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andres-abrigo/DB0201EN-Week4-2-2-PeerAssign-v5-py.ipynb

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DB0201EN-Week4-2-2-PeerAssign-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>Assignment: Notebook for Peer Assignment</font></h1>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Introduction\n",
"\n",
"Using this Python notebook you will:\n",
"1. Understand 3 Chicago datasets \n",
"1. Load the 3 datasets into 3 tables in a Db2 database\n",
"1. Execute SQL queries to answer assignment questions "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Understand the datasets \n",
"To complete the assignment problems in this notebook you will be using three datasets that are available on the city of Chicago's Data Portal:\n",
"1. <a href=\"https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-socioeconomic-indicators-in-C/kn9c-c2s2\">Socioeconomic Indicators in Chicago</a>\n",
"1. <a href=\"https://data.cityofchicago.org/Education/Chicago-Public-Schools-Progress-Report-Cards-2011-/9xs2-f89t\">Chicago Public Schools</a>\n",
"1. <a href=\"https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2\">Chicago Crime Data</a>\n",
"\n",
"### 1. Socioeconomic Indicators in Chicago\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",
"For this assignment you will use a snapshot of this dataset which can be downloaded from:\n",
"https://ibm.box.com/shared/static/05c3415cbfbtfnr2fx4atenb2sd361ze.csv\n",
"\n",
"A detailed description of this dataset and the original dataset can be obtained from the Chicago Data Portal at:\n",
"https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-socioeconomic-indicators-in-C/kn9c-c2s2\n",
"\n",
"\n",
"\n",
"### 2. Chicago Public Schools\n",
"\n",
"This dataset shows all school level performance data used to create CPS School Report Cards for the 2011-2012 school year. This dataset is provided by the city of Chicago's Data Portal.\n",
"\n",
"For this assignment you will use a snapshot of this dataset which can be downloaded from:\n",
"https://ibm.box.com/shared/static/f9gjvj1gjmxxzycdhplzt01qtz0s7ew7.csv\n",
"\n",
"A detailed description of this dataset and the original dataset can be obtained from the Chicago Data Portal at:\n",
"https://data.cityofchicago.org/Education/Chicago-Public-Schools-Progress-Report-Cards-2011-/9xs2-f89t\n",
"\n",
"\n",
"\n",
"\n",
"### 3. Chicago Crime Data \n",
"\n",
"This dataset reflects reported incidents of crime (with the exception of murders where data exists for each victim) that occurred in the City of Chicago from 2001 to present, minus the most recent seven days. \n",
"\n",
"This dataset is quite large - over 1.5GB in size with over 6.5 million rows. For the purposes of this assignment we will use a much smaller sample of this dataset which can be downloaded from:\n",
"https://ibm.box.com/shared/static/svflyugsr9zbqy5bmowgswqemfpm1x7f.csv\n",
"\n",
"A detailed description of this dataset and the original dataset can be obtained from the Chicago Data Portal at:\n",
"https://data.cityofchicago.org/Public-Safety/Crimes-2001-to-present/ijzp-q8t2\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Download the datasets\n",
"In many cases the dataset to be analyzed is available as a .CSV (comma separated values) file, perhaps on the internet. Click on the links below to download and save the datasets (.CSV files):\n",
