From 015b5ec9775f4a7bd506a18271bf737b60fd89f4 Mon Sep 17 00:00:00 2001
From: Priyanshu Raj Jindal <96648176+priyanshu11137@users.noreply.github.com>
Date: Tue, 11 Jul 2023 20:30:48 +0530
Subject: [PATCH] Add files via upload

---
 Graded Assignment 2/FuTSA2_210787.ipynb | 1712 +++++++++++++++++++++++
 1 file changed, 1712 insertions(+)
 create mode 100644 Graded Assignment 2/FuTSA2_210787.ipynb

diff --git a/Graded Assignment 2/FuTSA2_210787.ipynb b/Graded Assignment 2/FuTSA2_210787.ipynb
new file mode 100644
index 0000000..b2fcee9
--- /dev/null
+++ b/Graded Assignment 2/FuTSA2_210787.ipynb	
@@ -0,0 +1,1712 @@
+{
+  "cells": [
+    {
+      "cell_type": "code",
+      "execution_count": 119,
+      "metadata": {
+        "id": "WvrEJNY69ToL"
+      },
+      "outputs": [],
+      "source": [
+        "import pandas as pd\n",
+        "import matplotlib.pyplot as plt\n",
+        "import pathlib\n",
+        "from pathlib import Path\n",
+        "import pandas as pd\n",
+        "import csv\n",
+        "import math\n",
+        "import numpy as np"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 120,
+      "metadata": {
+        "id": "oS8lh4NWFolD"
+      },
+      "outputs": [],
+      "source": [
+        "def read_data():\n",
+        "    url1='https://drive.google.com/file/d/17bqqiJZ7JCXPt0EwseFBPZbBIuV7-YcG/view?usp=sharing'\n",
+        "    url1='https://drive.google.com/uc?id=' + url1.split('/')[-2]\n",
+        "    url2='https://drive.google.com/file/d/1kulRVMpepw903zqZAIETRHR3CKCw9hCT/view?usp=sharing'\n",
+        "    url2='https://drive.google.com/uc?id=' + url2.split('/')[-2]\n",
+        "\n",
+        "    df1= pd.read_csv(url1)\n",
+        "    df2= pd.read_csv(url2)\n",
+        "    return df1,df2"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 121,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "QYGbk7YF_WTC",
+        "outputId": "8870cf90-3ca6-44d0-dc31-937ee561bbf5"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              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"
+            ],
+            "text/html": [
+              "\n",
+              "\n",
+              "  <div id=\"df-319fe113-5e97-447f-8556-1e009060f402\">\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>value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>21410.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>85.968188</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>13.553070</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>2.084721</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>83.082867</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>89.251290</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>93.910748</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>108.510543</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-319fe113-5e97-447f-8556-1e009060f402')\"\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 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-26ee2522-3480-4ad4-acc9-b56db44eba95\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-26ee2522-3480-4ad4-acc9-b56db44eba95')\"\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",
+              "    </div>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    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-quickchart: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",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart: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",
+              "      async function quickchart(key) {\n",
+              "        const containerElement = document.querySelector('#' + key);\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      }\n",
+              "    </script>\n",
+              "\n",
+              "      <script>\n",
+              "\n",
+              "function displayQuickchartButton(domScope) {\n",
+              "  let quickchartButtonEl =\n",
+              "    domScope.querySelector('#df-26ee2522-3480-4ad4-acc9-b56db44eba95 button.colab-df-quickchart');\n",
+              "  quickchartButtonEl.style.display =\n",
+              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "}\n",
+              "\n",
+              "        displayQuickchartButton(document);\n",
+              "      </script>\n",
+              "      <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      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",
+              "    [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-319fe113-5e97-447f-8556-1e009060f402 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-319fe113-5e97-447f-8556-1e009060f402');\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": 121
