Abalone.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# ABALONE DATA ANALYSIS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Lenght</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",
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      ],
      "text/plain": [
       "  Sex  Lenght  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": [
    "#Defining the column and adding it to the dataset.\n",
    "column = ['Sex', 'Lenght', 'Diameter','Height', 'Whole weight', 'Shucked weight', 'Viscera weight', 'Shell weight', 'Rings']\n",
    "path=pd.read_csv('abalone.data.csv', names=column)\n",
    "data=pd.DataFrame(path)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Sex</th>\n",
       "      <th>Lenght</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>16.5</td>\n",
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       "    <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>8.5</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>10.5</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>11.5</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>8.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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      ],
      "text/plain": [
       "  Sex  Lenght  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   16.5  \n",
       "1         0.070    8.5  \n",
       "2         0.210   10.5  \n",
       "3         0.155   11.5  \n",
       "4         0.055    8.5  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ring= np.array(data['Rings']) + 1.5\n",
    "ring.reshape(4177,1)\n",
    "Ring= pd.DataFrame(ring, columns=['Ring'])\n",
    "data['Rings']=Ring\n",
    "data.head()\n",
    "#data['Rings']= ['Ring']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th>Lenght</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",
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       "    <tr>\n",
       "      <th>M</th>\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",
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       "      <th>M</th>\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>8.5</td>\n",
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       "    <tr>\n",
       "      <th>F</th>\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",
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       "      <td>0.210</td>\n",
       "      <td>10.5</td>\n",
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       "    <tr>\n",
       "      <th>M</th>\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>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\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>8.5</td>\n",
       "    </tr>\n",
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      ],
      "text/plain": [
       "     Lenght  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "Sex                                                                           \n",
       "M     0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "M     0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "F     0.530     0.420   0.135        0.6770          0.2565          0.1415   \n",
       "M     0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "I     0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "\n",
       "     Shell weight  Rings  \n",
       "Sex                       \n",
       "M           0.150   16.5  \n",
       "M           0.070    8.5  \n",
       "F           0.210   10.5  \n",
       "M           0.155   11.5  \n",
       "I           0.055    8.5  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat= data.set_index('Sex')\n",
    "dat.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lenght</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",
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       "      <th>Rings</th>\n",
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       "    <tr>\n",
       "      <th>Sex</th>\n",
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       "      <th>I</th>\n",
       "      <td>0.075</td>\n",
       "      <td>0.055</td>\n",
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       "      <td>0.0020</td>\n",
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       "      <td>0.150</td>\n",
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       "      <td>0.0150</td>\n",
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       "      <td>0.0040</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>3.5</td>\n",
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       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.210</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.050</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>0.0155</td>\n",
       "      <td>0.0085</td>\n",
       "      <td>0.0100</td>\n",
       "      <td>4.5</td>\n",
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       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.215</td>\n",
       "      <td>0.150</td>\n",
       "      <td>0.055</td>\n",
       "      <td>0.0410</td>\n",
       "      <td>0.0150</td>\n",
       "      <td>0.0090</td>\n",
       "      <td>0.0125</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.165</td>\n",
       "      <td>0.120</td>\n",
       "      <td>0.030</td>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.0070</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>0.0050</td>\n",
       "      <td>4.5</td>\n",
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      ],
      "text/plain": [
       "     Lenght  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "Sex                                                                           \n",
       "I     0.075     0.055   0.010        0.0020          0.0010          0.0005   \n",
       "I     0.150     0.100   0.025        0.0150          0.0045          0.0040   \n",
       "M     0.210     0.150   0.050        0.0385          0.0155          0.0085   \n",
       "I     0.215     0.150   0.055        0.0410          0.0150          0.0090   \n",
