Data_Exploration.ipynb

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      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import pandas as pd\n",
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "from datetime import datetime\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mPdFd-1_GveZ"
      },
      "source": [
        "### 0. Data Paths"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "7aRmJHXRGves"
      },
      "outputs": [],
      "source": [
        "DATA_DIR = f\"/content/drive/MyDrive/CS6140_final_project/Data/raw_data\"\n",
        "ERRORS_F = 'PdM_errors.csv'\n",
        "FAILURES_F = 'PdM_failures.csv'\n",
        "MACHINES_F = 'PdM_machines.csv'\n",
        "MAINT_F = 'PdM_maint.csv'\n",
        "TELEMETRY_F = 'PdM_telemetry.csv'\n",
        "\n",
        "ERRORS_F = os.path.join(DATA_DIR, ERRORS_F)\n",
        "FAILURES_F = os.path.join(DATA_DIR, FAILURES_F)\n",
        "MACHINES_F = os.path.join(DATA_DIR, MACHINES_F)\n",
        "MAINT_F = os.path.join(DATA_DIR, MAINT_F)\n",
        "TELEMETRY_F = os.path.join(DATA_DIR, TELEMETRY_F)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YqNoJG6RGvet"
      },
      "source": [
        "### 1. Errors CSV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "RYD9QC20Gvet",
        "outputId": "25064728-25ca-4d36-a071-8a443a0399ea",
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          "height": 221
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "- 3,919 total rows in errors_df.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             datetime  machineID errorID\n",
              "0 2015-01-03 07:00:00          1  error1\n",
              "1 2015-01-03 20:00:00          1  error3\n",
              "2 2015-01-04 06:00:00          1  error5\n",
              "3 2015-01-10 15:00:00          1  error4\n",
              "4 2015-01-22 10:00:00          1  error4"
            ],
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              "      <td>2015-01-03 07:00:00</td>\n",
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              "      <td>1</td>\n",
              "      <td>error3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2015-01-04 06:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>error5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2015-01-10 15:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>error4</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-22 10:00:00</td>\n",
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              "\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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            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "errors_df",
              "summary": "{\n  \"name\": \"errors_df\",\n  \"rows\": 3919,\n  \"fields\": [\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2015-01-01 06:00:00\",\n        \"max\": \"2016-01-01 05:00:00\",\n        \"num_unique_values\": 2720,\n        \"samples\": [\n          \"2015-04-06 17:00:00\",\n          \"2015-07-09 06:00:00\",\n          \"2015-04-16 07:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"machineID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 1,\n        \"max\": 100,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          84,\n          54,\n          71\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"errorID\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 5,\n        \"samples\": [\n          \"error3\",\n          \"error2\",\n          \"error5\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 5
        }
      ],
      "source": [
        "errors_df = pd.read_csv(\n",
        "    ERRORS_F,\n",
        ")\n",
        "errors_df['machineID'] = errors_df['machineID'].astype(int)\n",
        "errors_df['datetime'] = pd.to_datetime(errors_df['datetime'])\n",
        "errors_df['errorID'] = errors_df['errorID'].astype(str)\n",
        "\n",
        "print(f\"- {format(len(errors_df), ',')} total rows in errors_df.\")\n",
        "errors_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "errors_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HVkLXbxNb1QW",
        "outputId": "f3014398-5c94-458a-8ac2-3d1b4a3f588d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3919 entries, 0 to 3918\n",
            "Data columns (total 3 columns):\n",
            " #   Column     Non-Null Count  Dtype         \n",
            "---  ------     --------------  -----         \n",
            " 0   datetime   3919 non-null   datetime64[ns]\n",
            " 1   machineID  3919 non-null   int64         \n",
            " 2   errorID    3919 non-null   object        \n",
            "dtypes: datetime64[ns](1), int64(1), object(1)\n",
            "memory usage: 92.0+ KB\n"
          ]
        }
      ]
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              "<Figure size 1500x600 with 1 Axes>"
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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# total errors per machine\n",
        "errors_per_machine = errors_df.groupby('machineID').size()\n",
        "\n",
        "plt.figure(\n",
        "    figsize = (15, 6)\n",
        ")\n",
        "errors_per_machine.plot(\n",
