Wine_Quality_edited_1.ipynb

{
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "      <th>quality</th>\n",
       "      <th>color</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7.0</td>\n",
       "      <td>0.27</td>\n",
       "      <td>0.36</td>\n",
       "      <td>20.7</td>\n",
       "      <td>0.045</td>\n",
       "      <td>45.0</td>\n",
       "      <td>170.0</td>\n",
       "      <td>1.0010</td>\n",
       "      <td>3.00</td>\n",
       "      <td>0.45</td>\n",
       "      <td>8.8</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>6.3</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.34</td>\n",
       "      <td>1.6</td>\n",
       "      <td>0.049</td>\n",
       "      <td>14.0</td>\n",
       "      <td>132.0</td>\n",
       "      <td>0.9940</td>\n",
       "      <td>3.30</td>\n",
       "      <td>0.49</td>\n",
       "      <td>9.5</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.1</td>\n",
       "      <td>0.28</td>\n",
       "      <td>0.40</td>\n",
       "      <td>6.9</td>\n",
       "      <td>0.050</td>\n",
       "      <td>30.0</td>\n",
       "      <td>97.0</td>\n",
       "      <td>0.9951</td>\n",
       "      <td>3.26</td>\n",
       "      <td>0.44</td>\n",
       "      <td>10.1</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.2</td>\n",
       "      <td>0.23</td>\n",
       "      <td>0.32</td>\n",
       "      <td>8.5</td>\n",
       "      <td>0.058</td>\n",
       "      <td>47.0</td>\n",
       "      <td>186.0</td>\n",
       "      <td>0.9956</td>\n",
       "      <td>3.19</td>\n",
       "      <td>0.40</td>\n",
       "      <td>9.9</td>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fixed acidity  volatile acidity  citric acid  residual sugar  chlorides  \\\n",
       "0            7.0              0.27         0.36            20.7      0.045   \n",
       "1            6.3              0.30         0.34             1.6      0.049   \n",
       "2            8.1              0.28         0.40             6.9      0.050   \n",
       "3            7.2              0.23         0.32             8.5      0.058   \n",
       "4            7.2              0.23         0.32             8.5      0.058   \n",
       "\n",
       "   free sulfur dioxide  total sulfur dioxide  density    pH  sulphates  \\\n",
       "0                 45.0                 170.0   1.0010  3.00       0.45   \n",
       "1                 14.0                 132.0   0.9940  3.30       0.49   \n",
       "2                 30.0                  97.0   0.9951  3.26       0.44   \n",
       "3                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "4                 47.0                 186.0   0.9956  3.19       0.40   \n",
       "\n",
       "   alcohol  quality  color  \n",
       "0      8.8        6  white  \n",
       "1      9.5        6  white  \n",
       "2     10.1        6  white  \n",
       "3      9.9        6  white  \n",
       "4      9.9        6  white  "
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "wine_df = pd.read_csv('winequality_edited.csv')\n",
    "wine_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7.853333</td>\n",
       "      <td>0.517000</td>\n",
       "      <td>0.281000</td>\n",
       "      <td>5.140000</td>\n",
       "      <td>0.077033</td>\n",
       "      <td>39.216667</td>\n",
       "      <td>122.033333</td>\n",
       "      <td>0.995744</td>\n",
       "      <td>3.257667</td>\n",
       "      <td>0.506333</td>\n",
       "      <td>10.215000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7.288889</td>\n",
       "      <td>0.457963</td>\n",
       "      <td>0.272315</td>\n",
       "      <td>4.153704</td>\n",
       "      <td>0.060056</td>\n",
       "      <td>20.636574</td>\n",
       "      <td>103.432870</td>\n",
       "      <td>0.994833</td>\n",
       "      <td>3.231620</td>\n",
       "      <td>0.505648</td>\n",
       "      <td>10.180093</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>7.326801</td>\n",
       "      <td>0.389614</td>\n",
       "      <td>0.307722</td>\n",
       "      <td>5.804116</td>\n",
       "      <td>0.064666</td>\n",
       "      <td>30.237371</td>\n",
       "      <td>120.839102</td>\n",
       "      <td>0.995849</td>\n",
       "      <td>3.212189</td>\n",
       "      <td>0.526403</td>\n",
       "      <td>9.837783</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7.177257</td>\n",
       "      <td>0.313863</td>\n",
       "      <td>0.323583</td>\n",
       "      <td>5.549753</td>\n",
       "      <td>0.054157</td>\n",
       "      <td>31.165021</td>\n",
       "      <td>115.410790</td>\n",
       "      <td>0.994558</td>\n",
       "      <td>3.217726</td>\n",
       "      <td>0.532549</td>\n",
       "      <td>10.587553</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>7.128962</td>\n",
       "      <td>0.288800</td>\n",
       "      <td>0.334764</td>\n",
       "      <td>4.731696</td>\n",
       "      <td>0.045272</td>\n",
       "      <td>30.422150</td>\n",
       "      <td>108.498610</td>\n",
       "      <td>0.993126</td>\n",
       "      <td>3.228072</td>\n",
       "      <td>0.547025</td>\n",
       "      <td>11.386006</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>6.835233</td>\n",
       "      <td>0.291010</td>\n",
       "      <td>0.332539</td>\n",
       "      <td>5.382902</td>\n",
       "      <td>0.041124</td>\n",
       "      <td>34.533679</td>\n",
       "      <td>117.518135</td>\n",
       "      <td>0.992514</td>\n",
       "      <td>3.223212</td>\n",
       "      <td>0.512487</td>\n",
       "      <td>11.678756</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7.420000</td>\n",
       "      <td>0.298000</td>\n",
