Univariate_Exploration_Diamond_Data.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 59,
   "metadata": {},
   "outputs": [],
   "source": [
    "# import all packages and set plots to be embedded inline\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sb\n",
    "\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {},
   "outputs": [],
   "source": [
    "# load in the dataset into a pandas dataframe\n",
    "diamonds = pd.read_csv('diamonds.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset Overview\n",
    "\n",
    "Before running straight into the exploration, let's just take a moment to get a high-level overview of the dataset. We can refer back to these points if we need to get our bearings on what we should expect to see from each variable."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(53940, 10)\n",
      "carat      float64\n",
      "cut         object\n",
      "color       object\n",
      "clarity     object\n",
      "depth      float64\n",
      "table      float64\n",
      "price        int64\n",
      "x          float64\n",
      "y          float64\n",
      "z          float64\n",
      "dtype: object\n"
     ]
    },
    {
     "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",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>0.24</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>J</td>\n",
       "      <td>VVS2</td>\n",
       "      <td>62.8</td>\n",
       "      <td>57.0</td>\n",
       "      <td>336</td>\n",
       "      <td>3.94</td>\n",
       "      <td>3.96</td>\n",
       "      <td>2.48</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.24</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>I</td>\n",
       "      <td>VVS1</td>\n",
       "      <td>62.3</td>\n",
       "      <td>57.0</td>\n",
       "      <td>336</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.47</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.26</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>H</td>\n",
       "      <td>SI1</td>\n",
       "      <td>61.9</td>\n",
       "      <td>55.0</td>\n",
       "      <td>337</td>\n",
       "      <td>4.07</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2.53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>0.22</td>\n",
       "      <td>Fair</td>\n",
       "      <td>E</td>\n",
       "      <td>VS2</td>\n",
       "      <td>65.1</td>\n",
       "      <td>61.0</td>\n",
       "      <td>337</td>\n",
       "      <td>3.87</td>\n",
       "      <td>3.78</td>\n",
       "      <td>2.49</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>0.23</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>H</td>\n",
       "      <td>VS1</td>\n",
       "      <td>59.4</td>\n",
       "      <td>61.0</td>\n",
       "      <td>338</td>\n",
       "      <td>4.00</td>\n",
       "      <td>4.05</td>\n",
       "      <td>2.39</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\n",
       "5   0.24  Very Good     J    VVS2   62.8   57.0    336  3.94  3.96  2.48\n",
       "6   0.24  Very Good     I    VVS1   62.3   57.0    336  3.95  3.98  2.47\n",
       "7   0.26  Very Good     H     SI1   61.9   55.0    337  4.07  4.11  2.53\n",
       "8   0.22       Fair     E     VS2   65.1   61.0    337  3.87  3.78  2.49\n",
       "9   0.23  Very Good     H     VS1   59.4   61.0    338  4.00  4.05  2.39"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# high-level overview of data shape and composition\n",
    "print(diamonds.shape)\n",
    "print(diamonds.dtypes)\n",
    "diamonds.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# descriptive statistics for numeric variables\n",
    "diamonds.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Univariate Exploration\n",
    "\n",
    "Let's start our exploration by looking at the main variable of interest: price. Is the distribution skewed or symmetric? Is it unimodal or multimodal?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49223abeb8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# univariate plot of diamond prices\n",
    "# for the numerical variable price, I would choose histogram \n",
    "bin_edges = np.arange(0 , diamonds['price'].max()+20 , 20)\n",
    "plt.hist(data = diamonds , x = 'price', bins = bin_edges);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Throughout this case study, you'll see comments in Markdown cells like the one below, where the text has been colored in white. Unlike the exercises in earlier lessons, a reference plot will not be provided: you'll need to assess on your own whether or not your plot matches the comments. To view these comments, you can either highlight the text, or double click the Markdown cell. Remove or change the 'color=white' style on the text to un-spoiler the commentary."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Price distribution comment 1: The initial plots should show that diamond price follows a highly right skewed distribution. Now I want to try a log scaling"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4922488208>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bin_edges = 10**np.arange(0, 5+0.1 , 0.1)  \n",
    "# 0 means 10^0 is the start point, 5 means 10^5 is the ending point , 10^0.1 is the gap\n",
    "# do i need to increase the max tick\n",
    "ticks = [0.1 , 0.3 , 1 , 3 , 10 , 30 , 100 , 300 , 1000 , 3000 , 10000, 30000 , 100000]\n",
    "labels = ['{}'.format(v) for v in ticks]\n",
    "plt.hist(data = diamonds , x = 'price', bins = bin_edges);\n",
    "plt.xticks(ticks,labels)\n",
    "plt.xlabel('Price')\n",
