Diamonds Data Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
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    "_kg_hide-input": false,
    "_uuid": "8f2839f25d086af736a60e9eeb907d3b93b6e0e5"
   },
   "source": [
    "# Diamonds Dataset Analysis and Pre-Processing  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Context\n",
    "### This classic dataset contains the prices and other attributes of almost 54,000 diamonds. It's a great dataset for  data analysis and visualization. This notebook takes a quick analysis of the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Content\n",
    "\n",
    "* price price in US dollars (\\$326--\\$18,823)\n",
    "\n",
    "* carat weight of the diamond (0.2--5.01)\n",
    " \n",
    "* cut quality of the cut (Fair, Good, Very Good, Premium, Ideal)\n",
    " \n",
    "* color diamond colour, from J (worst) to D (best)\n",
    " \n",
    "* clarity a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))\n",
    " \n",
    "* x length in mm (0--10.74)\n",
    " \n",
    "* y width in mm (0--58.9)\n",
    " \n",
    "* z depth in mm (0--31.8)\n",
    " \n",
    "* depth total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)\n",
    " \n",
    "* table width of top of diamond relative to widest point (43--95)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Topics:\n",
    "## 1. Exploring Data\n",
    "## 2. Handling Data \n",
    "## 3. Visualization of Data\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Import Libraries"
   ]
  },
  {
   "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",
    "import seaborn as sns\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Extract Data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "_cell_guid": "79c7e3d0-c299-4dcb-8224-4455121ee9b0",
    "_kg_hide-input": true,
    "_uuid": "d629ff2d2480ee46fbb7e2d37f6b5fab8052498a"
   },
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  carat      cut color clarity  depth  table  price     x     y  \\\n",
       "0           1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98   \n",
       "1           2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84   \n",
       "2           3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07   \n",
       "3           4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23   \n",
       "4           5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35   \n",
       "\n",
       "      z  \n",
       "0  2.43  \n",
       "1  2.31  \n",
       "2  2.31  \n",
       "3  2.63  \n",
       "4  2.75  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "#data = pd.read_csv('/kaggle/input/diamonds/diamonds.csv')\n",
    "data = pd.read_csv('datasets\\d8\\diamonds2.csv')\n",
    "\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1. Exploring Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.1 Data Size & Shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "593340"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53940, 11)"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.2 Get general information about data columns, data types"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53940 entries, 0 to 53939\n",
      "Data columns (total 11 columns):\n",
      " #   Column      Non-Null Count  Dtype  \n",
      "---  ------      --------------  -----  \n",
      " 0   Unnamed: 0  53940 non-null  int64  \n",
      " 1   carat       53940 non-null  float64\n",
      " 2   cut         53940 non-null  object \n",
      " 3   color       53940 non-null  object \n",
      " 4   clarity     53940 non-null  object \n",
      " 5   depth       53940 non-null  float64\n",
      " 6   table       53940 non-null  float64\n",
      " 7   price       53940 non-null  int64  \n",
      " 8   x           53940 non-null  float64\n",
      " 9   y           53940 non-null  float64\n",
      " 10  z           53940 non-null  float64\n",
      "dtypes: float64(6), int64(2), object(3)\n",
      "memory usage: 4.5+ MB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.3 Get Statistical Summary  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>26970.500000</td>\n",
       "      <td>0.797940</td>\n",
       "      <td>61.749405</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>3932.799722</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>3.538734</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>15571.281097</td>\n",
       "      <td>0.474011</td>\n",
       "      <td>1.432621</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>3989.439738</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.705699</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>43.000000</td>\n",
       "      <td>326.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>13485.750000</td>\n",
       "      <td>0.400000</td>\n",
       "      <td>61.000000</td>\n",
       "      <td>56.000000</td>\n",
       "      <td>950.000000</td>\n",
       "      <td>4.710000</td>\n",
       "      <td>4.720000</td>\n",
       "      <td>2.910000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>26970.500000</td>\n",
       "      <td>0.700000</td>\n",
       "      <td>61.800000</td>\n",
       "      <td>57.000000</td>\n",
       "      <td>2401.000000</td>\n",
       "      <td>5.700000</td>\n",
       "      <td>5.710000</td>\n",
       "      <td>3.530000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>40455.250000</td>\n",
       "      <td>1.040000</td>\n",
       "      <td>62.500000</td>\n",
       "      <td>59.000000</td>\n",
       "      <td>5324.250000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>6.540000</td>\n",
       "      <td>4.040000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>53940.000000</td>\n",
