VendorRating.ipynb

{
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  "metadata": {
    "colab": {
      "name": "VendorRating.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "kOeoc6mTuMgh",
        "colab_type": "code",
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 72
        },
        "outputId": "d95e9370-eb3b-40d6-8fd2-ae1321541fd0"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded = files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-805e2880-486a-4631-97ea-f934fc8e4527\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-805e2880-486a-4631-97ea-f934fc8e4527\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving compiled.csv to compiled.csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RtIOyLS-wPbb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 71
        },
        "outputId": "3f6e177f-8144-407e-e00b-24115da2fec6"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from plotly.offline import init_notebook_mode,iplot\n",
        "import plotly.graph_objects as go\n",
        "import cufflinks as cf\n",
        "import numpy as np\n",
        "init_notebook_mode(connected=True)\n",
        "import plotly.express as px\n",
        "import matplotlib.pyplot as plt\n",
        "import io\n",
        "import warnings\n",
        "warnings.filterwarnings('ignore')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/statsmodels/tools/_testing.py:19: FutureWarning: pandas.util.testing is deprecated. Use the functions in the public API at pandas.testing instead.\n",
            "  import pandas.util.testing as tm\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "        <script type=\"text/javascript\">\n",
              "        window.PlotlyConfig = {MathJaxConfig: 'local'};\n",
              "        if (window.MathJax) {MathJax.Hub.Config({SVG: {font: \"STIX-Web\"}});}\n",
              "        if (typeof require !== 'undefined') {\n",
              "        require.undef(\"plotly\");\n",
              "        requirejs.config({\n",
              "            paths: {\n",
              "                'plotly': ['https://cdn.plot.ly/plotly-latest.min']\n",
              "            }\n",
              "        });\n",
              "        require(['plotly'], function(Plotly) {\n",
              "            window._Plotly = Plotly;\n",
              "        });\n",
              "        }\n",
              "        </script>\n",
              "        "
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "vo3R3Vhi-iZa",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "df = pd.read_csv(io.StringIO(uploaded['compiled.csv'].decode('utf-8')))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JHgT7BfBxKJg",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 134
        },
        "outputId": "a20d84cc-ee2f-45f8-e4aa-b019fa43e107"
      },
      "source": [
        "df.iloc[26:27 ]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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>Vendor</th>\n",
              "      <th>PurchaseOrder</th>\n",
              "      <th>Item</th>\n",
              "      <th>Description</th>\n",
              "      <th>Quantity</th>\n",
              "      <th>CancelQuantity</th>\n",
              "      <th>QuantityDemandedFinal</th>\n",
              "      <th>EnteredReceivedQuantity</th>\n",
              "      <th>EarlyDeliveryDate</th>\n",
              "      <th>ReceivedDate</th>\n",
              "      <th>DeliveryTime</th>\n",
              "      <th>RemainingQuantity</th>\n",
              "      <th>PercentOfQuantityReceived</th>\n",
              "      <th>ReturnQuantity</th>\n",
              "      <th>PercentOfQuantityReturned</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>8</td>\n",
              "      <td>49</td>\n",
              "      <td>34</td>\n",
              "      <td>STEM HIP FITMORE  A SIZE 4.5</td>\n",
              "      <td>3.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>3</td>\n",
              "      <td>2017-05-18</td>\n",
              "      <td>2017-05-09</td>\n",
              "      <td>9</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>0.666667</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "    Vendor  PurchaseOrder  ... ReturnQuantity PercentOfQuantityReturned\n",
              "26       8             49  ...            2.0                  0.666667\n",
              "\n",
              "[1 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_M8CRom8xQyT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "outputId": "bc537625-bc0c-4ae4-c758-10707a26f943"
      },
      "source": [
        "df.info()\n",
        "# RemainingQty = EnteredReceivedQuantity - QuantityDemandedFinal\n",
        "# DeliveryTime = EarlyDeliveryDate - ReceivedDate"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1503 entries, 0 to 1502\n",
            "Data columns (total 15 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   Vendor                     1503 non-null   int64  \n",
            " 1   PurchaseOrder              1503 non-null   int64  \n",
            " 2   Item                       1503 non-null   object \n",
            " 3   Description                1503 non-null   object \n",
            " 4   Quantity                   1503 non-null   float64\n",
            " 5   CancelQuantity             1503 non-null   float64\n",
            " 6   QuantityDemandedFinal      1503 non-null   float64\n",
            " 7   EnteredReceivedQuantity    1503 non-null   int64  \n",
            " 8   EarlyDeliveryDate          1503 non-null   object \n",
            " 9   ReceivedDate               1503 non-null   object \n",
            " 10  DeliveryTime               1503 non-null   int64  \n",
            " 11  RemainingQuantity          1503 non-null   float64\n",
            " 12  PercentOfQuantityReceived  1503 non-null   float64\n",
            " 13  ReturnQuantity             1503 non-null   float64\n",
            " 14  PercentOfQuantityReturned  1496 non-null   float64\n",
            "dtypes: float64(7), int64(4), object(4)\n",
            "memory usage: 176.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HFE4LC496qAH",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "9d24296f-b7e3-4d3c-de53-83b90850a15b"
      },
      "source": [
        "# Because there are some null values in PercentOfQuantityReturned\n",
        "df.fillna(0,inplace=True)\n",
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1503 entries, 0 to 1502\n",
            "Data columns (total 15 columns):\n",
            " #   Column                     Non-Null Count  Dtype  \n",
            "---  ------                     --------------  -----  \n",
            " 0   Vendor                     1503 non-null   int64  \n",
            " 1   PurchaseOrder              1503 non-null   int64  \n",
            " 2   Item                       1503 non-null   object \n",
            " 3   Description                1503 non-null   object \n",
            " 4   Quantity                   1503 non-null   float64\n",
            " 5   CancelQuantity             1503 non-null   float64\n",
            " 6   QuantityDemandedFinal      1503 non-null   float64\n",
            " 7   EnteredReceivedQuantity    1503 non-null   int64  \n",
            " 8   EarlyDeliveryDate          1503 non-null   object \n",
            " 9   ReceivedDate               1503 non-null   object \n",
            " 10  DeliveryTime               1503 non-null   int64  \n",
            " 11  RemainingQuantity          1503 non-null   float64\n",
            " 12  PercentOfQuantityReceived  1503 non-null   float64\n",
            " 13  ReturnQuantity             1503 non-null   float64\n",
            " 14  PercentOfQuantityReturned  1503 non-null   float64\n",
            "dtypes: float64(7), int64(4), object(4)\n",
            "memory usage: 176.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DOQgfQYw2Ixl",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 386
        },
        "outputId": "eac606bd-75d4-4dcc-d978-f22fee54813b"
      },
      "source": [
        "df['EarlyDeliveryDate'] = pd.to_datetime(df['EarlyDeliveryDate'])\n",
        "df['ReceivedDate'] = pd.to_datetime(df['ReceivedDate'])\n",
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1503 entries, 0 to 1502\n",
            "Data columns (total 15 columns):\n",
            " #   Column                     Non-Null Count  Dtype         \n",
            "---  ------                     --------------  -----         \n",
            " 0   Vendor                     1503 non-null   int64         \n",
            " 1   PurchaseOrder              1503 non-null   int64         \n",
            " 2   Item                       1503 non-null   object        \n",
            " 3   Description                1503 non-null   object        \n",
            " 4   Quantity                   1503 non-null   float64       \n",
            " 5   CancelQuantity             1503 non-null   float64       \n",
            " 6   QuantityDemandedFinal      1503 non-null   float64       \n",
            " 7   EnteredReceivedQuantity    1503 non-null   int64         \n",
            " 8   EarlyDeliveryDate          1503 non-null   datetime64[ns]\n",
            " 9   ReceivedDate               1503 non-null   datetime64[ns]\n",
            " 10  DeliveryTime               1503 non-null   int64         \n",
            " 11  RemainingQuantity          1503 non-null   float64       \n",
            " 12  PercentOfQuantityReceived  1503 non-null   float64       \n",
            " 13  ReturnQuantity             1503 non-null   float64       \n",
            " 14  PercentOfQuantityReturned  1503 non-null   float64       \n",
            "dtypes: datetime64[ns](2), float64(7), int64(4), object(2)\n",
            "memory usage: 176.3+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iTF6WqaM3m1m",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "41f34c48-5502-4d54-a5f7-55564b8f2435"
      },
      "source": [
        "vendor1=df[df['Vendor']==1].count()[0]\n",
        "vendor2=df[df['Vendor']==2].count()[0]\n",
        "vendor5=df[df['Vendor']==5].count()[0]\n",
        "vendor8=df[df['Vendor']==8].count()[0]\n",
        "Remaining_vendors=df[(df['Vendor']!=1) & (df['Vendor']!=2) & (df['Vendor']!=5) & (df['Vendor']!=8)].count()[0]\n",