       "I     0.165     0.120   0.030        0.0215          0.0070          0.0050   \n",
       "\n",
       "     Shell weight  Rings  \n",
       "Sex                       \n",
       "I          0.0015    2.5  \n",
       "I          0.0050    3.5  \n",
       "M          0.0100    4.5  \n",
       "I          0.0125    4.5  \n",
       "I          0.0050    4.5  "
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat.sort_values(by=['Rings'], ascending=True).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "SEPAPARATING THE SEX TYPE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.370</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.2405</td>\n",
       "      <td>0.1040</td>\n",
       "      <td>0.0535</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.290</td>\n",
       "      <td>0.225</td>\n",
       "      <td>0.075</td>\n",
       "      <td>0.1400</td>\n",
       "      <td>0.0515</td>\n",
       "      <td>0.0235</td>\n",
       "      <td>0.0400</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.360</td>\n",
       "      <td>0.270</td>\n",
       "      <td>0.090</td>\n",
       "      <td>0.1885</td>\n",
       "      <td>0.0845</td>\n",
       "      <td>0.0385</td>\n",
       "      <td>0.0550</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.275</td>\n",
       "      <td>0.195</td>\n",
       "      <td>0.070</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>0.0310</td>\n",
       "      <td>0.0215</td>\n",
       "      <td>0.0250</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>0.445</td>\n",
       "      <td>0.335</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.4355</td>\n",
       "      <td>0.2025</td>\n",
       "      <td>0.1095</td>\n",
       "      <td>0.1195</td>\n",
       "      <td>7.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Lenght  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "Sex                                                                           \n",
       "F     0.370     0.275   0.085        0.2405          0.1040          0.0535   \n",
       "F     0.290     0.225   0.075        0.1400          0.0515          0.0235   \n",
       "F     0.360     0.270   0.090        0.1885          0.0845          0.0385   \n",
       "F     0.275     0.195   0.070        0.0800          0.0310          0.0215   \n",
       "F     0.445     0.335   0.110        0.4355          0.2025          0.1095   \n",
       "\n",
       "     Shell weight  Rings  \n",
       "Sex                       \n",
       "F          0.0700    6.5  \n",
       "F          0.0400    6.5  \n",
       "F          0.0550    6.5  \n",
       "F          0.0250    6.5  \n",
       "F          0.1195    7.5  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat1=dat.loc['F'].sort_values(by=['Rings'], ascending=True)\n",
    "dat1.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "\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>Lenght</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>count</th>\n",
       "      <td>1307.000000</td>\n",
       "      <td>1307.000000</td>\n",
       "      <td>1307.000000</td>\n",
       "      <td>1307.000000</td>\n",
       "      <td>1307.000000</td>\n",
       "      <td>1307.000000</td>\n",
       "      <td>1307.000000</td>\n",
       "      <td>1307.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.579093</td>\n",
       "      <td>0.454732</td>\n",
       "      <td>0.158011</td>\n",
       "      <td>1.046532</td>\n",
       "      <td>0.446188</td>\n",
       "      <td>0.230689</td>\n",
       "      <td>0.302010</td>\n",
       "      <td>12.629304</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.086160</td>\n",
       "      <td>0.070954</td>\n",
       "      <td>0.039984</td>\n",
       "      <td>0.430316</td>\n",
       "      <td>0.198663</td>\n",
       "      <td>0.097617</td>\n",
       "      <td>0.125649</td>\n",
       "      <td>3.104256</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.275000</td>\n",
       "      <td>0.195000</td>\n",
       "      <td>0.015000</td>\n",
       "      <td>0.080000</td>\n",
       "      <td>0.031000</td>\n",
       "      <td>0.021000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>6.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.525000</td>\n",
       "      <td>0.410000</td>\n",
       "      <td>0.140000</td>\n",
       "      <td>0.730000</td>\n",
       "      <td>0.295000</td>\n",
       "      <td>0.159000</td>\n",
       "      <td>0.213250</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.590000</td>\n",
       "      <td>0.465000</td>\n",
       "      <td>0.160000</td>\n",
       "      <td>1.038500</td>\n",
       "      <td>0.440500</td>\n",
       "      <td>0.224000</td>\n",
       "      <td>0.295000</td>\n",
       "      <td>11.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.640000</td>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.175000</td>\n",
       "      <td>1.320250</td>\n",
       "      <td>0.573250</td>\n",
       "      <td>0.297250</td>\n",
       "      <td>0.375000</td>\n",
       "      <td>13.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.815000</td>\n",
       "      <td>0.650000</td>\n",
       "      <td>1.130000</td>\n",
       "      <td>2.657000</td>\n",
       "      <td>1.488000</td>\n",
       "      <td>0.590000</td>\n",
       "      <td>1.005000</td>\n",
       "      <td>30.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Lenght     Diameter       Height  Whole weight  Shucked weight  \\\n",
       "count  1307.000000  1307.000000  1307.000000   1307.000000     1307.000000   \n",
       "mean      0.579093     0.454732     0.158011      1.046532        0.446188   \n",
       "std       0.086160     0.070954     0.039984      0.430316        0.198663   \n",
       "min       0.275000     0.195000     0.015000      0.080000        0.031000   \n",
       "25%       0.525000     0.410000     0.140000      0.730000        0.295000   \n",
       "50%       0.590000     0.465000     0.160000      1.038500        0.440500   \n",
       "75%       0.640000     0.505000     0.175000      1.320250        0.573250   \n",
