WiDS_workshop2018_part1.ipynb

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   "metadata": {
    "slideshow": {
     "slide_type": "slide"
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   "source": [
    "# WiDS DATATHON WORKSHOP\n",
    "## FEBRUARY 12th, 2018\n",
    "<img src=\"images/inst_logos.pdf\" alt=\"Harvard IACS\" style=\"height: 80px;\" align=\"left\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
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   "source": [
    "### INSTRUCTORS: \n",
    "- **Anastasiya Belyaeva (MIT)**\n",
    "- **Weiwei Pan (Harvard IACS)**\n",
    "\n",
    "### SET-UP INSTRUCTIONS:\n",
    "**I. Workshop WiFi Access**\n",
    "\n",
    "- WiFi network: \n",
    "- WiFi access code:\n",
    "  \n",
    "**II. Workshop Materials & Computing**\n",
    "\n",
    "All workshop data, slides, `ipython` notebooks are available on the Workshop Canvas site.\n",
    "1. **Go to the workshop Canvas site:** *https://canvas.harvard.edu*\n",
    "2. **Login using XID:** *(see check-in slip)*\n",
    "3. **Login using password:** *(see check-in slip)*\n",
    "4. **Choose the course titled:** *WiDS Datathon Workshop* \n",
    "5. **For workshop materials:** *select 'Modules'*\n",
    "6. **For computing:** *select 'JupyterHub', following instructions to start a notebook server through the Harvard SEAS JupyterHub*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "metadata": {
    "collapsed": true,
    "slideshow": {
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   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import matplotlib\n",
    "import pandas as pd # package for data analysis, reading in the data, summarizing data, manipulating data\n",
    "import numpy as np # package for math operations\n",
    "import matplotlib.pyplot as plt # package for plotting\n",
    "from IPython.display import Image\n",
    "from sklearn import preprocessing, neighbors"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# PART 0: INTRODUCTION TO KAGGLE COMPETITIONS"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Hands-on experience with data science and machine learning\n",
    "* You only learn by doing!\n",
    "* Register yourself on [Kaggle](https://www.kaggle.com/)\n",
    "* Fill out Name, Location (both city and country) and Occupation so that you can win prizes in the state of Massachusetts\n",
    "* **February 15, 2018** - Entry deadline. You must accept the competition rules by this date in order to compete.\n",
    "* **February 15, 2018** - Team Merger deadline. This is the last day competitors may join or merge teams.\n",
    "* **February 28, 2018** - Final submission deadline\n",
    "* Note the submission instructions and how you will be evaluated"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
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e+rXsl7DNtI3Q/2X7tHO7rv92H29uiwfvw4i1/5XLe+5l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\nmeCWS79MlI9cWQa4svTh7unTp2NJsENEREREvMb60NVbTsr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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 194,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 900
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='images/submission.PNG', width = 900, height = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Winning strategies\n",
    "* Feature engineering - creating variables that might correlate well with the target\n",
    "* Testing many hypotheses for feature engineering\n",
    "* Model choice: ensembles of different models\n",
    "* Save predictions from all of your models to build an ensemble of models later\n",
    "* [Take a look at past solutions](https://www.hackerearth.com/practice/machine-learning/advanced-techniques/winning-tips-machine-learning-competitions-kazanova-current-kaggle-3/tutorial/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Dataset - Hubway Bike Rentals\n",
    "* Bike trips taken with variety of metrics collected on the trip\n",
    "* Questions that the data can answer:\n",
    "  * Where do Hubway users ride? \n",
    "  * When do they ride? \n",
    "  * How far do they go? \n",
    "  * Which stations are most popular? \n",
    "  * On what days of the week are most rides taken? \n",
    "  * How do user patterns differ between members and casual riders? "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# PART I: DATA EXPLORATION\n",
    "\n",
    "# First look at the data\n",
    "\n",
    "* Read in raw data - we will be using [Pandas](http://pandas.pydata.org/pandas-docs/stable/10min.html) package to make a DataFrame\n",
    "* What are the attributes of the data?\n",
    "* Summary statistics"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seq_id</th>\n",
       "      <th>hubway_id</th>\n",
       "      <th>status</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>strt_statn</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_statn</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subsc_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Closed</td>\n",
       "      <td>9</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Closed</td>\n",
       "      <td>220</td>\n",
       "      <td>7/28/2011 10:21:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:25:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>Closed</td>\n",
       "      <td>56</td>\n",
       "      <td>7/28/2011 10:33:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:34:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>Closed</td>\n",
       "      <td>64</td>\n",
       "      <td>7/28/2011 10:35:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:36:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>Closed</td>\n",
       "      <td>12</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seq_id  hubway_id  status  duration          start_date  strt_statn  \\\n",
       "0       1          8  Closed         9  7/28/2011 10:12:00        23.0   \n",
       "1       2          9  Closed       220  7/28/2011 10:21:00        23.0   \n",
       "2       3         10  Closed        56  7/28/2011 10:33:00        23.0   \n",
       "3       4         11  Closed        64  7/28/2011 10:35:00        23.0   \n",
       "4       5         12  Closed        12  7/28/2011 10:37:00        23.0   \n",
       "\n",
       "             end_date  end_statn bike_nr  subsc_type zip_code  birth_date  \\\n",
       "0  7/28/2011 10:12:00       23.0  B00468  Registered   '97217      1976.0   \n",
       "1  7/28/2011 10:25:00       23.0  B00554  Registered   '02215      1966.0   \n",
       "2  7/28/2011 10:34:00       23.0  B00456  Registered   '02108      1943.0   \n",
       "3  7/28/2011 10:36:00       23.0  B00554  Registered   '02116      1981.0   \n",
       "4  7/28/2011 10:37:00       23.0  B00554  Registered   '97214      1983.0   \n",
       "\n",
       "   gender  \n",
       "0    Male  \n",
       "1    Male  \n",
       "2    Male  \n",
       "3  Female  \n",
       "4  Female  "
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubway_data = pd.read_csv('hubway_trips.csv') # read in the data as a DataFrame\n",
    "hubway_data.head() # shows the first couple of rows of the dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "What is the dimensionality of our dataset (number of rows, number of columns)?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(1579025, 13)\n"
     ]
    }
   ],
   "source": [
    "print(hubway_data.shape) # what is the dimensionality of our dataset (number of rows, number of columns)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Summary statistics for our dataset for both numerical and categorical variables:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 197,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seq_id</th>\n",
       "      <th>hubway_id</th>\n",
       "      <th>status</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>strt_statn</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_statn</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subsc_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>1.579025e+06</td>\n",
       "      <td>1.579025e+06</td>\n",
       "      <td>1579025</td>\n",
       "      <td>1.579025e+06</td>\n",
       "      <td>1579025</td>\n",
       "      <td>1.579011e+06</td>\n",
