{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np \n",
    "import pandas as pd \n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "import streamlit as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<>:1: SyntaxWarning: invalid escape sequence '\\$'\n",
      "<>:1: SyntaxWarning: invalid escape sequence '\\$'\n",
      "C:\\Users\\danie\\AppData\\Local\\Temp\\ipykernel_9696\\1163327148.py:1: SyntaxWarning: invalid escape sequence '\\$'\n",
      "  \"\"\"\"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'\"\\nContent\\nprice price in US dollars (\\\\$326--\\\\$18,823)\\n\\ncarat weight of the diamond (0.2--5.01)\\n\\ncut quality of the cut (Fair, Good, Very Good, Premium, Ideal)\\n\\ncolor diamond colour, from J (worst) to D (best)\\n\\nclarity a measurement of how clear the diamond is (I1 (worst), SI2, SI1, VS2, VS1, VVS2, VVS1, IF (best))\\n\\nx length in mm (0--10.74)\\n\\ny width in mm (0--58.9)\\n\\nz depth in mm (0--31.8)\\n\\ndepth total depth percentage = z / mean(x, y) = 2 * z / (x + y) (43--79)\\n\\ntable width of top of diamond relative to widest point (43--95)\\n'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\"\"\"\"\n",
    "Content\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)\n",
    "\"\"\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "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>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": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_diamonds = pd.read_csv('C:/Users/danie/Desktop/Python Project/diamonds.csv')\n",
    "df_diamonds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "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>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>53935</th>\n",
       "      <td>53936</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>60.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.75</td>\n",
       "      <td>5.76</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53936</th>\n",
       "      <td>53937</td>\n",
       "      <td>0.72</td>\n",
       "      <td>Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>63.1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.69</td>\n",
       "      <td>5.75</td>\n",
       "      <td>3.61</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53937</th>\n",
       "      <td>53938</td>\n",
       "      <td>0.70</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>60.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.66</td>\n",
       "      <td>5.68</td>\n",
       "      <td>3.56</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53938</th>\n",
       "      <td>53939</td>\n",
       "      <td>0.86</td>\n",
       "      <td>Premium</td>\n",
       "      <td>H</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.0</td>\n",
       "      <td>58.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>6.15</td>\n",
       "      <td>6.12</td>\n",
       "      <td>3.74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>53939</th>\n",
       "      <td>53940</td>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>D</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.83</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.64</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Unnamed: 0  carat        cut color clarity  depth  table  price     x  \\\n",
       "53935       53936   0.72      Ideal     D     SI1   60.8   57.0   2757  5.75   \n",
       "53936       53937   0.72       Good     D     SI1   63.1   55.0   2757  5.69   \n",
       "53937       53938   0.70  Very Good     D     SI1   62.8   60.0   2757  5.66   \n",
       "53938       53939   0.86    Premium     H     SI2   61.0   58.0   2757  6.15   \n",
       "53939       53940   0.75      Ideal     D     SI2   62.2   55.0   2757  5.83   \n",
       "\n",
       "          y     z  \n",
       "53935  5.76  3.50  \n",
       "53936  5.75  3.61  \n",
       "53937  5.68  3.56  \n",
       "53938  6.12  3.74  \n",
       "53939  5.87  3.64  "
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_diamonds.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53940, 11)"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_diamonds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "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": [
    "#First we clean the data that we intend on using\n",
    "#Since the first value is called unnamed and isn't important, we can remove it\n",
    "df_diamonds.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>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",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\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>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>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>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>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>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>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": [
       "              carat         depth         table         price             x  \\\n",