       "max       0.815000     0.650000     1.130000      2.657000        1.488000   \n",
       "\n",
       "       Viscera weight  Shell weight        Rings  \n",
       "count     1307.000000   1307.000000  1307.000000  \n",
       "mean         0.230689      0.302010    12.629304  \n",
       "std          0.097617      0.125649     3.104256  \n",
       "min          0.021000      0.025000     6.500000  \n",
       "25%          0.159000      0.213250    10.500000  \n",
       "50%          0.224000      0.295000    11.500000  \n",
       "75%          0.297250      0.375000    13.500000  \n",
       "max          0.590000      1.005000    30.500000  "
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat1_desc = dat1.describe()\n",
    "dat1_desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lenght</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",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></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>I</th>\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>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.425</td>\n",
       "      <td>0.300</td>\n",
       "      <td>0.095</td>\n",
       "      <td>0.3515</td>\n",
       "      <td>0.1410</td>\n",
       "      <td>0.0775</td>\n",
       "      <td>0.120</td>\n",
       "      <td>9.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.355</td>\n",
       "      <td>0.280</td>\n",
       "      <td>0.085</td>\n",
       "      <td>0.2905</td>\n",
       "      <td>0.0950</td>\n",
       "      <td>0.0395</td>\n",
       "      <td>0.115</td>\n",
       "      <td>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.380</td>\n",
       "      <td>0.275</td>\n",
       "      <td>0.100</td>\n",
       "      <td>0.2255</td>\n",
       "      <td>0.0800</td>\n",
       "      <td>0.0490</td>\n",
       "      <td>0.085</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>0.240</td>\n",
       "      <td>0.175</td>\n",
       "      <td>0.045</td>\n",
       "      <td>0.0700</td>\n",
       "      <td>0.0315</td>\n",
       "      <td>0.0235</td>\n",
       "      <td>0.020</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Lenght  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "Sex                                                                           \n",
       "I     0.330     0.255   0.080        0.2050          0.0895          0.0395   \n",
       "I     0.425     0.300   0.095        0.3515          0.1410          0.0775   \n",
       "I     0.355     0.280   0.085        0.2905          0.0950          0.0395   \n",
       "I     0.380     0.275   0.100        0.2255          0.0800          0.0490   \n",
       "I     0.240     0.175   0.045        0.0700          0.0315          0.0235   \n",
       "\n",
       "     Shell weight  Rings  \n",
       "Sex                       \n",
       "I           0.055    8.5  \n",
       "I           0.120    9.5  \n",
       "I           0.115    8.5  \n",
       "I           0.085   11.5  \n",
       "I           0.020    6.5  "
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat2 = dat.loc['I']\n",
    "dat2.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lenght</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>count</th>\n",
       "      <td>1342.000000</td>\n",
       "      <td>1342.000000</td>\n",
       "      <td>1342.000000</td>\n",
       "      <td>1342.000000</td>\n",
       "      <td>1342.000000</td>\n",
       "      <td>1342.000000</td>\n",
       "      <td>1342.000000</td>\n",
       "      <td>1342.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.427746</td>\n",
       "      <td>0.326494</td>\n",
       "      <td>0.107996</td>\n",
       "      <td>0.431363</td>\n",
       "      <td>0.191035</td>\n",
       "      <td>0.092010</td>\n",
       "      <td>0.128182</td>\n",
       "      <td>9.390462</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.108858</td>\n",
       "      <td>0.088109</td>\n",
       "      <td>0.031995</td>\n",
       "      <td>0.286275</td>\n",
       "      <td>0.128405</td>\n",
       "      <td>0.062536</td>\n",
       "      <td>0.084927</td>\n",
       "      <td>2.511554</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.075000</td>\n",
       "      <td>0.055000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.002000</td>\n",
       "      <td>0.001000</td>\n",
       "      <td>0.000500</td>\n",
       "      <td>0.001500</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.360000</td>\n",
       "      <td>0.270000</td>\n",
       "      <td>0.085000</td>\n",
       "      <td>0.205500</td>\n",
       "      <td>0.090000</td>\n",
       "      <td>0.042500</td>\n",
       "      <td>0.064125</td>\n",
       "      <td>7.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.435000</td>\n",
       "      <td>0.335000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.384000</td>\n",
       "      <td>0.169750</td>\n",
       "      <td>0.080500</td>\n",
       "      <td>0.113000</td>\n",
       "      <td>9.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.510000</td>\n",
       "      <td>0.390000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.599375</td>\n",
       "      <td>0.270375</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.178500</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.725000</td>\n",
       "      <td>0.550000</td>\n",
       "      <td>0.220000</td>\n",
       "      <td>2.049500</td>\n",
       "      <td>0.773500</td>\n",
       "      <td>0.440500</td>\n",
       "      <td>0.655000</td>\n",
       "      <td>22.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Lenght     Diameter       Height  Whole weight  Shucked weight  \\\n",
       "count  1342.000000  1342.000000  1342.000000   1342.000000     1342.000000   \n",
       "mean      0.427746     0.326494     0.107996      0.431363        0.191035   \n",
       "std       0.108858     0.088109     0.031995      0.286275        0.128405   \n",
       "min       0.075000     0.055000     0.000000      0.002000        0.001000   \n",
