maintenance-ocp.ipynb

maintenance-ocp.ipynb

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      "execution_count": 1,
      "metadata": {
        "id": "KX1Oji0g_IF1"
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      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "dataset_path = \"/content/21_02_2019_lcmp_unique_pdm_b8.xlsx\""
      ],
      "metadata": {
        "id": "kNA1uB1V_RPQ"
      },
      "execution_count": 2,
      "outputs": []
    },
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      "cell_type": "code",
      "source": [
        "df = pd.read_excel(dataset_path)"
      ],
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        "id": "dPffLthA_pM5"
      },
      "execution_count": 3,
      "outputs": []
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      "source": [
        "df.head()"
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              "  Ronde Activity Codification                    Asset N              Plan  \\\n",
              "0   BF1         AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-M      Q1-BIS-BF1-M   \n",
              "1   BF1         AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-R      Q1-BIS-BF1-R   \n",
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              "\n",
              "  Etat Ligne                                   FULL DESCRIPTION Fré  \\\n",
              "0          M  Q1-BIS-BF1-M : Mesure de vibration - RMS (TSD-...   M   \n",
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              "   Numero des ouvriers  Temps pour la tache   MH                     TSD  \n",
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          "metadata": {},
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      "source": [
        "# Preprocessing Data"
      ],
      "metadata": {
        "id": "KCF-YjUjWlne"
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      "source": [
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          "data": {
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              "(587, 11)"
            ]
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          "metadata": {},
          "execution_count": 5
        }
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      "cell_type": "code",
      "source": [
        "# deleting not used columns\n",
        "df.drop([\"MH\", \"TSD\"], axis = 1, inplace = True)\n",
        "\n",
        "df.shape"
      ],
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      "execution_count": 6,
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            "text/plain": [
              "(587, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
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    },
    {
      "cell_type": "code",
      "source": [
        "# renaming columns\n",
        "cols = [\"Lign\", \"ActivityCode\", \"Asset\", \"Plan\", \"Lign Status\", \"Description\", \"Frq\", \"EmployesNbr\", \"TimeNeeded\"]\n",
        "df.columns = cols\n",
        "\n",
        "df.columns"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oc5kdU-DZb_4",
        "outputId": "c108aa84-99f3-43fd-cb0f-a11ed137d098"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Lign', 'ActivityCode', 'Asset', 'Plan', 'Lign Status', 'Description',\n",
              "       'Frq', 'EmployesNbr', 'TimeNeeded'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# how many lings we have?\n",
        "print(\"ligns: \", len(df.Lign.value_counts()))\n",
        "# How many machines we have?\n",
        "print(\"Machines: \", len(df.ActivityCode.value_counts()))\n",
        "# How many plans we have?\n",
        "print(\"Plans: \", len(df.Plan.value_counts()))\n",
        "# Statics for Etat Ligne & Frequance & Nbr employes & time?\n",
        "print(\"Lign Status: \", len(df[\"Lign Status\"].value_counts()))\n",
        "print(\"Frequances: \", len(df.Frq.value_counts()))\n",
        "print(\"Employes Nbr: \", len(df.EmployesNbr.value_counts()))\n",
        "print(\"Time Needed: \", len(df.TimeNeeded.value_counts()))\n"
      ],
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        },
        "id": "nJr80pWuYDZw",
        "outputId": "d4373d27-f012-4e80-a09c-0f6f982f250f"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ligns:  20\n",
            "Machines:  76\n",
            "Plans:  255\n",
            "Lign Status:  2\n",
            "Frequances:  6\n",
            "Employes Nbr:  1\n",
            "Time Needed:  7\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "So, we have 20 ligns, with 76 Machines and 255 plans. The lign status can be boolean and the Frequence has 6 possible values. For all those operations we need a fix number of employes _(2)_ and the time needed can varie between 7 values."
      ],
      "metadata": {
        "id": "QPyUgAri50D8"
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    {
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      "source": [
