{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "80bbac67-72f2-41ad-9272-341d97f1e07a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv(\"abalone.data\", sep=',', decimal='.', header=None, names=['Sex', 'Length', 'Diameter', 'Height', 'Whole weight',\n",
    "\n",
    "'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "d32e09ba-7165-4d5b-b28e-33a99ab2c9a7",
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex  Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   Shell weight  Rings  \n",
       "0         0.150     15  \n",
       "1         0.070      7  \n",
       "2         0.210      9  \n",
       "3         0.155     10  \n",
       "4         0.055      7  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "f4a14cf5-f0a9-4c2c-bcd2-07268abb2758",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'F'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# manipulating the data\n",
    "df['Sex'][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "2e021a6f-30ca-405a-a802-58e8dc8d71f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'M'"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#.loc and .iloc\n",
    "df.iloc[3]['Sex']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "f96a457a-501d-4605-95cd-28a5183ef66d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       False\n",
       "1       False\n",
       "2       False\n",
       "3       False\n",
       "4       False\n",
       "        ...  \n",
       "4172    False\n",
       "4173    False\n",
       "4174    False\n",
       "4175    False\n",
       "4176    False\n",
       "Name: Rings, Length: 4177, dtype: bool"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# masking\n",
    "mask_rings_22 = df['Rings'] == 22\n",
    "mask_rings_22"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "73a2e6a8-0361-49a6-99a2-b485144a56d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "232    0.215\n",
       "270    0.215\n",
       "275    0.215\n",
       "351    0.170\n",
       "433    0.140\n",
       "675    0.180\n",
       "Name: Height, dtype: float64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[mask_rings_22,'Height']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "c3022d5b-2d8a-4404-8afd-6a52cc31195a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9     689\n",
       "10    634\n",
       "8     568\n",
       "11    487\n",
       "7     391\n",
       "12    267\n",
       "6     259\n",
       "13    203\n",
       "14    126\n",
       "5     115\n",
       "15    103\n",
       "16     67\n",
       "17     58\n",
       "4      57\n",
       "18     42\n",
       "19     32\n",
       "20     26\n",
       "3      15\n",
       "21     14\n",
       "23      9\n",
       "22      6\n",
       "27      2\n",
       "24      2\n",
       "1       1\n",
       "26      1\n",
       "29      1\n",
       "2       1\n",
       "25      1\n",
       "Name: Rings, dtype: int64"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['Rings'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "94d73784-290f-46d5-bbf2-856b926fb7b3",
   "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>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rings</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>0.075000</td>\n",
       "      <td>0.055000</td>\n",
       "      <td>0.010000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.001500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.150000</td>\n",
       "      <td>0.100000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.004500</td>\n",
       "      <td>0.004000</td>\n",
       "      <td>0.005000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.176000</td>\n",
       "      <td>0.128667</td>\n",
       "      <td>0.041667</td>\n",
       "      <td>0.028400</td>\n",
       "      <td>0.011767</td>\n",
       "      <td>0.006267</td>\n",
       "      <td>0.008933</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.221491</td>\n",
       "      <td>0.161579</td>\n",
       "      <td>0.053947</td>\n",
       "      <td>0.059605</td>\n",
       "      <td>0.024719</td>\n",
       "      <td>0.012956</td>\n",
       "      <td>0.018000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.285739</td>\n",
       "      <td>0.210696</td>\n",
       "      <td>0.069913</td>\n",
       "      <td>0.125500</td>\n",
       "      <td>0.061696</td>\n",
       "      <td>0.027330</td>\n",
       "      <td>0.036770</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.369363</td>\n",
       "      <td>0.278861</td>\n",
       "      <td>0.091776</td>\n",
       "      <td>0.273174</td>\n",
       "      <td>0.123158</td>\n",
       "      <td>0.058371</td>\n",
       "      <td>0.078388</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.422033</td>\n",
       "      <td>0.321535</td>\n",
       "      <td>0.105921</td>\n",