"1. __CENSUS_DATA:__ https://ibm.box.com/shared/static/05c3415cbfbtfnr2fx4atenb2sd361ze.csv\n",
"1. __CHICAGO_PUBLIC_SCHOOLS__ https://ibm.box.com/shared/static/f9gjvj1gjmxxzycdhplzt01qtz0s7ew7.csv\n",
"1. __CHICAGO_CRIME_DATA:__ https://ibm.box.com/shared/static/svflyugsr9zbqy5bmowgswqemfpm1x7f.csv\n",
"\n",
"__NOTE:__ Ensure you have downloaded the datasets using the links above instead of directly from the Chicago Data Portal. The versions linked here are subsets of the original datasets and have some of the column names modified to be more database friendly which will make it easier to complete this assignment."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Store the datasets in database tables\n",
"To analyze the data using SQL, it first needs to be stored in the database.\n",
"\n",
"While it is easier to read the dataset into a Pandas dataframe and then PERSIST it into the database as we saw in Week 3 Lab 3, it results in mapping to default datatypes which may not be optimal for SQL querying. For example a long textual field may map to a CLOB instead of a VARCHAR. \n",
"\n",
"Therefore, __it is highly recommended to manually load the table using the database console LOAD tool, as indicated in Week 2 Lab 1 Part II__. The only difference with that lab is that in Step 5 of the instructions you will need to click on create \"(+) New Table\" and specify the name of the table you want to create and then click \"Next\". \n",
"\n",
"<img src = \"https://ibm.box.com/shared/static/uc4xjh1uxcc78ks1i18v668simioz4es.jpg\">\n",
"\n",
"##### Now open the Db2 console, open the LOAD tool, Select / Drag the .CSV file for the first dataset, Next create a New Table, and then follow the steps on-screen instructions to load the data. Name the new tables as folows:\n",
"1. __CENSUS_DATA__\n",
"1. __CHICAGO_PUBLIC_SCHOOLS__\n",
"1. __CHICAGO_CRIME_DATA__"
]
},
{
"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": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"The sql extension is already loaded. To reload it, use:\n",
" %reload_ext sql\n"
]
}
],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"In the next cell enter your db2 connection string. Recall you created Service Credentials for your Db2 instance in first lab in Week 3. From the __uri__ field of your Db2 service credentials copy everything after db2:// (except the double quote at the end) and paste it in the cell below after ibm_db_sa://\n",
"\n",
"<img src =\"https://ibm.box.com/shared/static/hzhkvdyinpupm2wfx49lkr71q9swbpec.jpg\">"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Connected: sfz51159@BLUDB'"
]
},
"execution_count": 8,
"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",
"%sql ibm_db_sa://sfz51159:r172j9g-gm6hgk1q@dashdb-txn-sbox-yp-dal09-08.services.dal.bluemix.net/BLUDB"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Problems\n",
"Now write and execute SQL queries to solve assignment problems\n",
"\n",
"### Problem 1\n",
"\n",
"##### Find the total number of crimes recorded in the CRIME table"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://sfz51159:***@dashdb-txn-sbox-yp-dal09-08.services.dal.bluemix.net/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <td>533</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(Decimal('533'),)]"
]
},
"execution_count": 31,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql \n",