        "df.head()"
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        "id": "mTCyZ9PY4F8O",
        "outputId": "f4d18659-e741-46cb-da73-30f109a87c4b"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  Lign   ActivityCode                      Asset              Plan  \\\n",
              "0  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-M      Q1-BIS-BF1-M   \n",
              "1  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-R      Q1-BIS-BF1-R   \n",
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              "3  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-V      Q1-BIS-BF1-V   \n",
              "4  BF1  AJIAM.TCM-BF1    JL-MP-HE-Q1-BIS-BF1-ACC    Q1-BIS-BF1-ACC   \n",
              "\n",
              "  Lign Status                                        Description Frq  \\\n",
              "0           M  Q1-BIS-BF1-M : Mesure de vibration - RMS (TSD-...   M   \n",
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              "3           M   Q1-BIS-BF1-V : Prendre une image thermographique   M   \n",
              "4           M  Q1-BIS-BF1-ACC : Prendre une image thermograph...   T   \n",
              "\n",
              "   EmployesNbr  TimeNeeded  \n",
              "0            2         0.2  \n",
              "1            2         0.2  \n",
              "2            2         0.2  \n",
              "3            2         0.1  \n",
              "4            2         0.2  "
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              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BF1</td>\n",
              "      <td>AJIAM.MCM-BF1</td>\n",
              "      <td>JL-MP-HE-Q1-BIS-BF1-V</td>\n",
              "      <td>Q1-BIS-BF1-V</td>\n",
              "      <td>M</td>\n",
              "      <td>Q1-BIS-BF1-V : Prendre une image thermographique</td>\n",
              "      <td>M</td>\n",
              "      <td>2</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BF1</td>\n",
              "      <td>AJIAM.TCM-BF1</td>\n",
              "      <td>JL-MP-HE-Q1-BIS-BF1-ACC</td>\n",
              "      <td>Q1-BIS-BF1-ACC</td>\n",
              "      <td>M</td>\n",
              "      <td>Q1-BIS-BF1-ACC : Prendre une image thermograph...</td>\n",
              "      <td>T</td>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5a8bb15a-5e70-49ff-b441-dc864264fed1')\"\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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              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Lign Status\"].value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YmZkUb1w9Ghz",
        "outputId": "967298dd-1d9f-4caa-806d-79c6e16e21c3"
      },
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "M    570\n",
              "A     17\n",
              "Name: Lign Status, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Frq.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dLMqgN729OZc",
        "outputId": "9e8e6d02-9262-44cc-cdc1-f12ead05fca0"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "T    320\n",
              "S    145\n",
              "M     73\n",
              "B     32\n",
              "X     14\n",
              "Z      3\n",
              "Name: Frq, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.TimeNeeded.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q61iQJQK9T28",
        "outputId": "3b75ddb3-045a-48b7-df6c-bd4ab9749433"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.20    279\n",
              "0.10    153\n",
              "0.30     83\n",
              "0.50     58\n",
              "0.15     12\n",
              "0.70      1\n",
              "3.00      1\n",
              "Name: TimeNeeded, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "id": "kr7mE4NiKHpf",
        "outputId": "a72efcc9-55bd-4ebc-82ab-2d35dcc90030"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Lign   ActivityCode                      Asset              Plan  \\\n",
              "0  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-M      Q1-BIS-BF1-M   \n",
              "1  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-R      Q1-BIS-BF1-R   \n",
              "2  BF1  AJIAM.MCM-BF1  JL-MP-HE-Q1-BIS-BF1-TAM01  Q1-BIS-BF1-TAM01   \n",
              "3  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-V      Q1-BIS-BF1-V   \n",
              "4  BF1  AJIAM.TCM-BF1    JL-MP-HE-Q1-BIS-BF1-ACC    Q1-BIS-BF1-ACC   \n",
              "\n",
              "  Lign Status                                        Description Frq  \\\n",
              "0           M  Q1-BIS-BF1-M : Mesure de vibration - RMS (TSD-...   M   \n",
              "1           M  Q1-BIS-BF1-R : Mesure de vibration - RMS et te...   M   \n",
              "2           M  Q1-BIS-BF1-TAM01 : Mesure de vibration - RMS e...   M   \n",
              "3           M   Q1-BIS-BF1-V : Prendre une image thermographique   M   \n",
