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DB0201EN-Week3-1-4-Analyzing-v5-py.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<a href=\"https://cognitiveclass.ai\"><img src = \"https://ibm.box.com/shared/static/ugcqz6ohbvff804xp84y4kqnvvk3bq1g.png\" width = 300, align = \"center\"></a>\n",
"\n",
"<h1 align=center><font size = 5>Lab: Analyzing a real world data-set with SQL and Python</font></h1>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"This notebook shows how to store a dataset into a database using and analyze data using SQL and Python. In this lab you will:\n",
"1. Understand a dataset of selected socioeconomic indicators in Chicago\n",
"1. Learn how to store data in an Db2 database on IBM Cloud instance\n",
"1. Solve example problems to practice your SQL skills "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Selected Socioeconomic Indicators in Chicago\n",
"\n",
"The city of Chicago released a dataset of socioeconomic data to the Chicago City Portal.\n",
"This dataset contains a selection of six socioeconomic indicators of public health significance and a “hardship index,” for each Chicago community area, for the years 2008 – 2012.\n",
"\n",
"Scores on the hardship index can range from 1 to 100, with a higher index number representing a greater level of hardship.\n",
"\n",
"A detailed description of the dataset can be found on [the city of Chicago's website](\n",
"https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-socioeconomic-indicators-in-C/kn9c-c2s2), but to summarize, the dataset has the following variables:\n",
"\n",
"* **Community Area Number** (`ca`): Used to uniquely identify each row of the dataset\n",
"\n",
"* **Community Area Name** (`community_area_name`): The name of the region in the city of Chicago \n",
"\n",
"* **Percent of Housing Crowded** (`percent_of_housing_crowded`): Percent of occupied housing units with more than one person per room\n",
"\n",
"* **Percent Households Below Poverty** (`percent_households_below_poverty`): Percent of households living below the federal poverty line\n",
"\n",
"* **Percent Aged 16+ Unemployed** (`percent_aged_16_unemployed`): Percent of persons over the age of 16 years that are unemployed\n",
"\n",
"* **Percent Aged 25+ without High School Diploma** (`percent_aged_25_without_high_school_diploma`): Percent of persons over the age of 25 years without a high school education\n",
"\n",
"* **Percent Aged Under** 18 or Over 64:Percent of population under 18 or over 64 years of age (`percent_aged_under_18_or_over_64`): (ie. dependents)\n",
"\n",
"* **Per Capita Income** (`per_capita_income_`): Community Area per capita income is estimated as the sum of tract-level aggragate incomes divided by the total population\n",
"\n",
"* **Hardship Index** (`hardship_index`): Score that incorporates each of the six selected socioeconomic indicators\n",
"\n",
"In this Lab, we'll take a look at the variables in the socioeconomic indicators dataset and do some basic analysis with Python.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"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": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Connected: vvp63794@BLUDB'"
]
},
"execution_count": 3,
"metadata": {},
"output_type": "execute_result"
}
],
"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",
"%sql ibm_db_sa://vvp63794:jt9sq0+x950wvj6z@dashdb-txn-sbox-yp-dal09-03.services.dal.bluemix.net:50000/BLUDB"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Store the dataset in a Table\n",
"##### In many cases the dataset to be analyzed is available as a .CSV (comma separated values) file, perhaps on the internet. To analyze the data using SQL, it first needs to be stored in the database.\n",
"\n",
"##### We will first read the dataset source .CSV from the internet into pandas dataframe\n",
"\n",
"##### Then we need to create a table in our Db2 database to store the dataset. The PERSIST command in SQL \"magic\" simplifies the process of table creation and writing the data from a `pandas` dataframe into the table"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://vvp63794:***@dashdb-txn-sbox-yp-dal09-03.services.dal.bluemix.net:50000/BLUDB\n"
]
},
{
"data": {
"text/plain": [
"'Persisted chicago_socioeconomic_data'"
]
},
"execution_count": 4,
"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": "markdown",
"metadata": {},