"SELECT COUNT(*) FROM CHICAGO_CRIME_DATA"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 2\n",
"\n",
"##### Retrieve first 10 rows from the CRIME table\n"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://sfz51159:***@dashdb-txn-sbox-yp-dal09-08.services.dal.bluemix.net/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>id</th>\n",
" <th>case_number</th>\n",
" <th>DATE</th>\n",
" <th>block</th>\n",
" <th>iucr</th>\n",
" <th>primary_type</th>\n",
" <th>description</th>\n",
" <th>location_description</th>\n",
" <th>arrest</th>\n",
" <th>domestic</th>\n",
" <th>beat</th>\n",
" <th>district</th>\n",
" <th>ward</th>\n",
" <th>community_area_number</th>\n",
" <th>fbicode</th>\n",
" <th>x_coordinate</th>\n",
" <th>y_coordinate</th>\n",
" <th>YEAR</th>\n",
" <th>updatedon</th>\n",
" <th>latitude</th>\n",
" <th>longitude</th>\n",
" <th>location</th>\n",
" </tr>\n",
" <tr>\n",
" <td>3512276</td>\n",
" <td>HK587712</td>\n",
" <td>2004-08-28 17:50:56</td>\n",
" <td>047XX S KEDZIE AVE</td>\n",
" <td>890</td>\n",
" <td>THEFT</td>\n",
" <td>FROM BUILDING</td>\n",
" <td>SMALL RETAIL STORE</td>\n",
" <td>FALSE</td>\n",
" <td>FALSE</td>\n",
" <td>911</td>\n",
" <td>9</td>\n",
" <td>14</td>\n",
" <td>58</td>\n",
" <td>6</td>\n",
" <td>1155838</td>\n",
" <td>1873050</td>\n",
" <td>2004</td>\n",
" <td>2/10/2018 15:50</td>\n",
" <td>41.80744050</td>\n",
" <td>-87.70395585</td>\n",
" <td>(41.8074405, -87.703955849)</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3406613</td>\n",
" <td>HK456306</td>\n",
" <td>2004-06-26 12:40:00</td>\n",
" <td>009XX N CENTRAL PARK AVE</td>\n",
" <td>820</td>\n",
" <td>THEFT</td>\n",
" <td>$500 AND UNDER</td>\n",
" <td>OTHER</td>\n",
" <td>FALSE</td>\n",
" <td>FALSE</td>\n",
" <td>1112</td>\n",
" <td>11</td>\n",
" <td>27</td>\n",
" <td>23</td>\n",
" <td>6</td>\n",
" <td>1152206</td>\n",
" <td>1906127</td>\n",
" <td>2004</td>\n",
" <td>2/28/2018 15:56</td>\n",
" <td>41.89827996</td>\n",
" <td>-87.71640551</td>\n",
" <td>(41.898279962, -87.716405505)</td>\n",
" </tr>\n",
" <tr>\n",
" <td>8002131</td>\n",
" <td>HT233595</td>\n",
" <td>2011-04-04 05:45:00</td>\n",
" <td>043XX S WABASH AVE</td>\n",
" <td>820</td>\n",
" <td>THEFT</td>\n",
" <td>$500 AND UNDER</td>\n",
" <td>NURSING HOME/RETIREMENT HOME</td>\n",
" <td>FALSE</td>\n",
" <td>FALSE</td>\n",
" <td>221</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>6</td>\n",
" <td>1177436</td>\n",
" <td>1876313</td>\n",
" <td>2011</td>\n",
" <td>2/10/2018 15:50</td>\n",
" <td>41.81593313</td>\n",
" <td>-87.62464213</td>\n",
" <td>(41.815933131, -87.624642127)</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7903289</td>\n",
" <td>HT133522</td>\n",
" <td>2010-12-30 16:30:00</td>\n",
" <td>083XX S KINGSTON AVE</td>\n",
" <td>840</td>\n",
" <td>THEFT</td>\n",
" <td>FINANCIAL ID THEFT: OVER $300</td>\n",
" <td>RESIDENCE</td>\n",
" <td>FALSE</td>\n",
" <td>FALSE</td>\n",
" <td>423</td>\n",
" <td>4</td>\n",
" <td>7</td>\n",
" <td>46</td>\n",
" <td>6</td>\n",
" <td>1194622</td>\n",
" <td>1850125</td>\n",
" <td>2010</td>\n",
" <td>2/10/2018 15:50</td>\n",
" <td>41.74366532</td>\n",
" <td>-87.56246276</td>\n",
" <td>(41.743665322, -87.562462756)</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10402076</td>\n",
" <td>HZ138551</td>\n",
" <td>2016-02-02 19:30:00</td>\n",
" <td>033XX W 66TH ST</td>\n",
" <td>820</td>\n",