        "    kind = 'bar'\n",
        ")\n",
        "plt.title('Total Errors per Machine')\n",
        "plt.xlabel('Machine ID')\n",
        "plt.ylabel('Number of Errors')\n",
        "plt.xticks(rotation = 45, fontsize = 6)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "6Ce1Le3WGvew",
        "outputId": "6ffa5ddb-b9f5-4950-c94d-78bfe51b6f4d",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# time difference between first and last error, per machine\n",
        "time_diff_per_machine = errors_df.groupby('machineID')['datetime'].agg(\n",
        "    [min, max]\n",
        ").apply(\n",
        "    lambda x: x['max'] - x['min'],\n",
        "    axis = 1\n",
        ")\n",
        "# calculate time difference in seconds and then convert to days\n",
        "time_diff_per_machine_days = time_diff_per_machine.dt.total_seconds() / (60 * 60 * 24)\n",
        "\n",
        "plt.figure(\n",
        "    figsize = (15, 6)\n",
        ")\n",
        "time_diff_per_machine_days.plot(kind = 'bar')\n",
        "plt.title('Time Difference Between First and Last Errors per Machine (Days)')\n",
        "plt.xlabel('Machine ID')\n",
        "plt.ylabel('Time Difference (Days)')\n",
        "plt.xticks(rotation = 45, fontsize = 6)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Tedc6DWIGvew",
        "outputId": "a0ade0e7-b79f-4f58-a827-95cda3c14173",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# row/error totals by unique errorID\n",
        "error_counts = errors_df['errorID'].value_counts()\n",
        "\n",
        "# Visualizing row/error totals by unique `errorID`\n",
        "plt.figure(\n",
        "    figsize = (10, 6)\n",
        ")\n",
        "error_counts.plot(kind = 'bar')\n",
        "plt.title('Error Totals by Unique `errorID`')\n",
        "plt.xlabel('Error ID')\n",
        "plt.ylabel('Total Occurrences')\n",
        "plt.xticks(rotation = 0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "UyyIbOQVGvex"
      },
      "source": [
        "### 2. Failures CSV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "-k2jw4KAGvex",
        "outputId": "253d0d92-c46e-48c9-c0c1-d8a4379584f4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "- 761 total rows in failures_df.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             datetime  machineID failure\n",
              "0 2015-01-05 06:00:00          1   comp4\n",
              "1 2015-03-06 06:00:00          1   comp1\n",
              "2 2015-04-20 06:00:00          1   comp2\n",
              "3 2015-06-19 06:00:00          1   comp4\n",
              "4 2015-09-02 06:00:00          1   comp4"
            ],
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              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-85806899-5ad9-45e9-9a86-a456221fe9ee button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-85806899-5ad9-45e9-9a86-a456221fe9ee');\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",
              "\n",
              "\n",
              "<div id=\"df-41da7e8a-2522-464a-9be5-e17973c39ce1\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-41da7e8a-2522-464a-9be5-e17973c39ce1')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-41da7e8a-2522-464a-9be5-e17973c39ce1 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "failures_df",
              "summary": "{\n  \"name\": \"failures_df\",\n  \"rows\": 761,\n  \"fields\": [\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2015-01-02 03:00:00\",\n        \"max\": \"2015-12-31 06:00:00\",\n        \"num_unique_values\": 302,\n        \"samples\": [\n          \"2015-04-16 06:00:00\",\n          \"2015-07-12 06:00:00\",\n          \"2015-11-24 06:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"machineID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 1,\n        \"max\": 100,\n        \"num_unique_values\": 98,\n        \"samples\": [\n          64,\n          42,\n          97\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"failure\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"comp1\",\n          \"comp3\",\n          \"comp4\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 12
        }
      ],
      "source": [
        "failures_df = pd.read_csv(\n",
        "    FAILURES_F,\n",
        ")\n",
        "failures_df['machineID'] = failures_df['machineID'].astype(int)\n",
        "failures_df['datetime'] = pd.to_datetime(failures_df['datetime'])\n",
        "failures_df['failure'] = failures_df['failure'].astype(str)\n",
        "\n",
        "print(f\"- {format(len(failures_df), ',')} total rows in failures_df.\")\n",
        "failures_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "failures_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "GyUyVpoR-o1J",
        "outputId": "66b0dae8-b942-4e18-b2e6-7d600d800ddd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 761 entries, 0 to 760\n",
            "Data columns (total 3 columns):\n",
            " #   Column     Non-Null Count  Dtype         \n",
            "---  ------     --------------  -----         \n",
            " 0   datetime   761 non-null    datetime64[ns]\n",
            " 1   machineID  761 non-null    int64         \n",
            " 2   failure    761 non-null    object        \n",
