22March.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "18984645",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ans1:\n",
    "import numpy as np\n",
    "data1={'time':[1,2,3,4,5],'score':[30,40,50,60,70]}\n",
    "import pandas as pd\n",
    "df1=pd.DataFrame(data1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "dca1dac8",
   "metadata": {},
   "outputs": [],
   "source": [
    "corr1=df1.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "7638bf1d",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>time</th>\n",
       "      <th>score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>time</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>score</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       time  score\n",
       "time    1.0    1.0\n",
       "score   1.0    1.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "corr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "903947fe",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ans2:\n",
    "import pandas as pd\n",
    "data={'amount':[2,4,5,6,8,9,10,11,15,14],'level':[1,2,3,4,5,6,7,8,9,10]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "7d765ffd",
   "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>amount</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>amount</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.987879</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>level</th>\n",
       "      <td>0.987879</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          amount     level\n",
       "amount  1.000000  0.987879\n",
       "level   0.987879  1.000000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.DataFrame(data)\n",
    "df.corr(method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3133eec7",
   "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>amount</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>8</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>9</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>10</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>15</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>14</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   amount  level\n",
       "0       2      1\n",
       "1       4      2\n",
       "2       5      3\n",
       "3       6      4\n",
       "4       8      5\n",
       "5       9      6\n",
       "6      10      7\n",
       "7      11      8\n",
       "8      15      9\n",
       "9      14     10"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "fbba9de6",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ans3:\n",
    "data={'hours':[4, 6, 3, 5, 7, 2, 3, 6, 4, 5, 7, 2, 3, 5, 4, 6, 7, 3, 5, 4, 2, 6, 3, 5, 4, 6, 7, 2, 3, 5, 4, 6, 3, 5, 4, 6, 7, 2, 3, 5, 4, 6, 3, 5, 4, 6, 7, 2, 3,4],\n",
    "     'BMI':[22.5, 24.3, 23.1, 25.8, 26.7, 21.9, 23.5, 24.6, 23.4, 26.2, 27.8, 21.3, 23.6, 24.5, 22.9, 25.4, 26.5, 23.3, 24.8, 23.9, 21.5, 26.4, 23.7, 24.9, 23.7, 26.1, 26.9, 21.4, 23.8, 25.3, 23.6, 25.1, 22.8, 25.6, 24.4, 26.3, 27.1, 21.8, 24.2, 25.7, 23.4, 26.8, 22.1, 24.7, 26.0, 23.2, 26.6, 27.3, 21.7, 24.1]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "fedaa24c",
   "metadata": {},
   "outputs": [],
   "source": [
    "l1=[22.5, 24.3, 23.1, 25.8, 26.7, 21.9, 23.5, 24.6, 23.4, 26.2, 27.8, 21.3, 23.6, 24.5, 22.9, 25.4, 26.5, 23.3, 24.8, 23.9, 21.5, 26.4, 23.7, 24.9, 23.7, 26.1, 26.9, 21.4, 23.8, 25.3, 23.6, 25.1, 22.8, 25.6, 24.4, 26.3, 27.1, 21.8, 24.2, 25.7, 23.4, 26.8, 22.1, 24.7, 26.0, 23.2, 26.6, 27.3, 21.7, 24.1, 25.5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "7116bed1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "51"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(l1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "99198ce0",
   "metadata": {},
   "outputs": [],
   "source": [
    "l2= [4, 6, 3, 5, 7, 2, 3, 6, 4, 5, 7, 2, 3, 5, 4, 6, 7, 3, 5, 4, 2, 6, 3, 5, 4, 6, 7, 2, 3, 5, 4, 6, 3, 5, 4, 6, 7, 2, 3, 5, 4, 6, 3, 5, 4, 6, 7, 2, 3,4]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "041efde0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "50"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(l2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "9db3bba1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df3=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "6a3c4870",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hours</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hours</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.771803</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.771803</td>\n",
       "      <td>1.000000</td>\n",
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      ],
      "text/plain": [
       "          hours       BMI\n",
       "hours  1.000000  0.771803\n",
       "BMI    0.771803  1.000000"
      ]
     },
     "execution_count": 39,
     "metadata": {},
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    }
   ],
   "source": [
    "df3.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "c33d8462",
   "metadata": {},
   "outputs": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hours</th>\n",
       "      <th>BMI</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hours</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.766449</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>BMI</th>\n",
       "      <td>0.766449</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "          hours       BMI\n",
       "hours  1.000000  0.766449\n",
       "BMI    0.766449  1.000000"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df3.corr(method='spearman')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "9c7fa54a",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ans4:\n",
    "data={'hours':[4, 6, 3, 5, 7, 2, 3, 6, 4, 5, 7, 2, 3, 5, 4, 6, 7, 3, 5, 4, 2, 6, 3, 5, 4, 6, 7, 2, 3, 5, 4, 6, 3, 5, 4, 6, 7, 2, 3, 5, 4, 6, 3, 5, 4, 6, 7, 2, 3,4],\n",
    "     'level':[11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,33,36,39,35,34,36,74,39,54,36,85,74,10,15,12,13,5,6,9,8,7,4,1,4,5,6]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "7be9eac0",
   "metadata": {},
   "outputs": [],
   "source": [
    "l=[11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27,28,29,30,31,32,33,34,33,36,39,35,34,36,74,39,54,36,85,74,10,15,12,13,5,6,9,8,7,4,1,4,5,6,7,8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "f44ca301",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(l)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "b04e15f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "df4=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d78caffe",
   "metadata": {},
   "outputs": [
    {
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>hours</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6</td>\n",