              "4           M  Q1-BIS-BF1-ACC : Prendre une image thermograph...   T   \n",
              "\n",
              "   EmployesNbr  TimeNeeded  \n",
              "0            2         0.2  \n",
              "1            2         0.2  \n",
              "2            2         0.2  \n",
              "3            2         0.1  \n",
              "4            2         0.2  "
            ],
            "text/html": [
              "\n",
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              "      <th>1</th>\n",
              "      <td>BF1</td>\n",
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              "      <td>Q1-BIS-BF1-R</td>\n",
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              "      <th>2</th>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BF1</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BF1</td>\n",
              "      <td>AJIAM.TCM-BF1</td>\n",
              "      <td>JL-MP-HE-Q1-BIS-BF1-ACC</td>\n",
              "      <td>Q1-BIS-BF1-ACC</td>\n",
              "      <td>M</td>\n",
              "      <td>Q1-BIS-BF1-ACC : Prendre une image thermograph...</td>\n",
              "      <td>T</td>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
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              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "  "
            ]
          },
          "metadata": {},
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Understanding values\n",
        "- **Lign**: the line code in the factory\n",
        "- **ActivityCode**: the maintenance ActivityCode\n",
        "- **Asset**: The Specific machine concerned by the maintenance operation\n",
        "- **Plan**: the specific piece of the machine to be verified\n",
        "- **Lign status**: Boolean if the should be lign:\n",
        "  - **M**: Marché\n",
        "  - **A**: Arrét\n",
        "- **Description**: descreption for the operation process\n",
        "- **Frq**: frequency of the operations, how many times should be verified per year\n",
        "  - **T**: 1 d\n",
        "  - **B**: 15 d\n",
        "  - **M**: 30 d\n",
        "  - **S**: 182 d\n",
        "  - **X**: 365 d\n",
        "  - **Z**: 730 d\n",
        "- **EmployesNbr**: number of employes needed for this operation\n",
        "- **TimeNeeded**: how much time needed for this operation per employe."
      ],
      "metadata": {
        "id": "RENGctXI-Giu"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Modifying Values\n",
        "we are going to change the abstract values to a numerecal values in the Frq column.\n"
      ],
      "metadata": {
        "id": "WaHYExZACtNW"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df.Frq = df.Frq.replace([\"T\", \"B\", \"M\", \"S\", \"X\", \"Z\"], [1,15,30,182,365,730])"
      ],
      "metadata": {
        "id": "O7v8dY01C42_"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# check changes\n",
        "df.Frq.value_counts()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "g2aRSazRDcnL",
        "outputId": "4358d35b-dfae-4dbc-9e81-b84fad65e595"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "1      320\n",
              "182    145\n",
              "30      73\n",
              "15      32\n",
              "365     14\n",
              "730      3\n",
              "Name: Frq, dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Data Visualization"
      ],
      "metadata": {
        "id": "3s-Cyssm-jPy"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "metadata": {
        "id": "F_heMLXX9p3B"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Lign Status\"].value_counts().plot.pie(autopct=\"%.1f%%\", explode=[0.05]*2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "5kjvbpNJ-p8S",
        "outputId": "f77b87a1-ba02-46a8-a720-6a9bcffe3061"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"Frq\"].value_counts().plot.pie(autopct=\"%.1f%%\", explode=[0.005]*6, figsize=(10,10))\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 575
        },
        "id": "pmzHiA36_VC5",
        "outputId": "98263cd9-39ad-4b8a-a8fc-87a14f5a9930"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x720 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"TimeNeeded\"].value_counts().plot.pie(autopct=\"%.1f%%\", explode=[0.005]*7)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "id": "_BtNTSlJBDth",
