Q_Zhu_w6_assn.ipynb

{
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  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
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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": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# improt data from github\n",
    "import pandas as pd\n",
    "from pandas import Series, DataFrame\n",
    "\n",
    "path1 = \"https://raw.githubusercontent.com/Johnnydaszhu/DAV-5400/master/diamonds.csv\"\n",
    "\n",
    "df = pd.read_csv(path1)\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>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>count</th>\n",
       "      <td>53940.000000</td>\n",
       "      <td>53940</td>\n",
       "      <td>53940</td>\n",
       "      <td>53940</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>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>5</td>\n",
       "      <td>7</td>\n",
       "      <td>8</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>G</td>\n",
       "      <td>SI1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>21551</td>\n",
       "      <td>11292</td>\n",
       "      <td>13065</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>0.797940</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</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",
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      ],
      "text/plain": [
       "               carat    cut  color clarity         depth         table  \\\n",
       "count   53940.000000  53940  53940   53940  53940.000000  53940.000000   \n",
       "unique           NaN      5      7       8           NaN           NaN   \n",
       "top              NaN  Ideal      G     SI1           NaN           NaN   \n",
       "freq             NaN  21551  11292   13065           NaN           NaN   \n",
       "mean        0.797940    NaN    NaN     NaN     61.749405     57.457184   \n",
       "std         0.474011    NaN    NaN     NaN      1.432621      2.234491   \n",
       "min         0.200000    NaN    NaN     NaN     43.000000     43.000000   \n",
       "25%         0.400000    NaN    NaN     NaN     61.000000     56.000000   \n",
       "50%         0.700000    NaN    NaN     NaN     61.800000     57.000000   \n",
       "75%         1.040000    NaN    NaN     NaN     62.500000     59.000000   \n",
       "max         5.010000    NaN    NaN     NaN     79.000000     95.000000   \n",
       "\n",
       "               price             x             y             z  \n",
       "count   53940.000000  53940.000000  53940.000000  53940.000000  \n",
       "unique           NaN           NaN           NaN           NaN  \n",
       "top              NaN           NaN           NaN           NaN  \n",
       "freq             NaN           NaN           NaN           NaN  \n",
       "mean     3932.799722      5.731157      5.734526      3.538734  \n",
       "std      3989.439738      1.121761      1.142135      0.705699  \n",
       "min       326.000000      0.000000      0.000000      0.000000  \n",
       "25%       950.000000      4.710000      4.720000      2.910000  \n",
       "50%      2401.000000      5.700000      5.710000      3.530000  \n",
       "75%      5324.250000      6.540000      6.540000      4.040000  \n",
       "max     18823.000000     10.740000     58.900000     31.800000  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe(include = 'all') # have a glance of the data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 1. What proportion of diamonds are between .30 and 1.08 carats?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7468298109010011"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "proportion = (len(df[df['carat'].between(0.30, 1.08, inclusive=True)]))/(len(df)) #use 'between' to range and then count them\n",
