From 09bc348c099c2f8445ed00a8cfc61d49b6b8f7ac Mon Sep 17 00:00:00 2001
From: Debarghya Jana
 <110213272+Debarghya-Jana-221306@users.noreply.github.com>
Date: Thu, 1 Jun 2023 23:59:42 +0530
Subject: [PATCH] Add files via upload

---
 Assignment_2.ipynb | 922 +++++++++++++++++++++++++++++++++++++++++++++
 1 file changed, 922 insertions(+)
 create mode 100644 Assignment_2.ipynb

diff --git a/Assignment_2.ipynb b/Assignment_2.ipynb
new file mode 100644
index 0000000..8dd9281
--- /dev/null
+++ b/Assignment_2.ipynb
@@ -0,0 +1,922 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "96dfd580",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
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+       "\n",
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+       "\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>value</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>02-12-2013 21:15</td>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22695 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "              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",
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+       "...                 ...        ...\n",
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+       "22693  19-02-2014 15:20  98.056852\n",
+       "22694  19-02-2014 15:25  96.903861\n",
+       "\n",
+       "[22695 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "from pathlib import Path\n",
+    "\n",
+    "# Read the dataset into a DataFrame\n",
+    "df = pd.read_csv(Path('machine_temp_failure_missing.csv'))\n",
+    "\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "89088522",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 22695 entries, 0 to 22694\n",
+      "Data columns (total 2 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   timestamp  22695 non-null  object \n",
+      " 1   value      21410 non-null  float64\n",
+      "dtypes: float64(1), object(1)\n",
+      "memory usage: 354.7+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Inspect the dataset\n",
+    "df.info()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "id": "1d5730e4",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "timestamp       0\n",
+       "value        1285\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Look for missing values\n",
+    "df.isnull().sum()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "ac95e95e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "id": "237eacdb",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/var/folders/gh/x_3x_yf907z7c2w3yx7k55r00000gn/T/ipykernel_23718/4114835346.py:5: FutureWarning: The default value of numeric_only in DataFrame.mean is deprecated. In a future version, it will default to False. In addition, specifying 'numeric_only=None' is deprecated. Select only valid columns or specify the value of numeric_only to silence this warning.\n",