       "      <td>1579025</td>\n",
       "      <td>1.578980e+06</td>\n",
       "      <td>1578559</td>\n",
       "      <td>1579025</td>\n",
       "      <td>1106259</td>\n",
       "      <td>350644.000000</td>\n",
       "      <td>1106414</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>521432</td>\n",
       "      <td>NaN</td>\n",
       "      <td>515102</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1163</td>\n",
       "      <td>2</td>\n",
       "      <td>530</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Closed</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7/30/2013 17:18:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10/19/2013 16:26:00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>B00490</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02118</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1579025</td>\n",
       "      <td>NaN</td>\n",
       "      <td>25</td>\n",
       "      <td>NaN</td>\n",
       "      <td>27</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2138</td>\n",
       "      <td>1106414</td>\n",
       "      <td>121749</td>\n",
       "      <td>NaN</td>\n",
       "      <td>834714</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>7.895130e+05</td>\n",
       "      <td>8.865317e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.200280e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.438039e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>5.425603e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1976.295322</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>4.558254e+05</td>\n",
       "      <td>5.064783e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.653539e+04</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.364295e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.347219e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>10.999742</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000e+00</td>\n",
       "      <td>8.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>-6.900000e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>3.000000e+00</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1932.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>3.947570e+05</td>\n",
       "      <td>4.465250e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.120000e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.700000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2.900000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1969.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>7.895130e+05</td>\n",
       "      <td>8.950440e+05</td>\n",
       "      <td>NaN</td>\n",
       "      <td>6.600000e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.800000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>4.800000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1979.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>1.184269e+06</td>\n",
       "      <td>1.328083e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.082000e+03</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.400000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.400000e+01</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1985.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>1.579025e+06</td>\n",
       "      <td>1.748022e+06</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.199446e+07</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1.450000e+02</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>1995.000000</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              seq_id     hubway_id   status      duration          start_date  \\\n",
       "count   1.579025e+06  1.579025e+06  1579025  1.579025e+06             1579025   \n",
       "unique           NaN           NaN        1           NaN              521432   \n",
       "top              NaN           NaN   Closed           NaN  7/30/2013 17:18:00   \n",
       "freq             NaN           NaN  1579025           NaN                  25   \n",
       "mean    7.895130e+05  8.865317e+05      NaN  1.200280e+03                 NaN   \n",
       "std     4.558254e+05  5.064783e+05      NaN  2.653539e+04                 NaN   \n",
       "min     1.000000e+00  8.000000e+00      NaN -6.900000e+03                 NaN   \n",
       "25%     3.947570e+05  4.465250e+05      NaN  4.120000e+02                 NaN   \n",
       "50%     7.895130e+05  8.950440e+05      NaN  6.600000e+02                 NaN   \n",
       "75%     1.184269e+06  1.328083e+06      NaN  1.082000e+03                 NaN   \n",
       "max     1.579025e+06  1.748022e+06      NaN  1.199446e+07                 NaN   \n",
       "\n",
       "          strt_statn             end_date     end_statn  bike_nr  subsc_type  \\\n",
       "count   1.579011e+06              1579025  1.578980e+06  1578559     1579025   \n",
       "unique           NaN               515102           NaN     1163           2   \n",
       "top              NaN  10/19/2013 16:26:00           NaN   B00490  Registered   \n",
       "freq             NaN                   27           NaN     2138     1106414   \n",
       "mean    5.438039e+01                  NaN  5.425603e+01      NaN         NaN   \n",
       "std     3.364295e+01                  NaN  3.347219e+01      NaN         NaN   \n",
       "min     3.000000e+00                  NaN  3.000000e+00      NaN         NaN   \n",
       "25%     2.700000e+01                  NaN  2.900000e+01      NaN         NaN   \n",
       "50%     4.800000e+01                  NaN  4.800000e+01      NaN         NaN   \n",
       "75%     7.400000e+01                  NaN  7.400000e+01      NaN         NaN   \n",
       "max     1.450000e+02                  NaN  1.450000e+02      NaN         NaN   \n",
       "\n",
       "       zip_code     birth_date   gender  \n",
       "count   1106259  350644.000000  1106414  \n",
       "unique      530            NaN        2  \n",
       "top      '02118            NaN     Male  \n",
       "freq     121749            NaN   834714  \n",
       "mean        NaN    1976.295322      NaN  \n",
       "std         NaN      10.999742      NaN  \n",
       "min         NaN    1932.000000      NaN  \n",
       "25%         NaN    1969.000000      NaN  \n",
       "50%         NaN    1979.000000      NaN  \n",
       "75%         NaN    1985.000000      NaN  \n",
       "max         NaN    1995.000000      NaN  "
      ]
     },
     "execution_count": 197,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubway_data.describe(include = 'all') # get counts and summary statistics for the data (include is to show results for numerical and categorical data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Selecting and Filtering Data\n",
    "* Select columns you are interested in"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>start_date</th>\n",
       "      <th>end_date</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>7/28/2011 10:21:00</td>\n",
       "      <td>7/28/2011 10:25:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>7/28/2011 10:33:00</td>\n",
       "      <td>7/28/2011 10:34:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>7/28/2011 10:35:00</td>\n",
       "      <td>7/28/2011 10:36:00</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           start_date            end_date\n",
       "0  7/28/2011 10:12:00  7/28/2011 10:12:00\n",
       "1  7/28/2011 10:21:00  7/28/2011 10:25:00\n",
       "2  7/28/2011 10:33:00  7/28/2011 10:34:00\n",
       "3  7/28/2011 10:35:00  7/28/2011 10:36:00\n",
       "4  7/28/2011 10:37:00  7/28/2011 10:37:00"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "columns_you_want = ['start_date', 'end_date'] # specify columns you're interested in\n",
    "chosen_columns = hubway_data[columns_you_want] # select the columns\n",
    "chosen_columns.head() # show first couple of lines of this new variable "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Filter the data - let's get the data of all the millennials"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 199,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seq_id</th>\n",
       "      <th>hubway_id</th>\n",
