Diamond_Analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "ec68b602",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "48aa2ea2",
   "metadata": {
    "scrolled": 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>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": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv(\"https://raw.githubusercontent.com/AliHaider20/AIM-5001/main/diamonds.csv\").drop(\"Unnamed: 0\", axis=1)\n",
    "# Checking if the dataframe is correctly loaded.\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8e36208d",
   "metadata": {},
   "source": [
    "# Data Summary\n",
    "\n",
    "- **Price** - Price in US dollars (326 USD -- 18,823 USD)\n",
    "    + **Mean** - 3932.799722\n",
    "    + **Standard Deviation** - 3989.439738\t\n",
    "\n",
    "- **Carat** - Weight of the diamond (0.2--5.01)\n",
    "    + **Mean** - 0.797940\n",
    "    + **Standard Deviation** - 0.474011\n",
    "\n",
    "- **Cut** - Quality of the cut (Fair, Good, Very Good, Premium, Ideal). Here are the percentages of the Cut values present in the dataset.\n",
    "    + Ideal: 0.399537, Premium: 0.255673, Very Good: 0.223990, Good: 0.090953, Fair: 0.029848.\n",
    "\n",
    "- **Color** - diamond colour, from D (best) to J (worst). Here are the percentages of the Color values present in the dataset.\n",
    "    + G: 0.209344, E: 0.181628, F: 0.176900, H: 0.153949, D: 0.125603, I: 0.100519, J: 0.052058.\n",
    "    \n",
    "- **Clarity** - a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best)). Here are the percentages of the Clarity values present in the dataset.\n",
    "    + SI1: 0.242214, VS2: 0.227253, SI2: 0.170449, VS1: 0.151483, VVS2: 0.093919, VVS1: 0.067760, IF: 0.033185, I1: 0.013737.\n",
    "\n",
    "- **X** - length in mm (0--10.74)\n",
    "    + **Mean** - 5.731157\n",
    "    + **Standard Deviation** - 1.121761\n",
    "\n",
    "- **Y** - y width in mm (0--58.9)\n",
    "    + **Mean** - 5.734526\n",
    "    + **Standard Deviation** - 1.142135\n",
    "\n",
    "- **Z** - depth in mm (0--31.8)\n",
    "    + **Mean** - 3.538734\n",
    "    + **Standard Deviation** - 0.705699\n",
    "\n",
    "- **Depth** - total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)\n",
    "    + **Mean** - 61.749405\n",
    "    + **Standard Deviation** - 1.432621\n",
    "\n",
    "- **Table** - width of top of diamond relative to widest point (43--95)\n",
    "    + **Mean** - 57.457184\n",
    "    + **Standard Deviation** - 2.234491"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5503b8d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>count</th>\n",
       "      <th>mean</th>\n",
       "      <th>std</th>\n",
       "      <th>min</th>\n",
       "      <th>25%</th>\n",
       "      <th>50%</th>\n",
       "      <th>75%</th>\n",
       "      <th>max</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>carat</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>0.797940</td>\n",
       "      <td>0.474011</td>\n",
       "      <td>0.2</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.70</td>\n",
       "      <td>1.04</td>\n",
       "      <td>5.01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>depth</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>61.749405</td>\n",
       "      <td>1.432621</td>\n",
       "      <td>43.0</td>\n",
       "      <td>61.00</td>\n",
       "      <td>61.80</td>\n",
       "      <td>62.50</td>\n",
       "      <td>79.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>table</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>57.457184</td>\n",
       "      <td>2.234491</td>\n",
       "      <td>43.0</td>\n",
       "      <td>56.00</td>\n",
       "      <td>57.00</td>\n",
       "      <td>59.00</td>\n",
       "      <td>95.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>price</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>3932.799722</td>\n",
       "      <td>3989.439738</td>\n",
       "      <td>326.0</td>\n",
       "      <td>950.00</td>\n",
       "      <td>2401.00</td>\n",
       "      <td>5324.25</td>\n",
       "      <td>18823.00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>x</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>5.731157</td>\n",
       "      <td>1.121761</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.71</td>\n",
       "      <td>5.70</td>\n",
       "      <td>6.54</td>\n",
       "      <td>10.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>5.734526</td>\n",
       "      <td>1.142135</td>\n",
       "      <td>0.0</td>\n",
       "      <td>4.72</td>\n",
       "      <td>5.71</td>\n",
       "      <td>6.54</td>\n",
       "      <td>58.90</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>z</th>\n",
       "      <td>53940.0</td>\n",
       "      <td>3.538734</td>\n",
       "      <td>0.705699</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2.91</td>\n",
       "      <td>3.53</td>\n",
       "      <td>4.04</td>\n",
       "      <td>31.80</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         count         mean          std    min     25%      50%      75%  \\\n",
       "carat  53940.0     0.797940     0.474011    0.2    0.40     0.70     1.04   \n",
       "depth  53940.0    61.749405     1.432621   43.0   61.00    61.80    62.50   \n",
       "table  53940.0    57.457184     2.234491   43.0   56.00    57.00    59.00   \n",