+      "  mean = df2.mean()\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "value    81.100634\n",
+       "dtype: float64"
+      ]
+     },
+     "execution_count": 30,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Calculate mean\n",
+    "df2 = df.fillna(value = 0)\n",
+    "df2\n",
+    "df2.isnull().sum()\n",
+    "mean = df2.mean()\n",
+    "mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "b7f5ed85",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
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+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
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+       "\n",
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+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
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+       "      <th></th>\n",
+       "      <th>timestamp</th>\n",
+       "      <th>temperature</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
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+       "      <th>2</th>\n",
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+       "      <td>76.124162</td>\n",
+       "    </tr>\n",
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+       "      <th>3</th>\n",
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+       "    </tr>\n",
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+       "      <td>96.903861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22695 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "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",
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+       "22693  19-02-2014 15:20    98.056852\n",
+       "22694  19-02-2014 15:25    96.903861\n",
+       "\n",
+       "[22695 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "# Rename value column to temperature\n",
+    "df.rename(columns={'value':'temperature'}, inplace=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "id": "df015eeb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "timestamp    31-12-2013 23:55\n",
+       "value              108.510543\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.max()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "id": "ec7900fb",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "timestamp    01-01-2014 00:00\n",
+       "value                     0.0\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 33,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.min()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 36,
+   "id": "3844178b",
+   "metadata": {
+    "scrolled": false
+   },
+   "outputs": [
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+       "      <td>96.903861</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22695 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "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",
