From 87a3d087dd53570db46485cfcdfac41f8b81af41 Mon Sep 17 00:00:00 2001
From: Devanksnpi <131532374+Devanksnpi@users.noreply.github.com>
Date: Fri, 2 Jun 2023 00:18:08 +0530
Subject: [PATCH] Add files via upload

220339
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 Graded Assignment 2/Untitled4 (1).ipynb | 1 +
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+{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyNoZ4VxUbl25YyzBDc6huE4"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"code","execution_count":80,"metadata":{"id":"rR30Ob1b7mvy","executionInfo":{"status":"ok","timestamp":1685534224582,"user_tz":-330,"elapsed":4,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}}},"outputs":[],"source":["import numpy as np \n","import pandas as pd\n","from pathlib import Path"]},{"cell_type":"code","source":["from pathlib import Path\n","import urllib.request\n","import pandas as pd\n","\n","drive_link = \"https://drive.google.com/file/d/17bqqiJZ7JCXPt0EwseFBPZbBIuV7-YcG/view\"\n","file_id = drive_link.split(\"=\")[-1]  \n","download_url = f\"https://drive.google.com/u/0/uc?id=17bqqiJZ7JCXPt0EwseFBPZbBIuV7-YcG&export=download\"\n","local_file = Path(\"data.csv\")\n","urllib.request.urlretrieve(download_url, local_file)\n","Machine_temp_failure_missing= pd.read_csv(local_file)\n","print(Machine_temp_failure_missing.head())\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"pnRM6Hv5ZXaG","executionInfo":{"status":"ok","timestamp":1685534227247,"user_tz":-330,"elapsed":1938,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}},"outputId":"6bf5666e-a403-4245-9df5-3f7c0fe6c6bb"},"execution_count":81,"outputs":[{"output_type":"stream","name":"stdout","text":["          timestamp      value\n","0  02-12-2013 21:15  73.967322\n","1  02-12-2013 21:20  74.935882\n","2  02-12-2013 21:25  76.124162\n","3  02-12-2013 21:30  78.140707\n","4  02-12-2013 21:35  79.329836\n"]}]},{"cell_type":"code","source":["empty_rows = Machine_temp_failure_missing.isnull().all(axis=1).sum()\n","print(empty_rows) # no of empty rows"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"-9BedEWtfuS7","executionInfo":{"status":"ok","timestamp":1685534227802,"user_tz":-330,"elapsed":4,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}},"outputId":"63a2ed16-fa09-459b-bb1c-7480db58d6d1"},"execution_count":82,"outputs":[{"output_type":"stream","name":"stdout","text":["0\n"]}]},{"cell_type":"code","source":["Machine_temp_failure_missing[\"value\"].describe() # to tell mean , median , max , min "],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"ga-R_2s_gNlK","executionInfo":{"status":"ok","timestamp":1685534229762,"user_tz":-330,"elapsed":4,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}},"outputId":"54fc489f-ac1c-4035-b16a-4f12b5529724"},"execution_count":83,"outputs":[{"output_type":"execute_result","data":{"text/plain":["count    21410.000000\n","mean        85.968188\n","std         13.553070\n","min          2.084721\n","25%         83.082867\n","50%         89.251290\n","75%         93.910748\n","max        108.510543\n","Name: value, dtype: float64"]},"metadata":{},"execution_count":83}]},{"cell_type":"code","source":["Machine_temp_failure_missing[\"temperature\"]=Machine_temp_failure_missing['value'] # rename value column to temperature\n","Machine_temp_failure_missing.drop('value',axis=1,inplace=True)\n"],"metadata":{"id":"zy-bynSugqpU","executionInfo":{"status":"ok","timestamp":1685534230762,"user_tz":-330,"elapsed":3,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}}},"execution_count":84,"outputs":[]},{"cell_type":"code","source":["Machine_temp_failure_missing"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":423},"id":"14qMiNCTWoOg","executionInfo":{"status":"ok","timestamp":1685534232439,"user_tz":-330,"elapsed":8,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}},"outputId":"083452cd-cbd3-4bb0-dacc-e9966b940ab9"},"execution_count":85,"outputs":[{"output_type":"execute_result","data":{"text/plain":["              timestamp  temperature\n","0      02-12-2013 21:15    73.967322\n","1      02-12-2013 21:20    74.935882\n","2      02-12-2013 21:25    76.124162\n","3      02-12-2013 21:30    78.140707\n","4      02-12-2013 21:35    79.329836\n","...                 ...          ...\n","22690  19-02-2014 15:05    98.185415\n","22691  19-02-2014 15:10    97.804168\n","22692  19-02-2014 15:15    97.135468\n","22693  19-02-2014 15:20    98.056852\n","22694  19-02-2014 15:25    96.903861\n","\n","[22695 rows x 2 columns]"],"text/html":["\n","  <div id=\"df-9f40f3e6-1299-49e8-bb7a-631681ba22f2\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>timestamp</th>\n","      <th>temperature</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>02-12-2013 21:15</td>\n","      <td>73.967322</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>02-12-2013 21:20</td>\n","      <td>74.935882</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>02-12-2013 21:25</td>\n","      <td>76.124162</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>02-12-2013 21:30</td>\n","      <td>78.140707</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>02-12-2013 21:35</td>\n","      <td>79.329836</td>\n","    </tr>\n","    <tr>\n","      <th>...