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Lab6 Analyzing Data with Python .ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#Connect to the database\n",
"#Let us first load the SQL extension and establish a connection with the database"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"# Remember the connection string is of the format:\n",
"# %sql ibm_db_sa://my-username:my-password@my-hostname:my-port/my-db-name\n",
"# Enter the connection string for your Db2 on Cloud database instance below\n",
"# i.e. copy after db2:// from the URI string in Service Credentials of your Db2 instance. Remove the double quotes at the end.\n"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Connected: dfk30111@BLUDB'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql ibm_db_sa://dfk30111:b6q01rtcjz%5Eq4sk8@dashdb-txn-sbox-yp-dal09-04.services.dal.bluemix.net:50000/BLUDB"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"#Store the dataset in a Table"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://dfk30111:***@dashdb-txn-sbox-yp-dal09-04.services.dal.bluemix.net:50000/BLUDB\n"
]
},
{
"data": {
"text/plain": [
"'Persisted chicago_socioeconomic_data'"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import pandas\n",
"chicago_socioeconomic_data = pandas.read_csv('https://data.cityofchicago.org/resource/jcxq-k9xf.csv')\n",
"%sql PERSIST chicago_socioeconomic_data"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"#Verify that the table creation was successful by making a basic query"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://dfk30111:***@dashdb-txn-sbox-yp-dal09-04.services.dal.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>index</th>\n",
" <th>ca</th>\n",
" <th>community_area_name</th>\n",
" <th>hardship_index</th>\n",
" <th>per_capita_income_</th>\n",
" <th>percent_aged_16_unemployed</th>\n",
" <th>percent_aged_25_without_high_school_diploma</th>\n",
" <th>percent_aged_under_18_or_over_64</th>\n",
" <th>percent_households_below_poverty</th>\n",
" <th>percent_of_housing_crowded</th>\n",
" </tr>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>Rogers Park</td>\n",
" <td>39.0</td>\n",
" <td>23939</td>\n",
" <td>8.7</td>\n",
" <td>18.2</td>\n",
" <td>27.5</td>\n",
" <td>23.6</td>\n",
" <td>7.7</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>West Ridge</td>\n",
" <td>46.0</td>\n",
" <td>23040</td>\n",
" <td>8.8</td>\n",
" <td>20.8</td>\n",
" <td>38.5</td>\n",
" <td>17.2</td>\n",
" <td>7.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" <td>Uptown</td>\n",
" <td>20.0</td>\n",
" <td>35787</td>\n",
" <td>8.9</td>\n",
" <td>11.8</td>\n",
" <td>22.2</td>\n",
" <td>24.0</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>4.0</td>\n",
" <td>Lincoln Square</td>\n",
" <td>17.0</td>\n",
" <td>37524</td>\n",
" <td>8.2</td>\n",
" <td>13.4</td>\n",
" <td>25.5</td>\n",
" <td>10.9</td>\n",
" <td>3.4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>5.0</td>\n",
" <td>North Center</td>\n",
" <td>6.0</td>\n",
" <td>57123</td>\n",
" <td>5.2</td>\n",
" <td>4.5</td>\n",
" <td>26.2</td>\n",
" <td>7.5</td>\n",
" <td>0.3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>6.0</td>\n",
" <td>Lake View</td>\n",
" <td>5.0</td>\n",
" <td>60058</td>\n",
" <td>4.7</td>\n",
" <td>2.6</td>\n",
" <td>17.0</td>\n",
" <td>11.4</td>\n",
" <td>1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>7.0</td>\n",
" <td>Lincoln Park</td>\n",
" <td>2.0</td>\n",
" <td>71551</td>\n",
" <td>5.1</td>\n",
" <td>3.6</td>\n",
" <td>21.5</td>\n",
" <td>12.3</td>\n",
" <td>0.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>8.0</td>\n",
" <td>Near North Side</td>\n",
" <td>1.0</td>\n",
" <td>88669</td>\n",
" <td>7.0</td>\n",
" <td>2.5</td>\n",
" <td>22.6</td>\n",
" <td>12.9</td>\n",
" <td>1.9</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8</td>\n",
" <td>9.0</td>\n",
