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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": 2,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Connected: ptw45118@BLUDB'"
]
},
"execution_count": 2,
"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://ptw45118:jvv%40j81ln9dm07dv@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.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": 3,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://ptw45118:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\n"
]
},
{
"data": {
"text/plain": [
"'Persisted chicago_socioeconomic_data'"
]
},
"execution_count": 3,
"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": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://ptw45118:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.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": 4,
"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": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://ptw45118:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.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": 5,
"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": 7,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://ptw45118:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.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": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT COUNT(community_area_name) 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": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://ptw45118:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.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": 8,
"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": 12,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://ptw45118:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.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": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT community_area_name FROM chicago_socioeconomic_data WHERE hardship_index = (SELECT MAX(hardship_index) FROM chicago_socioeconomic_data)"
]
},
{
"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://ptw45118:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.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": 14,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql SELECT community_area_name 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": 18,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"\n",
"\n",
"plot = sns.jointplot(x=\"per_capita_income_\", y= \"hardship_index\", data = chicago_socioeconomic_data)"
]
},
{
"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"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"\n",
"\n",
"plot = sns.jointplot(x=\"per_capita_income_\", y= \"percent_households_below_poverty\", data = chicago_socioeconomic_data)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x432 with 3 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"import seaborn as sns\n",
"\n",
"\n",
"plot = sns.jointplot(x=\"per_capita_income_\", y= \"percent_aged_16_unemployed\", data = chicago_socioeconomic_data)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": []
}
],
"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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