4 - 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-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDB0201ENSkillsNetwork20127838-2022-01-01\" 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"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\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-SkillsNetworkCoursesIBMDeveloperSkillsNetworkDB0201ENSkillsNetwork20127838-2022-01-01), 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 notebook, 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"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "%load_ext sql"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The connection string is of the format:\n",
    "# %sql ibm_db_sa://my-username:my-password@hostname:port/BLUDB?security=SSL\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",
    "\n",
    "#username = hhf49271\n",
    "#password = Rrplpk5JraUR4qsg\n",
    "#hostname = 54a2f15b-5c0f-46df-8954-7e38e612c2bd.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud\n",
    "#port = 32733\n",
    "\n",
    "%sql ibm_db_sa://hhf49271:Rrplpk5JraUR4qsg@54a2f15b-5c0f-46df-8954-7e38e612c2bd.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32733/BLUDB?security=SSL"
   ]
  },
  {
   "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": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></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",
       "      <th>0</th>\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",
       "      <th>1</th>\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",
       "      <th>2</th>\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",
       "      <th>3</th>\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",
       "      <th>4</th>\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",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>73</th>\n",
       "      <td>74.0</td>\n",
       "      <td>Mount Greenwood</td>\n",
       "      <td>1.0</td>\n",
       "      <td>3.4</td>\n",
       "      <td>8.7</td>\n",
       "      <td>4.3</td>\n",
       "      <td>36.8</td>\n",
       "      <td>34381</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>74</th>\n",
       "      <td>75.0</td>\n",
       "      <td>Morgan Park</td>\n",
       "      <td>0.8</td>\n",
       "      <td>13.2</td>\n",
       "      <td>15.0</td>\n",
       "      <td>10.8</td>\n",
       "      <td>40.3</td>\n",
       "      <td>27149</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75</th>\n",
       "      <td>76.0</td>\n",
       "      <td>O'Hare</td>\n",
       "      <td>3.6</td>\n",
       "      <td>15.4</td>\n",
       "      <td>7.1</td>\n",
       "      <td>10.9</td>\n",
       "      <td>30.3</td>\n",
       "      <td>25828</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>76</th>\n",
       "      <td>77.0</td>\n",
       "      <td>Edgewater</td>\n",
       "      <td>4.1</td>\n",
       "      <td>18.2</td>\n",
       "      <td>9.2</td>\n",
       "      <td>9.7</td>\n",
       "      <td>23.8</td>\n",
       "      <td>33385</td>\n",
       "      <td>19.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>77</th>\n",
       "      <td>NaN</td>\n",
       "      <td>CHICAGO</td>\n",
       "      <td>4.7</td>\n",
       "      <td>19.7</td>\n",
       "      <td>12.9</td>\n",
       "      <td>19.5</td>\n",
       "      <td>33.5</td>\n",
       "      <td>28202</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>78 rows × 9 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      ca community_area_name  percent_of_housing_crowded  \\\n",
       "0    1.0         Rogers Park                         7.7   \n",
       "1    2.0          West Ridge                         7.8   \n",
       "2    3.0              Uptown                         3.8   \n",
       "3    4.0      Lincoln Square                         3.4   \n",
       "4    5.0        North Center                         0.3   \n",
       "..   ...                 ...                         ...   \n",
       "73  74.0     Mount Greenwood                         1.0   \n",
       "74  75.0         Morgan Park                         0.8   \n",
       "75  76.0              O'Hare                         3.6   \n",
       "76  77.0           Edgewater                         4.1   \n",
       "77   NaN             CHICAGO                         4.7   \n",
       "\n",
       "    percent_households_below_poverty  percent_aged_16_unemployed  \\\n",
       "0                               23.6                         8.7   \n",
       "1                               17.2                         8.8   \n",
       "2                               24.0                         8.9   \n",
       "3                               10.9                         8.2   \n",
       "4                                7.5                         5.2   \n",
       "..                               ...                         ...   \n",
       "73                               3.4                         8.7   \n",
       "74                              13.2                        15.0   \n",
       "75                              15.4                         7.1   \n",
       "76                              18.2                         9.2   \n",
       "77                              19.7                        12.9   \n",
       "\n",
       "    percent_aged_25_without_high_school_diploma  \\\n",
       "0                                          18.2   \n",
       "1                                          20.8   \n",
       "2                                          11.8   \n",
       "3                                          13.4   \n",
       "4                                           4.5   \n",
       "..                                          ...   \n",
       "73                                          4.3   \n",
       "74                                         10.8   \n",
       "75                                         10.9   \n",
       "76                                          9.7   \n",
       "77                                         19.5   \n",
       "\n",