       "      <th>status</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>strt_statn</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_statn</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subsc_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>Closed</td>\n",
       "      <td>12</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>Closed</td>\n",
       "      <td>8</td>\n",
       "      <td>7/28/2011 11:01:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 11:01:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>Closed</td>\n",
       "      <td>1108</td>\n",
       "      <td>7/28/2011 11:55:00</td>\n",
       "      <td>47.0</td>\n",
       "      <td>7/28/2011 12:13:00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>B00550</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'01867</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>Male</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>31</td>\n",
       "      <td>Closed</td>\n",
       "      <td>1020</td>\n",
       "      <td>7/28/2011 12:01:00</td>\n",
       "      <td>38.0</td>\n",
       "      <td>7/28/2011 12:18:00</td>\n",
       "      <td>36.0</td>\n",
       "      <td>B00147</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02118</td>\n",
       "      <td>1987.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>33</td>\n",
       "      <td>Closed</td>\n",
       "      <td>1264</td>\n",
       "      <td>7/28/2011 12:01:00</td>\n",
       "      <td>38.0</td>\n",
       "      <td>7/28/2011 12:22:00</td>\n",
       "      <td>44.0</td>\n",
       "      <td>B00165</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02139</td>\n",
       "      <td>1985.0</td>\n",
       "      <td>Female</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    seq_id  hubway_id  status  duration          start_date  strt_statn  \\\n",
       "4        5         12  Closed        12  7/28/2011 10:37:00        23.0   \n",
       "8        9         16  Closed         8  7/28/2011 11:01:00        23.0   \n",
       "9       10         17  Closed      1108  7/28/2011 11:55:00        47.0   \n",
       "18      19         31  Closed      1020  7/28/2011 12:01:00        38.0   \n",
       "19      20         33  Closed      1264  7/28/2011 12:01:00        38.0   \n",
       "\n",
       "              end_date  end_statn bike_nr  subsc_type zip_code  birth_date  \\\n",
       "4   7/28/2011 10:37:00       23.0  B00554  Registered   '97214      1983.0   \n",
       "8   7/28/2011 11:01:00       23.0  B00554  Registered   '97214      1983.0   \n",
       "9   7/28/2011 12:13:00       40.0  B00550  Registered   '01867      1994.0   \n",
       "18  7/28/2011 12:18:00       36.0  B00147  Registered   '02118      1987.0   \n",
       "19  7/28/2011 12:22:00       44.0  B00165  Registered   '02139      1985.0   \n",
       "\n",
       "    gender  \n",
       "4   Female  \n",
       "8   Female  \n",
       "9     Male  \n",
       "18  Female  \n",
       "19  Female  "
      ]
     },
     "execution_count": 199,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "millennials = hubway_data[hubway_data.birth_date > 1982] # select the birth_date column and only take entries that are larger than year 1982\n",
    "millennials.head() # display first couple of lines"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Split Data by Groups\n",
    "* [Split](https://pandas.pydata.org/pandas-docs/stable/groupby.html) bike trips by type of user (registered vs. casual)\n",
    "* Are the bike trips between registered and casual users different in duration?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "subsc_type\n",
       "Casual        2293.442214\n",
       "Registered     733.328984\n",
       "Name: duration, dtype: float64"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grouped_data = hubway_data.groupby('subsc_type') # split data\n",
    "grouped_data.mean()['duration'] # calculate the mean for the variable duration"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Applying a Function\n",
    "* Apply a function to a column of a DataFrame\n",
    "* Let's transform start date to get a year when the bike was checked out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 239,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def get_age(x):\n",
    "    \"\"\"\n",
    "    Calculate the age of the user.\n",
    "    x : birth date and start date in that order\n",
    "    \"\"\"\n",
    "    birthdate = x[0]\n",
    "    startdate = x[1]\n",
    "    # start date comes in the form of \"7/28/2011 10:12:00\"\n",
    "    # get just the year digits\n",
    "    check_out_year = int(startdate[-13:-9])\n",
    "    age = check_out_year - birthdate\n",
    "    return age"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    35.0\n",
       "1    45.0\n",
       "2    68.0\n",
       "3    30.0\n",
       "4    28.0\n",
       "Name: age, dtype: float64"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubway_data['age'] = hubway_data[['birth_date', 'start_date']].apply(get_age, axis=1)\n",
    "hubway_data['age'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Join data from another dataset \n",
    "* Location (latitude and longitude) corresponsing to each bike station\n",
    "* DataFrame.join is a convenient method for combining the columns of two potentially differently-indexed DataFrames into a single result DataFrame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 203,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "\n",
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       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>42.340021</td>\n",
       "      <td>-71.100812</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4</td>\n",
       "      <td>42.345392</td>\n",
       "      <td>-71.069616</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>5</td>\n",
       "      <td>42.341814</td>\n",
       "      <td>-71.090179</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>6</td>\n",
       "      <td>42.361285</td>\n",
       "      <td>-71.065140</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>7</td>\n",
       "      <td>42.353412</td>\n",
       "      <td>-71.044624</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   id        lat        lng\n",
       "0   3  42.340021 -71.100812\n",
       "1   4  42.345392 -71.069616\n",
       "2   5  42.341814 -71.090179\n",
       "3   6  42.361285 -71.065140\n",
       "4   7  42.353412 -71.044624"
      ]
     },
     "execution_count": 203,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "station_data = pd.read_csv('hubway_stations.csv')[['id', 'lat', 'lng']]\n",
    "station_data.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
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       "        text-align: right;\n",
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       "    }\n",
       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seq_id</th>\n",
       "      <th>hubway_id</th>\n",
       "      <th>status</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>strt_statn</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_statn</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subsc_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>lat</th>\n",
       "      <th>lng</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Closed</td>\n",
       "      <td>9</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Closed</td>\n",
       "      <td>220</td>\n",
       "      <td>7/28/2011 10:21:00</td>\n",
       "      <td>23</td>\n",
       "      <td>7/28/2011 10:25:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>45.0</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>Closed</td>\n",
       "      <td>56</td>\n",
       "      <td>7/28/2011 10:33:00</td>\n",
       "      <td>23</td>\n",
       "      <td>7/28/2011 10:34:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>68.0</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>Closed</td>\n",
       "      <td>64</td>\n",
       "      <td>7/28/2011 10:35:00</td>\n",
       "      <td>23</td>\n",
       "      <td>7/28/2011 10:36:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>30.0</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>Closed</td>\n",
       "      <td>12</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>28.0</td>\n",
       "      <td>42.359677</td>\n",