+        }
+      ],
+      "source": [
+        "df1,df2=read_data()\n",
+        "df1.describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 122,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 300
+        },
+        "id": "wUTJphxLCVwA",
+        "outputId": "53b4a872-6e86-4553-8a3f-e4f0cbfebe2c"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              value\n",
+              "count  22695.000000\n",
+              "mean      85.926498\n",
+              "std       13.746912\n",
+              "min        2.084721\n",
+              "25%       83.080078\n",
+              "50%       89.408246\n",
+              "75%       94.016252\n",
+              "max      108.510543"
+            ],
+            "text/html": [
+              "\n",
+              "\n",
+              "  <div id=\"df-f60de90d-375e-43f6-b0ae-541cd16e7410\">\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>value</th>\n",
+              "    </tr>\n",
+              "  </thead>\n",
+              "  <tbody>\n",
+              "    <tr>\n",
+              "      <th>count</th>\n",
+              "      <td>22695.000000</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>mean</th>\n",
+              "      <td>85.926498</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>std</th>\n",
+              "      <td>13.746912</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>min</th>\n",
+              "      <td>2.084721</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>25%</th>\n",
+              "      <td>83.080078</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>50%</th>\n",
+              "      <td>89.408246</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>75%</th>\n",
+              "      <td>94.016252</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>max</th>\n",
+              "      <td>108.510543</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-f60de90d-375e-43f6-b0ae-541cd16e7410')\"\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 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-5647177b-6853-4874-90f8-c7a726925f52\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5647177b-6853-4874-90f8-c7a726925f52')\"\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",
+              "    </div>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    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-quickchart: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",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart: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",
+              "      async function quickchart(key) {\n",
+              "        const containerElement = document.querySelector('#' + key);\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      }\n",
+              "    </script>\n",
+              "\n",
+              "      <script>\n",
+              "\n",
+              "function displayQuickchartButton(domScope) {\n",
+              "  let quickchartButtonEl =\n",
+              "    domScope.querySelector('#df-5647177b-6853-4874-90f8-c7a726925f52 button.colab-df-quickchart');\n",
+              "  quickchartButtonEl.style.display =\n",
+              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "}\n",
+              "\n",
+              "        displayQuickchartButton(document);\n",
+              "      </script>\n",
+              "      <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      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",
+              "    [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-f60de90d-375e-43f6-b0ae-541cd16e7410 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-f60de90d-375e-43f6-b0ae-541cd16e7410');\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": 122
+        }
+      ],
+      "source": [
+        "df2.describe()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 123,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "AHVDpVjfGlEb",
+        "outputId": "0bfa8985-2715-4e3b-d5aa-8613aa0775c4"
+      },
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "df1: timestamp       0\n",
+            "value        1285\n",
+            "dtype: int64\n",
+            "df2: timestamp    0\n",
+            "value        0\n",
+            "dtype: int64\n"
+          ]
+        }
+      ],
+      "source": [
+        "print(f\"df1: {df1.isnull().sum()}\")\n",
+        "print(f\"df2: {df2.isnull().sum()}\")"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 124,