       "25%       0.360000     0.270000     0.085000      0.205500        0.090000   \n",
       "50%       0.435000     0.335000     0.110000      0.384000        0.169750   \n",
       "75%       0.510000     0.390000     0.130000      0.599375        0.270375   \n",
       "max       0.725000     0.550000     0.220000      2.049500        0.773500   \n",
       "\n",
       "       Viscera weight  Shell weight        Rings  \n",
       "count     1342.000000   1342.000000  1342.000000  \n",
       "mean         0.092010      0.128182     9.390462  \n",
       "std          0.062536      0.084927     2.511554  \n",
       "min          0.000500      0.001500     2.500000  \n",
       "25%          0.042500      0.064125     7.500000  \n",
       "50%          0.080500      0.113000     9.500000  \n",
       "75%          0.130000      0.178500    10.500000  \n",
       "max          0.440500      0.655000    22.500000  "
      ]
     },
     "execution_count": 63,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat2_desc = dat2.describe()\n",
    "dat2_desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lenght</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",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></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>M</th>\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>16.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\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>8.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\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>11.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.475</td>\n",
       "      <td>0.370</td>\n",
       "      <td>0.125</td>\n",
       "      <td>0.5095</td>\n",
       "      <td>0.2165</td>\n",
       "      <td>0.1125</td>\n",
       "      <td>0.165</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.430</td>\n",
       "      <td>0.350</td>\n",
       "      <td>0.110</td>\n",
       "      <td>0.4060</td>\n",
       "      <td>0.1675</td>\n",
       "      <td>0.0810</td>\n",
       "      <td>0.135</td>\n",
       "      <td>11.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     Lenght  Diameter  Height  Whole weight  Shucked weight  Viscera weight  \\\n",
       "Sex                                                                           \n",
       "M     0.455     0.365   0.095        0.5140          0.2245          0.1010   \n",
       "M     0.350     0.265   0.090        0.2255          0.0995          0.0485   \n",
       "M     0.440     0.365   0.125        0.5160          0.2155          0.1140   \n",
       "M     0.475     0.370   0.125        0.5095          0.2165          0.1125   \n",
       "M     0.430     0.350   0.110        0.4060          0.1675          0.0810   \n",
       "\n",
       "     Shell weight  Rings  \n",
       "Sex                       \n",
       "M           0.150   16.5  \n",
       "M           0.070    8.5  \n",
       "M           0.155   11.5  \n",
       "M           0.165   10.5  \n",
       "M           0.135   11.5  "
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat3 = dat.loc['M']\n",
    "dat3.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lenght</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>count</th>\n",
       "      <td>1528.000000</td>\n",
       "      <td>1528.000000</td>\n",
       "      <td>1528.000000</td>\n",
       "      <td>1528.000000</td>\n",
       "      <td>1528.000000</td>\n",
       "      <td>1528.000000</td>\n",
       "      <td>1528.000000</td>\n",
       "      <td>1528.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.561391</td>\n",
       "      <td>0.439287</td>\n",
       "      <td>0.151381</td>\n",
       "      <td>0.991459</td>\n",
       "      <td>0.432946</td>\n",
       "      <td>0.215545</td>\n",
       "      <td>0.281969</td>\n",
       "      <td>12.205497</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.102697</td>\n",
       "      <td>0.084398</td>\n",
       "      <td>0.034804</td>\n",
       "      <td>0.470581</td>\n",
       "      <td>0.223000</td>\n",
       "      <td>0.104919</td>\n",
       "      <td>0.130834</td>\n",
       "      <td>3.026349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>0.155000</td>\n",
       "      <td>0.110000</td>\n",
       "      <td>0.025000</td>\n",
       "      <td>0.015500</td>\n",
       "      <td>0.006500</td>\n",
       "      <td>0.003000</td>\n",
       "      <td>0.005000</td>\n",
       "      <td>4.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>0.505000</td>\n",
       "      <td>0.395000</td>\n",
       "      <td>0.130000</td>\n",
       "      <td>0.671625</td>\n",
       "      <td>0.276375</td>\n",
       "      <td>0.145875</td>\n",
       "      <td>0.190000</td>\n",
       "      <td>10.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>0.580000</td>\n",
       "      <td>0.455000</td>\n",
       "      <td>0.155000</td>\n",
       "      <td>0.975750</td>\n",
       "      <td>0.421750</td>\n",
       "      <td>0.210000</td>\n",
       "      <td>0.276000</td>\n",
       "      <td>11.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.175000</td>\n",
       "      <td>1.265625</td>\n",
       "      <td>0.566000</td>\n",
       "      <td>0.278000</td>\n",
       "      <td>0.355500</td>\n",
       "      <td>13.500000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.515000</td>\n",
       "      <td>2.825500</td>\n",
       "      <td>1.351000</td>\n",
       "      <td>0.760000</td>\n",
       "      <td>0.897000</td>\n",
       "      <td>28.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Lenght     Diameter       Height  Whole weight  Shucked weight  \\\n",
       "count  1528.000000  1528.000000  1528.000000   1528.000000     1528.000000   \n",
       "mean      0.561391     0.439287     0.151381      0.991459        0.432946   \n",
       "std       0.102697     0.084398     0.034804      0.470581        0.223000   \n",
       "min       0.155000     0.110000     0.025000      0.015500        0.006500   \n",