       "    percent_aged_under_18_or_over_64  per_capita_income_  hardship_index  \n",
       "0                               27.5               23939            39.0  \n",
       "1                               38.5               23040            46.0  \n",
       "2                               22.2               35787            20.0  \n",
       "3                               25.5               37524            17.0  \n",
       "4                               26.2               57123             6.0  \n",
       "..                               ...                 ...             ...  \n",
       "73                              36.8               34381            16.0  \n",
       "74                              40.3               27149            30.0  \n",
       "75                              30.3               25828            24.0  \n",
       "76                              23.8               33385            19.0  \n",
       "77                              33.5               28202             NaN  \n",
       "\n",
       "[78 rows x 9 columns]"
      ]
     },
     "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",
    "chicago_socioeconomic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://hhf49271:***@54a2f15b-5c0f-46df-8954-7e38e612c2bd.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32733/BLUDB\n",
      "Done.\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "[]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql DROP TABLE chicago_socioeconomic_data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://hhf49271:***@54a2f15b-5c0f-46df-8954-7e38e612c2bd.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32733/BLUDB\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "'Persisted chicago_socioeconomic_data'"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql --persist chicago_socioeconomic_data"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can verify that the table creation was successful by making a basic query like:\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "##### How many rows are in the dataset?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://hhf49271:***@54a2f15b-5c0f-46df-8954-7e38e612c2bd.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32733/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": [
       "[(78,)]"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql SELECT COUNT(*) FROM chicago_socioeconomic_data;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### How many community areas in Chicago have a hardship index greater than 50.0?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://hhf49271:***@54a2f15b-5c0f-46df-8954-7e38e612c2bd.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32733/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": [
       "[(38,)]"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql SELECT COUNT(*) FROM chicago_socioeconomic_data WHERE hardship_index > 50;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### What is the maximum value of hardship index in this dataset?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://hhf49271:***@54a2f15b-5c0f-46df-8954-7e38e612c2bd.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32733/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": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql SELECT MAX(hardship_index) FROM chicago_socioeconomic_data;"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Which community area which has the highest hardship index?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://hhf49271:***@54a2f15b-5c0f-46df-8954-7e38e612c2bd.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32733/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": 10,
     "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": [
    "##### Which Chicago community areas have per-capita incomes greater than $60,000?\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://hhf49271:***@54a2f15b-5c0f-46df-8954-7e38e612c2bd.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32733/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": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "%sql SELECT community_area_name FROM chicago_socioeconomic_data WHERE per_capita_income_ > 60000;\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### 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": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://hhf49271:***@54a2f15b-5c0f-46df-8954-7e38e612c2bd.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32733/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 seaborn as sns\n",
    "\n",
    "income_vs_hardship_index = %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_index.DataFrame())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### We have done basic exploratory data analysis using SQL and python visualization tools. \n",
    "\n",
    "##### We 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`. \n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      " * ibm_db_sa://hhf49271:***@54a2f15b-5c0f-46df-8954-7e38e612c2bd.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32733/BLUDB\n",
      "Done.\n",
      " * ibm_db_sa://hhf49271:***@54a2f15b-5c0f-46df-8954-7e38e612c2bd.c1ogj3sd0tgtu0lqde00.databases.appdomain.cloud:32733/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"
    },
    {
     "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": [
    "per_capita_income_vs_percent_below_pvty = %sql  SELECT per_capita_income_,percent_households_below_poverty FROM chicago_socioeconomic_data;\n",
    "plot2 = sns.jointplot(x='per_capita_income_', y='percent_households_below_poverty', data=per_capita_income_vs_percent_below_pvty.DataFrame())\n",
    "\n",
    "per_capita_income_vs_percent_16_unemployed = %sql  SELECT per_capita_income_,percent_aged_16_unemployed FROM chicago_socioeconomic_data;\n",
    "plot3 = sns.jointplot(x='per_capita_income_', y='percent_aged_16_unemployed', data=per_capita_income_vs_percent_16_unemployed.DataFrame())"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.9.7"
  },
  "widgets": {
   "state": {},
   "version": "1.1.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}