loan_info_cleaning.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Cleaning data example\n",
    "\n",
    "This uses the loan information example we use to calculate APR.\n",
    "\n",
    "### Start by setting up your enviornment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "/Users/alexandradobre/Desktop/Python\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import math\n",
    "import os\n",
    "import subprocess\n",
    "import sys\n",
    "\n",
    "# Install numpy-financial\n",
    "def install(package):\n",
    "    subprocess.check_call([sys.executable, \"-m\", \"pip\", \"install\", package])\n",
    "install(\"numpy-financial\")\n",
    "\n",
    "# Get path\n",
    "path = os.getcwd()\n",
    "print(path)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Load in the data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>years</th>\n",
       "      <th>months</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>total_fees</th>\n",
       "    </tr>\n",
       "  </thead>\n",
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       "      <td>471000</td>\n",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>291000</td>\n",
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       "      <td>360</td>\n",
       "      <td>4.0</td>\n",
       "      <td>2500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111000</td>\n",
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       "      <td>4.5</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>271000</td>\n",
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       "      <td>3.0</td>\n",
       "      <td>7500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   loan_amount  years  months  interest_rate  total_fees\n",
       "0       471000     15     180            4.5        1000\n",
       "1       291000     30     360            4.0        2500\n",
       "2       111000     35     420            6.0        5000\n",
       "3       414000     30     360            4.5        3000\n",
       "4       271000     20     240            3.0        7500"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.read_csv('loan_info.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Create new variables\n",
    "\n",
    "Present value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>years</th>\n",
       "      <th>months</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>total_fees</th>\n",
       "      <th>present_value</th>\n",
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       "  </thead>\n",
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       "      <td>4.5</td>\n",
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       "      <td>470000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>291000</td>\n",
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       "      <td>3.0</td>\n",
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       "      <td>263500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "   loan_amount  years  months  interest_rate  total_fees  present_value\n",
       "0       471000     15     180            4.5        1000         470000\n",
       "1       291000     30     360            4.0        2500         288500\n",
       "2       111000     35     420            6.0        5000         106000\n",
       "3       414000     30     360            4.5        3000         411000\n",
       "4       271000     20     240            3.0        7500         263500"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Present value of the loan = loan amount - loan fees\n",
    "present_val = df[\"loan_amount\"] - df[\"total_fees\"]\n",
    "df[\"present_value\"] = present_val\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Monthly payment"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "ename": "TypeError",
     "evalue": "'('interest_rate', 0     180\n1     360\n2     420\n3     360\n4     240\n5     180\n6     180\n7     180\n8     480\n9     360\n10    120\n11    300\n12    180\n13    360\n14    420\n15    360\n16    540\n17    300\n18    240\nName: months, dtype: int64)' is an invalid key",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-12-5b7c60b8e0f0>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m     14\u001b[0m     \u001b[0;32mreturn\u001b[0m \u001b[0mpayment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m \u001b[0mmonthly_payment\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcalc_mortgage\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"loan_amount\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"interest_rate\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"months\"\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"monthly_pmt\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mmonthly_payment\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   2900\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2901\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2902\u001b[0;31m             \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2903\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2904\u001b[0m                 \u001b[0mindexer\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m~/opt/anaconda3/lib/python3.8/site-packages/pandas/core/indexes/base.py\u001b[0m in \u001b[0;36mget_loc\u001b[0;34m(self, key, method, tolerance)\u001b[0m\n\u001b[1;32m   2893\u001b[0m             \u001b[0mcasted_key\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2894\u001b[0m             \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 2895\u001b[0;31m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mcasted_key\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   2896\u001b[0m             \u001b[0;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   2897\u001b[0m                 \u001b[0;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0merr\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;32mpandas/_libs/index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[0;34m()\u001b[0m\n",
      "\u001b[0;31mTypeError\u001b[0m: '('interest_rate', 0     180\n1     360\n2     420\n3     360\n4     240\n5     180\n6     180\n7     180\n8     480\n9     360\n10    120\n11    300\n12    180\n13    360\n14    420\n15    360\n16    540\n17    300\n18    240\nName: months, dtype: int64)' is an invalid key"
     ]
    }
   ],
   "source": [
    "# Define calc_mortgage, which calculates monthly payment amount\n",
    "def calc_mortgage(principal, interest, months):\n",
    "    \n",
    "    # monthly rate from annual percentage rate\n",
    "    interest_rate = interest/(100 * 12)\n",