        "\n",
        "trace = go.Bar(x = [\"Vendor1\",\"Vendor2\",\"Vendor5\",\"Vendor8\",\"Remaining_Vendors\"],y=[vendor1,vendor2,vendor5,vendor8,Remaining_vendors],marker=dict(color=[vendor1,vendor2,vendor5,vendor8,Remaining_vendors],colorscale='Viridis',showscale=True))\n",
        "data=[trace]\n",
        "#defining layout\n",
        "layout = go.Layout(xaxis=dict(title='VendorID'),yaxis=dict(title='Top 4 vendors Vs Remaining_Vendors '),hovermode='closest')\n",
        "#defining figure and plotting\n",
        "figure = go.Figure(data=data,layout=layout)\n",
        "figure.update_layout(paper_bgcolor=\"LightSteelBlue\")\n",
        "figure.show(renderer='colab')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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          },
          "metadata": {
            "tags": []
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      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rHCUsA0KICb2",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "Vendor1_late=len(df[df['Vendor'] == 1]['DeliveryTime'][df[df['Vendor'] == 1]['DeliveryTime'] < 0])\n",
        "Vendor1_total=len(df[df['Vendor'] == 1]['DeliveryTime'])\n",
        "Vendor1_fraction=Vendor1_late/Vendor1_total\n",
        "Vendor2_late=len(df[df['Vendor'] == 2]['DeliveryTime'][df[df['Vendor'] == 2]['DeliveryTime'] < 0])\n",
        "Vendor2_total=len(df[df['Vendor'] == 2]['DeliveryTime'])\n",
        "Vendor2_fraction=Vendor2_late/Vendor2_total\n",
        "Vendor5_late=len(df[df['Vendor'] == 5]['DeliveryTime'][df[df['Vendor'] == 5]['DeliveryTime'] < 0])\n",
        "Vendor5_total=len(df[df['Vendor'] == 5]['DeliveryTime'])\n",
        "Vendor5_fraction=Vendor5_late/Vendor5_total\n",
        "Vendor8_late=len(df[df['Vendor'] == 8]['DeliveryTime'][df[df['Vendor'] == 8]['DeliveryTime'] < 0])\n",
        "Vendor8_total=len(df[df['Vendor'] == 8]['DeliveryTime'])\n",
        "Vendor8_fraction=Vendor8_late/Vendor8_total"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DtauzdUZIHrq",
        "colab_type": "code",
        "colab": {
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          "height": 542
        },
        "outputId": "16ef1989-2a93-434d-9ad7-a96f7e4805b3"
      },
      "source": [
        "fig1 = go.Figure(data=[\n",
        "    go.Bar(name='Vendor1', x=['No. of late deliveries (Comparison between top 4 vendors)','Total deliveries (Comparison between top4 vendors)'], y=[Vendor1_late,Vendor1_total]),\n",
        "    go.Bar(name='Vendor2', x=['No. of late deliveries (Comparison between top 4 vendors)','Total deliveries (Comparison between top4 vendors)'], y=[Vendor2_late,Vendor2_total]),\n",
        "    go.Bar(name='Vendor5', x=['No. of late deliveries (Comparison between top 4 vendors)','Total deliveries (Comparison between top4 vendors)'], y=[Vendor5_late,Vendor5_total]),\n",
        "    go.Bar(name='Vendor8', x=['No. of late deliveries (Comparison between top 4 vendors)','Total deliveries (Comparison between top4 vendors)'], y=[Vendor8_late,Vendor8_total])\n",
        "])\n",
        "fig1.update_layout(barmode='group',paper_bgcolor=\"LightSteelBlue\")\n",
        "fig1.show(renderer=\"colab\")"
      ],
      "execution_count": null,
      "outputs": [
        {
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        "fig2 = go.Figure(data=[\n",
        "    go.Bar(name='Vendor1', x=['Fraction of late-deliveries (Comparison between top4 vendors)'], y=[Vendor1_fraction]),\n",
        "    go.Bar(name='Vendor2', x=['Fraction of late-deliveries (Comparison between top4 vendors)'], y=[Vendor2_fraction]),\n",
        "    go.Bar(name='Vendor5', x=['Fraction of late-deliveries (Comparison between top4 vendors)'], y=[Vendor5_fraction]),\n",
        "    go.Bar(name='Vendor8', x=['Fraction of late-deliveries (Comparison between top4 vendors)'], y=[Vendor8_fraction])\n",
        "])\n",
        "fig2.update_layout(barmode='group',paper_bgcolor=\"LightSteelBlue\")\n",
        "fig2.show(renderer=\"colab\")"
      ],
      "execution_count": null,
      "outputs": [
        {
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\"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}},\n",
              "                        {\"responsive\": true}\n",
              "                    ).then(function(){\n",
              "                            \n",
              "var gd = document.getElementById('c10eeff1-9e94-459a-859b-ca723db65efb');\n",
              "var x = new MutationObserver(function (mutations, observer) {{\n",
              "        var display = window.getComputedStyle(gd).display;\n",
              "        if (!display || display === 'none') {{\n",
              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
              "        }}\n",
              "}});\n",
              "\n",
              "// Listen for the removal of the full notebook cells\n",
              "var notebookContainer = gd.closest('#notebook-container');\n",
              "if (notebookContainer) {{\n",
              "    x.observe(notebookContainer, {childList: true});\n",
              "}}\n",
              "\n",
              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MtZ2rU6F4HLQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "fd2acaa4-ccda-41d3-b930-878806dc9970"
      },
      "source": [
        "plt.figure(figsize = (20,7))\n",
        "sns.distplot(df[df['Vendor'] == 1]['DeliveryTime'], kde = False, color = 'r') #1\n",
        "sns.distplot(df[df['Vendor'] == 2]['DeliveryTime'], kde = False, color = 'b')  #2\n",
        "sns.distplot(df[df['Vendor'] == 5]['DeliveryTime'], kde = False, color = 'g')  #3\n",
        "sns.distplot(df[df['Vendor'] == 8]['DeliveryTime'], kde = False, color = 'y') #4 #worst"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f32ec6a07f0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1440x504 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nIxnk1-84hSU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "5e44a215-fc04-43b7-fe4c-6c5f8078d3b4"
      },
      "source": [
        "vendorID,count=np.unique(df[df['RemainingQuantity'] < 0]['Vendor'], return_counts = True)\n",
        "# number of occurences of vendors where the quantities are delivered less then demanded\n",
        "# 1 -> 43\n",
        "# 2 -> 6\n",
        "# 5 -> 4\n",
        "# 8 -> 32\n",
        "trace = go.Bar(x = [\"Vendor1\",\"Vendor2\",\"Vendor5\",\"Vendor8\"],y=[43,6,4,32],marker=dict(color=count,colorscale='Viridis',showscale=True))\n",
        "data=[trace]\n",
        "#defining layout\n",
        "layout = go.Layout(xaxis=dict(title='VendorID'),yaxis=dict(title='Count of top 4 Vendors with less supply '),hovermode='closest')\n",
        "#defining figure and plotting\n",
        "figure = go.Figure(data=data,layout=layout)\n",
        "figure.update_layout(\n",
        "    paper_bgcolor=\"LightBlue\",\n",
        ")\n",
        "figure.show(renderer='colab')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "                        [{\"marker\": {\"color\": [43, 6, 4, 3, 4, 32, 1, 2, 1, 2], \"colorscale\": [[0.0, \"#440154\"], [0.1111111111111111, \"#482878\"], [0.2222222222222222, \"#3e4989\"], [0.3333333333333333, \"#31688e\"], [0.4444444444444444, \"#26828e\"], [0.5555555555555556, \"#1f9e89\"], [0.6666666666666666, \"#35b779\"], [0.7777777777777778, \"#6ece58\"], [0.8888888888888888, \"#b5de2b\"], [1.0, \"#fde725\"]], \"showscale\": true}, \"type\": \"bar\", \"x\": [\"Vendor1\", \"Vendor2\", \"Vendor5\", \"Vendor8\"], \"y\": [43, 6, 4, 32]}],\n",
              "                        {\"hovermode\": \"closest\", \"paper_bgcolor\": \"LightBlue\", \"template\": {\"data\": {\"bar\": [{\"error_x\": {\"color\": \"#2a3f5f\"}, \"error_y\": {\"color\": \"#2a3f5f\"}, \"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"bar\"}], \"barpolar\": [{\"marker\": {\"line\": {\"color\": \"#E5ECF6\", \"width\": 0.5}}, \"type\": \"barpolar\"}], \"carpet\": [{\"aaxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"baxis\": {\"endlinecolor\": \"#2a3f5f\", \"gridcolor\": \"white\", \"linecolor\": \"white\", \"minorgridcolor\": \"white\", \"startlinecolor\": \"#2a3f5f\"}, \"type\": \"carpet\"}], \"choropleth\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"type\": \"choropleth\"}], \"contour\": [{\"colorbar\": {\"outlinewidth\": 0, \"ticks\": \"\"}, \"colorscale\": [[0.0, \"#0d0887\"], [0.1111111111111111, 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\"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"radialaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"scene\": {\"xaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"yaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}, \"zaxis\": {\"backgroundcolor\": \"#E5ECF6\", \"gridcolor\": \"white\", \"gridwidth\": 2, \"linecolor\": \"white\", \"showbackground\": true, \"ticks\": \"\", \"zerolinecolor\": \"white\"}}, \"shapedefaults\": {\"line\": {\"color\": \"#2a3f5f\"}}, \"ternary\": {\"aaxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"baxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}, \"bgcolor\": \"#E5ECF6\", \"caxis\": {\"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\"}}, \"title\": {\"x\": 0.05}, \"xaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}, \"yaxis\": {\"automargin\": true, \"gridcolor\": \"white\", \"linecolor\": \"white\", \"ticks\": \"\", \"title\": {\"standoff\": 15}, \"zerolinecolor\": \"white\", \"zerolinewidth\": 2}}}, \"xaxis\": {\"title\": {\"text\": \"VendorID\"}}, \"yaxis\": {\"title\": {\"text\": \"Count of top 4 Vendors with less supply \"}}},\n",
              "                        {\"responsive\": true}\n",
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              "</body>\n",
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          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cF2aLNiB4rVU",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "fee52056-891b-4448-d0ef-2f025540cffd"
      },
      "source": [
        "vendor1=df[df['Vendor']==1].count()[0]\n",
        "vendor2=df[df['Vendor']==2].count()[0]\n",
        "vendor5=df[df['Vendor']==5].count()[0]\n",
        "vendor8=df[df['Vendor']==8].count()[0]\n",
        "Remaining_vendors=df[(df['Vendor']!=1) & (df['Vendor']!=2) & (df['Vendor']!=5) & (df['Vendor']!=8)].count()[0]\n",
        "\n",