+      "metadata": {
+        "id": "X3z52rb-IFyB"
+      },
+      "outputs": [],
+      "source": [
+        "df1 = df1.rename(columns={\"value\": \"temp\"})"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 125,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 423
+        },
+        "id": "nkz-DOs-HLcZ",
+        "outputId": "42cb2ddc-54bb-4585-8dcc-32ca81aca389"
+      },
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              timestamp       temp  delta_temp\n",
+              "0      02-12-2013 21:15  73.967322         NaN\n",
+              "1      02-12-2013 21:20  74.935882    0.968560\n",
+              "2      02-12-2013 21:25  76.124162    1.188280\n",
+              "3      02-12-2013 21:30  78.140707    2.016546\n",
+              "4      02-12-2013 21:35  79.329836    1.189128\n",
+              "...                 ...        ...         ...\n",
+              "22690  19-02-2014 15:05  98.185415         NaN\n",
+              "22691  19-02-2014 15:10  97.804168   -0.381246\n",
+              "22692  19-02-2014 15:15  97.135468   -0.668700\n",
+              "22693  19-02-2014 15:20  98.056852    0.921384\n",
+              "22694  19-02-2014 15:25  96.903861   -1.152991\n",
+              "\n",
+              "[22695 rows x 3 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "\n",
+              "  <div id=\"df-44969bfc-7ac3-4c7e-9a3b-3b1f57baf9cb\">\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>temp</th>\n",
+              "      <th>delta_temp</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>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>1</th>\n",
+              "      <td>02-12-2013 21:20</td>\n",
+              "      <td>74.935882</td>\n",
+              "      <td>0.968560</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>2</th>\n",
+              "      <td>02-12-2013 21:25</td>\n",
+              "      <td>76.124162</td>\n",
+              "      <td>1.188280</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>3</th>\n",
+              "      <td>02-12-2013 21:30</td>\n",
+              "      <td>78.140707</td>\n",
+              "      <td>2.016546</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>4</th>\n",
+              "      <td>02-12-2013 21:35</td>\n",
+              "      <td>79.329836</td>\n",
+              "      <td>1.189128</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>NaN</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22691</th>\n",
+              "      <td>19-02-2014 15:10</td>\n",
+              "      <td>97.804168</td>\n",
+              "      <td>-0.381246</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22692</th>\n",
+              "      <td>19-02-2014 15:15</td>\n",
+              "      <td>97.135468</td>\n",
+              "      <td>-0.668700</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22693</th>\n",
+              "      <td>19-02-2014 15:20</td>\n",
+              "      <td>98.056852</td>\n",
+              "      <td>0.921384</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>22694</th>\n",
+              "      <td>19-02-2014 15:25</td>\n",
+              "      <td>96.903861</td>\n",
+              "      <td>-1.152991</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>22695 rows × 3 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-44969bfc-7ac3-4c7e-9a3b-3b1f57baf9cb')\"\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 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
+              "  </svg>\n",
+              "      </button>\n",
+              "\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-6c2856cf-8a17-4791-9689-ad10b0255286\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-6c2856cf-8a17-4791-9689-ad10b0255286')\"\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",
+              "    </div>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    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-quickchart: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",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart: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",
+              "      async function quickchart(key) {\n",
+              "        const containerElement = document.querySelector('#' + key);\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      }\n",
+              "    </script>\n",
+              "\n",
+              "      <script>\n",
+              "\n",
+              "function displayQuickchartButton(domScope) {\n",
+              "  let quickchartButtonEl =\n",