    "proportion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 2. How many of the diamonds have equal x and y dimensions?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "17"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[df['x'] == df['y']]) # count the rows when 'x' equal to 'y'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 3. How many of the diamonds have a carat less than the mean carat value?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "31127"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[df['carat'] < df[\"carat\"].mean()]) # count the rows when 'carat' less then the mean of carat"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 4. How many diamonds have a Premium cut or better? \n",
    "Note that the possible values for the quality of a cut are\n",
    "ranked in ascending order as follows: **Fair / Good / Very Good / Premium / Ideal**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "35342"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df.loc[(df['cut'] == 'Ideal') | (df['cut'] == 'Premium')]) #count each quality of 'cut' with \"Ideal\" or 'Premium'"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 5. Which diamond has the highest price per carat? What is its value?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    }\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>27635</th>\n",
       "      <td>1.04</td>\n",
       "      <td>Very Good</td>\n",
       "      <td>D</td>\n",
       "      <td>IF</td>\n",
       "      <td>61.3</td>\n",
       "      <td>56.0</td>\n",
       "      <td>18542</td>\n",
       "      <td>6.53</td>\n",
       "      <td>6.55</td>\n",
       "      <td>4.01</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       carat        cut color clarity  depth  table  price     x     y     z\n",
       "27635   1.04  Very Good     D      IF   61.3   56.0  18542  6.53  6.55  4.01"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ppc = (df['price']/(df['carat'])).idxmax() # Return index of first occurrence of maximum over requested axis\n",
    "\n",
    "df.loc[[ppc]] # using the return index to get the row"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value is 18542\n"
     ]
    }
   ],
   "source": [
    "print('The value is', df.loc[ppc]['price']) # use index to get the price of that row"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 6. Make a bar plot of the ‘cut’ data. \n",
    "What does it tell us about the frequencies of the individual cut values within the data set?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The data tend to have larger number of cut values of diamonds \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": [
    "df['cut'].value_counts().plot('bar') # use 'count' to get the frequencies and then plot them\n",
    "\n",
    "print('The data tend to have larger number of cut values of diamonds ')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 7. Make boxplots of the diamond price for each cut and discuss any conclusions you can draw from the appearance of the boxplots."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conclusions: The Ideal cut has lowest median and the Premium has the least outliners. There is no abvious relationship we could see from the boxplot between price range, median and cut\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": [
    "bp_df = df[['cut','price']] # get dataframe of 'cut' and 'price'\n",
    "bp = bp_df.boxplot(by='cut') # boxplot the dataframe just created\n",
    "\n",
    "print('Conclusions: The Ideal cut has lowest median and the Premium has the least outliners. There is no abvious relationship we could see from the boxplot between price range, median and cut')\n",
    "    "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 8. Make histograms of the length, width, depth, and total depth attributes\n",