        "trace = go.Bar(x = [\"Vendor1\",\"Vendor2\",\"Vendor5\",\"Vendor8\",\"Remaining_Vendors\"],y=[vendor1,vendor2,vendor5,vendor8,Remaining_vendors],marker=dict(color=[vendor1,vendor2,vendor5,vendor8,Remaining_vendors],colorscale='Viridis',showscale=True))\n",
        "data=[trace]\n",
        "#defining layout\n",
        "layout = go.Layout(xaxis=dict(title='VendorID'),yaxis=dict(title='Top 4 vendors Vs Remaining_Vendors '),hovermode='closest')\n",
        "#defining figure and plotting\n",
        "figure = go.Figure(data=data,layout=layout)\n",
        "figure.update_layout(\n",
        "    paper_bgcolor=\"Turquoise\",\n",
        ")\n",
        "figure.show(renderer='colab')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                        {\"responsive\": true}\n",
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              "var notebookContainer = gd.closest('#notebook-container');\n",
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          "metadata": {
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    {
      "cell_type": "code",
      "metadata": {
        "id": "Di2KH6WdLdAb",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
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        "outputId": "9085f77d-ed62-43df-c476-f5ed44e03d98"
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      "source": [
        "fig = px.scatter(df, x=\"PercentOfQuantityReceived\", color=\"Vendor\",\n",
        "                  hover_data=['Vendor'])\n",
        "fig.update_layout(\n",
        "    paper_bgcolor=\"LightBlue\",\n",
        ")\n",
        "fig.show(renderer='colab')"
      ],
      "execution_count": null,
      "outputs": [
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              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NOzq3X1y2UsQ",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "e13c12bf-b446-4f1c-f97d-4ef6d8dfdcb5"
      },
      "source": [
        "fig = px.scatter(df, x=\"PercentOfQuantityReceived\", color=\"Vendor\",\n",
        "                  hover_data=['Vendor'])\n",
        "fig.update_layout(\n",
        "    paper_bgcolor=\"LightSteelBlue\",\n",
        ")\n",
        "fig.show(renderer='colab')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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1.0], \"title\": {\"text\": \"PercentOfQuantityReceived\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0]}},\n",
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          "metadata": {
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bnaDSXrX4fpM",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "778488cf-a7c6-4098-b368-eb3f6b0f54dc"
      },
      "source": [
        "fig = px.scatter(df, x=\"DeliveryTime\", color=\"Vendor\",\n",
        "                  hover_data=['Vendor'])\n",
        "fig.update_layout(\n",
        "    paper_bgcolor=\"Turquoise\",\n",
        ")\n",
        "fig.show(renderer='colab')"
      ],
      "execution_count": null,
      "outputs": [
        {
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1.0], \"title\": {\"text\": \"DeliveryTime\"}}, \"yaxis\": {\"anchor\": \"x\", \"domain\": [0.0, 1.0]}},\n",
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          "metadata": {
            "tags": []
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    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZcoK-i08KJKW",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "for i in range(df['DeliveryTime'].shape[0]):\n",
        "    if((df[\"DeliveryTime\"].iloc[i]>0) and (df[\"DeliveryTime\"].iloc[i]<=3)):\n",
        "        df[\"DeliveryTime\"].iloc[i]=np.log(df[\"DeliveryTime\"].iloc[i])\n",
        "df['DeliveryTime'] = [x/10 if x>0 else x for x in df.DeliveryTime]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jCC-UCY7eDQc",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "6d80d6dd-c410-4570-db14-2d1adf79b300"
      },
      "source": [
        "df['NormDeliveryTime'] = 0-df.DeliveryTime.abs()\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scale = MinMaxScaler()\n",
        "df['NormDeliveryTime'] = scale.fit_transform(df[['NormDeliveryTime']])\n",
        "fig = px.scatter(df, x=\"NormDeliveryTime\", color=\"Vendor\",\n",
        "                  hover_data=['Vendor'])\n",
        "fig.update_layout(\n",
        "    paper_bgcolor=\"LightBlue\",\n",
        ")\n",
        "fig.show(renderer='colab')"
      ],
      "execution_count": null,
      "outputs": [
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              "      <th>Vendor</th>\n",
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              "      <th>0</th>\n",
              "      <td>8</td>\n",
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              "      <td>SCREW BN CANC TI FT 6.5 X 25</td>\n",
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              "      <td>6</td>\n",
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              "      <td>15</td>\n",
              "      <td>PORTABLE X-RAY MACHINE</td>\n",
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              "      <td>XPS 15</td>\n",
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              "      <td>1.0</td>\n",
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              "      <td>2</td>\n",
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              "      <td>MARQUE CURVED DOUBLE RECEPTION S</td>\n",
              "      <td>Marque Curved Double Reception Station - 124.2...</td>\n",
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              "      <td>0.0</td>\n",
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              "   Vendor  PurchaseOrder  ... PercentOfQuantityReturned NormDeliveryTime\n",
              "0       8              1  ...                       0.0         1.000000\n",
              "1       6              2  ...                       0.0         1.000000\n",
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              "4       8              6  ...                       0.0         0.999943\n",
              "\n",
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          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1503 entries, 0 to 1502\n",
            "Data columns (total 16 columns):\n",
            " #   Column                     Non-Null Count  Dtype         \n",
            "---  ------                     --------------  -----         \n",
            " 0   Vendor                     1503 non-null   int64         \n",
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            " 5   CancelQuantity             1503 non-null   float64       \n",
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            " 7   EnteredReceivedQuantity    1503 non-null   int64         \n",
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            " 9   ReceivedDate               1503 non-null   datetime64[ns]\n",
            " 10  DeliveryTime               1503 non-null   float64       \n",
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            " 12  PercentOfQuantityReceived  1503 non-null   float64       \n",
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            " 14  PercentOfQuantityReturned  1503 non-null   float64       \n",
            " 15  NormDeliveryTime           1503 non-null   float64       \n",
            "dtypes: datetime64[ns](2), float64(9), int64(3), object(2)\n",
            "memory usage: 188.0+ KB\n"
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              "      <th>QuantityDemandedFinal</th>\n",
              "      <th>EnteredReceivedQuantity</th>\n",
              "      <th>EarlyDeliveryDate</th>\n",
              "      <th>ReceivedDate</th>\n",
              "      <th>DeliveryTime</th>\n",
              "      <th>RemainingQuantity</th>\n",
              "      <th>PercentOfQuantityReceived</th>\n",
              "      <th>ReturnQuantity</th>\n",
              "      <th>PercentOfQuantityReturned</th>\n",
              "      <th>NormDeliveryTime</th>\n",
              "      <th>PercentKept</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>8</td>\n",
              "      <td>1</td>\n",
              "      <td>9</td>\n",
              "      <td>SCREW BN CANC TI FT 6.5 X 25</td>\n",
              "      <td>10.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>10.0</td>\n",
              "      <td>10</td>\n",
              "      <td>2016-11-29</td>\n",
              "      <td>2016-11-29</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>15</td>\n",
              "      <td>PORTABLE X-RAY MACHINE</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-12-03</td>\n",
              "      <td>2016-12-03</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>3</td>\n",
              "      <td>16</td>\n",
              "      <td>XPS 15</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-12-05</td>\n",
              "      <td>2016-12-06</td>\n",
              "      <td>-1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.999174</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2</td>\n",
              "      <td>4</td>\n",
              "      <td>MARQUE CURVED DOUBLE RECEPTION S</td>\n",
              "      <td>Marque Curved Double Reception Station - 124.2...</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-12-08</td>\n",
              "      <td>2016-12-08</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>8</td>\n",
              "      <td>6</td>\n",
              "      <td>6</td>\n",
              "      <td>SURFACE ART LPS-FLEX EF 3-4 10</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>1</td>\n",
              "      <td>2016-12-16</td>\n",
              "      <td>2016-12-14</td>\n",
              "      <td>0.069315</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.999943</td>\n",
              "      <td>1.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Vendor  PurchaseOrder  ... NormDeliveryTime PercentKept\n",
              "0       8              1  ...         1.000000         1.0\n",
              "1       6              2  ...         1.000000         1.0\n",
              "2       3              3  ...         0.999174         1.0\n",
              "3       2              4  ...         1.000000         1.0\n",