+              "    domScope.querySelector('#df-6c2856cf-8a17-4791-9689-ad10b0255286 button.colab-df-quickchart');\n",
+              "  quickchartButtonEl.style.display =\n",
+              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "}\n",
+              "\n",
+              "        displayQuickchartButton(document);\n",
+              "      </script>\n",
+              "      <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      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",
+              "    [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-44969bfc-7ac3-4c7e-9a3b-3b1f57baf9cb 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-44969bfc-7ac3-4c7e-9a3b-3b1f57baf9cb');\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": 125
+        }
+      ],
+      "source": [
+        "df1['delta_temp']=df1['temp']-df1['temp'].shift(1)\n",
+        "df1"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 70,
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 455
+        },
+        "id": "yZwxYebfIL-R",
+        "outputId": "9b6ffd20-dd1a-4a14-f2bd-967a27a6837c"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "plt.scatter(df1['timestamp'],df1['temp'],color='red')\n",
+        "# plt.scatter(df1['timestamp'],df1['delta_temp'],color='blue')\n",
+        "plt.xlabel('Time')\n",
+        "plt.ylabel('Temperature')\n",
+        "cbar = plt.colorbar()\n",
+        "cbar.set_label('Temperature Color')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "execution_count": 73,
+      "metadata": {
+        "id": "2UcxrGd2IbW7",
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 455
+        },
+        "outputId": "2f67407d-0892-4f5b-d77b-22ad694e65a2"
+      },
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 640x480 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ],
+      "source": [
+        "set_colour=['blue' if temp<70.0 else 'red' for temp in df1['temp']]\n",
+        "plt.scatter(df1['timestamp'],df1['temp'],color=set_colour)\n",
+        "plt.xlabel('Time')\n",
+        "plt.ylabel('Temperature')\n",
+        "cbar = plt.colorbar()\n",
+        "cbar.set_label('Temperature Color')\n",
+        "plt.show()"
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "# filling values using univariate imputation\n",
+        "df1['ffill'] = df1['temp'].fillna(method='ffill')\n",
+        "df1['bfill'] = df1['temp'].fillna(method='bfill')\n",
+        "df1['mean'] = df1['temp'].fillna(df1['temp'].mean())"
+      ],
+      "metadata": {
+        "id": "QxOw5YC6O1_Y"
+      },
+      "execution_count": 126,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "from sklearn.impute import SimpleImputer\n",
+        "strategy = [\n",
+        "    ('Mean Strategy', 'mean'),\n",
+        "    ('Median Strategy', 'median'),\n",
+        "    ('Most Frequent Strategy', 'most_frequent')]\n",
+        "temp_vals = df1['temp'].values.reshape(-1,1)\n",
+        "for strategy_name, method in strategy:\n",
+        "    df1[strategy_name] = (\n",
+        "        SimpleImputer(strategy=method).fit_transform(temp_vals))"
+      ],
+      "metadata": {
+        "id": "AyWpdZAhRe7W"
+      },
+      "execution_count": 127,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "interpolation_methods = [\n",
+        "    'linear',\n",
+        "    'quadratic',\n",
+        "    'nearest',\n",
+        "    'cubic'\n",
+        "]\n",
+        "import numpy as np\n",
+        "for methods in interpolation_methods:\n",
+        "    df1[methods] = df1['temp'].interpolate(method=methods)"
+      ],
+      "metadata": {
+        "id": "M8KhjoDSSJuM"
+      },
+      "execution_count": 128,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 869
+        },
+        "id": "AtiAGi6AUQFa",
+        "outputId": "4feaaf14-37fb-4e0c-b62d-3775c9fd222b"
+      },
+      "execution_count": 129,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "              timestamp       temp  delta_temp      ffill      bfill  \\\n",
+              "0      02-12-2013 21:15  73.967322         NaN  73.967322  73.967322   \n",
+              "1      02-12-2013 21:20  74.935882    0.968560  74.935882  74.935882   \n",
+              "2      02-12-2013 21:25  76.124162    1.188280  76.124162  76.124162   \n",
+              "3      02-12-2013 21:30  78.140707    2.016546  78.140707  78.140707   \n",
+              "4      02-12-2013 21:35  79.329836    1.189128  79.329836  79.329836   \n",
+              "...                 ...        ...         ...        ...        ...   \n",
+              "22690  19-02-2014 15:05  98.185415         NaN  98.185415  98.185415   \n",