       "      <td>-71.059364</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seq_id  hubway_id  status  duration          start_date strt_statn  \\\n",
       "0       1          8  Closed         9  7/28/2011 10:12:00         23   \n",
       "1       2          9  Closed       220  7/28/2011 10:21:00         23   \n",
       "2       3         10  Closed        56  7/28/2011 10:33:00         23   \n",
       "3       4         11  Closed        64  7/28/2011 10:35:00         23   \n",
       "4       5         12  Closed        12  7/28/2011 10:37:00         23   \n",
       "\n",
       "             end_date  end_statn bike_nr  subsc_type zip_code  birth_date  \\\n",
       "0  7/28/2011 10:12:00       23.0  B00468  Registered   '97217      1976.0   \n",
       "1  7/28/2011 10:25:00       23.0  B00554  Registered   '02215      1966.0   \n",
       "2  7/28/2011 10:34:00       23.0  B00456  Registered   '02108      1943.0   \n",
       "3  7/28/2011 10:36:00       23.0  B00554  Registered   '02116      1981.0   \n",
       "4  7/28/2011 10:37:00       23.0  B00554  Registered   '97214      1983.0   \n",
       "\n",
       "   gender   age        lat        lng  \n",
       "0    Male  35.0  42.359677 -71.059364  \n",
       "1    Male  45.0  42.359677 -71.059364  \n",
       "2    Male  68.0  42.359677 -71.059364  \n",
       "3  Female  30.0  42.359677 -71.059364  \n",
       "4  Female  28.0  42.359677 -71.059364  "
      ]
     },
     "execution_count": 204,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubway_data_with_gps = hubway_data.join(station_data.set_index('id'), on='strt_statn')\n",
    "hubway_data_with_gps.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# DATA VISUALIZATION\n",
    "* identify hidden patterns and trends\n",
    "* formulate hypothesis\n",
    "* determine best steps for modeling\n",
    "* communicate results"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's explore users that only ride for a relatively short amount of time (less than 2 hours). We will also remove trips where some information about the trip is missing using [dropna() function](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.dropna.html) to make plotting easier."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 205,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>seq_id</th>\n",
       "      <th>hubway_id</th>\n",
       "      <th>status</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>strt_statn</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_statn</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subsc_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Closed</td>\n",
       "      <td>9</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>35.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Closed</td>\n",
       "      <td>220</td>\n",
       "      <td>7/28/2011 10:21:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:25:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>45.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>Closed</td>\n",
       "      <td>56</td>\n",
       "      <td>7/28/2011 10:33:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:34:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>Male</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>Closed</td>\n",
       "      <td>64</td>\n",
       "      <td>7/28/2011 10:35:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:36:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>30.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>Closed</td>\n",
       "      <td>12</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>Female</td>\n",
       "      <td>28.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seq_id  hubway_id  status  duration          start_date  strt_statn  \\\n",
       "0       1          8  Closed         9  7/28/2011 10:12:00        23.0   \n",
       "1       2          9  Closed       220  7/28/2011 10:21:00        23.0   \n",
       "2       3         10  Closed        56  7/28/2011 10:33:00        23.0   \n",
       "3       4         11  Closed        64  7/28/2011 10:35:00        23.0   \n",
       "4       5         12  Closed        12  7/28/2011 10:37:00        23.0   \n",
       "\n",
       "             end_date  end_statn bike_nr  subsc_type zip_code  birth_date  \\\n",
       "0  7/28/2011 10:12:00       23.0  B00468  Registered   '97217      1976.0   \n",
       "1  7/28/2011 10:25:00       23.0  B00554  Registered   '02215      1966.0   \n",
       "2  7/28/2011 10:34:00       23.0  B00456  Registered   '02108      1943.0   \n",
       "3  7/28/2011 10:36:00       23.0  B00554  Registered   '02116      1981.0   \n",
       "4  7/28/2011 10:37:00       23.0  B00554  Registered   '97214      1983.0   \n",
       "\n",
       "   gender   age  \n",
       "0    Male  35.0  \n",
       "1    Male  45.0  \n",
       "2    Male  68.0  \n",
       "3  Female  30.0  \n",
       "4  Female  28.0  "
      ]
     },
     "execution_count": 205,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "short_distance_trips = hubway_data[hubway_data.duration < 7200].dropna()\n",
    "short_distance_trips.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Scatter plots - numerical data - useful for exploring correlations in data\n",
    "* Age vs. duration of bike trip "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Duration (in seconds)')"
      ]
     },
     "execution_count": 206,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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dAffO+U9t5NRxhdG5l58ypuhhb42IqaiWuGlOgkIjT1C1Uq2+PThL7ulVhg3O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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6df51cd68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(short_distance_trips['age'], short_distance_trips['duration'])\n",
    "plt.title('Scatter plot of Duration by User Ages')\n",
    "plt.xlabel('Age in years')\n",
    "plt.ylabel('Duration (in seconds)')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Histograms - distribution of the variable\n",
    "* Useful for identifying outliers, multi-modality"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 207,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Histogram of User Age')"
      ]
     },
     "execution_count": 207,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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RI+jbty8nnXQSq1atqr8PElBtXbaPkwEDBkRTe3jax23qsx+e9vGzePFijjzyyFKH0eA2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\nAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6c3dd7898>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(short_distance_trips['age'], bins = 10)\n",
    "plt.axvline(short_distance_trips['age'].mean(), color='green', label='Average Age')\n",
    "plt.legend()\n",
    "plt.xlabel('Age')\n",
    "plt.ylabel('Frequency')\n",
    "plt.title('Histogram of User Age')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "* Bar plot - useful for categorical data\n",
    "* Let's [obtain counts](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.Series.value_counts.html) of number of users by gender and plot them"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Male      262841\n",
       "Female     86684\n",
       "Name: gender, dtype: int64"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "gender_counts = short_distance_trips['gender'].value_counts()\n",
    "gender_counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 281,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Users by Gender')"
      ]
     },
     "execution_count": 281,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6874752e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(2), gender_counts, align='center', color=['gray', 'teal'])\n",
    "plt.xticks([0, 1], ['male', 'female'])\n",
    "plt.title('Users by Gender')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# DATA CLEANING\n",
    "* Wrong values\n",
    "* Messy format\n",
    "* Too many observations - do preliminary analysis on a subset of data\n",
    "* Missing data\n",
    "* Drop samples with problematic values\n",
    "* Use mean, median or most common value of the feature\n",
    "* Use a model to estimate the value\n",