       "price  53940.0  3932.799722  3989.439738  326.0  950.00  2401.00  5324.25   \n",
       "x      53940.0     5.731157     1.121761    0.0    4.71     5.70     6.54   \n",
       "y      53940.0     5.734526     1.142135    0.0    4.72     5.71     6.54   \n",
       "z      53940.0     3.538734     0.705699    0.0    2.91     3.53     4.04   \n",
       "\n",
       "            max  \n",
       "carat      5.01  \n",
       "depth     79.00  \n",
       "table     95.00  \n",
       "price  18823.00  \n",
       "x         10.74  \n",
       "y         58.90  \n",
       "z         31.80  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe().T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "1f251ece",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "53940\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 53940 entries, 0 to 53939\n",
      "Data columns (total 10 columns):\n",
      " #   Column   Non-Null Count  Dtype  \n",
      "---  ------   --------------  -----  \n",
      " 0   carat    53940 non-null  float64\n",
      " 1   cut      53940 non-null  object \n",
      " 2   color    53940 non-null  object \n",
      " 3   clarity  53940 non-null  object \n",
      " 4   depth    53940 non-null  float64\n",
      " 5   table    53940 non-null  float64\n",
      " 6   price    53940 non-null  int64  \n",
      " 7   x        53940 non-null  float64\n",
      " 8   y        53940 non-null  float64\n",
      " 9   z        53940 non-null  float64\n",
      "dtypes: float64(6), int64(1), object(3)\n",
      "memory usage: 4.1+ MB\n"
     ]
    }
   ],
   "source": [
    "print(len(df))\n",
    "df.info();"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dc725200",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ideal        0.399537\n",
       "Premium      0.255673\n",
       "Very Good    0.223990\n",
       "Good         0.090953\n",
       "Fair         0.029848\n",
       "Name: cut, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.cut.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "8a7dd632",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SI1     0.242214\n",
       "VS2     0.227253\n",
       "SI2     0.170449\n",
       "VS1     0.151483\n",
       "VVS2    0.093919\n",
       "VVS1    0.067760\n",
       "IF      0.033185\n",
       "I1      0.013737\n",
       "Name: clarity, dtype: float64"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.clarity.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "75cfdfb5",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G    0.209344\n",
       "E    0.181628\n",
       "F    0.176900\n",
       "H    0.153949\n",
       "D    0.125603\n",
       "I    0.100519\n",
       "J    0.052058\n",
       "Name: color, dtype: float64"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.color.value_counts(normalize=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f6367c28",
   "metadata": {},
   "source": [
    "# EDA"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "e350dd2d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.title(\"Z (Depth of the Diamonds)\")\n",
    "plt.hist(df.z);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5a805be5",
   "metadata": {},
   "source": [
    "# Obeservations\n",
    "- The data is normally distributed.\n",
    "- There are very few Diamonds with depth less than 5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "5154494a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.title(\"Y (Width of the Diamonds)\")\n",
    "sns.histplot(x='y', data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b35885f1",
   "metadata": {},
   "source": [
    "# Observations\n",
    "- The data looks to be rightly skewed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ca0d0b20",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.title(\"X (Length of the Diamonds)\")\n",
    "sns.histplot(x='x', data=df, bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c53af500",
   "metadata": {},
   "source": [
    "# Observations\n",
    "- The length data looks to be normally distributed.\n",
    "- Most of the Diamonds have length less than 6.2.\n",
    "- Most of the Diamonds length's fall in the range 4-5."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "96265e9c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.title(\"Price of the Diamonds\")\n",
    "sns.histplot(x='price', data=df, bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a3306665",
   "metadata": {},
   "source": [
    "# Observations\n",
    "- The Price data is positively skewed.\n",
    "- Most of the data value lie between 0 and 4500."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "baa19d34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.title(\" Table (width of top of diamond relative to widest point\")\n",
    "sns.histplot(x='table', data=df, bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4dcfec1c",
   "metadata": {},
   "source": [
    "# Observations\n",
    "- The data is table normally distributed.\n",
    "- Most of the table data points lie between 50 and 50.8."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "a68ce006",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.title(\"X (Depth of the Diamonds)\")\n",
    "sns.histplot(x='depth', data=df, bins=50);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a1c0e72e",
   "metadata": {},
   "source": [
    "# Observations\n",
