Experiment no.4 - Statistical Description divyani.ipynb

{
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  {
   "cell_type": "markdown",
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   "metadata": {},
   "source": [
    "# Statistical Description"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1c4242e",
   "metadata": {},
   "source": [
    "Experiment no.: 4"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "50367cd1",
   "metadata": {},
   "source": [
    "Aim: Statistical Description"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "3c9a0452",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Name:Divyani Sadafale\n",
    "#Roll no.: 50\n",
    "#Sec: B\n",
    "#Subject: Data Science and Statistics (Lab 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "fb52c9f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "94454629",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "99d7fa30",
   "metadata": {},
   "outputs": [],
   "source": [
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "d9414f1d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "76f53b1e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5a511360",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\hp\\\\Downloads'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "cf4ec052",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('C:\\\\Users\\\\hp\\\\Desktop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5e48b63f",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"Salary_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "a8ce4717",
   "metadata": {},
   "outputs": [
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       "   Unnamed: 0  YearsExperience   Salary\n",
       "0           0              1.2  39344.0\n",
       "1           1              1.4  46206.0\n",
       "2           2              1.6  37732.0\n",
       "3           3              2.1  43526.0\n",
       "4           4              2.3  39892.0"
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   "source": [
    "df.head()"
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  {
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   "execution_count": 11,
   "id": "250867ba",
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   "outputs": [
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       "      <td>9.7</td>\n",
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       "    Unnamed: 0  YearsExperience    Salary\n",
       "25          25              9.1  105583.0\n",
       "26          26              9.6  116970.0\n",
       "27          27              9.7  112636.0\n",
       "28          28             10.4  122392.0\n",
       "29          29             10.6  121873.0"
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   "source": [
    "df.tail()"
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  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "30a538e6",
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   "outputs": [
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       "      <td>57082.0</td>\n",
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       "      <td>81364.0</td>\n",
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       "      <td>93941.0</td>\n",
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       "      <td>91739.0</td>\n",
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       "      <td>101303.0</td>\n",
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       "      <td>8.3</td>\n",
       "      <td>113813.0</td>\n",
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       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>8.8</td>\n",
       "      <td>109432.0</td>\n",
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       "    <tr>\n",
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       "      <td>9.1</td>\n",
       "      <td>105583.0</td>\n",
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       "      <td>9.6</td>\n",
       "      <td>116970.0</td>\n",
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       "      <td>9.7</td>\n",
       "      <td>112636.0</td>\n",
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       "      <td>122392.0</td>\n",
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       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>10.6</td>\n",
       "      <td>121873.0</td>\n",
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      "text/plain": [
       "    Unnamed: 0  YearsExperience    Salary\n",
       "0            0              1.2   39344.0\n",
       "1            1              1.4   46206.0\n",
       "2            2              1.6   37732.0\n",
       "3            3              2.1   43526.0\n",
       "4            4              2.3   39892.0\n",
       "5            5              3.0   56643.0\n",
       "6            6              3.1   60151.0\n",
       "7            7              3.3   54446.0\n",
       "8            8              3.3   64446.0\n",
       "9            9              3.8   57190.0\n",
       "10          10              4.0   63219.0\n",
       "11          11              4.1   55795.0\n",
       "12          12              4.1   56958.0\n",
       "13          13              4.2   57082.0\n",
       "14          14              4.6   61112.0\n",
       "15          15              5.0   67939.0\n",
       "16          16              5.2   66030.0\n",
       "17          17              5.4   83089.0\n",
       "18          18              6.0   81364.0\n",
       "19          19              6.1   93941.0\n",
       "20          20              6.9   91739.0\n",
       "21          21              7.2   98274.0\n",
       "22          22              8.0  101303.0\n",
       "23          23              8.3  113813.0\n",
       "24          24              8.8  109432.0\n",
       "25          25              9.1  105583.0\n",
       "26          26              9.6  116970.0\n",
       "27          27              9.7  112636.0\n",
       "28          28             10.4  122392.0\n",
       "29          29             10.6  121873.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a39b9c89",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 30 entries, 0 to 29\n",
      "Data columns (total 3 columns):\n",
      " #   Column           Non-Null Count  Dtype  \n",
      "---  ------           --------------  -----  \n",
      " 0   Unnamed: 0       30 non-null     int64  \n",
      " 1   YearsExperience  30 non-null     float64\n",
      " 2   Salary           30 non-null     float64\n",
      "dtypes: float64(2), int64(1)\n",
      "memory usage: 852.0 bytes\n"
     ]
    }
   ],
   "source": [
    "df.info()\n",
    "#attribute"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c0d8dff9",
   "metadata": {},
   "outputs": [
    {
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       "      <th>count</th>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "      <td>30.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>14.500000</td>\n",
       "      <td>5.413333</td>\n",
       "      <td>76004.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>8.803408</td>\n",
       "      <td>2.837888</td>\n",
       "      <td>27414.429785</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.200000</td>\n",
       "      <td>37732.000000</td>\n",
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       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>7.250000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>56721.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>14.500000</td>\n",
       "      <td>4.800000</td>\n",
       "      <td>65238.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>21.750000</td>\n",
       "      <td>7.800000</td>\n",
       "      <td>100545.750000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>29.000000</td>\n",
       "      <td>10.600000</td>\n",
       "      <td>122392.000000</td>\n",
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      ],
      "text/plain": [
       "       Unnamed: 0  YearsExperience         Salary\n",
       "count   30.000000        30.000000      30.000000\n",
       "mean    14.500000         5.413333   76004.000000\n",
       "std      8.803408         2.837888   27414.429785\n",
       "min      0.000000         1.200000   37732.000000\n",
       "25%      7.250000         3.300000   56721.750000\n",
       "50%     14.500000         4.800000   65238.000000\n",
       "75%     21.750000         7.800000  100545.750000\n",
       "max     29.000000        10.600000  122392.000000"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()\n",
    "#record"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "b30cac44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 3)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0095f774",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "dfe3df4f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim"
   ]
  }
 ],
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