       "25%       0.505000     0.395000     0.130000      0.671625        0.276375   \n",
       "50%       0.580000     0.455000     0.155000      0.975750        0.421750   \n",
       "75%       0.630000     0.500000     0.175000      1.265625        0.566000   \n",
       "max       0.780000     0.630000     0.515000      2.825500        1.351000   \n",
       "\n",
       "       Viscera weight  Shell weight        Rings  \n",
       "count     1528.000000   1528.000000  1528.000000  \n",
       "mean         0.215545      0.281969    12.205497  \n",
       "std          0.104919      0.130834     3.026349  \n",
       "min          0.003000      0.005000     4.500000  \n",
       "25%          0.145875      0.190000    10.500000  \n",
       "50%          0.210000      0.276000    11.500000  \n",
       "75%          0.278000      0.355500    13.500000  \n",
       "max          0.760000      0.897000    28.500000  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dat3_desc = dat3.describe()\n",
    "dat3_desc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "M    1528\n",
       "I    1342\n",
       "F    1307\n",
       "Name: Sex, dtype: int64"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ds=data.Sex.value_counts()\n",
    "ds"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  1,   1,   1,   1,   1,   2,   2,   6,   9,  14,  15,  26,  32,\n",
       "        42,  57,  58,  67, 103, 115, 126, 203, 259, 267, 391, 487, 568,\n",
       "       634, 689], dtype=int64)"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dr=data.Rings.value_counts()\n",
    "np.sort(dr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Rings</th>\n",
       "      <th>Max Rings</th>\n",
       "      <th>Min Rings</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>F</th>\n",
       "      <td>30.5</td>\n",
       "      <td>6.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>22.5</td>\n",
       "      <td>2.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>28.5</td>\n",
       "      <td>4.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Rings  Max Rings  Min Rings\n",
       "Sex                        \n",
       "F           30.5        6.5\n",
       "I           22.5        2.5\n",
       "M           28.5        4.5"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ftt = data.groupby('Sex').Rings.max()\n",
    "fts = data.groupby('Sex').Rings.min()\n",
    "ft=pd.concat([ftt, fts], axis=1, ignore_index=True)\n",
    "ft.columns=['Max Rings', 'Min Rings']\n",
    "ft.columns.name='Rings'\n",
    "ft"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Rings</th>\n",
       "      <th>2.5</th>\n",
       "      <th>3.5</th>\n",
       "      <th>4.5</th>\n",
       "      <th>5.5</th>\n",
       "      <th>6.5</th>\n",
       "      <th>7.5</th>\n",
       "      <th>8.5</th>\n",
       "      <th>9.5</th>\n",
       "      <th>10.5</th>\n",
       "      <th>11.5</th>\n",
       "      <th>...</th>\n",
       "      <th>20.5</th>\n",
       "      <th>21.5</th>\n",
       "      <th>22.5</th>\n",
       "      <th>23.5</th>\n",
       "      <th>24.5</th>\n",
       "      <th>25.5</th>\n",
       "      <th>26.5</th>\n",
       "      <th>27.5</th>\n",
       "      <th>28.5</th>\n",
       "      <th>30.5</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></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>F</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.0</td>\n",
       "      <td>16.0</td>\n",
       "      <td>44.0</td>\n",
       "      <td>122.0</td>\n",
       "      <td>238.0</td>\n",
       "      <td>248.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>51.0</td>\n",
       "      <td>100.0</td>\n",
       "      <td>216.0</td>\n",
       "      <td>267.0</td>\n",
       "      <td>274.0</td>\n",
       "      <td>173.0</td>\n",
       "      <td>92.0</td>\n",
       "      <td>...</td>\n",
       "      <td>2.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>11.0</td>\n",
       "      <td>27.0</td>\n",
       "      <td>80.0</td>\n",
       "      <td>172.0</td>\n",
       "      <td>278.0</td>\n",
       "      <td>294.0</td>\n",
       "      <td>...</td>\n",
       "      <td>15.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 28 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "Rings  2.5   3.5   4.5   5.5    6.5    7.5    8.5    9.5    10.5   11.5  ...  \\\n",
       "Sex                                                                      ...   \n",
       "F       0.0   0.0   0.0   0.0    4.0   16.0   44.0  122.0  238.0  248.0  ...   \n",
       "I       1.0   1.0  12.0  51.0  100.0  216.0  267.0  274.0  173.0   92.0  ...   \n",
       "M       0.0   0.0   3.0   6.0   11.0   27.0   80.0  172.0  278.0  294.0  ...   \n",
       "\n",
       "Rings  20.5  21.5  22.5  23.5  24.5  25.5  26.5  27.5  28.5  30.5  \n",
       "Sex                                                                \n",
       "F      15.0  12.0   7.0   3.0   6.0   1.0   1.0   0.0   1.0   1.0  \n",
       "I       2.0   2.0   1.0   0.0   0.0   0.0   0.0   0.0   0.0   0.0  \n",
       "M      15.0  12.0   6.0   3.0   3.0   1.0   0.0   1.0   1.0   0.0  \n",
       "\n",
       "[3 rows x 28 columns]"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "dt= data.groupby(['Sex', 'Rings'])\n",
    "dts = dt.size().unstack().fillna(0)\n",
    "inds = dts.sum(1).argsort()\n",
    "dtt= dts.take(inds)\n",
    "dtt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rings\n",
       "2.5       0.0\n",
       "3.5       0.0\n",
       "4.5       0.0\n",
       "5.5       0.0\n",
       "6.5       4.0\n",
       "7.5      16.0\n",
       "8.5      44.0\n",
       "9.5     122.0\n",
       "10.5    238.0\n",
       "11.5    248.0\n",
       "12.5    200.0\n",
       "13.5    128.0\n",
       "14.5     88.0\n",
       "15.5     56.0\n",
       "16.5     41.0\n",
       "17.5     30.0\n",
       "18.5     26.0\n",
       "19.5     19.0\n",
       "20.5     15.0\n",
       "21.5     12.0\n",
       "22.5      7.0\n",
       "23.5      3.0\n",