       "      <td>0.397386</td>\n",
       "      <td>0.182657</td>\n",
       "      <td>0.085899</td>\n",
       "      <td>0.111648</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.498776</td>\n",
       "      <td>0.384798</td>\n",
       "      <td>0.128556</td>\n",
       "      <td>0.640753</td>\n",
       "      <td>0.293773</td>\n",
       "      <td>0.138502</td>\n",
       "      <td>0.178609</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.546865</td>\n",
       "      <td>0.425218</td>\n",
       "      <td>0.142721</td>\n",
       "      <td>0.852013</td>\n",
       "      <td>0.387938</td>\n",
       "      <td>0.187803</td>\n",
       "      <td>0.236509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.574629</td>\n",
       "      <td>0.449290</td>\n",
       "      <td>0.154093</td>\n",
       "      <td>1.008748</td>\n",
       "      <td>0.447217</td>\n",
       "      <td>0.223128</td>\n",
       "      <td>0.282976</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.599374</td>\n",
       "      <td>0.470595</td>\n",
       "      <td>0.161253</td>\n",
       "      <td>1.142559</td>\n",
       "      <td>0.503977</td>\n",
       "      <td>0.252393</td>\n",
       "      <td>0.319866</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>0.589457</td>\n",
       "      <td>0.462434</td>\n",
       "      <td>0.161292</td>\n",
       "      <td>1.102904</td>\n",
       "      <td>0.472781</td>\n",
       "      <td>0.239575</td>\n",
       "      <td>0.321157</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.578892</td>\n",
       "      <td>0.456453</td>\n",
       "      <td>0.160887</td>\n",
       "      <td>1.075453</td>\n",
       "      <td>0.434638</td>\n",
       "      <td>0.235421</td>\n",
       "      <td>0.320828</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>0.580198</td>\n",
       "      <td>0.458294</td>\n",
       "      <td>0.163571</td>\n",
       "      <td>1.079992</td>\n",
       "      <td>0.427190</td>\n",
       "      <td>0.235258</td>\n",
       "      <td>0.329960</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>0.575728</td>\n",
       "      <td>0.456262</td>\n",
       "      <td>0.161359</td>\n",
       "      <td>1.040951</td>\n",
       "      <td>0.402471</td>\n",
       "      <td>0.227539</td>\n",
       "      <td>0.321650</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.587537</td>\n",
       "      <td>0.468433</td>\n",
       "      <td>0.171866</td>\n",
       "      <td>1.133388</td>\n",
       "      <td>0.421716</td>\n",
       "      <td>0.239769</td>\n",
       "      <td>0.377858</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>0.601034</td>\n",
       "      <td>0.475345</td>\n",
       "      <td>0.173966</td>\n",
       "      <td>1.234328</td>\n",
       "      <td>0.467052</td>\n",
       "      <td>0.251233</td>\n",
       "      <td>0.393414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>0.596071</td>\n",
       "      <td>0.471310</td>\n",
       "      <td>0.171548</td>\n",
       "      <td>1.194333</td>\n",
       "      <td>0.446833</td>\n",
       "      <td>0.242762</td>\n",
       "      <td>0.386762</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>0.595625</td>\n",
       "      <td>0.470781</td>\n",
       "      <td>0.170313</td>\n",
       "      <td>1.166797</td>\n",
       "      <td>0.440625</td>\n",
       "      <td>0.248859</td>\n",
       "      <td>0.365312</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>0.603654</td>\n",
       "      <td>0.482308</td>\n",
       "      <td>0.173846</td>\n",
       "      <td>1.251750</td>\n",
       "      <td>0.458115</td>\n",
       "      <td>0.251135</td>\n",
       "      <td>0.425885</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>0.618214</td>\n",
       "      <td>0.485357</td>\n",
       "      <td>0.171071</td>\n",
       "      <td>1.250607</td>\n",
       "      <td>0.447464</td>\n",
       "      <td>0.246607</td>\n",
       "      <td>0.444643</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>0.595000</td>\n",
       "      <td>0.472500</td>\n",
       "      <td>0.189167</td>\n",
       "      <td>1.221250</td>\n",
       "      <td>0.405000</td>\n",
       "      <td>0.217167</td>\n",
       "      <td>0.390000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>0.587222</td>\n",
       "      <td>0.463889</td>\n",
       "      <td>0.172778</td>\n",
       "      <td>1.170389</td>\n",
       "      <td>0.399444</td>\n",
       "      <td>0.254833</td>\n",
       "      <td>0.370556</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>0.695000</td>\n",
       "      <td>0.540000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>1.798750</td>\n",
       "      <td>0.600250</td>\n",
       "      <td>0.332750</td>\n",
       "      <td>0.632500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>0.645000</td>\n",