" <td>Edison Park</td>\n",
" <td>8.0</td>\n",
" <td>40959</td>\n",
" <td>6.5</td>\n",
" <td>7.4</td>\n",
" <td>35.3</td>\n",
" <td>3.3</td>\n",
" <td>1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>9</td>\n",
" <td>10.0</td>\n",
" <td>Norwood Park</td>\n",
" <td>21.0</td>\n",
" <td>32875</td>\n",
" <td>9.0</td>\n",
" <td>11.5</td>\n",
" <td>39.5</td>\n",
" <td>5.4</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10</td>\n",
" <td>11.0</td>\n",
" <td>Jefferson Park</td>\n",
" <td>25.0</td>\n",
" <td>27751</td>\n",
" <td>12.4</td>\n",
" <td>13.4</td>\n",
" <td>35.5</td>\n",
" <td>8.6</td>\n",
" <td>2.7</td>\n",
" </tr>\n",
" <tr>\n",
" <td>11</td>\n",
" <td>12.0</td>\n",
" <td>Forest Glen</td>\n",
" <td>11.0</td>\n",
" <td>44164</td>\n",
" <td>6.8</td>\n",
" <td>4.9</td>\n",
" <td>40.5</td>\n",
" <td>7.5</td>\n",
" <td>1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>12</td>\n",
" <td>13.0</td>\n",
" <td>North Park</td>\n",
" <td>33.0</td>\n",
" <td>26576</td>\n",
" <td>9.9</td>\n",
" <td>14.4</td>\n",
" <td>39.0</td>\n",
" <td>13.2</td>\n",
" <td>3.9</td>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>14.0</td>\n",
" <td>Albany Park</td>\n",
" <td>53.0</td>\n",
" <td>21323</td>\n",
" <td>10.0</td>\n",
" <td>32.9</td>\n",
" <td>32.0</td>\n",
" <td>19.2</td>\n",
" <td>11.3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>14</td>\n",
" <td>15.0</td>\n",
" <td>Portage Park</td>\n",
" <td>35.0</td>\n",
" <td>24336</td>\n",
" <td>12.6</td>\n",
" <td>19.3</td>\n",
" <td>34.0</td>\n",
" <td>11.6</td>\n",
" <td>4.1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>15</td>\n",
" <td>16.0</td>\n",
" <td>Irving Park</td>\n",
" <td>34.0</td>\n",
" <td>27249</td>\n",
" <td>10.0</td>\n",
" <td>22.4</td>\n",
" <td>31.6</td>\n",
" <td>13.1</td>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>16</td>\n",
" <td>17.0</td>\n",
" <td>Dunning</td>\n",
" <td>28.0</td>\n",
" <td>26282</td>\n",
" <td>10.0</td>\n",
" <td>16.2</td>\n",
" <td>33.6</td>\n",
" <td>10.6</td>\n",
" <td>5.2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>17</td>\n",
" <td>18.0</td>\n",
" <td>Montclaire</td>\n",
" <td>50.0</td>\n",
" <td>22014</td>\n",
" <td>13.8</td>\n",
" <td>23.5</td>\n",
" <td>38.6</td>\n",
" <td>15.3</td>\n",
" <td>8.1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>19.0</td>\n",
" <td>Belmont Cragin</td>\n",
" <td>70.0</td>\n",
" <td>15461</td>\n",
" <td>14.6</td>\n",
" <td>37.3</td>\n",
" <td>37.3</td>\n",
" <td>18.7</td>\n",
" <td>10.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>20.0</td>\n",
" <td>Hermosa</td>\n",
" <td>71.0</td>\n",
" <td>15089</td>\n",
" <td>13.1</td>\n",
" <td>41.6</td>\n",
" <td>36.4</td>\n",
" <td>20.5</td>\n",
" <td>6.9</td>\n",
" </tr>\n",
" <tr>\n",
" <td>20</td>\n",
" <td>21.0</td>\n",
" <td>Avondale</td>\n",
" <td>42.0</td>\n",
" <td>20039</td>\n",
" <td>9.2</td>\n",
" <td>24.7</td>\n",
" <td>31.0</td>\n",
" <td>15.3</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>21</td>\n",
" <td>22.0</td>\n",
" <td>Logan Square</td>\n",
" <td>23.0</td>\n",
" <td>31908</td>\n",
" <td>8.2</td>\n",
" <td>14.8</td>\n",
" <td>26.2</td>\n",
" <td>16.8</td>\n",
" <td>3.2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>23.0</td>\n",
" <td>Humboldt park</td>\n",
" <td>85.0</td>\n",
" <td>13781</td>\n",
" <td>17.3</td>\n",
" <td>35.4</td>\n",
" <td>38.0</td>\n",
" <td>33.9</td>\n",
" <td>14.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>23</td>\n",
" <td>24.0</td>\n",
" <td>West Town</td>\n",
" <td>10.0</td>\n",
" <td>43198</td>\n",
" <td>6.6</td>\n",
" <td>12.9</td>\n",
" <td>21.7</td>\n",
" <td>14.7</td>\n",
" <td>2.3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>25.0</td>\n",
" <td>Austin</td>\n",
" <td>73.0</td>\n",
" <td>15957</td>\n",
" <td>22.6</td>\n",
" <td>24.4</td>\n",
" <td>37.9</td>\n",
" <td>28.6</td>\n",
