            "Name: Row ID, Length: 9994, dtype: int64\n",
            "Order ID CA-2017-100111    14\n",
            "CA-2017-157987    12\n",
            "US-2016-108504    11\n",
            "CA-2016-165330    11\n",
            "US-2015-126977    10\n",
            "                  ..\n",
            "CA-2015-162544     1\n",
            "CA-2017-126788     1\n",
            "US-2016-152373     1\n",
            "CA-2016-155446     1\n",
            "US-2017-117723     1\n",
            "Name: Order ID, Length: 5009, dtype: int64\n",
            "Order Date 2016-09-05    38\n",
            "2017-09-02    36\n",
            "2016-11-10    35\n",
            "2017-12-02    34\n",
            "2017-12-01    34\n",
            "              ..\n",
            "2016-02-25     1\n",
            "2014-01-15     1\n",
            "2014-09-24     1\n",
            "2017-07-12     1\n",
            "2015-12-09     1\n",
            "Name: Order Date, Length: 1237, dtype: int64\n",
            "Ship Date 2015-12-16    35\n",
            "2017-09-26    34\n",
            "2017-11-21    32\n",
            "2017-12-06    32\n",
            "2017-09-06    30\n",
            "              ..\n",
            "2014-12-24     1\n",
            "2017-04-06     1\n",
            "2016-08-07     1\n",
            "2016-08-25     1\n",
            "2017-07-22     1\n",
            "Name: Ship Date, Length: 1334, dtype: int64\n",
            "Ship Mode Standard Class    5968\n",
            "Second Class      1945\n",
            "First Class       1538\n",
            "Same Day           543\n",
            "Name: Ship Mode, dtype: int64\n",
            "Customer ID WB-21850    37\n",
            "JL-15835    34\n",
            "MA-17560    34\n",
            "PP-18955    34\n",
            "SV-20365    32\n",
            "            ..\n",
            "RE-19405     1\n",
            "AO-10810     1\n",
            "LD-16855     1\n",
            "JR-15700     1\n",
            "CJ-11875     1\n",
            "Name: Customer ID, Length: 793, dtype: int64\n",
            "Customer Name William Brown        37\n",
            "Paul Prost           34\n",
            "John Lee             34\n",
            "Matt Abelman         34\n",
            "Seth Vernon          32\n",
            "                     ..\n",
            "Carl Jackson          1\n",
            "Ricardo Emerson       1\n",
            "Lela Donovan          1\n",
            "Anthony O'Donnell     1\n",
            "Jocasta Rupert        1\n",
            "Name: Customer Name, Length: 793, dtype: int64\n",
            "Segment Consumer       5191\n",
            "Corporate      3020\n",
            "Home Office    1783\n",
            "Name: Segment, dtype: int64\n",
            "Country United States    9994\n",
            "Name: Country, dtype: int64\n",
            "City New York City    915\n",
            "Los Angeles      747\n",
            "Philadelphia     537\n",
            "San Francisco    510\n",
            "Seattle          428\n",
            "                ... \n",
            "Goldsboro          1\n",
            "Lake Elsinore      1\n",
            "Chapel Hill        1\n",
            "Waukesha           1\n",
            "Melbourne          1\n",
            "Name: City, Length: 531, dtype: int64\n",
            "State California              2001\n",
            "New York                1128\n",
            "Texas                    985\n",
            "Pennsylvania             587\n",
            "Washington               506\n",
            "Illinois                 492\n",
            "Ohio                     469\n",
            "Florida                  383\n",
            "Michigan                 255\n",
            "North Carolina           249\n",
            "Arizona                  224\n",
            "Virginia                 224\n",
            "Georgia                  184\n",
            "Tennessee                183\n",
            "Colorado                 182\n",
            "Indiana                  149\n",
            "Kentucky                 139\n",
            "Massachusetts            135\n",
            "New Jersey               130\n",
            "Oregon                   124\n",
            "Wisconsin                110\n",
            "Maryland                 105\n",
            "Delaware                  96\n",
            "Minnesota                 89\n",
            "Connecticut               82\n",
            "Missouri                  66\n",
            "Oklahoma                  66\n",
            "Alabama                   61\n",
            "Arkansas                  60\n",
            "Rhode Island              56\n",
            "Mississippi               53\n",