            "dtypes: datetime64[ns](1), int64(1), object(1)\n",
            "memory usage: 18.0+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "iVrFTIX2Gvey",
        "outputId": "ba3bd74d-7719-47a9-80e4-fc337b92dc59",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
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          },
          "metadata": {}
        }
      ],
      "source": [
        "# total failures per machine\n",
        "failures_per_machine = failures_df.groupby('machineID').size()\n",
        "\n",
        "plt.figure(\n",
        "    figsize = (15, 6)\n",
        ")\n",
        "failures_per_machine.plot(\n",
        "    kind = 'bar'\n",
        ")\n",
        "plt.title('Total Failures per Machine')\n",
        "plt.xlabel('Machine ID')\n",
        "plt.ylabel('Number of Failures')\n",
        "plt.xticks(rotation = 45, fontsize = 6)\n",
        "plt.yticks(np.arange(0, 21, 1))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "zT6MiqhYGvez",
        "outputId": "98fb73a5-38a9-4dc3-e50d-d6b116105910",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# time difference between first and last failure, per machine\n",
        "time_diff_per_machine = failures_df.groupby('machineID')['datetime'].agg(\n",
        "    [min, max]\n",
        ").apply(\n",
        "    lambda x: x['max'] - x['min'],\n",
        "    axis = 1\n",
        ")\n",
        "# calculate time difference in seconds and then convert to days\n",
        "time_diff_per_machine_days = time_diff_per_machine.dt.total_seconds() / (60 * 60 * 24)\n",
        "\n",
        "plt.figure(\n",
        "    figsize = (15, 6)\n",
        ")\n",
        "time_diff_per_machine_days.plot(kind = 'bar')\n",
        "plt.title('Time Difference Between First and Last Failures per Machine (Days)')\n",
        "plt.xlabel('Machine ID')\n",
        "plt.ylabel('Time Difference (Days)')\n",
        "plt.xticks(rotation = 45, fontsize = 6)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "Z_855UICGvez",
        "outputId": "cca301cd-bcc7-43cf-e152-a56ca458d7cb",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 565
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# row/failure totals by unique failure\n",
        "failure_counts = failures_df['failure'].value_counts()\n",
        "\n",
        "# Visualizing row/failure totals by unique `failure`\n",
        "plt.figure(\n",
        "    figsize = (10, 6)\n",
        ")\n",
        "failure_counts.plot(kind = 'bar')\n",
        "plt.title('Failure Totals by Unique `failure`')\n",
        "plt.xlabel('Failure (ID)')\n",
        "plt.ylabel('Total Occurrences')\n",
        "plt.xticks(rotation = 0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "nmudbrwUGve0"
      },
      "source": [
        "### 3. Machines CSV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "d5IPVe91Gve0",
        "outputId": "b933f0e3-f0ab-4690-90dd-d7b35dc83f2e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "- 100 total rows in machines_df.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   machineID   model  age\n",
              "0          1  model3   18\n",
              "1          2  model4    7\n",
              "2          3  model3    8\n",
              "3          4  model3    7\n",
              "4          5  model3    2"
            ],
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              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-beb4adca-52cc-499d-968e-3012b11d639c button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-beb4adca-52cc-499d-968e-3012b11d639c');\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",
              "\n",
              "\n",
              "<div id=\"df-debe29c0-0069-44eb-b9be-67d3ec6137da\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-debe29c0-0069-44eb-b9be-67d3ec6137da')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-debe29c0-0069-44eb-b9be-67d3ec6137da button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "machines_df",
              "summary": "{\n  \"name\": \"machines_df\",\n  \"rows\": 100,\n  \"fields\": [\n    {\n      \"column\": \"machineID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29,\n        \"min\": 1,\n        \"max\": 100,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          84,\n          54,\n          71\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"model\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"model4\",\n          \"model1\",\n          \"model3\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"age\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 5,\n        \"min\": 0,\n        \"max\": 20,\n        \"num_unique_values\": 20,\n        \"samples\": [\n          18,\n          4,\n          5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 16
        }
      ],
      "source": [
        "machines_df = pd.read_csv(\n",
        "    MACHINES_F,\n",
        ")\n",
        "machines_df['machineID'] = machines_df['machineID'].astype(int)\n",
        "machines_df['model'] = machines_df['model'].astype(str)\n",
        "machines_df['age'] = machines_df['age'].astype(int)\n",
        "\n",
        "print(f\"- {format(len(machines_df), ',')} total rows in machines_df.\")\n",