       "      <td>0.386000</td>\n",
       "      <td>4.120000</td>\n",
       "      <td>0.027400</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>0.991460</td>\n",
       "      <td>3.308000</td>\n",
       "      <td>0.466000</td>\n",
       "      <td>12.180000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      "text/plain": [
       "         fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "quality                                                                 \n",
       "3             7.853333          0.517000     0.281000        5.140000   \n",
       "4             7.288889          0.457963     0.272315        4.153704   \n",
       "5             7.326801          0.389614     0.307722        5.804116   \n",
       "6             7.177257          0.313863     0.323583        5.549753   \n",
       "7             7.128962          0.288800     0.334764        4.731696   \n",
       "8             6.835233          0.291010     0.332539        5.382902   \n",
       "9             7.420000          0.298000     0.386000        4.120000   \n",
       "\n",
       "         chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
       "quality                                                                   \n",
       "3         0.077033            39.216667            122.033333  0.995744   \n",
       "4         0.060056            20.636574            103.432870  0.994833   \n",
       "5         0.064666            30.237371            120.839102  0.995849   \n",
       "6         0.054157            31.165021            115.410790  0.994558   \n",
       "7         0.045272            30.422150            108.498610  0.993126   \n",
       "8         0.041124            34.533679            117.518135  0.992514   \n",
       "9         0.027400            33.400000            116.000000  0.991460   \n",
       "\n",
       "               pH  sulphates    alcohol  \n",
       "quality                                  \n",
       "3        3.257667   0.506333  10.215000  \n",
       "4        3.231620   0.505648  10.180093  \n",
       "5        3.212189   0.526403   9.837783  \n",
       "6        3.217726   0.532549  10.587553  \n",
       "7        3.228072   0.547025  11.386006  \n",
       "8        3.223212   0.512487  11.678756  \n",
       "9        3.308000   0.466000  12.180000  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df.groupby('quality').mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
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       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
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       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>red</th>\n",
       "      <td>8.360000</td>\n",
       "      <td>0.884500</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>2.635000</td>\n",
       "      <td>0.122500</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>0.997464</td>\n",
       "      <td>3.398000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>9.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.333250</td>\n",
       "      <td>0.336000</td>\n",
       "      <td>6.392500</td>\n",
       "      <td>0.054300</td>\n",
       "      <td>53.325000</td>\n",
       "      <td>170.600000</td>\n",
       "      <td>0.994884</td>\n",
       "      <td>3.187500</td>\n",
       "      <td>0.474500</td>\n",
       "      <td>10.345000</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>red</th>\n",
       "      <td>7.779245</td>\n",
       "      <td>0.693962</td>\n",
       "      <td>0.174151</td>\n",
       "      <td>2.694340</td>\n",
       "      <td>0.090679</td>\n",
       "      <td>12.264151</td>\n",
       "      <td>36.245283</td>\n",
       "      <td>0.996542</td>\n",
       "      <td>3.381509</td>\n",
       "      <td>0.596415</td>\n",
       "      <td>10.265094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>7.129448</td>\n",
       "      <td>0.381227</td>\n",
       "      <td>0.304233</td>\n",
       "      <td>4.628221</td>\n",
       "      <td>0.050098</td>\n",
       "      <td>23.358896</td>\n",
       "      <td>125.279141</td>\n",
       "      <td>0.994277</td>\n",
       "      <td>3.182883</td>\n",
       "      <td>0.476135</td>\n",
       "      <td>10.152454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>red</th>\n",
       "      <td>8.167254</td>\n",
       "      <td>0.577041</td>\n",
       "      <td>0.243686</td>\n",
       "      <td>2.528855</td>\n",
       "      <td>0.092736</td>\n",
       "      <td>16.983847</td>\n",
       "      <td>56.513950</td>\n",
       "      <td>0.997104</td>\n",
       "      <td>3.304949</td>\n",
       "      <td>0.620969</td>\n",
       "      <td>9.899706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>6.933974</td>\n",
       "      <td>0.302011</td>\n",
       "      <td>0.337653</td>\n",
       "      <td>7.334969</td>\n",
       "      <td>0.051546</td>\n",
       "      <td>36.432052</td>\n",
       "      <td>150.904598</td>\n",
       "      <td>0.995263</td>\n",
       "      <td>3.168833</td>\n",
       "      <td>0.482203</td>\n",
       "      <td>9.808840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6</th>\n",
       "      <th>red</th>\n",
       "      <td>8.347179</td>\n",
       "      <td>0.497484</td>\n",
       "      <td>0.273824</td>\n",
       "      <td>2.477194</td>\n",
       "      <td>0.084956</td>\n",
       "      <td>15.711599</td>\n",
       "      <td>40.869906</td>\n",
       "      <td>0.996615</td>\n",
       "      <td>3.318072</td>\n",
       "      <td>0.675329</td>\n",
       "      <td>10.629519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>6.837671</td>\n",
       "      <td>0.260564</td>\n",