              "4       8              6  ...         0.999943         1.0\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ilJ74L0r8irf",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "e052fa20-7e85-4f92-866d-96d5217489b4"
      },
      "source": [
        "import datetime as dt\n",
        "\n",
        "df['ReceivedMonth'] = df['ReceivedDate'].dt.month\n",
        "df['ReceivedYear'] = df['ReceivedDate'].dt.year\n",
        "\n",
        "\n",
        "grouped_monthly = df.groupby(['ReceivedYear', \n",
        "                              'ReceivedMonth', \n",
        "                              'Vendor']).agg(MonthlyNormDeliveryTime = pd.NamedAgg(column = 'NormDeliveryTime', \n",
        "                                                                                   aggfunc='mean'), \n",
        "                                             MonthlyPercentReceived = pd.NamedAgg(column = 'PercentOfQuantityReceived', \n",
        "                                                                                   aggfunc='mean'), \n",
        "                                             MonthlyPercentKept = pd.NamedAgg(column = 'PercentKept', \n",
        "                                                                                  aggfunc='mean'), \n",
        "                                             VendorId = pd.NamedAgg(column = 'Vendor', \n",
        "                                                                                  aggfunc='first'))\n",
        "grouped_monthly.head(60)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<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></th>\n",
              "      <th></th>\n",
              "      <th>MonthlyNormDeliveryTime</th>\n",
              "      <th>MonthlyPercentReceived</th>\n",
              "      <th>MonthlyPercentKept</th>\n",
              "      <th>VendorId</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ReceivedYear</th>\n",
              "      <th>ReceivedMonth</th>\n",
              "      <th>Vendor</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th rowspan=\"9\" valign=\"top\">2016</th>\n",
              "      <th rowspan=\"2\" valign=\"top\">1</th>\n",
              "      <th>1</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">11</th>\n",
              "      <th>1</th>\n",
              "      <td>0.988981</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.999174</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.998347</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">12</th>\n",
              "      <th>2</th>\n",
              "      <td>0.999711</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.999174</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.999971</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"51\" valign=\"top\">2017</th>\n",
              "      <th>1</th>\n",
              "      <th>8</th>\n",
              "      <td>0.999955</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">2</th>\n",
              "      <th>1</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.999939</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <th>2</th>\n",
              "      <td>0.676033</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"3\" valign=\"top\">4</th>\n",
              "      <th>1</th>\n",
              "      <td>0.999835</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"5\" valign=\"top\">5</th>\n",
              "      <th>1</th>\n",
              "      <td>0.999520</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.928571</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.998498</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.895833</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1005</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1005</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1006</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1006</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">6</th>\n",
              "      <th>1</th>\n",
              "      <td>0.999909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.999339</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.999909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.999807</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
              "      <th>1</th>\n",
              "      <td>0.999377</td>\n",
              "      <td>0.908333</td>\n",
              "      <td>0.976190</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.999909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">8</th>\n",
              "      <th>1</th>\n",
              "      <td>0.999127</td>\n",
              "      <td>0.959829</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.999922</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"7\" valign=\"top\">9</th>\n",
              "      <th>1</th>\n",
              "      <td>0.998827</td>\n",
              "      <td>0.976190</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.999779</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.998868</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.999909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.999932</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1021</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"10\" valign=\"top\">10</th>\n",
              "      <th>1</th>\n",
              "      <td>0.999500</td>\n",
              "      <td>0.955556</td>\n",
              "      <td>0.997061</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.999587</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.995305</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.852381</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.999771</td>\n",
              "      <td>0.929240</td>\n",
              "      <td>0.960902</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1003</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1012</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1012</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1021</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"8\" valign=\"top\">11</th>\n",
              "      <th>1</th>\n",
              "      <td>0.999880</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.998889</td>\n",
              "      <td>0.844444</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.918787</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.996281</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.999372</td>\n",
              "      <td>0.994152</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1003</th>\n",
              "      <td>0.999587</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1003</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1021</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1021</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">12</th>\n",
              "      <th>1</th>\n",
              "      <td>0.999924</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.999981</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.999810</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   MonthlyNormDeliveryTime  ...  VendorId\n",
              "ReceivedYear ReceivedMonth Vendor                           ...          \n",
              "2016         1             1                      1.000000  ...         1\n",
              "                           5                      1.000000  ...         5\n",
              "             11            1                      0.988981  ...         1\n",
              "                           5                      0.999174  ...         5\n",
              "                           8                      0.998347  ...         8\n",
              "             12            2                      0.999711  ...         2\n",
              "                           3                      0.999174  ...         3\n",
              "                           6                      1.000000  ...         6\n",
              "                           8                      0.999971  ...         8\n",
              "2017         1             8                      0.999955  ...         8\n",
              "             2             1                      1.000000  ...         1\n",
              "                           8                      0.999939  ...         8\n",
              "             3             2                      0.676033  ...         2\n",
              "             4             1                      0.999835  ...         1\n",
              "                           2                      1.000000  ...         2\n",
              "                           8                      1.000000  ...         8\n",
              "             5             1                      0.999520  ...         1\n",
              "                           5                      1.000000  ...         5\n",
              "                           8                      0.998498  ...         8\n",
              "                           1005                   1.000000  ...      1005\n",
              "                           1006                   1.000000  ...      1006\n",
              "             6             1                      0.999909  ...         1\n",
              "                           2                      0.999339  ...         2\n",
              "                           5                      0.999909  ...         5\n",
              "                           8                      0.999807  ...         8\n",
              "             7             1                      0.999377  ...         1\n",
              "                           5                      0.999909  ...         5\n",
              "             8             1                      0.999127  ...         1\n",
              "                           3                      1.000000  ...         3\n",
              "                           5                      0.999922  ...         5\n",
              "                           8                      1.000000  ...         8\n",
              "             9             1                      0.998827  ...         1\n",
              "                           2                      1.000000  ...         2\n",