    "- The data is normally distributed.\n",
    "- Most of Diamonds have depth of 4.37 mm."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "895419ab",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "plt.title(\"Clarity of the diamonds\")\n",
    "sns.countplot(x=\"clarity\", data = df);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21a2005a",
   "metadata": {},
   "source": [
    "# Observations\n",
    "- Most of the diamonds are of SI2, SI1, and VS2 i.e average quality.\n",
    "- There are only few diamonds with IF (best) quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "a25e9517",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "df.carat.plot(kind='hist');"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6469e166",
   "metadata": {},
   "source": [
    "# Observations\n",
    "- The carat datapoints are positively skewed.\n",
    "- Most of the datapoints lie between 0.2 and 0.9."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "2bc520ce",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.countplot(x= 'cut', data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cc179f02",
   "metadata": {},
   "source": [
    "# Observations\n",
    "- Most of the diamonds have cut quality of Ideal and Premium which are considered to be best cut quality."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "0550202f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 576x360 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(8,5))\n",
    "sns.countplot(x= 'color', data=df);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0ebd1b31",
   "metadata": {},
   "source": [
    "# Observations\n",
    "- Most of the diamonds have colors E, H, F, and G which are considered as Good.\n",
    "- We've more than 6000 daimonds with best color.\n",
    "- There are very less daimonds with worst color i.e J."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1a20a499",
   "metadata": {},
   "source": [
    "# Inferences"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e4e911e4",
   "metadata": {},
   "source": [
    "## Q1: What proportion of diamonds are between .30 and 1.08 carats?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6a1d28a7",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The proportion of the diamonds whos carat values comes between .30 and 1.08 (exclusive) is 0.6939933259176864\n"
     ]
    }
   ],
   "source": [
    "print(\"The proportion of the diamonds whos carat values comes between .30 and 1.08 (exclusive) is\", len(df[(df.carat > .30) & (df.carat < 1.08)])/len(df))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4b862008",
   "metadata": {},
   "source": [
    "## Q2: How many of the diamonds have equal x and y dimensions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "b2f223c5",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 17 datapoints where length and width of the diamond are the same\n"
     ]
    }
   ],
   "source": [
    "print(f\"There are {len(df[df.x == df.y])} datapoints where length and width of the diamond are the same\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cbf1bb45",
   "metadata": {},
   "source": [
    "## Q3: How many of the diamonds have a carat value that is less than the mean carat value?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "76bd7e63",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 31127 diamonds whose carat values are less than mean carat value\n"
     ]
    }
   ],
   "source": [
    "print(f\"There are {len(df[df.carat < df.carat.mean()])} diamonds whose carat values are less than mean carat value\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "789c86aa",
   "metadata": {},
   "source": [
    "## Q4: How many diamonds have a Premium cut or better?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "069cc871",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There are 35342 diamonds who has Premium cut or Ideal\n"
     ]
    }
   ],
   "source": [
    "print(f\"There are {len(df[(df.cut == 'Premium') | (df.cut == 'Ideal')])} diamonds who has Premium cut or Ideal\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12eed145",
   "metadata": {},
   "source": [
    "## Q5: Which diamond has the highest price per carat? What is its value?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "bc117c6e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Diamond number 27635 has highest price per carat value\n"
     ]
    }
   ],
   "source": [
    "price_per_carat = (df.price/df.carat)\n",
    "print(\"Diamond number {0} has highest price per carat value\".format(price_per_carat[price_per_carat == price_per_carat.max()].index[0]))"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f800beb8",
   "metadata": {},
   "source": [
    "## Q6: Using both Matplolib and Seaborn, make boxplots of the diamond price for each distinct cut value and discuss any conclusions you can draw from the appearance of the boxplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "7a1f0faf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x576 with 6 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(2,3, figsize=(15,8))\n",
    "axs[0,0].boxplot(df[df.cut == \"Premium\"].price, vert=False)\n",
    "axs[0,0].set_title(\"Premium\")\n",
    "axs[0,1].boxplot(df[df.cut == \"Ideal\"].price, vert=False)\n",
    "axs[0,1].set_title(\"Ideal\")\n",
    "axs[0,2].boxplot(df[df.cut == \"Good\"].price, vert=False)\n",
    "axs[0,2].set_title(\"Good\")\n",