"source": [
"##### You can verify that the table creation was successful by making a basic query like:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://vvp63794:***@dashdb-txn-sbox-yp-dal09-03.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>percent_of_housing_crowded</th>\n",
" <th>percent_households_below_poverty</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>per_capita_income_</th>\n",
" <th>hardship_index</th>\n",
" </tr>\n",
" <tr>\n",
" <td>0</td>\n",
" <td>1.0</td>\n",
" <td>Rogers Park</td>\n",
" <td>7.7</td>\n",
" <td>23.6</td>\n",
" <td>8.7</td>\n",
" <td>18.2</td>\n",
" <td>27.5</td>\n",
" <td>23939</td>\n",
" <td>39.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>1</td>\n",
" <td>2.0</td>\n",
" <td>West Ridge</td>\n",
" <td>7.8</td>\n",
" <td>17.2</td>\n",
" <td>8.8</td>\n",
" <td>20.8</td>\n",
" <td>38.5</td>\n",
" <td>23040</td>\n",
" <td>46.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>2</td>\n",
" <td>3.0</td>\n",
" <td>Uptown</td>\n",
" <td>3.8</td>\n",
" <td>24.0</td>\n",
" <td>8.9</td>\n",
" <td>11.8</td>\n",
" <td>22.2</td>\n",
" <td>35787</td>\n",
" <td>20.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3</td>\n",
" <td>4.0</td>\n",
" <td>Lincoln Square</td>\n",
" <td>3.4</td>\n",
" <td>10.9</td>\n",
" <td>8.2</td>\n",
" <td>13.4</td>\n",
" <td>25.5</td>\n",
" <td>37524</td>\n",
" <td>17.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" <td>5.0</td>\n",
" <td>North Center</td>\n",
" <td>0.3</td>\n",
" <td>7.5</td>\n",
" <td>5.2</td>\n",
" <td>4.5</td>\n",
" <td>26.2</td>\n",
" <td>57123</td>\n",
" <td>6.0</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(0, 1.0, 'Rogers Park', 7.7, 23.6, 8.7, 18.2, 27.5, 23939, 39.0),\n",
" (1, 2.0, 'West Ridge', 7.8, 17.2, 8.8, 20.8, 38.5, 23040, 46.0),\n",
" (2, 3.0, 'Uptown', 3.8, 24.0, 8.9, 11.8, 22.2, 35787, 20.0),\n",
" (3, 4.0, 'Lincoln Square', 3.4, 10.9, 8.2, 13.4, 25.5, 37524, 17.0),\n",
" (4, 5.0, 'North Center', 0.3, 7.5, 5.2, 4.5, 26.2, 57123, 6.0)]"
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT * FROM chicago_socioeconomic_data limit 5;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problems\n",
"\n",
"### Problem 1\n",
"\n",
"##### How many rows are in the dataset?"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://vvp63794:***@dashdb-txn-sbox-yp-dal09-03.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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT count (*) FROM chicago_socioeconomic_data;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click __here__ for the solution.\n",
"\n",
"<!-- Hint:\n",
"\n",
"%sql SELECT COUNT(*) FROM chicago_socioeconomic_data;\n",
"\n",
"Correct answer: 78\n",
"\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 2\n",
"\n",
"##### How many community areas in Chicago have a hardship index greater than 50.0?"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://vvp63794:***@dashdb-txn-sbox-yp-dal09-03.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>percent_of_housing_crowded</th>\n",
" <th>percent_households_below_poverty</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>per_capita_income_</th>\n",
" <th>hardship_index</th>\n",
" </tr>\n",
" <tr>\n",
" <td>13</td>\n",
" <td>14.0</td>\n",
" <td>Albany Park</td>\n",
" <td>11.3</td>\n",
" <td>19.2</td>\n",
" <td>10.0</td>\n",
" <td>32.9</td>\n",
" <td>32.0</td>\n",
" <td>21323</td>\n",
" <td>53.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>18</td>\n",
" <td>19.0</td>\n",
" <td>Belmont Cragin</td>\n",
" <td>10.8</td>\n",
" <td>18.7</td>\n",
" <td>14.6</td>\n",
" <td>37.3</td>\n",
" <td>37.3</td>\n",
" <td>15461</td>\n",
" <td>70.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>19</td>\n",
" <td>20.0</td>\n",
" <td>Hermosa</td>\n",
" <td>6.9</td>\n",
" <td>20.5</td>\n",
" <td>13.1</td>\n",
" <td>41.6</td>\n",
" <td>36.4</td>\n",
" <td>15089</td>\n",
" <td>71.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>22</td>\n",
" <td>23.0</td>\n",
" <td>Humboldt park</td>\n",
" <td>14.8</td>\n",
" <td>33.9</td>\n",
" <td>17.3</td>\n",
" <td>35.4</td>\n",
" <td>38.0</td>\n",
" <td>13781</td>\n",
" <td>85.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>24</td>\n",
" <td>25.0</td>\n",
" <td>Austin</td>\n",
" <td>6.3</td>\n",
" <td>28.6</td>\n",
" <td>22.6</td>\n",
" <td>24.4</td>\n",
" <td>37.9</td>\n",
" <td>15957</td>\n",
