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+{
+  "nbformat": 4,
+  "nbformat_minor": 0,
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "name": "python3",
+      "display_name": "Python 3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": null,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "lrvQKaJrswRe",
+        "outputId": "755ccc8b-6bfd-4305-e718-d130534e6a9d"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
+            "Requirement already satisfied: hvplot in /usr/local/lib/python3.9/dist-packages (0.8.3)\n",
+            "Requirement already satisfied: panel>=0.11.0 in /usr/local/lib/python3.9/dist-packages (from hvplot) (0.14.4)\n",
+            "Requirement already satisfied: bokeh>=1.0.0 in /usr/local/lib/python3.9/dist-packages (from hvplot) (2.4.3)\n",
+            "Requirement already satisfied: packaging in /usr/local/lib/python3.9/dist-packages (from hvplot) (23.1)\n",
+            "Requirement already satisfied: param>=1.9.0 in /usr/local/lib/python3.9/dist-packages (from hvplot) (1.13.0)\n",
+            "Requirement already satisfied: numpy>=1.15 in /usr/local/lib/python3.9/dist-packages (from hvplot) (1.22.4)\n",
+            "Requirement already satisfied: pandas in /usr/local/lib/python3.9/dist-packages (from hvplot) (1.5.3)\n",
+            "Requirement already satisfied: holoviews>=1.11.0 in /usr/local/lib/python3.9/dist-packages (from hvplot) (1.15.4)\n",
+            "Requirement already satisfied: colorcet>=2 in /usr/local/lib/python3.9/dist-packages (from hvplot) (3.0.1)\n",
+            "Requirement already satisfied: tornado>=5.1 in /usr/local/lib/python3.9/dist-packages (from bokeh>=1.0.0->hvplot) (6.2)\n",
+            "Requirement already satisfied: pillow>=7.1.0 in /usr/local/lib/python3.9/dist-packages (from bokeh>=1.0.0->hvplot) (8.4.0)\n",
+            "Requirement already satisfied: PyYAML>=3.10 in /usr/local/lib/python3.9/dist-packages (from bokeh>=1.0.0->hvplot) (6.0)\n",
+            "Requirement already satisfied: Jinja2>=2.9 in /usr/local/lib/python3.9/dist-packages (from bokeh>=1.0.0->hvplot) (3.1.2)\n",
+            "Requirement already satisfied: typing-extensions>=3.10.0 in /usr/local/lib/python3.9/dist-packages (from bokeh>=1.0.0->hvplot) (4.5.0)\n",
+            "Requirement already satisfied: pyct>=0.4.4 in /usr/local/lib/python3.9/dist-packages (from colorcet>=2->hvplot) (0.5.0)\n",
+            "Requirement already satisfied: pyviz-comms>=0.7.4 in /usr/local/lib/python3.9/dist-packages (from holoviews>=1.11.0->hvplot) (2.2.1)\n",
+            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.9/dist-packages (from pandas->hvplot) (2.8.2)\n",
+            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.9/dist-packages (from pandas->hvplot) (2022.7.1)\n",
+            "Requirement already satisfied: setuptools>=42 in /usr/local/lib/python3.9/dist-packages (from panel>=0.11.0->hvplot) (67.7.2)\n",
+            "Requirement already satisfied: markdown in /usr/local/lib/python3.9/dist-packages (from panel>=0.11.0->hvplot) (3.4.3)\n",
+            "Requirement already satisfied: bleach in /usr/local/lib/python3.9/dist-packages (from panel>=0.11.0->hvplot) (6.0.0)\n",
+            "Requirement already satisfied: requests in /usr/local/lib/python3.9/dist-packages (from panel>=0.11.0->hvplot) (2.27.1)\n",
+            "Requirement already satisfied: tqdm>=4.48.0 in /usr/local/lib/python3.9/dist-packages (from panel>=0.11.0->hvplot) (4.65.0)\n",
+            "Requirement already satisfied: MarkupSafe>=2.0 in /usr/local/lib/python3.9/dist-packages (from Jinja2>=2.9->bokeh>=1.0.0->hvplot) (2.1.2)\n",
+            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.9/dist-packages (from python-dateutil>=2.8.1->pandas->hvplot) (1.16.0)\n",
+            "Requirement already satisfied: webencodings in /usr/local/lib/python3.9/dist-packages (from bleach->panel>=0.11.0->hvplot) (0.5.1)\n",
+            "Requirement already satisfied: importlib-metadata>=4.4 in /usr/local/lib/python3.9/dist-packages (from markdown->panel>=0.11.0->hvplot) (6.4.1)\n",
+            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.9/dist-packages (from requests->panel>=0.11.0->hvplot) (2022.12.7)\n",
+            "Requirement already satisfied: urllib3<1.27,>=1.21.1 in /usr/local/lib/python3.9/dist-packages (from requests->panel>=0.11.0->hvplot) (1.26.15)\n",
+            "Requirement already satisfied: charset-normalizer~=2.0.0 in /usr/local/lib/python3.9/dist-packages (from requests->panel>=0.11.0->hvplot) (2.0.12)\n",
+            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.9/dist-packages (from requests->panel>=0.11.0->hvplot) (3.4)\n",
+            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.9/dist-packages (from importlib-metadata>=4.4->markdown->panel>=0.11.0->hvplot) (3.15.0)\n"
+          ]
+        }
+      ],
+      "source": [
+        "import pandas as pd\n",
+        "import numpy as np\n",
+        "from pathlib import Path\n",
+        "import matplotlib.pyplot as plt\n",
+        "import seaborn as sns\n",