            "Utah                      53\n",
            "Louisiana                 42\n",
            "South Carolina            42\n",
            "Nevada                    39\n",
            "Nebraska                  38\n",
            "New Mexico                37\n",
            "Iowa                      30\n",
            "New Hampshire             27\n",
            "Kansas                    24\n",
            "Idaho                     21\n",
            "Montana                   15\n",
            "South Dakota              12\n",
            "Vermont                   11\n",
            "District of Columbia      10\n",
            "Maine                      8\n",
            "North Dakota               7\n",
            "West Virginia              4\n",
            "Wyoming                    1\n",
            "Name: State, dtype: int64\n",
            "Postal Code 10035    263\n",
            "10024    230\n",
            "10009    229\n",
            "94122    203\n",
            "10011    193\n",
            "        ... \n",
            "52240      1\n",
            "94509      1\n",
            "53186      1\n",
            "32503      1\n",
            "60025      1\n",
            "Name: Postal Code, Length: 631, dtype: int64\n",
            "Region West       3203\n",
            "East       2848\n",
            "Central    2323\n",
            "South      1620\n",
            "Name: Region, dtype: int64\n",
            "Product ID OFF-PA-10001970    19\n",
            "TEC-AC-10003832    18\n",
            "FUR-FU-10004270    16\n",
            "FUR-CH-10001146    15\n",
            "TEC-AC-10002049    15\n",
            "                   ..\n",
            "OFF-PA-10001001     1\n",
            "OFF-PA-10001661     1\n",
            "OFF-LA-10003388     1\n",
            "OFF-AP-10002203     1\n",
            "TEC-MA-10001695     1\n",
            "Name: Product ID, Length: 1862, dtype: int64\n",
            "Category Office Supplies    6026\n",
            "Furniture          2121\n",
            "Technology         1847\n",
            "Name: Category, dtype: int64\n",
            "Sub-Category Binders        1523\n",
            "Paper          1370\n",
            "Furnishings     957\n",
            "Phones          889\n",
            "Storage         846\n",
            "Art             796\n",
            "Accessories     775\n",
            "Chairs          617\n",
            "Appliances      466\n",
            "Labels          364\n",
            "Tables          319\n",
            "Envelopes       254\n",
            "Bookcases       228\n",
            "Fasteners       217\n",
            "Supplies        190\n",
            "Machines        115\n",
            "Copiers          68\n",
            "Name: Sub-Category, dtype: int64\n",
            "Product Name Staple envelope                                             48\n",
            "Staples                                                     46\n",
            "Easy-staple paper                                           46\n",
            "Avery Non-Stick Binders                                     20\n",
            "Staples in misc. colors                                     19\n",
            "                                                            ..\n",
            "Stride Job 150 Highlighters, Chisel Tip, Assorted Colors     1\n",
            "Socket Bluetooth Cordless Hand Scanner (CHS)                 1\n",
            "Xerox 20                                                     1\n",
            "Hewlett-Packard Desktjet 6988DT Refurbished Printer          1\n",
            "Cubify CubeX 3D Printer Triple Head Print                    1\n",
            "Name: Product Name, Length: 1850, dtype: int64\n",
            "Sales 12.960     56\n",
            "15.552     39\n",
            "19.440     39\n",
            "10.368     36\n",
            "32.400     28\n",
            "           ..\n",
            "775.728     1\n",
            "65.520      1\n",
            "83.136      1\n",
            "114.460     1\n",
            "334.768     1\n",
            "Name: Sales, Length: 6144, dtype: int64\n",
            "Quantity 3     2409\n",
            "2     2402\n",
            "5     1230\n",
            "4     1191\n",
            "1      899\n",
            "7      606\n",
            "6      572\n",
            "9      258\n",
            "8      257\n",
            "10      57\n",
            "11      34\n",
            "14      29\n",
            "13      27\n",
            "12      23\n",
            "Name: Quantity, dtype: int64\n",
            "Discount 0.00    4798\n",
            "0.20    3657\n",
            "0.70     418\n",
            "0.80     300\n",
            "0.30     227\n",