" <td>THEFT</td>\n",
" <td>$500 AND UNDER</td>\n",
" <td>ALLEY</td>\n",
" <td>FALSE</td>\n",
" <td>FALSE</td>\n",
" <td>831</td>\n",
" <td>8</td>\n",
" <td>15</td>\n",
" <td>66</td>\n",
" <td>6</td>\n",
" <td>1155240</td>\n",
" <td>1860661</td>\n",
" <td>2016</td>\n",
" <td>2/10/2018 15:50</td>\n",
" <td>41.77345530</td>\n",
" <td>-87.70648047</td>\n",
" <td>(41.773455295, -87.706480471)</td>\n",
" </tr>\n",
" <tr>\n",
" <td>7732712</td>\n",
" <td>HS540106</td>\n",
" <td>2010-09-29 07:59:00</td>\n",
" <td>006XX W CHICAGO AVE</td>\n",
" <td>810</td>\n",
" <td>THEFT</td>\n",
" <td>OVER $500</td>\n",
" <td>PARKING LOT/GARAGE(NON.RESID.)</td>\n",
" <td>FALSE</td>\n",
" <td>FALSE</td>\n",
" <td>1323</td>\n",
" <td>12</td>\n",
" <td>27</td>\n",
" <td>24</td>\n",
" <td>6</td>\n",
" <td>1171668</td>\n",
" <td>1905607</td>\n",
" <td>2010</td>\n",
" <td>2/10/2018 15:50</td>\n",
" <td>41.89644677</td>\n",
" <td>-87.64493868</td>\n",
" <td>(41.896446772, -87.644938678)</td>\n",
" </tr>\n",
" <tr>\n",
" <td>10769475</td>\n",
" <td>HZ534771</td>\n",
" <td>2016-11-30 01:15:00</td>\n",
" <td>050XX N KEDZIE AVE</td>\n",
" <td>810</td>\n",
" <td>THEFT</td>\n",
" <td>OVER $500</td>\n",
" <td>STREET</td>\n",
" <td>FALSE</td>\n",
" <td>FALSE</td>\n",
" <td>1713</td>\n",
" <td>17</td>\n",
" <td>33</td>\n",
" <td>14</td>\n",
" <td>6</td>\n",
" <td>1154133</td>\n",
" <td>1933314</td>\n",
" <td>2016</td>\n",
" <td>2/10/2018 15:50</td>\n",
" <td>41.97284491</td>\n",
" <td>-87.70860008</td>\n",
" <td>(41.972844913, -87.708600079)</td>\n",
" </tr>\n",
" <tr>\n",
" <td>4494340</td>\n",
" <td>HL793243</td>\n",
" <td>2005-12-16 16:45:00</td>\n",
" <td>005XX E PERSHING RD</td>\n",
" <td>860</td>\n",
" <td>THEFT</td>\n",
" <td>RETAIL THEFT</td>\n",
" <td>GROCERY FOOD STORE</td>\n",
" <td>TRUE</td>\n",
" <td>FALSE</td>\n",
" <td>213</td>\n",
" <td>2</td>\n",
" <td>3</td>\n",
" <td>38</td>\n",
" <td>6</td>\n",
" <td>1180448</td>\n",
" <td>1879234</td>\n",
" <td>2005</td>\n",
" <td>2/28/2018 15:56</td>\n",
" <td>41.82387989</td>\n",
" <td>-87.61350386</td>\n",
" <td>(41.823879885, -87.613503857)</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3778925</td>\n",
" <td>HL149610</td>\n",
" <td>2005-01-28 17:00:00</td>\n",
" <td>100XX S WASHTENAW AVE</td>\n",
" <td>810</td>\n",
" <td>THEFT</td>\n",
" <td>OVER $500</td>\n",
" <td>STREET</td>\n",
" <td>FALSE</td>\n",
" <td>FALSE</td>\n",
" <td>2211</td>\n",
" <td>22</td>\n",
" <td>19</td>\n",
" <td>72</td>\n",
" <td>6</td>\n",
" <td>1160129</td>\n",
" <td>1838040</td>\n",
" <td>2005</td>\n",
" <td>2/28/2018 15:56</td>\n",
" <td>41.71128051</td>\n",
" <td>-87.68917910</td>\n",
" <td>(41.711280513, -87.689179097)</td>\n",
" </tr>\n",
" <tr>\n",
" <td>3324217</td>\n",
" <td>HK361551</td>\n",
" <td>2004-05-13 14:15:00</td>\n",
" <td>033XX W BELMONT AVE</td>\n",
" <td>820</td>\n",
" <td>THEFT</td>\n",
" <td>$500 AND UNDER</td>\n",
" <td>SMALL RETAIL STORE</td>\n",
" <td>FALSE</td>\n",
" <td>FALSE</td>\n",
" <td>1733</td>\n",
" <td>17</td>\n",
" <td>35</td>\n",
" <td>21</td>\n",
" <td>6</td>\n",
" <td>1153590</td>\n",
" <td>1921084</td>\n",
" <td>2004</td>\n",
" <td>2/28/2018 15:56</td>\n",
" <td>41.93929582</td>\n",
" <td>-87.71092344</td>\n",
