Assignment 1 - Basic Stats Level -1.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "91ce6c2d",
   "metadata": {},
   "source": [
    "## Question no.7 & its Answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ee5e8b30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "1a5ceeec",
   "metadata": {},
   "outputs": [
    {
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       "        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>Unnamed: 0</th>\n",
       "      <th>Points</th>\n",
       "      <th>Score</th>\n",
       "      <th>Weigh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Unnamed: 0  Points  Score  Weigh\n",
       "0          Mazda RX4    3.90  2.620  16.46\n",
       "1      Mazda RX4 Wag    3.90  2.875  17.02\n",
       "2         Datsun 710    3.85  2.320  18.61\n",
       "3     Hornet 4 Drive    3.08  3.215  19.44\n",
       "4  Hornet Sportabout    3.15  3.440  17.02"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "que7_data = pd.read_csv(\"Q7.csv\")\n",
    "que7_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "365e541b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(32, 4)"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "que7_data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "c169b8b0",
   "metadata": {},
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>Points</th>\n",
       "      <th>Score</th>\n",
       "      <th>Weigh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>32</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>32.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>32</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.596563</td>\n",
       "      <td>3.217250</td>\n",
       "      <td>17.848750</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0.534679</td>\n",
       "      <td>0.978457</td>\n",
       "      <td>1.786943</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>NaN</td>\n",
       "      <td>2.760000</td>\n",
       "      <td>1.513000</td>\n",
       "      <td>14.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.080000</td>\n",
       "      <td>2.581250</td>\n",
       "      <td>16.892500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.695000</td>\n",
       "      <td>3.325000</td>\n",
       "      <td>17.710000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>NaN</td>\n",
       "      <td>3.920000</td>\n",
       "      <td>3.610000</td>\n",
       "      <td>18.900000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>NaN</td>\n",
       "      <td>4.930000</td>\n",
       "      <td>5.424000</td>\n",
       "      <td>22.900000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0     Points      Score      Weigh\n",
       "count          32  32.000000  32.000000  32.000000\n",
       "unique         32        NaN        NaN        NaN\n",
       "top     Mazda RX4        NaN        NaN        NaN\n",
       "freq            1        NaN        NaN        NaN\n",
       "mean          NaN   3.596563   3.217250  17.848750\n",
       "std           NaN   0.534679   0.978457   1.786943\n",
       "min           NaN   2.760000   1.513000  14.500000\n",
       "25%           NaN   3.080000   2.581250  16.892500\n",
       "50%           NaN   3.695000   3.325000  17.710000\n",
       "75%           NaN   3.920000   3.610000  18.900000\n",
       "max           NaN   4.930000   5.424000  22.900000"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "que7_data.describe(include=\"all\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "5b497a47",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cars_Name</th>\n",
       "      <th>Points</th>\n",
       "      <th>Score</th>\n",
       "      <th>Weigh</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Mazda RX4</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.620</td>\n",
       "      <td>16.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Mazda RX4 Wag</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.875</td>\n",
       "      <td>17.02</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Datsun 710</td>\n",
       "      <td>3.85</td>\n",
       "      <td>2.320</td>\n",
       "      <td>18.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Hornet 4 Drive</td>\n",
       "      <td>3.08</td>\n",
       "      <td>3.215</td>\n",
       "      <td>19.44</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Hornet Sportabout</td>\n",
       "      <td>3.15</td>\n",
       "      <td>3.440</td>\n",
       "      <td>17.02</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           Cars_Name  Points  Score  Weigh\n",
       "0          Mazda RX4    3.90  2.620  16.46\n",
       "1      Mazda RX4 Wag    3.90  2.875  17.02\n",
       "2         Datsun 710    3.85  2.320  18.61\n",
       "3     Hornet 4 Drive    3.08  3.215  19.44\n",
       "4  Hornet Sportabout    3.15  3.440  17.02"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "que7_data.rename(columns = {'Unnamed: 0':'Cars_Name'}, inplace = True)\n",
    "que7_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "3934f34a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Points    0.285881\n",
       "Score     0.957379\n",
       "Weigh     3.193166\n",
       "dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import warnings\n",
    "warnings.filterwarnings(\"ignore\")\n",
    "\n",
    "que7_data.var()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "07aab897",
   "metadata": {},
   "source": [
    "## Question No. 8 & its answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "3114047f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Expected Value of the Weight of randomly choosen patient is : 145.3334\n"
     ]
    }
   ],
   "source": [
    "values = np.array([108, 110, 123, 134, 135, 145, 167, 187, 199])\n",
    "prob = np.array([1/9, 1/9, 1/9, 1/9,  1/9, 1/9, 1/9, 1/9, 1/9])\n",