       "      <td>0.490000</td>\n",
       "      <td>0.215000</td>\n",
       "      <td>1.406000</td>\n",
       "      <td>0.426500</td>\n",
       "      <td>0.228500</td>\n",
       "      <td>0.510000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>0.600000</td>\n",
       "      <td>0.495000</td>\n",
       "      <td>0.195000</td>\n",
       "      <td>1.057500</td>\n",
       "      <td>0.384000</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>0.375000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>0.607500</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.202500</td>\n",
       "      <td>1.698000</td>\n",
       "      <td>0.539000</td>\n",
       "      <td>0.298000</td>\n",
       "      <td>0.705000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>0.700000</td>\n",
       "      <td>0.585000</td>\n",
       "      <td>0.185000</td>\n",
       "      <td>1.807500</td>\n",
       "      <td>0.705500</td>\n",
       "      <td>0.321500</td>\n",
       "      <td>0.475000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Length  Diameter    Height  Whole weight  Shucked weight  \\\n",
       "Rings                                                               \n",
       "1      0.075000  0.055000  0.010000      0.002000        0.001000   \n",
       "2      0.150000  0.100000  0.025000      0.015000        0.004500   \n",
       "3      0.176000  0.128667  0.041667      0.028400        0.011767   \n",
       "4      0.221491  0.161579  0.053947      0.059605        0.024719   \n",
       "5      0.285739  0.210696  0.069913      0.125500        0.061696   \n",
       "6      0.369363  0.278861  0.091776      0.273174        0.123158   \n",
       "7      0.422033  0.321535  0.105921      0.397386        0.182657   \n",
       "8      0.498776  0.384798  0.128556      0.640753        0.293773   \n",
       "9      0.546865  0.425218  0.142721      0.852013        0.387938   \n",
       "10     0.574629  0.449290  0.154093      1.008748        0.447217   \n",
       "11     0.599374  0.470595  0.161253      1.142559        0.503977   \n",
       "12     0.589457  0.462434  0.161292      1.102904        0.472781   \n",
       "13     0.578892  0.456453  0.160887      1.075453        0.434638   \n",
       "14     0.580198  0.458294  0.163571      1.079992        0.427190   \n",
       "15     0.575728  0.456262  0.161359      1.040951        0.402471   \n",
       "16     0.587537  0.468433  0.171866      1.133388        0.421716   \n",
       "17     0.601034  0.475345  0.173966      1.234328        0.467052   \n",
       "18     0.596071  0.471310  0.171548      1.194333        0.446833   \n",
       "19     0.595625  0.470781  0.170313      1.166797        0.440625   \n",
       "20     0.603654  0.482308  0.173846      1.251750        0.458115   \n",
       "21     0.618214  0.485357  0.171071      1.250607        0.447464   \n",
       "22     0.595000  0.472500  0.189167      1.221250        0.405000   \n",
       "23     0.587222  0.463889  0.172778      1.170389        0.399444   \n",
       "24     0.695000  0.540000  0.200000      1.798750        0.600250   \n",
       "25     0.645000  0.490000  0.215000      1.406000        0.426500   \n",
       "26     0.600000  0.495000  0.195000      1.057500        0.384000   \n",
       "27     0.607500  0.500000  0.202500      1.698000        0.539000   \n",
       "29     0.700000  0.585000  0.185000      1.807500        0.705500   \n",
       "\n",
       "       Viscera weight  Shell weight  \n",
       "Rings                                \n",
       "1            0.000500      0.001500  \n",
       "2            0.004000      0.005000  \n",
       "3            0.006267      0.008933  \n",
       "4            0.012956      0.018000  \n",
       "5            0.027330      0.036770  \n",
       "6            0.058371      0.078388  \n",
       "7            0.085899      0.111648  \n",
       "8            0.138502      0.178609  \n",
       "9            0.187803      0.236509  \n",
       "10           0.223128      0.282976  \n",
       "11           0.252393      0.319866  \n",
       "12           0.239575      0.321157  \n",
       "13           0.235421      0.320828  \n",
       "14           0.235258      0.329960  \n",
       "15           0.227539      0.321650  \n",
       "16           0.239769      0.377858  \n",
       "17           0.251233      0.393414  \n",
       "18           0.242762      0.386762  \n",
       "19           0.248859      0.365312  \n",
       "20           0.251135      0.425885  \n",
       "21           0.246607      0.444643  \n",
       "22           0.217167      0.390000  \n",
       "23           0.254833      0.370556  \n",
       "24           0.332750      0.632500  \n",
       "25           0.228500      0.510000  \n",
       "26           0.190000      0.375000  \n",