" <td>(41.939295821, -87.710923442)</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(3512276, 'HK587712', datetime.datetime(2004, 8, 28, 17, 50, 56), '047XX S KEDZIE AVE', '890', 'THEFT', 'FROM BUILDING', 'SMALL RETAIL STORE', 'FALSE', 'FALSE', 911, 9, 14, 58, '6', 1155838, 1873050, 2004, '2/10/2018 15:50', Decimal('41.80744050'), Decimal('-87.70395585'), '(41.8074405, -87.703955849)'),\n",
" (3406613, 'HK456306', datetime.datetime(2004, 6, 26, 12, 40), '009XX N CENTRAL PARK AVE', '820', 'THEFT', '$500 AND UNDER', 'OTHER', 'FALSE', 'FALSE', 1112, 11, 27, 23, '6', 1152206, 1906127, 2004, '2/28/2018 15:56', Decimal('41.89827996'), Decimal('-87.71640551'), '(41.898279962, -87.716405505)'),\n",
" (8002131, 'HT233595', datetime.datetime(2011, 4, 4, 5, 45), '043XX S WABASH AVE', '820', 'THEFT', '$500 AND UNDER', 'NURSING HOME/RETIREMENT HOME', 'FALSE', 'FALSE', 221, 2, 3, 38, '6', 1177436, 1876313, 2011, '2/10/2018 15:50', Decimal('41.81593313'), Decimal('-87.62464213'), '(41.815933131, -87.624642127)'),\n",
" (7903289, 'HT133522', datetime.datetime(2010, 12, 30, 16, 30), '083XX S KINGSTON AVE', '840', 'THEFT', 'FINANCIAL ID THEFT: OVER $300', 'RESIDENCE', 'FALSE', 'FALSE', 423, 4, 7, 46, '6', 1194622, 1850125, 2010, '2/10/2018 15:50', Decimal('41.74366532'), Decimal('-87.56246276'), '(41.743665322, -87.562462756)'),\n",
" (10402076, 'HZ138551', datetime.datetime(2016, 2, 2, 19, 30), '033XX W 66TH ST', '820', 'THEFT', '$500 AND UNDER', 'ALLEY', 'FALSE', 'FALSE', 831, 8, 15, 66, '6', 1155240, 1860661, 2016, '2/10/2018 15:50', Decimal('41.77345530'), Decimal('-87.70648047'), '(41.773455295, -87.706480471)'),\n",
" (7732712, 'HS540106', datetime.datetime(2010, 9, 29, 7, 59), '006XX W CHICAGO AVE', '810', 'THEFT', 'OVER $500', 'PARKING LOT/GARAGE(NON.RESID.)', 'FALSE', 'FALSE', 1323, 12, 27, 24, '6', 1171668, 1905607, 2010, '2/10/2018 15:50', Decimal('41.89644677'), Decimal('-87.64493868'), '(41.896446772, -87.644938678)'),\n",
" (10769475, 'HZ534771', datetime.datetime(2016, 11, 30, 1, 15), '050XX N KEDZIE AVE', '810', 'THEFT', 'OVER $500', 'STREET', 'FALSE', 'FALSE', 1713, 17, 33, 14, '6', 1154133, 1933314, 2016, '2/10/2018 15:50', Decimal('41.97284491'), Decimal('-87.70860008'), '(41.972844913, -87.708600079)'),\n",
" (4494340, 'HL793243', datetime.datetime(2005, 12, 16, 16, 45), '005XX E PERSHING RD', '860', 'THEFT', 'RETAIL THEFT', 'GROCERY FOOD STORE', 'TRUE', 'FALSE', 213, 2, 3, 38, '6', 1180448, 1879234, 2005, '2/28/2018 15:56', Decimal('41.82387989'), Decimal('-87.61350386'), '(41.823879885, -87.613503857)'),\n",
" (3778925, 'HL149610', datetime.datetime(2005, 1, 28, 17, 0), '100XX S WASHTENAW AVE', '810', 'THEFT', 'OVER $500', 'STREET', 'FALSE', 'FALSE', 2211, 22, 19, 72, '6', 1160129, 1838040, 2005, '2/28/2018 15:56', Decimal('41.71128051'), Decimal('-87.68917910'), '(41.711280513, -87.689179097)'),\n",
" (3324217, 'HK361551', datetime.datetime(2004, 5, 13, 14, 15), '033XX W BELMONT AVE', '820', 'THEFT', '$500 AND UNDER', 'SMALL RETAIL STORE', 'FALSE', 'FALSE', 1733, 17, 35, 21, '6', 1153590, 1921084, 2004, '2/28/2018 15:56', Decimal('41.93929582'), Decimal('-87.71092344'), '(41.939295821, -87.710923442)')]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql \n",
"SELECT * FROM CHICAGO_CRIME_DATA LIMIT 10"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 3\n",
"\n",
"##### How many crimes involve an arrest?"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://sfz51159:***@dashdb-txn-sbox-yp-dal09-08.services.dal.bluemix.net/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <td>163</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(Decimal('163'),)]"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql \n",