" <td>6.3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>26.0</td>\n",
" <td>West Garfield Park</td>\n",
" <td>92.0</td>\n",
" <td>10934</td>\n",
" <td>25.8</td>\n",
" <td>24.5</td>\n",
" <td>43.6</td>\n",
" <td>41.7</td>\n",
" <td>9.4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>27.0</td>\n",
" <td>East Garfield Park</td>\n",
" <td>83.0</td>\n",
" <td>12961</td>\n",
" <td>19.6</td>\n",
" <td>21.3</td>\n",
" <td>43.2</td>\n",
" <td>42.4</td>\n",
" <td>8.2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>27</td>\n",
" <td>28.0</td>\n",
" <td>Near West Side</td>\n",
" <td>15.0</td>\n",
" <td>44689</td>\n",
" <td>10.7</td>\n",
" <td>9.6</td>\n",
" <td>22.2</td>\n",
" <td>20.6</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>29.0</td>\n",
" <td>North Lawndale</td>\n",
" <td>87.0</td>\n",
" <td>12034</td>\n",
" <td>21.2</td>\n",
" <td>27.6</td>\n",
" <td>42.7</td>\n",
" <td>43.1</td>\n",
" <td>7.4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>30.0</td>\n",
" <td>South Lawndale</td>\n",
" <td>96.0</td>\n",
" <td>10402</td>\n",
" <td>15.8</td>\n",
" <td>54.8</td>\n",
" <td>33.8</td>\n",
" <td>30.7</td>\n",
" <td>15.2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>31.0</td>\n",
" <td>Lower West Side</td>\n",
" <td>76.0</td>\n",
" <td>16444</td>\n",
" <td>15.8</td>\n",
" <td>40.7</td>\n",
" <td>32.6</td>\n",
" <td>25.8</td>\n",
" <td>9.6</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>32.0</td>\n",
" <td>Loop</td>\n",
" <td>3.0</td>\n",
" <td>65526</td>\n",
" <td>5.7</td>\n",
" <td>3.1</td>\n",
" <td>13.5</td>\n",
" <td>14.7</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>32</td>\n",
" <td>33.0</td>\n",
" <td>Near South Side</td>\n",
" <td>7.0</td>\n",
" <td>59077</td>\n",
" <td>4.9</td>\n",
" <td>7.4</td>\n",
" <td>21.8</td>\n",
" <td>13.8</td>\n",
" <td>1.3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>34.0</td>\n",
" <td>Armour Square</td>\n",
" <td>82.0</td>\n",
" <td>16148</td>\n",
" <td>16.7</td>\n",
" <td>34.5</td>\n",
" <td>38.3</td>\n",
" <td>40.1</td>\n",
" <td>5.7</td>\n",
" </tr>\n",
" <tr>\n",
" <td>34</td>\n",
" <td>35.0</td>\n",
" <td>Douglas</td>\n",
" <td>47.0</td>\n",
" <td>23791</td>\n",
" <td>18.2</td>\n",
" <td>14.3</td>\n",
" <td>30.7</td>\n",
" <td>29.6</td>\n",
" <td>1.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>36.0</td>\n",
" <td>Oakland</td>\n",
" <td>78.0</td>\n",
" <td>19252</td>\n",
" <td>28.7</td>\n",
" <td>18.4</td>\n",
" <td>40.4</td>\n",
" <td>39.7</td>\n",
" <td>1.3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>37.0</td>\n",
" <td>Fuller Park</td>\n",
" <td>97.0</td>\n",
" <td>10432</td>\n",
" <td>33.9</td>\n",
" <td>26.6</td>\n",
" <td>44.9</td>\n",
" <td>51.2</td>\n",
" <td>3.2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>38.0</td>\n",
" <td>Grand Boulevard</td>\n",
" <td>57.0</td>\n",
" <td>23472</td>\n",
" <td>24.3</td>\n",
" <td>15.9</td>\n",
" <td>39.5</td>\n",
" <td>29.3</td>\n",
" <td>3.3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" <td>39.0</td>\n",
" <td>Kenwood</td>\n",
" <td>26.0</td>\n",
" <td>35911</td>\n",
" <td>15.7</td>\n",
" <td>11.3</td>\n",
" <td>35.4</td>\n",
" <td>21.7</td>\n",
" <td>2.4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>40.0</td>\n",
" <td>Washington Park</td>\n",
" <td>88.0</td>\n",
" <td>13785</td>\n",
" <td>28.6</td>\n",
" <td>25.4</td>\n",
" <td>42.8</td>\n",
" <td>42.1</td>\n",
" <td>5.6</td>\n",
" </tr>\n",
" <tr>\n",
" <td>40</td>\n",
" <td>41.0</td>\n",
" <td>Hyde Park</td>\n",
" <td>14.0</td>\n",
" <td>39056</td>\n",
" <td>8.4</td>\n",
" <td>4.3</td>\n",
" <td>26.2</td>\n",
" <td>18.4</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>42.0</td>\n",
" <td>Woodlawn</td>\n",
" <td>58.0</td>\n",
" <td>18672</td>\n",
" <td>23.4</td>\n",
" <td>16.5</td>\n",
" <td>36.1</td>\n",
" <td>30.7</td>\n",