+              "22691  19-02-2014 15:10  97.804168   -0.381246  97.804168  97.804168   \n",
+              "22692  19-02-2014 15:15  97.135468   -0.668700  97.135468  97.135468   \n",
+              "22693  19-02-2014 15:20  98.056852    0.921384  98.056852  98.056852   \n",
+              "22694  19-02-2014 15:25  96.903861   -1.152991  96.903861  96.903861   \n",
+              "\n",
+              "            mean  Mean Strategy  Median Strategy  Most Frequent Strategy  \\\n",
+              "0      73.967322      73.967322        73.967322               73.967322   \n",
+              "1      74.935882      74.935882        74.935882               74.935882   \n",
+              "2      76.124162      76.124162        76.124162               76.124162   \n",
+              "3      78.140707      78.140707        78.140707               78.140707   \n",
+              "4      79.329836      79.329836        79.329836               79.329836   \n",
+              "...          ...            ...              ...                     ...   \n",
+              "22690  98.185415      98.185415        98.185415               98.185415   \n",
+              "22691  97.804168      97.804168        97.804168               97.804168   \n",
+              "22692  97.135468      97.135468        97.135468               97.135468   \n",
+              "22693  98.056852      98.056852        98.056852               98.056852   \n",
+              "22694  96.903861      96.903861        96.903861               96.903861   \n",
+              "\n",
+              "          linear  quadratic    nearest      cubic  \n",
+              "0      73.967322  73.967322  73.967322  73.967322  \n",
+              "1      74.935882  74.935882  74.935882  74.935882  \n",
+              "2      76.124162  76.124162  76.124162  76.124162  \n",
+              "3      78.140707  78.140707  78.140707  78.140707  \n",
+              "4      79.329836  79.329836  79.329836  79.329836  \n",
+              "...          ...        ...        ...        ...  \n",
+              "22690  98.185415  98.185415  98.185415  98.185415  \n",
+              "22691  97.804168  97.804168  97.804168  97.804168  \n",
+              "22692  97.135468  97.135468  97.135468  97.135468  \n",
+              "22693  98.056852  98.056852  98.056852  98.056852  \n",
+              "22694  96.903861  96.903861  96.903861  96.903861  \n",
+              "\n",
+              "[22695 rows x 13 columns]"
+            ],
+            "text/html": [
+              "\n",
+              "\n",
+              "  <div id=\"df-eb74bb3d-e985-49e0-bb2b-df6a4fe5c9be\">\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>temp</th>\n",
+              "      <th>delta_temp</th>\n",
+              "      <th>ffill</th>\n",
+              "      <th>bfill</th>\n",
+              "      <th>mean</th>\n",
+              "      <th>Mean Strategy</th>\n",
+              "      <th>Median Strategy</th>\n",
+              "      <th>Most Frequent Strategy</th>\n",
+              "      <th>linear</th>\n",
+              "      <th>quadratic</th>\n",
+              "      <th>nearest</th>\n",
+              "      <th>cubic</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>NaN</td>\n",
+              "      <td>73.967322</td>\n",
+              "      <td>73.967322</td>\n",
+              "      <td>73.967322</td>\n",
+              "      <td>73.967322</td>\n",
+              "      <td>73.967322</td>\n",
+              "      <td>73.967322</td>\n",
+              "      <td>73.967322</td>\n",
+              "      <td>73.967322</td>\n",
+              "      <td>73.967322</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",
+              "      <td>0.968560</td>\n",
+              "      <td>74.935882</td>\n",
+              "      <td>74.935882</td>\n",
+              "      <td>74.935882</td>\n",
+              "      <td>74.935882</td>\n",
+              "      <td>74.935882</td>\n",
+              "      <td>74.935882</td>\n",
+              "      <td>74.935882</td>\n",
+              "      <td>74.935882</td>\n",
+              "      <td>74.935882</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",
+              "      <td>1.188280</td>\n",
+              "      <td>76.124162</td>\n",
+              "      <td>76.124162</td>\n",
+              "      <td>76.124162</td>\n",
+              "      <td>76.124162</td>\n",
+              "      <td>76.124162</td>\n",
+              "      <td>76.124162</td>\n",
+              "      <td>76.124162</td>\n",