    "and discuss any conclusions you can draw from the shape of the resulting distributions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Conclusion: x is most likely to be a normal distribution\n"
     ]
    },
    {
     "data": {
      "image/png": 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J04FPAXMj4mSyG78XA9cB16eY9gGLWxXTePIQHIVzstPcCSwYVNay88nJoXELgdXp+WrgQ636YknHAH8O3AEQEb+NiP1lxjTI2cBzEfGLCsR0ODBJ0uHA0cAu4CzggRJjGi9/GIIjIn4LDAzBYR0gIn4E7B1U3LLzycmhPgH8QNKWNJwBQFdE7AJIf09oYTzvBH4NfF3SE5JulzS55JjyLgbuSc9Liykifgn8D+AFsqTwMrAF2B8R/Wm1PrKhLNpRrSE42nVbRqvWOTkRtOx8cnKoz5kRcTpZ9X2ppD8vOZ7DgdOBWyPiNOA1Wt+sVVNqv/8g8K0KxDKV7ErrROBtwGSyf8PB2rU/d11DcHSoqp2THcfJoQ4R8WL6u4esHf0MYLekaQDp754WhtQH9EXEprT8AFmyKDOmAecDj0fE7rRcZkzvB34eEb+OiN8BDwL/DpiSmpmgvYetmLBDcAxxTk4ELTufnBxGIGmypDcPPAfOBZ4iG+pgUVptEfBQq2KKiF8BOyW9OxWdTTbUc2kx5VzCwSYlKDemF4D5ko6WJA7up43Ah0uKaTxNyCE4hjknJ4KWnU/+hfQIJL2T7MoEsuacb0bEtZKOA+4H3k72n9BFETH45lEz4zoVuB04Enge+DhZsi8zpqPJ2sDfGREvp7Ky99M/AB8B+oEngE+QtcvfCxybyj4aEa+3KqbxJOkC4AYODsFxbckhNd1Q52SJITWFpHuAbrIhuncD1wDfoUXnk5NDB5H034D5EfGXubKbgd9HxFXlRWbWPJI+Quq5lxwBPBIR3eVE1BmcHDpIaoPsBaZHxP7Urv4icH5EbCk3OrPmS928NwE3RMTXyo6nnfmeQwdJXdt+BFyUihYALzkx2EQg6Q3AN4EeJ4axc3LoPKuBj6bnHwW+UWIsZq10LfBmsl/F2xi5WanDSDqK7Adf/wF4FDgpIl4oNyqz5pJ0MbAC+LcR8euy4+kETg4dSNJtZJPVvxQRZ5Udj1kzSToN+AFwTkQ8WXY8ncLNSp1pNXAKblKyiWEhMBX4saQD6fFw2UG1O9ccOpCktwM/A/4oIl4pOx4zaz+uOXSY1GPj08C9Tgxm1qjDR17F2kUaSmA38AuK48CbmdXNzUpmZlbgZiUzMyto22al448/PmbNmlUof+2115g8eXLrAxoDx9wag2PesmXLSxHx1hJDGpV2P+Yd5/hpNMZRHfMR0ZaPOXPmRC0bN26sWV5ljrk1BscMbI4KHMv1Ptr9mHec46fRGEdzzLtZyczMCkZMDpKOkvQTSf8s6ek0Pj5pgpFNkrZLui9NNoKkN6bl3vT6rNxnXZ3Kn5V0Xq58QSrrlVSJ6S7NzCayemoOrwNnRcR7gVOBBZLmA9cB10fEbGAfsDitvxjYFxHvAq5P6yHpJLKZqv6UrJvlP0k6TNJhwFfJppc8CbgkrWtmZiUZ8YZ0aqc6kBaPSI8AzgL+OpWvBj4P3Er2U/bPp/IHgFvSFI0LyX6Y9Trwc0m9HJz3tTcingeQdG9a95lGNmjrL1/m8uXfG9V7dqy4sJGvMmtrs0Z5noDPlYmkrt5K6ep+C/Ausqv854D9EdGfVukjm3qR9HcnQET0S3oZOC6VP5r72Px7dg4qnzdEHEuAJQBdXV309PQU1umaBMtO6S+UD6fW57TSgQMHSo9htByzWWerKzlExO+BUyVNIZu79T21Vkt/NcRrQ5XXatqq+cu8iFgJrASYO3dudHd3F9a5+e6H+PLW0fXQ3XFp8XNaqaenh1rbUmWO2ayzjaq3UkTsB3qA+cCUNA0lwAyy6Sghu/KfCZBefwuwN18+6D1DlZuZWUnq6a301lRjQNIk4P3ANmAj8OG02iLgofR8TVomvf7DdN9iDXBx6s10IjAb+AnwGDA79X46kuym9Zrx2DgzM2tMPe0v04DV6b7DG4D7I+K7kp4B7pX0JeAJ4I60/h3AN9IN571k/9kTEU9Lup/sRnM/sDQ1VyHpCmAdcBiwKiKeHrctNDOzUaunt9JPgdNqlD/Pwd5G+fJ/4eAE94Nfu5ZsntfB5WuBtXXEa2ZmLeBfSJuZWYGTg9kgHhXAzMnBrBaPCmATnpOD2SBpAMuhRgV4IJWvBj6Uni9My6TXzx48KkBE/BwYGBXgDNKoABHxW2BgVACzymjb+RzMmqmdRgVo9Jffox1JAMY2mkC7/EK9HeJsRYxODmY1tNOoAI3+8nu0Y5DB2EYTaJdfqLdDnK2I0c1KZsPwqAA2UTk5mA3iUQHM3KxkVotHBbAJz8nBbBCPCmDmZiUzM6vBycHMzAqcHMzMrMDJwczMCnxDmsYmWgdPtm5mncs1BzMzK3ByMDOzAicHMzMrcHIwM7MCJwczMytwcjAzs4IRk4OkmZI2StqW5tO9MpUfK2l9mk93vaSpqVySbkpz4/5U0um5z1qU1t8uaVGufI6krek9N6VZtMzMrCT11Bz6gWUR8R6yMe2XpvlulwMb0ny6G9IyZPPizk6PJcCtkCUT4BqyGa/OAK4ZSChpnSW59y0Y+6aZmVmjRkwOEbErIh5Pz18lG9d+OofOmzt4Pt270jy8j5JNkDINOA9YHxF7I2IfsJ5s4vZpwDER8UgaA/+u3GeZmVkJRvULaUmzyIYy3gR0RcQuyBKIpBPSan+YTzcZmDd3uPK+GuW1vn/E+XS7JjU2N24jxmsO13aYs3Ywx2zW2epODpLeBHwbuCoiXhnmtsBo59MdqrxYWMd8ujff/RBf3tqaUUHGMp9uXjvMWTuYYzbrbHX1VpJ0BFliuDsiHkzFu1OTEOnvnlQ+2nlz+9LzweVmZlaSenoriWwaxG0R8ZXcS/l5cwfPp3tZ6rU0H3g5NT+tA86VNDXdiD4XWJdee1XS/PRdl+U+y6zl3EPPrL6aw5nAx4CzJD2ZHhcAK4BzJG0HzknLkE19+DzQC9wG/C1AROwFvkg2ufpjwBdSGcAngdvTe54DHh6HbTNrlHvo2YQ3YuN8RPyY2vcFAM6usX4AS4f4rFXAqhrlm4GTR4rFrBVSbXags8WrkvI99LrTaquBHuAz5HroAY9KGuih103qoQcgaaCHXg+ph14qH+ih54siqwzP52A2jHbooddoL6xGevWNpbdXu/QWa4c4WxGjk4PZENqlh16jvbAub2CSq7H00GuX3mLtEGcrYnRyMKthuB56qdZQbw+97kHlPYxzD72tv3y5of/ozYbjgffMBnEPPTPXHMxqGeiht1XSk6nss2Q98u6XtBh4AbgovbYWuICst91vgI9D1kNP0kAPPSj20LsTmER2I9o3o61SnBzMBnEPPTM3K5mZWQ1ODmZmVuDkYGZmBU4OZmZW4ORgZmYFTg5mZlbg5GBmZgVODmZmVuDkYGZmBU4OZmZW4ORgZmYFTg5mZlbg5GBmZgVODmZmVjBicpC0StIeSU/lyo6VtF7S9vR3aiqXpJsk9Ur6qaTTc+9ZlNbfLmlRrnyOpK3pPTdpmLkYzcysNeqpOdwJLBhUthzYEBGzgQ1pGeB8YHZ6LAFuhSyZANcA84AzgGsGEkpaZ0nufYO/y8zMWmzE5BARPwL2DipeCKxOz1cDH8qV3xWZR4Epaa7d84D1EbE3IvYB64EF6bVjIuKRNGHKXbnPMjOzkjQ6E1xXmgeXNNn6Cal8OrAzt15fKhuuvK9GeU2SlpDVMujq6qKnp6cY2CRYdkr/KDenMbW+vxEHDhwYt89qlU6PWdIq4APAnog4OZUdC9wHzAJ2AH8VEftSU+iNZFOF/ga4PCIeT+9ZBHwufeyXImJ1Kp/DwWlC1wJXpgsks0oY72lCa90viAbKa4qIlcBKgLlz50Z3d3dhnZvvfogvb23N7Kc7Li1+fyN6enqotS1VNgFivhO4haw2O2CgOXWFpOVp+TMc2pw6j6ypdF6uOXUu2XG9RdKaVHseaE59lCw5LMDzSFuFNNpbaXdqEiL93ZPK+4CZufVmAC+OUD6jRrlZqdycahNdo8lhDTDQ42gR8FCu/LLUa2k+8HJqfloHnCtparoRfS6wLr32qqT5qWp+We6zzKrmkOZUoCXNqWZlGLH9RdI9QDdwvKQ+smryCuB+SYuBF4CL0uprydpde8naXj8OEBF7JX0ReCyt94WIGLgq+yQH214fxlVraz9Na07tpPts7XKfqh3ibEWMIyaHiLhkiJfOrrFuAEuH+JxVwKoa5ZuBk0eKw6wCdkualjph1Nuc2j2ovIdRNKd20n22drlP1Q5xtiJG/0LarH5uTrUJozWXG2Ztxs2pNtE5OZjV4OZUm+jcrGRmZgVODmZmVuDkYGZmBU4OZmZW4ORgZmYFTg5mZlbg5GBmZgVODmZmVuDkYGZmBU4OZmZW4ORgZmYFTg5mZlbg5GBmZgVODmZmVuDkYGZmBU4OZmZW4ORgZmYFTg5mZlZQmeQgaYGkZyX1SlpedjxmzeZj3qqsEslB0mHAV4HzgZOASySdVG5UZs3jY96qrhLJATgD6I2I5yPit8C9wMKSYzJrJh/zVmmHlx1AMh3YmVvuA+YNXknSEmBJWjwg6dkan3U88NK4R1iDrhu3j2pZzOOoE2J+R1mBMDGP+XY5ZtohzkZjrPuYr0pyUI2yKBRErARWDvtB0uaImDtegbWCY26NisU84Y55xzl+WhFjVZqV+oCZueUZwIslxWLWCj7mrdKqkhweA2ZLOlHSkcDFwJqSYzJrJh/zVmmVaFaKiH5JVwDrgMOAVRHxdIMfN2wVvKIcc2tUJuYJesw7zvHT9BgVUWjmNDOzCa4qzUpmZlYhTg5mZlbQUcmhHYYjkDRT0kZJ2yQ9LenKVH6spPWStqe/U8uONU/SYZKekPTdtHyipE0p3vvSTdXKkDRF0gOSfpb29fuqvo8bUdVjXtIqSXskPZUrq9T+b5dzUdJRkn4i6Z9TnP+Qypt6DnZMcmij4Qj6gWUR8R5gPrA0xbkc2BARs4ENablKrgS25ZavA65P8e4DFpcS1dBuBL4fEX8CvJcs9qrv41Gp+DF/J7BgUFnV9n+7nIuvA2dFxHuBU4EFkubT7HMwIjriAbwPWJdbvhq4uuy46oj7IeAc4FlgWiqbBjxbdmy5GGeQnSRnAd8l+wHXS8DhtfZ92Q/gGODnpA4XufLK7uMGt7PSxzwwC3iqXfZ/m5yLRwOPk/2avqnnYMfUHKg9HMH0kmKpi6RZwGnAJqArInYBpL8nlBdZwQ3A3wH/mpaPA/ZHRH9artq+fifwa+DrqSnsdkmTqfY+bkS7HfOV3f9VPxdTs+6TwB5gPfAcTT4HOyk51DUcQVVIehPwbeCqiHil7HiGIukDwJ6I2JIvrrFqlfb14cDpwK0RcRrwGuU3DTRD1f8d2kI7nIsR8fuIOJWsFn8G8J5aq43nd3ZScmib4QgkHUF2MN4dEQ+m4t2SpqXXp5FdIVTBmcAHJe0gGzn0LLKaxBRJAz+irNq+7gP6ImJTWn6ALFlUdR83qm2O+aRy+7/NzkUiYj/QQ3aPpKnnYCclh7YYjkCSgDuAbRHxldxLa4BF6fkisvbP0kXE1RExIyJmke3TH0bEpcBG4MNptcrECxARvwJ2Snp3KjobeIaK7uMxaItjPqdS+79dzkVJb5U0JT2fBLyfrINFc8/Bsm+wjPPNmguA/0fWHvf3ZcczRIz/nqz691PgyfS4gKwdfwOwPf09tuxYa8TeDXw3PX8n8BOgF/gW8May4xsU66nA5rSfvwNMbYd93MB2VvKYB+4BdgG/I6vhLK7a/m+XcxH4M+CJFOdTwH9P5U09Bz18hpmZFXRSs5KZmY0TJwczMytwcjAzswInBzMzK3ByMDOzAicHMzMrcHIwM7OC/w8vukt9Fxtj+gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df2 = df[['x','y','z','depth']] # create dataframe of 'x','y','x','depth'\n",
    "df2.hist() # use the dataframe to create histograms\n",
    "\n",
    "print('Conclusion: x is most likely to be a normal distribution')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 9. Make a scatter plot of price vs. carat. \n",
    "What can we say about the relationship between those two attributes?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "There is a positive relationship between price and carat\n"
     ]
    },
    {
     "data": {
      "image/png": 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PsLYAFKrw3K3jqngaEf0RwJkAlgLojBkoxKOZNIaLpzFucDv6i1/Pq+Qlu/0ubdwVeyrojQ/fDS817sLcReshSwRFFbERbFPrYdz41Fq0h6x+6b4BGaoQjnYkSzxq6whh2kPL0R05LpJBH+H9Oy+InXvLvnb834a9+I+3tiASN7Ide0I5Nu/vRDwBn4TVd87Ea817tRtAHOUBGUqczebr/lrzXtz/8kYQRNKuUjF7ZYIggl/SbkSnjeiHj3YccdzGjESAXwJC9u0NAGglgi+bOAyvrN+DaBr1Kd/86Yy0K2emUzzNrQ9/t/5PglZWgWESyKds0Pj1rjlrJBZ+tDPjyUvJ7Il/KgDg2c3m5y82WYT19hebMK1uUNInjWN6hEiy0b5T4pHh4vrZwkYYg3RVAKtaDmLWpBEJ28ZjJ/YA4JMIqz9vw70vb7B934hqMdxHK1sOxm5y3WElDYeM0WJSwHBXpCP2gHa+TmIPAAqAl5r2pLXfMr+UtElOpnAl+EII9tszjuRTNqjdeoYIZTJjM5U9xr4Noc/khJwxAo8qasIoOqIIbNh9BDPGnhCbf5BAOBaxikkoKvCPz62zjPaTJR7NnjI6NvKcVjcIsiQhqt9MIorA3MXrMbyqzFHsneiOKLjtzx8jSRBLDFkibNh9NOEmVyx4mWULuBR8IhoMYC6A8QDKjOVCiJke2cUUENkqAuXWf+xmIq4nE6fxTzCp7PHqJphqFK2h+Y3Nk6c3zW+w+M6BxNH+1gMdtntrbD0cE/ydh7oQkCXLvvyShHe3HOzhGWmjZtWFfneHFRztihSl2N992TjPwzLdtjhcAK094RhoUTrbAHzokU1MgZGtZttuQyRTTZom284Ju1IBTvZ4VRLBbgQej08Cxg/vZ1lW1cePey4fhzK/hL7+xOQiY7T/q1c/td3npJr+sfISdhOdEVXFjJMHpXk26SNJhPbuiOfHyTZBH2GCKZLJK9wKfrUQ4mkAESHEO0KIHwCY4qFdTAGR7WxQN71k49ebPbW2x71ak4k3gKT2JLsJbth9tFfNLpLFmBv4JODRaybFJqkfe2sLzv21dqN6cNlG3H3pOPzhu2ch6Ev86R8LKwgrxrPBcWZPrcWGPUdjN7zLnliJa+pHJpx3/ZjqhN6wskS4pn5kwj57SplPTnhKKQZCUZG0mXomcTtpa9xS9xDRpdAmcEd6YxJTaORjNqjderdeMLZHE6dOrptk9tjdBLsiUdw0vyEhY9NMqonvSSnmAWSJMK1uEBas2Y77Xt4Qc30YIvngKxux6o6Z+M3VyX37FUEf/u1bp6E7osTKQhiROcY1WNiwE8tumZ7Ql/fWC8binNHV2N7WgVHVFbEaNndcfCoef2sL/mfNdvTGGxNWVEyvGwSZ0Kv95Bt+CZ5P2ALuBf9fiagKwO0AHgfQD8BtnlnFFBzZyKI1SJaElEose5q8lOoJxm6/8TfBsKJo0R02GZvGaHzB2h14ckVLQnil+fw6wwquqR+JhQ07bW0NyDL+9N5W/OGdrbbvx9+o7Hz7YUVBvz6+mFg3tR6GT6KE/XSGFctEtF1CFYCYG+iFhtZei/Qt59ehbkglbrtwrGO8fT7gkwg3nTcG81dvQ2c4lYsRWcm0dRuH/yyAW4UQh/W/BwL4re7ayRgch1/8OIlyb8I6vQ7DNMfVp7M/45yOdIXx4wUfW2LiywMy7p81HqGoigeWbUAoLmDbLxPW/MsFthPAP7twLAI+Cf/2t00WsQ76CKpA0knNMr+EVXfMjF3flxp34XZTiKVE2lOCOWegvTuaEBsf9BFe+cl5sRE+gIT4fOMcNDeMAkmihPfTwRzv39YRwtm/fNPVRG+u8EsESYLj52Em/rNxixdx+KcbYg8AQogvieiMtKxiSh6nqJX47Mp0BLq3YZhubjQ9fYIxRv92pX47wwruXdqcdPRnhFcOr+oTK7xlnM+jb27GqjtmYmB5wHIjmnnKYLzavM92fwGZ8PBVpwMA3t18AEe7wpjz4npLKKQqAFU5XvdnzqImJHr1NZ/zxb97DwGZEFZUXHbacAgb9Y2Y9tXb4f1F44dYrnu68wKk/8vWDEBEFWkdTAI8z7R1K/gSEQ0QQhwCYiN816WVmcKnt0lVTqGbiz/aiV/9bRMAIKxrQzphnb0Jw0wndLI3dYDMLh5zedxUj/qrP2/D0yu/SKjFYjeHUB6Qcenj7yXdFxGhYduXlqSpVAgB+GR7aY2qAlFd5Jc07Xa3w16wtGkvgHX44fQT0frlMQR8Mroi7v3eAtZSBvnGsYiKBWu3e+rWcSvajwB4n4gWQbtm1wD4pWdWMXlFb+PJ2zpCWLFpf4IfWCbCvS81Y9knexO2kSX35WJ7GoaZ7SbShjiv2LQf9728AR2p0jUBPLNqm23hLXP4p3Ej3nmoCz5JQgj2+w1F1bQTo7Rj509UzNKmPfjbJ3u10XMRsrBhJ24+78S0yyu4xW2m7XwiagAwE9pT0beEEBs9sYjJK8x1vNMVxZZ97Xhm1RdYvG4XfBIlRCF0hhVbsQc0V4DbsM74CdKuSNSSyOPXXRnx9u481JXghvCygJUhzpNq+rsKLZQlzfa4UjgI+LQwyPgG33dfOg5hJbPi7JeAy08fhsUfez+Cd0uxir2BOckt06TT8WojABb5EiJZHW83Warx2aDmqPO+AQnHUrgy7r3cOesw3sUUH3USNQmqRFo5gHjKA3JCoa2Q0vN4aLNNgLV2jvkpKawoUFyIVplPTrz2MvD8D8/BsYgai64xbsT3vNQMFzEYaRFRgb/kkdiXAqlCb3sD++EZW5zqeKdKqnLKBu3rl3D56cPxUuNudCcZ5f7iG6fGSuXakczFVF0RRFUff0Laf0CWY1m/ZhHuDCsI+qzr9rSAlVOHq7svHYcHX9mYsslGPIoQuGT8EEsRrqgCXPfUWr07kxq3ftpmu6K4x9P5xeyptZ6N7gEWfCYOo6b41gPtCT534Lg7wamV25/es48BB7SJqWXrncX+5hknOdrn5HdPFjPfvOsIvjNvteUm0d4dTRDNqJJ+hnCqDlf3v7wBAZvM1lR8fdyQhIqLAkbki/dJOkx2ue2COtz2tVM8PQYLPhMjPiY7noBMePUn05OOQNwV9bKPTJEl4IFZEyydj+wig+waSphdTHZZv3Yj7DmLmmzdH5SkWYUTKTtcyVLCk5JPAmRJgkyJma4AUOYjvNpsP7/BFCdPrPgcJw6uyHiVWTMs+AwATVznLmqyFfvyoAxFj41PJvapinpNqqlCy/6OhMiUoExQAdwX1+bOzm0jAMxd1JSQoBTvYoqPmbcTZJkkKDaBen45/Sqa2lyAQ4crRcW9s8bjwWUbLedjzDn86NkPE24IitCuTbSY6gcwjkRV4WmUGMCCz+B42KQ2crYKTB+/hPsvH4/zTz0hadLSht1H8d6W/Y7HaGw9AtnGRWRMmj74ykZcPGFoLEnJrjUfQAliH0ziYoqPmY938yhCtR3hK2rq6CDjyaM8IOPV5r14ckULFAfBVwVQGfRh1R0zLT1eqyuCmDF2MK48Y4SlVAIB+E59Df7c0OpoB1N89KRsdzqw4Jc45vonXTZp76pAUrFP5QKKR1EFgj57N4b5i27ntpFJSkit7OuX8YfvnoUZYwcDcI6SsSvuBsCShOSXCb+5+vjNw65F4oK1O/DE8i1QFJEk2j0RY+RmuJbi5xHi6+IIAM+t7VkjEaaw6YpEPW2C4pngE1ENgPkAhkLL3JgnhPidV8dj0sc8krbDEMBDnWGs2LTf0t+04Yu2tMTe4LtTRmFSTRVufaHRElXSFYmiPCDjsbe24InlW2xcHCogrIofVVUMryqzxPsHZPsestPqBmHed+txtCuMfn20tn4A8Mz3z8GuQ8cQiiqYXjc4dn7xfWLPPbEa72w+0OMUJEVRce/SDYiqIvbU8s8vNLquT86UBhlOo0jAVfG0Hu2YaBiAYUKIdURUCeAjAFc6JWxx8bTs8u7m/fjH59bFuh4Bmj96zkWn4MTB5Rg/vAq/e3NzQn/T7oiatFpjKoxgFbsORz4JtjcQv0x45NsTAWgROULVGlWX+SWEImrKsEFjglQIgbAiEJA1EZckggTE9iWEwA+mjcH44f3w04XF2UKPyX9+/c0JuNYhLDkeL4qnpY0QYg+APfrrdiL6FMAIcPJWXqCNYBMnQBUhcPnE4aiuCKLhizbb/qa9wemJwOk9YyJr3LB++MbjKwEI15UXoypi/VcBxJ4eFJOgG/v6fZKywgyTLVoO2Dd6zwRZeaIkotEAzgCwNhvHY5zRInLWO06AvtS4C9/505ocWWgl4DveLrEzrCAosyOEKV5GDyxAH74BEVUAWAzgNiHEUZv3bwZwMwDU1tbGv814wIK1OxISjswToIZv32t/olvMkTNuCqXJpPU+1UoTKIiqIm/OhWFSsb8j7Nm+PRV8IvJDE/sFQoi/2K0jhJgHYB6g+fC9tIfRRvdPrtiSsFwRItb42k254UwR9CWGWhr0DchQhbCEXRoRNz9/sRF2FRBmnFyNf/+O1qrBiLBZvG4nfvXqJs/OgWEyiZfN4L2M0iEATwP4VAjxqFfHYdJj56EuBGQZoai1BOMt59c59mMFtMqJ102uxcKGndpkZ1QFEcVqohtIlDghGx/h75cJ910+HhNGVKF59xHc+1JzzIfvk4D7Z03AhBFVtvX3jcSqe5Y04xVTNuqsiUPx2HVnxf42XFOPvrFZb9QhYr1Qgz4JUUWzP+jTOjGpIv26MfHnNWpgGW6afiLW7z7a44ltpnSZNLIK9WOqPdu/lyP8aQC+C+ATImrUl/1CCPGqh8dkUmAn5kEf4frJx91p8XHroWgU3z6rFn8/bTTqhlRamoGvajmYUMbAEOovDnTguqfWIqKImCj6JeDfv3NGrF8qAEys6Y+Lxw9NSEpKxSnD+uGNT/eBiCCEwIV/N9Tyvl3YqSwR5t1wJloPdcXaA0bDmnUBGRCgpNE5EmkCb347fs3tX