" <td>73.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>25</td>\n",
" <td>26.0</td>\n",
" <td>West Garfield Park</td>\n",
" <td>9.4</td>\n",
" <td>41.7</td>\n",
" <td>25.8</td>\n",
" <td>24.5</td>\n",
" <td>43.6</td>\n",
" <td>10934</td>\n",
" <td>92.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>26</td>\n",
" <td>27.0</td>\n",
" <td>East Garfield Park</td>\n",
" <td>8.2</td>\n",
" <td>42.4</td>\n",
" <td>19.6</td>\n",
" <td>21.3</td>\n",
" <td>43.2</td>\n",
" <td>12961</td>\n",
" <td>83.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>28</td>\n",
" <td>29.0</td>\n",
" <td>North Lawndale</td>\n",
" <td>7.4</td>\n",
" <td>43.1</td>\n",
" <td>21.2</td>\n",
" <td>27.6</td>\n",
" <td>42.7</td>\n",
" <td>12034</td>\n",
" <td>87.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>29</td>\n",
" <td>30.0</td>\n",
" <td>South Lawndale</td>\n",
" <td>15.2</td>\n",
" <td>30.7</td>\n",
" <td>15.8</td>\n",
" <td>54.8</td>\n",
" <td>33.8</td>\n",
" <td>10402</td>\n",
" <td>96.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>30</td>\n",
" <td>31.0</td>\n",
" <td>Lower West Side</td>\n",
" <td>9.6</td>\n",
" <td>25.8</td>\n",
" <td>15.8</td>\n",
" <td>40.7</td>\n",
" <td>32.6</td>\n",
" <td>16444</td>\n",
" <td>76.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>33</td>\n",
" <td>34.0</td>\n",
" <td>Armour Square</td>\n",
" <td>5.7</td>\n",
" <td>40.1</td>\n",
" <td>16.7</td>\n",
" <td>34.5</td>\n",
" <td>38.3</td>\n",
" <td>16148</td>\n",
" <td>82.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>35</td>\n",
" <td>36.0</td>\n",
" <td>Oakland</td>\n",
" <td>1.3</td>\n",
" <td>39.7</td>\n",
" <td>28.7</td>\n",
" <td>18.4</td>\n",
" <td>40.4</td>\n",
" <td>19252</td>\n",
" <td>78.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>36</td>\n",
" <td>37.0</td>\n",
" <td>Fuller Park</td>\n",
" <td>3.2</td>\n",
" <td>51.2</td>\n",
" <td>33.9</td>\n",
" <td>26.6</td>\n",
" <td>44.9</td>\n",
" <td>10432</td>\n",
" <td>97.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>37</td>\n",
" <td>38.0</td>\n",
" <td>Grand Boulevard</td>\n",
" <td>3.3</td>\n",
" <td>29.3</td>\n",
" <td>24.3</td>\n",
" <td>15.9</td>\n",
" <td>39.5</td>\n",
" <td>23472</td>\n",
" <td>57.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>39</td>\n",
" <td>40.0</td>\n",
" <td>Washington Park</td>\n",
" <td>5.6</td>\n",
" <td>42.1</td>\n",
" <td>28.6</td>\n",
" <td>25.4</td>\n",
" <td>42.8</td>\n",
" <td>13785</td>\n",
" <td>88.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>41</td>\n",
" <td>42.0</td>\n",
" <td>Woodlawn</td>\n",
" <td>2.9</td>\n",
" <td>30.7</td>\n",
" <td>23.4</td>\n",
" <td>16.5</td>\n",
" <td>36.1</td>\n",
" <td>18672</td>\n",
" <td>58.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>42</td>\n",
" <td>43.0</td>\n",
" <td>South Shore</td>\n",
" <td>2.8</td>\n",
" <td>31.1</td>\n",
" <td>20.0</td>\n",
" <td>14.0</td>\n",
" <td>35.7</td>\n",
" <td>19398</td>\n",
" <td>55.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>43</td>\n",
" <td>44.0</td>\n",
" <td>Chatham</td>\n",
" <td>3.3</td>\n",
" <td>27.8</td>\n",
" <td>24.0</td>\n",
" <td>14.5</td>\n",
" <td>40.3</td>\n",
" <td>18881</td>\n",
" <td>60.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>45</td>\n",
" <td>46.0</td>\n",
" <td>South Chicago</td>\n",
" <td>4.7</td>\n",
" <td>29.8</td>\n",
" <td>19.7</td>\n",
" <td>26.6</td>\n",
" <td>41.1</td>\n",
" <td>16579</td>\n",
" <td>75.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>46</td>\n",
" <td>47.0</td>\n",
" <td>Burnside</td>\n",
" <td>6.8</td>\n",
" <td>33.0</td>\n",
" <td>18.6</td>\n",
" <td>19.3</td>\n",
" <td>42.7</td>\n",
" <td>12515</td>\n",
" <td>79.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>48</td>\n",
" <td>49.0</td>\n",
" <td>Roseland</td>\n",
" <td>2.5</td>\n",
" <td>19.8</td>\n",
" <td>20.3</td>\n",
" <td>16.9</td>\n",
" <td>41.2</td>\n",
" <td>17949</td>\n",
" <td>52.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>49</td>\n",
" <td>50.0</td>\n",
" <td>Pullman</td>\n",
" <td>1.5</td>\n",
" <td>21.6</td>\n",
" <td>22.8</td>\n",
" <td>13.1</td>\n",
" <td>38.6</td>\n",
" <td>20588</td>\n",
" <td>51.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>50</td>\n",
" <td>51.0</td>\n",
" <td>South Deering</td>\n",
" <td>4.0</td>\n",
" <td>29.2</td>\n",
" <td>16.3</td>\n",
" <td>21.0</td>\n",
" <td>39.5</td>\n",
" <td>14685</td>\n",
" <td>65.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>51</td>\n",