            "0.40     206\n",
            "0.60     138\n",
            "0.10      94\n",
            "0.50      66\n",
            "0.15      52\n",
            "0.32      27\n",
            "0.45      11\n",
            "Name: Discount, dtype: int64\n",
            "Profit  0.0000     65\n",
            " 6.2208     43\n",
            " 9.3312     38\n",
            " 5.4432     32\n",
            " 3.6288     32\n",
            "            ..\n",
            " 6.7032      1\n",
            " 46.8996     1\n",
            " 5.5341      1\n",
            "-4.0600      1\n",
            "-10.9611     1\n",
            "Name: Profit, Length: 7545, dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# CARDINALITY\n",
        "for name in data.columns:\n",
        "  print(name , end=\" \")\n",
        "  # Aggregate to view smaller but more readable data!\n",
        "  print(data[name].value_counts().count())"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pq7oCfTBabzd",
        "outputId": "98dc7a2f-380d-4846-ce62-087aedb330dc"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Row ID 9994\n",
            "Order ID 5009\n",
            "Order Date 1237\n",
            "Ship Date 1334\n",
            "Ship Mode 4\n",
            "Customer ID 793\n",
            "Customer Name 793\n",
            "Segment 3\n",
            "Country 1\n",
            "City 531\n",
            "State 49\n",
            "Postal Code 631\n",
            "Region 4\n",
            "Product ID 1862\n",
            "Category 3\n",
            "Sub-Category 17\n",
            "Product Name 1850\n",
            "Sales 6144\n",
            "Quantity 14\n",
            "Discount 12\n",
            "Profit 7545\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "OBSERVATION: \n",
        "> columns Sales, Quantity, Discount, Profit can be chosen for continuous data\n",
        "> but Quantity, Discount appear to be DISCRETE (less values)\n",
        "> now Profit is more varied than Sales, as it depends on Discount\n",
        "\n",
        "**Therefore, we consider Sales as the continuous data, on which we will create a time-series analysis**"
      ],
      "metadata": {
        "id": "n3R4chP7lq02"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.tail()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 612
        },
        "id": "Qm7nSnsMcIdt",
        "outputId": "2b9a53b4-01d4-4e3c-b4be-1b393ece856d"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "      Row ID        Order ID Order Date  Ship Date       Ship Mode  \\\n",
              "9989    9990  CA-2014-110422 2014-01-21 2014-01-23    Second Class   \n",
              "9990    9991  CA-2017-121258 2017-02-26 2017-03-03  Standard Class   \n",
              "9991    9992  CA-2017-121258 2017-02-26 2017-03-03  Standard Class   \n",
              "9992    9993  CA-2017-121258 2017-02-26 2017-03-03  Standard Class   \n",
              "9993    9994  CA-2017-119914 2017-05-04 2017-05-09    Second Class   \n",
              "\n",
              "     Customer ID     Customer Name   Segment        Country         City  ...  \\\n",
              "9989    TB-21400  Tom Boeckenhauer  Consumer  United States        Miami  ...   \n",
              "9990    DB-13060       Dave Brooks  Consumer  United States   Costa Mesa  ...   \n",
              "9991    DB-13060       Dave Brooks  Consumer  United States   Costa Mesa  ...   \n",
              "9992    DB-13060       Dave Brooks  Consumer  United States   Costa Mesa  ...   \n",
              "9993    CC-12220      Chris Cortes  Consumer  United States  Westminster  ...   \n",
              "\n",
              "     Postal Code  Region       Product ID         Category Sub-Category  \\\n",
              "9989       33180   South  FUR-FU-10001889        Furniture  Furnishings   \n",
              "9990       92627    West  FUR-FU-10000747        Furniture  Furnishings   \n",
              "9991       92627    West  TEC-PH-10003645       Technology       Phones   \n",
              "9992       92627    West  OFF-PA-10004041  Office Supplies        Paper   \n",
              "9993       92683    West  OFF-AP-10002684  Office Supplies   Appliances   \n",
              "\n",
              "                                           Product Name    Sales  Quantity  \\\n",
              "9989                             Ultra Door Pull Handle   25.248         3   \n",
              "9990  Tenex B1-RE Series Chair Mats for Low Pile Car...   91.960         2   \n",
              "9991                              Aastra 57i VoIP phone  258.576         2   \n",
              "9992  It's Hot Message Books with Stickers, 2 3/4\" x 5\"   29.600         4   \n",
              "9993  Acco 7-Outlet Masterpiece Power Center, Wihtou...  243.160         2   \n",