              "                           3                      0.999779  ...         3\n",
              "                           5                      0.998868  ...         5\n",
              "                           6                      0.999909  ...         6\n",
              "                           8                      0.999932  ...         8\n",
              "                           1021                   1.000000  ...      1021\n",
              "             10            1                      0.999500  ...         1\n",
              "                           2                      0.999587  ...         2\n",
              "                           3                      1.000000  ...         3\n",
              "                           4                      1.000000  ...         4\n",
              "                           5                      0.995305  ...         5\n",
              "                           6                      1.000000  ...         6\n",
              "                           8                      0.999771  ...         8\n",
              "                           1003                   1.000000  ...      1003\n",
              "                           1012                   1.000000  ...      1012\n",
              "                           1021                   1.000000  ...      1021\n",
              "             11            1                      0.999880  ...         1\n",
              "                           2                      1.000000  ...         2\n",
              "                           3                      0.998889  ...         3\n",
              "                           5                      0.918787  ...         5\n",
              "                           6                      0.996281  ...         6\n",
              "                           8                      0.999372  ...         8\n",
              "                           1003                   0.999587  ...      1003\n",
              "                           1021                   1.000000  ...      1021\n",
              "             12            1                      0.999924  ...         1\n",
              "                           3                      0.999981  ...         3\n",
              "                           5                      1.000000  ...         5\n",
              "                           8                      0.999810  ...         8\n",
              "\n",
              "[60 rows x 4 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KekPSR8htUsz",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "\n",
        "import numpy as np\n",
        "from numpy import linalg as LA"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4l-9o6F8pdIR",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def Rater(Alternatives):\n",
        "  length=Alternatives.shape[0]\n",
        "  matrix=np.ones((length,length))\n",
        "  for i in range(length):\n",
        "      if(Alternatives[i]==0):\n",
        "        Alternatives[i]=0.000000001    #Columns with zero value aren't acceptable in AHP algo ; So just assigned it a close to zero value\n",
        "      div_array=Alternatives/Alternatives[i]\n",
        "      for k in range(length):\n",
        "          matrix[k][i]=div_array[k]\n",
        "          \n",
        "  return matrix\n",
        "    \n",
        "def Weight(matrix):\n",
        "  eigen_v,eigen_vec=LA.eig(matrix)\n",
        "  eigen_v=np.real(eigen_v)\n",
        "  eigen_vec=np.real(eigen_vec)\n",
        "  index=np.argmax(eigen_v)\n",
        "  weight_arr=eigen_vec[:,index]/eigen_vec[:,index].sum()\n",
        "  return np.abs(weight_arr)\n",
        "\n",
        "def main(matrix):\n",
        "    \n",
        "    criteria=np.array([[1,2,0.5],[0.5,1,1/3],[2,3,1]]) #Need to ask mam about this factors\n",
        "    w,v=LA.eig(criteria)\n",
        "    w=np.real(w)\n",
        "    v=np.real(v)\n",
        "    \n",
        "    index=np.argmax(w)\n",
        "    matrix_b=v[:,index]/v[:,index].sum()\n",
        "    matrix_b=matrix_b.reshape(-1,1)\n",
        "    \n",
        "    \n",
        "    matrix_1=Rater(matrix[:,0])\n",
        "    matrix_2=Rater(matrix[:,1])\n",
        "    matrix_3=Rater(matrix[:,2])\n",
        "    weight_1=Weight(matrix_1).reshape(-1,1)\n",
        "    weight_2=Weight(matrix_2).reshape(-1,1)\n",
        "    weight_3=Weight(matrix_3).reshape(-1,1)\n",
        "    \n",
        "    \n",
        "    matrix_a=np.concatenate((weight_1,weight_2,weight_3),axis=1)\n",
        "    \n",
        "    return matrix_a.dot(matrix_b)\n",
        "    \n",
        "        \n",
        "    \n",
        "        "
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zDzsJof9guC0",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 54
        },
        "outputId": "59deda1b-7624-4816-ca5c-b562900349ea"
      },
      "source": [
        "Rating_df = df.groupby(['ReceivedYear', 'ReceivedMonth', 'Vendor']).agg(MonthlyNormDeliveryTime = pd.NamedAgg(column = 'NormDeliveryTime', aggfunc='mean'), MonthlyPercentReceived = pd.NamedAgg(column = 'PercentOfQuantityReceived', aggfunc='mean'), MonthlyPercentKept = pd.NamedAgg(column = 'PercentKept', aggfunc='mean'),AHP_Score = pd.NamedAgg(column = 'Vendor', aggfunc='first'),Vendor_Monthly_Score=pd.NamedAgg(column='Vendor',aggfunc='first') ,Month = pd.NamedAgg(column = 'ReceivedMonth', aggfunc='first'),Year = pd.NamedAgg(column = 'ReceivedYear', aggfunc='first'))\n",
        "Year = Rating_df['Year'].unique()   \n",
        "Month =Rating_df['Month'].unique()\n",
        "Vendor=grouped_monthly['VendorId'].unique()\n",
        "Vendor=np.sort(Vendor)\n",
        "Month =np.sort(Month)\n",
        "\n",
        "res = {Vendor[i]: 0 for i in range(len(Vendor))} \n",
        "print(res)\n",
        "\n",
        "\n",
        "Rating_df['Year']=Rating_df['Year'].astype(int)\n",
        "Rating_df['Month']=Rating_df['Month'].astype(int)\n",
        "\n",
        "ans=None\n",
        "ans1=None\n",
        "for i in Year:\n",
        "  for j in Month:\n",
        "    matrix=np.array(Rating_df[(Rating_df['Month']==j) & (Rating_df['Year']==i)][['MonthlyNormDeliveryTime','MonthlyPercentReceived','MonthlyPercentKept']])\n",
        "    temp=Rating_df[(Rating_df['Month']==j) & (Rating_df['Year']==i)]['AHP_Score']\n",
        "    if(matrix.shape[0]!=0):\n",
        "      part_ans=main(matrix)\n",
        "      maxim=np.max(part_ans)\n",
        "      ind=[i for i, j in enumerate(part_ans) if j == maxim]\n",
        "      for k in ind:\n",
        "          wy=res[temp[k]]+1\n",
        "          res[temp[k]]=wy\n",
        "      part_ans1=part_ans/maxim\n",
        "      if ans is None:\n",
        "        ans = part_ans\n",
        "        ans1 = part_ans1\n",
        "      else:\n",
        "        ans = np.concatenate(([ans , part_ans ]), axis=0)\n",
        "        ans1 = np.concatenate(([ans1 , part_ans1 ]), axis=0)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "{1: 0, 2: 0, 3: 0, 4: 0, 5: 0, 6: 0, 7: 0, 8: 0, 9: 0, 1003: 0, 1005: 0, 1006: 0, 1007: 0, 1012: 0, 1021: 0, 1025: 0, 1027: 0, 1029: 0, 1034: 0, 1036: 0, 1042: 0, 1043: 0, 1044: 0, 1045: 0, 1046: 0, 1113: 0, 1128: 0, 1130: 0, 1133: 0, 1136: 0}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hw5SwQoMtMxV",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "1c05c57f-b090-4054-fbc4-f6ba3596b85c"
      },
      "source": [
        "x=[]\n",
        "y=[]\n",
        "for key, value in res.items():\n",
        "    x.append(\"Vendor\"+str(key))\n",
        "    y.append(value)\n",
        "    \n",
        "trace = go.Bar(x = x,y=y,marker=dict(color=y,colorscale='Viridis',showscale=True))\n",
        "data=[trace]\n",
        "#defining layout\n",
        "layout = go.Layout(xaxis=dict(title='The count of number of times vendor had the highest score in Month'),yaxis=dict(title='count'),hovermode='closest')\n",
        "#defining figure and plotting\n",
        "figure = go.Figure(data=data,layout=layout)\n",
        "figure.update_layout(\n",
        "    paper_bgcolor=\"Turquoise\",\n",
        ")\n",
        "figure.show(renderer='colab')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
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              "var notebookContainer = gd.closest('#notebook-container');\n",
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          },
          "metadata": {
            "tags": []
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        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2K_diVlzuEvY",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "a99e0933-43e4-4f1a-9a08-31c114c31c95"
      },
      "source": [
        "Rating_df['AHP_Score']=ans\n",
        "Rating_df['Vendor_Monthly_Score']=ans1\n",
        "Rating_df.head(60)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <td>0.250432</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>8</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"7\" valign=\"top\">9</th>\n",
              "      <th>1</th>\n",
              "      <td>0.998827</td>\n",
              "      <td>0.976190</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.148179</td>\n",
              "      <td>0.995594</td>\n",
              "      <td>9</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.148834</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.999779</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.148825</td>\n",
              "      <td>0.999937</td>\n",
              "      <td>9</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.998868</td>\n",
              "      <td>0.444444</td>\n",
              "      <td>0.666667</td>\n",
              "      <td>0.107665</td>\n",
              "      <td>0.723389</td>\n",
              "      <td>9</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.999909</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.148831</td>\n",
              "      <td>0.999974</td>\n",
              "      <td>9</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.999932</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.148832</td>\n",
              "      <td>0.999981</td>\n",