       "      <td>5.010000</td>\n",
       "      <td>79.000000</td>\n",
       "      <td>95.000000</td>\n",
       "      <td>18823.000000</td>\n",
       "      <td>10.740000</td>\n",
       "      <td>58.900000</td>\n",
       "      <td>31.800000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         Unnamed: 0         carat         depth         table         price  \\\n",
       "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
       "mean   26970.500000      0.797940     61.749405     57.457184   3932.799722   \n",
       "std    15571.281097      0.474011      1.432621      2.234491   3989.439738   \n",
       "min        1.000000      0.200000     43.000000     43.000000    326.000000   \n",
       "25%    13485.750000      0.400000     61.000000     56.000000    950.000000   \n",
       "50%    26970.500000      0.700000     61.800000     57.000000   2401.000000   \n",
       "75%    40455.250000      1.040000     62.500000     59.000000   5324.250000   \n",
       "max    53940.000000      5.010000     79.000000     95.000000  18823.000000   \n",
       "\n",
       "                  x             y             z  \n",
       "count  53940.000000  53940.000000  53940.000000  \n",
       "mean       5.731157      5.734526      3.538734  \n",
       "std        1.121761      1.142135      0.705699  \n",
       "min        0.000000      0.000000      0.000000  \n",
       "25%        4.710000      4.720000      2.910000  \n",
       "50%        5.700000      5.710000      3.530000  \n",
       "75%        6.540000      6.540000      4.040000  \n",
       "max       10.740000     58.900000     31.800000  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Minimum value of (x,y,z) or (length,width,depth) is equal to zero, which is wrong (not possible) and we will handle in Handling Data section"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.4 Correlation Table"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
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       "    <tr>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.377983</td>\n",
       "      <td>-0.034800</td>\n",
       "      <td>-0.100830</td>\n",
       "      <td>-0.306873</td>\n",
       "      <td>-0.405440</td>\n",
       "      <td>-0.395843</td>\n",
       "      <td>-0.399208</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>carat</th>\n",
       "      <td>-0.377983</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.028224</td>\n",
       "      <td>0.181618</td>\n",
       "      <td>0.921591</td>\n",
       "      <td>0.975094</td>\n",
       "      <td>0.951722</td>\n",
       "      <td>0.953387</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depth</th>\n",
       "      <td>-0.034800</td>\n",
       "      <td>0.028224</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.295779</td>\n",
       "      <td>-0.010647</td>\n",
       "      <td>-0.025289</td>\n",
       "      <td>-0.029341</td>\n",
       "      <td>0.094924</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table</th>\n",
       "      <td>-0.100830</td>\n",
       "      <td>0.181618</td>\n",
       "      <td>-0.295779</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.127134</td>\n",
       "      <td>0.195344</td>\n",
       "      <td>0.183760</td>\n",
       "      <td>0.150929</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>-0.306873</td>\n",
       "      <td>0.921591</td>\n",
       "      <td>-0.010647</td>\n",
       "      <td>0.127134</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.884435</td>\n",
       "      <td>0.865421</td>\n",
       "      <td>0.861249</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>-0.405440</td>\n",
       "      <td>0.975094</td>\n",
       "      <td>-0.025289</td>\n",
       "      <td>0.195344</td>\n",
       "      <td>0.884435</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.974701</td>\n",
       "      <td>0.970772</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>-0.395843</td>\n",
       "      <td>0.951722</td>\n",
       "      <td>-0.029341</td>\n",
       "      <td>0.183760</td>\n",
       "      <td>0.865421</td>\n",
       "      <td>0.974701</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.952006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>-0.399208</td>\n",
       "      <td>0.953387</td>\n",
       "      <td>0.094924</td>\n",
       "      <td>0.150929</td>\n",
       "      <td>0.861249</td>\n",
       "      <td>0.970772</td>\n",
       "      <td>0.952006</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Unnamed: 0     carat     depth     table     price         x  \\\n",
       "Unnamed: 0    1.000000 -0.377983 -0.034800 -0.100830 -0.306873 -0.405440   \n",
       "carat        -0.377983  1.000000  0.028224  0.181618  0.921591  0.975094   \n",
       "depth        -0.034800  0.028224  1.000000 -0.295779 -0.010647 -0.025289   \n",
       "table        -0.100830  0.181618 -0.295779  1.000000  0.127134  0.195344   \n",
       "price        -0.306873  0.921591 -0.010647  0.127134  1.000000  0.884435   \n",
       "x            -0.405440  0.975094 -0.025289  0.195344  0.884435  1.000000   \n",
       "y            -0.395843  0.951722 -0.029341  0.183760  0.865421  0.974701   \n",
       "z            -0.399208  0.953387  0.094924  0.150929  0.861249  0.970772   \n",
       "\n",
       "                   y         z  \n",
       "Unnamed: 0 -0.395843 -0.399208  \n",
       "carat       0.951722  0.953387  \n",
       "depth      -0.029341  0.094924  \n",
       "table       0.183760  0.150929  \n",
       "price       0.865421  0.861249  \n",
       "x           0.974701  0.970772  \n",
