02_Basic_Preprocessing.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "8ee1d57d",
   "metadata": {},
   "source": [
    "# <center>Preprocessing Techniques in Python: A Basic Guide</center>"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fc841e2a",
   "metadata": {},
   "source": [
    "## Introduction to Data Preprocessing\n",
    "\n",
    "Data preprocessing is a crucial step in the data analysis pipeline, as it involves cleaning, transforming, and organizing raw data to make it suitable for further analysis. This process helps improve the quality and reliability of data, enabling accurate insights and predictions. In this IPython Notebook (ipynb), we will explore some fundamental preprocessing techniques using Python and popular libraries such as pandas. \n",
    "\n",
    "The notebook will cover various tasks, including importing a dataset, examining its structure and contents, manipulating data using pandas functions, and performing basic statistical analysis. By following this guide, you will gain a solid understanding of the essential preprocessing steps and how to apply them to your own datasets. \n",
    "\n",
    "Now let's dive into the specific questions and tasks covered in this notebook. "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56bed895",
   "metadata": {},
   "source": [
    "## Questions and Descriptions"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bcf61307",
   "metadata": {},
   "source": [
    "### Import the dataset: \n",
    "\n",
    "- To begin with, we will import the dataset into our notebook. This involves reading the data from a file (e.g., CSV, Excel) or fetching it from an external source (e.g,. API). We will use appropriate pandas functions or other libraries to accomplish this task. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "85b01eee",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79260485",
   "metadata": {},
   "outputs": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>competitorname</th>\n",
       "      <th>chocolate</th>\n",
       "      <th>fruity</th>\n",
       "      <th>caramel</th>\n",
       "      <th>peanutyalmondy</th>\n",
       "      <th>nougat</th>\n",
       "      <th>crispedricewafer</th>\n",
       "      <th>hard</th>\n",
       "      <th>bar</th>\n",
       "      <th>pluribus</th>\n",
       "      <th>sugarpercent</th>\n",
       "      <th>pricepercent</th>\n",
       "      <th>winpercent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100 Grand</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.860</td>\n",
       "      <td>66.971725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3 Musketeers</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.511</td>\n",
       "      <td>67.602936</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Air Heads</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.511</td>\n",
       "      <td>52.341465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Almond Joy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.767</td>\n",
       "      <td>50.347546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Baby Ruth</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.767</td>\n",
       "      <td>56.914547</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>78</td>\n",
       "      <td>Twizzlers</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.116</td>\n",
       "      <td>45.466282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>79</td>\n",
       "      <td>Warheads</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.116</td>\n",
       "      <td>39.011898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>80</td>\n",
       "      <td>Welch's Fruit Snacks</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.313</td>\n",
       "      <td>44.375519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>81</td>\n",
       "      <td>Werther's Original Caramel</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.267</td>\n",
       "      <td>41.904308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>82</td>\n",
       "      <td>Whoppers</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.848</td>\n",
       "      <td>49.524113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id              competitorname chocolate fruity caramel peanutyalmondy  \\\n",
       "0    0                   100 Grand       Yes     No     Yes             No   \n",
       "1    1                3 Musketeers       Yes     No      No             No   \n",
       "2    2                   Air Heads        No    Yes      No             No   \n",
       "3    3                  Almond Joy       Yes     No      No            Yes   \n",
       "4    4                   Baby Ruth       Yes     No     Yes            Yes   \n",
       "..  ..                         ...       ...    ...     ...            ...   \n",
       "78  78                   Twizzlers        No    Yes      No             No   \n",
       "79  79                    Warheads        No    Yes      No             No   \n",
       "80  80        Welch's Fruit Snacks        No    Yes      No             No   \n",
       "81  81  Werther's Original Caramel        No     No     Yes             No   \n",
       "82  82                    Whoppers       Yes     No      No             No   \n",
       "\n",