              "      <td>9</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1021</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.148834</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>9</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"10\" valign=\"top\">10</th>\n",
              "      <th>1</th>\n",
              "      <td>0.999500</td>\n",
              "      <td>0.955556</td>\n",
              "      <td>0.997061</td>\n",
              "      <td>0.100340</td>\n",
              "      <td>0.990999</td>\n",
              "      <td>10</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.999587</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.101239</td>\n",
              "      <td>0.999879</td>\n",
              "      <td>10</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.101251</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.101251</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.995305</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.852381</td>\n",
              "      <td>0.092992</td>\n",
              "      <td>0.918428</td>\n",
              "      <td>10</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.101251</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.999771</td>\n",
              "      <td>0.929240</td>\n",
              "      <td>0.960902</td>\n",
              "      <td>0.097924</td>\n",
              "      <td>0.967139</td>\n",
              "      <td>10</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1003</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.101251</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1012</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.101251</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1021</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.101251</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>10</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"8\" valign=\"top\">11</th>\n",
              "      <th>1</th>\n",
              "      <td>0.999880</td>\n",
              "      <td>0.975000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.135589</td>\n",
              "      <td>0.996126</td>\n",
              "      <td>11</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.136116</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.998889</td>\n",
              "      <td>0.844444</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.132821</td>\n",
              "      <td>0.975791</td>\n",
              "      <td>11</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>0.918787</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.133069</td>\n",
              "      <td>0.977608</td>\n",
              "      <td>11</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>0.996281</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.058305</td>\n",
              "      <td>0.428347</td>\n",
              "      <td>11</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.999372</td>\n",
              "      <td>0.994152</td>\n",
              "      <td>0.947368</td>\n",
              "      <td>0.131883</td>\n",
              "      <td>0.968895</td>\n",
              "      <td>11</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1003</th>\n",
              "      <td>0.999587</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.136101</td>\n",
              "      <td>0.999886</td>\n",
              "      <td>11</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1021</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.136116</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th rowspan=\"4\" valign=\"top\">12</th>\n",
              "      <th>1</th>\n",
              "      <td>0.999924</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.166666</td>\n",
              "      <td>0.999977</td>\n",
              "      <td>12</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.999981</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.166669</td>\n",
              "      <td>0.999994</td>\n",
              "      <td>12</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.166670</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>12</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>0.999810</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.166660</td>\n",
              "      <td>0.999944</td>\n",
              "      <td>12</td>\n",
              "      <td>2017</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                   MonthlyNormDeliveryTime  ...  Year\n",
              "ReceivedYear ReceivedMonth Vendor                           ...      \n",
              "2016         1             1                      1.000000  ...  2016\n",
              "                           5                      1.000000  ...  2016\n",
              "             11            1                      0.988981  ...  2016\n",
              "                           5                      0.999174  ...  2016\n",
              "                           8                      0.998347  ...  2016\n",
              "             12            2                      0.999711  ...  2016\n",
              "                           3                      0.999174  ...  2016\n",
              "                           6                      1.000000  ...  2016\n",
              "                           8                      0.999971  ...  2016\n",
              "2017         1             8                      0.999955  ...  2017\n",
              "             2             1                      1.000000  ...  2017\n",
              "                           8                      0.999939  ...  2017\n",
              "             3             2                      0.676033  ...  2017\n",
              "             4             1                      0.999835  ...  2017\n",
              "                           2                      1.000000  ...  2017\n",
              "                           8                      1.000000  ...  2017\n",
              "             5             1                      0.999520  ...  2017\n",
              "                           5                      1.000000  ...  2017\n",
              "                           8                      0.998498  ...  2017\n",
              "                           1005                   1.000000  ...  2017\n",
              "                           1006                   1.000000  ...  2017\n",
              "             6             1                      0.999909  ...  2017\n",
              "                           2                      0.999339  ...  2017\n",
              "                           5                      0.999909  ...  2017\n",
              "                           8                      0.999807  ...  2017\n",
              "             7             1                      0.999377  ...  2017\n",
              "                           5                      0.999909  ...  2017\n",
              "             8             1                      0.999127  ...  2017\n",
              "                           3                      1.000000  ...  2017\n",
              "                           5                      0.999922  ...  2017\n",
              "                           8                      1.000000  ...  2017\n",
              "             9             1                      0.998827  ...  2017\n",
              "                           2                      1.000000  ...  2017\n",
              "                           3                      0.999779  ...  2017\n",
              "                           5                      0.998868  ...  2017\n",
              "                           6                      0.999909  ...  2017\n",
              "                           8                      0.999932  ...  2017\n",
              "                           1021                   1.000000  ...  2017\n",
              "             10            1                      0.999500  ...  2017\n",
              "                           2                      0.999587  ...  2017\n",
              "                           3                      1.000000  ...  2017\n",
              "                           4                      1.000000  ...  2017\n",
              "                           5                      0.995305  ...  2017\n",
              "                           6                      1.000000  ...  2017\n",
              "                           8                      0.999771  ...  2017\n",
              "                           1003                   1.000000  ...  2017\n",
              "                           1012                   1.000000  ...  2017\n",
              "                           1021                   1.000000  ...  2017\n",
              "             11            1                      0.999880  ...  2017\n",
              "                           2                      1.000000  ...  2017\n",
              "                           3                      0.998889  ...  2017\n",
              "                           5                      0.918787  ...  2017\n",
              "                           6                      0.996281  ...  2017\n",
              "                           8                      0.999372  ...  2017\n",
              "                           1003                   0.999587  ...  2017\n",
              "                           1021                   1.000000  ...  2017\n",
              "             12            1                      0.999924  ...  2017\n",
              "                           3                      0.999981  ...  2017\n",
              "                           5                      1.000000  ...  2017\n",
              "                           8                      0.999810  ...  2017\n",
              "\n",
              "[60 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4OQAThPM25dG",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 50
        },
        "outputId": "ea00c02e-8571-4bf6-b3b2-a2f50540baf8"
      },
      "source": [
        "from xgboost import XGBRegressor\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import r2_score\n",