+              "      <td>76.124162</td>\n",
+              "      <td>76.124162</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",
+              "      <td>2.016546</td>\n",
+              "      <td>78.140707</td>\n",
+              "      <td>78.140707</td>\n",
+              "      <td>78.140707</td>\n",
+              "      <td>78.140707</td>\n",
+              "      <td>78.140707</td>\n",
+              "      <td>78.140707</td>\n",
+              "      <td>78.140707</td>\n",
+              "      <td>78.140707</td>\n",
+              "      <td>78.140707</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",
+              "      <td>1.189128</td>\n",
+              "      <td>79.329836</td>\n",
+              "      <td>79.329836</td>\n",
+              "      <td>79.329836</td>\n",
+              "      <td>79.329836</td>\n",
+              "      <td>79.329836</td>\n",
+              "      <td>79.329836</td>\n",
+              "      <td>79.329836</td>\n",
+              "      <td>79.329836</td>\n",
+              "      <td>79.329836</td>\n",
+              "      <td>79.329836</td>\n",
+              "    </tr>\n",
+              "    <tr>\n",
+              "      <th>...</th>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\n",
+              "      <td>...</td>\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>NaN</td>\n",
+              "      <td>98.185415</td>\n",
+              "      <td>98.185415</td>\n",
+              "      <td>98.185415</td>\n",
+              "      <td>98.185415</td>\n",
+              "      <td>98.185415</td>\n",
+              "      <td>98.185415</td>\n",
+              "      <td>98.185415</td>\n",
+              "      <td>98.185415</td>\n",
+              "      <td>98.185415</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",
+              "      <td>-0.381246</td>\n",
+              "      <td>97.804168</td>\n",
+              "      <td>97.804168</td>\n",
+              "      <td>97.804168</td>\n",
+              "      <td>97.804168</td>\n",
+              "      <td>97.804168</td>\n",
+              "      <td>97.804168</td>\n",
+              "      <td>97.804168</td>\n",
+              "      <td>97.804168</td>\n",
+              "      <td>97.804168</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",
+              "      <td>-0.668700</td>\n",
+              "      <td>97.135468</td>\n",
+              "      <td>97.135468</td>\n",
+              "      <td>97.135468</td>\n",
+              "      <td>97.135468</td>\n",
+              "      <td>97.135468</td>\n",
+              "      <td>97.135468</td>\n",
+              "      <td>97.135468</td>\n",
+              "      <td>97.135468</td>\n",
+              "      <td>97.135468</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",
+              "      <td>0.921384</td>\n",
+              "      <td>98.056852</td>\n",
+              "      <td>98.056852</td>\n",
+              "      <td>98.056852</td>\n",
+              "      <td>98.056852</td>\n",
+              "      <td>98.056852</td>\n",
+              "      <td>98.056852</td>\n",
+              "      <td>98.056852</td>\n",
+              "      <td>98.056852</td>\n",
+              "      <td>98.056852</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",
+              "      <td>-1.152991</td>\n",
+              "      <td>96.903861</td>\n",
+              "      <td>96.903861</td>\n",
+              "      <td>96.903861</td>\n",
+              "      <td>96.903861</td>\n",
+              "      <td>96.903861</td>\n",
+              "      <td>96.903861</td>\n",
+              "      <td>96.903861</td>\n",
+              "      <td>96.903861</td>\n",
+              "      <td>96.903861</td>\n",
+              "      <td>96.903861</td>\n",
+              "    </tr>\n",
+              "  </tbody>\n",
+              "</table>\n",
+              "<p>22695 rows × 13 columns</p>\n",
+              "</div>\n",
+              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-eb74bb3d-e985-49e0-bb2b-df6a4fe5c9be')\"\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",
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+              "  </svg>\n",
+              "      </button>\n",
+              "\n",
+              "\n",
+              "\n",
+              "    <div id=\"df-5681f831-f4fb-4056-957b-957145c466a6\">\n",
+              "      <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-5681f831-f4fb-4056-957b-957145c466a6')\"\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",