" <td>52.0</td>\n",
" <td>East Side</td>\n",
" <td>6.8</td>\n",
" <td>19.2</td>\n",
" <td>12.1</td>\n",
" <td>31.9</td>\n",
" <td>42.8</td>\n",
" <td>17104</td>\n",
" <td>64.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>52</td>\n",
" <td>53.0</td>\n",
" <td>West Pullman</td>\n",
" <td>3.3</td>\n",
" <td>25.9</td>\n",
" <td>19.4</td>\n",
" <td>20.5</td>\n",
" <td>42.1</td>\n",
" <td>16563</td>\n",
" <td>62.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>54.0</td>\n",
" <td>Riverdale</td>\n",
" <td>5.8</td>\n",
" <td>56.5</td>\n",
" <td>34.6</td>\n",
" <td>27.5</td>\n",
" <td>51.5</td>\n",
" <td>8201</td>\n",
" <td>98.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>56</td>\n",
" <td>57.0</td>\n",
" <td>Archer Heights</td>\n",
" <td>8.5</td>\n",
" <td>14.1</td>\n",
" <td>16.5</td>\n",
" <td>35.9</td>\n",
" <td>39.2</td>\n",
" <td>16134</td>\n",
" <td>67.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>57</td>\n",
" <td>58.0</td>\n",
" <td>Brighton Park</td>\n",
" <td>14.4</td>\n",
" <td>23.6</td>\n",
" <td>13.9</td>\n",
" <td>45.1</td>\n",
" <td>39.3</td>\n",
" <td>13089</td>\n",
" <td>84.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>58</td>\n",
" <td>59.0</td>\n",
" <td>McKinley Park</td>\n",
" <td>7.2</td>\n",
" <td>18.7</td>\n",
" <td>13.4</td>\n",
" <td>32.9</td>\n",
" <td>35.6</td>\n",
" <td>16954</td>\n",
" <td>61.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>60</td>\n",
" <td>61.0</td>\n",
" <td>New City</td>\n",
" <td>11.9</td>\n",
" <td>29.0</td>\n",
" <td>23.0</td>\n",
" <td>41.5</td>\n",
" <td>38.9</td>\n",
" <td>12765</td>\n",
" <td>91.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>61</td>\n",
" <td>62.0</td>\n",
" <td>West Elsdon</td>\n",
" <td>11.1</td>\n",
" <td>15.6</td>\n",
" <td>16.7</td>\n",
" <td>37.0</td>\n",
" <td>37.7</td>\n",
" <td>15754</td>\n",
" <td>69.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>62</td>\n",
" <td>63.0</td>\n",
" <td>Gage Park</td>\n",
" <td>15.8</td>\n",
" <td>23.4</td>\n",
" <td>18.2</td>\n",
" <td>51.5</td>\n",
" <td>38.8</td>\n",
" <td>12171</td>\n",
" <td>93.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>64</td>\n",
" <td>65.0</td>\n",
" <td>West Lawn</td>\n",
" <td>5.8</td>\n",
" <td>14.9</td>\n",
" <td>9.6</td>\n",
" <td>33.6</td>\n",
" <td>39.6</td>\n",
" <td>16907</td>\n",
" <td>56.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>65</td>\n",
" <td>66.0</td>\n",
" <td>Chicago Lawn</td>\n",
" <td>7.6</td>\n",
" <td>27.9</td>\n",
" <td>17.1</td>\n",
" <td>31.2</td>\n",
" <td>40.6</td>\n",
" <td>13231</td>\n",
" <td>80.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>66</td>\n",
" <td>67.0</td>\n",
" <td>West Englewood</td>\n",
" <td>4.8</td>\n",
" <td>34.4</td>\n",
" <td>35.9</td>\n",
" <td>26.3</td>\n",
" <td>40.7</td>\n",
" <td>11317</td>\n",
" <td>89.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>67</td>\n",
" <td>68.0</td>\n",
" <td>Englewood</td>\n",
" <td>3.8</td>\n",
" <td>46.6</td>\n",
" <td>28.0</td>\n",
" <td>28.5</td>\n",
" <td>42.5</td>\n",
" <td>11888</td>\n",
" <td>94.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>68</td>\n",
" <td>69.0</td>\n",
" <td>Greater Grand Crossing</td>\n",
" <td>3.6</td>\n",
" <td>29.6</td>\n",
" <td>23.0</td>\n",
" <td>16.5</td>\n",
" <td>41.0</td>\n",
" <td>17285</td>\n",
" <td>66.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>70</td>\n",
" <td>71.0</td>\n",
" <td>Auburn Gresham</td>\n",
" <td>4.0</td>\n",
" <td>27.6</td>\n",
" <td>28.3</td>\n",
" <td>18.5</td>\n",
" <td>41.9</td>\n",
" <td>15528</td>\n",
" <td>74.0</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(13, 14.0, 'Albany Park', 11.3, 19.2, 10.0, 32.9, 32.0, 21323, 53.0),\n",
" (18, 19.0, 'Belmont Cragin', 10.8, 18.7, 14.6, 37.3, 37.3, 15461, 70.0),\n",
" (19, 20.0, 'Hermosa', 6.9, 20.5, 13.1, 41.6, 36.4, 15089, 71.0),\n",
" (22, 23.0, 'Humboldt park', 14.8, 33.9, 17.3, 35.4, 38.0, 13781, 85.0),\n",
" (24, 25.0, 'Austin', 6.3, 28.6, 22.6, 24.4, 37.9, 15957, 73.0),\n",
" (25, 26.0, 'West Garfield Park', 9.4, 41.7, 25.8, 24.5, 43.6, 10934, 92.0),\n",
" (26, 27.0, 'East Garfield Park', 8.2, 42.4, 19.6, 21.3, 43.2, 12961, 83.0),\n",
" (28, 29.0, 'North Lawndale', 7.4, 43.1, 21.2, 27.6, 42.7, 12034, 87.0),\n",
" (29, 30.0, 'South Lawndale', 15.2, 30.7, 15.8, 54.8, 33.8, 10402, 96.0),\n",
" (30, 31.0, 'Lower West Side', 9.6, 25.8, 15.8, 40.7, 32.6, 16444, 76.0),\n",