+        "!pip install hvplot\n",
+        "import hvplot.pandas\n",
+        "\n"
+      ]
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Task** **1**"
+      ],
+      "metadata": {
+        "id": "zEmVROGowh_T"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def read_datasets():\n",
+        "    # Read machine_temp_failure_missing.csv into a DataFrame\n",
+        "    data_path = Path('/content/machine_temp_failure_missing.csv')\n",
+        "    df = pd.read_csv(data_path)\n",
+        "    \n",
+        "    # Check for missing values\n",
+        "    missing_values = df.isnull().sum()\n",
+        "    print(\"Missing Values:\")\n",
+        "    print(missing_values)\n",
+        "    \n",
+        "    # Get statistics\n",
+        "    stats = df.describe()\n",
+        "    print(\"Statistics:\")\n",
+        "    print(stats)\n",
+        "    \n",
+        "    # Rename 'value' column to 'temperature'\n",
+        "    df.rename(columns={'value': 'temperature'}, inplace=True)\n",
+        "    \n",
+        "    return df"
+      ],
+      "metadata": {
+        "id": "VjhtNRljtFIg"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Task 2**"
+      ],
+      "metadata": {
+        "id": "G-77WT-fwnh3"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def create_delta_temperature(df):\n",
+        "    df['delta_temperature'] = df['temperature'].diff()\n",
+        "    return df\n"
+      ],
+      "metadata": {
+        "id": "z8VVZ-IvwsEB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Task 3**"
+      ],
+      "metadata": {
+        "id": "sR8jzeOVw1XV"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def plot_dfs(df):\n",
+        "    # Using Matplotlib\n",
+        "    plt.figure(figsize=(12, 6))\n",
+        "    plt.subplot(2, 1, 1)\n",
+        "    plt.plot(df['temperature'])\n",
+        "    plt.title('Temperature')\n",
+        "    plt.subplot(2, 1, 2)\n",
+        "    plt.plot(df['delta_temperature'])\n",
+        "    plt.title('Delta Temperature')\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()\n",
+        "    # Using hvplot\n",
+        "    df.hvplot.line(y='temperature', width=800, height=300, xlabel='Index', ylabel='Temperature', title='Temperature') * \\\n",
+        "    df.hvplot.line(y='delta_temperature', width=800, height=300, xlabel='Index', ylabel='Delta Temperature', title='Delta Temperature')"
+      ],
+      "metadata": {
+        "id": "Nf0V1vicw5GB"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Task 4**"
+      ],
+      "metadata": {
+        "id": "uD5HGCDCx9uW"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def plot_temperature_color(df):\n",
+        "    plt.figure(figsize=(12, 6))\n",
+        "    colors = np.where(df['temperature'] > 70.0, 'red', 'blue')\n",
+        "    plt.scatter(df.index, df['temperature'], c=colors)\n",
+        "    plt.xlabel('Index')\n",
+        "    plt.ylabel('Temperature')\n",
+        "    plt.title('Temperature (Color-coded)')\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "TZNs0e95yBO-"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Task 5**"
+      ],
+      "metadata": {
+        "id": "Syt7WrElyI5w"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def rmse_score(df_missing, df_original):\n",
+        "    rmse = np.sqrt(((df_missing['temperature'] - df_original['temperature']) ** 2).mean())\n",
+        "    return rmse"
+      ],
+      "metadata": {
+        "id": "nZ2J6wFOyLED"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "Task 6"
+      ],
+      "metadata": {
+        "id": "8exR0yFCyNmq"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.impute import SimpleImputer\n",
+        "\n",
+        "def rmse_score(df_missing, df_original):\n",
+        "    # Perform missing value imputation on df_missing\n",
+        "    imputer = SimpleImputer(strategy='mean')\n",
+        "    df_missing_imputed = imputer.fit_transform(df_missing[['temperature']])\n",
+        "    \n",
+        "    # Calculate RMSE score\n",
+        "    from sklearn.metrics import mean_squared_error\n",
+        "    rmse = np.sqrt(mean_squared_error(df_missing_imputed, df_original['value']))\n",
+        "    return rmse\n"
+      ],
+      "metadata": {
+        "id": "ayH0GZSayPxp"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Task 7**"
+      ],
+      "metadata": {
+        "id": "c7zH5xpryZDQ"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def resample_hourly(df):\n",
+        "    df['timestamp'] = pd.to_datetime(df['timestamp'])\n",
+        "    df = df.set_index('timestamp')\n",
+        "    df_hourly = df.resample('H').mean()\n",
+        "    return df_hourly"
+      ],
+      "metadata": {
+        "id": "khZ6_t-EydaK"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Task 8**"
+      ],
+      "metadata": {
+        "id": "jaVpmMISyfwU"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def box_plots(df):\n",
+        "    plt.figure(figsize=(12, 6))\n",
+        "    plt.subplot(2, 1, 1)\n",
+        "    sns.boxplot(data=df, x='temperature')\n",