        "machines_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "machines_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2NxkLqkl-wnE",
        "outputId": "550ed0b8-87f5-4d20-aec7-d2d808ae9790"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 100 entries, 0 to 99\n",
            "Data columns (total 3 columns):\n",
            " #   Column     Non-Null Count  Dtype \n",
            "---  ------     --------------  ----- \n",
            " 0   machineID  100 non-null    int64 \n",
            " 1   model      100 non-null    object\n",
            " 2   age        100 non-null    int64 \n",
            "dtypes: int64(2), object(1)\n",
            "memory usage: 2.5+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GlF3EYoJGve2",
        "outputId": "f97a1f83-cfdb-408a-ea4a-6f32bac8d89a",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# unique model counts\n",
        "plt.figure(\n",
        "    figsize = (10, 6)\n",
        ")\n",
        "machines_df['model'].value_counts().plot(kind = 'bar')\n",
        "plt.title('Total Number of Unique Models')\n",
        "plt.xlabel('Model')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xticks(rotation = 0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "264rJgtXGve2",
        "outputId": "085116a5-81ab-43b4-b0d7-8250d1109eb6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# distribution of machine age\n",
        "plt.figure(\n",
        "    figsize = (10, 6)\n",
        ")\n",
        "machines_df['age'].plot(\n",
        "    kind = 'hist',\n",
        "    bins = 20\n",
        ")\n",
        "plt.title('Distribution of Machine Ages')\n",
        "plt.xlabel('Age')\n",
        "plt.ylabel('Frequency')\n",
        "plt.xticks(np.arange(0, 21, 1))\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "SY1PHX8KGve3"
      },
      "source": [
        "### 4. Maintenance CSV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "8vhwc48cGve3",
        "outputId": "eb5b1745-55a3-4a7c-e562-e9359ab9c30f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "- 3,286 total rows in maint_df.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             datetime  machineID   comp\n",
              "0 2014-06-01 06:00:00          1  comp2\n",
              "1 2014-07-16 06:00:00          1  comp4\n",
              "2 2014-07-31 06:00:00          1  comp3\n",
              "3 2014-12-13 06:00:00          1  comp1\n",
              "4 2015-01-05 06:00:00          1  comp4"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>datetime</th>\n",
              "      <th>machineID</th>\n",
              "      <th>comp</th>\n",
              "    </tr>\n",
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              "      <th>0</th>\n",
              "      <td>2014-06-01 06:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>comp2</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
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              "      <td>1</td>\n",
              "      <td>comp4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2014-07-31 06:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>comp3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2014-12-13 06:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>comp1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2015-01-05 06:00:00</td>\n",
              "      <td>1</td>\n",
              "      <td>comp4</td>\n",
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              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    .colab-df-buttons div {\n",
              "      margin-bottom: 4px;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-52f0ddd4-aea8-4485-895f-73dd35f7f999 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-52f0ddd4-aea8-4485-895f-73dd35f7f999');\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",
              "\n",
              "\n",
              "<div id=\"df-b1971aa9-a1fa-4afd-8e9a-dabfbbc62392\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-b1971aa9-a1fa-4afd-8e9a-dabfbbc62392')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-b1971aa9-a1fa-4afd-8e9a-dabfbbc62392 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "maint_df",
              "summary": "{\n  \"name\": \"maint_df\",\n  \"rows\": 3286,\n  \"fields\": [\n    {\n      \"column\": \"datetime\",\n      \"properties\": {\n        \"dtype\": \"date\",\n        \"min\": \"2014-06-01 06:00:00\",\n        \"max\": \"2016-01-01 06:00:00\",\n        \"num_unique_values\": 374,\n        \"samples\": [\n          \"2015-11-09 06:00:00\",\n          \"2015-02-17 06:00:00\",\n          \"2015-06-19 06:00:00\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"machineID\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 28,\n        \"min\": 1,\n        \"max\": 100,\n        \"num_unique_values\": 100,\n        \"samples\": [\n          84,\n          54,\n          71\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"comp\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 4,\n        \"samples\": [\n          \"comp4\",\n          \"comp1\",\n          \"comp2\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 21
        }
      ],
      "source": [
        "maint_df = pd.read_csv(\n",