       "27           0.298000      0.705000  \n",
       "29           0.321500      0.475000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# check mean value\n",
    "df.groupby(['Rings']).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "0c5973bc-4a82-4741-a278-d448c0c05993",
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2051</th>\n",
       "      <td>F</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.355</td>\n",
       "      <td>1.130</td>\n",
       "      <td>0.5940</td>\n",
       "      <td>0.3320</td>\n",
       "      <td>0.1160</td>\n",
       "      <td>0.1335</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1417</th>\n",
       "      <td>M</td>\n",
       "      <td>0.705</td>\n",
       "      <td>0.565</td>\n",
       "      <td>0.515</td>\n",
       "      <td>2.2100</td>\n",
       "      <td>1.1075</td>\n",
       "      <td>0.4865</td>\n",
       "      <td>0.5120</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1763</th>\n",
       "      <td>M</td>\n",
       "      <td>0.775</td>\n",
       "      <td>0.630</td>\n",
       "      <td>0.250</td>\n",
       "      <td>2.7795</td>\n",
       "      <td>1.3485</td>\n",
       "      <td>0.7600</td>\n",
       "      <td>0.5780</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1428</th>\n",
       "      <td>F</td>\n",
       "      <td>0.815</td>\n",
       "      <td>0.650</td>\n",
       "      <td>0.250</td>\n",
       "      <td>2.2550</td>\n",
       "      <td>0.8905</td>\n",
       "      <td>0.4200</td>\n",
       "      <td>0.7975</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2179</th>\n",
       "      <td>F</td>\n",
       "      <td>0.595</td>\n",
       "      <td>0.470</td>\n",
       "      <td>0.250</td>\n",
       "      <td>1.2830</td>\n",
       "      <td>0.4620</td>\n",
       "      <td>0.2475</td>\n",
       "      <td>0.4450</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Sex  Length  Diameter  Height  Whole weight  Shucked weight  \\\n",
       "2051   F   0.455     0.355   1.130        0.5940          0.3320   \n",
       "1417   M   0.705     0.565   0.515        2.2100          1.1075   \n",
       "1763   M   0.775     0.630   0.250        2.7795          1.3485   \n",
       "1428   F   0.815     0.650   0.250        2.2550          0.8905   \n",
       "2179   F   0.595     0.470   0.250        1.2830          0.4620   \n",
       "\n",
       "      Viscera weight  Shell weight  Rings  \n",
       "2051          0.1160        0.1335      8  \n",
       "1417          0.4865        0.5120     10  \n",
       "1763          0.7600        0.5780     12  \n",
       "1428          0.4200        0.7975     14  \n",
       "2179          0.2475        0.4450     14  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.sort_values(by='Height', ascending = False).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "838ef286-79da-42c8-b3a3-77f049ccf382",
   "metadata": {},
   "outputs": [],
   "source": [
    "#create new columns\n",
    "df['Ratio'] = (df['Length']/df['Height']).round(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "775aa137-b01a-4f12-875f-3445580f6840",
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "      <th>Ratio</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
       "      <td>4.79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
       "      <td>3.89</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
       "      <td>3.93</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "      <td>3.52</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "      <td>4.12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex  Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   Shell weight  Rings  Ratio  \n",
       "0         0.150     15   4.79  \n",
       "1         0.070      7   3.89  \n",
       "2         0.210      9   3.93  \n",
       "3         0.155     10   3.52  \n",
       "4         0.055      7   4.12  "
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "01a712cf-29d5-4cf5-85cf-20e6c52b41c9",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.drop(columns=['Ratio'],inplace=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "ee6b85ad-8f7e-4d5a-96db-cd8637546639",
   "metadata": {},
   "outputs": [],
   "source": [
    "df.to_csv('processed_df.csv',index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "76279646-6972-41cf-93aa-7fe3bb9b760f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Sex'>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#visualisation\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "df['Sex'].value_counts().plot(kind='pie', autopct= '%.2f')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "a46ba082-b988-4b25-8f21-abee877936ef",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Frequency'>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#histogram\n",