       "      <td>0.338025</td>\n",
       "      <td>6.441606</td>\n",
       "      <td>0.045217</td>\n",
       "      <td>35.650591</td>\n",
       "      <td>137.047316</td>\n",
       "      <td>0.993961</td>\n",
       "      <td>3.188599</td>\n",
       "      <td>0.491106</td>\n",
       "      <td>10.575372</td>\n",
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       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
       "      <th>red</th>\n",
       "      <td>8.872362</td>\n",
       "      <td>0.403920</td>\n",
       "      <td>0.375176</td>\n",
       "      <td>2.720603</td>\n",
       "      <td>0.076588</td>\n",
       "      <td>14.045226</td>\n",
       "      <td>35.020101</td>\n",
       "      <td>0.996104</td>\n",
       "      <td>3.290754</td>\n",
       "      <td>0.741256</td>\n",
       "      <td>11.465913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>6.734716</td>\n",
       "      <td>0.262767</td>\n",
       "      <td>0.325625</td>\n",
       "      <td>5.186477</td>\n",
       "      <td>0.038191</td>\n",
       "      <td>34.125568</td>\n",
       "      <td>125.114773</td>\n",
       "      <td>0.992452</td>\n",
       "      <td>3.213898</td>\n",
       "      <td>0.503102</td>\n",
       "      <td>11.367936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>red</th>\n",
       "      <td>8.566667</td>\n",
       "      <td>0.423333</td>\n",
       "      <td>0.391111</td>\n",
       "      <td>2.577778</td>\n",
       "      <td>0.068444</td>\n",
       "      <td>13.277778</td>\n",
       "      <td>33.444444</td>\n",
       "      <td>0.995212</td>\n",
       "      <td>3.267222</td>\n",
       "      <td>0.767778</td>\n",
       "      <td>12.094444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>6.657143</td>\n",
       "      <td>0.277400</td>\n",
       "      <td>0.326514</td>\n",
       "      <td>5.671429</td>\n",
       "      <td>0.038314</td>\n",
       "      <td>36.720000</td>\n",
       "      <td>126.165714</td>\n",
       "      <td>0.992236</td>\n",
       "      <td>3.218686</td>\n",
       "      <td>0.486229</td>\n",
       "      <td>11.636000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>white</th>\n",
       "      <td>7.420000</td>\n",
       "      <td>0.298000</td>\n",
       "      <td>0.386000</td>\n",
       "      <td>4.120000</td>\n",
       "      <td>0.027400</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>0.991460</td>\n",
       "      <td>3.308000</td>\n",
       "      <td>0.466000</td>\n",
       "      <td>12.180000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "quality color                                                                 \n",
       "3       red         8.360000          0.884500     0.171000        2.635000   \n",
       "        white       7.600000          0.333250     0.336000        6.392500   \n",
       "4       red         7.779245          0.693962     0.174151        2.694340   \n",
       "        white       7.129448          0.381227     0.304233        4.628221   \n",
       "5       red         8.167254          0.577041     0.243686        2.528855   \n",
       "        white       6.933974          0.302011     0.337653        7.334969   \n",
       "6       red         8.347179          0.497484     0.273824        2.477194   \n",
       "        white       6.837671          0.260564     0.338025        6.441606   \n",
       "7       red         8.872362          0.403920     0.375176        2.720603   \n",
       "        white       6.734716          0.262767     0.325625        5.186477   \n",
       "8       red         8.566667          0.423333     0.391111        2.577778   \n",
       "        white       6.657143          0.277400     0.326514        5.671429   \n",
       "9       white       7.420000          0.298000     0.386000        4.120000   \n",
       "\n",
       "               chlorides  free sulfur dioxide  total sulfur dioxide   density  \\\n",
       "quality color                                                                   \n",
       "3       red     0.122500            11.000000             24.900000  0.997464   \n",
       "        white   0.054300            53.325000            170.600000  0.994884   \n",
       "4       red     0.090679            12.264151             36.245283  0.996542   \n",
       "        white   0.050098            23.358896            125.279141  0.994277   \n",
       "5       red     0.092736            16.983847             56.513950  0.997104   \n",
       "        white   0.051546            36.432052            150.904598  0.995263   \n",
       "6       red     0.084956            15.711599             40.869906  0.996615   \n",
       "        white   0.045217            35.650591            137.047316  0.993961   \n",
       "7       red     0.076588            14.045226             35.020101  0.996104   \n",
       "        white   0.038191            34.125568            125.114773  0.992452   \n",
       "8       red     0.068444            13.277778             33.444444  0.995212   \n",
       "        white   0.038314            36.720000            126.165714  0.992236   \n",
       "9       white   0.027400            33.400000            116.000000  0.991460   \n",
       "\n",
       "                     pH  sulphates    alcohol  \n",
       "quality color                                  \n",
       "3       red    3.398000   0.570000   9.955000  \n",
       "        white  3.187500   0.474500  10.345000  \n",
       "4       red    3.381509   0.596415  10.265094  \n",
       "        white  3.182883   0.476135  10.152454  \n",
       "5       red    3.304949   0.620969   9.899706  \n",
       "        white  3.168833   0.482203   9.808840  \n",
