Analyzing a real world data-set with SQL and Python.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<p style=\"text-align:center\">\n",
    "    <a href=\"https://skills.network/?utm_medium=Exinfluencer&utm_source=Exinfluencer&utm_content=000026UJ&utm_term=10006555&utm_id=NA-SkillsNetwork-Channel-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDB0201ENSkillsNetwork22-2022-01-01\" target=\"_blank\">\n",
    "    <img src=\"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/assets/logos/SN_web_lightmode.png\" width=\"200\" alt=\"Skills Network Logo\"  />\n",
    "    </a>\n",
    "</p>\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 SQLite database.\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",
    "\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": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext sql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv, 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": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Connected: @socioeconomic.db'"
      ]
     },
     "execution_count": 5,
     "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": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas\n",
    "df = pandas.read_csv('https://data.cityofchicago.org/resource/jcxq-k9xf.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": 11,
   "metadata": {},
   "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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql SELECT * FROM chicago_socioeconomic_data limit 5;\n"
   ]
  },
  {
   "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": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///socioeconomic.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>COUNT(*)</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>78</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(78,)]"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql Select  COUNT(*) 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": 16,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///socioeconomic.db\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "    <thead>\n",
       "        <tr>\n",
       "            <th>COUNT(*)</th>\n",
       "        </tr>\n",
       "    </thead>\n",
       "    <tbody>\n",
       "        <tr>\n",
       "            <td>38</td>\n",
       "        </tr>\n",
       "    </tbody>\n",
       "</table>"
      ],
      "text/plain": [
       "[(38,)]"
      ]
     },
     "execution_count": 16,
     "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": 17,
   "metadata": {},
   "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": 17,
     "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": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///socioeconomic.db\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": 20,
     "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": [
    "<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": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * sqlite:///socioeconomic.db\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": 28,
     "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": 30,
   "metadata": {},
   "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 600x600 with 3 Axes>"
      ]
     },
     "metadata": {},
     "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",
    "plot = sns.jointplot(x='per_capita_income_',y='hardship_index', data=income_vs_hardship.DataFrame())"
   ]
  },
  {
   "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",
    "| 2022-03-04        | 2.3     | Lakshmi Holla     | Made changes in markdown cells |\n",
    "| 2021-07-09        | 2.2     | Malika        | Updated connection string          |\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"
   ]
  }
 ],
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