              "\n",
              "      Discount   Profit  \n",
              "9989       0.2   4.1028  \n",
              "9990       0.0  15.6332  \n",
              "9991       0.2  19.3932  \n",
              "9992       0.0  13.3200  \n",
              "9993       0.0  72.9480  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-edfa6cd7-fe8d-4957-84d5-f7867a5fb28a\">\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>Row ID</th>\n",
              "      <th>Order ID</th>\n",
              "      <th>Order Date</th>\n",
              "      <th>Ship Date</th>\n",
              "      <th>Ship Mode</th>\n",
              "      <th>Customer ID</th>\n",
              "      <th>Customer Name</th>\n",
              "      <th>Segment</th>\n",
              "      <th>Country</th>\n",
              "      <th>City</th>\n",
              "      <th>...</th>\n",
              "      <th>Postal Code</th>\n",
              "      <th>Region</th>\n",
              "      <th>Product ID</th>\n",
              "      <th>Category</th>\n",
              "      <th>Sub-Category</th>\n",
              "      <th>Product Name</th>\n",
              "      <th>Sales</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>Discount</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>9989</th>\n",
              "      <td>9990</td>\n",
              "      <td>CA-2014-110422</td>\n",
              "      <td>2014-01-21</td>\n",
              "      <td>2014-01-23</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>TB-21400</td>\n",
              "      <td>Tom Boeckenhauer</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Miami</td>\n",
              "      <td>...</td>\n",
              "      <td>33180</td>\n",
              "      <td>South</td>\n",
              "      <td>FUR-FU-10001889</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Furnishings</td>\n",
              "      <td>Ultra Door Pull Handle</td>\n",
              "      <td>25.248</td>\n",
              "      <td>3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>4.1028</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9990</th>\n",
              "      <td>9991</td>\n",
              "      <td>CA-2017-121258</td>\n",
              "      <td>2017-02-26</td>\n",
              "      <td>2017-03-03</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>DB-13060</td>\n",
              "      <td>Dave Brooks</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Costa Mesa</td>\n",
              "      <td>...</td>\n",
              "      <td>92627</td>\n",
              "      <td>West</td>\n",
              "      <td>FUR-FU-10000747</td>\n",
              "      <td>Furniture</td>\n",
              "      <td>Furnishings</td>\n",
              "      <td>Tenex B1-RE Series Chair Mats for Low Pile Car...</td>\n",
              "      <td>91.960</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>15.6332</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9991</th>\n",
              "      <td>9992</td>\n",
              "      <td>CA-2017-121258</td>\n",
              "      <td>2017-02-26</td>\n",
              "      <td>2017-03-03</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>DB-13060</td>\n",
              "      <td>Dave Brooks</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Costa Mesa</td>\n",
              "      <td>...</td>\n",
              "      <td>92627</td>\n",
              "      <td>West</td>\n",
              "      <td>TEC-PH-10003645</td>\n",
              "      <td>Technology</td>\n",
              "      <td>Phones</td>\n",
              "      <td>Aastra 57i VoIP phone</td>\n",
              "      <td>258.576</td>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>19.3932</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9992</th>\n",
              "      <td>9993</td>\n",
              "      <td>CA-2017-121258</td>\n",
              "      <td>2017-02-26</td>\n",
              "      <td>2017-03-03</td>\n",
              "      <td>Standard Class</td>\n",
              "      <td>DB-13060</td>\n",
              "      <td>Dave Brooks</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Costa Mesa</td>\n",
              "      <td>...</td>\n",
              "      <td>92627</td>\n",
              "      <td>West</td>\n",
              "      <td>OFF-PA-10004041</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Paper</td>\n",
              "      <td>It's Hot Message Books with Stickers, 2 3/4\" x 5\"</td>\n",
              "      <td>29.600</td>\n",
              "      <td>4</td>\n",
              "      <td>0.0</td>\n",
              "      <td>13.3200</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9993</th>\n",
