Frew parking analysis.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import scipy as sp\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib as mpl\n",
    "import seaborn as sns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Import the assignment data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "fn = 'Assignment 3 data.xlsx'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel(fn, na_values='error')\n",
    "df.dropna(inplace=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Take a look at the data that I've imported to see if there are any issues."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "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>Purchase_Date_Local</th>\n",
       "      <th>Pay_Unit___Name</th>\n",
       "      <th>$ paid</th>\n",
       "      <th>Minutes bought</th>\n",
       "      <th>duration</th>\n",
       "      <th>hour of the day</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day of the month</th>\n",
       "      <th>year</th>\n",
       "      <th>street</th>\n",
       "      <th>Meter ID</th>\n",
       "      <th>price per minute</th>\n",
       "      <th>DAYMONTH</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01OCT13:06:02:00</td>\n",
       "      <td>Card</td>\n",
       "      <td>6</td>\n",
       "      <td>279</td>\n",
       "      <td>9600</td>\n",
       "      <td>6</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>FREWST</td>\n",
       "      <td>410141</td>\n",
       "      <td>2.25</td>\n",
       "      <td>01OCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>01OCT13:07:35:00</td>\n",
       "      <td>Card</td>\n",
       "      <td>9</td>\n",
       "      <td>266</td>\n",
       "      <td>14400</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>FREWST</td>\n",
       "      <td>410141</td>\n",
       "      <td>2.25</td>\n",
       "      <td>01OCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>01OCT13:07:42:00</td>\n",
       "      <td>Card</td>\n",
       "      <td>3</td>\n",
       "      <td>99</td>\n",
       "      <td>4800</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>FREWST</td>\n",
       "      <td>410141</td>\n",
       "      <td>2.25</td>\n",
       "      <td>01OCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>01OCT13:07:43:00</td>\n",
       "      <td>Card</td>\n",
       "      <td>2</td>\n",
       "      <td>71</td>\n",
       "      <td>3180</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>FREWST</td>\n",
       "      <td>410142</td>\n",
       "      <td>2.25</td>\n",
       "      <td>01OCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>32</th>\n",
       "      <td>01OCT13:07:51:00</td>\n",
       "      <td>Card</td>\n",
       "      <td>9</td>\n",
       "      <td>250</td>\n",
       "      <td>14400</td>\n",
       "      <td>7</td>\n",
       "      <td>10</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2013</td>\n",
       "      <td>FREWST</td>\n",
       "      <td>410145</td>\n",
       "      <td>2.25</td>\n",
       "      <td>01OCT</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Purchase_Date_Local Pay_Unit___Name  $ paid  Minutes bought  duration  \\\n",
       "0     01OCT13:06:02:00            Card       6             279      9600   \n",
       "22    01OCT13:07:35:00            Card       9             266     14400   \n",
       "25    01OCT13:07:42:00            Card       3              99      4800   \n",
       "27    01OCT13:07:43:00            Card       2              71      3180   \n",
       "32    01OCT13:07:51:00            Card       9             250     14400   \n",
       "\n",
       "    hour of the day  month  weekday  day of the month  year  street  Meter ID  \\\n",
       "0                 6     10        3                 1  2013  FREWST    410141   \n",
       "22                7     10        3                 1  2013  FREWST    410141   \n",
       "25                7     10        3                 1  2013  FREWST    410141   \n",
       "27                7     10        3                 1  2013  FREWST    410142   \n",
       "32                7     10        3                 1  2013  FREWST    410145   \n",
       "\n",
       "    price per minute DAYMONTH  \n",
       "0               2.25    01OCT  \n",
       "22              2.25    01OCT  \n",
       "25              2.25    01OCT  \n",
       "27              2.25    01OCT  \n",
       "32              2.25    01OCT  "
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Group the data by day of month\n",
    "This sums the total number of minutes bought per day. Unfortunately it also sums the price per minute, which will then need to be corrected."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_day = df.groupby('DAYMONTH', as_index=False).sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Look at the dataframe. This is how I noticed that the price per min is wrong now."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "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>DAYMONTH</th>\n",
