pandas_01.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# How to Import Dataset into Jupyter Notebook using Pandas\n",
    "Written by: Faheem Siddiqi\\\n",
    "Date: 26-03-2024\\\n",
    "Email: faheemsiddiqi789@gmail.com"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 1\n",
    "# Importing liberaries\n",
    "\n",
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Step 2\n",
    "# Importing the Dataset\n",
    "\n",
    "df=pd.read_excel(\"pandas_01_day6.xlsx\") \n",
    "\n",
    "# write file name if file exist in same forlder otherwise give the path to that file."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>favourite_dish</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.8</td>\n",
       "      <td>101</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.2</td>\n",
       "      <td>92</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "      <td>Karahi Ghosht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.9</td>\n",
       "      <td>95</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.9</td>\n",
       "      <td>55</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>5.4</td>\n",
       "      <td>55</td>\n",
       "      <td>Pasta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>5.9</td>\n",
       "      <td>75</td>\n",
       "      <td>Karahi Ghosht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5.4</td>\n",
       "      <td>61</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.2</td>\n",
       "      <td>68</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>5.1</td>\n",
       "      <td>96</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   height weight favourite_dish\n",
       "0     5.8    101  Chicken Pulao\n",
       "1     6.2     92        Biryani\n",
       "2     5.9     62  Karahi Ghosht\n",
       "3     5.9     95  Chicken Pulao\n",
       "4     5.9     55  Chicken Pulao\n",
       "..    ...    ...            ...\n",
       "82    5.4     55          Pasta\n",
       "83    5.9     75  Karahi Ghosht\n",
       "84    5.4     61        Biryani\n",
       "85    6.2     68  Chicken Pulao\n",
       "86    5.1     96        Biryani\n",
       "\n",
       "[87 rows x 3 columns]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Step 3\n",
    "# Reading the Dataset\n",
    "\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
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       "      <th>favourite_dish</th>\n",
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       "      <th>1</th>\n",
       "      <td>6.2</td>\n",
       "      <td>92</td>\n",
       "      <td>Biryani</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "      <td>Karahi Ghosht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.9</td>\n",
       "      <td>95</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.9</td>\n",
       "      <td>55</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  height weight favourite_dish\n",
       "0    5.8    101  Chicken Pulao\n",
       "1    6.2     92        Biryani\n",
       "2    5.9     62  Karahi Ghosht\n",
       "3    5.9     95  Chicken Pulao\n",
       "4    5.9     55  Chicken Pulao"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see header of Dataset\n",
    "# By defaul it shows first five enteries\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "      <th>favourite_dish</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.2</td>\n",
       "      <td>92</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5.9</td>\n",
       "      <td>62</td>\n",
       "      <td>Karahi Ghosht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5.9</td>\n",
       "      <td>95</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.9</td>\n",
       "      <td>55</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5.3</td>\n",
       "      <td>59</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>5.6</td>\n",
       "      <td>50</td>\n",
       "      <td>Pasta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>5.9</td>\n",
       "      <td>76</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>5.1</td>\n",
       "      <td>78</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5.4</td>\n",
       "      <td>68</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  height weight favourite_dish\n",
       "0    5.8    101  Chicken Pulao\n",
       "1    6.2     92        Biryani\n",
       "2    5.9     62  Karahi Ghosht\n",
       "3    5.9     95  Chicken Pulao\n",
       "4    5.9     55  Chicken Pulao\n",
       "5    5.3     59        Biryani\n",
       "6    5.6     50          Pasta\n",
       "7    5.9     76        Biryani\n",
       "8    5.1     78        Biryani\n",
       "9    5.4     68        Biryani"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see header of Dataset for first 10 enteries\n",
    "df.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>favourite_dish</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>82</th>\n",
       "      <td>5.4</td>\n",
       "      <td>55</td>\n",
       "      <td>Pasta</td>\n",
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       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>5.9</td>\n",
       "      <td>75</td>\n",
       "      <td>Karahi Ghosht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5.4</td>\n",
       "      <td>61</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.2</td>\n",
       "      <td>68</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>5.1</td>\n",
       "      <td>96</td>\n",
       "      <td>Biryani</td>\n",