3bjWFTFDZNH4ZqzRmLFZ/uxZV8H+vXxYXrdIBzuiuLwsTD+462WpOc1sn8QE4ZV4fVN+y03TgnAY9edgT5+CU07j+DEQX2xfucRbNh9FFV9/Fjx2QFPSwcTAX7SYqyvnDQCf23cban6SQCuPXskKsv8WPvFQazf2e54A/VJwB9vPAtdERXb2zrw0fbDWP7ZAff2AJheNxB7j4RQXRFA34APg8oD2HqwA+t2HLE8n36lbhBkmTCyfx8cOhZCKKri5CGV+LthVbjzL+td9SZIZoNxjvEDHR8BspxY5O78UwbhionDMXdxs6WctV8Cnv7+2T2ywy1eRumsRPpdyBiPie+8FFFU3HPZ+KSjaKOx9tKm3Vj88U5L85Odh7owrW4QVt0xM5Z5avQ4ra4IYuehLi0WXjn+NFHm96FfH39sEtbsqjESqFKRGGGk/R+flm7nmiIA//T8x/ARJfwQgz4fbp5xIp58uwUSEpPD+vhlrehbilDmX726CRVBGVFV4JqzRuLdloPwSxKWfbIXF40bgpfX73Hc/kBHBGNOqID6qTV7WQXwxcFOXD+5FoMqy1AekDGwogxfPfUEHO2K4p0tBxMfrVwSXzXUDiEA/d6IRet2Jbwf8BGuPWcUmncdwbz3vki6H79MkAi45fyTMal2AACgZmBfTB5TjZNOqMD81dtd9QvoG5BxxaSRCYmBbR0hnPvr5ZZ9rN3+pW2/2LaOUMITajqYt4zfjWzTKSwgA38/bQzGD6/Cb7+dmAzoZXtDwMM4/J7AcfjZw00P2baOUEJj6qCP8INpY/DMqm0IyNY6N/G1b6bVDUrY3oiJN2+bbves+B+zQWXQh+d+NDlWPycSVXD90x8g7DI7zHj6OHVoJTbtbcfPFjZaEsD8MpBGR70e08evia+dDkmk1fsxcgoyhV9O/mSTDjdNH4P/en9bUhHt45MQVlRIEiEgS+iOKCDS8iJ6cnTjxmr+HjW1HsaNT621NIwv80n47bcnomZg34RMbPNTqpG0F5DTL5MdlAmCCEE5eTY3oN1ciRD7jfSmdSiQXhw+C34JYifkZX4pYQRk98OxI+jT2kPH15VfdcdMy48prChQBSzCYndcJ7sfeX0znv/APhcrUIjvAAAdnUlEQVQgIAM/nH4inlm1DRBacpaRzFXml6DqyVbx8ft+SWv0YSCR5vohaDH7QZ8EQOBrp56AZUkahDPe8f1zR+Gy04bhLx/vwqKPdmplQOJu4ubvkd3327yeMXcTn4ltLsuxYtN+3PXXTxIa5JgxsrDN+152y3R0hpXY067WZ3il7QAlne++E3mReMXkB3Ylhu1cHXZFm9yEQAL2dW7smmwf6Qrj/y1YZ6nlbj6uuSiZ2TXU1hHCU+9txVPvbYVTrlVYSUycMn5n0aiK2VNH4b9Xb0/YLn6fqgBU0w/d+LF6JfaJJesYM/NXb8dza3ZAIoGwAvikxKslE+lzQMDRrjAusuklAMB0Ezjer+C2Fxrxi2+ciq+eckJsHsknwVbsfQT89GsnY1R1BY50R2IVULsjUXz15MHY3taJDXva8eSKFvgkQiiqJPVrS+S+b3Om4BF+EZOsM1QyV82fZtcnTJgubdyFuYvX2/q0zdsmG+Gb97VgzXbctaTZsq0xUdX6ZRceWLYRqhCImEodXHDqELzWvDeP2minh4R8agFe3GTrxilLhKBPgqIK3P61sXixoRVbepAd65OA126dYRnc9AR26TAp3TaGkNu15DP7Q43yx0e7wrj9xfW2j6azp9aiftRAzFnUBJkkRFUFP5x+IqaeVB27gbR1hDD5V2/alk9gUWRKFZm0+RNFCNx3ubUBkFvYpcOkdNvEN/0ORdXYI64R7bKy5aCl8faFpw7B6xv3JrhA/veDVpwypB8AQjiqICo018rv39kai7ePKGrSWjks9kypoghA0aPN7lrSDBAc+zn3Fhb8IiVZb1dzre3qiiAaWw8njNpVRcWG3UcTmpC8kqTlXkQRuGfpBssElvm9u5Y0Q+7tCTFMCXD/0o24ePxQz/z6XIWqSDHi7cv8EiqDPpT5Jdx96TjsPNSFto4QAM1d8/hbmxO2DavA8k/3wS+5/3rYib3l/fTMZ5iSRJYQy1HxAh7hFzHmCJnmXUcSui1tazuWNOplwQc7oHqYtckwTCKqQGF2vGJyh10o5nfmrU7oEes0X++XScuqzKNJfYYpZmTJ2mLTC1jwiwy7UMxR1eWJ1S8FaU3FFXtnS1QPjexpjRGDfkEJR0M8LcswqfBJWqMfL2Effg5p2deORQ2taNnXblne1hHCu5sP4N3N+9HWEUr422l/cxZpE63toSi6Iyp+/mIjVm7ZbynSBAAhRUVX0rh6CT//+im2qfvpfmFY7BnGHQFZ9tR/D/AIP+sY7pYFa7dbyucapX0XrNmOe15qRrKMblkCHpg1AacMqcBrG/ZBJkCFAAng6VXbErYLK8BvXtfq37tNTOkXlPHQa59BsXHnsHwzjDeEFcVT/z3Agp9VDHeLBOBY3Gzp0qa92HbwXazf1W6/sY6iIiFb1S1uvfEHOiM92j/DMD0nogisajmYVjHBdGGXTpYw12aPF3uDVGLPMEzxogpgzqL1jm7b3sKCnyWMzFeGYRgnvPTjswJlCbeVJxmGKTzcdHrSigymxks/Pgt+lrDLfD2rtsqyznl11fDxJ8IwBcXkMQPwu2snIegj+CWrqPsk4PavjcWbP52Bhf9wLn7xjVOT7ofj8IsMc+arkRTVsq8dja2HMammP+qGVMaqUwICw6v6YPeRbny29yi2t3WiIxTF0sY9HCnDZI2+PmBoVV8IIbC3vQtdKebzywNAZ9h5nTIJ6Hb5Ja7p3wdjh5YjFBEgEljZ8mVC8MG4YRXYsr8TJATCKjD9pGqMHtQXo6vLMXZoJXYc7MTrn+7H+5+3ObYzNDqeDe0XjPViNr8nSYltIG86bwzuunQcAMR+2+UBGbuPdMOuP/PEmv4AtDaYZrRSyeehbkiluwvTQ1jws0x1RdDyBagbUmn5kON7u9YNqcSMsYP11n5vsdgzWaVbAXYd6dY7iDmvW+aX8Psbz8JNzzY4dooiWYKfRKyjmBO7j3Rhb3s3ynwyQlEFQb9kKfldHpDRcuCYRZwbdhzC76474/jvbCxwWs0AfJyie1uZ34eagX211za9mG+cUpvQYGf+6m34x6+cFPtdG8e0E24jJPvUoZWoCMqWpMY+fl/aLRV7Agu+B5hH6fF3+J7sy+gWJZMELkPGZBNVIGVD8fKgDEUVuPvScTjaFbUV+z5+CV26UHc5tS2LQxGAohzvThWfaBKKKgjIMswPFT3t3mauJmtXaXb88Cr08cuWhMVQVOD5tTvwkwtOdty3OQPeaPWZ7NhewoKfYV5q3IXbTU2LfRLw6DWTbBuEx9e7iV9m/pKEotGk9eQZJldcUz8CN0weHSvOpyiJX9Kgj3DjlFH403tfZOy4Rq9iVQWOqdZBkJ14GnNoc01lR66pH4mFDTstZUiM32LCumeNxO0vNiIUTbyZPbGiBZdMGJq0c5U5JNsob+KTgIBM8EkSFGE9tpew4GeQto4Q5i5qsghzVAV++udGjBvWL/aFMDcWMb5o7d1R3L9sIwIyIaqPlh58ZaPlS8Iw+caSj3fjmrNqYt9VO1RVoH+fzEqN8RuzO+Ldl42zFU+7ObRbLxibMPCKX7c8IOOyJ1baij0AQAh84/GVCMpSQsc4wL4ZkUwEAdLT391F72QCFvwMsvNQFySbAgaKAC753bso8/sQikahCK3bvfEFuPWFxtgWYf3J9Z6lzfBx3D6T54QVge/8cY2jZkVU4LHlLVmxp29AwoThWvSb3VN0/ByaE8a6Ta2HE4sPmtBcWAJh/S5kdIwzjmPnToptoz+cxG/jFSz4GaQ8ICOcxE8YUYFIbMLIekOwGzcoKqBw3D5TAChAyrodSUfHGSYU0dw5dlVj492qTuuYbxblARkhG1dVeVBGJKpCksjydBM/hxDvTgpFlZTbeAULfoZYsGY77nt5A1ijGcZb/DLhkglDsbRpT8J7kkT44kAH5ixaj3D0uDs0fgRt51e36+XcFYmCiGIJS2V+7dXdl47DhBFVMXePGbs5BDsXUaptvIB9BhlgwZrtuGtJMyKKcF2gjGGYnqGqAq+sTxR7QBO0a59aG3OvGAhVWEoW2JU68UuSpZdze0gLlIgoIhZ5pKoCy26ZjhumjMJEPXcmPqEy2QRsdUUw7W0yDY/we0lbR6jH1SsZhkkfp9D9ZPH/IUWgPCDH/rbzq2t/C0d/vU+SsPtItyXOPn4yGACaWg/bRuwk2yYbYg/wCL/XrP78YK5NYJiCor62CrLLwJQ+PoLf5co+mWIul3jK/JIlscmu1MnDV52O8cOrHOP1j0UU3DS/AUsbd1mWG6P3lS0HMe2h5bjxqbWY9tDyhPXstsmW2AMs+L1me9uxXJvAMDmhp8GEDTuOQHK5sSDCN8+wTrbabeuXCS/8aHLS/aiqdYQPaKPsVXfMxHM/moxVd8zErEkjEm4EPgkJ9a1CURVzFyeWMTbPCxzvONeU0NEul7Dg95C2jhCaWg8jwNXOmBJEAvD4dWegb5yIAgAREEjxszCH7BuD8jK/lHATmTVxGJY27bYem2B5QpAl4L7Lx6N+THVMrIP6CsbPU5IIlz2xMunI3DyZO6q6HMtumY7nfjQZa39xIZ75/tkJ52lE1Ri0dYSwYtN++OLuRmFF4OLH3sOCNdudL0iWYB9+D/jjO5/jt69/BgiBZFniAZkgAEuND4YpFCQ4t7OcVleNqSdVI6oklvoQArj1a2Pxn29/nrI+THlAxv2zxmNSTX/sPtKNm+Y3WEo5LPl4d8KgKuiTMeeiU7DtYAee/6AVAZ+EB1/ZiMoyn8U3HokquP7pDwBVjYVAOsW724VpTqzpj/HDq6DGtfs0R9UY28lEtucbVYQ2z0fADZNH2eYHZAvPBJ+IngFwGYD9QogJXh2nt6R78ee+2IiFHyX3yxmEFdHjR16GySUyaaN0pxDj91racKgzjG+fVYsFH+xIeL886LPtiRyPIgTOP/UEVFcE0RlW4JMIZkeJUEVCBm9nWMHDr22KdY4L6zeduYvXWzLaN+w+Ap9EKevsAPZhmnMWNcVuDuY4+rCi4MdfrUvYLhX3v7wREMCDr2x0zA/wEi9H+P8N4AkA8z08Rq9IlZxh3AwOtnejaecRlPslV2JvwGN7phDRk0BT0th6GH8/bbSt4E+vG4SHrzodcxY1OSZdmcsgLFizLWGEHBHQHhmg3YiMB2a7NqGKInDJY+/BL0sIRRQQIaH+VLJG4XblD8yF0YwnhwVrd+DJFS2Y9+5WPPl2C3781TrHqB4zPgm4f9lGx/wAr/FM8IUQ7xLRaK/231ucEi8A4Kn3tuJP725FlhIEGabgMHo4zJ5ai/mrj4v+7Km1sbLf/fv68cNnG2xdm+UBOVYGoWVfe8rBVCrvaEQvQRkx3Ew266sCto3CRw7oE3tSMPPEihZcP7k2Jsj/+XYLQlE15nZ6YsUWuJ2+jioCAZ8UK58CZC/D1qBkZxyTJV4sWLsD5/zyTfz+HRZ7pnhxG+poMGmktTubIeoA8MAVp+HNn87Ab68+HW/+dAYeuOK02HrDq/okncdShIiNthtbD6dlT0+JKMI2wqa6Iohbzk8scRyQj0/O2mlGQJZxy/l1lqgev0yoDPogE+CTCOVBGWV+CfdePj6hAUu2MmwNcj5pS0Q3A7gZAGpra7N2XLvEi7Ci4InlW1KOJBgmnzG7PnwSQEQW0Q3I2jK3Tsegj/D098/Goc6wpTubmfhGPgadYQVlcU1LACDgs2aXTtI7QaXL988dhYUNO3EsjeYhyUbV10+uxRMrWiyTxmZBTpasdf3kWlw/udaSeGX3uroiiMoyn6XscrYybA1yLvhCiHkA5gFAfX191qTWPBEjEyGiqPjBtDF46r2tqTdmmDxl9tRa3HrBWEsDnlUtBxNEBtBcmEIVluxUv0y47pwa2zrx1RXBtFvw2Y1eAzLh1Z9Mt+zLzjVkxmyX8Xu99/LxuHjCULzwYWvS4/cNSDgWThRpO7uqK4L4zdWnJxVku5r68e+b92X3OlcZtgYkXMyk93jnmg9/mdsonfr6etHQ0OCZPXYsWLM9Voc+HFWQhS5jTJFz0uC+2N52DKrqHNrY1084Fjn++xtWGcC+jjACsgRVqPjKySegzC/BJxM6QwqCPsLEmgEoD/oQiiqYXjcYA8oDWP15Gw52dGN63eCkguzUcMeuB2smQweXNu5KEMlkkSlGj+cBff1oPdSFoE/CiAF9HO0y9m/cvAIygQi457LxmDCiCs27j+DBZe4jY1Kdey7DKu0goo+EEPWu1vVK8InofwF8FcAgAPsA3CuEeNppm2wLvtYndnnKFm5MflLbvwxRVcWgyiA+2d2O+K/ypROGYFLtAHy29yiWNu1J2T/VjARg4T9Mwd6jIfz8xSZ0m74jfgm4+qyRiCgqhvQrw8knVODD7YfRv48P3zxjZKwZvSGmm/a2xwQZQELTerN45JuYZAqvz8t8ve06TxXrdQXSE3wvo3Su82rfmeLuJZ+w2BcoQZ+Ef/3W6ZgxdjCaWg/jxrgG1ZVBH27+Sh0m1vRHW0cIyz7ZmzrMw0R50Ae/T8bUk6oTgjBkWcLPLzrVIhxXnlljWcepoXV80/r4x/9iEyTA+/NKtf9iva7pUrJROi372vFq875cm8H0kFBUjRWxSjaZZvhp4+ujBF0U5DK2T1Zki8WDKURyPmmbK5zCwMZU98UXXBQtK5x70kA0bDuU4G4JyBIkSWs08WVnGP/x5uaEAbpRxGrVHTMdJ9OAxMmy+IlMp4bWuZ5oY5hMUXKCb/jynGqetX7JYp8OAZlwz6V/h3te3gg1jSmhgAw8MGuC1v0nLmzwqe+dFZuoA7SQuZebduOh1zahy6Y1nBtRNj/Wp9PQOn5bhilUSkrwjYgcCNUxGocTro7jkwBZkhznOoI+GWUBH8oDPosfHdBG6mGbfqDadj6tLorN6HzG2BMs61ZXBHH5xOH49WubLMvjXTfpiHKp+M8ZxqBkBN9oQ8ikR1QFKEWCTkRVMammf4IfXUvwcd5u5IA+mFjT35XLJFUcNMMwzpSE4HMbwt4RcfDTGBmTRp/OZAk+5obQZT7ZNmnFjXCzP51hek5JCP4f3/k81yYUBT6JLLVA4jMmk4lxfL/P3oo1u14YpmeUhOB/uP3LXJtQFJw6tAItBzotI/j4GHOzGLd1hCwp/nYp6AzDZI+SEPwZJ1Xj4x1Hcm1GwfOj6WNw3tgTXI3QX2rchdsXNsbqkftlwiPfnpjVZg8Mw1gpesG/Z8knmL/GviATc5z4lnZ9fIQuU7jSsH6BWDZpqhF6W0cIcxc1WZpPRBSBOYuy2+yBYRgrRS34LfvaS1rs/+WisajqG8A9SzekrCMT8Et47gfnYFvbsVidlyXrWrHsk7247LShCaUDnNh5qAsySQCssa+yRFlt9sAwjJWiFvxsNVXIR3wSYdeRbjz61hZHsS8PylBUgYevOh31Y6pRP6Y69t6VZ9akJfQGIwf0gSJsWtCpwrYsLcMw2aEoBb+tI4S/rNuJv6xLXie72ImqIuXTTXlAxv2Xj481kc4UWl3xifhZnA//N1dzzDzD5JKiE/yXGnfh1hcac21GQaAIkXGxNzBCNO2idBiGyQ1FJfhtHSHc/mcW+2QYZRICcnayVKsrgpgxdrBn+2cYJj2KSvBXf97G/WhNXDphKN76bL8lbp6zVBmmdCkawS/W8MsrJg5DVV9/0l6fQZ+E2782Fr99fbOlSFnQR3jgygl4AImZrSz0DFOaFIXgF2v45ayJQ/G7684EAMyeMhrPrPoCi9ft0itQKrjl/JNx/eRaVFcEMbSqzFVzZYZhShdPm5inS0972j65fAt+8/pmDyzKHAP7+jDlxIEYM6gCIwf0jTVmPtQZxsqWgxhUEUQfv4Tlnx2w9EaNx6k3ZzH37WQYxp686GmbTR5/K3/FPiATXv3n82zFG9BG3+b3Lhg31HF/ToXDuKgYwzBOFLzg3/3X9eh2aGaSC2SJ0NcvJy0wxjAMkwsKWvDbOkL4n7X5l1ylqAL/9q3TMPWkah5xMwyTNzh0ds1/Zjz0Zq5NSMrBjhCLPcMweUVBC35nxLt99/U79OZzwSAWe4Zh8oyCFnwvWXrLefA5XB2fpMXA260iS4SpJ1XbvMMwDJM7WPBteOzaSagbUon7Z02wfT/ok/DoNZPw/p0z8dcfT8NtF9TBJxECsoSgT8K/XzOR3TkMw+QdBT1pm2m+N6UW/3zh2JhY3zBlFEDAfUs3QJYIqqriJzPHxpKdAC0UcmJNf3x36miOgWcYJq9hwdeZPbUW919xWsLyGyaPwsXjh6YUc46BZxgm3ylowT93TH+8/0V6TU76BiREFYF7Lx+PyWMGorH1cKzDUzJYzBmGKQYKWvCf/4dpGH3nKynX6xckrJhzAYDEQmKcFMUwTKlQ0IIPANt+fSmu/+MqvP/FYcgEXHjqCfiVQ513HqkzDFOqFLzgA9pIn2EYhnHG07BMIrqYiD4johYiutPLYzEMwzDOeCb4RCQDeBLAJQDGAbiOiMZ5dTyGYRjGGS9H+OcAaBFCbBVChAG8AOAKD4/HMAzDOOCl4I8AYC5luVNfxjAMw+QALwXfrvpYQnstIrqZiBqIqOHAgQMemsMwDFPaeCn4OwHUmP4eCWB3/EpCiHlCiHohRP3gwYM9NIdhGKa08aynLRH5AGwGcAGAXQA+BHC9EGKDwzYHAGxPsetBAA5myk4PKQQ72cbMwDZmBraxZ4wSQrgaLXsWhy+EiBLRLQD+D4AM4Bknsde3SWk0ETW4bdibSwrBTrYxM7CNmYFt9B5PE6+EEK8CeNXLYzAMwzDu4Hr4DMMwJUIhCv68XBvgkkKwk23MDGxjZmAbPcazSVuGYRgmvyjEET7DMAzTAwpK8HNZjI2IaohoBRF9SkQbiOhWffl9RLSLiBr1f98wbfMvuq2fEdFF2TgPItpGRJ/otjToywYS0RtEtEX/f4C+nIjoMd2O9UR0pmk/39PX30JE38ugfaeYrlUjER0lottyfR2J6Bki2k9EzaZlGbtuRHSW/rm06NvaJSb2xMbfENEm3Y6/ElF/ffloIuoyXc8/pLIl2flmyM6Mfb5ENIaI1up2/pmIAhmy8c8m+7YRUaO+PGfXMuMIIQriH7TQzs8BnAggAKAJwLgsHn8YgDP115XQcgzGAbgPwM9t1h+n2xgEMEa3Xfb6PABsAzAobtnDAO7UX98J4CH99TcA/A1aVvQUAGv15QMBbNX/H6C/HuDRZ7oXwKhcX0cAMwCcCaDZi+sG4AMAU/Vt/gbgkgzZ+HUAPv31QyYbR5vXi9uPrS3JzjdDdmbs8wWwEMC1+us/APinTNgY9/4jAO7J9bXM9L9CGuHntBibEGKPEGKd/rodwKdwrg10BYAXhBAhIcQXAFqgnUMuzuMKAM/qr58FcKVp+XyhsQZAfyIaBuAiAG8IIb4UQhwC8AaAiz2w6wIAnwshnJLtsnIdhRDvAvjS5ti9vm76e/2EEKuFpgDzTfvqlY1CiNeFEFH9zzXQMtqTksKWZOfbazsdSOvz1UfQMwEs6o2dTjbqx7gGwP867SMb1zLTFJLg500xNiIaDeAMAGv1Rbfoj9TPmB7dktnr9XkIAK8T0UdEdLO+bIgQYg+g3bgAnJBjGw2uhfVHlU/XEcjcdRuhv/bSVgD4AbRRpsEYIvqYiN4hovP0ZU62JDvfTJGJz7cawGHTTc6La3kegH1CiC2mZfl2LXtEIQm+q2JsnhtBVAFgMYDbhBBHAfwewEkAJgHYA+1REEhur9fnMU0IcSa0PgQ/JqIZDuvmykboftdZAF7UF+XbdXQiXZuycT3vAhAFsEBftAdArRDiDAA/A/A8EfXLhi1JyNTnmw37r4N1IJJv17LHFJLguyrG5iVE5Icm9guEEH8BACHEPiGEIoRQAfwJ2qOok72enocQYrf+/34Af9Xt2ac/fhqPoftzaaPOJQDWCSH26fbm1XXUydR12wmrqyWjtuqTw5cBuEF3LUB3kbTprz+C5g8fm8KWZOfbazL4+R6E5kLzxS3PCPp+vwXgzybb8+pa9oZCEvwPAZysz9AHoLkDlmbr4Lpf72kAnwohHjUtH2Za7ZsAjFn/pQCuJaIgEY0BcDK0CR7PzoOIyomo0ngNbUKvWd+/ETHyPQAvmWycTRpTABzRHz//D8DXiWiA/uj9dX1ZJrGMovLpOprIyHXT32snoin692i2aV+9goguBnAHgFlCiGOm5YNJ6zoHIjoR2nXbmsKWZOebCTsz8vnqN7QVAK72wk4AFwLYJISIuWry7Vr2ilzPGqfzD1p0xGZod9i7snzs6dAe19YDaNT/fQPA/wD4RF++FMAw0zZ36bZ+BlNUhlfnAS2ioUn/t8HYNzS/51sAtuj/D9SXE7Q2lJ/r51Bv2tcPoE2gtQD4+wxfy74A2gBUmZbl9DpCu/nsARCBNnL7YSavG4B6aCL3OYAnoCc9ZsDGFmi+buM7+Qd93av070ATgHUALk9lS7LzzZCdGft89e/5B/q5vwggmAkb9eX/DeAf49bN2bXM9D/OtGUYhikRCsmlwzAMw/QCFnyGYZgSgQWfYRimRGDBZxiGKRFY8BmGYUoEFnyGcYCIHiCiC3NtB8NkAg7LZJgkEJEshFBybQfDZAoe4TMliV7jfBMRPasX9FpERH1Jq4N+DxGtBPBtIvpvIrpa3+ZsInqfiJqI6AMiqiQimbSa9B/q+/mHHJ8awySFBZ8pZU4BME8IcTqAowD+n768WwgxXQjxgrGint7/ZwC3CiEmQkvB74KWRXpECHE2gLMB3KSXCGCYvIMFnyllWoUQq/TXz0ErnwGYCmeZOAXAHiHEhwAghDgqtBK9X4dWV6cRWrnsami1Vhgm7/ClXoVhipb4CSzj706bdclmfWP5T4QQmS4uxzAZh0f4TClTS0RT9dfXAVjpsO4mAMOJ6GwA0P33PmgVMv9JL50NIhqrVyplmLyDBZ8pZT4F8D0iWg+tD+3vk60otDZ73wHwOBE1QWtfWAbgKQAbAawjrSH2H8FPzkyewmGZTElCWpvKZUKICTk2hWGyBo/wGYZhSgQe4TMMw5QIPMJnGIYpEVjwGYZhSgQWfIZhmBKBBZ9hGKZEYMFnGIYpEVjwGYZhSoT/D8oqjRvrJNG1AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3 = df[['price','carat']] # create a dataframe with 'price' and 'carat'\n",
    "ax2 = df3.plot.scatter(x='price',  # use the dataframe just created to plot a scatter\n",
    "                        y='carat')\n",
    "ax2;\n",
    "\n",
    "print('There is a positive relationship between price and carat')"
   ]
  },
  {
   "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": "3.7.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}