    "    \n",
    "expected_val = (values*prob).round(4).sum()\n",
    "\n",
    "print(\"Expected Value of the Weight of randomly choosen patient is :\" ,expected_val)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a43ee8f8",
   "metadata": {},
   "source": [
    "## Question No. 9 & its answer"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "48efae96",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Index</th>\n",
       "      <th>speed</th>\n",
       "      <th>dist</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   Index  speed  dist\n",
       "0      1      4     2\n",
       "1      2      4    10\n",
       "2      3      7     4\n",
       "3      4      7    22\n",
       "4      5      8    16"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "que9a_cars_data = pd.read_csv(\"E:\\Data Science\\Data Science\\Assignments\\Basic Stats Level-1\\Q9_a.csv\")\n",
    "que9a_cars_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "3ca875b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index    0.000000\n",
       "speed   -0.117510\n",
       "dist     0.806895\n",
       "dtype: float64"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "que9a_cars_data.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "3e28c19a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index   -1.200000\n",
       "speed   -0.508994\n",
       "dist     0.405053\n",
       "dtype: float64"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "que9a_cars_data.kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "4bdd46c4",
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
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       "      <th>SP</th>\n",
       "      <th>WT</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>104.185353</td>\n",
       "      <td>28.762059</td>\n",
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       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.466833</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>105.461264</td>\n",
       "      <td>30.193597</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>113.461264</td>\n",
       "      <td>30.632114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>104.461264</td>\n",
       "      <td>29.889149</td>\n",
       "    </tr>\n",
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       "</table>\n",
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      ],
      "text/plain": [
       "   Unnamed: 0          SP         WT\n",
       "0           1  104.185353  28.762059\n",
       "1           2  105.461264  30.466833\n",
       "2           3  105.461264  30.193597\n",
       "3           4  113.461264  30.632114\n",
       "4           5  104.461264  29.889149"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "que9b_cars_data = pd.read_csv(\"E:\\Data Science\\Data Science\\Assignments\\Basic Stats Level-1\\Q9_b.csv\")\n",
    "que9b_cars_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "ff343140",
   "metadata": {},
   "outputs": [
    {
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       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>113.461264</td>\n",
       "      <td>30.632114</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>104.461264</td>\n",
       "      <td>29.889149</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77</td>\n",
       "      <td>169.598513</td>\n",
       "      <td>16.132947</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>78</td>\n",
       "      <td>150.576579</td>\n",
       "      <td>37.923113</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>78</th>\n",
       "      <td>79</td>\n",
       "      <td>151.598513</td>\n",
       "      <td>15.769625</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>79</th>\n",
       "      <td>80</td>\n",
       "      <td>167.944460</td>\n",
       "      <td>39.423099</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>80</th>\n",
       "      <td>81</td>\n",
       "      <td>139.840817</td>\n",
       "      <td>34.948615</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>81 rows × 3 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    Cars          SP         WT\n",
       "0      1  104.185353  28.762059\n",
       "1      2  105.461264  30.466833\n",
       "2      3  105.461264  30.193597\n",
       "3      4  113.461264  30.632114\n",
       "4      5  104.461264  29.889149\n",
       "..   ...         ...        ...\n",
       "76    77  169.598513  16.132947\n",
       "77    78  150.576579  37.923113\n",
       "78    79  151.598513  15.769625\n",
       "79    80  167.944460  39.423099\n",
       "80    81  139.840817  34.948615\n",
       "\n",
       "[81 rows x 3 columns]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "que9b_cars_data.rename(columns = {'Unnamed: 0': 'Cars'}, inplace = True)\n",
    "que9b_cars_data                       "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "f9582fe8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cars    0.000000\n",
       "SP      1.611450\n",
       "WT     -0.614753\n",
       "dtype: float64"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "que9b_cars_data.skew()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d55f2b66",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Cars   -1.200000\n",
       "SP      2.977329\n",
       "WT      0.950291\n",
       "dtype: float64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "que9b_cars_data.kurt()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "23daaa7c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f495ddc0",
   "metadata": {},
   "outputs": [],
   "source": [
    "from scipy import stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3a59d637",
   "metadata": {},
   "outputs": [],
   "source": [
    "stats.norm.cdf()"
   ]
  }
 ],
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