2.4.2+Drill+-+Prepare+the+Data.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Prepare this data to model with multivariable regression (including data cleaning if necessary) according to this specification:\n",
    "\n",
    "Propertycrime=α+Population+Population2+Murder+Robbery\n",
    "\n",
    " \n",
    "The 'population' variable is already set for you, but you will need to create the last three features. Robbery and Murder are currently continuous variables. For this model, please use these variables to create categorical features where values greater than 0 are coded 1, and values equal to 0 are coded 0. You'll use this data and model in a later assignment- for now, just write the code you need to get the data ready. Don't forget basic data cleaning procedures, either! Do some graphing to see if there are any anomalous cases, and decide how you want to deal with them."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                  City  Population  Violent\\ncrime  \\\n",
      "343      Woodbury Town       10685               3   \n",
      "344  Woodridge Village         829               7   \n",
      "345     Woodstock Town        5931               2   \n",
      "346            Yonkers      199134            1036   \n",
      "347      Yorktown Town       36643              15   \n",
      "\n",
      "     Murder and\\nnonnegligent\\nmanslaughter  Rape\\n(revised\\ndefinition)1  \\\n",
      "343                                       0                           NaN   \n",
      "344                                       0                           NaN   \n",
      "345                                       0                           NaN   \n",
      "346                                       6                           NaN   \n",
      "347                                       0                           NaN   \n",
      "\n",
      "     Rape\\n(legacy\\ndefinition)2  Robbery  Aggravated\\nassault  \\\n",
      "343                            0        2                    1   \n",
      "344                            0        0                    7   \n",
      "345                            0        0                    2   \n",
      "346                           25      390                  615   \n",
      "347                            0        2                   13   \n",
      "\n",
      "     Property\\ncrime  Burglary  Larceny-\\ntheft  Motor\\nvehicle\\ntheft  Arson3  \n",
      "343              541         9              529                      3     NaN  \n",
      "344               17         8                9                      0     0.0  \n",
      "345               58        13               45                      0     NaN  \n",
      "346             2368       470             1662                    236    10.0  \n",
      "347              334        45              287                      2     NaN  \n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\maken\\lib\\site-packages\\ipykernel\\__main__.py:8: ParserWarning: Falling back to the 'python' engine because the 'c' engine does not support skipfooter; you can avoid this warning by specifying engine='python'.\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "\n",
    "# Read the csv file. 'thousands' is used to format out the ',' when it appears in the numbers, skiprows will get rid of the first four rows (could also use skipheader), \n",
    "# skipfooter will remove the bottom three rows where the footnotes are located.\n",
    "df = pd.read_csv('C:\\\\Users\\\\maken\\\\table_8_offenses_known_to_law_enforcement_new_york_by_city_2013.csv', thousands = ',', skiprows = 4, skipfooter = 3)\n",
    "\n",
    "# Print the last few rows to ensure we are getting all of the data\n",
    "print(df.tail())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Population</th>\n",
       "      <th>Murder</th>\n",
       "      <th>Robbery</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adams Village</td>\n",
       "      <td>1861</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Addison Town and Village</td>\n",
       "      <td>2577</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Akron Village</td>\n",
       "      <td>2846</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Albany</td>\n",
       "      <td>97956</td>\n",
       "      <td>8</td>\n",
       "      <td>227</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Albion Village</td>\n",
       "      <td>6388</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                       City  Population  Murder  Robbery\n",
       "0             Adams Village        1861       0        0\n",
       "1  Addison Town and Village        2577       0        0\n",
       "2             Akron Village        2846       0        0\n",
       "3                    Albany       97956       8      227\n",
       "4            Albion Village        6388       0        4"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Looks good. Let's grab the columns we need. \n",
    "\n",
    "df = df.iloc[:, [0, 1, 3, 6]].dropna()\n",
    "df.columns = ['City', 'Population', 'Murder', 'Robbery']\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "# Time to make some features.\n",
    "\n",
    "df['Robbery_0'] = np.where(df['Robbery'] == 0, 0, 1)\n",
    "df['Murder_0'] = np.where(df['Murder'] == 0, 0, 1)\n",
    "df['Population2'] = df['Population'] * df['Population']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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jhDAAAEYIYQAAjBDCAAAYIYQBADDy/wGbApsc8auBzAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aaca3820f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Plot Robbery_0 to make sure there are no anomalies\n",
    "\n",
    "plt.plot(df['Robbery_0'], 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aac886d208>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looks fine. \n",
    "\n",
    "# Do the same with Murder_0\n",
    "\n",
    "plt.plot(df['Murder_0'], 'ro')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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oeBAhDMAKXKIELyKEAdjBDPEYsBghDMAKOZcoFbAcQD4RwgCskHs4umDFAPKKEAZgBzPk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\nVVXtpOhG3gAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aaca0786d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Looks fine.\n",
    "\n",
    "# Let's see what the population looks like. \n",
    "\n",
    "plt.plot(df['Population'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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BABWF8yYlz89Jh3bWM1bADw0NYWJiwv1u2zYqlYp028TEBIaHh0OPcfztzr6zZ88Ove5b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\n99JhkvfBN9K1lzSQgI9BFmR3fNQzy0tN9K3EHDOcBypba1o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tW7eO/eAHP+hG0TLjgQce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\nCIIgiAJCAp4gCIIgCggJeIIgCIIoIP8/j08E2WkgvlkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x1aaca28e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# NYC obviously has a much higher population... let's plot the murder per capita\n",
    "\n",
    "plt.plot(df['Murder']/df['Population'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "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": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}