       "      <th>$ paid</th>\n",
       "      <th>Minutes bought</th>\n",
       "      <th>duration</th>\n",
       "      <th>hour of the day</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day of the month</th>\n",
       "      <th>year</th>\n",
       "      <th>Meter ID</th>\n",
       "      <th>price per minute</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01DEC</td>\n",
       "      <td>15.75</td>\n",
       "      <td>538</td>\n",
       "      <td>32280</td>\n",
       "      <td>67</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>12078</td>\n",
       "      <td>2460857</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01NOV</td>\n",
       "      <td>1738.25</td>\n",
       "      <td>55387</td>\n",
       "      <td>3316320</td>\n",
       "      <td>4755</td>\n",
       "      <td>4400</td>\n",
       "      <td>2400</td>\n",
       "      <td>400</td>\n",
       "      <td>805200</td>\n",
       "      <td>164057124</td>\n",
       "      <td>800.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01OCT</td>\n",
       "      <td>2086.75</td>\n",
       "      <td>56243</td>\n",
       "      <td>3361740</td>\n",
       "      <td>5287</td>\n",
       "      <td>4420</td>\n",
       "      <td>1326</td>\n",
       "      <td>442</td>\n",
       "      <td>889746</td>\n",
       "      <td>181283166</td>\n",
       "      <td>994.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02DEC</td>\n",
       "      <td>1280.50</td>\n",
       "      <td>45645</td>\n",
       "      <td>2731320</td>\n",
       "      <td>4564</td>\n",
       "      <td>4296</td>\n",
       "      <td>716</td>\n",
       "      <td>716</td>\n",
       "      <td>720654</td>\n",
       "      <td>146831117</td>\n",
       "      <td>626.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02JAN</td>\n",
       "      <td>395.75</td>\n",
       "      <td>13972</td>\n",
       "      <td>836460</td>\n",
       "      <td>1020</td>\n",
       "      <td>90</td>\n",
       "      <td>450</td>\n",
       "      <td>180</td>\n",
       "      <td>181260</td>\n",
       "      <td>36912868</td>\n",
       "      <td>157.5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DAYMONTH   $ paid  Minutes bought  duration  hour of the day  month  \\\n",
       "0    01DEC    15.75             538     32280               67     72   \n",
       "1    01NOV  1738.25           55387   3316320             4755   4400   \n",
       "2    01OCT  2086.75           56243   3361740             5287   4420   \n",
       "3    02DEC  1280.50           45645   2731320             4564   4296   \n",
       "4    02JAN   395.75           13972    836460             1020     90   \n",
       "\n",
       "   weekday  day of the month    year   Meter ID  price per minute  \n",
       "0        6                 6   12078    2460857              10.5  \n",
       "1     2400               400  805200  164057124             800.0  \n",
       "2     1326               442  889746  181283166             994.5  \n",
       "3      716               716  720654  146831117             626.5  \n",
       "4      450               180  181260   36912868             157.5  "
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_day.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Correct price per minute for each of the days\n",
    "- Create a dictionary of price by month\n",
    "- Create a new column with just the month (no date) by stripping numbers from the DAYMONTH\n",
    "- Map the dictionary of price by month with the new month column to correct the price per minute data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "price = {'DEC':1.75,\n",
    "        'JAN': 1.75,\n",
    "        'OCT': 2.25,\n",
    "        'NOV': 2.0}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "df_day['Month'] = df_day['DAYMONTH'].map(lambda x: x.lstrip('0123456789'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "df_day['price per minute'] = df_day['Month'].map(price)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "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>DAYMONTH</th>\n",
       "      <th>$ paid</th>\n",
       "      <th>Minutes bought</th>\n",
       "      <th>duration</th>\n",
       "      <th>hour of the day</th>\n",
       "      <th>month</th>\n",
       "      <th>weekday</th>\n",
       "      <th>day of the month</th>\n",
       "      <th>year</th>\n",
       "      <th>Meter ID</th>\n",
       "      <th>price per minute</th>\n",
       "      <th>Month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>01DEC</td>\n",
       "      <td>15.75</td>\n",
       "      <td>538</td>\n",
       "      <td>32280</td>\n",
       "      <td>67</td>\n",
       "      <td>72</td>\n",
       "      <td>6</td>\n",
       "      <td>6</td>\n",
       "      <td>12078</td>\n",
       "      <td>2460857</td>\n",
       "      <td>1.75</td>\n",
       "      <td>DEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>01NOV</td>\n",
       "      <td>1738.25</td>\n",
       "      <td>55387</td>\n",