</th>\n","      <td>...</td>\n","      <td>...</td>\n","    </tr>\n","    <tr>\n","      <th>22690</th>\n","      <td>19-02-2014 15:05</td>\n","      <td>98.185415</td>\n","    </tr>\n","    <tr>\n","      <th>22691</th>\n","      <td>19-02-2014 15:10</td>\n","      <td>97.804168</td>\n","    </tr>\n","    <tr>\n","      <th>22692</th>\n","      <td>19-02-2014 15:15</td>\n","      <td>97.135468</td>\n","    </tr>\n","    <tr>\n","      <th>22693</th>\n","      <td>19-02-2014 15:20</td>\n","      <td>98.056852</td>\n","    </tr>\n","    <tr>\n","      <th>22694</th>\n","      <td>19-02-2014 15:25</td>\n","      <td>96.903861</td>\n","    </tr>\n","  </tbody>\n","</table>\n","<p>22695 rows × 2 columns</p>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-9f40f3e6-1299-49e8-bb7a-631681ba22f2')\"\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 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-9f40f3e6-1299-49e8-bb7a-631681ba22f2');\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":85}]},{"cell_type":"code","source":["for i in range(22694):\n","   Machine_temp_failure_missing.loc[i,\"delta_temperature\"]=Machine_temp_failure_missing.loc[i+1,\"temperature\"]-df.loc[i,\"temperature\"]\n","Machine_temp_failure_missing.head()  # created new column delta_temperature "],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"WfjxyVV1g8hW","executionInfo":{"status":"ok","timestamp":1685534239225,"user_tz":-330,"elapsed":4248,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}},"outputId":"22fa79a0-bdf6-4505-a9d1-d7d61036fcd2"},"execution_count":86,"outputs":[{"output_type":"execute_result","data":{"text/plain":["          timestamp  temperature  delta_temperature\n","0  02-12-2013 21:15    73.967322           0.968560\n","1  02-12-2013 21:20    74.935882           1.188280\n","2  02-12-2013 21:25    76.124162           2.016546\n","3  02-12-2013 21:30    78.140707           1.189128\n","4  02-12-2013 21:35    79.329836          -0.619417"],"text/html":["\n","  <div id=\"df-c10c2cbd-5239-48dd-af5d-4b31fe1ac446\">\n","    <div class=\"colab-df-container\">\n","      <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>timestamp</th>\n","      <th>temperature</th>\n","      <th>delta_temperature</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>02-12-2013 21:15</td>\n","      <td>73.967322</td>\n","      <td>0.968560</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>02-12-2013 21:20</td>\n","      <td>74.935882</td>\n","      <td>1.188280</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>02-12-2013 21:25</td>\n","      <td>76.124162</td>\n","      <td>2.016546</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>02-12-2013 21:30</td>\n","      <td>78.140707</td>\n","      <td>1.189128</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>02-12-2013 21:35</td>\n","      <td>79.329836</td>\n","      <td>-0.619417</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-c10c2cbd-5239-48dd-af5d-4b31fe1ac446')\"\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","       width=\"24px\">\n","    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n","    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 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'block' : 'none';\n","\n","        async function convertToInteractive(key) {\n","          const element = document.querySelector('#df-c10c2cbd-5239-48dd-af5d-4b31fe1ac446');\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":86}]},{"cell_type":"code","source":["import matplotlib.pyplot as plt\n","plt.plot(Machine_temp_failure_missing.index, Machine_temp_failure_missing['temperature'],linewidth=1)\n","plt.title('Temperature')\n","plt.xlabel('Index')\n","plt.ylabel('Temperature')\n","plt.show()\n","\n","plt.plot(Machine_temp_failure_missing.index, Machine_temp_failure_missing['delta_temperature'])\n","plt.title('Delta Temperature')\n","plt.xlabel('Index')\n","plt.ylabel('Delta Temperature')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":927},"id":"5qQ4IbB5iv6k","executionInfo":{"status":"ok","timestamp":1685534239227,"user_tz":-330,"elapsed":25,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}},"outputId":"24f8e5b5-dd77-4c32-e522-9686f7562ff9"},"execution_count":87,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}},{"output_type":"display_data","data":{"text/plain":["<Figure 