              "      <td>9994</td>\n",
              "      <td>CA-2017-119914</td>\n",
              "      <td>2017-05-04</td>\n",
              "      <td>2017-05-09</td>\n",
              "      <td>Second Class</td>\n",
              "      <td>CC-12220</td>\n",
              "      <td>Chris Cortes</td>\n",
              "      <td>Consumer</td>\n",
              "      <td>United States</td>\n",
              "      <td>Westminster</td>\n",
              "      <td>...</td>\n",
              "      <td>92683</td>\n",
              "      <td>West</td>\n",
              "      <td>OFF-AP-10002684</td>\n",
              "      <td>Office Supplies</td>\n",
              "      <td>Appliances</td>\n",
              "      <td>Acco 7-Outlet Masterpiece Power Center, Wihtou...</td>\n",
              "      <td>243.160</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0</td>\n",
              "      <td>72.9480</td>\n",
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          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Order Date']\n",
        "# multiple date entries in the record!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vKMnhhb1cJLk",
        "outputId": "07891137-592c-4d2f-df3d-ce473396239e"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0      2016-11-08\n",
              "1      2016-11-08\n",
              "2      2016-06-12\n",
              "3      2015-10-11\n",
              "4      2015-10-11\n",
              "          ...    \n",
              "9989   2014-01-21\n",
              "9990   2017-02-26\n",
              "9991   2017-02-26\n",
              "9992   2017-02-26\n",
              "9993   2017-05-04\n",
              "Name: Order Date, Length: 9994, dtype: datetime64[ns]"
            ]
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          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "data['Order Date'].min(), data['Order Date'].max()\n",
        "# OBSERVATION-> Jan1,2 of 2014 is missing!\n",
        "# 31st dec of 2017-> missing NOT null!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VhaQPGaCc_Mi",
        "outputId": "4e8c2488-d139-4baf-c06a-70df81385d5a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(Timestamp('2014-01-03 00:00:00'), Timestamp('2017-12-30 00:00:00'))"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = data.groupby('Order Date')\n",
        "df['Order Date'].value_counts().count()\n",
        "# ONLY 1237 entries for all the days of 2014 to 2017!"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZUxWlJO6dKoB",
        "outputId": "cfd35985-410b-42ab-db8c-6a9bef9894d2"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1237"
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "OBSERVATION:\n",
        "* 4 years X 365.25 days = 1461 (expected)\n",
        "* reality-> 1237 entries!\n",
        "> Missing values= 1461 - 1237 = 224 \n",
        "\n",
        "**Therefore, 224 values need to be filled/created *(RESAMPLE)***"
      ],
      "metadata": {
        "id": "_p4F-vWCmYJN"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "data.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kVu19Zac_6o4",
        "outputId": "ac0177c9-4508-4fa7-886d-f3c0705d4153"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 9994 entries, 0 to 9993\n",
            "Data columns (total 21 columns):\n",
            " #   Column         Non-Null Count  Dtype         \n",
            "---  ------         --------------  -----         \n",
            " 0   Row ID         9994 non-null   int64         \n",
            " 1   Order ID       9994 non-null   object        \n",
            " 2   Order Date     9994 non-null   datetime64[ns]\n",
            " 3   Ship Date      9994 non-null   datetime64[ns]\n",
            " 4   Ship Mode      9994 non-null   object        \n",
            " 5   Customer ID    9994 non-null   object        \n",
            " 6   Customer Name  9994 non-null   object        \n",
            " 7   Segment        9994 non-null   object        \n",
            " 8   Country        9994 non-null   object        \n",
            " 9   City           9994 non-null   object        \n",
            " 10  State          9994 non-null   object        \n",
            " 11  Postal Code    9994 non-null   int64         \n",
            " 12  Region         9994 non-null   object        \n",
            " 13  Product ID     9994 non-null   object        \n",
            " 14  Category       9994 non-null   object        \n",
            " 15  Sub-Category   9994 non-null   object        \n",
            " 16  Product Name   9994 non-null   object        \n",
            " 17  Sales          9994 non-null   float64       \n",
            " 18  Quantity       9994 non-null   int64         \n",