        "outputId": "75550a02-f069-414c-d985-da0e01c06d19"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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AGGHZwNuOm8CoRNTeU/jCo6yLHvSe1viQ79Rcto0ptF8Y8qMt3Cyal33P5o8fB7+fzAnHknPAmdu9Xv/tDBrn/xcsVqzp2eSdcAW2nIF4alaz6e27UX4fecdNxTG0EOX3seG1W8g//WYs9ohdVx3B7igiqcDngWNswBtKqVt32McBPI+RL6AG+K1Sqqo3woJp+U/FiFGeB6CUWiMiZhgxnlv+3jA0sB0FYBcfV9tf52r76w1si3D8AHjgK9/ua6Z7ptk3kjuG7XsLozBqyCUEyu9j07v3I7YUQKj/bhZpYyaRMmDbJJCndg1K+RGseBtq2Pzhoww841bcc17H39YMYqH2s+cY/H+lNMx7B29jxKvOhRJh2grcDtxJoPafiLynlOpYiPZdYAKwGmP26xd6ORAejPnblFJKRNpH+81KTJiILX9vyMK46m/NFHOQdSFzrZf6MGrFtfcW3qpVmUuu91zU9l//xPaEpx0HHOPuYtpaXYlq3cLg392NLSuP1Y+eT+NP79P/qIu37pOx26H0O3IKFnsqtZ89R+NPHwDg2bSCzH1OIGXQWGreuRt/SyMNP7xH9qTTI9nqQ2izPRbgHxhl6GowpptHsn0Vai9GBa2vRWQ6cKeIiOpFue1ghL0mIv8CckXkIuAC4IlQTxQG4u7LGmWsGEFQYzBKqtFPGnk85T6ATWy7KLwKVL7i/fXmW7znp7dhH4dxQcgzRXUIbFk4ew+xpYy15w6eCWDvN+yQ5mXfp3PUtvJxqSP32rp/a3XFwRi3RDOtmXkTG757Ox8RZUnPbd707gON+H0DMvc88v0Iy24IYd/9McaA3sT4HOdi9N46MhRoz6n5W4zo1TyMzzgkgsnkc4+IHAPUY7QYtyilPgz1RGFAz5f3ngHAIYENgLNtsznbNrsF48t23yMLx7knrBhUVNdv7Jb63AK2ZAxO89nSBwkMEPw+QfkEv1dQfosonyi/X/AHfldKRPlF+f0WQVnw+0Fhwa8E47GgEFFYQAkKAREUFkBEISgxnoPA6xYxostFULKydl36tMrPx0prc2rrI3+YfOyuk1asSElRi92tjteaNuZYsKAQufmDR3afV71ooMfntdh8XusphYeuOGXlz8Ov3bB88IYtten5Gf2ap+198ooHvnxxV7vV5h/5+l+OUmC59tDzq3YfNKZZEAuIRRAJxLa3PzakIhaErY9BrBhaLRjPWTFacEsgPsayuuSLsmGlh74dxOc0FFillDpORHKBr+gif4WIjMQoDNrUy+9EcIYKmN0Mw3cklgqGJAqpwJ5bRHZ5tKi5tPSZJc+MqlpyXYfX3V5r6lJ3tnNdXe7YLe7sApoyhmQ227MGI5YxtN+KtU9M9jRB2VXHNIgOq9s6gL1GHcX8qi+5+vRnrHe++cfC/pmDfFZHLt+29JuglN/z4/IvU5bWu3N/c+B561/6/F/DPT6PZeKEyXX3z3lpbNH+p1XlZ+c1v/b1q6Me/2HGiFblVSk2R9uKhnV2q8Xiu/KDOwumHXPWj+AD5RXwofAJyi8QuPbht4DfopTfAn6r8VjZUIGfKCsou/EzcJ0zLhD+qwlttk8pVSciFewc1VqNMR18GEaY/dkE0uqHSndpvJbTzcellBrdmxP2gVC6T5oQqHCkNAP5JefbLrrtBe8du62mBMPKOTZfy6/yaivJq63seIgCqptT835054zeXJszprUhe6StOXVAP5/VMRwRJ2HuqeVk5LHv6COYs+h9trTWpw3KHcny9Qss+489BrFkDRNg0ZpKCkccxGtfPVeQ7shBHNCqBu6XlT4CW0rBYLEPQkkmDS3r6Zc5FK/fm7H/2GMYmT+Opz66jZTMokjl2g/2fnwLRmuOiKRhBJJ9vsM+s4BijFW2M4BPenO/D91/QPvt8NgCnAVcA/zQm5P1EW3+CLEgJcUb+LXfLb+3Tb3xZd8dE6pUCV2neRNgWFpLzbC0lhoGr99ugWerX6yLGjKHranLHVvvzhnta8wYmtbqyMlXFtso+jDvXTB4D1JTMrh/1pXUN9WQ7sjm4MIi3pn7DCPyx1G3ZRNL15bT0FzHwJxh1DVu5OmP/krxkTfw/Kd34vG20uppIsWWykn7X8Ccyvf5bMEMLGIhLyuigXgtQe63Djgk0OL7MRaS3SUitwHfKaVmAU8BMzEyWnkwWv5e0V0arxoAEbEAvweuBX4EipRSC3t7wj7QaMI5k4KfHSkdW47sv59jvfyaN3x/33+x+jOhTxU6LMq3R07Dij1yGlbAqo86vlbTZs/41p09akNd7pim+uwCS1P6oGyPLWMIImPoYdWk1WLlt4dczrMf305Gag6H73kqQ/o7jXWZAey2VDJTs7FZt82w9c8axBWT70UpxX1vTSczNZfBuSNoamuksdlNVloukyddGOKfGRLNQe43CNiIkYSmAbhXKbXAWFUPInIYcD9GhqC3lFKnth8oIj6gPPBwpVJqck8n667bb8cY2b8S+B9wilLKzPBa3fJHiF9SUnb8HmTec4b1mstm+W4/dIEqIXzZkPJSPFvy8mvKya8p7/i8X8GKpvRBK+pyRtfW5Yz1NGQNt7ek9s/zW1JGIjIcsPh8Xv5XUcbJk6Zw1F7bgntOmng+AAtWzmHi2KPZb4xR0Om2V4q54Oibt+4nIuRm5HPS/ucz++cZHLXXmZx18GXM+vYpRg+OaGrIup52CAwOPoxxL78aY6R/BoBS6pbAPk7gPIze96wd3qJZKRVS2rDuuv3LMeYU78fIkLOXiGydR1FKvRnKicKANn+EqLZZO5voTntosvW6VrvvjqN/VCVEdkWlRaAgo2l9QUbTeoau/arja1t8Fnt5XeaIddOX/bxbblr/1hPHHb24VfkGIdbRdMgnOX7kQXy+YCb7jj6Cqg0VpKVkkJOxbQZz8ZqfyMnIY2DOMDzeVsQY0KfNG2yvvNcEMyC3P7BEKbUMQEReAX4DbO1lt0fyiUhYVtV2Z/6PMAYqJgS2jiiMuchoos0fGepbLZau5vgdj59gLWmx+0pPmquuw5y1BRlWv2dC1bryCfNqVrJryjruefncXQGm5Q2o/cVnWVbvyG08Za9TKzJzR9q+s6VOuuWlcwalpmTYfnfEdVvnH5RSvD/vxa09gYMLi3j2k9vx+3389tArIqlfsS35bXd0nL8Ho/Wf1MW+nZEqIt9hNNilSqmZPR0Q1Kq+mMCV4yD4gRNN8CweXzDiW7qvyuQ963Pf7Wd8qa4B0qOkqy94FZZljRlDVtXljnHX5YzxNmYOc7Q6+g3wW+0FwC5R1LJp6mNH9rj2QkTOAI5XSl0YePx7jEQ6O+WsFJFngXeUUm90eG6oUqpaREYBnwBHKaWW7nhsR4JZ2DMII954F6XUCSKyO3CgUuqpno4NKy53K66cOnTizXCzgZ5H4G2vHWa9sSXFf8fvPvVPJ/ZDrW2Cf9esLdW7Zm2pZnj1Zx1fc3utqd+7s53rjdiFUTRlDM5ss2cORixjMVLTh5O1Qe7XPn/fzrDAc0GhlKoO/FwmIrMx1uP0zfzAsxir+W4MPP4FI0Q0uuY3WERoXSFNz6wnuNyI1lkHWP7caqd0yn/9U4nfZCQ5Nl/Lvl3ELqxuTs37oS5n9OY6I3bB3pw6IMdndYxApIDeLZIK1sBzgbFinKcaYwrv/4I5UET6YSy5bw0U1j0YuKun44Ix/wCl1GsicgOAUsobmFYwA23+8LOe4JdnWz7Y13JDi507p5b5L8ZIjJooCDA8raVmeFpLDUN2jl2obMgcXl2XO6bBnTPa15g5NK01JSdfWWyj6X5JdbetbzsBX03DWKlpBZ4OTPNtneMXkYkYMwD9gJNF5C9KqT0wlnf/KzAQaMG45+9xOj4Y828RkTy25fA7AHAH8wdFgEUmnTdh8RrzygNCOEQ+28tS0mbnnitn+otJjlwCgdiFqj1yGqo6i134xp09amNd7tim+mxne+zCLoiElH1KKfUuxpLdjs/d0uH3uRi3Azse1+UagO4IxvxXYcwpjhaRLzE+7DNCPVGYqOx5F00oLLPbG+lFd/brQss1rXbuK3ndfw4xnKcuCnQbu9DqyP0SvjZLW7d0V6UXAKXUPOBwjBRUfwT2UErNj7SwLtAtf5gpd6S09fbYeWMsV/7lHMsbhDAwlURYBApSW+vWmC2kK3o0fyDy6ESMjDLHApeJyFWRFtYFSzAjbXgCs2D70N7Qj3dapt30e+vbwIowSUok3IWVFTF7YezR/MDbGCGFeRhTPO1b9HG5WzHSY2vCRGVKSp/TfP0yTP503fnWDwlycCuJWGC2gO4I5p5/mFJqr553ixqL2Dm7iaaXVNntYQnbrRosF155kfXZe5/wecVI+qKJ1Zv9AMG0/O+JyLERVxI8c80WkEC0NFgtYZuvrx4g513+J+t3KsZbvCjypdkCuiMY888BZohIs4jUi0iDiNRHWlg37JjcQNN7NhDmOoPr+8m5Uy+1LlDG8u9e80LtZiYvX8bJy5fx/OadQ+PdPh+XVa/mlOXL+e2KKha3tgKw2evldytXMHn5Mj5q2LYcZGr1ajZ4PX2R1Bvi3vz3YlS3TVdKZSulspRSZibT/BojiYGm76wnAkVGN+XIWX+8zLpc9bKXtri1ldfr6nh1pJMZzgJmb2lkRdv2kxKP19SwmyOVmQUF3DF4CLdvWA9AWUM9Z+Xk8upIJy/UGheNTxsbKHQ4GGgL18rkoFhSWFmxIZonDJVgzL8K+Lm3qYLCjsvdBHxvtowEIZi4/l5RlymnXjjdut7fi/vepW2t7JWWRprFgk2EiWnp27Xi7ftMSjfWGI1yOFjj8bDJ68WO0KIUbUphEcGrFM/X1jKlf9STE8d0qw/BmX8ZMFtEbhCRq9q3SAvrgU9NPn+iEJGWv52GdDlpypXWer/wRSjHjU1x8H1TE3U+H81+P59vaWTtDl32cY5UPmo0Lgjzm5tZ4/Gw3uulKDubTxobuHDVKi7un8fLdbVMzs4mzRLMVz2sJIT5l2OUok7B7Km+bXxg8vkThfVEuJbcllQ57vwrrW0+4ZNgjxntcHBh/zwuXLWSi1evYjdHKlbZPjXwRf37U+/zc2rVcl6qq6UwNRULkGW18tiw4bzudLJ7aiqzGxs5NiubW9at5Yrqan5sDjajVp/5OFon6i3xs56/I64cO0Z2FLMvQnHNWqu15NgRQ0ujcS6HR33xzL2+Jpuf40I99r6NGxlss3FOv36dvq6U4phlS5npLCDTui1s4c4N6zkiM5MVbR7sIhyblcX06mqeGD680/cJI+WFlRWxND3eKV22/CLyz8DPt0Vk1o5b9CR2gsvtQXf9+0xfQntDpdUuhxZfbc32WHgnmP1rvEZC4TUeDx81NlCUvf0Yc73PR1ug4