Lecture_SimpleiPythonExample_3_6053Fall19.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "from numpy import math\n",
    "\n",
    "loansData = pd.read_csv('loansData.csv')\n",
    "\n",
    "#Note, the data and schema can be found in the data folder on NYU Classes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Amount.Requested', 'Amount.Funded.By.Investors', 'Interest.Rate',\n",
       "       'Loan.Length', 'Loan.Purpose', 'Debt.To.Income.Ratio', 'State',\n",
       "       'Home.Ownership', 'Monthly.Income', 'FICO.Range',\n",
       "       'Open.CREDIT.Lines', 'Revolving.CREDIT.Balance',\n",
       "       'Inquiries.in.the.Last.6.Months', 'Employment.Length'], dtype=object)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loansData.columns.values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "81174     6541.67\n",
       "99592     4583.33\n",
       "80059    11500.00\n",
       "15825     3833.33\n",
       "33182     3195.00\n",
       "Name: Monthly.Income, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loansData['Monthly.Income'][0:5] # first five rows of Monthly.Income"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure()\n",
    "inc = loansData['Monthly.Income']\n",
    "h = inc.hist()\n",
    "plt.title('Histogram of Monthly Income')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 432x288 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "loansData['Monthly.LogIncome'] = [ math.log(x) for x in inc ]\n",
    "plt.figure() #creates a figure object\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "h = loansData['Monthly.LogIncome'].hist()\n",
    "plt.title('Histogram of Log(Monthly Income)')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2499.000000\n",
       "mean        8.501915\n",
       "std         0.523019\n",
       "min         6.377577\n",
       "25%         8.160518\n",
       "50%         8.517193\n",
       "75%         8.824678\n",
       "max        11.540054\n",
       "Name: Monthly.LogIncome, dtype: float64"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "loansData['Monthly.LogIncome'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.16"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}