       "6       red    3.318072   0.675329  10.629519  \n",
       "        white  3.188599   0.491106  10.575372  \n",
       "7       red    3.290754   0.741256  11.465913  \n",
       "        white  3.213898   0.503102  11.367936  \n",
       "8       red    3.267222   0.767778  12.094444  \n",
       "        white  3.218686   0.486229  11.636000  \n",
       "9       white  3.308000   0.466000  12.180000  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df.groupby(['quality','color']).mean() # means groupedby quality and color"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quality</th>\n",
       "      <th>color</th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>red</td>\n",
       "      <td>8.360000</td>\n",
       "      <td>0.884500</td>\n",
       "      <td>0.171000</td>\n",
       "      <td>2.635000</td>\n",
       "      <td>0.122500</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>24.900000</td>\n",
       "      <td>0.997464</td>\n",
       "      <td>3.398000</td>\n",
       "      <td>0.570000</td>\n",
       "      <td>9.955000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>white</td>\n",
       "      <td>7.600000</td>\n",
       "      <td>0.333250</td>\n",
       "      <td>0.336000</td>\n",
       "      <td>6.392500</td>\n",
       "      <td>0.054300</td>\n",
       "      <td>53.325000</td>\n",
       "      <td>170.600000</td>\n",
       "      <td>0.994884</td>\n",
       "      <td>3.187500</td>\n",
       "      <td>0.474500</td>\n",
       "      <td>10.345000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>red</td>\n",
       "      <td>7.779245</td>\n",
       "      <td>0.693962</td>\n",
       "      <td>0.174151</td>\n",
       "      <td>2.694340</td>\n",
       "      <td>0.090679</td>\n",
       "      <td>12.264151</td>\n",
       "      <td>36.245283</td>\n",
       "      <td>0.996542</td>\n",
       "      <td>3.381509</td>\n",
       "      <td>0.596415</td>\n",
       "      <td>10.265094</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>white</td>\n",
       "      <td>7.129448</td>\n",
       "      <td>0.381227</td>\n",
       "      <td>0.304233</td>\n",
       "      <td>4.628221</td>\n",
       "      <td>0.050098</td>\n",
       "      <td>23.358896</td>\n",
       "      <td>125.279141</td>\n",
       "      <td>0.994277</td>\n",
       "      <td>3.182883</td>\n",
       "      <td>0.476135</td>\n",
       "      <td>10.152454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "      <td>8.167254</td>\n",
       "      <td>0.577041</td>\n",
       "      <td>0.243686</td>\n",
       "      <td>2.528855</td>\n",
       "      <td>0.092736</td>\n",
       "      <td>16.983847</td>\n",
       "      <td>56.513950</td>\n",
       "      <td>0.997104</td>\n",
       "      <td>3.304949</td>\n",
       "      <td>0.620969</td>\n",
       "      <td>9.899706</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>white</td>\n",
       "      <td>6.933974</td>\n",
       "      <td>0.302011</td>\n",
       "      <td>0.337653</td>\n",
       "      <td>7.334969</td>\n",
       "      <td>0.051546</td>\n",
       "      <td>36.432052</td>\n",
       "      <td>150.904598</td>\n",
       "      <td>0.995263</td>\n",
       "      <td>3.168833</td>\n",
       "      <td>0.482203</td>\n",
       "      <td>9.808840</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>red</td>\n",
       "      <td>8.347179</td>\n",
       "      <td>0.497484</td>\n",
       "      <td>0.273824</td>\n",
       "      <td>2.477194</td>\n",
       "      <td>0.084956</td>\n",
       "      <td>15.711599</td>\n",
       "      <td>40.869906</td>\n",
       "      <td>0.996615</td>\n",
       "      <td>3.318072</td>\n",
       "      <td>0.675329</td>\n",
       "      <td>10.629519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "      <td>6.837671</td>\n",
       "      <td>0.260564</td>\n",
       "      <td>0.338025</td>\n",
       "      <td>6.441606</td>\n",
       "      <td>0.045217</td>\n",
       "      <td>35.650591</td>\n",
       "      <td>137.047316</td>\n",
       "      <td>0.993961</td>\n",
       "      <td>3.188599</td>\n",
       "      <td>0.491106</td>\n",
       "      <td>10.575372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>red</td>\n",
       "      <td>8.872362</td>\n",
       "      <td>0.403920</td>\n",
       "      <td>0.375176</td>\n",
       "      <td>2.720603</td>\n",
       "      <td>0.076588</td>\n",
       "      <td>14.045226</td>\n",
       "      <td>35.020101</td>\n",
       "      <td>0.996104</td>\n",
       "      <td>3.290754</td>\n",
       "      <td>0.741256</td>\n",
       "      <td>11.465913</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>white</td>\n",
       "      <td>6.734716</td>\n",
       "      <td>0.262767</td>\n",
       "      <td>0.325625</td>\n",
       "      <td>5.186477</td>\n",
       "      <td>0.038191</td>\n",
       "      <td>34.125568</td>\n",
       "      <td>125.114773</td>\n",
       "      <td>0.992452</td>\n",
       "      <td>3.213898</td>\n",
       "      <td>0.503102</td>\n",
       "      <td>11.367936</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>red</td>\n",
       "      <td>8.566667</td>\n",
       "      <td>0.423333</td>\n",
       "      <td>0.391111</td>\n",
       "      <td>2.577778</td>\n",
       "      <td>0.068444</td>\n",
       "      <td>13.277778</td>\n",
       "      <td>33.444444</td>\n",
       "      <td>0.995212</td>\n",
       "      <td>3.267222</td>\n",
       "      <td>0.767778</td>\n",
       "      <td>12.094444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8</td>\n",
       "      <td>white</td>\n",
       "      <td>6.657143</td>\n",
       "      <td>0.277400</td>\n",
       "      <td>0.326514</td>\n",