    "    \n",
    "    # total number of payments\n",
    "    payment_num = months\n",
    "    \n",
    "    # calculate monthly payment\n",
    "    payment = principal * \\\n",
    "        (interest_rate/(1-math.pow((1+interest_rate), (-payment_num))))\n",
    "\n",
    "    return payment\n",
    "\n",
    "monthly_payment = calc_mortgage(df[\"loan_amount\"], df[\"interest_rate\", df[\"months\"]])\n",
    "df[\"monthly_pmt\"] = monthly_payment\n",
    "\n",
    "df.head()\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>loan_amount</th>\n",
       "      <th>years</th>\n",
       "      <th>months</th>\n",
       "      <th>interest_rate</th>\n",
       "      <th>total_fees</th>\n",
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       "      <td>471000</td>\n",
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       "      <td>4.50</td>\n",
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       "      <th>1</th>\n",
       "      <td>291000</td>\n",
       "      <td>30</td>\n",
       "      <td>360</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2500</td>\n",
       "      <td>288500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>111000</td>\n",
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       "      <td>5000</td>\n",
       "      <td>106000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>414000</td>\n",
       "      <td>30</td>\n",
       "      <td>360</td>\n",
       "      <td>4.50</td>\n",
       "      <td>3000</td>\n",
       "      <td>411000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>271000</td>\n",
       "      <td>20</td>\n",
       "      <td>240</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7500</td>\n",
       "      <td>263500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>338000</td>\n",
       "      <td>15</td>\n",
       "      <td>180</td>\n",
       "      <td>3.75</td>\n",
       "      <td>3000</td>\n",
       "      <td>335000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>279000</td>\n",
       "      <td>15</td>\n",
       "      <td>180</td>\n",
       "      <td>4.00</td>\n",
       "      <td>2500</td>\n",
       "      <td>276500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>141000</td>\n",
       "      <td>15</td>\n",
       "      <td>180</td>\n",
       "      <td>7.00</td>\n",
       "      <td>1000</td>\n",
       "      <td>140000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>103000</td>\n",
       "      <td>40</td>\n",
       "      <td>480</td>\n",
       "      <td>8.00</td>\n",
       "      <td>5000</td>\n",
       "      <td>98000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>390000</td>\n",
       "      <td>30</td>\n",
       "      <td>360</td>\n",
       "      <td>2.00</td>\n",
       "      <td>6500</td>\n",
       "      <td>383500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>423000</td>\n",
       "      <td>10</td>\n",
       "      <td>120</td>\n",
       "      <td>3.00</td>\n",
       "      <td>9000</td>\n",
       "      <td>414000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>463000</td>\n",
       "      <td>25</td>\n",
       "      <td>300</td>\n",
       "      <td>4.50</td>\n",
       "      <td>4300</td>\n",
       "      <td>458700</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>423000</td>\n",
       "      <td>15</td>\n",
       "      <td>180</td>\n",
       "      <td>3.50</td>\n",
       "      <td>2100</td>\n",
       "      <td>420900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>377000</td>\n",
       "      <td>30</td>\n",
       "      <td>360</td>\n",
       "      <td>2.78</td>\n",
       "      <td>1000</td>\n",
       "      <td>376000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>222000</td>\n",
       "      <td>35</td>\n",
       "      <td>420</td>\n",
       "      <td>6.00</td>\n",
       "      <td>1200</td>\n",
       "      <td>220800</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>167000</td>\n",
       "      <td>30</td>\n",
       "      <td>360</td>\n",
       "      <td>3.00</td>\n",
       "      <td>7000</td>\n",
       "      <td>160000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>340000</td>\n",
       "      <td>45</td>\n",
       "      <td>540</td>\n",
       "      <td>5.00</td>\n",
       "      <td>8000</td>\n",
       "      <td>332000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>500000</td>\n",
       "      <td>25</td>\n",
       "      <td>300</td>\n",
       "      <td>5.25</td>\n",
       "      <td>3000</td>\n",
       "      <td>497000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>412000</td>\n",
       "      <td>20</td>\n",
       "      <td>240</td>\n",
       "      <td>5.00</td>\n",
       "      <td>2500</td>\n",
       "      <td>409500</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    loan_amount  years  months  interest_rate  total_fees  present_value\n",
       "0        471000     15     180           4.50        1000         470000\n",
       "1        291000     30     360           4.00        2500         288500\n",
       "2        111000     35     420           6.00        5000         106000\n",
       "3        414000     30     360           4.50        3000         411000\n",
       "4        271000     20     240           3.00        7500         263500\n",
       "5        338000     15     180           3.75        3000         335000\n",
       "6        279000     15     180           4.00        2500         276500\n",
       "7        141000     15     180           7.00        1000         140000\n",
       "8        103000     40     480           8.00        5000          98000\n",
       "9        390000     30     360           2.00        6500         383500\n",
       "10       423000     10     120           3.00        9000         414000\n",
       "11       463000     25     300           4.50        4300         458700\n",
       "12       423000     15     180           3.50        2100         420900\n",
       "13       377000     30     360           2.78        1000         376000\n",
       "14       222000     35     420           6.00        1200         220800\n",
       "15       167000     30     360           3.00        7000         160000\n",
       "16       340000     45     540           5.00        8000         332000\n",
       "17       500000     25     300           5.25        3000         497000\n",
       "18       412000     20     240           5.00        2500         409500"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
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