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+       "22693  19-02-2014 15:20    98.056852\n",
+       "22694  19-02-2014 15:25    96.903861\n",
+       "\n",
+       "[22695 rows x 2 columns]"
+      ]
+     },
+     "execution_count": 36,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.rename(columns={'value':'temperature'}, inplace=True)\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "id": "6c63c8d1",
+   "metadata": {},
+   "outputs": [
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+       "      <td>78.140707</td>\n",
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+       "    <tr>\n",
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+       "      <td>19-02-2014 15:20</td>\n",
+       "      <td>98.056852</td>\n",
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+       "    <tr>\n",
+       "      <th>22694</th>\n",
+       "      <td>19-02-2014 15:25</td>\n",
+       "      <td>96.903861</td>\n",
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+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22695 rows × 2 columns</p>\n",
+       "</div>"
+      ],
+      "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]"
+      ]
+     },
+     "execution_count": 41,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2.rename(columns={'value':'temperature'}, inplace=True)\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "id": "87aaa7ae",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<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",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\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",
+       "      <td>-0.381246</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22691</th>\n",
+       "      <td>19-02-2014 15:10</td>\n",
+       "      <td>97.804168</td>\n",
+       "      <td>-0.668700</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22692</th>\n",
+       "      <td>19-02-2014 15:15</td>\n",
+       "      <td>97.135468</td>\n",
+       "      <td>0.921384</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22693</th>\n",
+       "      <td>19-02-2014 15:20</td>\n",
+       "      <td>98.056852</td>\n",
+       "      <td>-1.152991</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>22694</th>\n",
+       "      <td>19-02-2014 15:25</td>\n",
+       "      <td>96.903861</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>22695 rows × 3 columns</p>\n",
+       "</div>"
+      ],
+      "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\n",
+       "...                 ...          ...                ...\n",
+       "22690  19-02-2014 15:05    98.185415          -0.381246\n",
+       "22691  19-02-2014 15:10    97.804168          -0.668700\n",
+       "22692  19-02-2014 15:15    97.135468           0.921384\n",
+       "22693  19-02-2014 15:20    98.056852          -1.152991\n",