" (33, 34.0, 'Armour Square', 5.7, 40.1, 16.7, 34.5, 38.3, 16148, 82.0),\n",
" (35, 36.0, 'Oakland', 1.3, 39.7, 28.7, 18.4, 40.4, 19252, 78.0),\n",
" (36, 37.0, 'Fuller Park', 3.2, 51.2, 33.9, 26.6, 44.9, 10432, 97.0),\n",
" (37, 38.0, 'Grand Boulevard', 3.3, 29.3, 24.3, 15.9, 39.5, 23472, 57.0),\n",
" (39, 40.0, 'Washington Park', 5.6, 42.1, 28.6, 25.4, 42.8, 13785, 88.0),\n",
" (41, 42.0, 'Woodlawn', 2.9, 30.7, 23.4, 16.5, 36.1, 18672, 58.0),\n",
" (42, 43.0, 'South Shore', 2.8, 31.1, 20.0, 14.0, 35.7, 19398, 55.0),\n",
" (43, 44.0, 'Chatham', 3.3, 27.8, 24.0, 14.5, 40.3, 18881, 60.0),\n",
" (45, 46.0, 'South Chicago', 4.7, 29.8, 19.7, 26.6, 41.1, 16579, 75.0),\n",
" (46, 47.0, 'Burnside', 6.8, 33.0, 18.6, 19.3, 42.7, 12515, 79.0),\n",
" (48, 49.0, 'Roseland', 2.5, 19.8, 20.3, 16.9, 41.2, 17949, 52.0),\n",
" (49, 50.0, 'Pullman', 1.5, 21.6, 22.8, 13.1, 38.6, 20588, 51.0),\n",
" (50, 51.0, 'South Deering', 4.0, 29.2, 16.3, 21.0, 39.5, 14685, 65.0),\n",
" (51, 52.0, 'East Side', 6.8, 19.2, 12.1, 31.9, 42.8, 17104, 64.0),\n",
" (52, 53.0, 'West Pullman', 3.3, 25.9, 19.4, 20.5, 42.1, 16563, 62.0),\n",
" (53, 54.0, 'Riverdale', 5.8, 56.5, 34.6, 27.5, 51.5, 8201, 98.0),\n",
" (56, 57.0, 'Archer Heights', 8.5, 14.1, 16.5, 35.9, 39.2, 16134, 67.0),\n",
" (57, 58.0, 'Brighton Park', 14.4, 23.6, 13.9, 45.1, 39.3, 13089, 84.0),\n",
" (58, 59.0, 'McKinley Park', 7.2, 18.7, 13.4, 32.9, 35.6, 16954, 61.0),\n",
" (60, 61.0, 'New City', 11.9, 29.0, 23.0, 41.5, 38.9, 12765, 91.0),\n",
" (61, 62.0, 'West Elsdon', 11.1, 15.6, 16.7, 37.0, 37.7, 15754, 69.0),\n",
" (62, 63.0, 'Gage Park', 15.8, 23.4, 18.2, 51.5, 38.8, 12171, 93.0),\n",
" (64, 65.0, 'West Lawn', 5.8, 14.9, 9.6, 33.6, 39.6, 16907, 56.0),\n",
" (65, 66.0, 'Chicago Lawn', 7.6, 27.9, 17.1, 31.2, 40.6, 13231, 80.0),\n",
" (66, 67.0, 'West Englewood', 4.8, 34.4, 35.9, 26.3, 40.7, 11317, 89.0),\n",
" (67, 68.0, 'Englewood', 3.8, 46.6, 28.0, 28.5, 42.5, 11888, 94.0),\n",
" (68, 69.0, 'Greater Grand Crossing', 3.6, 29.6, 23.0, 16.5, 41.0, 17285, 66.0),\n",
" (70, 71.0, 'Auburn Gresham', 4.0, 27.6, 28.3, 18.5, 41.9, 15528, 74.0)]"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT * FROM chicago_socioeconomic_data WHERE hardship_index > 50.0;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click __here__ for the solution.\n",
"\n",
"<!-- Hint:\n",
"\n",
"%sql SELECT COUNT(*) FROM chicago_socioeconomic_data WHERE hardship_index > 50.0;\n",
"Correct answer: 38\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 3\n",
"\n",
"##### What is the maximum value of hardship index in this dataset?"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://vvp63794:***@dashdb-txn-sbox-yp-dal09-03.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": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT MAX(hardship_index) FROM chicago_socioeconomic_data;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click __here__ for the solution.\n",
"\n",
"<!-- Hint:\n",
"\n",
"%sql SELECT MAX(hardship_index) FROM chicago_socioeconomic_data;\n",
"\n",
"Correct answer: 98.0\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 4\n",
"\n",
"##### Which community area which has the highest hardship index?\n"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://vvp63794:***@dashdb-txn-sbox-yp-dal09-03.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>percent_of_housing_crowded</th>\n",
" <th>percent_households_below_poverty</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>per_capita_income_</th>\n",
" <th>hardship_index</th>\n",
" </tr>\n",
" <tr>\n",
" <td>53</td>\n",
" <td>54.0</td>\n",
" <td>Riverdale</td>\n",
" <td>5.8</td>\n",
" <td>56.5</td>\n",
" <td>34.6</td>\n",
" <td>27.5</td>\n",
" <td>51.5</td>\n",
" <td>8201</td>\n",
" <td>98.0</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(53, 54.0, 'Riverdale', 5.8, 56.5, 34.6, 27.5, 51.5, 8201, 98.0)]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select * from chicago_socioeconomic_data where hardship_index = ( select max(hardship_index) from chicago_socioeconomic_data );\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click __here__ for the solution.\n",
"\n",
"<!-- Hint:\n",
"\n",
"## 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 ) \n",
"\n",
"Correct answer: 'Riverdale'\n",
"-->"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 5\n",
"\n",
