Experiment no.5 - Simple Linear Regression divyani.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "1eaee406",
   "metadata": {},
   "source": [
    "# Simple Linear Regression\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "77c06304",
   "metadata": {},
   "source": [
    "Aim: Simple Linear Regression"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fea4403a",
   "metadata": {},
   "source": [
    "Experiment no.: 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d7add9d6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Name: Divyani Sadafale\n",
    "#Roll no.: 50\n",
    "#Sec : B\n",
    "#Year: 3rd Year\n",
    "#Sub: DSS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b4d2bbe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "89c64505",
   "metadata": {},
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ae181da8",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "c166138d",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "722c4919",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'C:\\\\Users\\\\hp\\\\Desktop\\\\DSS Practicals'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "os.getcwd()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "318668ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "os.chdir('C:\\\\Users\\\\HP\\\\Desktop')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "852b650b",
   "metadata": {},
   "outputs": [],
   "source": [
    "df=pd.read_csv(\"Salary_dataset.csv\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "b59fb92d",
   "metadata": {},
   "outputs": [
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       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
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       "      <td>1.2</td>\n",
       "      <td>39344.0</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>46206.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>37732.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>43526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>39892.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\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"
      ]
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     "execution_count": 9,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b89da813",
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   "outputs": [
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       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
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       "  </thead>\n",
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       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>9.1</td>\n",
       "      <td>105583.0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>9.6</td>\n",
       "      <td>116970.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>9.7</td>\n",
       "      <td>112636.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>10.4</td>\n",
       "      <td>122392.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <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",
       "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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     "execution_count": 10,
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   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b6b12462",
   "metadata": {
    "scrolled": true
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   "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()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "666724a6",
   "metadata": {},
   "outputs": [
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       "      <th>YearsExperience</th>\n",
       "      <th>Salary</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <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",
       "    </tr>\n",
       "    <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",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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": 12,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "4277e29d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(30, 3)"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "c6f014d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "90"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "e9fc59a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "47d7958d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0         0\n",
       "YearsExperience    0\n",
       "Salary             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "9a05be7c",
   "metadata": {},
   "outputs": [
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       "      <th>3</th>\n",
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       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2.3</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"
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     "execution_count": 17,
     "metadata": {},
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   "source": [
    "df.head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "b9422b39",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Unnamed: 0', 'YearsExperience', 'Salary'], dtype='object')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
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    }
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    "df.columns"
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  {
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   "execution_count": 19,
   "id": "59106406",
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   "source": [
    "a=(1,2,3,4,5,6,7,8,9,10)"
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   "id": "40ef876b",
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   "execution_count": 21,
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    {
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       "10"
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     "execution_count": 21,
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       "10"
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    "a[9]"
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    "a[-10]"
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   "cell_type": "code",
   "execution_count": 24,
   "id": "4a939886",
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    {
     "data": {
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       "39892.0"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.loc[4,'Salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "01e12170",
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   "outputs": [
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       "      <th>3</th>\n",
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       "      <th>4</th>\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"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5a0b6d87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0             29.0\n",
       "YearsExperience        10.6\n",
       "Salary             121873.0\n",
       "Name: 29, dtype: float64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[29]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "519a6c88",
   "metadata": {},
   "outputs": [
    {
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       "      <td>56643.0</td>\n",
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       "      <th>6</th>\n",
       "      <td>6</td>\n",
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       "      <td>60151.0</td>\n",
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       "      <th>7</th>\n",
       "      <td>7</td>\n",
       "      <td>3.3</td>\n",
       "      <td>54446.0</td>\n",
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       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>8</td>\n",
       "      <td>3.3</td>\n",
       "      <td>64446.0</td>\n",
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       "      <th>9</th>\n",
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       "      <td>57190.0</td>\n",
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       "      <th>10</th>\n",
       "      <td>10</td>\n",
       "      <td>4.0</td>\n",
       "      <td>63219.0</td>\n",
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       "      <th>11</th>\n",