        "X=Rating_df[['MonthlyNormDeliveryTime',\t'MonthlyPercentReceived',\t'MonthlyPercentKept']]\n",
        "y=Rating_df['Vendor_Monthly_Score']\n",
        "X_train, X_test, y_train, y_test = train_test_split(X,y )\n",
        "model = XGBRegressor()\n",
        "model.fit(X_train, y_train)\n",
        "\n",
        "predictions = model.predict(X_test)\n",
        "r2_score(y_test,predictions)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[13:28:57] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9687782402506689"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8WvWvdllgw6O",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 136
        },
        "outputId": "b48dc6ab-eb9e-47cc-e4fc-e8f63eb721f1"
      },
      "source": [
        "\n",
        "\n",
        "rating = RandomForestRegressor(n_estimators=200,max_depth=5, random_state=0)\n",
        "rating.fit(Rating_df[['MonthlyNormDeliveryTime',\t'MonthlyPercentReceived',\t'MonthlyPercentKept']],Rating_df['Vendor_Monthly_Score'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=5, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=200, n_jobs=None, oob_score=False,\n",
              "                      random_state=0, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Q27CEr2co4YS",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "e082a70f-0d38-48c1-d3e5-c262a4d0b117"
      },
      "source": [
        "rating.score(Rating_df[['MonthlyNormDeliveryTime',\t'MonthlyPercentReceived',\t'MonthlyPercentKept']],Rating_df['Vendor_Monthly_Score'])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.9552971221176534"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jDJPiLf-9Lo3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "5f1f469d-33a4-4313-ed35-41abc4858bc4"
      },
      "source": [
        "fig4 = px.scatter_3d(grouped_monthly, x='MonthlyPercentReceived', y='MonthlyNormDeliveryTime', z='MonthlyPercentKept',\n",
        "                     color='VendorId')\n",
        "fig4.update_layout(\n",
        "    margin=dict(l=20, r=20, t=20, b=20),\n",
        "    paper_bgcolor=\"LightBlue\",\n",
        ")\n",
        "fig4.show(renderer='colab')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "// Listen for the clearing of the current output cell\n",
              "var outputEl = gd.closest('.output');\n",
              "if (outputEl) {{\n",
              "    x.observe(outputEl, {childList: true});\n",
              "}}\n",
              "\n",
              "                        })\n",
              "                };\n",
              "                \n",
              "            </script>\n",
              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Qh3ATYf093OL",
        "colab_type": "text"
      },
      "source": [
        "*Categorization* of orders into performing and non-performing along with the reason. (Promptness, Quantity, Quality)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6oV5b0yW6P5P",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "from sklearn.cluster import KMeans\n",
        "cls = KMeans(n_clusters = 4)\n",
        "cls_assignment = cls.fit_predict(grouped_monthly[['MonthlyPercentReceived',\n",
        "                                                  'MonthlyNormDeliveryTime',\n",
        "                                                  'MonthlyPercentKept']])\n",
        "grouped_monthly['label'] = cls_assignment"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ZQng0nlsMEWT",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "f5ea3986-d83d-44c1-c1d6-5810849ae16a"
      },
      "source": [
        "trace1=go.Scatter3d(\n",
        "    x=grouped_monthly[grouped_monthly['label']==0]['MonthlyPercentReceived'],\n",
        "    y=grouped_monthly[grouped_monthly['label']==0]['MonthlyNormDeliveryTime'],\n",
        "    z=grouped_monthly[grouped_monthly['label']==0]['MonthlyPercentKept'],\n",
        "    mode='markers',\n",
        "    marker=dict(\n",
        "        size=12,\n",
        "        color='red',                # set color to an array/list of desired values\n",
        "        colorscale='Viridis',   # choose a colorscale\n",
        "        opacity=0.8\n",
        "    ),\n",
        "     hovertemplate= '<b>Quantity</b>: %{x:.2f}' +\n",
        "                        '<br><b>Promptness</b>: %{y}<br>' +\n",
        "                        '<b>Quality</b> : %{z}',\n",
        "    name=\"Performing-Vendors\"\n",
        ")\n",
        "layout = go.Layout(scene = dict(\n",
        "                    xaxis_title='Quantity',\n",
        "                    yaxis_title='Promptness',\n",
        "                    zaxis_title='Quality'))\n",
        "trace2=go.Scatter3d(\n",
        "    x=grouped_monthly[grouped_monthly['label']==1]['MonthlyPercentReceived'],\n",
        "    y=grouped_monthly[grouped_monthly['label']==1]['MonthlyNormDeliveryTime'],\n",
        "    z=grouped_monthly[grouped_monthly['label']==1]['MonthlyPercentKept'],\n",
        "    mode='markers',\n",
        "    marker=dict(\n",
        "        size=12,\n",
        "        color='yellow',                # set color to an array/list of desired values\n",
        "        colorscale='Viridis',   # choose a colorscale\n",
        "        opacity=0.8\n",
        "    ),\n",
        "    hovertemplate= '<b>Quantity</b>: %{x:.2f}' +\n",
        "                        '<br><b>Promptness</b>: %{y}<br>' +\n",
        "                        '<b>Quality</b> : %{z}',\n",
        "    name=\"Non-Performing-Vendors(Due to Quantity issue)\"\n",
        ")\n",
        "trace3=go.Scatter3d(\n",
        "    x=grouped_monthly[grouped_monthly['label']==2]['MonthlyPercentReceived'],\n",
        "    y=grouped_monthly[grouped_monthly['label']==2]['MonthlyNormDeliveryTime'],\n",
        "    z=grouped_monthly[grouped_monthly['label']==2]['MonthlyPercentKept'],\n",
        "    mode='markers',\n",
        "    marker=dict(\n",
        "        size=12,\n",
        "        color='blue',                # set color to an array/list of desired values\n",
        "        colorscale='Viridis',   # choose a colorscale\n",
        "        opacity=0.8\n",
        "    ),\n",
        "    hovertemplate= '<b>Quantity</b>: %{x:.2f}' +\n",
        "                        '<br><b>Promptness</b>: %{y}<br>' +\n",
        "                        '<b>Quality</b> : %{z}',\n",
        "    name=\"Non-Performing-Vendors(Due to Quality issue)\"\n",
        ")\n",
        "trace4 =go.Scatter3d(\n",
        "    x=grouped_monthly[grouped_monthly['label']==3]['MonthlyPercentReceived'],\n",
        "    y=grouped_monthly[grouped_monthly['label']==3]['MonthlyNormDeliveryTime'],\n",
        "    z=grouped_monthly[grouped_monthly['label']==3]['MonthlyPercentKept'],\n",
        "    mode='markers',\n",
        "    marker=dict(\n",
        "        size=12,\n",
        "        color='green',                # set color to an array/list of desired values\n",
        "        colorscale='Viridis',   # choose a colorscale\n",
        "        opacity=0.8\n",
        "    ),\n",
        "    hovertemplate= '<b>Quantity</b>: %{x:.2f}' +\n",
        "                        '<br><b>Promptness</b>: %{y}<br>' +\n",
        "                        '<b>Quality</b> : %{z}',\n",
        "    name=\"Non-Performing-Vendors(Due to Promptness issue)\"\n",
        ")\n",
        "data=[trace1,trace2,trace3,trace4]\n",
        "\n",
        "\n",
        "fig2 = go.Figure(data,layout)\n",
        "\n",
        "fig2.update_layout(\n",
        "    margin=dict(l=20, r=20, t=20, b=20),\n",
        "    paper_bgcolor=\"LightBlue\",\n",
        ")\n",
        "fig2.show(renderer='colab')\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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    {
      "cell_type": "code",
      "metadata": {
        "id": "KBxMn8NuX8Cd",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 979
        },
        "outputId": "4ce54b49-7b1c-4bdb-a5e7-b4477e436578"
      },
      "source": [
        "grouped = grouped_monthly.groupby(\"VendorId\")\n",
        "vendor_output = pd.DataFrame(columns=['Vendor_ID', 'Performance', 'Performance_Percent','UnderPerformance (Quality)', 'UnderPerformance (Quantity)', 'UnderPerformance (Promptness)'])\n",
        "for name, group in grouped:\n",
        "    values = group['label'].value_counts()\n",
        "    values1 = values\n",
        "    for i in range(0,4):\n",
        "        if i not in values1.index:\n",
        "            values1[i] = 0\n",
        "    total = np.sum(group['label'].value_counts())\n",
        "\n",
        "    for i,value in enumerate(values):\n",
        "        values1[values[values == value].index] = value/total\n",
        "\n",
        "    maxperc_index = values1[values1 == np.max(values1)].index[0]\n",
        "    mydict = {0:'Performing', 1: 'Quality Issue', 2: 'Quantity Issue', 3: 'Promptness Issue'}\n",
        "    \n",
        "    if maxperc_index == 0 and values1[maxperc_index] < 0.75:\n",
        "            maxperc_index = values1.sort()[-2].index[0]\n",
        "    \n",
        "    vendor_output = vendor_output.append({'Vendor_ID': group['VendorId'][group['VendorId'].first_valid_index()],'Performance': mydict[maxperc_index], 'Performance_Percent': values1[0],'UnderPerformance (Quantity)': values1[2],'UnderPerformance (Promptness)' : values1[3], 'UnderPerformance (Quality)': values1[1]}, ignore_index=True)\n",
        "vendor_output"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "      <th></th>\n",
              "      <th>Vendor_ID</th>\n",
              "      <th>Performance</th>\n",
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              "      <td>1</td>\n",
              "      <td>Performing</td>\n",
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              "    <tr>\n",
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              "      <td>2</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>Performing</td>\n",
              "      <td>0.875000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.083333</td>\n",
              "      <td>0.041667</td>\n",
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              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>6</td>\n",
              "      <td>Performing</td>\n",