       "y           1.000000  0.952006  \n",
       "z           0.952006  1.000000  "
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## From Correlation table we get:\n",
    "### 1. Carat and Price are highly corelated with x,y and z\n",
    "### 2. But Depth doesn't have much impact, which is derived from x,y and z"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 1.5 Check for null values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Unnamed: 0    0\n",
       "carat         0\n",
       "cut           0\n",
       "color         0\n",
       "clarity       0\n",
       "depth         0\n",
       "table         0\n",
       "price         0\n",
       "x             0\n",
       "y             0\n",
       "z             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## No null values"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2 Handling Data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. We need to handle the records where values of x,y,z is equal to zero.\n",
    "## 2. We don't need Unnamed as a column"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.1 Drop Unnamed Column "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "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>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   carat      cut color clarity  depth  table  price     x     y     z\n",
       "0   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98  2.43\n",
       "1   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84  2.31\n",
       "2   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07  2.31\n",
       "3   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23  2.63\n",
       "4   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35  2.75"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.drop(['Unnamed: 0'],inplace = True, axis = 1)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 2.2 Handle wrong value of x,y,z "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2.1 rows of x,y,z with wrong values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows with x = 0 are 8\n",
      "Number of rows with x = 0 are 7\n",
      "Number of rows with x = 0 are 20\n"
     ]
    }
   ],
   "source": [
    "print('Number of rows with x = 0 are {}'.format((data.x==0).sum()))\n",
    "print('Number of rows with x = 0 are {}'.format((data.y==0).sum()))\n",
    "print('Number of rows with x = 0 are {}'.format((data.z==0).sum()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2.2 rows of x,y,z with wrong values set to NaN"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of rows with x = 0 are 0\n",
      "Number of rows with y = 0 are 0\n",
      "Number of rows with z = 0 are 0\n"
     ]
    }
   ],
   "source": [
    "data.x = data.x.replace(0,np.NaN)\n",
    "data.y = data.y.replace(0,np.NaN)\n",
    "data.z = data.z.replace(0,np.NaN)\n",
    "\n",
    "print(\"Number of rows with x = 0 are {}\".format((data.x==0).sum()))\n",
    "print(\"Number of rows with y = 0 are {}\".format((data.y==0).sum()))\n",
    "print(\"Number of rows with z = 0 are {}\".format((data.z==0).sum()))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat       0\n",
       "cut         0\n",
       "color       0\n",
       "clarity     0\n",
       "depth       0\n",
       "table       0\n",
       "price       0\n",
       "x           8\n",
       "y           7\n",
       "z          20\n",
       "dtype: int64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2.2.3 rows with NaN values removed"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "carat      0\n",
       "cut        0\n",
       "color      0\n",
       "clarity    0\n",
       "depth      0\n",
       "table      0\n",
       "price      0\n",
       "x          0\n",
       "y          0\n",
       "z          0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dropna(inplace=True)\n",
    "data.isna().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3. Visualization of Data "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.1 Count of diamonds based on cut"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18850f12a20>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=data, x='cut', kind = \"count\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.2 Clarity vs Price\n",
    "### clarity's info from content  : I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18853994d68>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=data,x='clarity', y= 'price', kind = 'bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.3 Color vs Price\n",
    "## *Nice coloured diamonds aren't the most costly.*\n",
    "### D (best) to J(worst) color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18853994438>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=data, x='color', y = 'price', kind = \"bar\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.4 Clarity vs Price\n",
    "### *Note median close to 1st quartile *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x18853a00080>"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.catplot(data=data, x='clarity', y = 'price', kind = \"box\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# 3.5 Cut vs Price\n",
    "### *Different cut doesn't impact much on price*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<seaborn.axisgrid.FacetGrid object at 0x0000018854111828>\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "print(sns.catplot(data=data, x='cut', y = 'price', kind = \"box\"))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Thank you\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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": "3.7.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}