    "* Data might not be missing at random"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Dropping missing values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "hubway_data_droppped = hubway_data.dropna()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Dropping wrong values\n",
    "* Explore outliers using histograms\n",
    "* Trip duration should be a positive number\n",
    "* Trip duration cannot be too long"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 211,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency')"
      ]
     },
     "execution_count": 211,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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20wZt3yTpSuB7NFdVt9FcdXwTWCvp0yW2qpyyCrhMUg/NFdDCUs9mSeuAu0o9Z9h+rvT3\nTGADzcy71bY3l7o+NkAbERFRwXCerHoEzVUJwO8AN/DCCQAds70cWN4vfB/NzLb+ZX8OnDJAPecA\n57SJX03zhdr+8bZtREREHcN5qN1htp8EkPRJ4Cu2/3CsOhYREbu/Tr8ndDDwbMv+s8CsUe9NRERM\nKJ1eCV0G3CzpH2imNf8ucOmY9SoiIiaETmfHnSPpGprVEgBOs33b2HUrIiImgk5vx0GzvM4Ttv+e\n5js7s8eoTxERMUF0uoDpcprpzWeV0MvYubXjIiIiOr4S+l3gRODfAGw/zAiX7YmIiOjTaRJ6tjw/\nyACSfmXsuhQRERNFp0lonaQv0Ky99iHg28DFY9etiIiYCDqdHfcZSe8FngDeAHzC9sYx7VlEROz2\nhkxC5UmlG2y/B0jiiYiIUTPk7biyKOhTkvYbh/5ERMQE0umKCT8H7pC0kTJDDsD2H49JryIiYkLo\nNAl9s7wiIiJGzaBJSNLBth+0vWa8OhQRERPHUJ8Jfb1vQ9JXx7gvERExwQyVhNSy/Zqx7EhEREw8\nQyUhD7C9UyTtL+lKST+UdLekd0h6laSNku4t71NKWUk6X1KPpNslHdZSz+JS/l5Ji1vih0u6o5xz\nviSVeNs2IiKijqGS0FskPSHpSeDNZfsJSU9KemIn2v174Fu23wi8BbgbWAZca3sOcG3ZBzgemFNe\nS4GLoEkoNI8IfzvNI7uXtySVi0rZvvPml/hAbURERAWDJiHbk2zva/uVtieX7b79fUfSoKR9gd8G\nVpU2nrX9GLAA6JsAsQY4qWwvAC51YxPN0kEHAccBG21vs72d5ou088uxfW3fWNa7u7RfXe3aiIiI\nCobzPKHR8hpgK/A/Jd0m6YtlQdRX234EoLwfWMpPBx5qOb+3xAaL97aJM0gbERFRQY0kNBk4DLjI\n9ttovvw62G0xtYl5BPGOSVoqqVtS99atW4dzakREDEONJNQL9Nq+qexfSZOUflJupVHet7SUn9ly\n/gzg4SHiM9rEGaSNF7C90naX7a5p06aNaJARETG0cU9Ctv8VeEjSG0roGOAuYD3QN8NtMXBV2V4P\nnFpmyc0DHi+30jYAx0qaUiYkHEuz0OojwJOS5pVZcaf2q6tdGxERUUGny/aMtg8DX5K0J3AfcBpN\nQlwnaQnwIHBKKXs1cALQAzxVymJ7m6RPAbeUcmfb3la2TwcuAfYBrikvgHMHaCMiIiqokoRsfx/o\nanPomDZlDZwxQD2rgdVt4t3AoW3iP23XRkRE1FHjM6GIiAggSSgiIipKEoqIiGqShCIiopokoYiI\nqCZJKCIiqkkSioiIapKEIiKimiShiIioJkkoIiKqSRKKiIhqkoQiIqKaJKGIiKgmSSgiIqpJEoqI\niGqShCIiopokoYiIqCZJKCIiqqmWhCRNknSbpG+U/dmSbpJ0r6QrJO1Z4nuV/Z5yfFZLHWeV+D2S\njmuJzy+xHknLWuJt24iIiDpqXgn9CXB3y/55wArbc4DtwJISXwJst/06YEUph6S5wELgEGA+cGFJ\nbJOAC4DjgbnAolJ2sDYiIqKCKklI0gzgfcAXy76Ao4ErS5E1wElle0HZpxw/ppRfAKy1/Yzt+4Ee\n4Mjy6rF9n+1ngbXAgiHaiIiICmpdCf0d8BfAL8v+AcBjtneU/V5getmeDjwEUI4/Xso/H+93zkDx\nwdp4AUlLJXVL6t66detIxxgREUMY9yQk6f3AFtu3tobbFPUQx0Yr/uKgvdJ2l+2uadOmtSsSERGj\nYHKFNt8JnCjpBGBvYF+aK6P9JU0uVyozgIdL+V5gJtAraTKwH7CtJd6n9Zx28UcHaSMiIioY9ysh\n22fZnmF7Fs3EgutsfwC4Hji5FFsMXFW215d9yvHrbLvEF5bZc7OBOcDNwC3AnDITbs/SxvpyzkBt\nREREBbvS94Q+BnxUUg/N5zerSnwVcECJfxRYBmB7M7AOuAv4FnCG7efKVc6ZwAaa2XfrStnB2oiI\niApq3I57nu3vAN8p2/fRzGzrX+bnwCkDnH8OcE6b+NXA1W3ibduIiIg6dqUroYiImGCShCIiopok\noYiIqCZJKCIiqkkSioiIapKEIiKimiShiIioJkkoIiKqSRKKiIhqkoQiIqKaJKGIiKgmSSgiIqpJ\nEoqIiGqShCIiopokoYiIqCZJKCIiqkkSioiIasY9CUmaKel6SXdL2izpT0r8VZI2Srq3vE8pcUk6\nX1KPpNslHdZS1+JS/l5Ji1vih0u6o5xzviQN1kZERNRR40poB/Bntt8EzAPOkDQXWAZca3sOcG3Z\nBzgemFNeS4GLoEkowHLg7TSP7F7eklQuKmX7zptf4gO1ERERFYx7ErL9iO3vle0ngbuB6cACYE0p\ntgY4qWwvAC51YxOwv6SDgOOAjba32d4ObATml2P72r7RtoFL+9XVro2IiKig6mdCkmYBbwNuAl5t\n+xFoEhVwYCk2HXio5bTeEhss3tsmziBtREREBdWSkKRXAF8F/tT2E4MVbRPzCOLD6dtSSd2Surdu\n3TqcUyMiYhiqJCFJL6NJQF+y/bUS/km5lUZ531LivcDMltNnAA8PEZ/RJj5YGy9ge6XtLttd06ZN\nG9kgIyJiSDVmxwlYBdxt+7Mth9YDfTPcFgNXtcRPLbPk5gGPl1tpG4BjJU0pExKOBTaUY09Kmlfa\nOrVfXe3aiIiICiZXaPOdwB8Ad0j6fol9HDgXWCdpCfAgcEo5djVwAtADPAWcBmB7m6RPAbeUcmfb\n3la2TwcuAfYBrikvBmkjIiIqGPckZPufaP+5DcAxbcobOGOAulYDq9vEu4FD28R/2q6NiIioIysm\nRERENUlCERFRTZJQRERUkyQUERHVJAlFREQ1SUIREVFNklBERFSTJBQREdUkCUVERDVJQhERUU2S\nUEREVJMkFBER1SQJRURENUlCERFRTZJQRERUkyQUERHVJAlFREQ1EzIJSZov6R5JPZKW1e5PRMRE\nNeGSkKRJwAXA8cBcYJGkuXV7FRExMU24JAQcCfTYvs/2s8BaYEHlPkVETEgTMQlNBx5q2e8tsYiI\nGGeTa3egArWJ+QUFpKXA0rL7M0n37ER7U4FHd+L8EdF5o15llXGMkYxl17O7jAMylj6/3kmhiZiE\neoGZLfszgIdbC9heCawcjcYkddvuGo26atpdxgEZy65odxkHZCzDNRFvx90CzJE0W9KewEJgfeU+\nRURMSBPuSsj2DklnAhuAScBq25srdysiYkKacEkIwPbVwNXj1Nyo3NbbBewu44CMZVe0u4wDMpZh\nke2hS0VERIyBifiZUERE7CKShEbBUMsASdpL0hXl+E2SZo1/LzvTwVg+KukuSbdLulZSR9Mwa+h0\neSZJJ0uypF1yRlMn45D0e+XfZbOkL493HzvVwc/XwZKul3Rb+Rk7oUY/hyJptaQtku4c4LgknV/G\nebukw8a7j53qYCwfKGO4XdK/SHrLqHbAdl478aKZ3PB/gdcAewI/AOb2K/NHwOfL9kLgitr93omx\nvBt4edk+/aU8llLulcANwCagq3a/R/hvMge4DZhS9g+s3e+dGMtK4PSyPRd4oHa/BxjLbwOHAXcO\ncPwE4Bqa7yXOA26q3eedGMtvtPxsHT/aY8mV0M7rZBmgBcCasn0lcIykdl+arW3Isdi+3vZTZXcT\nzfesdkWdLs/0KeCvgZ+PZ+eGoZNxfAi4wPZ2ANtbxrmPnepkLAb2Ldv70e87fLsK2zcA2wYpsgC4\n1I1NwP6SDhqf3g3PUGOx/S99P1uMwe98ktDO62QZoOfL2N4BPA4cMC69G57hLmm0hOavvV3RkGOR\n9DZgpu1vjGfHhqmTf5PXA6+X9M+SNkmaP269G55OxvJJ4Pcl9dLMYP3w+HRt1O2uy4ON+u/8hJyi\nPcqGXAaowzK7go77Ken3gS7gXWPao5EbdCyS9gBWAB8crw6NUCf/JpNpbskdRfNX6j9KOtT2Y2Pc\nt+HqZCyLgEts/62kdwCXlbH8cuy7N6peKr/zHZP0bpok9JujWW+uhHbekMsAtZaRNJnmNsNgl/K1\ndDIWJL0H+EvgRNvPjFPfhmuosbwSOBT4jqQHaO7br98FJyd0+vN1le1f2L4fuIcmKe1qOhnLEmAd\ngO0bgb1p1i97qenod+mlQtKbgS8CC2z/dDTrThLaeZ0sA7QeWFy2Twauc/mUbxcz5FjKLawv0CSg\nXfWzBxhiLLYftz3V9izbs2judZ9ou7tOdwfUyc/X12kmjCBpKs3tufvGtZed6WQsDwLHAEh6E00S\n2jquvRwd64FTyyy5ecDjth+p3amRkHQw8DXgD2z/aLTrz+24neQBlgGSdDbQbXs9sIrmtkIPzRXQ\nwno9HliHY/kb4BXAV8rcigdtn1it0wPocCy7vA7HsQE4VtJdwHPAfx/tv1ZHQ4dj+TPgYkkfobl9\n9cFd8Q82SZfT3P6cWj6/Wg68DMD252k+zzoB6AGeAk6r09OhdTCWT9B8hn1h+Z3f4VFc1DQrJkRE\nRDW5HRcREdUkCUVERDVJQhERUU2SUEREVJMkFBERzxtqQdN+ZVdI+n55/UjSsL8gnSQUMU4kPVd+\nWTdL+kFZkXzUfgclfVDSr7Xsf1HS3NGqPyaMS4COln6y/RHbb7X9VuB/0HyfaFiShCLGz9PlF/YQ\n4L003yNZPpwKJE0a5PAHgeeTkO0/tH3XSDoaE1e7BU0lvVbStyTdKukfJb2xzamLgMuH216SUEQF\nZbWJpcCZ5Vv1H5T0ub7jkr4h6aiy/TNJZ0u6CXiHpE9IukXSnZJWlvNPplnL70vlamsfSd/pW4ZI\n0iJJd5Rzzmtp52eSzilXZpskvXo8/zvES8ZK4MO2Dwf+HLiw9aCa54rNBq4bbsVJQhGV2L6P5nfw\nwCGK/grNs17ebvufgM/ZPsL2ocA+wPttXwl0Ax8oV1tP951cbtGdBxwNvBU4QtJJLXVvsv0Wmucq\nfWgUhxi7AUmvoHmm0FckfZ9m2a7+j6VYCFxp+7nh1p8kFFFXJ8+Veg74asv+u9U8ofcOmsRyyBDn\nHwF8x/bW8iiRL9E8yAzgWaDvURa3ArM67XhMGHsAj/V99lNeb+pXZiEjuBXXV3lEVCDpNTQJZguw\ngxf+Pu7dsv3zvr8wJe1NcyvkZNv/Abi4X9m2TQ1y7Bcta7M9R9aTjH5sPwHcL+kUeP7R5c8/4lvS\nG4ApwI0jqT9JKKICSdOAz9PcWjPwAPBWSXtImknzFNJ2+hLOo+U2ycktx56keURFfzcB75I0tUxs\nWAR8dxSGEbuhsqDpjcAbJPVKWgJ8AFgi6QfAZl74RNxFwNqRLjSbv3oixs8+5Z76y2iufC4DPluO\n/TNwP3AHcCfwvXYV2H5M0sWl3AM0j0focwnweUlPA+9oOecRSWcB19NcFV1t+6rRG1bsTmwvGuBQ\n22nbtj+5M+1lFe2IiKgmt+MiIqKaJKGIiKgmSSgiIqpJEoqIiGqShCIiopokoYiIqCZJKCIiqkkS\nioiIav4/XKr17LaXizoAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6c3dd7048>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(hubway_data.duration.dropna())\n",
    "plt.xlabel('Duration')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Minimum duration =  -6900\n",
      "Maximum duration =  11994458\n"
     ]
    }
   ],
   "source": [
    "print('Minimum duration = ', np.min(hubway_data.duration))\n",
    "print('Maximum duration = ', np.max(hubway_data.duration))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Currently the maximum trip duration is 11994458 seconds, which is approximately 138 days. Something must have gone wrong during the data recording process. The minimum trip duration is a negative value, which cannot occur. \n",
    "Let's filter data by duration:\n",
    "* Trip duration has to be positive and probably less than 8 hours (28800 seconds)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 213,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1571813, 14)"
      ]
     },
     "execution_count": 213,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubway_data_clean = hubway_data[(hubway_data.duration > 0) & (hubway_data.duration < 28800)] \n",
    "hubway_data_clean.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We can check the histogram for duraton of our filtered dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency')"
      ]
     },
     "execution_count": 214,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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gdmALcE1VrQOuadsAJwHr2mszcGEbzyHAOcArgWOBcwaSz4Wt7eR+G1p8uj4kST02W7LK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\nqfdMVpKk3jNZSZJ6z2QlSeq9/w8+F4adS7/cMgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe7a64d5080>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(hubway_data_clean.duration.dropna())\n",
    "plt.xlabel('Duration')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Filling in missing data with summary statistics\n",