"SELECT COUNT(*) FROM CHICAGO_CRIME_DATA WHERE CHICAGO_CRIME_DATA.ARREST = TRUE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 4\n",
"\n",
"##### Which unique types of crimes have been recorded at GAS STATION locations?\n"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://sfz51159:***@dashdb-txn-sbox-yp-dal09-08.services.dal.bluemix.net/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <td>4</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(Decimal('4'),)]"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT COUNT(DISTINCT CHICAGO_CRIME_DATA.PRIMARY_TYPE) FROM CHICAGO_CRIME_DATA WHERE CHICAGO_CRIME_DATA.LOCATION_DESCRIPTION = 'GAS STATION' "
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Hint: Which column lists types of crimes e.g. THEFT?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 5\n",
"\n",
"##### In the CENUS_DATA table list all Community Areas whose names start with the letter ‘B’."
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://sfz51159:***@dashdb-txn-sbox-yp-dal09-08.services.dal.bluemix.net/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>community_area_name</th>\n",
" </tr>\n",
" <tr>\n",
" <td>Belmont Cragin</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Burnside</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Brighton Park</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Bridgeport</td>\n",
" </tr>\n",
" <tr>\n",
" <td>Beverly</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[('Belmont Cragin',),\n",
" ('Burnside',),\n",
" ('Brighton Park',),\n",
" ('Bridgeport',),\n",
" ('Beverly',)]"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql \n",
"SELECT CENSUS_DATA.COMMUNITY_AREA_NAME FROM CENSUS_DATA WHERE CENSUS_DATA.COMMUNITY_AREA_NAME LIKE 'B%'"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 6\n",
"\n",
"##### Which schools in Community Areas 10 to 15 are healthy school certified?"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://sfz51159:***@dashdb-txn-sbox-yp-dal09-08.services.dal.bluemix.net/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>name_of_school</th>\n",
" </tr>\n",
" <tr>\n",
" <td>Rufus M Hitch Elementary School</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[('Rufus M Hitch Elementary School',)]"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT CHICAGO_PUBLIC_SCHOOLS.NAME_OF_SCHOOL \n",
"FROM CHICAGO_PUBLIC_SCHOOLS \n",
"WHERE CHICAGO_PUBLIC_SCHOOLS.COMMUNITY_AREA_NUMBER >= 10 \n",
"AND CHICAGO_PUBLIC_SCHOOLS.COMMUNITY_AREA_NUMBER <= 15 \n",
"AND CHICAGO_PUBLIC_SCHOOLS.HEALTHY_SCHOOL_CERTIFIED = TRUE"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 7\n",
"\n",
"##### What is the average school Safety Score? "
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://sfz51159:***@dashdb-txn-sbox-yp-dal09-08.services.dal.bluemix.net/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>1</th>\n",
" </tr>\n",
" <tr>\n",
" <td>49.504873</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(Decimal('49.504873'),)]"
]
},
"execution_count": 20,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT AVG(CHICAGO_PUBLIC_SCHOOLS.SAFETY_SCORE) FROM CHICAGO_PUBLIC_SCHOOLS"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 8\n",
"\n",