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       "      <td>55795.0</td>\n",
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       "      <th>12</th>\n",
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       "      <td>4.1</td>\n",
       "      <td>56958.0</td>\n",
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       "      <th>13</th>\n",
       "      <td>13</td>\n",
       "      <td>4.2</td>\n",
       "      <td>57082.0</td>\n",
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       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>14</td>\n",
       "      <td>4.6</td>\n",
       "      <td>61112.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>15</td>\n",
       "      <td>5.0</td>\n",
       "      <td>67939.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>16</td>\n",
       "      <td>5.2</td>\n",
       "      <td>66030.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>17</td>\n",
       "      <td>5.4</td>\n",
       "      <td>83089.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>18</td>\n",
       "      <td>6.0</td>\n",
       "      <td>81364.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>19</td>\n",
       "      <td>6.1</td>\n",
       "      <td>93941.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>20</td>\n",
       "      <td>6.9</td>\n",
       "      <td>91739.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>21</td>\n",
       "      <td>7.2</td>\n",
       "      <td>98274.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>22</td>\n",
       "      <td>8.0</td>\n",
       "      <td>101303.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>23</td>\n",
       "      <td>8.3</td>\n",
       "      <td>113813.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>24</td>\n",
       "      <td>8.8</td>\n",
       "      <td>109432.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>25</td>\n",
       "      <td>9.1</td>\n",
       "      <td>105583.0</td>\n",
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       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>26</td>\n",
       "      <td>9.6</td>\n",
       "      <td>116970.0</td>\n",
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       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>27</td>\n",
       "      <td>9.7</td>\n",
       "      <td>112636.0</td>\n",
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       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>28</td>\n",
       "      <td>10.4</td>\n",
       "      <td>122392.0</td>\n",
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       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>29</td>\n",
       "      <td>10.6</td>\n",
       "      <td>121873.0</td>\n",
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       "  </tbody>\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": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(30)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "be4574a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0             4.0\n",
       "YearsExperience        2.3\n",
       "Salary             39892.0\n",
       "Name: 4, dtype: float64"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "c2c4b274",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=(1,2,3,4,5,6,7,8,9,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "8707cc7e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4)"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1:4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "0fa4c53d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46206.0"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.iloc[1,2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "2dd8d001",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "    }\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>Unnamed: 0</th>\n",
       "      <th>YearsExperience</th>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>1.4</td>\n",
       "      <td>46206.0</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>1.6</td>\n",
       "      <td>37732.0</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>3</td>\n",
       "      <td>2.1</td>\n",
       "      <td>43526.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>4</td>\n",
       "      <td>2.3</td>\n",
       "      <td>39892.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "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"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "42229e8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46206.0"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[1,'Salary']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "9776f011",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=df.iloc[:,:-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "9ac0be8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "y=df.iloc[:,-1].values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "7dc92fc9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.   1.2]\n",
      " [ 1.   1.4]\n",
      " [ 2.   1.6]\n",
      " [ 3.   2.1]\n",
      " [ 4.   2.3]\n",
      " [ 5.   3. ]\n",
      " [ 6.   3.1]\n",
      " [ 7.   3.3]\n",
      " [ 8.   3.3]\n",
      " [ 9.   3.8]\n",
      " [10.   4. ]\n",
      " [11.   4.1]\n",
      " [12.   4.1]\n",
      " [13.   4.2]\n",
      " [14.   4.6]\n",
      " [15.   5. ]\n",
      " [16.   5.2]\n",
      " [17.   5.4]\n",
      " [18.   6. ]\n",
      " [19.   6.1]\n",
      " [20.   6.9]\n",
      " [21.   7.2]\n",
      " [22.   8. ]\n",
      " [23.   8.3]\n",
      " [24.   8.8]\n",
      " [25.   9.1]\n",
      " [26.   9.6]\n",
      " [27.   9.7]\n",
      " [28.  10.4]\n",
      " [29.  10.6]]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "7a796740",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 39344.  46206.  37732.  43526.  39892.  56643.  60151.  54446.  64446.\n",
      "  57190.  63219.  55795.  56958.  57082.  61112.  67939.  66030.  83089.\n",
      "  81364.  93941.  91739.  98274. 101303. 113813. 109432. 105583. 116970.\n",
      " 112636. 122392. 121873.]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "7db66a9a",
   "metadata": {},
   "outputs": [],
   "source": [
    "a=(1,2,3,4,5,6,7,8,9,10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6e14d1a2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 2)"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "5890d99a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(3, 4, 5, 6, 7, 8, 9, 10)"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[2:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "ce97cb57",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4, 6)"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1:6:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "85959b6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3, 4, 5, 6)"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "a[1:6:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8dd1ca16",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.   1.2]\n",
      " [ 1.   1.4]\n",
      " [ 2.   1.6]\n",
      " [ 3.   2.1]\n",
      " [ 4.   2.3]\n",
      " [ 5.   3. ]\n",
      " [ 6.   3.1]\n",
      " [ 7.   3.3]\n",
      " [ 8.   3.3]\n",
      " [ 9.   3.8]\n",
      " [10.   4. ]\n",
      " [11.   4.1]\n",
      " [12.   4.1]\n",
      " [13.   4.2]\n",
      " [14.   4.6]\n",
      " [15.   5. ]\n",
      " [16.   5.2]\n",
      " [17.   5.4]\n",
      " [18.   6. ]\n",
      " [19.   6.1]\n",
      " [20.   6.9]\n",
      " [21.   7.2]\n",
      " [22.   8. ]\n",
      " [23.   8.3]\n",
      " [24.   8.8]\n",
      " [25.   9.1]\n",
      " [26.   9.6]\n",
      " [27.   9.7]\n",
      " [28.  10.4]\n",
      " [29.  10.6]]\n"
     ]
    }
   ],
   "source": [
    "print(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "bb2e5faa",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 39344.  46206.  37732.  43526.  39892.  56643.  60151.  54446.  64446.\n",
      "  57190.  63219.  55795.  56958.  57082.  61112.  67939.  66030.  83089.\n",
      "  81364.  93941.  91739.  98274. 101303. 113813. 109432. 105583. 116970.\n",
      " 112636. 122392. 121873.]\n"
     ]
    }
   ],
   "source": [
    "print(y)"
   ]
  }
 ],
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