            " 19  Discount       9994 non-null   float64       \n",
            " 20  Profit         9994 non-null   float64       \n",
            "dtypes: datetime64[ns](2), float64(3), int64(3), object(13)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# creating time-series dataset\n",
        "monthly_df=data[['Order Date', 'Sales']].copy()\n",
        "monthly_df.set_index('Order Date', inplace=True)\n",
        "monthly_df.head()"
      ],
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      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "               Sales\n",
              "Order Date          \n",
              "2016-11-08  261.9600\n",
              "2016-11-08  731.9400\n",
              "2016-06-12   14.6200\n",
              "2015-10-11  957.5775\n",
              "2015-10-11   22.3680"
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        "OBSERVATION:\n",
        "* Resampling is done by **MONTH (MS)** because\n",
        "> Resampling by 1D leads to 4x365.25 dates => repeated values <br>\n",
        "> Each month has different no. of days => resampled by avg. dates"
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              "                  Sales\n",
              "Order Date             \n",
              "2014-01-01   14236.8950\n",
              "2014-02-01    4519.8920\n",
              "2014-03-01   55691.0090\n",
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              "2015-01-01   18174.0756\n",
              "2015-02-01   11951.4110\n",
              "2015-03-01   38726.2520\n",
              "2015-04-01   34195.2085\n",
              "2015-05-01   30131.6865\n",
              "2015-06-01   24797.2920\n",
              "2015-07-01   28765.3250\n",
              "2015-08-01   36898.3322\n",
              "2015-09-01   64595.9180\n",
              "2015-10-01   31404.9235\n",
              "2015-11-01   75972.5635\n",
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              "2016-12-01   96999.0430\n",
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              "2017-11-01  118447.8250\n",
              "2017-12-01   83829.3188"
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              "      <td>33946.3930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-08-01</th>\n",
              "      <td>27909.4685</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-09-01</th>\n",
              "      <td>81777.3508</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-10-01</th>\n",
              "      <td>31453.3930</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-11-01</th>\n",
              "      <td>78628.7167</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2014-12-01</th>\n",
              "      <td>69545.6205</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-01-01</th>\n",
              "      <td>18174.0756</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-02-01</th>\n",
              "      <td>11951.4110</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-03-01</th>\n",
              "      <td>38726.2520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-04-01</th>\n",
              "      <td>34195.2085</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-05-01</th>\n",
              "      <td>30131.6865</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-06-01</th>\n",
              "      <td>24797.2920</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-07-01</th>\n",
              "      <td>28765.3250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-08-01</th>\n",
              "      <td>36898.3322</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-09-01</th>\n",
              "      <td>64595.9180</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-10-01</th>\n",
              "      <td>31404.9235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-11-01</th>\n",
              "      <td>75972.5635</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2015-12-01</th>\n",
              "      <td>74919.5212</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-01-01</th>\n",
              "      <td>18542.4910</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-02-01</th>\n",
              "      <td>22978.8150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-03-01</th>\n",
              "      <td>51715.8750</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-04-01</th>\n",
              "      <td>38750.0390</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-05-01</th>\n",
              "      <td>56987.7280</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-06-01</th>\n",
              "      <td>40344.5340</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-07-01</th>\n",
