Analyzing  Socioeconomic data to the Chicago City Portal with SQL and Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<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: fhh86766@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://fhh86766:t5%2Bftpnsqmncjcd3@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://fhh86766:***@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://fhh86766:***@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": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://fhh86766:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>Rows</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(*) as \"Rows\" FROM chicago_socioeconomic_data;"
   ]
  },
  {
   "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://fhh86766:***@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 Areas</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>38</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(Decimal('38'),)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql SELECT COUNT(*) as \"Community Areas\" FROM chicago_socioeconomic_data where hardship_index>50.0 ;"
   ]
  },
  {
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://fhh86766:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <tr>\n",
       "        <th>Maximum_hardship_index</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "        <td>98.0</td>\n",
       "    </tr>\n",
       "</table>"
      ],
      "text/plain": [
       "[(98.0,)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql SELECT MAX(hardship_index) as \"Maximum_hardship_index\" FROM chicago_socioeconomic_data;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Problem 4\n",
    "\n",
    "##### Which community area which has the highest hardship index?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://fhh86766:***@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": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql select community_area_name from chicago_socioeconomic_data where hardship_index=98.0;\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://fhh86766:***@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": 18,
     "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": [
    "### Problem 5\n",
    "\n",
    "##### Which Chicago community areas have per-capita incomes greater than $60,000?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://fhh86766:***@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": 21,
     "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": [
    "### 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": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://fhh86766:***@dashdb-txn-sbox-yp-lon02-02.services.eu-gb.bluemix.net:50000/BLUDB\n",
      "Done.\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",
    "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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\n",
    "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`. "
   ]
  },
  {
   "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."
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.7"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}