       "   nougat crispedricewafer hard  bar pluribus  sugarpercent  pricepercent  \\\n",
       "0      No              Yes   No  Yes       No         0.732         0.860   \n",
       "1     Yes               No   No  Yes       No         0.604         0.511   \n",
       "2      No               No   No   No       No         0.906         0.511   \n",
       "3      No               No   No  Yes       No         0.465         0.767   \n",
       "4     Yes               No   No  Yes       No         0.604         0.767   \n",
       "..    ...              ...  ...  ...      ...           ...           ...   \n",
       "78     No               No   No   No       No         0.220         0.116   \n",
       "79     No               No  Yes   No       No         0.093         0.116   \n",
       "80     No               No   No   No      Yes         0.313         0.313   \n",
       "81     No               No  Yes   No       No         0.186         0.267   \n",
       "82     No              Yes   No   No      Yes         0.872         0.848   \n",
       "\n",
       "    winpercent  \n",
       "0    66.971725  \n",
       "1    67.602936  \n",
       "2    52.341465  \n",
       "3    50.347546  \n",
       "4    56.914547  \n",
       "..         ...  \n",
       "78   45.466282  \n",
       "79   39.011898  \n",
       "80   44.375519  \n",
       "81   41.904308  \n",
       "82   49.524113  \n",
       "\n",
       "[83 rows x 14 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Import the Dataset\n",
    "data = pd.read_csv('candy.csv')\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "72ea302e",
   "metadata": {},
   "source": [
    "### Display the head of the dataset\n",
    "\n",
    "- After importing the dataset, we will display the first few rows to get a glimpse of its structure and contents. This step helps us to understand the variables and their initial values, providing an overview of the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "01aeaa5a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>competitorname</th>\n",
       "      <th>chocolate</th>\n",
       "      <th>fruity</th>\n",
       "      <th>caramel</th>\n",
       "      <th>peanutyalmondy</th>\n",
       "      <th>nougat</th>\n",
       "      <th>crispedricewafer</th>\n",
       "      <th>hard</th>\n",
       "      <th>bar</th>\n",
       "      <th>pluribus</th>\n",
       "      <th>sugarpercent</th>\n",
       "      <th>pricepercent</th>\n",
       "      <th>winpercent</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100 Grand</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.860</td>\n",
       "      <td>66.971725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3 Musketeers</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
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       "      <td>0.511</td>\n",
       "      <td>67.602936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Air Heads</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.511</td>\n",
       "      <td>52.341465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Almond Joy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.767</td>\n",
       "      <td>50.347546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Baby Ruth</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.767</td>\n",
       "      <td>56.914547</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id competitorname chocolate fruity caramel peanutyalmondy nougat  \\\n",
       "0   0      100 Grand       Yes     No     Yes             No     No   \n",
       "1   1   3 Musketeers       Yes     No      No             No    Yes   \n",
       "2   2      Air Heads        No    Yes      No             No     No   \n",
       "3   3     Almond Joy       Yes     No      No            Yes     No   \n",
       "4   4      Baby Ruth       Yes     No     Yes            Yes    Yes   \n",
       "\n",
       "  crispedricewafer hard  bar pluribus  sugarpercent  pricepercent  winpercent  \n",
       "0              Yes   No  Yes       No         0.732         0.860   66.971725  \n",
       "1               No   No  Yes       No         0.604         0.511   67.602936  \n",
       "2               No   No   No       No         0.906         0.511   52.341465  \n",
       "3               No   No  Yes       No         0.465         0.767   50.347546  \n",
       "4               No   No  Yes       No         0.604         0.767   56.914547  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the head of the dataset\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "614fdb2f",
   "metadata": {},
   "source": [
    "### Display the tail of the dataset\n",
    "\n",
    "- Similar to displaying the head, we will also display the last few rows of the dataset. This allows us to check if there are any patterns or trends at the end of the data, which might be useful for analysis or preprocessing decisions. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "952876d3",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>competitorname</th>\n",
       "      <th>chocolate</th>\n",
       "      <th>fruity</th>\n",
       "      <th>caramel</th>\n",
       "      <th>peanutyalmondy</th>\n",
       "      <th>nougat</th>\n",
       "      <th>crispedricewafer</th>\n",
       "      <th>hard</th>\n",