       "24.5      6.0\n",
       "25.5      1.0\n",
       "26.5      1.0\n",
       "27.5      0.0\n",
       "28.5      1.0\n",
       "30.5      1.0\n",
       "Name: F, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data on Female\n",
    "dlf = dtt.loc['F']\n",
    "dlf\n",
    "#Has highest ring value of 248.0 at 11.5Rings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rings\n",
       "2.5       0.0\n",
       "3.5       0.0\n",
       "4.5       3.0\n",
       "5.5       6.0\n",
       "6.5      11.0\n",
       "7.5      27.0\n",
       "8.5      80.0\n",
       "9.5     172.0\n",
       "10.5    278.0\n",
       "11.5    294.0\n",
       "12.5    225.0\n",
       "13.5    118.0\n",
       "14.5     91.0\n",
       "15.5     56.0\n",
       "16.5     52.0\n",
       "17.5     30.0\n",
       "18.5     25.0\n",
       "19.5     18.0\n",
       "20.5     15.0\n",
       "21.5     12.0\n",
       "22.5      6.0\n",
       "23.5      3.0\n",
       "24.5      3.0\n",
       "25.5      1.0\n",
       "26.5      0.0\n",
       "27.5      1.0\n",
       "28.5      1.0\n",
       "30.5      0.0\n",
       "Name: M, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Data on Male\n",
    "dlm = dtt.loc['M']\n",
    "dlm\n",
    "#Has highest ring value of 294.0 at 11.5Rings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Rings\n",
       "2.5       1.0\n",
       "3.5       1.0\n",
       "4.5      12.0\n",
       "5.5      51.0\n",
       "6.5     100.0\n",
       "7.5     216.0\n",
       "8.5     267.0\n",
       "9.5     274.0\n",
       "10.5    173.0\n",
       "11.5     92.0\n",
       "12.5     62.0\n",
       "13.5     21.0\n",
       "14.5     24.0\n",
       "15.5     14.0\n",
       "16.5     10.0\n",
       "17.5      7.0\n",
       "18.5      7.0\n",
       "19.5      5.0\n",
       "20.5      2.0\n",
       "21.5      2.0\n",
       "22.5      1.0\n",
       "23.5      0.0\n",
       "24.5      0.0\n",
       "25.5      0.0\n",
       "26.5      0.0\n",
       "27.5      0.0\n",
       "28.5      0.0\n",
       "30.5      0.0\n",
       "Name: I, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#data on Infant\n",
    "dli = dtt.loc['I']\n",
    "dli\n",
    "#Has highest ring value of 274.0 at 9.5Rings"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "dlf.plot(color='r')\n",
    "dlm.plot(color='k')\n",
    "dli.plot(color='c')\n",
    "plt.show()\n",
    "#From the graph, it shows that "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Rings\n",
    "The male has more rings followed by the \n",
    "infant and the female."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex  Lenght\n",
       "F    0.625     43\n",
       "     0.580     40\n",
       "     0.620     39\n",
       "     0.600     35\n",
       "     0.645     34\n",
       "               ..\n",
       "M    0.300      1\n",
       "     0.315      1\n",
       "     0.435      1\n",
       "     0.755      1\n",
       "     0.780      1\n",
       "Name: Lenght, Length: 317, dtype: int64"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddl= data.groupby('Sex').Lenght.value_counts()\n",
    "#ddl.isnull().sum() gives a 0\n",
    "ddl"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddl.plot()\n",
    "plt.show()\n",
    "#The male posseses more lenght than the female and infant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex  Diameter\n",
       "F    0.500       64\n",
       "     0.475       56\n",
       "     0.450       49\n",
       "     0.485       42\n",
       "     0.480       39\n",
       "                 ..\n",
       "M    0.170        1\n",
       "     0.190        1\n",
       "     0.220        1\n",
       "     0.620        1\n",
       "     0.630        1\n",
       "Name: Diameter, Length: 268, dtype: int64"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddd= data.groupby('Sex').Diameter.value_counts()\n",
    "#ddd.isnull().sum() gives a 0\n",
    "ddd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddd.plot()\n",
    "plt.show()\n",
    "#The male posseses a larger diameter than the female and infant"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex  Height\n",
       "F    0.150     113\n",
       "     0.175      96\n",
       "     0.160      91\n",
       "     0.165      90\n",
       "     0.170      86\n",
       "              ... \n",
       "M    0.070       2\n",
       "     0.025       1\n",
       "     0.045       1\n",
       "     0.250       1\n",
       "     0.515       1\n",
       "Name: Height, Length: 125, dtype: int64"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddh= data.groupby('Sex').Height.value_counts()\n",
    "#ddh.isnull().sum() gives a 0\n",
    "ddh"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddh.plot()\n",
    "plt.show()\n",
    "#The height of the male, female and infant are close in range"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex  Whole weight\n",
       "F    1.1345          7\n",
       "     0.5885          4\n",
       "     0.6355          4\n",
       "     1.1155          4\n",
       "     1.2895          4\n",
       "                    ..\n",
       "M    2.5155          1\n",
       "     2.5480          1\n",
       "     2.5500          1\n",
       "     2.7795          1\n",
       "     2.8255          1\n",
       "Name: Whole weight, Length: 3271, dtype: int64"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddww= data.groupby('Sex')['Whole weight'].value_counts()\n",
    "#ddww.isnull().sum() gives a 0\n",
    "ddww"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddww.plot()\n",
    "plt.show()\n",
    "#The infant posseses more weight compared to the male and female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex  Shucked weight\n",
       "F    0.1650            6\n",
       "     0.3020            5\n",
       "     0.3910            5\n",
       "     0.5415            5\n",
       "     0.1920            4\n",
       "                      ..\n",
       "M    1.1945            1\n",
       "     1.2455            1\n",
       "     1.2530            1\n",