" <td>2.9</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>43.0</td>\n",
" <td>South Shore</td>\n",
" <td>55.0</td>\n",
" <td>19398</td>\n",
" <td>20.0</td>\n",
" <td>14.0</td>\n",
" <td>35.7</td>\n",
" <td>31.1</td>\n",
" <td>2.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>43</td>\n",
" <td>44.0</td>\n",
" <td>Chatham</td>\n",
" <td>60.0</td>\n",
" <td>18881</td>\n",
" <td>24.0</td>\n",
" <td>14.5</td>\n",
" <td>40.3</td>\n",
" <td>27.8</td>\n",
" <td>3.3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>44</td>\n",
" <td>45.0</td>\n",
" <td>Avalon Park</td>\n",
" <td>41.0</td>\n",
" <td>24454</td>\n",
" <td>21.1</td>\n",
" <td>10.6</td>\n",
" <td>39.3</td>\n",
" <td>17.2</td>\n",
" <td>1.4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45</td>\n",
" <td>46.0</td>\n",
" <td>South Chicago</td>\n",
" <td>75.0</td>\n",
" <td>16579</td>\n",
" <td>19.7</td>\n",
" <td>26.6</td>\n",
" <td>41.1</td>\n",
" <td>29.8</td>\n",
" <td>4.7</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>47.0</td>\n",
" <td>Burnside</td>\n",
" <td>79.0</td>\n",
" <td>12515</td>\n",
" <td>18.6</td>\n",
" <td>19.3</td>\n",
" <td>42.7</td>\n",
" <td>33.0</td>\n",
" <td>6.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>47</td>\n",
" <td>48.0</td>\n",
" <td>Calumet Heights</td>\n",
" <td>38.0</td>\n",
" <td>28887</td>\n",
" <td>20.0</td>\n",
" <td>11.0</td>\n",
" <td>44.0</td>\n",
" <td>11.5</td>\n",
" <td>2.1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>49.0</td>\n",
" <td>Roseland</td>\n",
" <td>52.0</td>\n",
" <td>17949</td>\n",
" <td>20.3</td>\n",
" <td>16.9</td>\n",
" <td>41.2</td>\n",
" <td>19.8</td>\n",
" <td>2.5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>49</td>\n",
" <td>50.0</td>\n",
" <td>Pullman</td>\n",
" <td>51.0</td>\n",
" <td>20588</td>\n",
" <td>22.8</td>\n",
" <td>13.1</td>\n",
" <td>38.6</td>\n",
" <td>21.6</td>\n",
" <td>1.5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>51.0</td>\n",
" <td>South Deering</td>\n",
" <td>65.0</td>\n",
" <td>14685</td>\n",
" <td>16.3</td>\n",
" <td>21.0</td>\n",
" <td>39.5</td>\n",
" <td>29.2</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51</td>\n",
" <td>52.0</td>\n",
" <td>East Side</td>\n",
" <td>64.0</td>\n",
" <td>17104</td>\n",
" <td>12.1</td>\n",
" <td>31.9</td>\n",
" <td>42.8</td>\n",
" <td>19.2</td>\n",
" <td>6.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>52</td>\n",
" <td>53.0</td>\n",
" <td>West Pullman</td>\n",
" <td>62.0</td>\n",
" <td>16563</td>\n",
" <td>19.4</td>\n",
" <td>20.5</td>\n",
" <td>42.1</td>\n",
" <td>25.9</td>\n",
" <td>3.3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>54.0</td>\n",
" <td>Riverdale</td>\n",
" <td>98.0</td>\n",
" <td>8201</td>\n",
" <td>34.6</td>\n",
" <td>27.5</td>\n",
" <td>51.5</td>\n",
" <td>56.5</td>\n",
" <td>5.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>54</td>\n",
" <td>55.0</td>\n",
" <td>Hegewisch</td>\n",
" <td>44.0</td>\n",
" <td>22677</td>\n",
" <td>9.6</td>\n",
" <td>19.2</td>\n",
" <td>42.9</td>\n",
" <td>17.1</td>\n",
" <td>3.3</td>\n",
" </tr>\n",
" <tr>\n",
" <td>55</td>\n",
" <td>56.0</td>\n",
" <td>Garfield Ridge</td>\n",
" <td>32.0</td>\n",
" <td>26353</td>\n",
" <td>11.3</td>\n",
" <td>19.3</td>\n",
" <td>38.1</td>\n",
" <td>8.8</td>\n",
" <td>2.6</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>57.0</td>\n",
" <td>Archer Heights</td>\n",
" <td>67.0</td>\n",
" <td>16134</td>\n",
" <td>16.5</td>\n",
" <td>35.9</td>\n",
" <td>39.2</td>\n",
" <td>14.1</td>\n",
" <td>8.5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>57</td>\n",
" <td>58.0</td>\n",
" <td>Brighton Park</td>\n",
" <td>84.0</td>\n",
" <td>13089</td>\n",
" <td>13.9</td>\n",
" <td>45.1</td>\n",
" <td>39.3</td>\n",
" <td>23.6</td>\n",
" <td>14.4</td>\n",
" </tr>\n",
" <tr>\n",
" <td>58</td>\n",
" <td>59.0</td>\n",
" <td>McKinley Park</td>\n",
" <td>61.0</td>\n",