       "      <th>bar</th>\n",
       "      <th>pluribus</th>\n",
       "      <th>sugarpercent</th>\n",
       "      <th>pricepercent</th>\n",
       "      <th>winpercent</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>78</td>\n",
       "      <td>Twizzlers</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.116</td>\n",
       "      <td>45.466282</td>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>79</td>\n",
       "      <td>Warheads</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.116</td>\n",
       "      <td>39.011898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>80</td>\n",
       "      <td>Welch's Fruit Snacks</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.313</td>\n",
       "      <td>44.375519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>81</td>\n",
       "      <td>Werther's Original Caramel</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.267</td>\n",
       "      <td>41.904308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>82</td>\n",
       "      <td>Whoppers</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.848</td>\n",
       "      <td>49.524113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    id              competitorname chocolate fruity caramel peanutyalmondy  \\\n",
       "78  78                   Twizzlers        No    Yes      No             No   \n",
       "79  79                    Warheads        No    Yes      No             No   \n",
       "80  80        Welch's Fruit Snacks        No    Yes      No             No   \n",
       "81  81  Werther's Original Caramel        No     No     Yes             No   \n",
       "82  82                    Whoppers       Yes     No      No             No   \n",
       "\n",
       "   nougat crispedricewafer hard bar pluribus  sugarpercent  pricepercent  \\\n",
       "78     No               No   No  No       No         0.220         0.116   \n",
       "79     No               No  Yes  No       No         0.093         0.116   \n",
       "80     No               No   No  No      Yes         0.313         0.313   \n",
       "81     No               No  Yes  No       No         0.186         0.267   \n",
       "82     No              Yes   No  No      Yes         0.872         0.848   \n",
       "\n",
       "    winpercent  \n",
       "78   45.466282  \n",
       "79   39.011898  \n",
       "80   44.375519  \n",
       "81   41.904308  \n",
       "82   49.524113  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the Tail of the Dataset\n",
    "data.tail()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1be7774d",
   "metadata": {},
   "source": [
    "### Display the Column names\n",
    "\n",
    "- In this task, we will extract and diisplay the column names of the dataset. By knowing the column names, we can refer to specific variables during data manipulation and analysis. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "9ad7d638",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "id\n",
      "competitorname\n",
      "chocolate\n",
      "fruity\n",
      "caramel\n",
      "peanutyalmondy\n",
      "nougat\n",
      "crispedricewafer\n",
      "hard\n",
      "bar\n",
      "pluribus\n",
      "sugarpercent\n",
      "pricepercent\n",
      "winpercent\n"
     ]
    }
   ],
   "source": [
    "# Display the column names\n",
    "for col in data.columns:\n",
    "    print(col)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00e8ba8c",
   "metadata": {},
   "source": [
    "### Display the datatypes of the columns\n",
    "\n",
    "- Understanding the datatypes of the columns is essential to perform accurate calculations, transformations and handle missing values effectively. We will retrieve and display the datatypes of each column in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "dc9d1000",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                    int64\n",
       "competitorname       object\n",
       "chocolate            object\n",
       "fruity               object\n",
       "caramel              object\n",
       "peanutyalmondy       object\n",
       "nougat               object\n",
       "crispedricewafer     object\n",
       "hard                 object\n",
       "bar                  object\n",
       "pluribus             object\n",
       "sugarpercent        float64\n",
       "pricepercent        float64\n",
       "winpercent          float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the Datatypes of the columns\n",
    "# datatypes = data.dtypes\n",
    "# print(datatypes)\n",
    "data.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "346d9027",
   "metadata": {},
   "source": [
    "### Display the statistical Information about the columns. \n",
    "\n",
    "- Statistical information provides insights into te distribution, central tendency, and spread of the data. We will compute and display various statistical measures such as mean, standard deviation, minimum, maximum, quartiles, ets., for each numerical column in the dataset. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1b3b6b32",
   "metadata": {
    "scrolled": false
   },
   "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>id</th>\n",
       "      <th>sugarpercent</th>\n",
       "      <th>pricepercent</th>\n",