    "* Impute missing data by replacing it with mean, median or the most frequent value\n",
    "* Most frequent value could be a good choice for categorical data\n",
    "* Imputation reduces variability within the dataset, which will impact your model's performance\n",
    "* Evaluate which imputation technique gives the best perfomance\n",
    "* Let's impute user's age and trip duration using the most frequent value\n",
    "* We will specify the parameters for the [imputer](http://scikit-learn.org/stable/modules/generated/sklearn.preprocessing.Imputer.html)\n",
    "* Fit the imputer (find most frequent value in this case) and transform the data accordingly"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 215,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "imp = preprocessing.Imputer(missing_values='NaN', strategy='most_frequent') # specify the imputer\n",
    "hubway_data[['age', 'duration']] = imp.fit_transform(hubway_data[['age', 'duration']]) # fit the imputer and transform the input"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Filling in missing data with k-nearest neighbors (k-NN)\n",
    "* Fit a model to the data that is not missing\n",
    "* Use the model to predict the values for missing data\n",
    "* k-NN finds $k$ samples closest in distance to the missing point and predicts the label from these closest points\n",
    "* k-NN classification: output is a category decided by majority vote of its $k$ neighbors\n",
    "* k-NN regression: output is the average of the values of its $k$ nearest neighbors\n",
    "* Weigh contribution of each point can be weighted by its distance from the point of interest\n",
    "* Distance metric matters, number of neighbors matters\n",
    "* Pick parameters that give you best performance on the final task"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/KnnClassification.svg/220px-KnnClassification.svg.png\" width=\"300\" height=\"300\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(url='https://upload.wikimedia.org/wikipedia/commons/thumb/e/e7/KnnClassification.svg/220px-KnnClassification.svg.png', width= 300, height= 300)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "We will use the duration column to impute missing birth dates."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 271,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "KNeighborsRegressor(algorithm='auto', leaf_size=30, metric='minkowski',\n",
       "          metric_params=None, n_jobs=1, n_neighbors=10, p=2,\n",
       "          weights='uniform')"
      ]
     },
     "execution_count": 271,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model = neighbors.KNeighborsRegressor(n_neighbors=10, weights = 'distance')\n",
    "model.fit(hubway_data_droppped[['duration']], hubway_data_droppped[['birth_date']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "missing_birth_dates = hubway_data[pd.isnull(hubway_data['birth_date'])]['duration']\n",
    "imputed_birthdates = model.predict(missing_birth_dates.values.reshape(-1, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 279,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0,0.5,'Frequency')"
      ]
     },
     "execution_count": 279,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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2S7KerlfZKroRpE8CvgIc14pfAhwFrAUeAF4HUFX3JDkDuKaVe2tVTXU6+E26Hm/bAx9r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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6874a5a90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(imputed_birthdates, bins = 20)\n",
    "plt.xlabel('Imputed birth date values')\n",
    "plt.ylabel('Frequency')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# FEATURE ENGINEERING\n",
    "* What is the question you are trying to answer\n",
    "* Creating new features that might correlate well with your prediction target\n",
    "* How to transform categorical data (male vs. female) to numerical"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Example: classify whether a trip was taken by a casual or registered user\n",
    "* What features might distinguish casual vs. registered users\n",
    "* Registered users probably use the bike on the regular basis to for example, to get to work\n",
    "* Registered users are likely using bikes around 9am and 5pm to get to and from work\n",
    "* Casual users might be more likely to rent a bike for city exploring, which might be in the afternoon\n",
    "* Let's engineer a create a new feature - the hour the bike was checked out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 216,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "def get_check_out_hours(s):\n",
    "    \"\"\"\n",
    "    Return the hour the bike was checked out.\n",
    "    s : date in form of 7/28/2011 10:12:00 or 11/30/2013 23:32:00\n",
    "    \"\"\"\n",
    "    # get just the hour digits\n",
    "    return int(s[-8:-6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 217,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Time of Day vs Checkouts')"
      ]
     },
     "execution_count": 217,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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OqxniDnRercmXT8aQJEkaUw7dSpIkjSkLPUmSpDFloSdJkjSmLPQk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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe6c3c6ce10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "hubway_data['check_out_hour'] = hubway_data['start_date'].apply(get_check_out_hours)\n",
    "plt.figure(figsize=(10, 5))\n",
    "check_out_counts = hubway_data['check_out_hour'].dropna().value_counts()\n",
    "plt.bar(range(0,24), check_out_counts.loc[range(0,24)])\n",
    "plt.xticks(range(24))\n",
    "plt.xlabel('Hour of Day')\n",
    "plt.ylabel('Number of Checkouts')\n",
    "plt.title('Time of Day vs Checkouts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's check our hypothesis by splitting the data into trips by subscription type, counting the number of checkouts corresponding to each hour and plotting a bar chart."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 218,
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [],
   "source": [
    "grouped = hubway_data.groupby('subsc_type') # split data by subscription type\n",
    "counts_by_group = grouped['check_out_hour'].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 219,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Time of Day vs Checkouts')"
      ]
     },
     "execution_count": 219,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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m1u40lOjdBvSMiCcLCyXtAiyuaKvMzMzMrGwNXaN3IVDsuWC9gN9XpjlmZmZm\nlpeGEr1dI+KeuoURcTswoHJNMjMzM7M8NJToNTTT1rNwzczMzNq4hhK9hZIOrlso6SCyW66YmZmZ\nWRvW0GSMk4G/Sfoq2cxbgKHAnsDoSjfMzMzMzMpTb49eRDwD7ArcA/RJr3uAAWmbmZmZmbVhDT4C\nLT327E8t1BYzMzMzy1Epj0AzMzMzs3bIiZ6ZmZlZlao30UvPtkXSuS3XHDMzMzPLS0PX6G0taV9g\njKRpgAo3RsTDFW2ZmZmZmZWloUTvZ8BpQG/gf+tsC2B4pRplZmZmZuWrN9GLiBuAGySdGRFnt2Cb\nzKwJJs0o/25HJ4/cPoeWmJlZW9Pg7VUAIuJsSWOAfVLRrIj4W2WbZWZmZmblanTWraRfAScBT6bX\nSanMzMzMzNqwRnv0gEOAgRHxEYCkKcAjwIRKNszMzMzMylPqffR6FCxvUomGmJmZmVm+SunR+xXw\niKS7yW6xsg/uzTOzJvKkEbN8+ZyyUpQyGeNaSbOA3ckSvVMj4rVKN8zMzMzMylNKjx4R8SowvcJt\nMTMzM7Mc+Vm3ZmZmZlXKiZ6ZmZlZlWow0ZO0nqT5LdUYMzMzM8tPg4leunfeY5I+neebSuoh6QZJ\nT0laIGlPSZtJmiFpYfq6aaorSRdIWiRpnqTBBXHGp/oLJY0vKB8i6fG0zwWSlGf7zczMzNqDUoZu\ntwaekDRT0vTaV5nv+3vgnxGxI7AbsAA4DZgZEf2AmWkd4CCgX3odB1wCIGkzYCLweWAYMLE2OUx1\njivYb1SZ7TUzMzNrd0qZdfs/eb6hpI3J7sV3FEBEfAB8IGkssF+qNgWYBZwKjAWuiogAHki9gVun\nujMiYlmKOwMYlW4Fs3FE3J/KrwIOA/6R53GYmZmZtXWN9uhFxD3A80CXtPwQ8HAZ7/lZYCnwJ0mP\nSLpc0kbAVuk2LrW3c9ky1e8FvFSwf00qa6i8pkj5OiQdJ2mOpDlLly4t45DMzMzM2p5GEz1J/wnc\nAPwhFfUCbi7jPTsDg4FLImIQ8A4fD9MWbUKRsmhG+bqFEZdFxNCIGNqzZ8+GW21mZmbWzpRyjd4P\ngC8AbwNExEI+7m1rjhqgJiIeTOs3kCV+i9OQLOnrkoL62xbs3xt4pZHy3kXKzczMzDqUUhK999N1\ndABI6kw9PWSlSI9Pe0nSDqloBPAk2ZM3amfOjgduScvTgW+n2bd7AMvT0O7twAGSNk2TMA4Abk/b\nVkjaI822/XZBLDMzM7MOo5TJGPdIOh3oJmkk8H3g1jLf97+AqZLWB54DjiZLOq+XdCzwInBEqnsb\ncDCwCHg31SUilkk6m+yaQYDl9YmTAAAU5ElEQVSzaidmAMcDVwLdyCZheCKGmZmZdTilJHqnAccC\njwPfJUu8Li/nTSPiUWBokU0jitQNsuHjYnEmA5OLlM8BdimnjWZmZmbtXaOJXkR8JGkK8CDZkO3T\nKfkyM7MSTZrxTNkxTh65fQ4taR0d/fjNWkujiZ6kQ4BLgWfJZrT2lfTdiPBwqJmZmVkbVsrQ7W+B\n/SNiEYCk7YC/4+vezMzMzNq0UmbdLqlN8pLn+PjWJ2ZmZmbWRtXboyfpK2nxCUm3AdeTXaN3BB/P\ndDUzMzOzNqqhodtDC5YXA/um5aXAphVrkZmZlcQTHMysMfUmehFxdEs2xMzMzMzyVcqs275kNzju\nU1g/IsZUrllmZmZmVq5SZt3eDFxB9jSMjyrbHDMzMzPLSymJ3qqIuKDiLTEzMzOzXJWS6P1e0kTg\nDuD92sKIeLhirTIzMzOzspWS6O0KfAsYzsdDt5HWzczMzKyNKiXR+zLw2Yj4oNKNMTOz1ufbtphV\nj1KejPEY0KPSDTEzMzOzfJXSo7cV8JSkh/jkNXq+vYqZmZlZG1ZKojex4q0wMzMzs9w1muhFxD0t\n0RAzMzMzy1cpT8ZYQTbLFmB9oAvwTkRsXMmGmZmZmVl5SunR6164LukwYFjFWmRmZmZmuShl1u0n\nRMTN+B56ZmZmZm1eKUO3XylYXQ8YysdDuWZmZmbWRpUy6/bQguU1wPPA2Iq0xszMzMxyU8o1eke3\nREPMzNoKPxnCzKpFvYmepJ81sF9ExNkVaI+ZmZmZ5aShHr13ipRtBBwLbA440TMzMzNrw+pN9CLi\nt7XLkroDJwFHA9OA39a3n5mZmZm1DQ1eoydpM+BHwDeAKcDgiHizJRpmZmZmZuVp6Bq984CvAJcB\nu0bEyhZrlZmZmbUKT0aqLg316P038D7wU+AMSbXlIpuM4UegmZlZSfZ48bIcopxf8ZiVjGvWGup9\nMkZErBcR3SKie0RsXPDqnkeSJ6mTpEck/S2t95X0oKSFkq6TtH4q3yCtL0rb+xTEmJDKn5Z0YEH5\nqFS2SNJp5bbVzMzMrD0q5YbJlXISsACoTRrPBSZFxDRJl5LN7r0kfX0zIj4naVyq9zVJ/YFxwM7A\nNsCdkmr7ii8GRgI1wEOSpkfEky11YGbWMvIYYgIPM5lZ9Wrys27zIKk3cAhweVoX2fNzb0hVpgCH\npeWxaZ20fUSqPxaYFhHvR8S/gUXAsPRaFBHPRcQHZLOE/SQPMzMz63Baq0fvd8BPgO5pfXPgrYhY\nk9ZrgF5puRfwEkBErJG0PNXvBTxQELNwn5fqlH8+7wMwM6tWvkbNrHq0eI+epNHAkoiYW1hcpGo0\nsq2p5cXacpykOZLmLF26tIFWm5mZmbU/rTF0+wVgjKTnyYZVh5P18PWQVNvD2Bt4JS3XANsCpO2b\nAMsKy+vsU1/5OiLisogYGhFDe/bsWf6RmZmZmbUhLZ7oRcSEiOgdEX3IJlPcFRHfAO4GDk/VxgO3\npOXpaZ20/a6IiFQ+Ls3K7Qv0A2YDDwH90ize9dN7TG+BQzMzMzNrU1pz1m1dpwLTJP0CeAS4IpVf\nAVwtaRFZT944gIh4QtL1wJPAGuAHEfEhgKQTgNuBTsDkiHiiRY/EzMzMrA1o1UQvImYBs9Lyc2Qz\nZuvWWQUcUc/+5wDnFCm/Dbgtx6aambU5njRhZo1pldurmJmZmVnlOdEzMzMzq1JO9MzMzMyqlBM9\nMzMzsyrVlmbdmpmZVSVPnLHW4kTPzKwF+A+9mbUGD92amZmZVSknemZmZmZVyomemZmZWZVyomdm\nZmZWpTwZw8zMzCpq0oxnyo5x8sjtc2hJx+MePTMzM7Mq5UTPzMzMrEo50TMzMzOrUk70zMzMzKqU\nEz0zMzOzKuVEz8zMzKxK+fYqZmZ1+Lm0ZlYt3KNnZmZmVqWc6JmZmZlVKSd6ZmZmZlXK1+iZmZm1\nQ76W1ErhHj0zMzOzKuVEz8zMzKxKOdEzMzMzq1K+Rs+shUya8UzZMU4euX0OLTEzs47CiZ6ZmZm1\nO/7nuTQeujUzMzOrUk70zMzMzKqUEz0zMzOzKtXiiZ6kbSXdLWmBpCcknZTKN5M0Q9LC9HXTVC5J\nF0haJGmepMEFscan+gsljS8oHyLp8bTPBZLU0sdpZmZm1tpao0dvDfDfEbETsAfwA0n9gdOAmRHR\nD5iZ1gEOAvql13HAJZAlhsBE4PPAMGBibXKY6hxXsN+oFjguMzMzszalxRO9iHg1Ih5OyyuABUAv\nYCwwJVWbAhyWlscCV0XmAaCHpK2BA4EZEbEsIt4EZgCj0raNI+L+iAjgqoJYZmZmZh1Gq95eRVIf\nYBDwILBVRLwKWTIoactUrRfwUsFuNamsofKaIuVmVmXyedYn+HmfZlatWm0yhqRPATcCP4yItxuq\nWqQsmlFerA3HSZojac7SpUsba7KZmZlZu9IqiZ6kLmRJ3tSI+GsqXpyGXUlfl6TyGmDbgt17A680\nUt67SPk6IuKyiBgaEUN79uxZ3kGZmZmZtTEtPnSbZsBeASyIiP8t2DQdGA/8On29paD8BEnTyCZe\nLE9Du7cDvyyYgHEAMCEilklaIWkPsiHhbwMXVvzAzMzMqkA+l0T4coi2ojWu0fsC8C3gcUmPprLT\nyRK86yUdC7wIHJG23QYcDCwC3gWOBkgJ3dnAQ6neWRGxLC0fD1wJdAP+kV5mZmZmHUqLJ3oR8f8o\nfh0dwIgi9QP4QT2xJgOTi5TPAXYpo5lmZmZm7Z6fjGFmZmZWpZzomZmZmVWpVr2PnpmZmVlbMmnG\nM2XHOHnk9jm0JB/u0TMzMzOrUu7RMzMzs4ryLVtaj3v0zMzMzKqUe/TMrEX4P3ozs5bnRM/MzMza\nHf/zWBoP3ZqZmZlVKSd6ZmZmZlXKQ7dmtg4PiZiZVQf36JmZmZlVKSd6ZmZmZlXKQ7dmZmZmSbVd\nuuJErwVV2/PzzMzMrG3z0K2ZmZlZlXKiZ2ZmZlalnOiZmZmZVSknemZmZmZVypMxWlDFZvLc/avy\nw+4/ofwYZmZm1qa4R8/MzMysSrlHrwrc/9wbZcfYc/86Be4lbDeq7Z5PZmaWHyd6ZkX4nodmZlYN\nPHRrZmZmVqWc6JmZmZlVKSd6ZmZmZlXK1+hZURWZ4GFmZmYtyj16ZmZmZlXKiZ6ZmZlZlfLQrVkL\n8f3uzMyspVVtj56kUZKelrRI0mmt3R4zMzOzllaVPXqSOgEXAyOBGuAhSdMj4snWbVnHdv8Vp5Qd\nY89j1+3R8s2NzczMiqvKRA8YBiyKiOcAJE0DxgJO9KpQJYZEPcxqZmbVoFqHbnsBLxWs16QyMzMz\nsw5DEdHabcidpCOAAyPiO2n9W8CwiPivOvWOA45LqzsAT7doQ4vbAni9g8asVNz2ErNScdtLzErF\nbS8xKxW3vcSsVNyOHLNScdtLzErFrVRbm+ozEdGzsUrVOnRbA2xbsN4beKVupYi4DMhjjC43kuZE\nxNCOGLNScdtLzErFbS8xKxW3vcSsVNz2ErNScTtyzErFbS8xKxW3Um2tlGodun0I6Cepr6T1gXHA\n9FZuk5mZmVmLqsoevYhYI+kE4HagEzA5Ip5o5WaZmZmZtaiqTPQAIuI24LbWbkczVGIoub3ErFTc\n9hKzUnHbS8xKxW0vMSsVt73ErFTcjhyzUnHbS8xKxW1Tl3w1pionY5iZmZlZ9V6jZ2ZmZtbhOdFr\nIyrxyDZJkyUtkTQ/j3gp5raS7pa0QNITkk7KIWZXSbMlPZZi/k8ebU2xO0l6RNLfcoz5vKTHJT0q\naU5OMXtIukHSU+mz3TOHmDukNta+3pb0wxzinpy+T/MlXSupaw4xT0rxniinjcV+5iVtJmmGpIXp\n66Y5xDwitfUjSU2efVdPzPPS93+epJsk9cgp7tkp5qOS7pC0TbkxC7adIikkbZFDO38u6eWCn9eD\nmxKzobZK+q/0+/UJSb/Joa3XFbTzeUmP5hBzoKQHan+vSBrWlJgNxN1N0v3pd9atkjZuYsyiv/PL\nOa8aiNns86qBmM0+rxqIWdY51eIiwq9WfpFNGHkW+CywPvAY0D+HuPsAg4H5ObZ1a2BwWu4OPFNu\nWwEBn0rLXYAHgT1yau+PgGuAv+X4GTwPbJHzz8AU4DtpeX2gRwV+xl4ju+9SOXF6Af8GuqX164Gj\nyoy5CzAf2JDsuuE7gX7NjLXOzzzwG+C0tHwacG4OMXciu/fmLGBoTu08AOicls9tajsbiLtxwfKJ\nwKXlxkzl25JNeHuhqedDPe38OXBKmT9LxeLun36mNkjrW+Zx/AXbfwv8LId23gEclJYPBmbldPwP\nAfum5WOAs5sYs+jv/HLOqwZiNvu8aiBms8+rBmKWdU619Ms9em3D2ke2RcQHQO0j28oSEfcCy8qN\nUyfmqxHxcFpeASygzKeORGZlWu2SXmVfPCqpN3AIcHm5sSop/Ye9D3AFQER8EBFv5fw2I4BnI+KF\nHGJ1BrpJ6kyWnK1zj8om2gl4ICLejYg1wD3Al5sTqJ6f+bFkiTTp62HlxoyIBRHR7Bus1xPzjnT8\nAA+Q3f8zj7hvF6xuRBPPrQZ+j0wCftLUeI3ELEs9cY8Hfh0R76c6S3KICYAkAV8Frs0hZgC1vW2b\n0Izzqp64OwD3puUZwP9pYsz6fuc3+7yqL2Y551UDMZt9XjUQs6xzqqU50Wsb2uUj2yT1AQaR9cCV\nG6tTGv5YAsyIiLJjAr8j+0P0UQ6xCgVwh6S5yp6uUq7PAkuBPykbZr5c0kY5xC00jib+MSomIl4m\ne4jvi8CrwPKIuKPMsPOBfSRtLmlDst6MbRvZpym2iohXIfvFDWyZY+xKOQb4R17BJJ0j6SXgG8DP\ncog3Bng5Ih4ru3GfdEIaEpvc1CH2BmwP7C3pQUn3SNo9p7gAewOLI2JhDrF+CJyXvk/nAxNyiAnZ\n+TUmLR9BGedWnd/5uZxXef4dKSFms8+rujHzPqcqyYle26AiZW36PwRJnwJuBH5Y57+bZomIDyNi\nINl/W8Mk7VJm+0YDSyJibrltK+ILETEYOAj4gaR9yozXmWy45ZKIGAS8QzYUkgtlNw0fA/wlh1ib\nkv0n3xfYBthI0jfLiRkRC8iGVGYA/yS7dGFNgztVMUlnkB3/1LxiRsQZEbFtinlCObFSMn4G+f9x\nuwTYDhhI9k/Eb3OK2xnYFNgD+DFwfeqJy8OR5PAPVHI8cHL6Pp1M6uHPwTFkv6fmkg0/ftCcIHn/\nzm/pmOWcV8Vi5nlOVZoTvbahpEe2tRWSupD90E+NiL/mGTsNWc4CRpUZ6gvAGEnPkw2FD5f05zJj\nAhARr6SvS4CbyIbey1ED1BT0Yt5Alvjl5SDg4YhYnEOsLwH/joilEbEa+CuwV7lBI+KKiBgcEfuQ\nDT3l0UNSa7GkrQHS1yYN3bUkSeOB0cA3Il0AlLNraOLQXRHbkSX6j6XzqzfwsKT/KCdoRCxO//B9\nBPyR8s+rWjXAX9MlIrPJevibNHmkmHTpwleA68qNlYwnO58g+6csl+OPiKci4oCIGEKWlD7b1Bj1\n/M4v67yqxN+R+mKWc16V0M48zqmKcqLXNrSbR7al/4SvABZExP/mFLNn7UwoSd3IkomnyokZERMi\nondE9CH7PO+KiLJ6nlL7NpLUvXaZ7ELfsmY1R8RrwEuSdkhFI4Any2roJ+XZ6/AisIekDdPPwgiy\n61bKImnL9PXTZH8882ovZOfS+LQ8Hrglx9i5kTQKOBUYExHv5hi3X8HqGMo/tx6PiC0jok86v2rI\nLlh/rZy4tUlD8mXKPK8K3AwMT++xPdlkpzweSP8l4KmIqMkhFmT/3O+bloeT0z87BefWesBPgUub\nuH99v/ObfV5V6O9I0ZjlnFcNxMz1nKq4urMz/GqdF9l1Sc+Q/bd1Rk4xryUbAllN9sv42BxifpFs\nWHke8Gh6HVxmzAHAIynmfJo4g62E+PuR06xbsuvpHkuvJ3L8Xg0E5qTP4GZg05zibgi8AWyS4+f5\nP2S/2OYDV5NmM5YZ819kye1jwIgy4qzzMw9sDswk+8M5E9gsh5hfTsvvA4uB23OIuYjsWt3a86rJ\nM/nqiXtj+l7NA24lu5i8rJh1tj9P02fdFmvn1cDjqZ3Tga1zOv71gT+nz+BhYHgexw9cCXwvx5/T\nLwJz0znwIDAkp7gnkf1teQb4NelBCU2IWfR3fjnnVQMxm31eNRCz2edVAzHLOqda+uUnY5iZmZlV\nKQ/dmpmZmVUpJ3pmZmZmVcqJnpmZmVmVcqJnZmZmVqWc6JmZmZlVKSd6ZtZhSFpZZ/0oSRe1cBuO\nkLRA0t11yvtIei89Bm+BpNnpRq9mZs3WubUbYGbW3knqFBEfllj9WOD7EXF3kW3PRvYYPCR9Fvir\npPUi4k95tdXMOhb36JmZAZI+I2mmpHnp66dT+ZWSDi+otzJ93U/S3ZKuIbvRb914R0p6XNJ8Seem\nsp+R3YT1UknnNdSeiHgO+BFwYtp3mKT7Uo/ffbVPUpH0L0kDC973/5M0oMyPw8yqhHv0zKwj6Sbp\n0YL1zfj4cYMXAVdFxBRJxwAXAIc1Em8YsEtE/LuwUNI2wLnAEOBN4A5Jh0XEWZKGA6dExJwS2vsw\nsGNafgrYJyLWSPoS8EuyZ2xeDhwF/DA94muDiJhXQmwz6wDco2dmHcl7ETGw9gX8rGDbnmQPKIfs\ncVxfLCHe7LpJXrI7MCsilkbEGmAqsE8z2quC5U2Av0iaD0wCdk7lfwFGp4evH0P2aC4zM8CJnplZ\nfWqfD7mG9LsyPeR8/YI679Szr+opb6pBwIK0fDZwd0TsAhwKdAWI7EHtM4CxwFf5OFk1M3OiZ2aW\n3AeMS8vfAP5fWn6ebAgWsmSqSwmxHgT2lbSFpE7AkcA9TWmMpD7A+cCFqWgT4OW0fFSd6peTDTU/\nFBHLmvI+ZlbdfI2emVnmRGCypB8DS4GjU/kfgVskzQZmUn8v3loR8aqkCcDdZL17t0XELSW0YTtJ\nj5D11q0ALiyYcfsbYIqkHwF31Xm/uZLeBjw718w+QRHReC0zM2uz0uSPWcCOEfFRKzfHzNoQD92a\nmbVjkr5NNlR8hpM8M6vLPXpmZmZmVco9emZmZmZVyomemZmZWZVyomdmZmZWpZzomZmZmVUpJ3pm\nZmZmVcqJnpmZmVmV+v8B2sCRFMspDCcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fe673ba8400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10, 5))\n",
    "plt.bar(range(0,24), counts_by_group['Registered'].loc[range(0,24)], alpha = 0.5, label = 'Registered')\n",
    "plt.bar(range(0,24), counts_by_group['Casual'].loc[range(0,24)], alpha = 0.5, label = 'Casual')\n",
    "plt.xticks(range(24))\n",
    "plt.legend()\n",
    "plt.xlabel('Hour of Day')\n",
    "plt.ylabel('Number of Checkouts')\n",
    "plt.title('Time of Day vs Checkouts')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# One-hot encoding\n",
    "* One-hot encoding is a representation of categorical variables as binary vectors\n",
    "* Could use integers to denote different categories\n",
    "* However categories will then be ordered, which is often not desired\n",