        "    MAINT_F,\n",
        ")\n",
        "maint_df['machineID'] = maint_df['machineID'].astype(int)\n",
        "maint_df['datetime'] = pd.to_datetime(maint_df['datetime'])\n",
        "maint_df['comp'] = maint_df['comp'].astype(str)\n",
        "\n",
        "print(f\"- {format(len(maint_df), ',')} total rows in maint_df.\")\n",
        "maint_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "maint_df.info()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iZVkeXzi-7AZ",
        "outputId": "7a19fb37-db0d-4074-d719-c099bac0b414"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 3286 entries, 0 to 3285\n",
            "Data columns (total 3 columns):\n",
            " #   Column     Non-Null Count  Dtype         \n",
            "---  ------     --------------  -----         \n",
            " 0   datetime   3286 non-null   datetime64[ns]\n",
            " 1   machineID  3286 non-null   int64         \n",
            " 2   comp       3286 non-null   object        \n",
            "dtypes: datetime64[ns](1), int64(1), object(1)\n",
            "memory usage: 77.1+ KB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "rJNOiGG0Gve4",
        "outputId": "d2c63d98-d5a9-4623-c185-99621a22f3d9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# total maintenance records per machine\n",
        "maint_per_machine = maint_df.groupby('machineID').size()\n",
        "\n",
        "plt.figure(\n",
        "    figsize = (15, 6)\n",
        ")\n",
        "maint_per_machine.plot(\n",
        "    kind = 'bar'\n",
        ")\n",
        "plt.title('Total Maintenance Records per Machine')\n",
        "plt.xlabel('Machine ID')\n",
        "plt.ylabel('Number of Maintenance Records')\n",
        "plt.xticks(rotation = 45, fontsize = 6)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "x39P1LWiGve4",
        "outputId": "fde09241-e1b1-4d34-a51c-42839bead01f",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 568
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1500x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ],
      "source": [
        "# time difference between first and last maint, per machine\n",
        "time_diff_per_machine = maint_df.groupby('machineID')['datetime'].agg(\n",
        "    [min, max]\n",
        ").apply(\n",
        "    lambda x: x['max'] - x['min'],\n",
        "    axis = 1\n",
        ")\n",
        "# calculate time difference in seconds and then convert to days\n",
        "time_diff_per_machine_days = time_diff_per_machine.dt.total_seconds() / (60 * 60 * 24)\n",
        "\n",
        "plt.figure(\n",
        "    figsize = (15, 6)\n",
        ")\n",
        "time_diff_per_machine_days.plot(kind = 'bar')\n",
        "plt.title('Time Difference Between First and Last Maintenance Records per Machine (Days)')\n",
        "plt.xlabel('Machine ID')\n",
        "plt.ylabel('Time Difference (Days)')\n",
        "plt.xticks(rotation = 45, fontsize = 6)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "9Aw4IlByGve5",
        "outputId": "c7728164-04c1-41f1-c489-b7dceb9b84a9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 564
        }
      },
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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          },
          "metadata": {}
        }
      ],
      "source": [
        "# row/maint totals by unique component\n",
        "maint_counts = maint_df['comp'].value_counts()\n",
        "\n",
        "# visualizing row/componen totals by unique `component`\n",
        "plt.figure(\n",
        "    figsize = (10, 6)\n",
        ")\n",
        "maint_counts.plot(kind = 'bar')\n",
        "plt.title('Maintenance Record Totals by Unique Component')\n",
        "plt.xlabel('Component (ID)')\n",
        "plt.ylabel('Total Occurrences')\n",
        "plt.xticks(rotation = 0)\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZoU-ZhbrGve5"
      },
      "source": [
        "### 5. Telemetry CSV"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "GnDmQ9yKGve6",
        "outputId": "21c084d4-a517-408a-d178-27ec7d563b86",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 221
        }
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "- 876,100 total rows in tel_df.\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "             datetime  machineID        volt      rotate    pressure  \\\n",
              "0 2015-01-01 06:00:00          1  176.217853  418.504078  113.077935   \n",
              "1 2015-01-01 07:00:00          1  162.879223  402.747490   95.460525   \n",
              "2 2015-01-01 08:00:00          1  170.989902  527.349825   75.237905   \n",
              "3 2015-01-01 09:00:00          1  162.462833  346.149335  109.248561   \n",
              "4 2015-01-01 10:00:00          1  157.610021  435.376873  111.886648   \n",
              "\n",
              "   vibration  \n",
              "0  45.087686  \n",
              "1  43.413973  \n",
              "2  34.178847  \n",
              "3  41.122144  \n",
              "4  25.990511  "
            ],
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              "        document.querySelector('#df-b39dd9ef-95d6-4214-98e2-011edf34a12e button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-b39dd9ef-95d6-4214-98e2-011edf34a12e');\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",