" <td>16954</td>\n",
" <td>13.4</td>\n",
" <td>32.9</td>\n",
" <td>35.6</td>\n",
" <td>18.7</td>\n",
" <td>7.2</td>\n",
" </tr>\n",
" <tr>\n",
" <td>59</td>\n",
" <td>60.0</td>\n",
" <td>Bridgeport</td>\n",
" <td>43.0</td>\n",
" <td>22694</td>\n",
" <td>13.7</td>\n",
" <td>22.2</td>\n",
" <td>31.3</td>\n",
" <td>18.9</td>\n",
" <td>4.5</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>61.0</td>\n",
" <td>New City</td>\n",
" <td>91.0</td>\n",
" <td>12765</td>\n",
" <td>23.0</td>\n",
" <td>41.5</td>\n",
" <td>38.9</td>\n",
" <td>29.0</td>\n",
" <td>11.9</td>\n",
" </tr>\n",
" <tr>\n",
" <td>61</td>\n",
" <td>62.0</td>\n",
" <td>West Elsdon</td>\n",
" <td>69.0</td>\n",
" <td>15754</td>\n",
" <td>16.7</td>\n",
" <td>37.0</td>\n",
" <td>37.7</td>\n",
" <td>15.6</td>\n",
" <td>11.1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>62</td>\n",
" <td>63.0</td>\n",
" <td>Gage Park</td>\n",
" <td>93.0</td>\n",
" <td>12171</td>\n",
" <td>18.2</td>\n",
" <td>51.5</td>\n",
" <td>38.8</td>\n",
" <td>23.4</td>\n",
" <td>15.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>63</td>\n",
" <td>64.0</td>\n",
" <td>Clearing</td>\n",
" <td>29.0</td>\n",
" <td>25113</td>\n",
" <td>9.5</td>\n",
" <td>18.8</td>\n",
" <td>37.6</td>\n",
" <td>8.9</td>\n",
" <td>2.7</td>\n",
" </tr>\n",
" <tr>\n",
" <td>64</td>\n",
" <td>65.0</td>\n",
" <td>West Lawn</td>\n",
" <td>56.0</td>\n",
" <td>16907</td>\n",
" <td>9.6</td>\n",
" <td>33.6</td>\n",
" <td>39.6</td>\n",
" <td>14.9</td>\n",
" <td>5.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>65</td>\n",
" <td>66.0</td>\n",
" <td>Chicago Lawn</td>\n",
" <td>80.0</td>\n",
" <td>13231</td>\n",
" <td>17.1</td>\n",
" <td>31.2</td>\n",
" <td>40.6</td>\n",
" <td>27.9</td>\n",
" <td>7.6</td>\n",
" </tr>\n",
" <tr>\n",
" <td>66</td>\n",
" <td>67.0</td>\n",
" <td>West Englewood</td>\n",
" <td>89.0</td>\n",
" <td>11317</td>\n",
" <td>35.9</td>\n",
" <td>26.3</td>\n",
" <td>40.7</td>\n",
" <td>34.4</td>\n",
" <td>4.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>67</td>\n",
" <td>68.0</td>\n",
" <td>Englewood</td>\n",
" <td>94.0</td>\n",
" <td>11888</td>\n",
" <td>28.0</td>\n",
" <td>28.5</td>\n",
" <td>42.5</td>\n",
" <td>46.6</td>\n",
" <td>3.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>68</td>\n",
" <td>69.0</td>\n",
" <td>Greater Grand Crossing</td>\n",
" <td>66.0</td>\n",
" <td>17285</td>\n",
" <td>23.0</td>\n",
" <td>16.5</td>\n",
" <td>41.0</td>\n",
" <td>29.6</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <td>69</td>\n",
" <td>70.0</td>\n",
" <td>Ashburn</td>\n",
" <td>37.0</td>\n",
" <td>23482</td>\n",
" <td>11.7</td>\n",
" <td>17.7</td>\n",
" <td>36.9</td>\n",
" <td>10.4</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>70</td>\n",
" <td>71.0</td>\n",
" <td>Auburn Gresham</td>\n",
" <td>74.0</td>\n",
" <td>15528</td>\n",
" <td>28.3</td>\n",
" <td>18.5</td>\n",
" <td>41.9</td>\n",
" <td>27.6</td>\n",
" <td>4.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>71</td>\n",
" <td>72.0</td>\n",
" <td>Beverly</td>\n",
" <td>12.0</td>\n",
" <td>39523</td>\n",
" <td>8.0</td>\n",
" <td>3.7</td>\n",
" <td>40.5</td>\n",
" <td>5.1</td>\n",
" <td>0.9</td>\n",
" </tr>\n",
" <tr>\n",
" <td>72</td>\n",
" <td>73.0</td>\n",
" <td>Washington Height</td>\n",
" <td>48.0</td>\n",
" <td>19713</td>\n",
" <td>20.8</td>\n",
" <td>13.7</td>\n",
" <td>42.6</td>\n",
" <td>16.9</td>\n",
" <td>1.1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>73</td>\n",
" <td>74.0</td>\n",
" <td>Mount Greenwood</td>\n",
" <td>16.0</td>\n",
" <td>34381</td>\n",
" <td>8.7</td>\n",
" <td>4.3</td>\n",
" <td>36.8</td>\n",
" <td>3.4</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>74</td>\n",
" <td>75.0</td>\n",
" <td>Morgan Park</td>\n",
" <td>30.0</td>\n",
" <td>27149</td>\n",
" <td>15.0</td>\n",
" <td>10.8</td>\n",
" <td>40.3</td>\n",
" <td>13.2</td>\n",