              "      <td>0.857143</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.142857</td>\n",
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              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>7</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>8</td>\n",
              "      <td>Performing</td>\n",
              "      <td>0.939394</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.030303</td>\n",
              "      <td>0.030303</td>\n",
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              "      <th>8</th>\n",
              "      <td>9</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>1003</td>\n",
              "      <td>Performing</td>\n",
              "      <td>0.882353</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.058824</td>\n",
              "      <td>0.058824</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>1005</td>\n",
              "      <td>Quantity Issue</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>1006</td>\n",
              "      <td>Quantity Issue</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>1007</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>1012</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>1021</td>\n",
              "      <td>Performing</td>\n",
              "      <td>0.800000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.200000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>1025</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>1027</td>\n",
              "      <td>Quality Issue</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>17</th>\n",
              "      <td>1029</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>1034</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>19</th>\n",
              "      <td>1036</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
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              "    <tr>\n",
              "      <th>20</th>\n",
              "      <td>1042</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>21</th>\n",
              "      <td>1043</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>22</th>\n",
              "      <td>1044</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>1045</td>\n",
              "      <td>Quantity Issue</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>1046</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25</th>\n",
              "      <td>1113</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>26</th>\n",
              "      <td>1128</td>\n",
              "      <td>Performing</td>\n",
              "      <td>0.750000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>1130</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>1133</td>\n",
              "      <td>Performing</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>29</th>\n",
              "      <td>1136</td>\n",
              "      <td>Quality Issue</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Vendor_ID  ... UnderPerformance (Promptness)\n",
              "0          1  ...                      0.000000\n",
              "1          2  ...                      0.045455\n",
              "2          3  ...                      0.000000\n",
              "3          4  ...                      0.000000\n",
              "4          5  ...                      0.041667\n",
              "5          6  ...                      0.142857\n",
              "6          7  ...                      0.000000\n",
              "7          8  ...                      0.030303\n",
              "8          9  ...                      0.000000\n",
              "9       1003  ...                      0.058824\n",
              "10      1005  ...                      0.000000\n",
              "11      1006  ...                      0.000000\n",
              "12      1007  ...                      0.000000\n",
              "13      1012  ...                      0.000000\n",
              "14      1021  ...                      0.000000\n",
              "15      1025  ...                      0.000000\n",
              "16      1027  ...                      0.000000\n",
              "17      1029  ...                      0.000000\n",
              "18      1034  ...                      0.000000\n",
              "19      1036  ...                      0.000000\n",
              "20      1042  ...                      0.000000\n",
              "21      1043  ...                      0.000000\n",
              "22      1044  ...                      0.000000\n",
              "23      1045  ...                      0.000000\n",
              "24      1046  ...                      0.000000\n",
              "25      1113  ...                      0.000000\n",
              "26      1128  ...                      0.250000\n",
              "27      1130  ...                      0.000000\n",
              "28      1133  ...                      0.000000\n",
              "29      1136  ...                      0.000000\n",
              "\n",
              "[30 rows x 6 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8TGHsCtlxZdz",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "outputId": "0074996c-d7e0-4e18-a586-e08464959489"
      },
      "source": [
        "distortions = []\n",
        "K = range(1,10)\n",
        "for k in K:\n",
        "    kmeanModel = KMeans(n_clusters=k)\n",
        "    kmeanModel.fit(grouped_monthly[['MonthlyPercentReceived','MonthlyNormDeliveryTime','MonthlyPercentKept']])\n",
        "    distortions.append(kmeanModel.inertia_)\n",
        "plt.figure(figsize=(7,6))\n",
        "plt.plot(K, distortions)\n",
        "plt.xlabel('k')\n",
        "plt.ylabel('Distortion')\n",
        "plt.title('The Elbow Method showing the optimal k')\n",
        "plt.show()#curve straightens out at 4, so optimal =4"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "r2OLyrYsYnwK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "count1 = vendor_output['Performance'].value_counts()[0]\n",
        "count2 = vendor_output['Performance'].value_counts()[1]\n",
        "count3 = vendor_output['Performance'].value_counts()[2]\n",
        "count4 = 0"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0M9OrAcLRt2W",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 542
        },
        "outputId": "a24e6568-52a7-451d-9af7-3bfd7d333907"
      },
      "source": [
        "label=[\"Performing_vendors\",\"Non-Performing_vendors_Promptness\",\"Non-Performing_vendor_Quality\",\"Non-Performing_vendors_Quantity\"]\n",
        "trace = go.Bar(x = label,y=[count1,count2,count3,count4],marker=dict(color=[count1,count2,count3,count4],colorscale='Viridis',showscale=True))\n",
        "data=[trace]\n",
        "#defining layout\n",
        "layout = go.Layout(xaxis=dict(title='Clustering-Output'),yaxis=dict(title='Count'),hovermode='closest')\n",
        "#defining figure and plotting\n",
        "figure = go.Figure(data=data,layout=layout)\n",
        "figure.show(renderer='colab')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
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              "                        {\"responsive\": true}\n",
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              "                            \n",
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              "            console.log([gd, 'removed!']);\n",
              "            Plotly.purge(gd);\n",
              "            observer.disconnect();\n",
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              "\n",
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              "                \n",
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              "        </div>\n",
              "</body>\n",
              "</html>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "mzDa-ciTh8N-",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 459
        },
        "outputId": "9010a584-be6e-48c5-c1fd-de404ac7674c"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier\n",
        "clf = RandomForestClassifier()\n",
        "X = grouped_monthly[['MonthlyPercentReceived','MonthlyNormDeliveryTime','MonthlyPercentKept']]\n",
        "y = grouped_monthly['label']\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import confusion_matrix , accuracy_score, classification_report\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
        "clf.fit(X_train,y_train)\n",
        "print(pd.Series(clf.predict(X)).value_counts())\n",
        "print(grouped_monthly.label.value_counts())\n",
        "clf.score(X_train,y_train),clf.score(X_test,y_test)\n",
        "pred = clf.predict(X_test)\n",
        "results = confusion_matrix(y_test, pred) \n",
        "print('Confusion Matrix :')\n",
        "print(results) \n",
        "print('Accuracy Score :',accuracy_score(y_test, pred)) \n",
        "print('Report : ')\n",
        "print(classification_report(y_test, pred)) "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "0    177\n",
            "2     13\n",
            "3      6\n",
            "1      3\n",
            "dtype: int64\n",
            "0    177\n",
            "2     13\n",
            "3      6\n",
            "1      3\n",
            "Name: label, dtype: int64\n",
            "Confusion Matrix :\n",
            "[[46  0  0]\n",
            " [ 0  3  0]\n",
            " [ 0  0  1]]\n",
            "Accuracy Score : 1.0\n",
            "Report : \n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       1.00      1.00      1.00        46\n",
            "           2       1.00      1.00      1.00         3\n",
            "           3       1.00      1.00      1.00         1\n",
            "\n",
            "    accuracy                           1.00        50\n",
            "   macro avg       1.00      1.00      1.00        50\n",
            "weighted avg       1.00      1.00      1.00        50\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "htWnsYCRx3D3",
        "colab_type": "code",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        },
        "outputId": "0e2eea55-43cb-4e01-d8fc-65ebe1fae3e5"
      },
      "source": [
        "!setup.py"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/bin/bash: setup.py: command not found\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}