       "count  53940.000000  53940.000000  53940.000000  53940.000000  53940.000000   \n",
       "mean       0.797940     61.749405     57.457184   3932.799722      5.731157   \n",
       "std        0.474011      1.432621      2.234491   3989.439738      1.121761   \n",
       "min        0.200000     43.000000     43.000000    326.000000      0.000000   \n",
       "25%        0.400000     61.000000     56.000000    950.000000      4.710000   \n",
       "50%        0.700000     61.800000     57.000000   2401.000000      5.700000   \n",
       "75%        1.040000     62.500000     59.000000   5324.250000      6.540000   \n",
       "max        5.010000     79.000000     95.000000  18823.000000     10.740000   \n",
       "\n",
       "                  y             z  \n",
       "count  53940.000000  53940.000000  \n",
       "mean       5.734526      3.538734  \n",
       "std        1.142135      0.705699  \n",
       "min        0.000000      0.000000  \n",
       "25%        4.720000      2.910000  \n",
       "50%        5.710000      3.530000  \n",
       "75%        6.540000      4.040000  \n",
       "max       58.900000     31.800000  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_diamonds = df_diamonds.drop([\"Unnamed: 0\"], axis=1)\n",
    "df_diamonds.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(53920, 10)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Potential diamonds with a impossible dimensions are dropped, for example a diamond that has a 0 in x, y or z should not be possible.  \n",
    "df_diamonds = df_diamonds.drop(df_diamonds[df_diamonds[\"x\"]==0].index)\n",
    "df_diamonds = df_diamonds.drop(df_diamonds[df_diamonds[\"y\"]==0].index)\n",
    "df_diamonds = df_diamonds.drop(df_diamonds[df_diamonds[\"z\"]==0].index)\n",
    "df_diamonds.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum value in the column: 18823\n",
      "Minimum value in the column: 326\n"
     ]
    }
   ],
   "source": [
    "max_value = df_diamonds['price'].max()\n",
    "min_value = df_diamonds['price'].min()\n",
    "\n",
    "print(f\"Maximum value in the column: {max_value}\")\n",
    "print(f\"Minimum value in the column: {min_value}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(df_diamonds['price'], bins=10, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Diamond Prices')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(df_diamonds['carat'], df_diamonds['price'])\n",
    "plt.xlabel('Carat')\n",
    "plt.ylabel('Price')\n",
    "plt.title('Price vs Carat of Diamonds')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.countplot(data=df_diamonds, x='clarity', order=['I1', 'SI2', 'SI1', 'VS2', 'VS1', 'VVS2', 'VVS1', 'IF'])\n",
    "plt.xlabel('Clarity')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Count of Diamonds by Clarity')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "x    0.977779\n",
      "y    0.953991\n",
      "z    0.961048\n",
      "Name: carat, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "\"\"\"The correlation coefficient ranges from -1 to 1, where:\n",
    "\n",
    "1 indicates a perfect positive correlation,\n",
    "-1 indicates a perfect negative correlation, and\n",
    "0 indicates no correlation.\"\"\"\n",
    "\n",
    "# Calculate the correlation matrix\n",
    "correlation_matrix = df_diamonds[['carat', 'x', 'y', 'z']].corr()\n",
    "\n",
    "# Extract the correlation between carat and the x, y, z values\n",
    "correlation_values = correlation_matrix.loc['carat', ['x', 'y', 'z']]\n",
    "\n",
    "print(correlation_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "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>11</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>VS1</td>\n",
       "      <td>62.8</td>\n",
       "      <td>56.0</td>\n",
       "      <td>340</td>\n",
       "      <td>3.93</td>\n",
       "      <td>3.90</td>\n",
       "      <td>2.46</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>0.31</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.2</td>\n",
       "      <td>54.0</td>\n",
       "      <td>344</td>\n",
       "      <td>4.35</td>\n",
       "      <td>4.37</td>\n",
       "      <td>2.71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>0.30</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>SI2</td>\n",
       "      <td>62.0</td>\n",
       "      <td>54.0</td>\n",
       "      <td>348</td>\n",
       "      <td>4.31</td>\n",
       "      <td>4.34</td>\n",
       "      <td>2.68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>39</th>\n",
       "      <td>0.33</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.8</td>\n",
       "      <td>55.0</td>\n",
       "      <td>403</td>\n",
       "      <td>4.49</td>\n",
       "      <td>4.51</td>\n",
       "      <td>2.78</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",
       "11   0.23  Ideal     J     VS1   62.8   56.0    340  3.93  3.90  2.46\n",
       "13   0.31  Ideal     J     SI2   62.2   54.0    344  4.35  4.37  2.71\n",