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\n"},"metadata":{}}]},{"cell_type":"code","source":["plt.scatter(Machine_temp_failure_missing.index, Machine_temp_failure_missing['temperature'],c=Machine_temp_failure_missing['temperature'].apply(lambda x: 'red' if x > 70.00 else 'blue'))\n","plt.title('Temperature')\n","plt.xlabel('Index')\n","plt.ylabel('Temperature')\n","plt.show()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":472},"id":"7_PPiZH5kmz0","executionInfo":{"status":"ok","timestamp":1685534242145,"user_tz":-330,"elapsed":1042,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}},"outputId":"04b9d7c8-3922-4079-8978-ebe0fb694f1e"},"execution_count":88,"outputs":[{"output_type":"display_data","data":{"text/plain":["<Figure size 640x480 with 1 Axes>"],"image/png":"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\n"},"metadata":{}}]},{"cell_type":"code","source":["from matplotlib.patches import Rectangle\n","import matplotlib.dates as mdates\n","import numpy as np\n","import sklearn\n","import statsmodels as sm\n","\n","def read_dataset(folder, file, date_col=None):\n","    '''\n","    folder: is a Path object\n","    file: the CSV filename in that Path object. \n","    date_col: specify a date_col to use as index_col \n","    \n","    returns: a pandas DataFrame with a DatetimeIndex\n","    '''\n","    df = pd.read_csv(folder / file, \n","                     index_col=date_col, \n","                     parse_dates=[date_col])\n","    return df"],"metadata":{"id":"uKZCvYV7nUr7","executionInfo":{"status":"ok","timestamp":1685534244452,"user_tz":-330,"elapsed":6,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}}},"execution_count":89,"outputs":[]},{"cell_type":"code","source":["def plot_dfs(df1, df2, col, title=None, xlabel=None, ylabel=None):\n","    '''\t\n","    df1: original dataframe without missing data\n","    df2: dataframe with missing data\n","    col: column name that contains missing data\n","    '''    \n","    Machine_temp_failure_missing = df2.rename(columns={col: 'missing'})\n","    \n","    columns = Machine_temp_failure_missing.loc[:, 'missing':].columns.tolist()\n","    subplots_size = len(columns)\n","    \n","    # subplots_size = df2.shape[1]\n","    fig, ax = plt.subplots(subplots_size+1, 1, sharex=True)\n","    plt.subplots_adjust(hspace=0.25)\n","    fig.suptitle = title \n","    \n","    df1[col].plot(ax=ax[0], figsize=(10, 16))\n","    ax[0].set_title('Original Dataset')\n","    ax[0].set_xlabel(xlabel)\n","    ax[0].set_ylabel(ylabel)    \n","    \n","    for i, colname in enumerate(columns):\n","        Machine_temp_failure_missing[colname].plot(ax=ax[i+1])\n","        ax[i+1].set_title(colname)\n","\n","    plt.show()\n"],"metadata":{"id":"TEc1lrNyuDRz","executionInfo":{"status":"ok","timestamp":1685534247221,"user_tz":-330,"elapsed":2,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}}},"execution_count":90,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","def rmse_score(df1, df2, col=None):\n","    '''\n","    df1: original dataframe without missing data\n","    df2: dataframe with missing data\n","    col: column name that contains missing data\n","\n","    returns: a list of scores\n","    '''\n","    Machine_temp_failure_missing = df2.rename(columns={col: 'missing'})\n","    columns = Machine_temp_failure_missing.loc[:, 'missing':].columns.tolist()\n","    scores = []\n","    for comp_col in columns[1:]:\n","        rmse = np.sqrt(np.mean((df1[col] - Machine_temp_failure_missing[comp_col])**2))\n","        scores.append(rmse)\n","        print(f'RMSE for {comp_col}: {rmse}')\n","    return scores\n"],"metadata":{"id":"ZkxCr8XpuG1Y","executionInfo":{"status":"ok","timestamp":1685534250054,"user_tz":-330,"elapsed":3,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}}},"execution_count":91,"outputs":[]},{"cell_type":"code","source":["drive_link = \"https://drive.google.com/file/d/1kulRVMpepw903zqZAIETRHR3CKCw9hCT/view?usp=sharing\"\n","file_id = drive_link.split(\"=\")[-1]  \n","download_url = f\"https://drive.google.com/u/0/uc?id=1kulRVMpepw903zqZAIETRHR3CKCw9hCT&export=download\"\n","local_file = Path(\"data.csv\")\n","urllib.request.urlretrieve(download_url, local_file)\n","Machine_temp_failure_original=pd.read_csv(local_file)\n","print(df.head())"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"3X6oG9FwujKH","executionInfo":{"status":"ok","timestamp":1685534253048,"user_tz":-330,"elapsed":1193,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}},"outputId":"d5e58a21-328f-4c51-bd60-d13cb3d401e2"},"execution_count":92,"outputs":[{"output_type":"stream","name":"stdout","text":["          timestamp  temperature\n","0  02-12-2013 21:15    73.967322\n","1  02-12-2013 21:20    74.935882\n","2  02-12-2013 21:25    76.124162\n","3  02-12-2013 21:30    78.140707\n","4  02-12-2013 21:35    