IT496_ViktoriiaO_Assignment11_CPM_PERT_final.ipynb

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        "import pandas as pd\n",
        "import numpy as np\n",
        "import tensorflow_probability as tfp\n",
        "tfd=tfp.distributions\n",
        "\n",
        "simulationDF=pd.read_csv('/content/drive/MyDrive/IT496/simulationDataset.csv')\n",
        "\n",
        "simulationDF.head(2)"
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              "          A         B         C         D         E         F         G  \\\n",
              "0  6.779389  4.518081  4.588156  2.693674  5.198787  4.360078  6.896625   \n",
              "1  6.653130  4.175826  2.546335  2.622374  5.509677  5.459227  3.933105   \n",
              "\n",
              "          H          I         J  EOP  \n",
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            "text/plain": [
              "        ADEJ      BCDEJ      BCFGJ      BCFHJ         BI maxElement\n",
              "0  17.253636  19.580484  22.944727  17.229141  18.485372      BCFGJ\n",
              "1  17.089749  17.158780  18.419062  19.552577  24.060752         BI"
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              "      <th>ADEJ</th>\n",
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              "      <th>1</th>\n",
              "      <td>17.089749</td>\n",
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              "                                                     [key], {});\n",
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              "\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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          "metadata": {},
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        }
      ],
      "source": [
        "paths = ['ADEJ','BCDEJ','BCFGJ', 'BCFHJ','BI']\n",
        "pathsDF=pd.DataFrame(columns=paths)\n",
        "ADEJ=[]\n",
        "BCDEJ=[]\n",
        "BCFGJ=[]\n",
        "BCFHJ=[]\n",
        "BI=[]\n",
        "\n",
        "for index, row in simulationDF.iterrows():\n",
        "  ADEJ.append(row['A']+row['D']+row['E']+row['J'])\n",
        "  BCDEJ.append(row['B']+row['C']+row['D']+row['E']+row['J'])\n",
        "  BCFGJ.append(row['B']+row['C']+row['F']+row['G']+row['J'])\n",
        "  BCFHJ.append(row['B']+row['C']+row['F']+row['H']+row['J'])\n",
        "  BI.append(row['B']+row['I'])\n",
        "\n",
        "pathsDF['ADEJ'] = ADEJ\n",
        "pathsDF['BCDEJ'] = BCDEJ\n",
        "pathsDF['BCFGJ'] = BCFGJ\n",
        "pathsDF['BCFHJ'] = BCFHJ\n",
        "pathsDF['BI'] = BI\n",
        "maxElement =pathsDF.idxmax(axis=1)\n",
        "pathsDF['maxElement'] = maxElement\n",
        "\n",
        "pathsDF.head(2)\n"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#getting Frequence (No of cases) for path with Max value\n",
        "\n",
        "pathsCounted = pd.DataFrame(columns=['maxElement','pathFrequency', 'relativeFrequency %'])\n",
        "pathFrequency = pathsDF['maxElement'].value_counts()\n",
        "relativeFrequency = round(pathFrequency*100/100000, 2) \n",
        "\n",
        "pathsCounted['pathFrequency'] =pathFrequency\n",
        "pathsCounted['relativeFrequency %'] =relativeFrequency\n",
        "pathsCounted['maxElement'] = pathFrequency.keys()\n",
        "\n",
        "print(pathsCounted)"
      ],
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        },
        "id": "g7TNf32bhgeL",
        "outputId": "03cdb11b-4313-44ed-a834-09f69c454a73"
      },
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      maxElement  pathFrequency  relativeFrequency %\n",
            "BCDEJ      BCDEJ          34074                34.07\n",
            "BCFGJ      BCFGJ          25620                25.62\n",
            "BI            BI          21770                21.77\n",
            "BCFHJ      BCFHJ          14629                14.63\n",
            "ADEJ        ADEJ           3907                 3.91\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "fig = plt.subplots(figsize = (10,5))\n",
        "\n",
        "data= relativeFrequency \n",
        "labels = pathsCounted['maxElement']\n",
        "plt.bar(range(len(data)), data)\n",
        "plt.xticks(range(len(data)), labels)\n",
        "\n",
        "plt.xlabel('paths')\n",
        "plt.ylabel('Relative Frequency in %')\n",
        "plt.title('Critical path frequency')\n",
        "\n",
        "for index,data in enumerate(data):\n",
        "    plt.text(x=index-0.2, y =data+0.5 , s=f\"{data}.%\" , fontdict=dict(fontsize=12))\n",
        "plt.tight_layout()\n",
        "\n",
        "plt.show()"
      ],
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        },
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      "execution_count": 51,
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        {
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            "text/plain": [
              "<Figure size 720x360 with 1 Axes>"
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\n"
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          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}