              "      <td>39261.9630</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-08-01</th>\n",
              "      <td>31115.3743</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-09-01</th>\n",
              "      <td>73410.0249</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-10-01</th>\n",
              "      <td>59687.7450</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-11-01</th>\n",
              "      <td>79411.9658</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2016-12-01</th>\n",
              "      <td>96999.0430</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-01-01</th>\n",
              "      <td>43971.3740</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-02-01</th>\n",
              "      <td>20301.1334</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-03-01</th>\n",
              "      <td>58872.3528</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-04-01</th>\n",
              "      <td>36521.5361</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-05-01</th>\n",
              "      <td>44261.1102</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-06-01</th>\n",
              "      <td>52981.7257</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-07-01</th>\n",
              "      <td>45264.4160</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-08-01</th>\n",
              "      <td>63120.8880</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-09-01</th>\n",
              "      <td>87866.6520</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-10-01</th>\n",
              "      <td>77776.9232</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-11-01</th>\n",
              "      <td>118447.8250</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2017-12-01</th>\n",
              "      <td>83829.3188</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
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              "                                                     [key], {});\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "monthly_df.plot(figsize=(10,5), title='Sales Trend over Order Date')\n",
        "plt.xlabel('Order Date')\n",
        "plt.ylabel('Sales')"
      ],
      "metadata": {
        "id": "CmvEmq4pHRvQ",
        "outputId": "207a5dfc-f064-4b50-b62f-83ea0e922eef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 378
        }
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0, 0.5, 'Sales')"
            ]
          },
          "metadata": {},
          "execution_count": 22
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "OBSERVATION:\n",
        "1. Sales trend is **POSITIVE**\n",
        "2. Sales **INCREASES FROM JULY-DEC (FESTIVE)**\n",
        "3. Sales **LESS FROM JAN-JUNE (MONEY SPENT)**"
      ],
      "metadata": {
        "id": "2vezDJDdQnup"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# vectors->\n",
        "# PERIOD or SEASON \n",
        "# dy/dx-> SLOPE of the data or DIRECTION of the graph\n",
        "# NOISE or RESIDUE\n",
        "\n",
        "# data -> dy/dx or slope is always constant \n",
        "#.     -> PERIOD-> sin + cos function that can be consistently\n",
        "#             placed on data\n",
        "#       -> whatever is left is called NOISE\n",
        "\n",
        "# ORIGINAL_GRAPH = SLOPE + Season + Noise\n",
        "# we predict the slope in time (always fixed)\n",
        "# we predict season in time (manipulate)\n",
        "# we predict noise in time (randomness)\n",
        "# we add the results to get the original prediction!"
      ],
      "metadata": {
        "id": "bXxavIsiMX2t"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "OBSERVATION:\n",
        "> There is a need to visualize these observations above properly. <br>\n",
        "**Thus, we DECOMPOSE the graph into 3 to view TREND, SEASONAL TREND (PATTERN), NOISE (EXTRA)**"
      ],
      "metadata": {
        "id": "Qk3tL0MhoFE7"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import statsmodels.api as sm\n",
        "decomposition = sm.tsa.seasonal_decompose(monthly_df, model='additive') # additive means graphs trend + seasonal + residual = observed\n",
        "decomposition.plot()\n",
        "plt.show()\n",
        "\n",
        "# Trend (T)    => POSITIVE\n",
        "# Seasonal (S) => Sales increases after July, Dips in January-Feb, Then gradually increases\n",
        "# Residual (R) => Noise"
      ],
      "metadata": {
        "id": "JPBA4WexoCb1",
        "outputId": "73f87342-8865-4892-eba3-2f79fc15e4ed",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 299
        }
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 4 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "-4kCtpB-szAw"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}