" <td>0.8</td>\n",
" </tr>\n",
" <tr>\n",
" <td>75</td>\n",
" <td>76.0</td>\n",
" <td>O&#x27;Hare</td>\n",
" <td>24.0</td>\n",
" <td>25828</td>\n",
" <td>7.1</td>\n",
" <td>10.9</td>\n",
" <td>30.3</td>\n",
" <td>15.4</td>\n",
" <td>3.6</td>\n",
" </tr>\n",
" <tr>\n",
" <td>76</td>\n",
" <td>77.0</td>\n",
" <td>Edgewater</td>\n",
" <td>19.0</td>\n",
" <td>33385</td>\n",
" <td>9.2</td>\n",
" <td>9.7</td>\n",
" <td>23.8</td>\n",
" <td>18.2</td>\n",
" <td>4.1</td>\n",
" </tr>\n",
" <tr>\n",
" <td>77</td>\n",
" <td>None</td>\n",
" <td>CHICAGO</td>\n",
" <td>None</td>\n",
" <td>28202</td>\n",
" <td>12.9</td>\n",
" <td>19.5</td>\n",
" <td>33.5</td>\n",
" <td>19.7</td>\n",
" <td>4.7</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(0, 1.0, 'Rogers Park', 39.0, 23939, 8.7, 18.2, 27.5, 23.6, 7.7),\n",
" (1, 2.0, 'West Ridge', 46.0, 23040, 8.8, 20.8, 38.5, 17.2, 7.8),\n",
" (2, 3.0, 'Uptown', 20.0, 35787, 8.9, 11.8, 22.2, 24.0, 3.8),\n",
" (3, 4.0, 'Lincoln Square', 17.0, 37524, 8.2, 13.4, 25.5, 10.9, 3.4),\n",
" (4, 5.0, 'North Center', 6.0, 57123, 5.2, 4.5, 26.2, 7.5, 0.3),\n",
" (5, 6.0, 'Lake View', 5.0, 60058, 4.7, 2.6, 17.0, 11.4, 1.1),\n",
" (6, 7.0, 'Lincoln Park', 2.0, 71551, 5.1, 3.6, 21.5, 12.3, 0.8),\n",
" (7, 8.0, 'Near North Side', 1.0, 88669, 7.0, 2.5, 22.6, 12.9, 1.9),\n",
" (8, 9.0, 'Edison Park', 8.0, 40959, 6.5, 7.4, 35.3, 3.3, 1.1),\n",
" (9, 10.0, 'Norwood Park', 21.0, 32875, 9.0, 11.5, 39.5, 5.4, 2.0),\n",
" (10, 11.0, 'Jefferson Park', 25.0, 27751, 12.4, 13.4, 35.5, 8.6, 2.7),\n",
" (11, 12.0, 'Forest Glen', 11.0, 44164, 6.8, 4.9, 40.5, 7.5, 1.1),\n",
" (12, 13.0, 'North Park', 33.0, 26576, 9.9, 14.4, 39.0, 13.2, 3.9),\n",
" (13, 14.0, 'Albany Park', 53.0, 21323, 10.0, 32.9, 32.0, 19.2, 11.3),\n",
" (14, 15.0, 'Portage Park', 35.0, 24336, 12.6, 19.3, 34.0, 11.6, 4.1),\n",
" (15, 16.0, 'Irving Park', 34.0, 27249, 10.0, 22.4, 31.6, 13.1, 6.3),\n",
" (16, 17.0, 'Dunning', 28.0, 26282, 10.0, 16.2, 33.6, 10.6, 5.2),\n",
" (17, 18.0, 'Montclaire', 50.0, 22014, 13.8, 23.5, 38.6, 15.3, 8.1),\n",
" (18, 19.0, 'Belmont Cragin', 70.0, 15461, 14.6, 37.3, 37.3, 18.7, 10.8),\n",
" (19, 20.0, 'Hermosa', 71.0, 15089, 13.1, 41.6, 36.4, 20.5, 6.9),\n",
" (20, 21.0, 'Avondale', 42.0, 20039, 9.2, 24.7, 31.0, 15.3, 6.0),\n",
" (21, 22.0, 'Logan Square', 23.0, 31908, 8.2, 14.8, 26.2, 16.8, 3.2),\n",
" (22, 23.0, 'Humboldt park', 85.0, 13781, 17.3, 35.4, 38.0, 33.9, 14.8),\n",
" (23, 24.0, 'West Town', 10.0, 43198, 6.6, 12.9, 21.7, 14.7, 2.3),\n",
" (24, 25.0, 'Austin', 73.0, 15957, 22.6, 24.4, 37.9, 28.6, 6.3),\n",
" (25, 26.0, 'West Garfield Park', 92.0, 10934, 25.8, 24.5, 43.6, 41.7, 9.4),\n",
" (26, 27.0, 'East Garfield Park', 83.0, 12961, 19.6, 21.3, 43.2, 42.4, 8.2),\n",
" (27, 28.0, 'Near West Side', 15.0, 44689, 10.7, 9.6, 22.2, 20.6, 3.8),\n",
" (28, 29.0, 'North Lawndale', 87.0, 12034, 21.2, 27.6, 42.7, 43.1, 7.4),\n",
" (29, 30.0, 'South Lawndale', 96.0, 10402, 15.8, 54.8, 33.8, 30.7, 15.2),\n",
" (30, 31.0, 'Lower West Side', 76.0, 16444, 15.8, 40.7, 32.6, 25.8, 9.6),\n",
" (31, 32.0, 'Loop', 3.0, 65526, 5.7, 3.1, 13.5, 14.7, 1.5),\n",
" (32, 33.0, 'Near South Side', 7.0, 59077, 4.9, 7.4, 21.8, 13.8, 1.3),\n",
" (33, 34.0, 'Armour Square', 82.0, 16148, 16.7, 34.5, 38.3, 40.1, 5.7),\n",
" (34, 35.0, 'Douglas', 47.0, 23791, 18.2, 14.3, 30.7, 29.6, 1.8),\n",
" (35, 36.0, 'Oakland', 78.0, 19252, 28.7, 18.4, 40.4, 39.7, 1.3),\n",
" (36, 37.0, 'Fuller Park', 97.0, 10432, 33.9, 26.6, 44.9, 51.2, 3.2),\n",
" (37, 38.0, 'Grand Boulevard', 57.0, 23472, 24.3, 15.9, 39.5, 29.3, 3.3),\n",
" (38, 39.0, 'Kenwood', 26.0, 35911, 15.7, 11.3, 35.4, 21.7, 2.4),\n",
" (39, 40.0, 'Washington Park', 88.0, 13785, 28.6, 25.4, 42.8, 42.1, 5.6),\n",
" (40, 41.0, 'Hyde Park', 14.0, 39056, 8.4, 4.3, 26.2, 18.4, 1.5),\n",
" (41, 42.0, 'Woodlawn', 58.0, 18672, 23.4, 16.5, 36.1, 30.7, 2.9),\n",