+       "22694  19-02-2014 15:25    96.903861           0.000000\n",
+       "\n",
+       "[22695 rows x 3 columns]"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df2['delta_temperature'] = 0\n",
+    "for i in range(len(df2) - 1) :\n",
+    "    df2.loc[i , 'delta_temperature'] = df2.loc[i+1 , 'temperature'] - df2.loc[i , 'temperature']\n",
+    "    \n",
+    "df2.loc[len(df2) - 1 , 'delta_temperature'] = 0\n",
+    "df2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "id": "7531a24c",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(df2['timestamp'], df2['temperature'])\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.ylabel('Temperature')\n",
+    "plt.title('Temperature over Time')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "id": "99da2875",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.plot(df2['timestamp'], df2['delta_temperature'].diff())\n",
+    "plt.xlabel('Timestamp')\n",
+    "plt.ylabel('Delta Temperature')\n",
+    "plt.title('Delta Temperature over Time')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "id": "38368380",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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69Tl58qTy8vI8fQAAgH+r8yqes2fP6vDhw57X+fn52rNnj5o3b67bb79dKSkpSktLU1xcnOLi4pSWlqbg4GCNGTNGkhQWFqaJEyfqt7/9rVq0aKHmzZvr6aefVufOnT2regAAgH+rc0DZtWuX+vTp43n91FNPSZLGjx+vpUuXatq0aXK73ZoyZYrOnDmjHj16aOPGjQoJCfG855VXXlGjRo00evRoud1u9e3bV0uXLpXT6fTBVwIAAPWdw7Isy+4i6qqkpERhYWEqLi5mPgrQwOzevVvdunVTbm6uunbtanc5AHyoLr/fPIsHAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOPU+WnGABqWQ4cOqbS01O4yPPbv3+/1X5OEhIQoLi7O7jIAv0BAAfzYoUOH1L59e7vLqNW4cePsLqFWX375JSEFuAkIKIAfqx45yczMVMeOHW2u5hK3260jR46obdu2CgoKsrscj/3792vcuHFGjTYBDRkBBYA6duyorl272l2GR8+ePe0uAYDNmCQLAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOPwLB7AjzkqLqhLVICCvv1SKuDvlWsJ+vZLdYkKkKPigt2lAH6BgAL4sSZnj2n3pFukrZOkrXZXY7aOknZPukX7zx6TlGh3OUCDR0AB/NiFW25X19fP6o9//KM6duhgdzlG23/ggMaOHavFg2+3uxTALxBQAD9mNWqizwqr5L61vRRzj93lGM1dWKXPCqtkNWpidymAX+CmMwAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMZp5OsLVlRUaObMmfrjH/+owsJCRUdHa8KECfr973+vgIBLeciyLM2aNUsZGRk6c+aMevTooVdffVWdOnXydTkAruH8+fOSpN27d9tcyT+53W4dOXJEbdu2VVBQkN3leOzfv9/uEgC/4vOAMnv2bC1atEjLli1Tp06dtGvXLv3iF79QWFiYfv3rX0uS5syZo3nz5mnp0qVq3769XnzxRfXv318HDx5USEiIr0sCcBUHDhyQJP3yl7+0uZL6g/9HATeHzwNKTk6Ohg0bpiFDhkiS2rZtqxUrVmjXrl2SLo2ezJ8/XzNmzNDw4cMlScuWLVNkZKSWL1+uSZMm+bokAFeRnJwsSerQoYOCg4PtLeb/279/v8aNG6fMzEx17NjR7nK8hISEKC4uzu4yAL/g84By3333adGiRfryyy/Vvn177d27V9u2bdP8+fMlSfn5+SosLNSAAQM873G5XOrdu7d27NhRa0ApKytTWVmZ53VJSYmvywb8UsuWLfXYY4/ZXUatOnbsqK5du9pdBgCb+DygTJ8+XcXFxerQoYOcTqcqKyv10ksv6ec//7kkqbCwUJIUGRnp9b7IyEgdPXq01mump6dr1qxZvi4VAAAYyuereFauXKnMzEwtX75cu3fv1rJlyzR37lwtW7bMq5/D4fB6bVlWjbZqqampKi4u9hzHjx/3ddkAAMAgPh9BeeaZZ/Tss8/qX//1XyVJnTt31tGjR5Wenq7x48crKipKkjwrfKoVFRXVGFWp5nK55HK5fF0qAAAwlM9HUM