+        "    plt.title('Box Plot')\n",
+        "    plt.subplot(2, 1, 2)\n",
+        "    sns.boxenplot(data=df, x='temperature')\n",
+        "    plt.title('Boxen Plot')\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "Se9gL9mOyiDb"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Task 9**"
+      ],
+      "metadata": {
+        "id": "9H70eA8UyogL"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def box_plots(df):\n",
+        "    plt.figure(figsize=(12, 6))\n",
+        "    plt.subplot(2, 1, 1)\n",
+        "    sns.boxplot(data=df, x='value')  # Update column name\n",
+        "    plt.title('Box Plot')\n",
+        "    plt.subplot(2, 1, 2)\n",
+        "    sns.boxenplot(data=df, x='value')  # Update column name\n",
+        "    plt.title('Boxen Plot')\n",
+        "    plt.tight_layout()\n",
+        "    plt.show()"
+      ],
+      "metadata": {
+        "id": "MJ46yJrZyrqr"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Task 10**"
+      ],
+      "metadata": {
+        "id": "Z3wnaZ9GywL4"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def iqr_outliers(df):\n",
+        "    Q1 = df['temperature'].quantile(0.25)\n",
+        "    Q3 = df['temperature'].quantile(0.75)\n",
+        "    IQR = Q3 - Q1\n",
+        "    lower_bound = Q1 - 1.5 * IQR\n",
+        "    upper_bound = Q3 + 1.5 * IQR\n",
+        "    outliers = df[(df['temperature'] < lower_bound) | (df['temperature'] > upper_bound)]\n",
+        "    return outliers"
+      ],
+      "metadata": {
+        "id": "v4Zu4HEfyyLW"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Task 11**"
+      ],
+      "metadata": {
+        "id": "txgIcQgIy1C0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def z_score(df):\n",
+        "    from scipy.stats import zscore\n",
+        "    df['z_score'] = zscore(df['value'])  # Update column name\n",
+        "    \n",
+        "    plt.figure(figsize=(12, 6))\n",
+        "    plt.plot(df['z_score'])\n",
+        "    plt.xlabel('Index')\n",
+        "    plt.ylabel('Z-Score')\n",
+        "    plt.title('Z-Score')\n",
+        "    plt.show()\n"
+      ],
+      "metadata": {
+        "id": "iRE_zNMqy4XD"
+      },
+      "execution_count": null,
+      "outputs": []
+    },
+    {
+      "cell_type": "markdown",
+      "source": [
+        "**Task 12**"
+      ],
+      "metadata": {
+        "id": "7NgUOxuEy8t0"
+      }
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "def complete_workflow():\n",
+        "    # Task 1: Read datasets\n",
+        "    df_missing = read_datasets()\n",
+        "    \n",
+        "    # Task 2: Create delta_temperature feature\n",
+        "    df_missing = create_delta_temperature(df_missing)\n",
+        "    \n",
+        "    # Task 3: Plot temperature and delta_temperature\n",
+        "    plot_dfs(df_missing)\n",
+        "    \n",
+        "    # Task 4: Plot temperature color-coded by value\n",
+        "    plot_temperature_color(df_missing)\n",
+        "    \n",
+        "    # Task 5: Read machine_temp_failure_original.csv\n",
+        "    df_original = pd.read_csv('/content/machine_temp_failure_original.csv')\n",
+        "    \n",
+        "    # Task 6: Calculate RMSE score\n",
+        "    rmse = rmse_score(df_missing, df_original)\n",
+        "    print(\"RMSE Score:\", rmse)\n",
+        "    \n",
+        "    # Task 7: Perform univariate imputation\n",
+        "    df_imputed = univariate_imputation(df_missing)\n",
+        "    \n",
+        "    # Task 8: Resample to hourly frequency\n",
+        "    df_hourly = resample_hourly(df_imputed)\n",
+        "    \n",
+        "    # Task 9: Box plot and Boxen plot\n",
+        "    box_plots(df_hourly)\n",
+        "    \n",
+        "    # Task 10: Lag plot\n",
+        "    lag_plot(df_hourly)\n",
+        "    \n",
+        "    # Task 11: Find IQR outliers\n",
+        "    outliers = iqr_outliers(df_hourly)\n",
+        "    print(\"IQR Outliers:\")\n",
+        "    print(outliers)\n",
+        "    \n",
+        "    # Task 12: Compute z-score and plot\n",
+        "    z_score(df_hourly)\n",
+        "\n",
+        "\n",
+        "# Run the complete workflow\n",
+        "complete_workflow()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 1000
+        },
+        "id": "YEtRf487y7zu",
+        "outputId": "c0454102-709d-450d-8eb7-45e7c1254691"
+      },
+      "execution_count": null,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "Missing Values:\n",
+            "timestamp       0\n",
+            "value        1285\n",
+            "dtype: int64\n",
+            "Statistics:\n",
+            "              value\n",
+            "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"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        },
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE Score: 3.961536496226712\n"
+          ]
+        },
+        {
+          "output_type": "error",
+          "ename": "ValueError",
+          "evalue": "ignored",
+          "traceback": [