" (42, 43.0, 'South Shore', 55.0, 19398, 20.0, 14.0, 35.7, 31.1, 2.8),\n",
" (43, 44.0, 'Chatham', 60.0, 18881, 24.0, 14.5, 40.3, 27.8, 3.3),\n",
" (44, 45.0, 'Avalon Park', 41.0, 24454, 21.1, 10.6, 39.3, 17.2, 1.4),\n",
" (45, 46.0, 'South Chicago', 75.0, 16579, 19.7, 26.6, 41.1, 29.8, 4.7),\n",
" (46, 47.0, 'Burnside', 79.0, 12515, 18.6, 19.3, 42.7, 33.0, 6.8),\n",
" (47, 48.0, 'Calumet Heights', 38.0, 28887, 20.0, 11.0, 44.0, 11.5, 2.1),\n",
" (48, 49.0, 'Roseland', 52.0, 17949, 20.3, 16.9, 41.2, 19.8, 2.5),\n",
" (49, 50.0, 'Pullman', 51.0, 20588, 22.8, 13.1, 38.6, 21.6, 1.5),\n",
" (50, 51.0, 'South Deering', 65.0, 14685, 16.3, 21.0, 39.5, 29.2, 4.0),\n",
" (51, 52.0, 'East Side', 64.0, 17104, 12.1, 31.9, 42.8, 19.2, 6.8),\n",
" (52, 53.0, 'West Pullman', 62.0, 16563, 19.4, 20.5, 42.1, 25.9, 3.3),\n",
" (53, 54.0, 'Riverdale', 98.0, 8201, 34.6, 27.5, 51.5, 56.5, 5.8),\n",
" (54, 55.0, 'Hegewisch', 44.0, 22677, 9.6, 19.2, 42.9, 17.1, 3.3),\n",
" (55, 56.0, 'Garfield Ridge', 32.0, 26353, 11.3, 19.3, 38.1, 8.8, 2.6),\n",
" (56, 57.0, 'Archer Heights', 67.0, 16134, 16.5, 35.9, 39.2, 14.1, 8.5),\n",
" (57, 58.0, 'Brighton Park', 84.0, 13089, 13.9, 45.1, 39.3, 23.6, 14.4),\n",
" (58, 59.0, 'McKinley Park', 61.0, 16954, 13.4, 32.9, 35.6, 18.7, 7.2),\n",
" (59, 60.0, 'Bridgeport', 43.0, 22694, 13.7, 22.2, 31.3, 18.9, 4.5),\n",
" (60, 61.0, 'New City', 91.0, 12765, 23.0, 41.5, 38.9, 29.0, 11.9),\n",
" (61, 62.0, 'West Elsdon', 69.0, 15754, 16.7, 37.0, 37.7, 15.6, 11.1),\n",
" (62, 63.0, 'Gage Park', 93.0, 12171, 18.2, 51.5, 38.8, 23.4, 15.8),\n",
" (63, 64.0, 'Clearing', 29.0, 25113, 9.5, 18.8, 37.6, 8.9, 2.7),\n",
" (64, 65.0, 'West Lawn', 56.0, 16907, 9.6, 33.6, 39.6, 14.9, 5.8),\n",
" (65, 66.0, 'Chicago Lawn', 80.0, 13231, 17.1, 31.2, 40.6, 27.9, 7.6),\n",
" (66, 67.0, 'West Englewood', 89.0, 11317, 35.9, 26.3, 40.7, 34.4, 4.8),\n",
" (67, 68.0, 'Englewood', 94.0, 11888, 28.0, 28.5, 42.5, 46.6, 3.8),\n",
" (68, 69.0, 'Greater Grand Crossing', 66.0, 17285, 23.0, 16.5, 41.0, 29.6, 3.6),\n",
" (69, 70.0, 'Ashburn', 37.0, 23482, 11.7, 17.7, 36.9, 10.4, 4.0),\n",
" (70, 71.0, 'Auburn Gresham', 74.0, 15528, 28.3, 18.5, 41.9, 27.6, 4.0),\n",
" (71, 72.0, 'Beverly', 12.0, 39523, 8.0, 3.7, 40.5, 5.1, 0.9),\n",
" (72, 73.0, 'Washington Height', 48.0, 19713, 20.8, 13.7, 42.6, 16.9, 1.1),\n",
" (73, 74.0, 'Mount Greenwood', 16.0, 34381, 8.7, 4.3, 36.8, 3.4, 1.0),\n",
" (74, 75.0, 'Morgan Park', 30.0, 27149, 15.0, 10.8, 40.3, 13.2, 0.8),\n",
" (75, 76.0, \"O'Hare\", 24.0, 25828, 7.1, 10.9, 30.3, 15.4, 3.6),\n",
" (76, 77.0, 'Edgewater', 19.0, 33385, 9.2, 9.7, 23.8, 18.2, 4.1),\n",
" (77, None, 'CHICAGO', None, 28202, 12.9, 19.5, 33.5, 19.7, 4.7)]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT * FROM chicago_socioeconomic_data limit 100;"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"#Problem 1\n",
"#How many rows are in the dataset?"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://dfk30111:***@dashdb-txn-sbox-yp-dal09-04.services.dal.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <td>78</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(Decimal('78'),)]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT COUNT(*) FROM chicago_socioeconomic_data;"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {},
"outputs": [],
"source": [
"#Problem 2\n",
"#How many community areas in Chicago have a hardship index greater than 50.0?"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://dfk30111:***@dashdb-txn-sbox-yp-dal09-04.services.dal.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <td>38</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(Decimal('38'),)]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT COUNT(*) FROM chicago_socioeconomic_data WHERE hardship_index > 50.0;"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [],
"source": [
"#Problem 3\n",