XrD7Wa/9PTtjF/V1sY6r5f90zNoUX4CFTZoUVEJEJ0RjZP0le6CfP4ATAPuiZKWUCkDesxQqumacocjqkuzPTY58A/XWOc+9w/fjBQfp3a37/Q11dT5fNhFuGngILKtVl6pqwXg7Nx+LGtr44a1axCEMY4U/jp4yHbHP7BpI9MHGAsOT8zK5rLqap7YXMNlA0JZwNhr4sL8XXb7ReQHpdQ+UdYTPK6c/sAaQiiBrNmeiwbnXzEnLe3+aJ/X4lc/Pnuvb3GqhzN73jvuqCqsrCgwW0QwdNfy53c3qq+UujcCeoLH5d6MK+dtorC8eJXbzx9mNrO+USECF//KzvQDjGvOQ9+08fDcNqwWKBpr465jds5xecFbzbzzi5eBGcLPl27LEnX9hy28t8TL3oOtPH+qkbL+xfltbGpSXHFA5K9py8IU2hsqfovsXXyV1fbMfb5/p7cFl60mjugxcWas0N1ovxUjn1lWF1ss8Fw0TmKzwD+OTWXh1EzmTMng4bkeFm708elyL28t8vDTnzJYcGkm1xzUuZfO29vO+7/bPu+lu0Uxb52P+ZdkkmKF8vU+mj2KZ370MHViVDzp22i1hjtfXdAoi+x53lXWfRtTo/MZRpH/mC0gWLpr+dcqpW6LmpLe8T4RnqsGGJJlYUjgcpflEArzLVTXK56Y10bJIQ4cNmMaamBG59fSw0baqKrbfqDJIuDxGSPVTR6F3Qr3fNXGZfunYLf2VPEyLM8Be2oAAA4jSURBVGxSIuZm4REZd8GVNtsTD3ifymliiqlawsOiwsqK/5ktIli6a/mj8g3sEy63F3gpmqesqvPzw1ofk4ZZ+aXGzxcrvEx6spHDn93C3Orgx8+yHMKJY23s868tDMm0kOMQvqn2ccpuUQtBjfhFM0hGXzTddnRNJo+ZLSQMPG62gFDozvxHRU1F34hat7GxTXH6a03cf3wq2Q7B64fNzYo5UzK4+5hUznqjiVDiJq472MGPf8rkH8elcvOnrdx2hIMn57Vx1utN/O3z1gj+JUAUAnxCYOQll9lO2pDDP80W0gdaieJ3MRx0aX6lVDBVRszH5Z5PH1eQBYPHZxj/3PF2Tis0Wudh2cJphXZEhP2HWrEIbGoKPWjqh7U+lIJxeRZeX+jhtTPTWVrrZ3FNRGfiwr6ir48Mm3ap7Yzq/kR99iFMvFFYWRFMWa6YIeoBzxHi2Ui+uVKKKbNaKBxg5aoDt43Cn7KbnU+rjGCUX2p8tPlgQHrod0s3f9rKX4904PGDLzA0YAGaIrt2MVa6/R0ZfOUfbedWDeRus4X0gn+ZLSBUEsX8zwC1kXrzL1f5eGG+h0+We9n7sUb2fqyRdxd7uGAfO8tqFXs+0sjZbzTz3ClpiAhrGvyc+FLT1uPP+U8TBz61hUU1fobd28BT87YF1s2s9LDfLhZ2ybKQmyrsPdjK+EcbafEpJgzuc4atLmkQ2QSYuTS7K/Kvm2Kb8ssulBJIFx8HVBRWVoS0eCkWiM/Y/s5w5dwC/MVsGfHCnFTH1RcNGfQPs3V0g/uWl3yP7LlSXU/sN1JTCisrnjZbRKjE+j81FB4giDroGoNyh8NrtoYeyLntXOu070fL7YBZFaKCYRXwgtkiekPimN/ldgMPmi0jXljgMCW4L1Sy7jzLetXXu8ntGKWnY5G7Cysr4jKzVOKY3+B+wMz8gnHDkhR7VHNa9YH0+061XvvpXnI7ELVViEFSDTwRzI4icryILBKRJSJS0snr54nIRhH5MbBdGHa1O5BY5ne5a4GHzJYRD6yx2dLM1hACqY8WWa9/b1+5E2M+PVa4vbCyoqWnnQKFbx4GTgB2B84JlLrfkVeVUnsHtifDrHUnEsv8BvcCDT3uldzUeoziq/GE45ljrSUzDpC7gKil4+mGlUCwBt0fWKKUWqaUagNeAX4TMWVBknjmd7k3Q1xHikWDWAvwCRb7y0dY//zyYZZ7gEaTtVxfWFkR7G3IUIyBwXZWB57bkdNFZL6IvCEiEU83lHjmN7gD4x+s6ZxYDPAJFuuMgy03Pnu05UHMG9+ZXVhZ8UqY3/NtwBmojvUhUQgVTkzzu9wNwGVmy4hhYimuvzdY3p1oueGxEywPE8Hgri7wYmS4CoVqoGNLPowdKhsrpWqUUu3jGU8C+/ZaYZAkpvkBXO6ZxFFihSjTY7d/9VOrqbisgsU3Lt7ptU3vbeLn837G29D57FvVPVUsvGQhK+7bvnDvqsdWsfimxax7Y902IbM2UP99rxpw+WRvyw0PTrY8AWzqzRv0kocKKytCLUc2FxgrIgUikgKcDWyXB1NEOuYhmwxU9E1mzySu+Q0uQw/+7USbYf5uB/z6HdIP59XOnY+taaNxQSP2vK5nCgecOIBhFw/b7rmWVS1YUiyM/dtYmpc342vy4anz0Ly0mex9ex9l/L89LNfdfbrlOYzeTKRZB7hCPUgp1d5b+ADD1K8ppRaIyG0i0p6H8nIRWSAiPwGXY1TGjiiJbX6XezVwk9kyYo0lKfYt9PDZZ4zLwJqx89qCdS+vY9BZ3Q8XZO6eiSV1h7e3gr/Nj/IrlFeBBTa8uYGBp/b97mPurpar//Zby6sYOR0jyTWFlRW96qYopd5VSu2qlBqtlPp74LlblFKzAr/foJTaQyk1QSl1hFKqMpzCOyOxzW/wT3Rl3+3obWhv/bx67P3spI0IPUQgdZdUbFk2lt66lOy9s2lb34ZSijRneMIN5o+yXH7z76wzMKbgIsGbhZUVUU0cE2kS3/wutx+4mNgND4065Y6UkFdz+Vv9bHxnY59a6iHnDmHMX8cw4IQBbHhzA4NOG8SGWRtY+fBKNs/ue/qIRcNlasl51vcxqkyFkzXARWF+T9NJfPMDuNw/AneaLSNWWJSSEvLn3rahjbaNbSy5eQmLrl6Ep9bD0luX4qkLPay9fl49qc5U/K1+2ja2MWLqCOq/q8ff2veCGsuGyMXXTLHOVrDzSGXvUEBxYWVFfCS3CYHkML/BrcDnZouIBVbZbTvnF++B1OGpFD5UyLh/jGPcP8Zh72dn9F9GY88NbYmA8ipq/ltD/on5+Nu2mX3rWEAYWDlQzr/ij9Y5ChaG4e3uL6ys+CgM7xNzJI/5XW4fcA7GSHcy07TFYum86F0HVj26imV/W0brulYqr6xk82ddN3zNy5upfnrbtPWy25ex6pFVNC5spPLKShrKt0241HxcQ+7BuVgcFlKHp6LaFItvWkyaM63TAcbesra//H7aJdb5Cub34W3mAzeES1OskTjJPILFlXM0xpRL8lz4tmf5+IIRnxGFqaRYoF+D+s9j//Q5JfSgmXrggMLKiojPt5tF8hnA5f6I5J7+i9e4/l5RmyWnX3S5dbWCb0I4zAecncjGhzgwfxDroA8TkXki4hWR4Ep3udx3AK+FW2ucEM9x/b2iPkN+M+UK62a/EGxBjWsLKyvei6ioGCCmzR/kOuiVGF3Yf4f49ucDP/VVYxwS73H9vaIxTU44/wpri0+Y3cOuTxVWVtwXDU1mE9PmJ4h10EqpKqXUfCC0eSKXuynwXtU97ZpgbCAJzQ/QnCpHn3eVFZ+FD7vY5Qvg0mhqMpNYN3+w66B7h8u9AqMyUdLMAKyw2eqBuEjgFwlaU+TXxVdZ070W3t3hpV+A00JYox/3xLr5I4/LvQg4Gki4II7OWOBIiaU0WKbQZpeD/3C1dUCbdevKupXA0YWVFdFcHWg6sW7+HtdBhwWXuxw4FnCH/b1jjN6E9iYiXpvs/4drrMOaU3gcw/irejwowYh18/e4DjpsuNzfYwwsmp0eKqJU9CK0N1HxW2Rw8dW2uwsrK8IVChxXxPQXIZh10CIyUURWA2cC/xKRUBMtbMPl/hojkUIsJIiMCMvjJ2V3pFkNHF5eXL7EbCFmkXwRfsHgyjkOeAtw9LRrnOEZ7xxeisjNZgsxmVXAEeXF5UvNFmImMd3ym4bL/QFwBIk3C7ARkaQK8OmExcCvk934oM3fNcYtwCSg97cRsUdSBvh04ENgUnlx+TKzhcQC2vzd4XJXAQdhjDkkAkkV178DDwInlBeXRzvbb8yizd8TLnc9UIQRZhzvJF1cP+ABLiovLp9eXlwey9V+o442fzC43D5c7mkY2YDj+QuUbN3+jcBR5cXlEa97F49o84eCy/1P4CSimyc+bGy2WGqBTLN1RIn5wMTy4vIvzBYSq2jzh4rL/T6wJ5EKNoogCx0pPVaUTRBeBQ4qLy5f0eOeSYzNbAFxicu9HvgNrpzzgAeA3lediCLze5myO45YD0wtLy7/j9lC4gHd8vcFl/tZYDzwqclKgmKhI6FDe58HdtfGD55E/jJEB5d7Jcay4OnEeFjwErs9EXt6q4ATy4vLi8uLy5NiZWa40OYPBy63wuV+ENgHIyFELKLW2azpZosIIwp4DNijvLg84VNuRYJEbAnMw8gNcBiunNOBu4BRJivqyGafSL7ZIsLEEuDC8uLyz8wWEs/olj8SuNz/wcg5eB1QZ7KadhJhjn8l8EeMe3tt/D6iV/VFGldOP+BajLLLGSYq+XR8wQgBfm2iht6yCrgdeLq8uDxp0mxFGm3+aOHKGQT8GaNoaMjlssLAq+MLRuwFFJpw7t5SjWH6J7Xpw48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          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Saving Data"
      ],
      "metadata": {
        "id": "gCYG_L63FvvO"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "json_data = df.to_json(orient=\"records\")\n",
        "with open(\"json_data.json\", \"w\") as jd:\n",