       "      <td>5.671429</td>\n",
       "      <td>0.038314</td>\n",
       "      <td>36.720000</td>\n",
       "      <td>126.165714</td>\n",
       "      <td>0.992236</td>\n",
       "      <td>3.218686</td>\n",
       "      <td>0.486229</td>\n",
       "      <td>11.636000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9</td>\n",
       "      <td>white</td>\n",
       "      <td>7.420000</td>\n",
       "      <td>0.298000</td>\n",
       "      <td>0.386000</td>\n",
       "      <td>4.120000</td>\n",
       "      <td>0.027400</td>\n",
       "      <td>33.400000</td>\n",
       "      <td>116.000000</td>\n",
       "      <td>0.991460</td>\n",
       "      <td>3.308000</td>\n",
       "      <td>0.466000</td>\n",
       "      <td>12.180000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    quality  color  fixed acidity  volatile acidity  citric acid  \\\n",
       "0         3    red       8.360000          0.884500     0.171000   \n",
       "1         3  white       7.600000          0.333250     0.336000   \n",
       "2         4    red       7.779245          0.693962     0.174151   \n",
       "3         4  white       7.129448          0.381227     0.304233   \n",
       "4         5    red       8.167254          0.577041     0.243686   \n",
       "5         5  white       6.933974          0.302011     0.337653   \n",
       "6         6    red       8.347179          0.497484     0.273824   \n",
       "7         6  white       6.837671          0.260564     0.338025   \n",
       "8         7    red       8.872362          0.403920     0.375176   \n",
       "9         7  white       6.734716          0.262767     0.325625   \n",
       "10        8    red       8.566667          0.423333     0.391111   \n",
       "11        8  white       6.657143          0.277400     0.326514   \n",
       "12        9  white       7.420000          0.298000     0.386000   \n",
       "\n",
       "    residual sugar  chlorides  free sulfur dioxide  total sulfur dioxide  \\\n",
       "0         2.635000   0.122500            11.000000             24.900000   \n",
       "1         6.392500   0.054300            53.325000            170.600000   \n",
       "2         2.694340   0.090679            12.264151             36.245283   \n",
       "3         4.628221   0.050098            23.358896            125.279141   \n",
       "4         2.528855   0.092736            16.983847             56.513950   \n",
       "5         7.334969   0.051546            36.432052            150.904598   \n",
       "6         2.477194   0.084956            15.711599             40.869906   \n",
       "7         6.441606   0.045217            35.650591            137.047316   \n",
       "8         2.720603   0.076588            14.045226             35.020101   \n",
       "9         5.186477   0.038191            34.125568            125.114773   \n",
       "10        2.577778   0.068444            13.277778             33.444444   \n",
       "11        5.671429   0.038314            36.720000            126.165714   \n",
       "12        4.120000   0.027400            33.400000            116.000000   \n",
       "\n",
       "     density        pH  sulphates    alcohol  \n",
       "0   0.997464  3.398000   0.570000   9.955000  \n",
       "1   0.994884  3.187500   0.474500  10.345000  \n",
       "2   0.996542  3.381509   0.596415  10.265094  \n",
       "3   0.994277  3.182883   0.476135  10.152454  \n",
       "4   0.997104  3.304949   0.620969   9.899706  \n",
       "5   0.995263  3.168833   0.482203   9.808840  \n",
       "6   0.996615  3.318072   0.675329  10.629519  \n",
       "7   0.993961  3.188599   0.491106  10.575372  \n",
       "8   0.996104  3.290754   0.741256  11.465913  \n",
       "9   0.992452  3.213898   0.503102  11.367936  \n",
       "10  0.995212  3.267222   0.767778  12.094444  \n",
       "11  0.992236  3.218686   0.486229  11.636000  \n",
       "12  0.991460  3.308000   0.466000  12.180000  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# set index to false if you don't want columns quality and index there\n",
    "wine_df.groupby(['quality','color'], as_index=False).mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>quality</th>\n",
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       "      <th>0</th>\n",
       "      <td>3</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>3</td>\n",
       "      <td>white</td>\n",
       "      <td>3.187500</td>\n",
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       "      <th>2</th>\n",
       "      <td>4</td>\n",
       "      <td>red</td>\n",
       "      <td>3.381509</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>white</td>\n",
       "      <td>3.182883</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>red</td>\n",
       "      <td>3.304949</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>5</td>\n",
       "      <td>white</td>\n",
       "      <td>3.168833</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>6</td>\n",
       "      <td>red</td>\n",
       "      <td>3.318072</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>6</td>\n",
       "      <td>white</td>\n",
       "      <td>3.188599</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>7</td>\n",
       "      <td>red</td>\n",
       "      <td>3.290754</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>7</td>\n",
       "      <td>white</td>\n",
       "      <td>3.213898</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>8</td>\n",
       "      <td>red</td>\n",
       "      <td>3.267222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>8</td>\n",
       "      <td>white</td>\n",
       "      <td>3.218686</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>9</td>\n",