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+              "    </g>\n",
+              "</svg>\n",
+              "      </button>\n",
+              "    </div>\n",
+              "\n",
+              "<style>\n",
+              "  .colab-df-quickchart {\n",
+              "    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-quickchart: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",
+              "  [theme=dark] .colab-df-quickchart {\n",
+              "    background-color: #3B4455;\n",
+              "    fill: #D2E3FC;\n",
+              "  }\n",
+              "\n",
+              "  [theme=dark] .colab-df-quickchart: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",
+              "      async function quickchart(key) {\n",
+              "        const containerElement = document.querySelector('#' + key);\n",
+              "        const charts = await google.colab.kernel.invokeFunction(\n",
+              "            'suggestCharts', [key], {});\n",
+              "      }\n",
+              "    </script>\n",
+              "\n",
+              "      <script>\n",
+              "\n",
+              "function displayQuickchartButton(domScope) {\n",
+              "  let quickchartButtonEl =\n",
+              "    domScope.querySelector('#df-5681f831-f4fb-4056-957b-957145c466a6 button.colab-df-quickchart');\n",
+              "  quickchartButtonEl.style.display =\n",
+              "    google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
+              "}\n",
+              "\n",
+              "        displayQuickchartButton(document);\n",
+              "      </script>\n",
+              "      <style>\n",
+              "    .colab-df-container {\n",
+              "      display:flex;\n",
+              "      flex-wrap:wrap;\n",
+              "      gap: 12px;\n",
+              "    }\n",
+              "\n",
+              "    .colab-df-convert {\n",
+              "      background-color: #E8F0FE;\n",
+              "      border: none;\n",
+              "      border-radius: 50%;\n",
+              "      cursor: pointer;\n",
+              "      display: none;\n",
+              "      fill: #1967D2;\n",
+              "      height: 32px;\n",
+              "      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",
+              "    [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-eb74bb3d-e985-49e0-bb2b-df6a4fe5c9be 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-eb74bb3d-e985-49e0-bb2b-df6a4fe5c9be');\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": 129
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "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",
+        "    min_score=10000000000000000\n",
+        "    method=\" \"\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",
+        "        if rmse<min_score:\n",
+        "          min_score=rmse\n",
+        "          method=comp_col\n",
+        "    print(f'Minimum RMSE for {method} which is {min_score}')\n",
+        "    return scores"
+      ],
+      "metadata": {
+        "id": "deS-clqGNyeB"
+      },
+      "execution_count": 130,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df2=df2.rename(columns={'value': 'temp'})\n",
+        "rmse_scores=rmse_score(df2,df1,'temp')\n",
+        "rmse_scores"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "8t6ncKc4SgFO",
+        "outputId": "e5e9c00c-f4c4-4e5b-c7e0-666a7c50f3d0"
+      },
+      "execution_count": 131,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
+            "RMSE for delta_temp: 87.02576617592636\n",
+            "RMSE for ffill: 2.4939956524274454\n",
+            "RMSE for bfill: 5.873128567455812\n",
+            "RMSE for mean: 3.961536496226712\n",
+            "RMSE for Mean Strategy: 3.961536496226712\n",
+            "RMSE for Median Strategy: 4.071585793081295\n",
+            "RMSE for Most Frequent Strategy: 20.176855010139285\n",
+            "RMSE for linear: 4.043203597694257\n",
+            "RMSE for quadratic: 11.547623967933907\n",
+            "RMSE for nearest: 4.538509533797259\n",
+            "RMSE for cubic: 12.63060501654674\n",
+            "Minimum RMSE for ffill which is 2.4939956524274454\n"
+          ]
+        },
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "[87.02576617592636,\n",
+              " 2.4939956524274454,\n",
+              " 5.873128567455812,\n",
+              " 3.961536496226712,\n",
+              " 3.961536496226712,\n",
+              " 4.071585793081295,\n",
+              " 20.176855010139285,\n",
+              " 4.043203597694257,\n",