79.329836\n"]}]},{"cell_type":"code","source":["Machine_temp_failure_missing['ffill'] = Machine_temp_failure_missing['temperature'].fillna(method='ffill')\n","Machine_temp_failure_missing['bfill'] = Machine_temp_failure_missing['temperature'].fillna(method='bfill')\n","Machine_temp_failure_missing['mean'] = Machine_temp_failure_missing['temperature'].fillna(Machine_temp_failure_missing['temperature'].mean())"],"metadata":{"id":"QW0lxZ_CvEGv","executionInfo":{"status":"ok","timestamp":1685534352317,"user_tz":-330,"elapsed":3,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}}},"execution_count":98,"outputs":[]},{"cell_type":"code","source":["import numpy as np\n","def rmse_score(df1, df2, col=None):\n","    '''\n","    df1: original dataframe without missing data\n","    df2: dataframe with missing data\n","    col: column name that contains missing data\n","\n","    returns: a list of scores\n","    '''\n","    df_missing = df2.rename(columns={col: 'missing'})\n","    columns = df_missing.loc[:,'missing':].columns.tolist()\n","    scores = []\n","    for comp_col in columns[1:]:\n","        rmse = np.sqrt(np.mean((df1[col] - df_missing[comp_col])**2))\n","        scores.append(rmse)\n","        print(f'RMSE for {comp_col}: {rmse}')\n","    return scores\n","rmse_score1 = rmse_score(Machine_temp_failure_original, \n","                    Machine_temp_failure_missing, \n","                    'temperature')\n"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":561},"id":"vM0cdfeLW3Cm","executionInfo":{"status":"error","timestamp":1685534601859,"user_tz":-330,"elapsed":773,"user":{"displayName":"Devank Saran Prajapati","userId":"05800905064799216101"}},"outputId":"9e3acbfb-f4d9-4f7d-dd9f-617a54b9ab8d"},"execution_count":102,"outputs":[{"output_type":"error","ename":"KeyError","evalue":"ignored","traceback":["\u001b[0;31m---------------------------------------------------------------------------\u001b[0m","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3801\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3802\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;32mpandas/_libs/hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[0;34m()\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 'temperature'","\nThe above exception was the direct cause of the following exception:\n","\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)","\u001b[0;32m<ipython-input-102-04e2ccad4c44>\u001b[0m in \u001b[0;36m<cell line: 18>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'RMSE for {comp_col}: {rmse}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     17\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mscores\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 18\u001b[0;31m rmse_score1 = rmse_score(Machine_temp_failure_original, \n\u001b[0m\u001b[1;32m     19\u001b[0m                     \u001b[0mMachine_temp_failure_missing\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     20\u001b[0m                     'temperature')\n","\u001b[0;32m<ipython-input-102-04e2ccad4c44>\u001b[0m in \u001b[0;36mrmse_score\u001b[0;34m(df1, df2, col)\u001b[0m\n\u001b[1;32m     12\u001b[0m     \u001b[0mscores\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     13\u001b[0m     \u001b[0;32mfor\u001b[0m \u001b[0mcomp_col\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 14\u001b[0;31m         \u001b[0mrmse\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msqrt\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcol\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m-\u001b[0m \u001b[0mdf_missing\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mcomp_col\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     15\u001b[0m         \u001b[0mscores\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrmse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     16\u001b[0m         \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34mf'RMSE for {comp_col}: {rmse}'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   3805\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3807\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3808\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3809\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   3802\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3803\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 3804\u001b[0;31m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   3805\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mTypeError\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   3806\u001b[0m                 \u001b[0;31m# If we have a listlike key, _check_indexing_error will raise\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n","\u001b[0;31mKeyError\u001b[0m: 'temperature'"]}]}]}
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