       "      <td>white</td>\n",
       "      <td>3.308000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    quality  color        pH\n",
       "0         3    red  3.398000\n",
       "1         3  white  3.187500\n",
       "2         4    red  3.381509\n",
       "3         4  white  3.182883\n",
       "4         5    red  3.304949\n",
       "5         5  white  3.168833\n",
       "6         6    red  3.318072\n",
       "7         6  white  3.188599\n",
       "8         7    red  3.290754\n",
       "9         7  white  3.213898\n",
       "10        8    red  3.267222\n",
       "11        8  white  3.218686\n",
       "12        9  white  3.308000"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# if you only want one column, you can index them as such\n",
    "wine_df.groupby(['quality', 'color'], as_index=False)['pH'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "# 1. find the mean quality of each wine by type (red and white) with groupby\n",
    "# 2. view the min 25%, 50%, 75%, max pH values with Pandas describe\n",
    "\n",
    "# 3. Bin edges that will be used to \"cut\" the data into groups\n",
    "# bin_edges = [ , , , , ] \n",
    "\n",
    "# 4. Labels for the four acidity level groups\n",
    "# bin_names = [ , , , ] # name each acidity level category\n",
    "\n",
    "# 5. Creates acidity_levels column\n",
    "# df['acidity_levels'] = pd.cut(df['pH'], bin_edges, labels=bin_names)\n",
    "\n",
    "# Checks for successful creation of this column\n",
    "# df.head()\n",
    "\n",
    "# 6. Find the mean quality of each acidity level with groupby\n",
    "\n",
    "# 7. Save changes for the next section\n",
    "# df.to_csv('winequality_edited.csv', index=False)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'\\n# selecting malignant records in cancer data\\ndf_m = df[df[\\'diagnosis\\'] == \\'M\\']\\ndf_m = df.query(\\'diagnosis == \"M\"\\')\\n\\n# selecting records of people making over $50K\\ndf_a = df[df[\\'income\\'] == \\' >50K\\']\\ndf_a = df.query(\\'income == \" >50K\"\\')\\n'"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Pandas query function\n",
    "\"\"\"\n",
    "# selecting malignant records in cancer data\n",
    "df_m = df[df['diagnosis'] == 'M']\n",
    "df_m = df.query('diagnosis == \"M\"')\n",
    "\n",
    "# selecting records of people making over $50K\n",
    "df_a = df[df['income'] == ' >50K']\n",
    "df_a = df.query('income == \" >50K\"')\n",
    "\"\"\"\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# visualization\n",
    "# matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "color\n",
       "red      5.636023\n",
       "white    5.877909\n",
       "Name: quality, dtype: float64"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "wine_df.groupby('color')['quality'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Quality')"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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ewNkrXpokaWWbzt5HB497XMBu7W0yvwYOXO6KJEm9mU4oPLe9D3AacBRw9ATt7gNuAn5W\nVV5FTZJWQcsMhao6Y+xxkqOBb4wfJkmaP4Y6eK2q3jRbhUiS+jcbex9JklZRU64pJLkfuB/Yvqp+\n3j6fzjEIVVXDnkJDktSzZf1wn0kTAncOPJckzUNThkJV7TbVc0nS/OI2BUlSx1CQJHWWtaF58BQX\n01VV5XWZJWkVs6wNzQcv53QLMBQkaRWzrFB47jJelyTNI8va+8jTWUjSCHFDsySp02soJFkryXlJ\nLkxycZK/6bMeSRp1Q4dCkk2SHJbkF0nuSnLfBLd7pzm5u4Hdq2oHYEdgzyRPH7YmSdLMGOr8REk2\nA84DNgIuBh4OXEnz4751O70LgFumM72qKuD29uma7c3TaEhST4ZdU/gQsDGwZ7t0D/CFqtqWJhS+\nA6wNvGK6E0yyepILgBuAU6vq3CFrkiTNkGFDYQ/g21X1vcEXqupq4FU0oTDtbQNVdV9V7QhsDuyc\n5MmDbZLsn2RxksVLly4dsmRJ0nQNGwob03QbjbmPJgQAqKrbgVOBlw1bSFX9Fjgd2HOC1w6vqkVV\ntWjBggXDTlqSNE3DhsKtwMPGPf8NsNlAm1uAaf1yJ1mQZP328drA84FLh6xJkjRDhr0QzpXAFuOe\nXwjsnmSdqrozyWrAC4Grpzm9TYCjk6xOE1BfrqqTh6xJkjRDhg2F7wP7J1mzqn4HHA0cA/wgyanA\ns4EnAYdOZ2JV9RPgaUPWIEmaJcOGwudpuow2BK6tqi8m+QPg7cBT2zZfAv525kqUJK0sQ4VCVf03\n8NGBYe9OcijNLqlXVNX1M1ifJGklGnZNYUJVtRRwX1FJWsV5QjxJUmfY01wcOc2mVVX7LUc9kqQe\nDdt9tO8yXi8g7b2hIEmrmGFDYatJhq8P7AT8NfAD4MAVKUqS1I9h9z66cpKXrgQuTPId4CfA92h2\nX5UkrUJmdENzVV0FnAS8cyanK0laOWZj76PrgW1mYbqSpFk2o6HQnsNod6Z5kR1J0twy7C6pu04x\nnS2AN9FcVvOIFaxLktSDYfc+Op2pL5cZ4Ezgr5a3IElSf4YNhUOYOBTupzlR3nlVdd4KVyVJ6sWw\nu6QePEt1SJLmgKFPiJdkZ2BXmmsqr0VzIrwlNNdu/p+ZLU+StDJNOxSS7AT8M7DD2KD2fqw76eYk\n762qLwyMt0ZV3bvClUqSZt20QiHJi4Gv0Vyf+RqaDc5X0QTD5sBuwKbAEUm2qaoPtOO9kObiOx+f\n6cIlSTNvmaGQZCPgeJqNyW8Fjqiq+wbarEZzArxPAe9rL825HvBvwN/NdNGSpNkxnTWFdwPrAq+s\nqq9P1KCq7gf+JclS4ATgX4EFwI3AhONIkuae6RzRvCdw7mSBMF5VfQM4l6Yr6RJgl6r6rxUrUZK0\nskwnFLaiOR32dP2AZuPzs6rq6uWqSpLUi+mEwprAPUNM83fA3VV1x/KVJEnqy3RC4VrgKUNM80nt\nOJKkVcx0QuFM4AVJtl1WwyTbAXsAZ6xoYZKklW86ofAZmi6kk5NsP1mjNhBOAlYHDpuZ8iRJK9My\nd0mtqiVJ/oHmzKc/SnIC8H2ag9cK2BJ4PvBHNAe3fayqlkxn5km2AI4BNqY5DuLwqvr08rwRSdKK\nm9YRzVX1viR3AB8E9gZeM9AkwL3AwVV1yBDzvxd4T1X9KMl6wJIkp1bVT4eYhiRphkz73EdVdUiS\no4E/BZ4FbEITBtcCZwNHVtWVw8y8qq5tx6eqbktyCbAZYChIUg+GPXX2lcBBs1FIkoXA02gOfpMk\n9WBGr9G8vJKsS3PCvXdV1a0TvL5/ksVJFi9dunTlFyhJI6L3UEiyJk0gHFtVJ0zUpqoOr6pFVbVo\nwYIFK7dASRohvYZCkgCfBy6pqk/2WYskqf81hWcBrwd2T3JBe3tRzzVJ0sga+nKcM6mqzuaBK7hJ\nknrW95qCJGkOMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQk\nSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1DQZLUMRQkSR1D\nQZLUMRQkSZ1eQyHJkUluSHJRn3VIkhp9rykcBezZcw2SpFavoVBVZwI391mDJOkBfa8pSJLmkFUi\nFJLsn2RxksVLly7tuxxJmrdWiVCoqsOralFVLVqwYEHf5UjSvLVKhIIkaeXoe5fU44FzgCcmuTrJ\nfn3WI0mjbo0+Z15V+/Q5f0nSg9l9JEnqGAqSpI6hIEnqGAqSpI6hIEnqGAqSpI6hIEnqGAqSpI6h\nIEnqGAqSpI6hIEnqGAqSpI6hIEnqGAqSpI6hIEnqGAqSpI6hIEnqGAqSpI6hIEnqGAqSpI6hIEnq\nGAqSpI6hIEnqGAqSpI6hIEnqGAqSpI6hIEnq9B4KSfZM8rMkv0hyYN/1SNIo6zUUkqwOHAbsBWwP\n7JNk+z5rkqRR1veaws7AL6rqsqq6B/gS8LKea5KkkbVGz/PfDLhq3POrgV0GGyXZH9i/fXp7kp+t\nhNpGwYbAjX0XsUxJ3xWoH34/Z85jp9uw71CY6NOshwyoOhw4fPbLGS1JFlfVor7rkCbi97MffXcf\nXQ1sMe755sA1PdUiSSOv71A4H9gmyVZJHgbsDZzYc02SNLJ67T6qqnuTvA34DrA6cGRVXdxnTSPG\nLjnNZX4/e5Cqh3ThS5JGVN/dR5KkOcRQkCR1DAVJUsdQkCR1+j54TStJkpOY4MDAMVX10pVYjvQQ\nSTYCDgU2raq92vOgPaOqPt9zaSPFNYXR8XHgE8DlwF3Av7S324GLeqxLGnMUze7pm7bPfw68q7dq\nRpS7pI6YJGdW1a7LGiatbEnOr6qdkvy4qp7WDrugqnbsu7ZR4prC6FmQZOuxJ0m2Ahb0WI805o4k\nG9B2cyZ5OnBLvyWNHrcpjJ53A6cnuax9vhB4S3/lSJ0DaE5z87gk/0mzsPKqfksaPXYfjaAkDwe2\nbZ9eWlV391mPBN338j7giTRnUP4ZsJrfz5XL7qMRk2Qd4K+At1XVhcCWSV7Sc1kSwDlVdW9VXVxV\nF1XV74Bz+i5q1Nh9NHq+ACwBntE+vxr4CnBybxVppCXZmOaCW2sneRoPXGflkcA6vRU2ogyF0fO4\nqnpNkn0AququZNW4dJTmrT2AfWmup/LJccNvAz7QR0GjzFAYPfckWZsH9vB4HGCfrXpTVUcDRyd5\nZVV9re96Rp2hMELaNYLPAd8GtkhyLPAsmqU0qRdJXldVXwQWJjlg8PWq+uQEo2mWGAojpKoqyTuB\nFwJPp+m7fWdVzf2Lo2s+e0R7v26vVQhwl9SRk+Qw4KiqOr/vWiTNPYbCiEnyU+AJwJXAHTRrC1VV\nT+21MI28JAuAP6M5oLLrxaiqP+2rplFk99Ho2avvAqRJfBM4C/gezUFs6oFrCpLmBE9+Nzd4RLOk\nueLkJC/qu4hR55qCpF4luY0HLgC1Ls1xM/e2z6uqHtlLYSPKUJA0JyT5V5ptCmdV1SV91zOqDAVJ\nc0KS3YFnA/8L2Br4MU1AfLrXwkaMoSBpzkiyOrAT8Fzgz4G7qmrbqcfSTHKXVElzQpLv0xzdfA5N\nN9JOVXVDv1WNHvc+kjRX/AS4B3gy8FTgye3JG7US2X0kaU5Jsi7wJuB/AxtX1cN7Lmmk2H0kaU5I\n8jaajcx/QHMaliNpupG0EhkKkuaKtWkusrOkqu5dVmPNDruPJEkdNzRLkjqGgiSpYyhIyynJFUmu\n6LsOaSYZCprXkmyb5B+TXJTkliT3JLkmybeS7Jdkrb5rlOYSNzRr3kryIeAgmoWfHwLnA7cDGwG7\n0ZxfZ0lVLVrO6V8BUFULV7xaaW5wl1TNS0k+APwNcBXwqqo6d4I2LwHes7Jrk+Yyu4807yRZCBwM\n/A540USBAFBVJwN7Doz76iRntl1NdyX5ryTvTzLto2qTPDzJgUl+kuTOJLcmOSvJqyeqNUklOSrJ\nE5L8W5IbktyfZLe2zdZJDk/yi7amm9u6Ppdkg+nWJU2Hawqaj94ErAl8qaoumqphVd099jjJocD7\ngRuB42i6mvYCDgX2SPKCqvrdVNNL8jDgO8BzgEuBw4B1gD8G/i3JjlX1gQlGfRxwLvBz4FiaA7lu\nTbIJTbfXI4FTgK8BawFbAa8HPgPcNFVN0lCqypu3eXUDvk9zJa83DzHOM9pxfkVzvp2x4WsAJ7Wv\nfWBgnCuAKwaGvb9tewqwxrjhj2nbF/DMccMXtsMKOHSCut7evvbOCV57BLB235+3t/l1s/tI89Em\n7f3VQ4zzp+39R6rqurGB1Zxu4T3A/cCbpzmdAg6ocadqqOYU0B9un040netptoFM5q7BAVV1R1U9\nZLi0IgwFzUdp74fZte732/vTBl+oqp/TBMxWSdafdKbJesDjgWuq6tIJmoxN+2kTvHZhjevKGudE\nmm6sw5J8Lcn+SZ6UJBO0lVaYoaD56Jr2fvMhxnlUe3/tJK9fO9BuRaYxUbBcN8EwqupKYGfgBOD5\nwD8DFwFXJnnHFLVIy8VQ0Hx0dnv/vCHGuaW933iS1zcZaDfT05h0raaqLqmq1wAbAIuAA2n+dz+d\nZL8p6pGGZihoPvoCze6or0yy/VQNx+1q+uP2frcJ2jyeZq3j8qr67WTTqqrbgF8CmyXZZoImz23v\nfzRl9ZNP/96qWlJVHwX2aQe/fHmmJU3GUNC8U1VX0Byn8DDgW0kmPGI5yZ7Av7dPj2zvP5hkwbg2\nqwMfp/lf+fw0Zn8kzTaNf2jHHZvOhsBfD8xrmZLsnGSjCV4aG3bndKclTYfHKWheqqpDk6xBc5qL\n85P8AFjMA6e52BXYph1GVf0gyceA9wIXJfkqcAfNcQpPpumS+odpzPrj7TgvAy5McgrNcQqvotkt\n9WNVdfYU4w96LfCXSc4AfgH8huaYhj8E7gY+NcS0pGXy3Eea15JsB/wFTdfNljQHft0EXAB8Ffhi\nPfgAtr2BtwE70BwA90uaA9k+UVX/MzDtK+Ch5z5qT7J3AM0P+uOAe4ELgcOq6viBtguBy4Gjq2rf\nCerfBdgXeCawBc1Bbb+muUzlJ2oZB+dJwzIUJEkdtylIkjqGgiSpYyhIkjqGgiSpYyhIkjqGgiSp\nYyhIkjqGgiSpYyhIkjqGgiSp8/8B6lRpmTPzyocAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc56d320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# plot \n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "% matplotlib inline\n",
    "\n",
    "\n",
    "wine_df.groupby('color')['quality'].mean().plot(kind='bar', title=\"Average Wine Quality by Color\", color=['red','white'], alpha=1 )\n",
    "\n",
    "plt.xlabel('Colors', fontsize=20)\n",
    "plt.ylabel('Quality', fontsize=20)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Quality')"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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CmwJuA86pqv+eseokSXNmhYHQzjU4DSDJVsDnqurc2S5MkjS3pnUeQlW9frYKkST1a8pA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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xdb0bcc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "colors = ['red', 'white']\n",
    "color_means = wine_df.groupby('color')['quality'].mean().plot(kind='bar', title=\"Average Wine Quality by Color\", color=colors, alpha=.7)\n",
    "plt.xlabel('Colors', fontsize=18)\n",
    "plt.ylabel('Quality', fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>fixed acidity</th>\n",
       "      <th>volatile acidity</th>\n",
       "      <th>citric acid</th>\n",
       "      <th>residual sugar</th>\n",
       "      <th>chlorides</th>\n",
       "      <th>free sulfur dioxide</th>\n",
       "      <th>total sulfur dioxide</th>\n",
       "      <th>density</th>\n",
       "      <th>pH</th>\n",
       "      <th>sulphates</th>\n",
       "      <th>alcohol</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>quality</th>\n",
       "      <th>color</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">3</th>\n",
       "      <th>red</th>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">4</th>\n",
       "      <th>red</th>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "      <td>53</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "      <td>163</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">5</th>\n",
       "      <th>red</th>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "      <td>681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>1457</td>\n",
       "      <td>1457</td>\n",
       "      <td>1457</td>\n",
       "      <td>1457</td>\n",
       "      <td>1457</td>\n",
       "      <td>1457</td>\n",
       "      <td>1457</td>\n",
       "      <td>1457</td>\n",
       "      <td>1457</td>\n",
       "      <td>1457</td>\n",
       "      <td>1457</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">6</th>\n",
       "      <th>red</th>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "      <td>638</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>2198</td>\n",
       "      <td>2198</td>\n",
       "      <td>2198</td>\n",
       "      <td>2198</td>\n",
       "      <td>2198</td>\n",
       "      <td>2198</td>\n",
       "      <td>2198</td>\n",
       "      <td>2198</td>\n",
       "      <td>2198</td>\n",
       "      <td>2198</td>\n",
       "      <td>2198</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">7</th>\n",
       "      <th>red</th>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "      <td>199</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>880</td>\n",