       "16   0.30  Ideal     I     SI2   62.0   54.0    348  4.31  4.34  2.68\n",
       "39   0.33  Ideal     I     SI2   61.8   55.0    403  4.49  4.51  2.78"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ideal_diamonds = df_diamonds[df_diamonds['cut'] == 'Ideal']\n",
    "\n",
    "ideal_diamonds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ideal_diamonds['price'], bins=10, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Diamond Prices')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "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>90</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.5</td>\n",
       "      <td>57.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.70</td>\n",
       "      <td>5.72</td>\n",
       "      <td>3.57</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>92</th>\n",
       "      <td>0.70</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>VS2</td>\n",
       "      <td>61.6</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2757</td>\n",
       "      <td>5.70</td>\n",
       "      <td>5.67</td>\n",
       "      <td>3.50</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>102</th>\n",
       "      <td>0.74</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "      <td>61.6</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2760</td>\n",
       "      <td>5.80</td>\n",
       "      <td>5.85</td>\n",
       "      <td>3.59</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>104</th>\n",
       "      <td>0.80</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>I</td>\n",
       "      <td>VS1</td>\n",
       "      <td>62.9</td>\n",
       "      <td>56.0</td>\n",
       "      <td>2760</td>\n",
       "      <td>5.94</td>\n",
       "      <td>5.87</td>\n",
       "      <td>3.72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>105</th>\n",
       "      <td>0.75</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "      <td>62.2</td>\n",
       "      <td>55.0</td>\n",
       "      <td>2760</td>\n",
       "      <td>5.87</td>\n",
       "      <td>5.80</td>\n",
       "      <td>3.63</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     carat    cut color clarity  depth  table  price     x     y     z\n",
       "90    0.70  Ideal     E     SI1   62.5   57.0   2757  5.70  5.72  3.57\n",
       "92    0.70  Ideal     G     VS2   61.6   56.0   2757  5.70  5.67  3.50\n",
       "102   0.74  Ideal     G     SI1   61.6   55.0   2760  5.80  5.85  3.59\n",
       "104   0.80  Ideal     I     VS1   62.9   56.0   2760  5.94  5.87  3.72\n",
       "105   0.75  Ideal     G     SI1   62.2   55.0   2760  5.87  5.80  3.63"
      ]
     },
     "execution_count": 57,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Set the price range for the subset\n",
    "min_price = 1000\n",
    "max_price = 5000\n",
    "\n",
    "# Create a subset of diamonds within the specified price range\n",
    "ideal_priced = ideal_diamonds[(ideal_diamonds['price'] >= min_price) & (ideal_diamonds['price'] <= max_price)]\n",
    "\n",
    "# Print or visualize the subset of diamonds within the price range\n",
    "ideal_priced.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(ideal_priced['price'], bins=10, color='skyblue', edgecolor='black')\n",
    "plt.xlabel('Price')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of Diamond Prices')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "       carat  size\n",
      "0       0.23  Tiny\n",
      "1       0.21  Tiny\n",
      "2       0.23  Tiny\n",
      "3       0.29  Tiny\n",
      "4       0.31  Tiny\n",
      "...      ...   ...\n",
      "53935   0.72  Tiny\n",
      "53936   0.72  Tiny\n",
      "53937   0.70  Tiny\n",
      "53938   0.86  Tiny\n",
      "53939   0.75  Tiny\n",
      "\n",
      "[53920 rows x 2 columns]\n"
     ]
    }
   ],
   "source": [
    "\"\"\"Define the bin edges and labels for categorizing carat values\n",
    "So basically what happens here is that we define that any diamond in the carat range 0 to 1 will be placed in the category 'Tiny'. \n",
    "Similarly this happens with the other categories as well. Thus we have 'Tiny', 'Small', 'Medium', 'Large', 'Huge'.\n",
    "\"\"\"\n",
    "bin_edges = [0, 1, 2, 3, 4, 5.1]\n",
    "bin_labels = ['Tiny', 'Small', 'Medium', 'Large', 'Huge']\n",
    "\n",
    "# Create a new column 'carat_category' based on the categorization\n",
    "df_diamonds['size'] = pd.cut(df_diamonds['carat'], bins=bin_edges, labels=bin_labels, include_lowest=True)\n",
    "\n",
    "print(df_diamonds[['carat', 'size']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Grouped diamonds by size (Carat =)\n",
    "sns.countplot(data=df_diamonds, x='size', order=['Tiny','Small', 'Medium', 'Large', 'Huge'])\n",
    "plt.xlabel('Size')\n",
    "plt.ylabel('Count')\n",
    "plt.title('Count of Diamonds by Clarity')\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "base",
   "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": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}