"##### Which Chicago community areas have per-capita incomes greater than $60,000?"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://vvp63794:***@dashdb-txn-sbox-yp-dal09-03.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>percent_of_housing_crowded</th>\n",
" <th>percent_households_below_poverty</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>per_capita_income_</th>\n",
" <th>hardship_index</th>\n",
" </tr>\n",
" <tr>\n",
" <td>5</td>\n",
" <td>6.0</td>\n",
" <td>Lake View</td>\n",
" <td>1.1</td>\n",
" <td>11.4</td>\n",
" <td>4.7</td>\n",
" <td>2.6</td>\n",
" <td>17.0</td>\n",
" <td>60058</td>\n",
" <td>5.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>6</td>\n",
" <td>7.0</td>\n",
" <td>Lincoln Park</td>\n",
" <td>0.8</td>\n",
" <td>12.3</td>\n",
" <td>5.1</td>\n",
" <td>3.6</td>\n",
" <td>21.5</td>\n",
" <td>71551</td>\n",
" <td>2.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7</td>\n",
" <td>8.0</td>\n",
" <td>Near North Side</td>\n",
" <td>1.9</td>\n",
" <td>12.9</td>\n",
" <td>7.0</td>\n",
" <td>2.5</td>\n",
" <td>22.6</td>\n",
" <td>88669</td>\n",
" <td>1.0</td>\n",
" </tr>\n",
" <tr>\n",
" <td>31</td>\n",
" <td>32.0</td>\n",
" <td>Loop</td>\n",
" <td>1.5</td>\n",
" <td>14.7</td>\n",
" <td>5.7</td>\n",
" <td>3.1</td>\n",
" <td>13.5</td>\n",
" <td>65526</td>\n",
" <td>3.0</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(5, 6.0, 'Lake View', 1.1, 11.4, 4.7, 2.6, 17.0, 60058, 5.0),\n",
" (6, 7.0, 'Lincoln Park', 0.8, 12.3, 5.1, 3.6, 21.5, 71551, 2.0),\n",
" (7, 8.0, 'Near North Side', 1.9, 12.9, 7.0, 2.5, 22.6, 88669, 1.0),\n",
" (31, 32.0, 'Loop', 1.5, 14.7, 5.7, 3.1, 13.5, 65526, 3.0)]"
]
},
"execution_count": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select * from chicago_socioeconomic_data where per_capita_income_ > 60000;"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Double-click __here__ for the solution.\n",
"\n",
"<!-- Hint:\n",
"\n",
"%sql SELECT community_area_name FROM chicago_socioeconomic_data WHERE per_capita_income_ > 60000;\n",
"\n",
"Correct answer:Lake View,Lincoln Park, Near North Side, Loop\n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 6\n",
"\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": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://vvp63794:***@dashdb-txn-sbox-yp-dal09-03.services.dal.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"image/png": 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8F3gNRfL8WES8FrgVOGMZ5x8KjR2EH85iNP9wJp+8ZoZ1P7HahCVJpW6S1d0R8ZMUc6KIiJcDdyz1jTPzWmBdi4dOWeo5h9FCawearCSp0E2yeh3Fl3pPiogZ4Gbg1X2Jaoz0agdhJxZLGmXddAN+F3hB2WJ+QGbe17+wxsdRU5PL7gacvw1JY2IxYMKSNBIWTVbl8kqtjgOQmX/a45jGSi92ELaUKGnUdTKyOqS8XgP8LEXHHsBLgK/0I6hRtVCpbjklvF6VEiWprhZNVpn5doCI+ALw9Eb5LyJ+D/h4X6MbIYuV6pYzAupFKVGS6qybeVbHAD9quv8jitXR1YF+7hjsxGJJo66bbsAPAF+NiE9RtK//MvD3fYlqBHVSqltqR18vSomSVGfddAO+IyI+Dzy3PPSazNzZn7BGz2KluuV29C23lChJddbttvbXUnxP9Sng+xFxTO9DGk2Ller6WSaUpGHX8cgqIl4PvA24E3gYCIpy4NP6E9poWaxUZ0efJLXXzXdWbwTWZOb3+xXMqFuoVGdHnyS1100Z8HvAvf0KZNzZ0SdJ7XUzsvoucGVEXA482DjoCha9sViZ0LX/JI2zbpLVreXlMeVFPdauTOjaf5LGXTet62/vZyBqz7X/JI27Thay/bPMPDciPk25l1WzzHxpXyIbcd2U9ewUlDTuOhlZfaC8/uN+BjJOui3r2Skoadwt2g2YmdeU119udWk8LyI+2c9AR0m3E4DtFJQ07rppsFjM8T0810jrtKzXXCqcWjXBgSsP4N49c3YDSho7vUxW+32fpdY6KevNLxXe88AckxMrePeZJ5mkJI2dbtcGVA90UtZzrUBJelQvR1bRw3ONtE629GhXKpyZ3cP6LdudHCxprHSVrCLiMcCTKEp+uzKzeTPGt/QysFG32JYe7UqFAXuPOzlY0rjouAwYEb8E/DPwF8BfATdFxIsaj2fmF3of3vhqVSpsLHPfzNKgpHHQzcjqT4Cfz8ybACLiJ4HLgc/1I7Bx16pU2GqkBU4OljT6uklWdzUSVem7wF09jkdN5pcK12/Z7uRgSWOpm2R1Q0R8FvgYRTXqDOBrEXE6QGZe0of4xs5CyzBt2rBmn3Z2cHKwpPHQTbI6iGKX4P9U3t8NrAZeQpG8TFbLtNgyTJ10EUrSKOpm1fXX9DMQdba6+mJdhJI0ijpZdf3NmflHEfGXtF51/Q19iWwMLXd1dTdolDSqOhlZ3Vhe7+hnIFre6upu0ChplC2arDLz0+X1Rf0PZ7wtp4HCDRoljbKOv7OKiJ8Cfhs4tvl1mXly78MaT0tpoGiU/pyDJWmUddMN+HHgb4ALgYcXea6WqJsGivmlv1acgyVpFHSTrB7KzPf0LRJ1rVXpr5lzsCSNik66AVeXNz8dEb8BfAp4sPF4Zv6gT7FpEQuV+KbtBpQ0QjoZWV1D0bLe2AJkU9NjiTsEV6Zd9+D01CRXbfarREmjo5NuwOMGEYi65/JLksZFN1uEnBERh5S33xoRl0TE2v6Fpk4cNPHor3BqcoLzTz/R0p+kkdPNtva/m5n3RcRzgQ3ARRTdgapAoxPwngfm9h578KFHKoxIkvqnm2TVqDX9EvCezLwUeEzvQ1InFpoELEmjpptkNRMRfwu8AvhsRBzY5evVQ8tdR1CShkk3yeYVwBXAqZk5S7E9yKaFX6J+OXRyoqvjkjTMuplnBXBl07EHcXHbykR0d1yShlm386yOAe4pb08BtwK2tvfBYtt9zDY1VjRrd1yShtmiZcDMPC4zj6coAb4kM5+QmY8HXoy7A/dFo9NvZnYPyaPbfWzdObP3Oe3W/HMtQEmjqJvvrH42Mz/buJOZn+PRLe7VQ510+m3asIbJiRX7PMcJwZJGVTcL2d4dEW8FPkhRFnw18P2+RDXmOun0W2g7EXcMljRquklWrwTeRrGQbQJfKY+pxzrdMbjVdiLuGCxpFHVUBoyIFcB5mfnGzFybmU/PzHNdcb0/llPic7KwpFHUUbLKzIeBZ/T6zSNiRUTsjIjPlPdXR8QXI+I75fVhvX7PYbBx7TTnn34i01OTBMUq6p2u+edkYUmjqJsy4M6IuIxix+D7GwczczkdgW8EbgQeV97fDGzLzC0Rsbm8/5ZlnH9odbNjcLNOS4iSNEy66QZcTdFQcTLwkvLy4qW+cUQcTbHO4IVNh0+jWCCX8nrjUs8/ruwSlDSKOh5ZZeZrevzefwa8GTik6dgRmXlH+X53RMQTW70wIs4BzgE45phjehzW8Gp0Ae6Ze5gVETyc6Y7B0ohp/vx7wo+Nz7/rjpNVRBwEvBZ4CnBQ43hm/lq3bxoRLwbuysxrIuL53b4+My8ALgBYt25ddvv6UTS/C/DhzL0jKhOVNDqaP/+O/+mnjc3nXzdlwA8AP0axl9WXgaOB+5b4vuuBl0bELcBHgZMj4oPAnRFxJEB5fdcSzz927AKUNMq6SVb/ITN/F7g/My+i+L7pxKW8aWael5lHZ+axwFnA9sx8NXAZcHb5tLOBS5dy/nHUqy7ArTtnWL9lO8dtvpz1W7bvs8STJFWlm2TVWCF1NiKeChwKHNvjeLYAL4yI7wAvLO+rA71YK7CTNQklqQrdJKsLynlPb6UYAX0LeOdyA8jMKzPzxeXt72fmKZl5QnntpOMO9aIL0FKipLrqZp7VB4CXUYymGu3lR/Q6IC3dQRMH7E02U5MT/N5Ln9JVc4UTiiXVVTfJ6lLgXor9rR7sTzhaivmdgAAPPvRI1+dxQrGkuuomWR2dmaf2LRIt2ULlu25GVps2rNkv6TmhWFIddPOd1T9GxJK6/9RfvSrfLWdNQknqp0VHVhFxHcWWICuB10TEdynKgAFkZj6tvyFqMb0s3y11TUJJ6qdOyoBLXv9Pg2H5TtKoWzRZZea/DCIQLd38XYOnVk2QCW+6+FredcUul1ySNPS6+c5KNbZx7TRXbT6Zd595Ej+ce4TZPXNO7JU0MkxWI8aJvZJGUTet6xoC3XQGNrYUuX12D0e5lYikGnNkNWI6XSPQdQAlDROT1YjpdI1Ay4WShollwBEzvzNwfnmvUfprNS8LXAdQUj2ZrEZQu4m9rdYQnM91ACXVkWXAMdKq9NfMicSS6sqR1RhZqMQ3bTegpBozWY2RQycnmN0zt9/xqckJrtp8cgURSVJnLAOOkYjujktSXZisxsjsA/uPqhY6Lkl1YbIaI51OGJakujFZjZFWE4YBHvjRQ65cIanWTFZjpLET8NTkxD7H73lgzqWWJNWayWrMbFw7zcEH7t8E6lJLkurM1vUR1m5V9W5WZpekOjBZjaj5Sys1VlWHoqGi1dqANlpIqivLgCNqoVXVO12ZXZLqwmQ1otqV9GZm9/Cmi6/lwJUHcNiqCYJiqaXzTz/RpZYk1ZZlwBHVrtQHkMDsnjkmJ1bw7jNPMklJqj1HViOq3ZyqZnYAShoWjqxG1PxNGLPN8+wAlDQMTFYjrHkTxvVbttsBKGloWQYcE3YAShpmjqzGxPyy4FEVbrbYbrKyJLVjshojzWXBqiw0Wbnq2CTVl2VADdRCk5UlqR1HVmNu0CU51yWUtBSOrMZYoyQ3U7a2N0py/dwqxA0gJS2FyWqMVVGSsytR0lJYBhwTrcp9VZTk6tSVKGl4mKzGQLsOvEMnJ5jdM7ff8/tdkqtDV6Kk4WIZcAy0K/dFYElO0lAwWY2BdmW92QfmOP/0E5memlzSViFbd86wfst2jtt8Oeu3bO9rY4ak8WYZcAwstDPwUktyTu6VNEiOrMZAPzrwnNwraZAcWY2BfnTgOblX0iCZrMZErzvwFiotSlKvWQbUkji5V9Igmay0JBvXTu/TSXjYqgkOXHkAb7r4WjsDJfWcyUpLtnHtNFdtPpl3n3kSP5x7hNk9cwNbY1DSeDFZadnsDJTUb5U0WETEjwN/D/wY8AhwQWb+eUSsBi4GjgVuAV6RmfdUEaM6101noLsES1qKqkZWDwG/lZk/DTwbeF1EPBnYDGzLzBOAbeV91Vyn235UsSWJpNFQSbLKzDsy8+vl7fuAG4Fp4DTgovJpFwEbq4hP3em0M9ByoaSGD199695LJyr/zioijgXWAlcDR2TmHVAkNOCJbV5zTkTsiIgdu3fvHlSoaqO5MxBgRcTeJNQ8anIisbR8zZ9/983+oOpwBqbSZBURjwU+CZybmf/W6esy84LMXJeZ6w4//PD+BaiObVw7vXeE9XAmsH+Zz12CpeVr/vw7ZGp11eEMTGXJKiImKBLVhzLzkvLwnRFxZPn4kcBdVcWn7i1W5nMisaSlqiRZRUQA7wVuzMw/bXroMuDs8vbZwKWDjk1Lt1iZb/5E4m63JJE0vqpaG3A98CvAdRFxbXnsfwBbgI9FxGuBW4EzKopPS9DJeoHuEixpKSpJVpn5D0C0efiUQcai3tm0Yc0+e1yBZT5JveGq6+qZ5W5F4oRhSe2YrNRT7jwsqR8qn2clgROGJS3MkZX6qtPSnhOGJS3EkZX6ppu1AJ0wLGkhJiv1TTelvUFMGN66c4b1W7Zz3ObL3SBSGjKWAdU33ZT2lttJuBgbOKThZrJS33QySbhZPycMLzTKM1lJ9WcZUH3Ti9Jer0p3NnBIw82RlfqmF5OEe1W663aUJ6leTFbqq+WU9npZunMpKGm4maxUW70s3fW7gUNSf5msVFu9Lt254rs0vGywUG25WaOkBpOVaquxWePU5MTeYwdN+FdWGkf+y1ftPfjQI3tv3/PAXNslmySNLpOVas3V2CWBDRaquV51BLqxozTcHFmp1nqxGns3q79LqieTlWqtFx2BlhKl4WcZULW21Mm8zWW/bPMc1wWUhofJSrXX7WTe+WsKtuO6gNLwsAyokdOq7Defk4ul4eLISiNnofJegN2A0hAyWWnktFtTcHpqkqs2n1xBRJKWyzKgRo5rCkqjx5GVhtJCk3zdDkQaPSYrDZ1OdhB2OxBptFgG1NBxkq80fhxZaej0cgfhYeVahxo3jqw0dHqxXuAwc61DjSOTlYbOuHf7WQbVOLIMqKEzLt1+7Up9lkE1jkxWGkqj3u23UMdju0nP41IG1XiyDCjV0EKlvnEvg2o8maykGlqo1Ldx7TTnn34i01OTBHDYqgkOXHkAb7r4WtZv2W6jhUaSyUqqocU6HjeuneaqzSfz7jNP4odzjzC7Z87OQI00k5VUQ52W+uwM1LiwwUKqoU47Hu0M1LgwWUk11UnHo52BGheWAaUhZmegxoUjK2mI9XqCtGsOqq5MVtKQ69UE6U62XpGqYhlQEmBnoerNkZUkoH+dhZYW1QuOrCQB/dl6xe1M1CsmK0lAfzoLLS2qVywDSgL6s/XKOE5atuzZHyYrSXv1euuVcZu0bEdl/9SuDBgRp0bEroi4KSI2Vx2PpKUbt0nLlj37p1Yjq4hYAfwv4IXAbcDXIuKyzPxWtZFJWopx2dW5YdBlz9UHP4ZXPeuYvpy7bmqVrIBnAjdl5ncBIuKjwGmAyUoaUqO+q3OzcSt7DlLdyoDTwPea7t9WHpOk2hu3sucg1W1kFS2O5X5PijgHOAfgmGPGYwgsqf4GUfYc18+/yNwvF1QmIp4D/F5mbijvnweQmee3e826detyx44dA4pQkvqu1X/aWxrRz7+Wf/66lQG/BpwQEcdFxGOAs4DLKo5JklSxWpUBM/OhiPjvwBXACuB9mXlDxWFJkipWq2QFkJmfBT5bdRySpPqoWxlQkqT9mKwkSbVnspIk1Z7JSpJUeyYrSVLtmawkSbVXqxUsliIidgP/0uPTPgG4u8fnXIq6xAHG0o6xtGYs++s0jrsz89ROThgRn+/0ucNu6JNVP0TEjsxcZxyPMpbWjKU1Y6lvHMPKMqAkqfZMVpKk2jNZtXZB1QGU6hIHGEs7xtKaseyvLnEMJb+zkiTVniMrSVLtmawkSbU3sskqIt4XEXdFxPVNx1ZHxBcj4jvl9WFNj50XETdFxK6I2NB0/BkRcV352F9ERJTHD4yIi8vjV0fEsW3i+PGI+FJE3BgRN0TEGyuM5aCI+GpEfKOM5e1VxdJ0nhURsTMiPlNlLBFxS3mOayNiR8WxTEXEJyLi2+Xfm+dU9PdlTfnzaFz+LSLOrSiWN5V/Z6+PiEKKSREAAAf1SURBVI+Uf5er+v28sYzjhog4tzxW2b+hsZGZI3kBngc8Hbi+6dgfAZvL25uBd5a3nwx8AzgQOA74Z2BF+dhXgedQbLX8OeBF5fHfAP6mvH0WcHGbOI4Enl7ePgT4f+X7VRFLAI8tb08AVwPPriKWpph+E/gw8Jmqfkfl47cAT5h3rKpYLgJ+vbz9GGCqyt9R+bwVwL8CPzHoWIBp4GZgsrz/MeC/VPEzAZ4KXA+sotgP8P8AJ1T9+xmHS+UB9PUPB8eyb7LaBRxZ3j4S2FXePg84r+l5V5R/iY4Evt10/JXA3zY/p7y9kmJmenQQ06XAC6uOpfzH9nXgWVXFAhwNbANO5tFkVVUst7B/shp4LMDjKD6Yo+pY5r3/LwBXVRELRbL6HrC6fM5nyniq+P2cAVzYdP93gTdX/fsZh8vIlgHbOCIz7wAor59YHm/8Y2i4rTw2Xd6ef3yf12TmQ8C9wOMXevNyOL+WYkRTSSxRlN2uBe4CvpiZlcUC/BnFP/RHmo5VFUsCX4iIayLinApjOR7YDfxdFOXRCyPi4IpiaXYW8JHy9kBjycwZ4I+BW4E7gHsz8wuDjqN0PfC8iHh8RKwCfhH48YpiGSvjlqzaiRbHcoHjC72m9RtEPBb4JHBuZv5bVbFk5sOZeRLFqOaZEfHUKmKJiBcDd2XmNQu8/0BiKa3PzKcDLwJeFxHPqyiWlRTl6/dk5lrgfoqyUhWxFE+OeAzwUuDjC8TRt1jK739OoyijHQUcHBGvHnQcAJl5I/BO4IvA5ylKfA9VEcu4GbdkdWdEHAlQXt9VHr+N4n9HDUcDt5fHj25xfJ/XRMRK4FDgB63eNCImKBLVhzLzkipjacjMWeBK4NSKYlkPvDQibgE+CpwcER+sKBYy8/by+i7gU8AzK4rlNuC2csQL8AmK5FXl35cXAV/PzDvL+4OO5QXAzZm5OzPngEuAn6sgDgAy872Z+fTMfF75nO9UFcs4GbdkdRlwdnn7bIrvjxrHzyq7cI6j+ML0q+Vw/r6IeHbZqfOr817TONfLge1ZFpmbla97L3BjZv5pxbEcHhFT5e1Jig+Bb1cRS2ael5lHZ+axFCWm7Zn56op+LgdHxCGN2xTfh1xf0c/lX4HvRcSa8tApwLeqiKXJK3m0BDj/9YOI5Vbg2RGxqnz9KcCNVf1MIuKJ5fUxwOnlz6bK3894qPpLs35dKP4C3QHMUfxP5bUUdd9tFP8T2gasbnr+71B06uyi7Mopj6+j+OD6Z+CveHTVj4MoyiI3UXT1HN8mjudSDOG/CVxbXn6xolieBuwsY7ke+J/l8YHHMi+u5/Nog0UVP5fjKco53wBuAH6nyp8LcBKwo/w9bQUOqzCWVcD3gUObjlXxO3o7xX+srgc+QNFdV9XP5P9S/AfiG8Apdfg3NA4Xl1uSJNXeuJUBJUlDyGQlSao9k5UkqfZMVpKk2jNZSZJqz2QlSao9k5XGXkT8fkS8oLx9brnm21LOc2FEPLm30UkCnGel+ouIlVks6DmI97oFWJeZdw/i/SR1xpGVBiIijo1iM8GLIuKbUWwuuCqKDei+XK52fkXT+mpXRsQfRsSXgTe2OecREfGpKDaT/EZE/Fx5fGt5vhvi0RXUiYh/j4g/iYivR8S2iDi8PP7+iHh5RLyBYqHUL0XEl8rH3hMRO6Jps8oF/oxXRsS6pvd6RxnXP0XEEYvE/JtRbOh3fTy6oV/jZ3ZhefxDEfGCiLgqik3+nlk+7+AoNhv9WhQrtZ+2jF+VVE9VL6HhZTwuFHuLJcXq5gDvAzYB/wgcXh47E3hfeftK4K8XOefFFKvYQ7E54KHl7dXl9STFcjaPL+8n8J/L2/8T+Kvy9vuBl5e3b6FpX6umc60oY3raAvFcSTEqa7zXS8rbfwS8tV3MwDOA64CDgcdSLPm0tvyZPQScSPEfy2vKn1tQrEK+tTzPHwKvLm9PUWzweXDVv3MvXnp5cWSlQfpeZl5V3v4gsIFi59UvRrHH1lvZdyXqixc538nAe2Dv1if3lsffEBHfAP6JYvXqE8rjjzSd84MU6zYu5hUR8XWKNRWfQrHzayd+RLFJIBRJ5tgFYn4u8KnMvD8z/51iVfH/WD7/5sy8LjMfoUhi2zIzKZJb45y/AGwuf4ZXUqwtd0yHcUpDYWXVAWiszP+C9D7ghsx8Tpvn39/tG0TE8ylWk39OZj4QEVdSfHh3Es/8cx0H/Dbws5l5T0S8f4FzzTdXJhWAh1n431qr/YsaHmy6/UjT/UeazhnAyzJzV4exSUPHkZUG6ZiIaCSmV1KMfA5vHIuIiYh4Shfn2wb8t/K1KyLicRRltXvKRPUk4NlNzz+AYssFgFcB/9DinPcBh5S3H0eRMO8tv3N6URexdRPzV4CN5Xd4BwO/TLGyd6euAF5fbjVBRKztQZxSrZisNEg3AmdHxDeB1cBfUiSPd5Zlu2spNtXr1BuBn4+I6yhKbU+h2L11Zfkef0CREBvuB54SEddQlON+v8U5LwA+FxFfysxvUJT/bqD4ruiqFs/v1n4xZ+bXKb43+ypwNXBhZu7s4px/AEwA34yI68v70kixdV0DERHHUuxZ9dQKY/j3zHxsVe8vaekcWUmSas+RlWovIn4HOGPe4Y9n5jsqiudTwHHzDr8lM6+oIp5WImID8M55h2/OzF+uIh5puUxWkqTaswwoSao9k5UkqfZMVpKk2jNZSZJq7/8DN3bBTyJgMUkAAAAASUVORK5CYII=\n",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"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": "markdown",
"metadata": {},
"source": [
"Double-click __here__ for the solution.\n",
"\n",
"<!-- Hint:\n",
"# 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())\n",
"\n",
"Correct answer:You can see that as Per Capita Income rises as the Hardship Index decreases. We see that the points on the scatter plot are somewhat closer to a straight line in the negative direction, so we have a negative correlation between the two variables. \n",
"-->\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Conclusion\n",
"\n",
"##### Now that you know how to do basic exploratory data analysis using SQL and python visualization tools, you can further explore this dataset to see how the variable `per_capita_income_` is related to `percent_households_below_poverty` and `percent_aged_16_unemployed`. Try to create interesting visualizations!"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Summary\n",
"\n",
"##### In this lab you learned how to store a real world data set from the internet in a database (Db2 on IBM Cloud), gain insights into data using SQL queries. You also visualized a portion of the data in the database to see what story it tells."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Copyright &copy; 2018 [cognitiveclass.ai](cognitiveclass.ai?utm_source=bducopyrightlink&utm_medium=dswb&utm_campaign=bdu). This notebook and its source code are released under the terms of the [MIT License](https://bigdatauniversity.com/mit-license/).\n"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python",
"language": "python",
"name": "conda-env-python-py"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.7"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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