ChicagoDataset_DataAnalysis_with_SQL_Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Analyzing a real world data-set with SQL and Python\n",
    "\n",
    "Estimated time needed: **15** minutes\n",
    "\n",
    "## Objectives\n",
    "\n",
    "\n",
    "*   Understand a dataset of selected socioeconomic indicators in Chicago\n",
    "*   Read Dataset using Python and store data in an SQLite database.\n",
    "*   Solve example problems to practice SQL skills\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## 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"
   ]
  },
  {
   "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",
    "\n",
    "##### The syntax for connecting to magic sql using sqllite is\n",
    "\n",
    "  **%sql sqlite://DatabaseName**   \n",
    "  \n",
    "where DatabaseName will be your **.db** file \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "tags": []
   },
   "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": "code",
   "execution_count": 40,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import sqlite3\n",
    "\n",
    "con = sqlite3.connect(\"socioeconomic.db\")\n",
    "cur = con.cursor()\n",
    "!pip install -q pandas==1.1.5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Connected: @socioeconomic.db'"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql sqlite:///socioeconomic.db"
   ]
  },
  {
   "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 csv files  from the given url  into pandas dataframes\n",
    "\n",
    "##### Next we will be using the  df.to_sql() function to convert each csv file  to a table in sqlite  with the csv data loaded in it.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas\n",
    "df = pandas.read_csv('Chicago_dataset.csv')\n",
    "df.to_sql(\"chicago_socioeconomic_data\", con, if_exists='replace', index=False,method=\"multi\")\n"
   ]
  },
  {
   "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": 43,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///socioeconomic.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\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>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>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>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>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>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": [
       "[(1.0, 'Rogers Park', 7.7, 23.6, 8.7, 18.2, 27.5, 23939, 39.0),\n",
       " (2.0, 'West Ridge', 7.8, 17.2, 8.8, 20.8, 38.5, 23040, 46.0),\n",
       " (3.0, 'Uptown', 3.8, 24.0, 8.9, 11.8, 22.2, 35787, 20.0),\n",
       " (4.0, 'Lincoln Square', 3.4, 10.9, 8.2, 13.4, 25.5, 37524, 17.0),\n",
       " (5.0, 'North Center', 0.3, 7.5, 5.2, 4.5, 26.2, 57123, 6.0)]"
      ]
     },
     "execution_count": 43,
     "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": 44,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///socioeconomic.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>TOTAL_ROWS</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>78</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(78,)]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql SELECT COUNT(*) AS TOTAL_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?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///socioeconomic.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>COMMUNITY_AREAS_WITH_BETTER_HARDSHIP_INDEX</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>38</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(38,)]"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "SELECT COUNT(community_area_name) AS COMMUNITY_AREAS_WITH_BETTER_HARDSHIP_INDEX\n",
    "FROM chicago_socioeconomic_data\n",
    "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?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///socioeconomic.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>MAX_HARDSHIP_INDEX</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": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "SELECT MAX(hardship_index) AS MAX_HARDSHIP_INDEX\n",
    "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": 48,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///socioeconomic.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>COMMUNITY_WITH_HIGHEST_INDEX</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Riverdale</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Riverdale',)]"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "SELECT community_area_name AS COMMUNITY_WITH_HIGHEST_INDEX\n",
    "FROM chicago_socioeconomic_data\n",
    "WHERE hardship_index = (\n",
    "    SELECT MAX(hardship_index)\n",
    "    FROM chicago_socioeconomic_data\n",
    ");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///socioeconomic.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>COMMUNITY_WITH_HIGHEST_INDEX</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>Riverdale</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[('Riverdale',)]"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "-- Another Technique to Retrieve the information\n",
    "\n",
    "SELECT community_area_name AS COMMUNITY_WITH_HIGHEST_INDEX\n",
    "FROM chicago_socioeconomic_data\n",
    "ORDER BY hardship_index DESC\n",
    "LIMIT 1;"
   ]
  },
  {
   "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": 50,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///socioeconomic.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>COMMUNITY_WITH_PCI_GT_$60000</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": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%%sql\n",
    "\n",
    "SELECT community_area_name AS COMMUNITY_WITH_PCI_GT_$60000\n",
    "FROM chicago_socioeconomic_data\n",
    "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.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///socioeconomic.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1000x1000 with 3 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "import seaborn as sns\n",
    "\n",
    "perCapitaIncome_vs_hardshipIndex = %sql SELECT per_capita_income_, hardship_index FROM chicago_socioeconomic_data;\n",
    "dfCopy = perCapitaIncome_vs_hardshipIndex.DataFrame()\n",
    "\n",
    "plot = sns.jointplot(x ='per_capita_income_', y='hardship_index', data = dfCopy, height=10, ratio=2)\n",
    "\n",
    "# Rename the axis labels\n",
    "plot.set_axis_labels('Per Capita Income (USD)', 'Hardship Index')\n",
    "\n",
    "# Adjust layout\n",
    "plt.tight_layout()\n",
    "\n",
    "# Display the plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "With the increase in Per Capita Income, the Hardship index is decreasing. Also, we can notice that the points in scatter plot are somehow close to a straight line. Hence, two variables are strongly negative correlated."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This project was conducted as a part of my certification program at Coursera on \"IBM Professional Data Analyst\" ."
   ]
  },
  {
   "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": "3.7.12"
  },
  "prev_pub_hash": "09ce986e3758d87b2cada309c08f7752eec5d24e24ca1cd547400d918772eeab"
 },
 "nbformat": 4,
 "nbformat_minor": 4
}