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DB0201EN-Week3-1-4-Analyzing-v5-py.ipynb
{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<center>\n",
" <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-DB0201EN-SkillsNetwork/labs/Module%205/images/IDSNlogo.png\" width=\"300\" alt=\"cognitiveclass.ai logo\" />\n",
"</center>\n",
"\n",
"# Analyzing a real world data-set with SQL and Python\n",
"\n",
"Estimated time needed: **15** minutes\n",
"\n",
"## Objectives\n",
"\n",
"After completing this lab you will be able to:\n",
"\n",
"- Understand a dataset of selected socioeconomic indicators in Chicago\n",
"- Learn how to store data in an Db2 database on IBM Cloud instance\n",
"- Solve example problems to practice your SQL skills\n"
]
},
{
"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](https://data.cityofchicago.org/Health-Human-Services/Census-Data-Selected-socioeconomic-indicators-in-C/kn9c-c2s2?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDB0201ENSkillsNetwork20127838-2021-01-01), 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",
"\n",
"Let us first load the SQL extension and establish a connection with the database\n"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Requirement already satisfied: sqlalchemy==1.3.9 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (1.3.9)\n",
"Requirement already satisfied: ibm_db_sa in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (0.3.3)\n",
"Requirement already satisfied: sqlalchemy>=0.7.3 in /home/jupyterlab/conda/envs/python/lib/python3.6/site-packages (from ibm_db_sa) (1.3.9)\n"
]
}
],
"source": [
"!pip install sqlalchemy==1.3.9\n",
"!pip install ibm_db_sa"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"%load_ext sql"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'Connected: cxm49223@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://cxm49223:f0d5n-tk66qd9w61@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",
"\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\n"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://cxm49223:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.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:\n"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://cxm49223:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <thead>\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",
" </thead>\n",
" <tbody>\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",
" </tbody>\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?\n"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://cxm49223:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <thead>\n",
" <tr>\n",
" <th>1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>78</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"[(Decimal('78'),)]"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select count(1) from chicago_socioeconomic_data"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details><summary>Click here for the solution</summary>\n",
"\n",
"```python\n",
"%sql SELECT COUNT(*) FROM chicago_socioeconomic_data;\n",
"\n",
"Correct answer: 78\n",
"```\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 2\n",
"\n",
"##### How many community areas in Chicago have a hardship index greater than 50.0?\n"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://cxm49223:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <thead>\n",
" <tr>\n",
" <th>1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>38</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>"
],
"text/plain": [
"[(Decimal('38'),)]"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%sql select count(*) from chicago_socioeconomic_data where hardship_index > 50"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details><summary>Click here for the solution</summary>\n",
"\n",
"```python\n",
"%sql SELECT COUNT(*) FROM chicago_socioeconomic_data WHERE hardship_index > 50.0;\n",
"\n",
"Correct answer: 38\n",
"```\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 3\n",
"\n",
"##### What is the maximum value of hardship index in this dataset?\n"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://cxm49223:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <thead>\n",
" <tr>\n",
" <th>1</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>98.0</td>\n",
" </tr>\n",
" </tbody>\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": [
"<details><summary>Click here for the solution</summary>\n",
"\n",
"```python\n",
"%sql SELECT MAX(hardship_index) FROM chicago_socioeconomic_data;\n",
"\n",
"Correct answer: 98.0\n",
"```\n",
"\n",
"</details>\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": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://cxm49223:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <thead>\n",
" <tr>\n",
" <th>community_area_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <td>Riverdale</td>\n",
" </tr>\n",
" </tbody>\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 in (select max(hardship_index) from chicago_socioeconomic_data)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details><summary>Click here for the solution</summary>\n",
"\n",
"```python\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",
" \n",
"```\n",
"\n",
"</details>\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Problem 5\n",
"\n",
"##### Which Chicago community areas have per-capita incomes greater than $60,000?\n"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://cxm49223:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\n",
"Done.\n"
]
},
{
"data": {
"text/html": [
"<table>\n",
" <thead>\n",
" <tr>\n",
" <th>community_area_name</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\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",
" </tbody>\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": [
"<details><summary>Click here for the solution</summary>\n",
"\n",
"```python\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",
"```\n",
"\n",
"</details>\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.\n"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {
"tags": []
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
" * ibm_db_sa://cxm49223:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\n",
"Done.\n",
"<class 'sql.run.ResultSet'>\n"
]
},
{
"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 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",
"print(type(income_vs_hardship))\n",
"plot = sns.jointplot(x='per_capita_income_',y='hardship_index', data=income_vs_hardship.DataFrame())\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"<details><summary>Click here for the solution</summary>\n",
"\n",
"```python\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",
"\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",
"```\n",
"\n",
"</details>\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!\n"
]
},
{
"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.\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Author\n",
"\n",
"<a href=\"https://www.linkedin.com/in/ravahuja/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDB0201ENSkillsNetwork20127838-2021-01-01\" target=\"_blank\">Rav Ahuja</a>\n",
"\n",
"## Change Log\n",
"\n",
"| Date (YYYY-MM-DD) | Version | Changed By | Change Description |\n",
"| ----------------- | ------- | ------------- | ---------------------------------- |\n",
"| 2021-05-06 | 2.1 | Malika Singla | Added libraries |\n",
"| 2020-08-28 | 2.0 | Lavanya | Moved lab to course repo in GitLab |\n",
"\n",
"<hr>\n",
"\n",
"## <h3 align=\"center\"> © IBM Corporation 2020. All rights reserved. <h3/>\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": "3.6.13"
},
"widgets": {
"state": {},
"version": "1.1.2"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
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