    "plt.xscale('log');\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Price distribution comment 2: Under a log scale, you should see that the data is roughly bimodal, with one large peak somewhere between 500 and 1000, and a second large peak between 4 and 5 thousand. The price distribution also seems to just cut off at its maximum, rather than declining in a smooth tail."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Next, create a plot of our first diamond 'C' metric: carat weight. Is there an interesting pattern in these values?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TrmfUkqxNcmbbfjnwTuB7k61qdKrq6qpaX1UzzP+9vbOq/mzCZY1MkjPaxQwkOQO4GBjr1XxTFwBVdQw4/riJA8DN0/y4iSRfAP4deGOSuSTbJl3TCF0IvI/5T4YPtJ93TbqoEToHuCvJt5n/YLO3qqb+0siOvA74RpJvAfcAt1XVV8dZwNRdBipJGszUzQAkSYMxACSpUwaAJHXKAJCkThkAktQpA0CSOmUASFKnDABJ6tT/AatNxHmz58X1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f492138ff98>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# univariate plot of carat weights\n",
    "# numeric variable so I still choose histgram \n",
    "bin_edges = np.arange(0 , diamonds['carat'].max()+0.1 , 0.1)\n",
    "plt.hist(data = diamonds , x = 'carat', bins = bin_edges);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Carat distribution comment: You should see an interesting pattern in carat weights, where there is a large spike around a single decimal value, then a gradual tailing off to the right until the next spike. In this case, it's a good idea to set the bin size small (e.g. 0.01) and zoom into a narrower range of values to clearly see the spikes in the data values. Perhaps these spikes come about as part of standard diamond size conventions?</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(0, 3)"
      ]
     },
     "execution_count": 66,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f492134ae80>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# let's try to zoom in \n",
    "bin_edges = np.arange(0 , diamonds['carat'].max()+0.1 , 0.1)\n",
    "plt.hist(data = diamonds , x = 'carat', bins = bin_edges);\n",
    "plt.xlim(0,3)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, let's move on to exploring the other three 'C' quality measures: cut, color, and clarity. For each of these measures, does the data we have tend to be higher on the quality scale, or lower?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* univariate plots of cut, color, and clarity grades\n",
    "* cut, color, and clarity are all categorical variables so I would choose a bar chart\n",
    "* since they're ordinal in nature, the bars should be sorted in order of increasing quality level. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {},
   "outputs": [],
   "source": [
    "# make them orderd \n",
    "ordinal_var_dict = {'cut': ['Fair','Good','Very Good','Premium','Ideal'],\n",
    "                    'color': ['J', 'I', 'H', 'G', 'F', 'E', 'D'],\n",
    "                    'clarity': ['I1', 'SI2', 'SI1', 'VS2', 'VS1', 'VVS2', 'VVS1', 'IF']}\n",
    "\n",
    "for var in ordinal_var_dict:\n",
    "    pd_ver = pd.__version__.split(\".\")\n",
    "    if (int(pd_ver[0]) > 0) or (int(pd_ver[1]) >= 21): # v0.21 or later\n",
    "        ordered_var = pd.api.types.CategoricalDtype(ordered = True,\n",
    "                                                    categories = ordinal_var_dict[var])\n",
    "        diamonds[var] = diamonds[var].astype(ordered_var)\n",
    "    else: # pre-v0.21\n",
    "        diamonds[var] = diamonds[var].astype('category', ordered = True,\n",
    "                                             categories = ordinal_var_dict[var])\n",
    "        \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f4921466828>"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f4921350278>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_color = sb.color_palette()[0]\n",
    "sb.countplot(data = diamonds , x = 'cut', color = base_color )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Cut distribution comment: The bar chart should show that the mode cut quality for diamonds in this dataset is the highest grade, Ideal. There is a consistent decrease in number of diamonds of lower cut grades.</span>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f492236c550>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f49224b20b8>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "#diamonds['color'].value_counts()\n",
    "base_color = sb.color_palette()[0]\n",
    "sb.countplot(data = diamonds , x = 'color', color = base_color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Color distribution comment: The distribution is roughly unimodal, with the most common color grade in this dataset belonging to the central grade represented, G. (There are other diamond grades that are worse than J, but they're not part of the collected data.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x7f49223d4860>"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f492145bfd0>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "base_color = sb.color_palette()[0]\n",
    "sb.countplot(data = diamonds , x = 'clarity', color = base_color)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Clarity distribution comment:There is a right-skew in the clarity grades represented in the dataset, with most of the diamonds collected having slight or very slight inclusions (SI2, SI1, VS2, VS1). There are very few diamonds with visible inclusions (I1)."
   ]
  },
  {
   "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": "3.6.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}