"##### List the top 5 Community Areas by average College Enrollment [number of students] "
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://sfz51159:***@dashdb-txn-sbox-yp-dal09-08.services.dal.bluemix.net/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>community_area_name</th>\n",
" <th>average</th>\n",
" </tr>\n",
" <tr>\n",
" <td>ARCHER HEIGHTS</td>\n",
" <td>2411.500000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>MONTCLARE</td>\n",
" <td>1317.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>WEST ELSDON</td>\n",
" <td>1233.333333</td>\n",
" </tr>\n",
" <tr>\n",
" <td>BRIGHTON PARK</td>\n",
" <td>1205.875000</td>\n",
" </tr>\n",
" <tr>\n",
" <td>BELMONT CRAGIN</td>\n",
" <td>1198.833333</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[('ARCHER HEIGHTS', Decimal('2411.500000')),\n",
" ('MONTCLARE', Decimal('1317.000000')),\n",
" ('WEST ELSDON', Decimal('1233.333333')),\n",
" ('BRIGHTON PARK', Decimal('1205.875000')),\n",
" ('BELMONT CRAGIN', Decimal('1198.833333'))]"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql \n",
"SELECT CHICAGO_PUBLIC_SCHOOLS.COMMUNITY_AREA_NAME, \n",
"AVG(CHICAGO_PUBLIC_SCHOOLS.COLLEGE_ENROLLMENT) AS AVERAGE \n",
"FROM CHICAGO_PUBLIC_SCHOOLS \n",
"GROUP BY CHICAGO_PUBLIC_SCHOOLS.COMMUNITY_AREA_NAME \n",
"ORDER BY AVG(CHICAGO_PUBLIC_SCHOOLS.COLLEGE_ENROLLMENT) DESC \n",
"LIMIT 5"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 9\n",
"\n",
"##### Use a sub-query to determine which Community Area has the least value for school Safety Score? "
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://sfz51159:***@dashdb-txn-sbox-yp-dal09-08.services.dal.bluemix.net/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>community_area_number</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>40</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</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(40, 'Washington Park', Decimal('5.6'), Decimal('42.1'), Decimal('28.6'), Decimal('25.4'), Decimal('42.8'), 13785, 88)]"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT *\n",
"FROM CENSUS_DATA \n",
"WHERE CENSUS_DATA.COMMUNITY_AREA_NUMBER \n",
"IN (SELECT CHICAGO_PUBLIC_SCHOOLS.COMMUNITY_AREA_NUMBER \n",
" FROM CHICAGO_PUBLIC_SCHOOLS\n",
" WHERE CHICAGO_PUBLIC_SCHOOLS.SAFETY_SCORE IN (SELECT MIN(CHICAGO_PUBLIC_SCHOOLS.SAFETY_SCORE) FROM CHICAGO_PUBLIC_SCHOOLS ))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 10\n",
"\n",
"##### [Without using an explicit JOIN operator] Find the Per Capita Income of the Community Area which has a school Safety Score of 1."
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://sfz51159:***@dashdb-txn-sbox-yp-dal09-08.services.dal.bluemix.net/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <tr>\n",
" <th>per_capita_income</th>\n",
" </tr>\n",
" <tr>\n",
" <td>13785</td>\n",
" </tr>\n",
"</table>"
],
"text/plain": [
"[(13785,)]"
]
},
"execution_count": 35,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%%sql\n",
"SELECT CENSUS_DATA.PER_CAPITA_INCOME \n",
"FROM CENSUS_DATA\n",
"WHERE CENSUS_DATA.COMMUNITY_AREA_NUMBER IN \n",
"(SELECT CHICAGO_PUBLIC_SCHOOLS.COMMUNITY_AREA_NUMBER FROM CHICAGO_PUBLIC_SCHOOLS WHERE CHICAGO_PUBLIC_SCHOOLS.SAFETY_SCORE = 1)"
]
},
{
"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": "3.6.10"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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