       "      <th>winpercent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.000000</td>\n",
       "      <td>83.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.489916</td>\n",
       "      <td>0.472627</td>\n",
       "      <td>50.584908</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>24.103942</td>\n",
       "      <td>0.276498</td>\n",
       "      <td>0.286503</td>\n",
       "      <td>14.748880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.034000</td>\n",
       "      <td>0.011000</td>\n",
       "      <td>22.445341</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>20.500000</td>\n",
       "      <td>0.267000</td>\n",
       "      <td>0.261000</td>\n",
       "      <td>39.163280</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>41.000000</td>\n",
       "      <td>0.465000</td>\n",
       "      <td>0.465000</td>\n",
       "      <td>48.982651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>61.500000</td>\n",
       "      <td>0.732000</td>\n",
       "      <td>0.703000</td>\n",
       "      <td>60.332349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>82.000000</td>\n",
       "      <td>0.988000</td>\n",
       "      <td>0.976000</td>\n",
       "      <td>84.180290</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              id  sugarpercent  pricepercent  winpercent\n",
       "count  83.000000     83.000000     83.000000   83.000000\n",
       "mean   41.000000      0.489916      0.472627   50.584908\n",
       "std    24.103942      0.276498      0.286503   14.748880\n",
       "min     0.000000      0.034000      0.011000   22.445341\n",
       "25%    20.500000      0.267000      0.261000   39.163280\n",
       "50%    41.000000      0.465000      0.465000   48.982651\n",
       "75%    61.500000      0.732000      0.703000   60.332349\n",
       "max    82.000000      0.988000      0.976000   84.180290"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display statistical iformation about suitable columns.\n",
    "data.describe()\n",
    "# data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "44cfe10c",
   "metadata": {},
   "source": [
    "### Uses of the .iloc function in columns. \n",
    "\n",
    "- The .loc function in pandas allows us to access and manipulate specific rows and columns based on their labels. We will explore the different ways touse the .loc function specifically for columns, which can be helpful for data extraction or transformation tasks. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e45afefb",
   "metadata": {
    "scrolled": true
   },
   "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>chocolate</th>\n",
       "      <th>caramel</th>\n",
       "      <th>fruity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   chocolate caramel fruity\n",
       "0        Yes     Yes     No\n",
       "1        Yes      No     No\n",
       "2         No      No    Yes\n",
       "3        Yes      No     No\n",
       "4        Yes     Yes     No\n",
       "..       ...     ...    ...\n",
       "78        No      No    Yes\n",
       "79        No      No    Yes\n",
       "80        No      No    Yes\n",
       "81        No     Yes     No\n",
       "82       Yes      No     No\n",
       "\n",
       "[83 rows x 3 columns]"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display all the rows of columns Chocolate, Caramel, Fruity\n",
    "data.loc[:,['chocolate', 'caramel', 'fruity']]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2040a9c7",
   "metadata": {},
   "source": [
    "### Display the total count of the column. \n",
    "\n",
    "- Counting the number of non-null values in a column is crucial for identifying missing or incomplete data. We will calculate and display the total count for each column, providing an overview of the data completeness."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "a87c1211",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "83"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display the total number of Competitors \n",
    "data.competitorname.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8a01a981",
   "metadata": {},
   "source": [
    "### Display by slicing the dataset using iloc and loc commands:\n",
    "\n",
    "- Slicing the dataset enables us to extract specific portions or subsets of the data based on row and column indices. We will demonstrate how to use the iloc and loc commands to perform slicing operations on the dataset, enabling flexible data extraction for further analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "db7fe097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0                      100 Grand\n",
       "1                   3 Musketeers\n",
       "2                      Air Heads\n",
       "3                     Almond Joy\n",
       "4                      Baby Ruth\n",
       "                 ...            \n",
       "78                     Twizzlers\n",
       "79                      Warheads\n",
       "80          Welch's Fruit Snacks\n",
       "81    Werther's Original Caramel\n",
       "82                      Whoppers\n",
       "Name: competitorname, Length: 83, dtype: object"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display by slicing the dataset using iloc and loc commands.\n",
    "data.loc[:, 'competitorname']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "66def24e",