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      "text/plain": [
       "   height weight favourite_dish\n",
       "82    5.4     55          Pasta\n",
       "83    5.9     75  Karahi Ghosht\n",
       "84    5.4     61        Biryani\n",
       "85    6.2     68  Chicken Pulao\n",
       "86    5.1     96        Biryani"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see tail of Dataset\n",
    "# By defaul it shows last five enteries\n",
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th>favourite_dish</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>5.5</td>\n",
       "      <td>85</td>\n",
       "      <td>Biryani</td>\n",
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       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>5.11</td>\n",
       "      <td>72</td>\n",
       "      <td>Karahi Ghosht</td>\n",
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       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>5.3</td>\n",
       "      <td>62</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>5.4</td>\n",
       "      <td>55</td>\n",
       "      <td>Pasta</td>\n",
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       "    <tr>\n",
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       "      <td>52</td>\n",
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       "    <tr>\n",
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       "      <td>5.4</td>\n",
       "      <td>55</td>\n",
       "      <td>Pasta</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>83</th>\n",
       "      <td>5.9</td>\n",
       "      <td>75</td>\n",
       "      <td>Karahi Ghosht</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5.4</td>\n",
       "      <td>61</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.2</td>\n",
       "      <td>68</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>5.1</td>\n",
       "      <td>96</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "   height weight favourite_dish\n",
       "77    5.5     85        Biryani\n",
       "78   5.11     72  Karahi Ghosht\n",
       "79    5.3     62        Biryani\n",
       "80    5.4     55          Pasta\n",
       "81    5.5     52  Karahi Ghosht\n",
       "82    5.4     55          Pasta\n",
       "83    5.9     75  Karahi Ghosht\n",
       "84    5.4     61        Biryani\n",
       "85    6.2     68  Chicken Pulao\n",
       "86    5.1     96        Biryani"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see tail of Dataset for last 10 enteries\n",
    "df.tail(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(87, 3)"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see the shape of te Dataset\n",
    "# It shows the number of rows(m) and number of coulumns(n)\n",
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "261"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see the size of Dataset\n",
    "df.size # m*n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 87 entries, 0 to 86\n",
      "Data columns (total 3 columns):\n",
      " #   Column          Non-Null Count  Dtype \n",
      "---  ------          --------------  ----- \n",
      " 0   height          87 non-null     object\n",
      " 1   weight          87 non-null     object\n",
      " 2   favourite_dish  87 non-null     object\n",
      "dtypes: object(3)\n",
      "memory usage: 2.2+ KB\n"
     ]
    }
   ],
   "source": [
    "# To get information about the Dataset\n",
    "df.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "RangeIndex(start=0, stop=87, step=1)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see index (Serial Number)\n",
    "df.index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['height', 'weight', 'favourite_dish'], dtype='object')"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see columns\n",
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
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       "      <th>height</th>\n",
       "      <th>weight</th>\n",
       "      <th>favourite_dish</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>87.0</td>\n",
       "      <td>87</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>19.0</td>\n",
       "      <td>42</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>5.9</td>\n",
       "      <td>55</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>11.0</td>\n",
       "      <td>6</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        height  weight favourite_dish\n",
       "count     87.0      87             87\n",
       "unique    19.0      42              5\n",
       "top        5.9      55        Biryani\n",
       "freq      11.0       6             40"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To see summary of Dataset\n",
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>78</th>\n",
       "      <th>79</th>\n",
       "      <th>80</th>\n",
       "      <th>81</th>\n",
       "      <th>82</th>\n",
       "      <th>83</th>\n",
       "      <th>84</th>\n",
       "      <th>85</th>\n",
       "      <th>86</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>height</th>\n",
       "      <td>5.8</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.6</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.1</td>\n",
       "      <td>5.4</td>\n",
       "      <td>...</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.11</td>\n",
       "      <td>5.3</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.5</td>\n",
       "      <td>5.4</td>\n",
       "      <td>5.9</td>\n",
       "      <td>5.4</td>\n",