    "df['Length'].plot(kind='hist',bins=25)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "89c2c44d-2102-4618-805a-881260d2059e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:ylabel='Density'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# density plots\n",
    "df['Shell weight'].plot(kind='density')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "40506862-c0ff-46a6-8003-b0cb4544a3aa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:>"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Boxplot\n",
    "df['Whole weight'].plot(kind='box')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bf926745-989c-4cde-ab7f-855a4d9b45fa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Whole weight'}, xlabel='Sex'>"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.dropna().boxplot(column='Whole weight', by ='Sex')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cdc297c9-f372-4474-b9c7-c289aab48862",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Rings', ylabel='Diameter'>"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# scatter plots\n",
    "df.plot(kind='scatter',x='Rings',y='Diameter')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e94979d6-31ae-4ff2-9e42-9b87a724218d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df['Sum of weights'] = df['Shucked weight']+df['Viscera weight']+df['Shell weight']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "54b2f4f4-93cb-45b5-9a14-a1ed78f3e2f3",
   "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>Sex</th>\n",
       "      <th>Length</th>\n",
       "      <th>Diameter</th>\n",
       "      <th>Height</th>\n",
       "      <th>Whole weight</th>\n",
       "      <th>Shucked weight</th>\n",
       "      <th>Viscera weight</th>\n",
       "      <th>Shell weight</th>\n",
       "      <th>Rings</th>\n",
       "      <th>Sum of weights</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>M</td>\n",
       "      <td>0.455</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.5140</td>\n",
       "      <td>0.2245</td>\n",
       "      <td>0.1010</td>\n",
       "      <td>0.150</td>\n",
       "      <td>15</td>\n",
       "      <td>0.4755</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>M</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.265</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0995</td>\n",
       "      <td>0.0485</td>\n",
       "      <td>0.070</td>\n",
       "      <td>7</td>\n",
       "      <td>0.2180</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>F</td>\n",
       "      <td>0.530</td>\n",
       "      <td>0.420</td>\n",
       "      <td>0.135</td>\n",
       "      <td>0.6770</td>\n",
       "      <td>0.2565</td>\n",
       "      <td>0.1415</td>\n",
       "      <td>0.210</td>\n",
       "      <td>9</td>\n",
       "      <td>0.6080</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>M</td>\n",
       "      <td>0.440</td>\n",
       "      <td>0.365</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5160</td>\n",
       "      <td>0.2155</td>\n",
       "      <td>0.1140</td>\n",
       "      <td>0.155</td>\n",
       "      <td>10</td>\n",
       "      <td>0.4845</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>I</td>\n",
       "      <td>0.330</td>\n",
       "      <td>0.255</td>\n",
       "      <td>0.080</td>\n",
       "      <td>0.2050</td>\n",
       "      <td>0.0895</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.055</td>\n",
       "      <td>7</td>\n",
       "      <td>0.1840</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Sex  Length  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "0   M   0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "1   M   0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "2   F   0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "3   M   0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "4   I   0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "   Shell weight  Rings  Sum of weights  \n",
       "0         0.150     15          0.4755  \n",
       "1         0.070      7          0.2180  \n",
       "2         0.210      9          0.6080  \n",
       "3         0.155     10          0.4845  \n",
       "4         0.055      7          0.1840  "
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "95b62219-f5fc-45dd-ba9c-7bf980f0e58d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:xlabel='Sum of weights', ylabel='Whole weight'>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df.plot(kind = 'scatter', x = 'Sum of weights',y='Whole weight')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0ff586e8-a5b1-4d15-a8e3-99ccb197b583",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.9 (XPython)",
   "language": "python",
   "name": "xpython"
  },
  "language_info": {
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "version": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}