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       "\n",
       "   loan_purpose income  \n",
       "0             1   None  \n",
       "1             1   None  \n",
       "2             1   None  \n",
       "3             1   None  \n",
       "4             2   None  "
      ]
     },
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     "execution_count": 26,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what are the five biggest loans in terms of dollar amount?  Practice ORDER BY.\n",
    "pd.read_sql(\"\"\"\n",
    "SELECT *\n",
    "FROM loans\n",
    "ORDER BY loan_amount DESC\n",
    "LIMIT 5\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 27,
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   "id": "c01b796c-ded3-4e11-81e7-69d7660da9cb",
   "metadata": {},
   "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>action_taken</th>\n",
       "      <th>loan_type</th>\n",
       "      <th>lei</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>interest_rate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Loan purchased by the institution</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>549300XWUSRVVOHPRY47</td>\n",
       "      <td>264185000.0</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Loan originated</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>AD6GFRVSDT01YPT1CS68</td>\n",
       "      <td>74755000.0</td>\n",
       "      <td>1.454</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Application withdrawn by applicant</td>\n",
       "      <td>FHA-insured</td>\n",
       "      <td>AD6GFRVSDT01YPT1CS68</td>\n",
       "      <td>66005000.0</td>\n",
       "      <td>NA</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Loan originated</td>\n",
       "      <td>Conventional</td>\n",
       "      <td>YQI2CPR3Z44KAR0HG822</td>\n",
       "      <td>65005000.0</td>\n",
       "      <td>3.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Loan originated</td>\n",
       "      <td>FHA-insured</td>\n",
       "      <td>254900YA1AQXNM8QVZ06</td>\n",
       "      <td>63735000.0</td>\n",
       "      <td>2.99</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         action_taken     loan_type                   lei  \\\n",
       "0   Loan purchased by the institution  Conventional  549300XWUSRVVOHPRY47   \n",
       "1                     Loan originated  Conventional  AD6GFRVSDT01YPT1CS68   \n",
       "2  Application withdrawn by applicant   FHA-insured  AD6GFRVSDT01YPT1CS68   \n",
       "3                     Loan originated  Conventional  YQI2CPR3Z44KAR0HG822   \n",
       "4                     Loan originated   FHA-insured  254900YA1AQXNM8QVZ06   \n",
       "\n",
       "   loan_amount interest_rate  \n",
       "0  264185000.0            NA  \n",
       "1   74755000.0         1.454  \n",
       "2   66005000.0            NA  \n",
       "3   65005000.0           3.0  \n",
       "4   63735000.0          2.99  "
      ]
     },
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     "execution_count": 27,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what are the actions taken and types for those loans (show the text, not numbers)?  Practice INNER JOIN.\n",
    "pd.read_sql(\"\"\"\n",
    "SELECT actions.action_taken, loan_types.loan_type, loans.lei, loans.loan_amount, loans.interest_rate\n",
    "FROM loans\n",
    "INNER JOIN actions ON loans.action_taken = actions.id\n",
    "INNER JOIN loan_types ON loans.loan_type = loan_types.id\n",
    "ORDER BY loan_amount DESC\n",
    "LIMIT 5\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 34,
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   "id": "b7d4a687-70bf-46e7-a715-013977358d05",
   "metadata": {},
   "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>lei</th>\n",
       "      <th>action_taken</th>\n",
       "      <th>loan_type</th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>loan_purpose</th>\n",
       "      <th>income</th>\n",
       "      <th>id</th>\n",
       "      <th>loan_purpose</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>None</td>\n",
       "      <td>3</td>\n",
       "      <td>Refinancing</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    lei action_taken loan_type loan_amount interest_rate loan_purpose income  \\\n",
       "0  None         None      None        None          None         None   None   \n",
       "\n",
       "   id loan_purpose  \n",
       "0   3  Refinancing  "
      ]
     },
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     "execution_count": 34,
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     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# what is a loan_purpose that doesn't appear in the loans table?  Practice LEFT/RIGHT JOIN.\n",
    "pd.read_sql(\"\"\"\n",
    "SELECT *\n",
    "FROM loans\n",
    "RIGHT JOIN purposes ON loans.loan_purpose = purposes.id\n",
    "WHERE loans.loan_purpose IS NULL\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 35,
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   "id": "fc73517c-cf57-4a91-bb9d-2fbfc76544d5",
   "metadata": {},
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   "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>COUNT(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>447367</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   COUNT(*)\n",
       "0    447367"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "source": [
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    "# how many rows are in the table?  Practice COUNT(*).\n",
    "pd.read_sql(\"\"\"\n",
    "SELECT COUNT(*)\n",
    "FROM loans\n",
    "\"\"\", conn)"
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   ]
  },
  {
   "cell_type": "code",
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   "id": "e91feeee-8689-4f57-a991-f6ca3fee2a6d",
   "metadata": {},