       "      <td>12</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>5</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>3</td>\n",
       "      <td>17</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>4</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>5</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>7</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>2</td>\n",
       "      <td>22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>3</td>\n",
       "      <td>23</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>5</td>\n",
       "      <td>24</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>4</td>\n",
       "      <td>25</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>6</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>7</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>3</td>\n",
       "      <td>28</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>5</td>\n",
       "      <td>29</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>4</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>2</td>\n",
       "      <td>31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>6</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>3</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>5</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>4</td>\n",
       "      <td>33</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>6</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>7</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>2</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>3</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>5</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>31</th>\n",
       "      <td>6</td>\n",
       "      <td>39</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>3</td>\n",
       "      <td>54</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>33</th>\n",
       "      <td>5</td>\n",
       "      <td>36</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>34</th>\n",
       "      <td>4</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35</th>\n",
       "      <td>6</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>36</th>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>37</th>\n",
       "      <td>2</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>38</th>\n",
       "      <td>3</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>40</th>\n",
       "      <td>4</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>41</th>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>42</th>\n",
       "      <td>3</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>43</th>\n",
       "      <td>5</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>44</th>\n",
       "      <td>4</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>45</th>\n",
       "      <td>6</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>46</th>\n",
       "      <td>7</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>47</th>\n",
       "      <td>2</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48</th>\n",
       "      <td>3</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49</th>\n",
       "      <td>4</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    hours  level\n",
       "0       4     11\n",
       "1       6     12\n",
       "2       3     13\n",
       "3       5     14\n",
       "4       7     15\n",
       "5       2     16\n",
       "6       3     17\n",
       "7       6     18\n",
       "8       4     19\n",
       "9       5     20\n",
       "10      7     21\n",
       "11      2     22\n",
       "12      3     23\n",
       "13      5     24\n",
       "14      4     25\n",
       "15      6     26\n",
       "16      7     27\n",
       "17      3     28\n",
       "18      5     29\n",
       "19      4     30\n",
       "20      2     31\n",
       "21      6     32\n",
       "22      3     33\n",
       "23      5     34\n",
       "24      4     33\n",
       "25      6     36\n",
       "26      7     39\n",
       "27      2     35\n",
       "28      3     34\n",
       "29      5     36\n",
       "30      4     74\n",
       "31      6     39\n",
       "32      3     54\n",
       "33      5     36\n",
       "34      4     85\n",
       "35      6     74\n",
       "36      7     10\n",
       "37      2     15\n",
       "38      3     12\n",
       "39      5     13\n",
       "40      4      5\n",
       "41      6      6\n",
       "42      3      9\n",
       "43      5      8\n",
       "44      4      7\n",
       "45      6      4\n",
       "46      7      1\n",
       "47      2      4\n",
       "48      3      5\n",
       "49      4      6"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "60313fe6",
   "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>hours</th>\n",
       "      <th>level</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>hours</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.008428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>level</th>\n",
       "      <td>0.008428</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          hours     level\n",
       "hours  1.000000  0.008428\n",
       "level  0.008428  1.000000"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df4.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "558ad535",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ans5:\n",
    "data={'Soft drink Preference ':['Coke','Pepsi','Mountain dew','Coke','Pepsi','Coke'],\n",
    "     'age':[25,42,37,19,31,28]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "64de2d41",
   "metadata": {},
   "outputs": [],
   "source": [
    "dg=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "16c6178b",
   "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>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>age</th>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     age\n",
       "age  1.0"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dg.corr(method='pearson')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "27efa6b9",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Ans6:\n",
    "data={'day':[25,23,27,28,26,32,21,24,25,26,28,30,36,42,52,56,23,28,31,35,39,40,21,25,26,42,58,41,32,23],\n",
    "     'week':[400,445,449,500,501,502,503,506,587,574,596,601,602,605,708,802,400,405,406,408,409,401,402,400,500,501,502,506,506,507]}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f14368be",
   "metadata": {},
   "outputs": [],
   "source": [
    "df8=pd.DataFrame(data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "7c5dd8d3",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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