       "      <td>3316320</td>\n",
       "      <td>4755</td>\n",
       "      <td>4400</td>\n",
       "      <td>2400</td>\n",
       "      <td>400</td>\n",
       "      <td>805200</td>\n",
       "      <td>164057124</td>\n",
       "      <td>2.00</td>\n",
       "      <td>NOV</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>01OCT</td>\n",
       "      <td>2086.75</td>\n",
       "      <td>56243</td>\n",
       "      <td>3361740</td>\n",
       "      <td>5287</td>\n",
       "      <td>4420</td>\n",
       "      <td>1326</td>\n",
       "      <td>442</td>\n",
       "      <td>889746</td>\n",
       "      <td>181283166</td>\n",
       "      <td>2.25</td>\n",
       "      <td>OCT</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>02DEC</td>\n",
       "      <td>1280.50</td>\n",
       "      <td>45645</td>\n",
       "      <td>2731320</td>\n",
       "      <td>4564</td>\n",
       "      <td>4296</td>\n",
       "      <td>716</td>\n",
       "      <td>716</td>\n",
       "      <td>720654</td>\n",
       "      <td>146831117</td>\n",
       "      <td>1.75</td>\n",
       "      <td>DEC</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>02JAN</td>\n",
       "      <td>395.75</td>\n",
       "      <td>13972</td>\n",
       "      <td>836460</td>\n",
       "      <td>1020</td>\n",
       "      <td>90</td>\n",
       "      <td>450</td>\n",
       "      <td>180</td>\n",
       "      <td>181260</td>\n",
       "      <td>36912868</td>\n",
       "      <td>1.75</td>\n",
       "      <td>JAN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  DAYMONTH   $ paid  Minutes bought  duration  hour of the day  month  \\\n",
       "0    01DEC    15.75             538     32280               67     72   \n",
       "1    01NOV  1738.25           55387   3316320             4755   4400   \n",
       "2    01OCT  2086.75           56243   3361740             5287   4420   \n",
       "3    02DEC  1280.50           45645   2731320             4564   4296   \n",
       "4    02JAN   395.75           13972    836460             1020     90   \n",
       "\n",
       "   weekday  day of the month    year   Meter ID  price per minute Month  \n",
       "0        6                 6   12078    2460857              1.75   DEC  \n",
       "1     2400               400  805200  164057124              2.00   NOV  \n",
       "2     1326               442  889746  181283166              2.25   OCT  \n",
       "3      716               716  720654  146831117              1.75   DEC  \n",
       "4      450               180  181260   36912868              1.75   JAN  "
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_day.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Plot some data\n",
    "First look at a violin plot of total minutes bought per day in each of the price categories."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x11273a3d0>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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9e/cmPz+fwsJC8vLyKCwsZPDgwVitVnQ6HSUlJeTm5rJu3Toeeuih74yjtjb6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\nwUkyIIQQQhicJANCCCGEwf1/20XpegJmEigAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x111f8f490>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='price per minute', y='Minutes bought',\n",
    "               data=df_day, saturation=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Impressions from the plot above\n",
    "It's weird, because the higher price also has higher mean number of min. But there are two months for the lowest price, so lets break those months out in the next plot."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Plot below\n",
    "Same as above, but using the *hue* to split out by month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x112051710>"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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NQUdHOy1pHNLV1YmGhtMw5AUXG9KCYDWAN4s4cvQwJMmnyTkJSVRvby8aGuox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\nhgEiIiKLYxggIiKyOIYBIiIii/v/tPxSwNoKG+IAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x112051ad0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.violinplot(x='price per minute', y='Minutes bought',\n",
    "               data=df_day, hue='Month', scale='count', saturation=0.5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Impressions from the plot above\n",
    "Now I see what's going on. Two months had rates of $1.75, and one of those is December. Which is when everyone finishes finals and doesn't come onto campus for a couple weeks. \n",
    "\n",
    "Other trends:\n",
    "\n",
    "- October, with the highest rate, has fewer days with low demand. The mean is nowhere near the median. \n",
    "- Distributions tend to be bi-modal. Maybe due to weekday vs weekend. I don't have the data set up well to do this right now, but it would be interesting to look at."
   ]
  }
 ],
 "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": "2.7.10"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}