6fP+9ZTlzN6XSqqqpKkhQbG6uoqCht2rTJc768vFxbtmxRYmKir8sBAAD1kM9HUIYOHaqXXnpJt99+uzp16qTPPvtM8+bN06OPPirp0q2dlJQUpaWlKS4uTnFxcUpLS1NwcLDGjBnj63IAAEA95POA8oc//EH/8R//oSlTpqioqEgxMTGaNGmSnnvuOU+fadOmye12a8qUKZ6N2jZu3Mj+AgAAQJLksCzLsruIuiopKVFYWJiKi4sVGhpqdzkAfGj37t3q1q2bcnNzWWYMNDB1+f3mWTwAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACwBjbtm1Tt27dJEndunXTtm3bbK4IgF0a2V0AAEiSw+Go0darVy9JkmVZN7scADZjBAWA7WoLJ3U5D6DhYQQFgE+cP39eBw4cqPP7du3adV39MjIy1L179zpfX5I6dOig4ODg7/VeAPYgoADwiQMHDnjmj9wIkyZN+t7vzc3NVdeuXX1YDYAbjYACwCc6dOig3NzcOr+vLqHm+1xfulQbgPqFgALAJ4KDg2/4KAWjIID/uCGTZE+cOKFx48apRYsWCg4O1j333OP1l49lWZo5c6ZiYmIUFBSkpKQk7du370aUAgAA6iGfB5QzZ86oZ8+eCgwM1IYNG/TFF1/o5Zdf1q233urpM2fOHM2bN08LFizQzp07FRUVpf79+6u0tNTX5QAAgHrIYfl4g4Fnn31W27dvV3Z2dq3nLctSTEyMUlJSNH36dElSWVmZIiMjNXv27OuaCFdSUqKwsDAVFxcrNDTUl+UDuMnqsoSY/VCA+q0uv98+H0FZu3atunfvrlGjRikiIkJdunTRG2+84Tmfn5+vwsJCDRgwwNPmcrnUu3dv7dixo9ZrlpWVqaSkxOsAAAANl88Dyt///nctXLhQcXFx+vjjjzV58mT96le/0ltvvSVJKiwslCRFRkZ6vS8yMtJz7krp6ekKCwvzHK1bt/Z12QAAwCA+DyhVVVXq2rWr0tLS1KVLF02aNEm//OUvtXDhQq9+Vw7rWpZ11aHe1NRUFRcXe47jx4/7umwAAGAQnweU6Oho/ehHP/Jq69ixo44dOyZJioqKkqQaoyVFRUU1RlWquVwuhYaGeh0AAKDh8nlA6dmzpw4ePOjV9uWXX6pNmzaSpNjYWEVFRWnTpk2e8+Xl5dqyZYsSExN9XQ4AAKiHfL5R229+8xslJiYqLS1No0eP1qeffqqMjAxlZGRIunRrJyUlRWlpaYqLi1NcXJzS0tIUHBysMWPG+LocAABQD/k8oNx777364IMPlJqaqhdeeEGxsbGaP3++xo4d6+kzbdo0ud1uTZkyRWfOnFGPHj20ceNGhYSE+LocAABQD/l8H5SbgX1QgIaDfVAA/2HrPigAAAA/FAEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAWArh8Ph034AGgYCCgBbOZ1On/YD0DAQUADYihEUALUhoAAAAOMQUADYyrIsn/YD0DAQUADYqqqqyqf9ADQMBBQAtiKgAKgNAQUAABjnhgeU9PR0ORwOpaSkeNosy9LMmTMVExOjoKAgJSUlad++fTe6FAAAUE/c0ICyc+dOZWRk6O677/ZqnzNnjubNm6cFCxZo586dioqKUv/+/VVaWnojywEAAPXEDQsoZ8+e1dixY/XGG2+oWbNmnnbLsjR//nzNmDFDw4cPV3x8vJYtW6bz589r+fLlN6ocAABQj9ywgPLEE09oyJAh6tevn1d7fn6+CgsLNWDAAE+by+VS7969tWPHjlqvVVZWppKSEq8DAAA0XDckoLzzzjvavXu30tPTa5wrLCyUJEVGRnq1R0ZGes5dKT09XWFhYZ6jdevWvi8agC3CwsJ82g9Aw+DzgHL8+HH9+te/VmZmppo0aXLVflduW21Z1lW3sk5NTVVxcbHnOH78uE9rBgAAZmnk6wvm5uaqqKhI3bp187RVVlZq69atWrBggQ4ePCjp0khKdHS0p09RUVGNUZVqLpdLLpfL16UCMEBZWZlP+wFoGHw+gtK3b1/93//9n/bs2eM5unfvrrFjx2rPnj264447FBUVpU2bNnneU15eri1btigxMdHX5QAwXGBgoE/7AWgYfD6CEhISovj4eK+2pk2bqkWLFp72lJQUpaWlKS4uTnFxcUpLS1NwcLDGjBnj63IAGC48PPy6thgIDw+/CdUAMIXPA8r1mDZtmtxut6ZMmaIzZ86oR48e2rhxo0JCQuwoB4CNzp0759N+ABoGh1UPHxFaUlKisLAwFRcXKzQ01O5yAPwAwcHBcrvd39kvKChI58+fvwkVAbhR6vL7zbN4ANiqvLzcp/0ANAwEFAC2qqys9Gk/AA0DAQUAABiHgAIAAIxDQAFgK/ZBAVAbAgoAWzEHBUBtCCgAbFVVVeXTfgAaBgIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADAOAQUAABiHgAIAAIxDQAEAAMYhoAAAAOMQUAAAgHEIKAAAwDgEFAAAYBwCCgAAMA4BBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcQgoAADAOAQUALYKCgryaT8ADQMBBYCtHA6HT/sBaBgIKABsdf78eZ/2A9AwEFAAAIBxCCgAAMA4BBQAtmrZsqVP+wFoGAgoAGwVHx/v034AGgYCCgBbJSQk+LQfgIaBgALAVv369fNpPwANAwEFgK2SkpIUHh5+zT4RERFKSkq6OQUBMAIBBYCtnE6nJkyYIKnmZmzVr8ePHy+n03mzSwNgIwIKAFtVVlbqvffe05