   "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>id</th>\n",
       "      <th>competitorname</th>\n",
       "      <th>chocolate</th>\n",
       "      <th>fruity</th>\n",
       "      <th>caramel</th>\n",
       "      <th>peanutyalmondy</th>\n",
       "      <th>nougat</th>\n",
       "      <th>crispedricewafer</th>\n",
       "      <th>hard</th>\n",
       "      <th>bar</th>\n",
       "      <th>pluribus</th>\n",
       "      <th>sugarpercent</th>\n",
       "      <th>pricepercent</th>\n",
       "      <th>winpercent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0</td>\n",
       "      <td>100 Grand</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.732</td>\n",
       "      <td>0.860</td>\n",
       "      <td>66.971725</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>3 Musketeers</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.511</td>\n",
       "      <td>67.602936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>Air Heads</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.906</td>\n",
       "      <td>0.511</td>\n",
       "      <td>52.341465</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>Almond Joy</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.465</td>\n",
       "      <td>0.767</td>\n",
       "      <td>50.347546</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>Baby Ruth</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.604</td>\n",
       "      <td>0.767</td>\n",
       "      <td>56.914547</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>78</td>\n",
       "      <td>Twizzlers</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.220</td>\n",
       "      <td>0.116</td>\n",
       "      <td>45.466282</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>79</td>\n",
       "      <td>Warheads</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.116</td>\n",
       "      <td>39.011898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>80</td>\n",
       "      <td>Welch's Fruit Snacks</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.313</td>\n",
       "      <td>44.375519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>81</th>\n",
       "      <td>81</td>\n",
       "      <td>Werther's Original Caramel</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.186</td>\n",
       "      <td>0.267</td>\n",
       "      <td>41.904308</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>82</td>\n",
       "      <td>Whoppers</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.872</td>\n",
       "      <td>0.848</td>\n",
       "      <td>49.524113</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id              competitorname chocolate fruity caramel peanutyalmondy  \\\n",
       "0    0                   100 Grand       Yes     No     Yes             No   \n",
       "1    1                3 Musketeers       Yes     No      No             No   \n",
       "2    2                   Air Heads        No    Yes      No             No   \n",
       "3    3                  Almond Joy       Yes     No      No            Yes   \n",
       "4    4                   Baby Ruth       Yes     No     Yes            Yes   \n",
       "..  ..                         ...       ...    ...     ...            ...   \n",
       "78  78                   Twizzlers        No    Yes      No             No   \n",
       "79  79                    Warheads        No    Yes      No             No   \n",
       "80  80        Welch's Fruit Snacks        No    Yes      No             No   \n",
       "81  81  Werther's Original Caramel        No     No     Yes             No   \n",
       "82  82                    Whoppers       Yes     No      No             No   \n",
       "\n",
       "   nougat crispedricewafer hard  bar pluribus  sugarpercent  pricepercent  \\\n",
       "0      No              Yes   No  Yes       No         0.732         0.860   \n",
       "1     Yes               No   No  Yes       No         0.604         0.511   \n",
       "2      No               No   No   No       No         0.906         0.511   \n",
       "3      No               No   No  Yes       No         0.465         0.767   \n",
       "4     Yes               No   No  Yes       No         0.604         0.767   \n",
       "..    ...              ...  ...  ...      ...           ...           ...   \n",
       "78     No               No   No   No       No         0.220         0.116   \n",
       "79     No               No  Yes   No       No         0.093         0.116   \n",
       "80     No               No   No   No      Yes         0.313         0.313   \n",
       "81     No               No  Yes   No       No         0.186         0.267   \n",
       "82     No              Yes   No   No      Yes         0.872         0.848   \n",
       "\n",
       "    winpercent  \n",
       "0    66.971725  \n",
       "1    67.602936  \n",
       "2    52.341465  \n",
       "3    50.347546  \n",
       "4    56.914547  \n",
       "..         ...  \n",
       "78   45.466282  \n",
       "79   39.011898  \n",
       "80   44.375519  \n",
       "81   41.904308  \n",
       "82   49.524113  \n",
       "\n",
       "[83 rows x 14 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Check the dataset for any null value and fill the null value with 0.01\n",