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   "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>COUNT(income)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>399948</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   COUNT(income)\n",
       "0         399948"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# how many non-null values are in the income column?  Practice COUNT(column).\n",
    "pd.read_sql(\"\"\"\n",
    "SELECT COUNT(income)\n",
    "FROM loans\n",
    "\"\"\", conn)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "d688a1a3-3740-4dcf-8de5-b8f9f3e928b3",
   "metadata": {},
   "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>id</th>\n",
       "      <th>loan_type</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>Conventional</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>FHA-insured</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>VA-guaranteed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>FSA/RHS-guaranteed</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id           loan_type\n",
       "0   1        Conventional\n",
       "1   2         FHA-insured\n",
       "2   3       VA-guaranteed\n",
       "3   4  FSA/RHS-guaranteed"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "source": [
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    "pd.read_sql(\"\"\"\n",
    "SELECT *\n",
    "FROM loan_types\n",
    "\"\"\", conn)"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 49,
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   "id": "12333532-0618-4ed4-b71b-260a4f35e581",
   "metadata": {},
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   "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>AVG(interest_rate)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2.21657</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   AVG(interest_rate)\n",
       "0             2.21657"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "source": [
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    "# what is the average interest rate for loans of type \"Conventional\"?  Practice AVG.\n",
    "pd.read_sql(\"\"\"\n",
    "SELECT AVG(interest_rate)\n",
    "FROM loans\n",
    "INNER JOIN loan_types ON loans.loan_type = loan_types.id\n",
    "WHERE loan_types.loan_type = 'Conventional'\n",
    "\"\"\", conn)"
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   ]
  },
  {
   "cell_type": "code",
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   "id": "23c00af3-e385-435a-8bf6-f5cfe64f02db",
   "metadata": {},
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   "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>loan_type</th>\n",
       "      <th>AVG(interest_rate)</th>\n",
       "      <th>COUNT(*)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Conventional</td>\n",
       "      <td>2.216570</td>\n",
       "      <td>389217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VA-guaranteed</td>\n",
       "      <td>1.919140</td>\n",
       "      <td>24551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FHA-insured</td>\n",
       "      <td>2.211670</td>\n",
       "      <td>30496</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>FSA/RHS-guaranteed</td>\n",
       "      <td>2.523942</td>\n",
       "      <td>3103</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            loan_type  AVG(interest_rate)  COUNT(*)\n",
       "0        Conventional            2.216570    389217\n",
       "1       VA-guaranteed            1.919140     24551\n",
       "2         FHA-insured            2.211670     30496\n",
       "3  FSA/RHS-guaranteed            2.523942      3103"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "source": [
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    "# how many loans are there of each type?  Practice GROUP BY.\n",
    "pd.read_sql(\"\"\"\n",
    "SELECT loan_types.loan_type, AVG(interest_rate), COUNT(*)\n",
    "FROM loans\n",
    "INNER JOIN loan_types ON loans.loan_type = loan_types.id\n",
    "GROUP BY loan_types.loan_type\n",
    "\"\"\", conn)"
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   ]
  },
  {
   "cell_type": "code",
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   "execution_count": 53,
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   "id": "2400a6a4-7056-47e0-b202-3bbb85a77b2f",
   "metadata": {},
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   "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>loan_type</th>\n",
       "      <th>AVG(interest_rate)</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Conventional</td>\n",
       "      <td>2.21657</td>\n",
       "      <td>389217</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>VA-guaranteed</td>\n",
       "      <td>1.91914</td>\n",
       "      <td>24551</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>FHA-insured</td>\n",
       "      <td>2.21167</td>\n",
       "      <td>30496</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       loan_type  AVG(interest_rate)   count\n",
       "0   Conventional             2.21657  389217\n",
       "1  VA-guaranteed             1.91914   24551\n",
       "2    FHA-insured             2.21167   30496"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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   "source": [
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    "# which loan types appear at least 10,000 times?  Practice HAVING.\n",
    "pd.read_sql(\"\"\"\n",
    "SELECT loan_types.loan_type, AVG(interest_rate), COUNT(*) as count\n",
    "FROM loans\n",
    "INNER JOIN loan_types ON loans.loan_type = loan_types.id\n",
    "GROUP BY loan_types.loan_type\n",
    "HAVING count >= 10000\n",
    "\"\"\", conn)"
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   ]
  }
 ],
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