133lkjoAQEBOjOO+/U+++/r8rKSpsqBGCHRnYXAMC/ZWdn68iRI3I4HBoyZIgGDRqkoKAgud1ubdiwQevXr5dlWcrOzuY2D+BHCCgAbHXixAlJ0sCBA/Xhhx8qIOCfA7uTJ0/Wgw8+qA0bNnj6AfAP3OIBYKtTp05JkoYPHy7LspSVlaUVK1YoKytLlmUpOTnZqx8A/8AICgBbVa/gee211/TSSy/pyJEjnnNt27ZVs2bNvPoB8A+MoACw1W233SZJ+uyzz+R2u5WRkaGCggJlZGTI7Xbrs88+8+oHwD84LMuy7C6irkpKShQWFqbi4mKFhobaXQ6AH6C8vFxNmzZV06ZNFRYWpmPHjnnOtWnTRt9++63OnTunc+fOqXHjxjZWCuCHqsvvN7d4ANhqx44dqqioUHFxsXr16qVp06Z5VvH8+c9/1rp16zz9WMUD+A8CCgBbnTx5UpKUmZmp3//+955AIkmxsbHKzMzUuHHjPP0A+AcCCgBbRUdHS5LuvPNOHT58WNnZ2Tp58qSio6PVq1cvffrpp179APgHn0+STU9P17333quQkBBFREQoOTlZBw8e9OpjWZZmzpypmJgYBQUFKSkpSfv27fN1KQDqgV69eqlt27ZKS0uTw+FQUlKSfv7znyspKUkOh0Pp6emKjY1Vr1697C4VwE3k8xGULVu26IknntC9996riooKzZgxQwMGDNAXX3yhpk2bSpLmzJmjefPmaenSpWrfvr1efPFF9e/fXwcPHlRISIivSwJgMKfTqZdfflkjR47UsGHDNHDgQK85KOvXr9f777/Ps3gAP3PDV/GcOnVKERER2rJli+6//35ZlqWYmBilpKRo+vTpkqSysjJFRkZq9uzZmjRp0ndek1U8QMMzbdo0vfLKK6qoqPC0NWrUSL/5zW80Z84cGysD4CtGreIpLi6WJDVv3lySlJ+fr8LCQg0YMMDTx+VyqXfv3tqxY0etAaWsrExlZWWe1yUlJTe4agA30+rVqzV37lwNHjxY7dq1k9vtVlBQkA4fPqy5c+cqISFBw4cPt7tMADfRDR1BsSxLw4YN05kzZ5SdnS3p0lLBnj176sSJE4qJifH0ffzxx3X06FF9/PHHNa4zc+ZMzZo1q0Y7IyhA/VdZWal27dqpZcuW+vrrr2vsJNuyZUudPn1ahw4d4jYPUM/VZQTlhu4kO3XqVH3++edasWJFjXNXPlbdsqwabdVSU1NVXFzsOY4fP35D6gVw81U/zTg3N1edO3dWTk6OSktLlZOTo86dOys3N1f5+fmeP3IA+IcbdovnySef1Nq1a7V161a1atXK0x4VFSVJKiws9Fo2WFRUpMjIyFqv5XK55HK5blSpAGx0+dOM16xZ43macUJCgtasWcPTjAE/5fMRFMuyNHXqVK1evVqffPKJYmNjvc7HxsYqKipKmzZt8rSVl5dry5YtSkxM9HU5AAx3+dOMq8NJtYCAAJ5mDPgpnweUJ554QpmZmVq+fLlCQkJUWFiowsJCud1uSZdu7aSkpCgtLU0ffPCB8vLyNGHCBAUHB2vMmDG+LgeA4aqfUrx69WpVVVV5nauqqtKaNWu8+gHwDz4PKAsXLlRxcbGSkpIUHR3tOVauXOnpM23aNKWkpGjKlCnq3r27Tpw4oY0bN7IHCuCHqp9SvGHDBiUnJ3vNQUlOTtaGDRu8+gHwDzzNGICtLl/FU1RUVONpxuHh4aziARoIo/ZBAYBrqd5JdsSIEQoKCvI6V1RUpKNHj2rVqlWEE8DP3NBlxgBwvWrbZsDhcFx1+wEADRu3eADYqvoWT+fOnbVq1Spt377d8zTjnj17asSIEcrLy+MWD9AAcIsHQL1RvVHbihUrFBgYqKSkJK/zqampSkxMVHZ2do1zABouAgoAW508eVKSFB8fr8rKSmVnZ3tGUHr16qX4+HivfgD8AwEFgK2qd5ResGCBXn/99RrP4nn88ce9+gHwD0ySBWCrXr16KTw8XKmpqYqPj/faByU+Pl6/+93vFBERoV69etldKoCbiIACwHaXr9SxLMtzAPBfBBQAtsrOzlZRUZHS09OVl5enxMREhYaGKjExUfv27VNaWpqKiop4mjHgZwgoAGxVPfl16tSpOnz4sDZv3qzly5dr8+bNOnTokKZOnerVD4B/YJIsAFtVT37Ny8tTQkJCjaXEeXl5Xv0A+Ac2agNgKzZqA/wHG7UBqDcufxZPaGioLly44DnXpEkTXbhwgWfxAH6IOSgAjFFWVnbN1wD8B7d4ANiqsrJS0dHROnXqlIYMGaLBgwcrKChIbrdbf/rTn7R+/XpFRESooKCAURSgnuMWD4B6IysrS6dOndJ9992ntWvXKiDgnwO7kydPVu/evbVt2zZlZWWpb9++NlYK4GbiFg8AW2VlZUmSZs2a5RVOJCkgIEDPP/+8Vz8A/oGAAgAAjENAAWCr6n1Pnn/+eVVVVXmdq6qq0syZM736AfAPzEEBYKukpCRFRERo27ZteuihhzRo0CDPJNkNGzZo+/btioiIIKAAfoZVPABst3r1ao0YMaJGu8PhkGVZWrVqlYYPH25DZQB8qS6/39ziAWCMy59qDMC/MYICwFaVlZWKiYlRUVER+6AADRz7oACoN7KyslRUVHTVfVDuv/9+bd++nX1QAD/DLR4AtvqufVCqV/GwDwrgXwgoAADAOAQUALb6rn1QZs2a5dUPgH8goACwVVJSksLDw7Vt2zYNGzZMOTk5Ki0tVU5OjoYNG6Zt27axDwrgh5gkC8BWTqdTixYt0ogRI/SXv/xF69at85wLCgqSJC1cuJAVPICfYQQFgO2GDx+uZ555RhcvXvRqv3jxop555hk2aQP8ECMoAGy3evVqzZ07V4MHD1a7du3kdrsVFBSkw4cPa+7cuUpISCCkAH6GjdoA2KqyslLt2rVTy5Yt9fXXX+vIkSOec23btlXLli11+vRpHTp0iNs8QD3HVvcA6o3s7GwdOXJEubm56ty5s9ck2c6dOys3N1f5+fnKzs62u1QANxEBBYCtTpw4IUkaOHCg1qxZo4SEBN1yyy1KSEjQmjVrNHDgQK9+APwDAQWArU6dOiXp0kTZ2naSTU5O9uoHwD8QUADYKjw8XNKlibK1bdS2Zs0ar34A/AMBBYCtbrvtNknShg0blJyc7DUHJTk5WRs2bPDqB8A/sIoHgK0uX8VTVFSkY8eOec61adNG4eHhrOIBGoi6/H6zDwoAWzmdTr388ssaMWKEZ+fYakVFRTp69KhWrVpFOAH8DLd4ABjB4XDU2lZbO4CGj1s8AGxVfYunc+fOWrVqlbZv366TJ08qOjpaPXv21IgRI5SXl8ctHqAB4BYPgHqjeqO2FStWKDAwsMZTi1NTU5WYmKjs7GyeaAz4EW7xALDVyZMnJUnx8fG1nq9ur+4HwD8QUADYKjo6WpKUl5enyspKZWVlacWKFcrKylJlZaXy8vK8+gHwD8xBAWArHhYI+A8eFgig3nA6nRo1apR27dolt9utjIwMFRQUKCMjQ263W7t27dLIkSMJJ4CfYQQFgK0uH0E5deqUjh496jnHCArQsLCKB0C9cfkqnnvvvVfZ2dmeZca9evXSp59+yioewA8RUADY6vJVPE6ns0YIYRUP4J+YgwLAVpev4qneOfbyg1U8gH9iDgoAW1XPQbl89c6VYmNjmYMCNAD1ZhXPa6+9ptjYWDVp0kTdunVTdna2neUAsIHT6bxmOJGk/Px8wgngZ2wLKCtXrlRKSopmzJihzz77TL169dKgQYO8HrUOoOG73ocB8tBAwL/YdounR48e6tq1qxYuXOhp69ixo5KTk5Wenu7Vt6ysTGVlZZ7XJSUlat26Nbd4gAbg8uBRUVFRYxVPo0b/nMtfD+9IA7iM8cuMy8vLlZubq2effdarfcCAAdqxY0eN/unp6Zo1a9bNKg+ATWpbxQPAP9lyi+frr79WZWWlIiMjvdojIyNVWFhYo39qaqqKi4s9x/Hjx29WqQAAwAa27oNy5T1ly7Jqvc/scrnkcrluVlkAbOJwOLxu4zDvBPBftgSUli1byul01hgtKSoqqjGqAqBhu/IPk6uFEuafAP7Flls8jRs3Vrdu3bRp0yav9k2bNikxMdGOkgDY6LvCB+EE8D+23eJ56qmn9Mgjj6h79+76l3/5F2VkZOjYsWOaPHmyXSUBsNHVbvESTgD/ZFtAefjhh3X69Gm98MILOnnypOLj4/WnP/1Jbdq0saskADYjjACoxlb3AADgpqg3W90DAADUhoACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4BBQAAGAcAgoAADCOrU8z/r6q95YrKSmxuRIAAHC9qn+3r2eP2HoZUEpLSyVJrVu3trkSAABQV6WlpQoLC7tmn3q51X1VVZUKCgoUEhJy1UezA6ifSkpK1Lp1ax0/fpxHWQANjGVZKi0tVUxMjAICrj3LpF4GFAANF8/aAiAxSRYAABiIgAIAAIxDQAFgFJfLpeeff14ul8vuUgDYiDkoAADAOIygAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFgBG2bt2qoUOHKiYmRg6HQ2vWrLG7JAA2IqAAMMK5c+f04x//WAsWLLC7FAAGqJdPMwbQ8AwaNEiDBg2yuwwAhmAEBQAAGIeAAgAAjENAAQAAxiGgAAAA4xBQAACAcVjFA8AIZ8+e1eHDhz2v8/PztWfPHjVv3ly33367jZUBsIPDsizL7iIAICsrS3369KnRPn78eC1duvTmFwTAVgQUAABgHOagAAAA4xBQAACAcQgoAADAOAQUAABgHAIKAAAwDgEFAAAYh4ACAACMQ0ABAADGIaAAAADjEFAAAIBxCCgAAMA4/w8ly9/7eudwfAAAAABJRU5ErkJggg==\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "plt.boxplot(df2['temperature'])\n",
+    "plt.title('Box Plot of Value')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "id": "b0c37417",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import seaborn as sns\n",
+    "\n",
+    "sns.boxenplot(df2['temperature'])\n",
+    "plt.title('Boxen Plot of Value')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "id": "368b3c18",
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "import pandas as pd\n",
+    "\n",
+    "pd.plotting.lag_plot(df2['temperature'])\n",
+    "plt.title('Lag Plot of Value')\n",
+    "plt.show()\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "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": "3.10.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}