Data_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 21,
   "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>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Tanu</td>\n",
       "      <td>23</td>\n",
       "      <td>69</td>\n",
       "      <td>Student</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ashika</td>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "      <td>Working</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Ashwin</td>\n",
       "      <td>22</td>\n",
       "      <td>65</td>\n",
       "      <td>Working</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank    Name  Age  Weight Occupation\n",
       "0     3    Tanu   23      69    Student\n",
       "1     2  Ashika   24      72    Working\n",
       "2     4  Ashwin   22      65    Working"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import style\n",
    "style.use(\"fivethirtyeight\")\n",
    "\n",
    "dictionary = {\"Rank\": [3, 2, 4, 5, 6], \"Name\": [\"Tanu\", \"Ashika\", \"Ashwin\", \"Mohit\", \"Sourabh\"], \"Age\": [23, 24, 22, 18, 9], \"Weight\": [69, 72, 65, 70, 40], \"Occupation\": [\"Student\", \"Working\", \"Working\", \"Student\", \"Student\"]}\n",
    "df = pd.DataFrame(dictionary)\n",
    "\n",
    "# Slicing the dataframe\n",
    "df.head(3)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "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>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Sourabh</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>Student</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank     Name  Age  Weight Occupation\n",
       "4     6  Sourabh    9      40    Student"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>Rank</th>\n",
       "      <th>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>Tanu</td>\n",
       "      <td>23</td>\n",
       "      <td>69</td>\n",
       "      <td>Student</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>Ashika</td>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "      <td>Working</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>Ashwin</td>\n",
       "      <td>22</td>\n",
       "      <td>65</td>\n",
       "      <td>Working</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>Mohit</td>\n",
       "      <td>18</td>\n",
       "      <td>70</td>\n",
       "      <td>Student</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>6</td>\n",
       "      <td>Sourabh</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>Student</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Tilo</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>Kitchen Queen</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Rank     Name  Age  Weight     Occupation\n",
       "0     3     Tanu   23      69        Student\n",
       "1     2   Ashika   24      72        Working\n",
       "2     4   Ashwin   22      65        Working\n",
       "3     5    Mohit   18      70        Student\n",
       "4     6  Sourabh    9      40        Student\n",
       "0     1     Tilo   80      80  Kitchen Queen"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dictionary1 = {\"Rank\": [1], \"Name\": [\"Tilo\"], \"Age\": [80], \"Weight\": [80], \"Occupation\": [\"Kitchen Queen\"] }\n",
    "df1 = pd.DataFrame(dictionary1)\n",
    "# Concatenating the two dataframes (df, df1)\n",
    "df_row = pd.concat([df, df1])\n",
    "df_row\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "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>Name</th>\n",
       "      <th>Age</th>\n",
       "      <th>Weight</th>\n",
       "      <th>Occupation</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rank</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Tilo</td>\n",
       "      <td>80</td>\n",
       "      <td>80</td>\n",
       "      <td>Kitchen Queen</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Ashika</td>\n",
       "      <td>24</td>\n",
       "      <td>72</td>\n",
       "      <td>Working</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Tanu</td>\n",
       "      <td>23</td>\n",
       "      <td>69</td>\n",
       "      <td>Student</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Ashwin</td>\n",
       "      <td>22</td>\n",
       "      <td>65</td>\n",
       "      <td>Working</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mohit</td>\n",
       "      <td>18</td>\n",
       "      <td>70</td>\n",
       "      <td>Student</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Sourabh</td>\n",
       "      <td>9</td>\n",
       "      <td>40</td>\n",
       "      <td>Student</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Name  Age  Weight     Occupation\n",
       "Rank                                     \n",
       "1        Tilo   80      80  Kitchen Queen\n",
       "2      Ashika   24      72        Working\n",
       "3        Tanu   23      69        Student\n",
       "4      Ashwin   22      65        Working\n",
       "5       Mohit   18      70        Student\n",
       "6     Sourabh    9      40        Student"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# To set the first as Rank\n",
    "df_row.set_index(\"Rank\", inplace = True)\n",
    "\n",
    "# To sort the data Rank in the ascending order\n",
    "df_row.sort_values(by=['Rank'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df_row.plot()\n",
    "plt.show()"
   ]
  },
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   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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