       "      <td>6.2</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>weight</th>\n",
       "      <td>101</td>\n",
       "      <td>92</td>\n",
       "      <td>62</td>\n",
       "      <td>95</td>\n",
       "      <td>55</td>\n",
       "      <td>59</td>\n",
       "      <td>50</td>\n",
       "      <td>76</td>\n",
       "      <td>78</td>\n",
       "      <td>68</td>\n",
       "      <td>...</td>\n",
       "      <td>85</td>\n",
       "      <td>72</td>\n",
       "      <td>62</td>\n",
       "      <td>55</td>\n",
       "      <td>52</td>\n",
       "      <td>55</td>\n",
       "      <td>75</td>\n",
       "      <td>61</td>\n",
       "      <td>68</td>\n",
       "      <td>96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>favourite_dish</th>\n",
       "      <td>Chicken Pulao</td>\n",
       "      <td>Biryani</td>\n",
       "      <td>Karahi Ghosht</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "      <td>Biryani</td>\n",
       "      <td>Pasta</td>\n",
       "      <td>Biryani</td>\n",
       "      <td>Biryani</td>\n",
       "      <td>Biryani</td>\n",
       "      <td>...</td>\n",
       "      <td>Biryani</td>\n",
       "      <td>Karahi Ghosht</td>\n",
       "      <td>Biryani</td>\n",
       "      <td>Pasta</td>\n",
       "      <td>Karahi Ghosht</td>\n",
       "      <td>Pasta</td>\n",
       "      <td>Karahi Ghosht</td>\n",
       "      <td>Biryani</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 87 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                           0        1              2              3   \\\n",
       "height                    5.8      6.2            5.9            5.9   \n",
       "weight                    101       92             62             95   \n",
       "favourite_dish  Chicken Pulao  Biryani  Karahi Ghosht  Chicken Pulao   \n",
       "\n",
       "                           4        5      6        7        8        9   ...  \\\n",
       "height                    5.9      5.3    5.6      5.9      5.1      5.4  ...   \n",
       "weight                     55       59     50       76       78       68  ...   \n",
       "favourite_dish  Chicken Pulao  Biryani  Pasta  Biryani  Biryani  Biryani  ...   \n",
       "\n",
       "                     77             78       79     80             81     82  \\\n",
       "height              5.5           5.11      5.3    5.4            5.5    5.4   \n",
       "weight               85             72       62     55             52     55   \n",
       "favourite_dish  Biryani  Karahi Ghosht  Biryani  Pasta  Karahi Ghosht  Pasta   \n",
       "\n",
       "                           83       84             85       86  \n",
       "height                    5.9      5.4            6.2      5.1  \n",
       "weight                     75       61             68       96  \n",
       "favourite_dish  Karahi Ghosht  Biryani  Chicken Pulao  Biryani  \n",
       "\n",
       "[3 rows x 87 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get transpose of Dataset(Row into columns)\n",
    "df.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <th>2</th>\n",
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       "      <td>75</td>\n",
       "      <td>Karahi Ghosht</td>\n",
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       "    <tr>\n",
       "      <th>84</th>\n",
       "      <td>5.4</td>\n",
       "      <td>61</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>85</th>\n",
       "      <td>6.2</td>\n",
       "      <td>68</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>86</th>\n",
       "      <td>5.1</td>\n",
       "      <td>96</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   height weight favourite_dish\n",
       "0     5.8    101  Chicken Pulao\n",
       "1     6.2     92        Biryani\n",
       "2     5.9     62  Karahi Ghosht\n",
       "3     5.9     95  Chicken Pulao\n",
       "4     5.9     55  Chicken Pulao\n",
       "..    ...    ...            ...\n",
       "82    5.4     55          Pasta\n",
       "83    5.9     75  Karahi Ghosht\n",
       "84    5.4     61        Biryani\n",
       "85    6.2     68  Chicken Pulao\n",
       "86    5.1     96        Biryani\n",
       "\n",
       "[87 rows x 3 columns]"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get sorted index for the Dataset\n",
    "df.sort_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
       "      <td>6.2</td>\n",
       "      <td>92</td>\n",
       "      <td>Biryani</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.8</td>\n",
       "      <td>101</td>\n",
       "      <td>Chicken Pulao</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>87 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "   height weight favourite_dish\n",
       "86    5.1     96        Biryani\n",
       "85    6.2     68  Chicken Pulao\n",
       "84    5.4     61        Biryani\n",
       "83    5.9     75  Karahi Ghosht\n",
       "82    5.4     55          Pasta\n",
       "..    ...    ...            ...\n",
       "4     5.9     55  Chicken Pulao\n",
       "3     5.9     95  Chicken Pulao\n",
       "2     5.9     62  Karahi Ghosht\n",
       "1     6.2     92        Biryani\n",
       "0     5.8    101  Chicken Pulao\n",
       "\n",
       "[87 rows x 3 columns]"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To get sorted index for the Dataset in Descending order\n",
    "df.sort_index(ascending=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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