    "data.fillna(value=0.01, inplace=True)\n",
    "data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "649961ec",
   "metadata": {},
   "source": [
    "### Display the mean for a particular column:\n",
    "\n",
    "- Calculating the mean value for a particular column can help us understand its central tendency and make informed decisions. We will compute and display the mean for a selected column in the dataset."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "ac5930e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50.58490762650603"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Find the mean winpercent\n",
    "data.winpercent.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "09070af4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "72    Tootsie Pop\n",
       "Name: competitorname, dtype: object"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display howmany competitors are both Chocolate and fruity\n",
    "dataf = data['competitorname']\n",
    "dataf.loc[(data.chocolate.isin(['Yes'])) & data.fruity.isin([\"Yes\"])]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3a07a8fd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Series([], Name: competitorname, dtype: object)"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display how many competitors are both hard and bar.\n",
    "dataf = data['competitorname']\n",
    "dataf.loc[(data.hard == 'Yes') & (data.bar == 'Yes')]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e9dd7570",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "id                                         50\n",
       "competitorname      Reese's Peanut Butter cup\n",
       "chocolate                                 Yes\n",
       "fruity                                     No\n",
       "caramel                                    No\n",
       "peanutyalmondy                            Yes\n",
       "nougat                                     No\n",
       "crispedricewafer                           No\n",
       "hard                                       No\n",
       "bar                                        No\n",
       "pluribus                                   No\n",
       "sugarpercent                             0.72\n",
       "pricepercent                            0.651\n",
       "winpercent                            84.1803\n",
       "Name: 50, dtype: object"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Display which competitor has the higher win percent. \n",
    "data.loc[(data['winpercent'].idxmax())]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d57df6ea",
   "metadata": {},
   "source": [
    "### Sort a given column:\n",
    "\n",
    "- Sorting a column allows us to arrange the data in ascending or descending order based on its values. We will demonstrate how to sort a given column and discuss its significance in data analysis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "11ec1b5b",
   "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>id</th>\n",
       "      <th>competitorname</th>\n",
       "      <th>chocolate</th>\n",
       "      <th>fruity</th>\n",
       "      <th>caramel</th>\n",
       "      <th>peanutyalmondy</th>\n",
       "      <th>nougat</th>\n",
       "      <th>crispedricewafer</th>\n",
       "      <th>hard</th>\n",
       "      <th>bar</th>\n",
       "      <th>pluribus</th>\n",
       "      <th>sugarpercent</th>\n",
       "      <th>pricepercent</th>\n",
       "      <th>winpercent</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>50</th>\n",
       "      <td>50</td>\n",
       "      <td>Reese's Peanut Butter cup</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.720</td>\n",
       "      <td>0.651</td>\n",
       "      <td>84.180290</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>49</td>\n",
       "      <td>Reese's Miniatures</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.034</td>\n",
       "      <td>0.279</td>\n",
       "      <td>81.866257</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>77</td>\n",
       "      <td>Twix</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.906</td>\n",
       "      <td>81.642914</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>Kit Kat</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.511</td>\n",
       "      <td>76.768600</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>62</th>\n",
       "      <td>62</td>\n",
       "      <td>Snickers</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>0.546</td>\n",
       "      <td>0.651</td>\n",
       "      <td>76.673782</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",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>Jawbusters</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.093</td>\n",
       "      <td>0.511</td>\n",
       "      <td>28.127439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>70</td>\n",
       "      <td>Super Bubble</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>0.162</td>\n",
       "      <td>0.116</td>\n",
       "      <td>27.303865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>Chiclets</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.046</td>\n",
       "      <td>0.325</td>\n",
       "      <td>24.524988</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>Boston Baked Beans</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.313</td>\n",