"#What is the maximum value of hardship index in this dataset?"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://dfk30111:***@dashdb-txn-sbox-yp-dal09-04.services.dal.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <td>98.0</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(98.0,)]"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT MAX(hardship_index) FROM chicago_socioeconomic_data;"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [],
"source": [
"#Problem 4\n",
"#Which community area which has the highest hardship index?"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://dfk30111:***@dashdb-txn-sbox-yp-dal09-04.services.dal.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>community_area_name</th>\n",
" </tr>\n",
" <tr>\n",
" <td>Riverdale</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[('Riverdale',)]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"## We can use the result of the last query to as an input to this query:\n",
"%sql SELECT community_area_name FROM chicago_socioeconomic_data where hardship_index=98.0\n",
"\n",
"## or another option:\n",
"#%sql SELECT community_area_name FROM chicago_socioeconomic_data ORDER BY hardship_index DESC NULLS LAST FETCH FIRST ROW ONLY;\n",
"\n",
"## or you can use a sub-query to determine the max hardship index:\n",
"#%sql select community_area_name from chicago_socioeconomic_data where hardship_index = ( select max(hardship_index) from chicago_socioeconomic_data ) "
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [],
"source": [
"#Problem 5\n",
"#Which Chicago community areas have per-capita incomes greater than $60,000?"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://dfk30111:***@dashdb-txn-sbox-yp-dal09-04.services.dal.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>community_area_name</th>\n",
" </tr>\n",
" <tr>\n",
" <td>Lake View</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Lincoln Park</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Near North Side</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Loop</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[('Lake View',), ('Lincoln Park',), ('Near North Side',), ('Loop',)]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT community_area_name FROM chicago_socioeconomic_data WHERE per_capita_income_ > 60000;"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [],
"source": [
"#Problem 6\n",
"#Create a scatter plot using the variables per_capita_income_ and hardship_index. Explain the correlation between the two variables."
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://dfk30111:***@dashdb-txn-sbox-yp-dal09-04.services.dal.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/jupyterlab/conda/lib/python3.6/site-packages/scipy/stats/stats.py:1713: FutureWarning: Using a non-tuple sequence for multidimensional indexing is deprecated; use `arr[tuple(seq)]` instead of `arr[seq]`. In the future this will be interpreted as an array index, `arr[np.array(seq)]`, which will result either in an error or a different result.\n",
" return np.add.reduce(sorted[indexer] * weights, axis=axis) / sumval\n"
]
},
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"# if the import command gives ModuleNotFoundError: No module named 'seaborn'\n",
"# then uncomment the following line i.e. delete the # to install the seaborn package \n",
"# !pip install seaborn\n",
"import matplotlib.pyplot as plt\n",
"%matplotlib inline\n",
"import seaborn as sns\n",
"\n",
"income_vs_hardship = %sql SELECT per_capita_income_, hardship_index FROM chicago_socioeconomic_data;\n",
"plot = sns.jointplot(x='per_capita_income_',y='hardship_index', data=income_vs_hardship.DataFrame())"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.8"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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