+              " 11.547623967933907,\n",
+              " 4.538509533797259,\n",
+              " 12.63060501654674]"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 131
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import seaborn as sns\n",
+        "sns.boxplot(df1['ffill'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "-fUviNz_T9uN",
+        "outputId": "9c9a2065-a716-4eb7-a991-7a5e2e827cb7"
+      },
+      "execution_count": 132,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 132
+        },
+        {
+          "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": [
+        "sns.boxenplot(df1['ffill'])"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 447
+        },
+        "id": "QN0sYWU7W3C-",
+        "outputId": "79d3d248-1d36-40d4-f4a7-aca8d89cbb4c"
+      },
+      "execution_count": 133,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "<Axes: >"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 133
+        },
+        {
+          "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": [
+        "df1['timestamp'] = pd.to_datetime(df1['timestamp'])\n",
+        "df1.set_index('timestamp',inplace=True)\n",
+        "fig, ax = plt.subplots(1, 2, figsize=(12, 7))\n",
+        "ax[0].plot(df1['ffill'], df1.index)\n",
+        "pd.plotting.lag_plot(df1['ffill'], lag=1,ax=ax[1])\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 621
+        },
+        "id": "3q7b9vTFW7_r",
+        "outputId": "c22d37c2-63ef-4fa9-e90f-2162d40969ca"
+      },
+      "execution_count": 134,
+      "outputs": [
+        {
+          "output_type": "display_data",
+          "data": {
+            "text/plain": [
+              "<Figure size 1200x700 with 2 Axes>"
+            ],
+            "image/png": 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\n"
+          },
+          "metadata": {}
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "outliers=df1['ffill']<50\n",
+        "f_outlier=[]\n",
+        "for _ in range(len(df1['ffill'])):\n",
+        "  if outliers[_]:\n",
+        "    f_outlier.append(df1['ffill'][_])\n",
+        "print(f_outlier)"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "3XpOAEIcXHOV",
+        "outputId": "c66fc6d8-dd7c-4a04-8747-018f0bfaeabb"
+      },
+      "execution_count": 135,
+      "outputs": [
+        {
+          "output_type": "stream",
+          "name": "stdout",
+          "text": [
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+          ]
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "import scipy.stats as stats"
+      ],
+      "metadata": {
+        "id": "xW5L1phnXsyW"
+      },
+      "execution_count": 136,
+      "outputs": []
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "df1['z_scores'] = stats.zscore(df1['ffill'])\n",
+        "df1['z_scores'].head()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/"
+        },
+        "id": "YO1w3YX_YlkO",
+        "outputId": "6fb6dfb9-b3a3-4b3d-b671-e4d3001d969a"
+      },
+      "execution_count": 137,
+      "outputs": [
+        {
+          "output_type": "execute_result",
+          "data": {
+            "text/plain": [
+              "timestamp\n",
+              "2013-02-12 21:15:00   -0.910091\n",
+              "2013-02-12 21:20:00   -0.837610\n",
+              "2013-02-12 21:25:00   -0.748688\n",
+              "2013-02-12 21:30:00   -0.597784\n",
+              "2013-02-12 21:35:00   -0.508798\n",
+              "Name: z_scores, dtype: float64"
+            ]
+          },
+          "metadata": {},
+          "execution_count": 137
+        }
+      ]
+    },
+    {
+      "cell_type": "code",
+      "source": [
+        "plt.plot(df1.index, df1['z_scores'])\n",
+        "plt.xlabel('Date_Time_index')\n",
+        "plt.ylabel('Z-Score')\n",
+        "plt.title('Z-Score of Temperature')\n",
+        "plt.show()"
+      ],
+      "metadata": {
+        "colab": {
+          "base_uri": "https://localhost:8080/",
+          "height": 472
+        },
+        "id": "6G6QhKbAYsyW",
+        "outputId": "5b7ade4e-9132-48b0-d33f-3d53eb46a9ca"
+      },
+      "execution_count": 138,
+      "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": [],
+      "metadata": {
+        "id": "mhrqgH2wZ0Jc"
+      },
+      "execution_count": 116,
+      "outputs": []
+    }
+  ],
+  "metadata": {
+    "colab": {
+      "provenance": []
+    },
+    "kernelspec": {
+      "display_name": "Python 3",
+      "name": "python3"
+    },
+    "language_info": {
+      "name": "python"
+    }
+  },
+  "nbformat": 4,
+  "nbformat_minor": 0
+}
\ No newline at end of file