       "      <td>0.511</td>\n",
       "      <td>23.417824</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>42</td>\n",
       "      <td>Nik L Nip</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>No</td>\n",
       "      <td>Yes</td>\n",
       "      <td>0.197</td>\n",
       "      <td>0.976</td>\n",
       "      <td>22.445341</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>83 rows × 14 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    id             competitorname chocolate fruity caramel peanutyalmondy  \\\n",
       "50  50  Reese's Peanut Butter cup       Yes     No      No            Yes   \n",
       "49  49         Reese's Miniatures       Yes     No      No            Yes   \n",
       "77  77                       Twix       Yes     No     Yes             No   \n",
       "26  26                    Kit Kat       Yes     No      No             No   \n",
       "62  62                   Snickers       Yes     No     Yes            Yes   \n",
       "..  ..                        ...       ...    ...     ...            ...   \n",
       "24  24                 Jawbusters        No    Yes      No             No   \n",
       "70  70               Super Bubble        No    Yes      No             No   \n",
       "10  10                   Chiclets        No    Yes      No             No   \n",
       "5    5         Boston Baked Beans        No     No      No            Yes   \n",
       "42  42                  Nik L Nip        No    Yes      No             No   \n",
       "\n",
       "   nougat crispedricewafer hard  bar pluribus  sugarpercent  pricepercent  \\\n",
       "50     No               No   No   No       No         0.720         0.651   \n",
       "49     No               No   No   No       No         0.034         0.279   \n",
       "77     No              Yes   No  Yes       No         0.546         0.906   \n",
       "26     No              Yes   No  Yes       No         0.313         0.511   \n",
       "62    Yes               No   No  Yes       No         0.546         0.651   \n",
       "..    ...              ...  ...  ...      ...           ...           ...   \n",
       "24     No               No  Yes   No      Yes         0.093         0.511   \n",
       "70     No               No   No   No       No         0.162         0.116   \n",
       "10     No               No   No   No      Yes         0.046         0.325   \n",
       "5      No               No   No   No      Yes         0.313         0.511   \n",
       "42     No               No   No   No      Yes         0.197         0.976   \n",
       "\n",
       "    winpercent  \n",
       "50   84.180290  \n",
       "49   81.866257  \n",
       "77   81.642914  \n",
       "26   76.768600  \n",
       "62   76.673782  \n",
       "..         ...  \n",
       "24   28.127439  \n",
       "70   27.303865  \n",
       "10   24.524988  \n",
       "5    23.417824  \n",
       "42   22.445341  \n",
       "\n",
       "[83 rows x 14 columns]"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Sort the Competitors by winpercent\n",
    "data.sort_values(['winpercent'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e7856e52",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "88f445e4",
   "metadata": {},
   "source": [
    "I have given the sample code with outputs above. By covering these tasks in our notebook, you will have a solid foundation in basic preprocessing techniques. These technipues serve as the builing blocks for more advanced data preprocessing steps, such as handling missing values, encoding categorical variables, scaling features, and more. \n",
    "\n",
    "Throughout the notebook, we will use the powerful pandas library, which provides efficient data structures and functions for data manipulation and analysis. You will learn how to leverage pandas' functionalities to import datasets, explore data characteristics, perform data slicing, calculate statistics, and sort columns. \n",
    "\n",
    "Remember, data preprocessing is a crucial step in the data analysis workflow. By effectively cleaning and preparing your data, you can ensure that your subsequent analysis or machine learning models yield accurate and reliable results."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0a5a72f9",
   "metadata": {},
   "source": [
    "## Contact\n",
    "\n",
    "If you have any questions, suggestions, or feedback regarding this IPython Notebook or any other topic related to data preprocessing, please feel free to reach out.\n",
    "\n",
    "You can contact me at:\n",
    "\n",
    "- Name: [Ruban Gino Singh](https://rubangino.in/)\n",
    "\n",
    "- Email: [info@rubangino.in](https://mailto:info@rubangino.in/)   \n",
    "\n",
    "- GitHub: [Ruban2205](https://github.com/Ruban2205/)\n",
    "\n",
    "- LinkedIn: [ruban-gino-singh](https://www.linkedin.com/in/ruban-gino-singh/)\n",
    "\n",
    "I am always eager to connect with fellow data enthusiasts and assist in any way possible. Don't hesitate to get in touch if you need further clarification or assistance with the preprocessing techniques covered in this notebook.\n",
    "\n",
    "Happy preprocessing and data analysis!"
   ]
  }
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