    "axs[1,0].boxplot(df[df.cut == \"Very Good\"].price, vert=False)\n",
    "axs[1,0].set_title(\"Very Good\")\n",
    "axs[1,1].boxplot(df[df.cut == \"Fair\"].price, vert=False)\n",
    "axs[1,1].set_title(\"Fair\");"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d12b3e38",
   "metadata": {},
   "source": [
    "## Observations\n",
    "- As it can be observed all the above boxplots are rightly skewed.\n",
    "- Compared to others price of Fair cut is not much skewed.\n",
    "- Among all the cut the maximum value close to 15000 is for the premium cut.\n",
    "- There are 7 values where x (length), y (width), and z depth are 0 which means the data is missing. The data can be replaced my NaN or left as it is. It can alter the Statistics if considered as 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "0726702d",
   "metadata": {},
   "source": [
    "## Q7: Using both Matplolib and Seaborn, make a scatter plot of price vs. carat. What can we say about the relationship between those two attributes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "1f7a846f",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/usr/local/lib/python3.10/dist-packages/seaborn/_decorators.py:36: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "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": [
    "sns.scatterplot(df.price, df.carat);"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c14379b9",
   "metadata": {},
   "source": [
    "## Observations\n",
    "- Above is the scatter plot of Price vs Carat.\n",
    "- It can be observed that Price of the diamond increases as the carat increases.\n",
    "- There are many diamonds who's carat value are low but the price is equal or more than the diamond who's carat value are high."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "64e95667",
   "metadata": {
    "scrolled": true
   },
   "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>11963</th>\n",
       "      <td>1.00</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>H</td>\n",
       "      <td>VS2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>53.0</td>\n",
       "      <td>5139</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15951</th>\n",
       "      <td>1.14</td>\n",
       "      <td>Fair</td>\n",
       "      <td>G</td>\n",
       "      <td>VS1</td>\n",
       "      <td>57.5</td>\n",
       "      <td>67.0</td>\n",
       "      <td>6381</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24520</th>\n",
       "      <td>1.56</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>54.0</td>\n",
       "      <td>12800</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26243</th>\n",
       "      <td>1.20</td>\n",
       "      <td>Premium</td>\n",
       "      <td>D</td>\n",
       "      <td>VVS1</td>\n",
       "      <td>62.1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>15686</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27429</th>\n",
       "      <td>2.25</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.8</td>\n",
       "      <td>59.0</td>\n",
       "      <td>18034</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49556</th>\n",
       "      <td>0.71</td>\n",
       "      <td>Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI2</td>\n",
       "      <td>64.1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>49557</th>\n",
       "      <td>0.71</td>\n",
       "      <td>Good</td>\n",
       "      <td>F</td>\n",
       "      <td>SI2</td>\n",
       "      <td>64.1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2130</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price    x    y    z\n",
       "11963   1.00  Very Good     H     VS2   63.3   53.0   5139  0.0  0.0  0.0\n",
       "15951   1.14       Fair     G     VS1   57.5   67.0   6381  0.0  0.0  0.0\n",
       "24520   1.56      Ideal     G     VS2   62.2   54.0  12800  0.0  0.0  0.0\n",
       "26243   1.20    Premium     D    VVS1   62.1   59.0  15686  0.0  0.0  0.0\n",
       "27429   2.25    Premium     H     SI2   62.8   59.0  18034  0.0  0.0  0.0\n",
       "49556   0.71       Good     F     SI2   64.1   60.0   2130  0.0  0.0  0.0\n",
       "49557   0.71       Good     F     SI2   64.1   60.0   2130  0.0  0.0  0.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Datapoints where length, width, and height are equal.\n",
    "df[(df.x == df.y) & (df.y == df.z) & (df.x == df.z)]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9061f9a7",
   "metadata": {},
   "source": [
    "# Conclusion\n",
    "- The purpose of this project is to analyse the Diamond dataset.\n",
    "- Firstly, we've identified that the continuous features have a many outliers and are rightly skewed using Boxplots.\n",
    "- Secondly, we've found there are 17 diamonds who has same length and width.\n",
    "- There are 7 values where x (length), y (width), and z depth are 0 which means the data is missing. The data can be replaced my NaN or left as it is. They can alter the Statistics if considered as 0."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6c1f23e4",
   "metadata": {},
   "source": [
    "# References\n",
    "\n",
    "- https://matplotlib.org/stable/api/_as_gen/matplotlib.pyplot.hist.html\n",
    "- https://seaborn.pydata.org/generated/seaborn.scatterplot.html"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.6"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}