       "      <td>880</td>\n",
       "      <td>880</td>\n",
       "      <td>880</td>\n",
       "      <td>880</td>\n",
       "      <td>880</td>\n",
       "      <td>880</td>\n",
       "      <td>880</td>\n",
       "      <td>880</td>\n",
       "      <td>880</td>\n",
       "      <td>880</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">8</th>\n",
       "      <th>red</th>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>white</th>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <th>white</th>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               fixed acidity  volatile acidity  citric acid  residual sugar  \\\n",
       "quality color                                                                 \n",
       "3       red               10                10           10              10   \n",
       "        white             20                20           20              20   \n",
       "4       red               53                53           53              53   \n",
       "        white            163               163          163             163   \n",
       "5       red              681               681          681             681   \n",
       "        white           1457              1457         1457            1457   \n",
       "6       red              638               638          638             638   \n",
       "        white           2198              2198         2198            2198   \n",
       "7       red              199               199          199             199   \n",
       "        white            880               880          880             880   \n",
       "8       red               18                18           18              18   \n",
       "        white            175               175          175             175   \n",
       "9       white              5                 5            5               5   \n",
       "\n",
       "               chlorides  free sulfur dioxide  total sulfur dioxide  density  \\\n",
       "quality color                                                                  \n",
       "3       red           10                   10                    10       10   \n",
       "        white         20                   20                    20       20   \n",
       "4       red           53                   53                    53       53   \n",
       "        white        163                  163                   163      163   \n",
       "5       red          681                  681                   681      681   \n",
       "        white       1457                 1457                  1457     1457   \n",
       "6       red          638                  638                   638      638   \n",
       "        white       2198                 2198                  2198     2198   \n",
       "7       red          199                  199                   199      199   \n",
       "        white        880                  880                   880      880   \n",
       "8       red           18                   18                    18       18   \n",
       "        white        175                  175                   175      175   \n",
       "9       white          5                    5                     5        5   \n",
       "\n",
       "                 pH  sulphates  alcohol  \n",
       "quality color                            \n",
       "3       red      10         10       10  \n",
       "        white    20         20       20  \n",
       "4       red      53         53       53  \n",
       "        white   163        163      163  \n",
       "5       red     681        681      681  \n",
       "        white  1457       1457     1457  \n",
       "6       red     638        638      638  \n",
       "        white  2198       2198     2198  \n",
       "7       red     199        199      199  \n",
       "        white   880        880      880  \n",
       "8       red      18         18       18  \n",
       "        white   175        175      175  \n",
       "9       white     5          5        5  "
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = wine_df.groupby(['quality', 'color']).count()\n",
    "counts\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,0,u'Count')"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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AmJkVygXAzKxQLgBmZoVyATAzK5QLgJlZoVwAzMwK5QJgZlYoFwAzs0K5AJiZFcoFwMys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\nrFD/H5eWm6Zppdc+AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x9c5c7f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "counts = wine_df.groupby(['quality', 'color']).count()['pH']\n",
    "counts.plot(kind=\"bar\", title=\"Counts by Wine Color and Quality\", color=colors, alpha=.7)\n",
    "plt.xlabel('Quality and Color', fontsize=18)\n",
    "plt.xlabel('Count', fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,u'Proportion')"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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p5HQaU54mlSWn06T/TU+2GTCxrmm0HqSLRd8ErgD+RLplcN0a0nlLmWkuz3Lr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\nMyvNQcPMzEpz0DAzs9L+PywexCvR72wBAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xddbe978>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "totals = wine_df.groupby('color').count()['pH']\n",
    "proportions = counts / totals\n",
    "proportions.plot(kind=\"bar\", title=\"Proportion by Wine Color and Quality\", color=colors, alpha=.7)\n",
    "plt.xlabel(\"Quality and Color\", fontsize=18)\n",
    "plt.ylabel(\"Proportion\", fontsize=18)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.14"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}