       "     1.3485            1\n",
       "     1.3510            1\n",
       "Name: Shucked weight, Length: 2531, dtype: int64"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddsw= data.groupby('Sex')['Shucked weight'].value_counts()\n",
    "#ddsw.isnull().sum() gives a 0\n",
    "ddsw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddsw.plot()\n",
    "plt.show()\n",
    "#The infant posseses more shucked weight than the male and female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex  Viscera weight\n",
       "F    0.1960            9\n",
       "     0.1715            8\n",
       "     0.3180            8\n",
       "     0.2070            7\n",
       "     0.2390            7\n",
       "                      ..\n",
       "M    0.5640            1\n",
       "     0.5745            1\n",
       "     0.5750            1\n",
       "     0.6415            1\n",
       "     0.7600            1\n",
       "Name: Viscera weight, Length: 1747, dtype: int64"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddvw= data.groupby('Sex')['Viscera weight'].value_counts()\n",
    "#ddvw.isnull().sum() gives a 0\n",
    "ddvw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddvw.plot()\n",
    "plt.show()\n",
    "#The infant has more Viscera weight than the male and female"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Sex  Shell weight\n",
       "F    0.320           19\n",
       "     0.265           18\n",
       "     0.275           18\n",
       "     0.315           18\n",
       "     0.355           17\n",
       "                     ..\n",
       "M    0.700            1\n",
       "     0.760            1\n",
       "     0.780            1\n",
       "     0.885            1\n",
       "     0.897            1\n",
       "Name: Shell weight, Length: 1489, dtype: int64"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ddshw= data.groupby('Sex')['Shell weight'].value_counts()\n",
    "#ddshw.isnull().sum() gives a 0\n",
    "ddshw"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "metadata": {
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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aOGzPXQB4ZokGYqSdkw7wY9ama3rEulApcRjAuUfuxXsdW/npE6/UwSKRZlIl7s1NxvcvnAQo7p4FPn7cWEAN4PWmVG4ZgH+feihTJ+5Jh0Iz/Z5UiTso7p4mKnaFbFJZpRHfi0nOUX8ndeKuvrrpo1wDnvq6149KvWUi8jnf0F2pQVw0NqkT92iUnbzBbNCiSVbqT7m4DJDfPlJV4t6fSaG4ayBTWqjG8dNApvSht18BKRR3JQ/LFlEIQPQ91frharcSkEJxjwZiyOtID6VymUA0961e/+tJufKATnHXPdS/SZ24AzSZ8f2HF7N2Y0fSpvRzKot2S7Nx57NL2SIhSQ1RWOaffj2X1Rs2J2yNSIpUivthe/mBTC8sfydhS0QldhvUCsDyte8lbEk/oMreL217D2XCXrvwyIJ25r2xto+NEmklleL+L6cfCOi1Mi2U6goJ8JdH7w2orOpJufIA2HfEIL52lmY16++kUtzVIJQd1LspnegeEqkWd3kdyVJtV0iADjWq9jnducJR3F0P3f5LKsVdFTNdlAsDqKzSiboUi3SKuypmZtCI4vpTIeQOxAcy6Y2qv5JKce8My6hiJkk1V1+z/9SP7qSKUWhTpFLcNXw6O6is0sn2cJneqPotqRT36FX/y3c9r8qZAsqNiIw8xDuefr1e5vR7rFJfSDrvoVtnLOGHjyzua5NECkmluA9qzTFysB8cs3TNxoStEeXYZ/jOACxfq2kR+5rupPDNNzdx/pQxbNy0lV8+owdvfySV4m5mXHnmIYDi7klSjZYMaGnm1EP2YIvKKVWYGV8/ZwInHDBCPZn6KakUd9AgjDRRKQrQklPa33pSTW+ZiNac8u33V1Ir7uo/nR1amo1NEpA+pyfvRsq3339Jrbir/3TyuCrlpFWee2rRTFn9l9SKe6u62GUGeYf1pYrOMtvJ5zRZdn8lteKumHt6qKQl8g7rQ0/mu25p1mTZ/ZXUirsGx2QHeYfppVVpCPotqRX3yHP/9h/m88uZ6qebBNU6e5F3+Gc/fJIfacBMn1Npmr04UdvV+TfO4O/u+CNbt0nk+wupFfcxQwdyxoRRrHxnE3fPWZa0Of2aSjHekw4YwXH7DWdx+3r+59k36mNUP6QnsnzcfiM4fvxw1mzczN1zlvHW+k01t0ukk9SKez7XxPUXTOKg0UMUz005R7xvKLd/4v0cs99wdV1NGQePHsJtl7yfT54wDkBdVvsRqRX3CHWzS47ueop5NazWh+6MYgqo91n/I/Xi7rvZKU6YBfI5k3ikFKXR7n9kQNxN3mDiVOcqRg2rom/oTXdGdS3uf6Re3PO5ZnmDGSHf3ESHxKPP6c4gpgil8+h/5GqxEzNbAqwDtgJbnHNttdgvKG9JknTXU2zJyXNPK0rn0f+oibgHTnLOrarh/gA1qKaBaj3FfGgfcc5VNaGEqB9qUO1/1FLc+4SW5ibe2rCZc77/OGdP2ouPHbV30iaJEkSv/uf84AkMGLJTC9ddMIlBramvZpmiJ4/NKOb+lbufZ9eBef7u/+3HiQeMrK1hIlXUKubugD+Y2Swzu7TwRzO71MxmmtnM9vb2bu341EP34Jhxu7G4fQO/12CmVHPC/iM48YARDGrN8V7HNh6e384r7RuSNksA+40cxBmHjWL0rjsx5/W3eeillUmbJPqYWrlUxzrnlpnZSOB+M3vJOTc9+tE5dwNwA0BbW1u3ArnHjBvOMeOG87GfPMX6TVtqZK7oCw7dcxdu/qspADyyoJ2LbnqazVu3JmxV49Cb3F8D8zmuv3ASAJOvekBtI/2Amnjuzrll4XMl8BtgSi32GyevtLKJ0ZMwQH571zv1q641vW3P8IPNVC6NTq/F3cx2NrPB0Xfgg8C83u63kJbmJjpUITNDPucFSA/k9JFXJ4V+QS3CMrsDvwneRA74b+fcvTXYbxfUza7+9CYMkG9uBtT1rpZUOzNWJZQmon/Qa3F3zr0MHF4DW8qikarJ0ZMwQIs89z6jt51MW5Qmol+Q+hGqEa3y3DPF9uHuKrPUoTQR/YPMiLvm6aw/vQkD5JXLpObUaqY8hWX6B5kZXdLS3MQ773Yw9brH/AIzPnvyeE46UAMx0kg0oOm7Dyzk9hmvdvmtqcn44mkHMWWfYUmY1u/J55qY9eqa7ffSLgPz/PAvJjEwnxk5EFWQmdI87dA9eGXVBrYF9+XxRat4eP5KiXsd6EmMd8SgVs6bPIY333lvh98ent/O44tWSdx7SG8zO3y0bQzNTX4n7es2MX1BO6+t3siBewypgXUiLWRG3NvGDuOmizvFQAMx+p7ehAGamoyr/3RC0d/GX3GPyq4H1Koj8JmHj+bMw0cDcP8LK/jrW2eqm3EDkpmYeyEaiJFdlBo4PXSmAtZI4kYju+Ku3jN1o9YJHjVmoXdYrztDdtKZCliOUqORXXGX95dZ1POpZ9Sqt0wcpQJuXDIr7hqI0ff0hZiAQmppQtPvNS7ZFXcNxMgsCqn1jlqGyTonzlZ5NBqZFXcNxKgftYzxgo/zKqTWfWqVWyaO5lZtXDLTFbKQfK6JmUvWcMa1jxb9ferE0Vz6gXF1tqqx6KvASUtzE48vWlWy7Irx18fvy1lH7NlHFvVfopHE37x3PjdMf3mH383gspPGc+qhe9TbNNFLMivufz55zPbGoEL++Nrb3DvvTYl7SvnYUXvzwIsrql7/icVv8eBLKyXufcDoXXfivMljWLV+U9Hfpy9cxSML2iXuGSSz4v7hCaP58ITRRX/7+M3PsHLdjiMjRc+odVfI86a8j/OmvK/q9T94zSMKwdE3DdzNZQabARx79YO69hklszH3cuQ1sUdDocklkkPXPrs0pLhrkExtcH3VF7KbqGdUV2r9JlUOzaOQXRpS3NWTprFQeSaHPPfs0pjinjN5eg2E+sUnh96asktjiruGt9eEdARlVJ6F1HrcQTn01pRdGlLcW5R3pqFoUQM5kEwbiMIy2SWzXSHL0ZJrYsPmrZz63ek12d+Iwa38+KI2WnPNNdlf1qhnA14xWnJNvLJqQ83KM6K5ybjyzEOYPFaThpSipbmJ55e9U/NrHzF4QI4b/7KNXQfm+2T//ZmGFPfTDx3FktisTb1h2dvv8ejCVax8ZxNjhg2sgXWiu5x75F5s3lLbfOPbnJ+o4ulXVmdO3Ov5sD1v8pjtaYFrzVvrN/PMkjUsbt/AkXtL3GtNQ4r7YXvtwg/+4sia7Ot3s9/gMz+f3T8blVISCTlh/xGcsP+Imu7TOcc+X7wnU/HkJHqmfvCQPfjgIX0zOvXJxW9x/o0zMlUGWaIhY+61JK+seVjScZk+wMx8ArN+XK5Jk8/5eqUy6Bsk7hVQvuvGpSWjPUEa5VGre6tvkbhXoKUfz1TTFylm04R6giRLvh/fW/VA4l6BKCyzSd5Fw5G1ATqN9qjd7rlnqAyyhMS9Ap1xwUa7taqnUcIAhWR1ur9GaQPJKyzTp0jcK7B9GjJVwIZDYZlk6QzLZO8BmwUasitkLYkq4Jd+N49v3PtSwtbUl7XvdiRtQp+Sb27iwZdWcsp/PpK0KVWxesPmpE2oKZHnfs0DC/jp468kbE3jIXGvwL7DB3H+lPex9t3GurGqZcSgVvYYMiBpM/qES47bh4cXrEzajG4xbsQgmpsaIyyz68AW/vr4fXjj7XeTNiUTPNDN9a3e+Sra2trczJkz63pMIYTIOmY2yznXVu36irkLIUQDInEXQogGROIuhBANiMRdCCEakJqIu5mdambzzWyRmV1ei30KIYToOb0WdzNrBq4HTgMOBs43s4N7u18hhBA9pxae+xRgkXPuZefcZuDnwNQa7FcIIUQPqYW47wm8Hvt/aVi2HTO71MxmmtnM9vb2GhxSCCFEOWoxQrXYcLkuI6OcczcANwCY2Tozm1+D49aT4cCqpI3oJrK5Psjm+iCbYe/urFwLcV8KjIn9vxewrMz687szyioNmNlM2dz3yOb6IJvrQ9I21yIs8www3sz2MbM8cB5wVw32K4QQoof02nN3zm0xs8uA+4Bm4Cbn3PO9tkwIIUSPqUlWSOfcPcA9Va5+Qy2OWWdkc32QzfVBNteHRG2ue1ZIIYQQfY/SDwghRAMicRdCiEbEOVf2D9gJeATfWDoWeBeYHfvLV9j+ImBh+LuoxDpfBeaG/f0BGB2WXxiWzwWeAA4Py/PAdCBXZzuvBN6I7fP0sLwFuAV4DngR+GJsmyVh+WxgZszO7wDn94WdsXU/jx9zMDz8b8C1wKJwTSfF1v0GMC/8/Xls+c3AKzH7Jobl54Rzi673vCrq0qnA/HD8y0us0wr8IqzzFDA2VuY/DddyDnBikW3vitsBfDesW2sbPwA8C2wBzi3y+5BQT66LLfvzcM2fB74ZW34x0B67vp8Iy8cAq4GdgRmhHK8Nn1/F96HuAH4cPq8rc05fDOfTHuyaD3yoYJ3Lwjou7Pt7wPpYnVkZ6up84DHg4LDdUcCGgnM9MpTTorBtFP4dBtyPr7v3A0OrqJdF63stjkHnvXgxvo474HexY5wclr1d6Rixa7wmXONy53E/sD52Hm8Am0L5PIZP4/LRUFe20Vlvngvr34yfmGlo2bpcRWX/NPCZ8H0sVdwgsW2HAS+Hz6Hh+w4GAUNi3/8e+GH4fkyscE4Dnoqt92XgwjrbeSXw+SLLLwB+Hr4PxIteJEpLCOIatxM/IGF6X9gZE4f7gFfpFPfTgf/DV/SjousJnBEqXA4vJjOjMgkVqZiAfRo/xmFgNdcbL7CLgX3xQj2HIBAF6/1trPzPA34RO95Pw/eRwCygKbbdOcB/01XcvwS8VG2d6IaNY4EJwK0lrs1/BVuuC//vBrwGjAj/3wKcHL5fTBFhDuf7FN4J+Eqw6/nw+UfgU/iHwcPhs6i444ViDnA43vFYDIwLn82x9Y4I57UEL2q34cU9qjNDojoDnAncGzvXN4GbY/t6Gjg61LP/A04Ly79JeGAClwPfqFAvS9b3WhwjXOMvhv0ejhf4TbFjLAEWANdVOMaPwzU+M5THYrx2FTuPk4B1dIr70+F6R+dxJXAvcFAou/XA+bF61IwX9s8BV5Srz9WEZS4EflfFesX4EHC/c261c24NXkBOLVzJOfdO7N+dCSNcnXNPhO3Aey97xdb7bbCtbnaWwQE7m1kO7w1sBt4pse6FeO/gVXyF7U6Ppe7YeQ3wBbqOFp4K3Oo8M4BdzWwUvhI94pzb4pzbgK+olc7/QvzD48NV2l5tDqKpePED+DVwsplZsHEagHNuJd6bagMws0HAPwBfK9jXaUCHme1RSxudc0ucc3PxXlUXzOxIYHf8G2jEvsAC51yUe+MB4E8r2HIhXjQuwJfzu3R61i8CnwB+CYwPn6WYGs7jdPzDaBH+4bgonG90Tn90zi0J/34FX3ei7W91zr0T1Rl8ehEXO9dpwQ5CfRrinHvSeUW6FTgrtq+obG8pWF6sXhat77U6Bv7Buj7sdy3+DeQ14FPhGCPxzg0VjhFd4zOAH9FZTl3OIxzj3/DalYudx7TYeRzni8O9iH/YbsB7+zjn3nLObQXuxj9sz6cMZcU9DEraN1boAOPMbHb4u77c9lSRdyZ2rKvM7HV8pf63Iqtcgn+yRcwDJtfbTuAyM5trZjeZ2dCw7Nf4QliOrxzfds6tDr854A9mNsvMPlVg5zxgn1rbaWZnAm845+ZUuf0c4DQzG2hmw/HeRXzU8VXhnK8xs9boegMPAsdXsLlbtsfXc85twd8QuwUbp5pZzsz2wb+eRjZ+Fe/hboxdg8jGGcCxNbaxKGbWFOz4p4KfFgEHmtnY4ACcRdfr+6fh+v7azMbEbL8fHyJZGta7Gy9K0fJ38Q+YciPCo3OKPqNzKnVug/Fe+fKC7TGzTwe7v4x/+4zO9RVgdGz9pbH9xY+ze7Tf8Dmy8BgF25Rb3ttjvAHsgw8Dxpc/BXwQ/ybzHp3XttwxBlP+GkfLL8OHDhfgPfXt5xGu7bfxdfXvw3H2x+vH98zsWTOLHrgz8Y5Nq5ntRgkqee7D8R5SnMXOuYnh79MVtq+Yd2b7QueucM6NAX6GvwidOzE7CS/u/xxbfyuw2cwG19HOH+BfaSfihfw7YfkUYCu+gu8D/KOZ7Rwoh3oAAAnWSURBVBt+O9Y5NwnvRV6Gf+2LeAt4q5Z2mtlA4AqKPyCLbu+c+wN+nMITwB3Ak/h4MvjX1gPxD9Jh+DKIrvdKOm/qSlR7jUutdxP+RpiJj6U/AWwxs4nAfs653xRs05c2luJvgXucc3GxIHidn8K3JTyKf92Pru/d+BDeBLxHf0vM9m10vUcfwb/Z7h6WH4V/cJTDCj7j51RYd0aH/d9YZHucc9fj68b38GGb6FzXhe0Kj1N4vEo2Fm7T3eXdOUYL3u7C5YvwovohYEUvjhG/xoZ/AHwUf+12WD9c2/Pwb2X/GhbngF3C/8cBZ5vZyXTW6bJ1u5K4vwsMqLBOObqbdwZ8rHL7K6uZTcC/nk51zr1VsG4r/ulaFzudcyucc1udc9vwN0D0WnsB3tvpCCGDxwkhA+fcsvC5En8jDy6wvzviUY2d4/APmDlmtiSs82wITZTc3jl3VXjAnIKvdNGr4PLwKrsJ36A5hc7rPSB8r5XtXdYLXu4uwOoQMvpcsHEq3oNdiI+7HhnO9TFgfzN7uI9tLMXR+De7JXgv7C/N7GoA59zdzrn3O+eOxjdKxl+1owf+jfg3ksh2R9f60YGv75/Ft9ecio/vVnNO0Wd0TsXO7Qi86D0TzmEgXlQKr8lPw7rRuV4ODAvnupSu4dP4cVaEUEQUvllZYGPhNuWW9/YYe+DFs3D5KLxnfT4+vl3NMdZR/hovxbfR7Id/eHwBf51/WeQ8nqEz/LMU/+a61jm3Ee+ATaKzTpev2+UC8j4MxOvAAFemUQp/w99aZPkw/Cvb0PD3CjCsyHrjY9//Dvh1+P6+cDGOKbLNbsCLdbZzVOz75+hsRP1nfIU3vAfzQijMnYHBYZ2d8d5me8zOB/BvGDW1s2CbJXQ2qJ5B10alp11nQ+Ju4fsEfLgoFz/nsM13gatj1/uf8Tf29uuNf9WcVsSOHL5RaR86GysPKbLep+naoPrL8H0gsHP4fgowvci2Xco92Pi/4VxrZmNs/Zsp0qAafruYrj1IRobPofgG0P2L1KmzgRkx24/G37wHhDIZS+g1AhyCfxhcF451D/D1sI+vx/Z5SDiPiXiv8GW8A/AysQbVEvVlfazOjI/qDPARYGZsm+8VXPdnwrpRI2HUq+xbdG2I/GaFelmyvtfiGOEajwr7nYC/b18J1/2icIyv0tmg+tVwjQuP8ZNwjafiG1RfxjeoljuP9bHzODd2HldG1zasuw4fqsnh9eIMvPP7Q3xoKVdYhtvLoZwwhAP8BPiTCqJ5LvCjEtt/HC/Qi4C/ii3/MdAWvt+Jr7xz8d7tnrF11tDZTWxmwTG/U2c7b8N3R5qLj51FwjcI+BW+N8MLwD+F5fuGQp8TfrsishP/5F7UF3aWuVkNP2vW4nAe0XkNCHa/gI9RT4xt/2BYdx5wOzAodr1nAIfRVTjbgPtK2H463itaTKylH/h34MyYLb8K5/c0vo0iKtP5eIF6ANi7yP67lDs+lPMq/saopY2T8V7VBnxo7fkqxP2O2DU+L7b866FuzAEeAg6MXd8f4cMgF9Ip7vMKjxE+H8OH0D5PrCtuWO+KcD7teE9yPj5MeA/+tf4e/Kv/Uny4aBm+PkVdIa/Hh4neC9s+ROyhF8rj97H/24K9i4N9URfC3fCNrwvDZyTURetlhfre62PQeS9+HH+fbCpyjMh7vi66tsWOEbvGa8L1i44RXeMu50GnuLfh0wJvDuXzEP6BfHY4dkcok3V0PqiuwzuXd5bV7irE/QjgtgrrfAuYUGlftfwD/gc4IO12lrqeofC+mlY7qziPk4HlRZZfRhDBpP/wPWjmptnGCvVkBb5Pfdl6Hda/HRgRfdbRzlb8Q76kB5nWv2o0o9g1Ttjm6HpfS+hOW+qvqtwyZvZx4BbnGzETJ0ot7Jy7tWB5quwsRbBzA957LGwIzgRmNhnv5X4trdfbzD6K7y3xw7TaWAozG4FvFP8sPkSQynptZuPxb9oPJ21LT8iKZkRE1xsfyr6x7LrViLsQQohsodwyQgjRgEjchRCiAZG4CyFEAyJxF3XDzK4ws+fDUPvZZvb+GuzzKDN7KuzvRTO7Miy/0sw+3819LQnpFzCz9b21rWDfPzazgyusc7OZnVtk+Vgzu6CW9ojGpybT7AlRCTM7Gp9kbJJzblMQ0XwNdn0L8GfOuTlm1owf8JM6nHOf6MXmY/GjoP+7NtaI/oA8d1EvRgGrXBhq75xb5UJqBjM70sweCcnV7jOzUSFB2DNmdmJY5+tmdlWR/Y7E5/nB+dQQL8R+O9jMHjazl80sSsaEmf2FmT0dvP0fhYdCRczsC9F+QhK1B8P3k83s9vD9g2b2ZEj09CvzGSsJdkRZLC8xswVh2Y1mdl3sMB8wsyeCzZEXfzVwfLD3c9XYKoTEXdSLPwBjgqh938xOADCzFvzw9XOdc0fiR5Ve5XxGyIuBH5jZKfg8Kl8pst9rgPlm9hsz+xszi+cYOhCfAGoK8GUzazGzg/ATZxzrnJuIT/h24Q57Lc50OrNgtgGDgv3HAY+Gt5F/xY+UnoRPcvYP8R2E5Fxfwg+BPyXYGGdU2N+H8aIOfoj7o87n1bmmSltFP0dhGVEXnHPrzef/Ph6fUvgXZnY5XgAPBe43M/B5biJP/Hkzuw2fkuJo53OsF+73383sZ/g0rRfgEz6dGH7+3/CmsMnMVuKzKZ6MT871TDjeTnQml6rELHySssH4oerP4kX+eHya1qPweecfD/vO49MHxJmCz52/GsDMfoXPQhjxW+cT071gZrtXaZcQOyBxF3UjjAJ8GHjYzJ7Dj7ychc/NcnSJzQ7D5zUpKXTOucV4D/9GoD2W4zqeXnkrvr4bfkTiF3tgf0fIgvhX+CRwc/EPqnH4nDfj8JNLlJtEoVi62jhxmyutK0RJFJYRdcHMDghDpyMm4pN6zQdGhAZXQujkkPD9HHySpg8A15rZrmH5183s7PD9DAtuMj5z4VZ2zO0fZxpwrpmNDNsPM7O9u3Eq0/EJpKbjc7N/Epjt/FDvGcCxZrZf2PdAM9u/YPungRPMbKj5lMaVZmQCnzRqcMW1hIghcRf1YhBwi5m9YGZz8eGLK0Oo5VzgG2Y2B5/985gQv74auMQ5F81j+V9hX4fh5+0E+Bg+5j4bn5DtwnJ5QkKD67/iZ8eai5/VaFQ3zuPRsP6TzrkV+EyJj4Z9t+PbCe4I+55BQUzdOfcG8B/4GX8ewGeJXFvhmHPxE5PMUYOqqBbllhGZw8zuc859KGk7eoqZDQptEDngN8BNbseZpIToFRJ3IeqMmX0bn0d8AL4X0WecbkRRYyTuQgjRgCjmLoQQDYjEXQghGhCJuxBCNCASdyGEaEAk7kII0YD8fy/uBWW0vkviAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ddshw.plot()\n",
    "plt.show()\n",
    "#The infant has more shell weight than the male and then the female"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Conclusion\n",
    "The male possese more of the Height, Lenght \n",
    "and Diameter when compared to the female and infant.\n",
    "The infant on the other hand posses more of the weight\n",
    "(i.e weight, Whole weight, Shucked weight, Viscera \n",
    "weight, Shell weight) than the male and female"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}