        "  jd.write(json_data)"
      ],
      "metadata": {
        "id": "1bX44hGABNyL"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "00ed-g2oHMrT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 354
        },
        "outputId": "f73f8c2f-7c46-45e7-af7a-f3596c227334"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Lign   ActivityCode                      Asset              Plan  \\\n",
              "0  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-M      Q1-BIS-BF1-M   \n",
              "1  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-R      Q1-BIS-BF1-R   \n",
              "2  BF1  AJIAM.MCM-BF1  JL-MP-HE-Q1-BIS-BF1-TAM01  Q1-BIS-BF1-TAM01   \n",
              "3  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-V      Q1-BIS-BF1-V   \n",
              "4  BF1  AJIAM.TCM-BF1    JL-MP-HE-Q1-BIS-BF1-ACC    Q1-BIS-BF1-ACC   \n",
              "\n",
              "  Lign Status                                        Description  Frq  \\\n",
              "0           M  Q1-BIS-BF1-M : Mesure de vibration - RMS (TSD-...   30   \n",
              "1           M  Q1-BIS-BF1-R : Mesure de vibration - RMS et te...   30   \n",
              "2           M  Q1-BIS-BF1-TAM01 : Mesure de vibration - RMS e...   30   \n",
              "3           M   Q1-BIS-BF1-V : Prendre une image thermographique   30   \n",
              "4           M  Q1-BIS-BF1-ACC : Prendre une image thermograph...    1   \n",
              "\n",
              "   EmployesNbr  TimeNeeded  \n",
              "0            2         0.2  \n",
              "1            2         0.2  \n",
              "2            2         0.2  \n",
              "3            2         0.1  \n",
              "4            2         0.2  "
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-95e21635-449d-450e-ba9c-43c09ac6d72b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
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              "        vertical-align: middle;\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Lign</th>\n",
              "      <th>ActivityCode</th>\n",
              "      <th>Asset</th>\n",
              "      <th>Plan</th>\n",
              "      <th>Lign Status</th>\n",
              "      <th>Description</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>0</th>\n",
              "      <td>BF1</td>\n",
              "      <td>AJIAM.MCM-BF1</td>\n",
              "      <td>JL-MP-HE-Q1-BIS-BF1-M</td>\n",
              "      <td>Q1-BIS-BF1-M</td>\n",
              "      <td>M</td>\n",
              "      <td>Q1-BIS-BF1-M : Mesure de vibration - RMS (TSD-...</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>BF1</td>\n",
              "      <td>AJIAM.MCM-BF1</td>\n",
              "      <td>JL-MP-HE-Q1-BIS-BF1-R</td>\n",
              "      <td>Q1-BIS-BF1-R</td>\n",
              "      <td>M</td>\n",
              "      <td>Q1-BIS-BF1-R : Mesure de vibration - RMS et te...</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BF1</td>\n",
              "      <td>AJIAM.MCM-BF1</td>\n",
              "      <td>JL-MP-HE-Q1-BIS-BF1-TAM01</td>\n",
              "      <td>Q1-BIS-BF1-TAM01</td>\n",
              "      <td>M</td>\n",
              "      <td>Q1-BIS-BF1-TAM01 : Mesure de vibration - RMS e...</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BF1</td>\n",
              "      <td>AJIAM.MCM-BF1</td>\n",
              "      <td>JL-MP-HE-Q1-BIS-BF1-V</td>\n",
              "      <td>Q1-BIS-BF1-V</td>\n",
              "      <td>M</td>\n",
              "      <td>Q1-BIS-BF1-V : Prendre une image thermographique</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>0.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BF1</td>\n",
              "      <td>AJIAM.TCM-BF1</td>\n",
              "      <td>JL-MP-HE-Q1-BIS-BF1-ACC</td>\n",
              "      <td>Q1-BIS-BF1-ACC</td>\n",
              "      <td>M</td>\n",
              "      <td>Q1-BIS-BF1-ACC : Prendre une image thermograph...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
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              "  </tbody>\n",
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              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-95e21635-449d-450e-ba9c-43c09ac6d72b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.shape"
      ],
      "metadata": {
        "id": "E1r7AtppIfk8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f2d8945-4752-45f6-c0ab-a5d259c5aa96"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(587, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Let's find if there's duplicates in our dataframe"
      ],
      "metadata": {
        "id": "TjwapuNFeeJB"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df[\"CustomId\"] = df.Lign + df.ActivityCode + df.Asset + df.Plan"
      ],
      "metadata": {
        "id": "smeCHZgteaF_"
      },
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 528