+            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+            "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+            "\u001b[0;32m<ipython-input-63-841662d353b1>\u001b[0m in \u001b[0;36m<cell line: 43>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     41\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     42\u001b[0m \u001b[0;31m# Run the complete workflow\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 43\u001b[0;31m \u001b[0mcomplete_workflow\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[0;32m<ipython-input-63-841662d353b1>\u001b[0m in \u001b[0;36mcomplete_workflow\u001b[0;34m()\u001b[0m\n\u001b[1;32m     26\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     27\u001b[0m     \u001b[0;31m# Task 9: Box plot and Boxen plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 28\u001b[0;31m     \u001b[0mbox_plots\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf_hourly\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     29\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     30\u001b[0m     \u001b[0;31m# Task 10: Lag plot\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m<ipython-input-60-03a3a3b13e53>\u001b[0m in \u001b[0;36mbox_plots\u001b[0;34m(df)\u001b[0m\n\u001b[1;32m      2\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfigure\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfigsize\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m12\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m6\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      3\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\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[0;32m----> 4\u001b[0;31m     \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mboxplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m'value'\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# Update column name\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      5\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtitle\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m'Box Plot'\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      6\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0msubplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;36m1\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[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mboxplot\u001b[0;34m(data, x, y, hue, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth, whis, ax, **kwargs)\u001b[0m\n\u001b[1;32m   2229\u001b[0m ):\n\u001b[1;32m   2230\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2231\u001b[0;31m     plotter = _BoxPlotter(x, y, hue, data, order, hue_order,\n\u001b[0m\u001b[1;32m   2232\u001b[0m                           \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2233\u001b[0m                           width, dodge, fliersize, linewidth)\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, x, y, hue, data, order, hue_order, orient, color, palette, saturation, width, dodge, fliersize, linewidth)\u001b[0m\n\u001b[1;32m    783\u001b[0m                  width, dodge, fliersize, linewidth):\n\u001b[1;32m    784\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 785\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_variables\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morient\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0morder\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mhue_order\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    786\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mestablish_colors\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcolor\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mpalette\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0msaturation\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    787\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;32m/usr/local/lib/python3.9/dist-packages/seaborn/categorical.py\u001b[0m in \u001b[0;36mestablish_variables\u001b[0;34m(self, x, y, hue, data, orient, order, hue_order, units)\u001b[0m\n\u001b[1;32m    539\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mvar\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mstr\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    540\u001b[0m                     \u001b[0merr\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34mf\"Could not interpret input '{var}'\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 541\u001b[0;31m                     \u001b[0;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[0;34m(\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[0m\u001b[1;32m    542\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    543\u001b[0m             \u001b[0;31m# Figure out the plotting orientation\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+            "\u001b[0;31mValueError\u001b[0m: Could not interpret input 'value'"
+          ]
+        },
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x600 with 1 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    }
+  ]
+}
\ No newline at end of file