              "\n",
              "\n",
              "<div id=\"df-3da9b94d-b90f-47d3-ba8d-8bc69a12be98\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-3da9b94d-b90f-47d3-ba8d-8bc69a12be98')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-3da9b94d-b90f-47d3-ba8d-8bc69a12be98 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "tel_df"
            }
          },
          "metadata": {},
          "execution_count": 26
        }
      ],
      "source": [
        "tel_df = pd.read_csv(\n",
        "    TELEMETRY_F,\n",
        ")\n",
        "tel_df['machineID'] = tel_df['machineID'].astype(int)\n",
        "tel_df['datetime'] = pd.to_datetime(tel_df['datetime'])\n",
        "tel_df['volt'] = tel_df['volt'].astype(float)\n",
        "tel_df['rotate'] = tel_df['rotate'].astype(float)\n",
        "tel_df['pressure'] = tel_df['pressure'].astype(float)\n",
        "tel_df['vibration'] = tel_df['vibration'].astype(float)\n",
        "\n",
        "print(f\"- {format(len(tel_df), ',')} total rows in tel_df.\")\n",
        "tel_df.head()"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "tel_df.info()"
      ],
      "metadata": {
        "id": "eDsEuiBC_FCk",
        "outputId": "6b613801-b7a3-4919-d569-a0cd127175d0",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 876100 entries, 0 to 876099\n",
            "Data columns (total 6 columns):\n",
            " #   Column     Non-Null Count   Dtype         \n",
            "---  ------     --------------   -----         \n",
            " 0   datetime   876100 non-null  datetime64[ns]\n",
            " 1   machineID  876100 non-null  int64         \n",
            " 2   volt       876100 non-null  float64       \n",
            " 3   rotate     876100 non-null  float64       \n",
            " 4   pressure   876100 non-null  float64       \n",
            " 5   vibration  876100 non-null  float64       \n",
            "dtypes: datetime64[ns](1), float64(4), int64(1)\n",
            "memory usage: 40.1 MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "C2PxFudAGve6",
        "outputId": "9d71f1a2-6dd9-469c-a6ec-b9da54bb609a",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    876100.000000\n",
              "mean        170.777736\n",
              "std          15.509114\n",
              "min          97.333604\n",
              "25%         160.304927\n",
              "50%         170.607338\n",
              "75%         181.004493\n",
              "max         255.124717\n",
              "Name: volt, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ],
      "source": [
        "tel_df['volt'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "uwFRwsBHGve7",
        "outputId": "f7ee815f-7d6f-4a8d-9a2d-a6f13a72ca4f",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    876100.000000\n",
              "mean        446.605119\n",
              "std          52.673886\n",
              "min         138.432075\n",
              "25%         412.305714\n",
              "50%         447.558150\n",
              "75%         482.176600\n",
              "max         695.020984\n",
              "Name: rotate, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ],
      "source": [
        "tel_df['rotate'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "vchvAdmVGve7",
        "outputId": "b575be75-367d-421a-ab7e-ff1aa42d3fe6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    876100.000000\n",
              "mean        100.858668\n",
              "std          11.048679\n",
              "min          51.237106\n",
              "25%          93.498181\n",
              "50%         100.425559\n",
              "75%         107.555231\n",
              "max         185.951998\n",
              "Name: pressure, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ],
      "source": [
        "tel_df['pressure'].describe()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "ocH0AfcbGve8",
        "outputId": "eba70134-90d4-48a7-c01b-7e0c774d529b",
        "colab": {
          "base_uri": "https://localhost:8080/"
        }
      },
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "count    876100.000000\n",
              "mean         40.385007\n",
              "std           5.370361\n",
              "min          14.877054\n",
              "25%          36.777299\n",
              "50%          40.237247\n",
              "75%          43.784938\n",
              "max          76.791072\n",
              "Name: vibration, dtype: float64"
            ]
          },
          "metadata": {},
          "execution_count": 30
        }
      ],
      "source": [
        "tel_df['vibration'].describe()"
      ]
    }
  ],
  "metadata": {
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    },
    "language_info": {
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "file_extension": ".py",
      "mimetype": "text/x-python",
      "name": "python",
      "nbconvert_exporter": "python",
      "pygments_lexer": "ipython3",
      "version": "3.11.5"
    },
    "colab": {
      "provenance": []
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}