        },
        "id": "Bb00R-JbexAs",
        "outputId": "ef3a709d-607b-4411-dd8e-b21921195b1b"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "execute_result",
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              "  Lign   ActivityCode                      Asset              Plan  \\\n",
              "0  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-M      Q1-BIS-BF1-M   \n",
              "1  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-R      Q1-BIS-BF1-R   \n",
              "2  BF1  AJIAM.MCM-BF1  JL-MP-HE-Q1-BIS-BF1-TAM01  Q1-BIS-BF1-TAM01   \n",
              "3  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-V      Q1-BIS-BF1-V   \n",
              "4  BF1  AJIAM.TCM-BF1    JL-MP-HE-Q1-BIS-BF1-ACC    Q1-BIS-BF1-ACC   \n",
              "\n",
              "  Lign Status                                        Description  Frq  \\\n",
              "0           M  Q1-BIS-BF1-M : Mesure de vibration - RMS (TSD-...   30   \n",
              "1           M  Q1-BIS-BF1-R : Mesure de vibration - RMS et te...   30   \n",
              "2           M  Q1-BIS-BF1-TAM01 : Mesure de vibration - RMS e...   30   \n",
              "3           M   Q1-BIS-BF1-V : Prendre une image thermographique   30   \n",
              "4           M  Q1-BIS-BF1-ACC : Prendre une image thermograph...    1   \n",
              "\n",
              "   EmployesNbr  TimeNeeded                                           CustomId  \n",
              "0            2         0.2  BF1AJIAM.MCM-BF1JL-MP-HE-Q1-BIS-BF1-MQ1-BIS-BF1-M  \n",
              "1            2         0.2  BF1AJIAM.MCM-BF1JL-MP-HE-Q1-BIS-BF1-RQ1-BIS-BF1-R  \n",
              "2            2         0.2  BF1AJIAM.MCM-BF1JL-MP-HE-Q1-BIS-BF1-TAM01Q1-BI...  \n",
              "3            2         0.1  BF1AJIAM.MCM-BF1JL-MP-HE-Q1-BIS-BF1-VQ1-BIS-BF1-V  \n",
              "4            2         0.2  BF1AJIAM.TCM-BF1JL-MP-HE-Q1-BIS-BF1-ACCQ1-BIS-...  "
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              "      <th></th>\n",
              "      <th>Lign</th>\n",
              "      <th>ActivityCode</th>\n",
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              "      <td>BF1</td>\n",
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              "      <td>M</td>\n",
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              "      <td>30</td>\n",
              "      <td>2</td>\n",
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              "      <td>BF1AJIAM.MCM-BF1JL-MP-HE-Q1-BIS-BF1-MQ1-BIS-BF1-M</td>\n",
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              "      <td>JL-MP-HE-Q1-BIS-BF1-R</td>\n",
              "      <td>Q1-BIS-BF1-R</td>\n",
              "      <td>M</td>\n",
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              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>BF1</td>\n",
              "      <td>AJIAM.MCM-BF1</td>\n",
              "      <td>JL-MP-HE-Q1-BIS-BF1-TAM01</td>\n",
              "      <td>Q1-BIS-BF1-TAM01</td>\n",
              "      <td>M</td>\n",
              "      <td>Q1-BIS-BF1-TAM01 : Mesure de vibration - RMS e...</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>BF1AJIAM.MCM-BF1JL-MP-HE-Q1-BIS-BF1-TAM01Q1-BI...</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>BF1</td>\n",
              "      <td>AJIAM.MCM-BF1</td>\n",
              "      <td>JL-MP-HE-Q1-BIS-BF1-V</td>\n",
              "      <td>Q1-BIS-BF1-V</td>\n",
              "      <td>M</td>\n",
              "      <td>Q1-BIS-BF1-V : Prendre une image thermographique</td>\n",
              "      <td>30</td>\n",
              "      <td>2</td>\n",
              "      <td>0.1</td>\n",
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              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>BF1</td>\n",
              "      <td>AJIAM.TCM-BF1</td>\n",
              "      <td>JL-MP-HE-Q1-BIS-BF1-ACC</td>\n",
              "      <td>Q1-BIS-BF1-ACC</td>\n",
              "      <td>M</td>\n",
              "      <td>Q1-BIS-BF1-ACC : Prendre une image thermograph...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>BF1AJIAM.TCM-BF1JL-MP-HE-Q1-BIS-BF1-ACCQ1-BIS-...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-3bb6ba01-f684-48a5-bc5a-e8c321a62c0b')\"\n",
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              "          const element = document.querySelector('#df-3bb6ba01-f684-48a5-bc5a-e8c321a62c0b');\n",
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              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.CustomId.duplicated().any()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tfb9UbjSeyIb",
        "outputId": "c48df4f5-a761-4ec5-d7f3-72e1ab916008"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "True"
            ]
          },
          "metadata": {},
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "**SO** we have duplicates rows in our dataset"
      ],
      "metadata": {
        "id": "2tu5JDXQgt81"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "df2 = df.drop_duplicates(subset=\"CustomId\", keep=\"first\")"
      ],
      "metadata": {
        "id": "aq48CpMLfU_u"
      },
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df2.head()"
      ],
      "metadata": {
        "colab": {
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          "height": 528
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        "id": "hTZ72V4tf7B_",
        "outputId": "bf4ddf5b-ecf2-47e1-dd3c-22ec81352983"
      },
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "  Lign   ActivityCode                      Asset              Plan  \\\n",
              "0  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-M      Q1-BIS-BF1-M   \n",
              "1  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-R      Q1-BIS-BF1-R   \n",
              "2  BF1  AJIAM.MCM-BF1  JL-MP-HE-Q1-BIS-BF1-TAM01  Q1-BIS-BF1-TAM01   \n",
              "3  BF1  AJIAM.MCM-BF1      JL-MP-HE-Q1-BIS-BF1-V      Q1-BIS-BF1-V   \n",
              "4  BF1  AJIAM.TCM-BF1    JL-MP-HE-Q1-BIS-BF1-ACC    Q1-BIS-BF1-ACC   \n",
              "\n",
              "  Lign Status                                        Description  Frq  \\\n",
              "0           M  Q1-BIS-BF1-M : Mesure de vibration - RMS (TSD-...   30   \n",
              "1           M  Q1-BIS-BF1-R : Mesure de vibration - RMS et te...   30   \n",
              "2           M  Q1-BIS-BF1-TAM01 : Mesure de vibration - RMS e...   30   \n",
              "3           M   Q1-BIS-BF1-V : Prendre une image thermographique   30   \n",
              "4           M  Q1-BIS-BF1-ACC : Prendre une image thermograph...    1   \n",
              "\n",
              "   EmployesNbr  TimeNeeded                                           CustomId  \n",
              "0            2         0.2  BF1AJIAM.MCM-BF1JL-MP-HE-Q1-BIS-BF1-MQ1-BIS-BF1-M  \n",
              "1            2         0.2  BF1AJIAM.MCM-BF1JL-MP-HE-Q1-BIS-BF1-RQ1-BIS-BF1-R  \n",
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              "      <th>0</th>\n",
              "      <td>BF1</td>\n",
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              "      <td>JL-MP-HE-Q1-BIS-BF1-M</td>\n",
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              "      <td>Q1-BIS-BF1-R</td>\n",
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              "      <th>2</th>\n",
              "      <td>BF1</td>\n",
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              "      <td>JL-MP-HE-Q1-BIS-BF1-TAM01</td>\n",
              "      <td>Q1-BIS-BF1-TAM01</td>\n",
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              "      <td>Q1-BIS-BF1-ACC</td>\n",
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              "      <td>Q1-BIS-BF1-ACC : Prendre une image thermograph...</td>\n",
              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>0.2</td>\n",
              "      <td>BF1AJIAM.TCM-BF1JL-MP-HE-Q1-BIS-BF1-ACCQ1-BIS-...</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-36b6940e-cb22-4493-a526-e9ec88e973fd')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
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              "    .colab-df-convert:hover {\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
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              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
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              "          document.querySelector('#df-36b6940e-cb22-4493-a526-e9ec88e973fd button.colab-df-convert');\n",
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              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-36b6940e-cb22-4493-a526-e9ec88e973fd');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
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              "          element.appendChild(docLink);\n",
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              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df2.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "MYv3iwUSf8M_",
        "outputId": "fbeeda57-34ed-433f-a4ef-7118830c3e58"
      },
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(399, 10)"
            ]
          },
          "metadata": {},
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Our cleaned data now contains **399** unique records"
      ],
      "metadata": {
        "id": "f0NiHE-hgy0q"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "json_data = df2.to_json(orient=\"records\")\n",
        "with open(\"json_data2.json\", \"w\") as jd:\n",
        "  jd.write(json_data)"
      ],
      "metadata": {
        "id": "VWGKdx05f-I5"
      },
      "execution_count": 38,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "boaLRR50hB3K"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}