    "* Solution: one-hot encoding"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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lGZNdqTAZqjJRrpSqTDLDvn37kn8prbuaSyIIWD4nys/49ttvyzOy3bhx49h3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\nR1EyrZFpaBgAMsJlQZ2n7cl7VK5tcayUU/JIckJHnZKigCv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gWEn7K1whpGOJ302jIVijxMdEiYXgjGPljSSOVca6wx+oSDgYEFcCLIe+8sU+dH27xmxET2bd4x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\nkPS3hnBte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A9ATxDCl9/rrrsu9uIUz8D16fXYbbfdZE7tTjvtZHr37h17zmIiPRUXXHCB\nCAdWvNhwww3VIVFEDq5oBeqQVH2qQ5I+sUP22msv88gjj8QMZl7KfvHFF029evWk5z9KBmBUOWLE\nCLPrrruaZ599NhaGvmQmAXYH741pPlZM8pHOzlatWsXC6ARm0YXNNttMOoXj4+ebBe2QQBo9hi4J\nYaREHZLAIWHImB54TyohtoV3SEaNGiUNu127dnIeYfRGEIdKzAjIQQcdJPus7b906VLz/PPPyz6O\nBEPT9Pyw5j/Ki23O22qrrcTZIX779u0lPuchlP20MdirVy/x1C+77LKiXV6SNDPdkF4KenzoEVKH\nRBE1uKIVRFUfYJwgg+JfaleDJXWSZ8gl75D4sMR4ynCSd3QyMX0c24S8wz6hTh511FGih8POU5Ym\nq2yRj9hz5KnPR4xr7BFsibDzlKXJlCxmYJx99tkiu6mTjOLxoru3y8LOyxcp6yrxUjsJUYckcEgY\nMWLk4qabbooRRY1tkYpDgqOAF+2FKAqf46wYQsVm/9ZbbzWbbLKJOBcIjfgpWDglOCgIYoSJd0ge\ne+wxaQjE//bbb2XosFDqWbaoDokiqnBFK4hiO+WZ+OYDK/IcccQR0hPN9nvvvSejvWHnKEsTWYzx\n8uabb8p0o8MOO0zkOtvkI51MYecpSxPd9+eff5qdd95ZDOd33nlHdPG+++4retXrRuXKST798ccf\nMv3t2muvFdvk8MMPl05S3rmkvoadpyxN8pFZMIhxHBDk4qGHHmqOOeYYscewm8POyxfVIalixCHB\nsH344YflRSZP3lWgUqbikDRt2lSmVnmHhLrAi2affPJJ7ByGUFlfHYeEMASFf4cEMO2Lyh/vkLzy\nyiuxF9J+/fVXebmqUOpZtqgOiSKqcEUriGI75ZneeOMNkSsY1MwzZ5uvY6tDkhy9Q4IOePXVVyUf\nMabZJh/VIUmedNQxNQvjj3qIU4deVmckNTJzgJkUPh9feuklqafqjKRGOn9x7mjL5CNT88PiRYHq\nkFQxJvsOSZhDwsgHceIdksaNG5dySJiuxXQwfw4OCe+F+BESXkbjGrxzAtk+4YQTSjkkCGh1SEpT\nHRJFVOGKVhDVduoNlUSGxVWWz7A8hGFxlSun5l9mqPmYGRZCPqpDUsWYikPCi+feIaFXhxfxiJOO\nQ8L1WYqP8+lR82C4OnGERB2SslSHRBFVuKIVFHs7VSqVSmV2WPAOCd4exjNThJimxLK/LDcb/y5D\nMTFZh4R4++23n6yCRX516dJFjsN0HRKmhnF+586dpf60bdtWXkLDKVGHpCxpfAynkv+szsL69eTh\n8uXLI/eyWSLVISkeuKIVqEOiVCqVymywoB0SnJFhw4aZTTfd1FSrVi1mULO93XbbyWoCYedVZeJo\n8E5H4jxB75DgtPkeeBwHn3c4HoxsEMc7JDh3fGgy3iFhDXCWAWaf69xzzz2yHDDXx4h+6KGHYuVw\n5plnmgsvvFBeNI13SJgL6h0SVuKqXr16URo65Ffz5s1j+RVPXvyPskOtDknxwBWtQB0SpVKpVGaD\nVWaEhF7meBbrCAkkP5iCFR/mCzq+55388XnHt0I4h23ylPg4EISz7c9hJCr+2ixbyzWIA32PP+S9\nEsh1/HHCeVnNn49jErW1sHPFxPyKZ3w5RZHqkBQPXNEK1CFRKpVKZTaITVQl3iFRKpW5ozokxQNX\ntALVB0qlUqnMBtUhUSqVKVMdkuKBK1qB6gOlUqlUZoPqkCiVypSpDknxwBWtgI+SoTSUSqVSqcwk\nsSvUIVEqlSlRHZLigStahUKhUChyCnVIlErlSqkOSfHAFa1CoVAoFDmFOiRKpXKlVIekeOCKVqFQ\nKBSKnKKUQ8LyoyztqlQqlZ7IhTA4G1ZRheCKVqFQKBSKnEIVkEKhSAvOhlVUIbiiVSgUCoUip1AF\npFAo0oKzYRVVCK5oFQqFQqHIKVQBKRSKtOBsWEUVgitahUKhUChyClVACoUiLTgbVlGF4IpWoVAo\nFIqcQhWQQqFIC86GVVQhuKKNLFh8RaFQKBRVD+qQKBSKtOBsWEUVgivaSOP99983u+22m9l7773N\nvvvuK0tTK1KHd+4eeughc+edd6qzlybmzJlj9ttvP6mPu+++u/nzzz8lXPMzNUyaNMkccMABsXzs\n37+/O6JIBZ07dy6Vj3PnznVHog91SBQKRVpwNqyiCsEVbWTRokULc8ghh7g9Y4455hhTq1Yt069f\nPxeiSAVNmzalHZvDDz9cDeg0MHbsWLPeeuuZ559/Xva//fZbU6NGDfP555/LviI5DB482Ky77rrm\no48+kv1PP/1U8vHXX3+VfUVy6NSpk7RnLw+ffvpp2Z8yZYrsRx3qkCgUirSAAauoWnBFG3l44/nf\nf/8VhUsPvyJ50KtfrVo1c9BBB5k77rhDftUhSR1XXnml5B3w+Xf55ZebDTfcULYVFWPZsmXmiiuu\nMKeddprs+3w89thjzfbbby/bioqxaNEi06xZMxnxjMd2221nLrzwQrcXbahDolAo0kJgwiqqElzR\nFgymTZsmDslNN93kQhTJgB7TN954Q7YfeOABGSlRhyQ1zJ49W6bGvPXWWy4kwP/+9z9Tt25dM2zY\nMBeiWBnIR6YXffjhhy4kwEsvvSSjn3///bcLUawM5ONGG21kfvvtNxcS4JxzzjHrr7++24s2Ck4B\n/fXXXyJI33zzTfP222+b5cuXuyPFB9LeunVrM3HiRBdSAuZZDx061O0pooRZs2aZli1bFvzc98CE\nVVQluKItGPz000/ikLRq1cqFKFIBOuT+++83Bx54oDokKWLy5Mlms802E1kejx49epiaNWvKr6Ji\nTJ061dSvX9989tlnLiTA999/b1ZffXWZzqWoGNOnTxdZ2KFDBxcS4LHHHjOrrbaajKBEHQWlgH7+\n+Wdz3HHHmR9++MG0b9/eNGjQwOy///5m5syZLkZxgQpWr1498/XXX7uQElAxn332WbeniAroxUCJ\nUT6//PKLCy1M2DQoqhhc0RYEFi5caLbcckuz9tpruxBFqsAJUYckPfAS9sYbb2x+/PFHFxKgd+/e\n4pDQeaqoGIzWrbnmmqZLly4uJAD2Hg7JkCFDXIhiZfCjxb169XIhAZ566inJR3VIsoB4oUmDpwAS\nh0yLBVQw5qq2a9fOhZSAfHnxxRfdniIK+PLLL6VccKoxpAr9hT2bFkUVgyvagsBll10mzgg9rIr0\noA5J+mAqEXI8cYQEw5qpRn379nUhipWB9svCAIkjJG3bthXHbuTIkS5EsTKUN0LCOyWMkBQCCs4h\niQcrXFAADz/8sAspLiTrkNArn1hRWc2CMKYPgT333FPmcT755JNmhx12kB6LW2+91QwcONDsuOOO\nplGjRmaPPfaICYelS5dKTxACuXHjxmadddYxBx98sFmwYIEcB1z/7rvvlhVcdt55Z7PpppuW6U0q\nJjzzzDMyDM0Lpcz1VIdEETW4oo0svNHcvHlzGSFnVRlF+lCHJH0wM6NJkybmtddecyEBPvnkE5m5\nwJQuRcXABsGGYJp5PF544QWzxhprmHnz5rkQxcpAPvLuUqJdcfrpp4usLAQUtEOCIY4OZam9YgQO\nyQYbbGD++OMPF1IC8sU7JBjA9NjED4ky55o4OCvgyCOPlFVXmA4HmLfJ8RNOOEGmRoBzzz3XnHrq\nqbLNvd955x3ZBosXL5b4ODEe7O+zzz5yDCC4cWCKXfHhSKtDoogiXNFGGu+99570+LVp08aFKNKF\nOiTpg9WhLrjgAunMiwcde+SnIjlgH5CPrKoVDzowvb2hqBh0BrMk+u233x57t9p3frL8byGgYB0S\njGQ+RlQonl82gFOAgV+9enUZ2ownzkUqDslhhx0mH9HxoELTOxE/+vLEE0+IA1Qejj76aHPyySe7\nvcAhie/16NOnjxgSxbwQAVCHRBFVuKKNLHh3kMc8//zzpQea+eVw1KhRsY4PRcVghHv48OHS8cTS\ntXxokm3yccmSJS6WoiJ0795ddCK9+RMmTJAVy7BJCuW7D1EBI53MymClLfQjHZu8n+M7QxXJgQ5l\n5CPfIWE2yyWXXCIzXkAhdDgUrEPCkD0Z/99//7mQ4oMfIUlc5g2QN6k6JHvttZdsA3p/6tSpY777\n7jsXYmQ6F3M948HShhgECGO881NOOcUdCZ7hgw8+cHtG5tSqQ6IOiSK6cEUbWfDBubvuukuW+eXb\nBRjT/N533336LkkKQCfcfPPNkn833nijGIBs33vvvWbGjBkuliIZ4NhdffXVUg8vvfRSnWKUJuiw\nvOaaa2L5qEgPTAunLfM9HBzkQhr5LCiHxGfsu+++K6sGFPP7CCDZd0hQPox2xDskTHcgTroOCUKX\nYcBNNtnE3HbbbcItttgi9nEjwPXVISkLdUgUUYUrWoVCoVAocoqCU0B8dIgpSvwWO5J1SHAeeNmp\nY8eOsg/wnomTrkPCUDXnz58/X/YB8z1POukkt6cOSXlQh0QRVbiiVSgUCoUipygoBdStWzcZGWF4\nmTmwTNeCGNXFaOT6KVssj5cIbAvmtQLyiiHlE088UfLLLwYAvUNy6KGHlno5D4ekdu3aZRySdddd\nV7aZqsW9eUeEOcd8qLJGjRqyooMH1090SIhTrA4J5UX+9+/fX/Lum2++kdErwgsRtnwVVQyuaBUK\nhUKhyCkKSgHxHYfzzjvPnHnmmaZZs2ayAhRk3mExvkuCo3HDDTfI3MtEkEd8WMgDx4M5w+TXnXfe\nKS8vXnTRRbFlevl4DutVe+A0MA+Rl6M8cE6uv/56txe8iMY7I8ccc4w4JF988UVsVAawKlfnzp3d\nnjHjx4+XuaHFupoLjuPxxx8vo0isKuLzrlBXiXM2rKIKwRWtQqFQKBQ5hSoghS73qEgLzoZVVCG4\nolUoFAqFIqdQBaRQKNKCs2EVVQiuaBUKhUKhyClUASkUirTgbFhFFYIrWoVCoVAocgpVQAqFIi04\nG1ZRheCKVqFQKBSKnEIVkEKhSAvOhlVUIbiiFfAl9H/++UepVCqVyozy77//NosXL3baJoA6JAqF\nIi04G1ZRheCKVqAOiVKpVCqzQXVIFApFxuBsWEUVgitagTokSqVSqcwGV+qQsPTr1KlTzbRp05RK\npTJG5EIYnA2rqEJwRStQh0SpVCqV2WCFDokqIKVSmUjkQhicDauoQnBFK1B9oFQqlcpsUB0SpVKZ\nMtUhKR64ohWoPlAqlUplNqgOiVKpTJnqkBQPXNEKVB8olUqlMhtUh0SpVKZMdUiKB65oBaoPlEql\nUpkNqkOiVCpTpjokxQNXtIKo6oN///3XTJ8+PcYpU6aExlNWzPh8JF/D4iiTIwuAaB6mT9pxfH3U\ndp0eWYSmEPJRHRKlUpky1SEpHriiFURRH8yZM8d8/vnn5qKLLjKXXnqpufLKK82IESNE8YbFV5bP\npUuXmiuuuELy8ZJLLjGfffaZmT9/vhgKYfGV4SS/Zs+ebR5++GHz4Ycfqh2VBmm/Q4cONVdddVWs\nPvbs2dPMmDEjNL4ynP/995/5888/JR8vu+wyycfx48dH0ilRh0SpVKZMdUiKB65oBVHTBxgtb775\nptl7771N165dTffu3c3OO+9stthiC9nX3unkiHFCL+o+++wjefnXX3+Zjz/+2Ky22mrm8ccfN7Nm\nzQo9T1mWfkn0hg0bIg/NBRdcYCZOnBgaVxlO6iPOx7bbbmvuvPNOact33XWXWX/99c3PP/8sI09h\n5ylLk3z87bffTN26dU2LFi3EMbn44ovNuuuuK502UXNK1CFR5oRUfIRI1BqAMj2qQ1I8cEUriKI+\n8NO1/HavXr3EELztttvMzJkzy8RXliW9zo8++qjk26JFiyQMJ6R58+Zm4403lp5qld0VE2ekc+fO\npn79+sIbbrjBnHPOOeqQpEjkDL35Bx98cKxTYeHChaZJkyZml112kVG7xHOUZclIyIknniijnvGj\nnJtuuqmMOjGKFx8/3yx4h4QEIDgXLFggpNIiFMLiVmXG54HPh7B4+SDl0alTJ3PzzTebbt26aa9l\nHFHy8eXG9JN4wRFVqkNSPHBFK4i6PkC2DB48WAxrpsswXSEsnrI0ybdjjz1WevP9lBjC6F1dffXV\nzbfffluUejVVkkft2rUzTzzxhOjgs88+Wx2SNDhq1ChxPl544YVYGDbOgw8+aGrVqiUOstbHijly\n5EizySabyJRWHzZv3jxz/PHHmwYNGkRu5LPgHRI8ZXrC1l57bbPOOuuYevXqmeHDhxfVkB7C7tZb\nbzUbbbSR5AGsU6eOGLdRaLSUxTvvvCNGwv/+9z+dA+pI2aD0fZnRo3bmmWfKPO6oOyXqkBQPXNEK\noqgPfKcUiowRkZNOOknk3+jRo9VoSZLozD322MNcc801MflMZ8nYsWNFbr///vual0kSmU4eopdx\nRtQhSZ0DBw4Unch0TB9GnrZs2VIc5A4dOkg+x5+jLMshQ4ZI+23Tpk0sDFmJzVyjRg0zbty4SI18\nFrRDgoB84403pMHTu7xs2TLp5aEAimn+MMLurLPOkt4tvN8lS5aY6667TvKBkYn4fKDyecZfIz48\n7HhF8eK3E+OyzTPMnTs39izxx/y23y8W0ra22WYbKSOmSTD/HWF76KGHRm4oNZHqkBQPXNEKoqgP\naCvXXnutyLvq1aub2rVriyzRl9qTJz3OTIVhilZ8Zx46lnz94IMP1CFJkeqQpM8BAwZIpwKjTT4M\nh4RFFnivqWPHjuqQJEE/Wsx7Nz4Mh+S+++6TfIzay+0FP0JCJWUkgG0qKC/jUQAI1mIZrvcOCYLP\nN1KM/1VXXdWce+65UgHJpz59+pgHHnhApjI8//zzkj++MrZu3dp06dLF/PDDDzIsSpxhw4bF7oFy\n53x/7LnnnjNPPvmkDAVyje+//958+umnZtKkSbFnoletffv2ouAQMNyTa3Kt33//3bz00kvSA8d1\nHnnkETk/akOI2SYjfJQZDRGnmiF+hlLJryg7aOqQFA9c0QqiqA9oO4yM0H7A/fffLzoApVtMI+WV\nIXJ59913LzVCAilv8vKjjz5ShyRFqkOSPhkhYbZL4gjJJ598Ip12vKej9bFiljdCcvfdd4tDgr2m\nDkmWSIXFoKYAWrVqVTQ9ZGEOCaNFOCTMFaT3HYdj6623Ns8884xp27atDM9ffvnlMUHJi0/0SPAi\n2ddff21OP/10efEJg5mGT+/95ptvLg4J3jZ5zDVYCYN85qWpAw44QIYAuR7TJbbffntz++23i8P4\nzTffyDn0bOAsYSywf8wxx5j33nvPfPHFF7L/3XffRdoQzybJp9NOO03yfdCgQeqQKCIBV7SCqOsD\nFBqyD1m01VZbSQ9hWDxlaU6YMEFeIOZFV++QoEsYvcUApMNJDcDUqA5J+sRBbtSokXnllVdiYXQ6\nPPbYY6ZmzZqRXCEqimQqJnbdl19+GQtjFs2pp54qU8Qx/uPj55tVwiEhETwnPfgYzYcddphkeljc\nqshEhwTFwUoz1apVk9EH9pnGgFfsRyB42Qlb49dff5X9k08+WaYKoZho6DggVGSWfsQpoecMBU8v\nJIKBpSAxnPGwUWAcP+SQQ0o5JCy/yVJ9TKlg6JXpFCg4HBIcG+Yw/vHHH/LM3JNGwhrZfpSl2MhL\nkCh/HL2o1191SIoHrmgFUdMHyH7kS3yvPh0wRx99tHTAxI/yKssnnUp0RiF//DtsyCB0xoYbbigv\nGasBmBrVIUmf2BHNmjWT98Gom9RH7FGcFDobim0mRbocM2aM2W233cQOIw9pw9hXLEnNNFddZSvD\nxDimt535rww5YwSzIghDVcXSo+MFX9OmTeUjTKw3TcM98MADpQx/+eUXGZ5jmhXHeTHsrbfekpfG\nGA3hGoyQXH311bHypmKw5vdTTz0lFQTBgJLHOUH5Mx2L4zg2TP1KxyFB+cUPGTJtCyGEU8R+MZG6\netNNN4njiJMY9akm6pAUD1zRCqKmDzBMWAgC2UYbot28/vrr0hnDlFBdZSs50inENwrohEIOMVqL\nHiUf0RfxDp9y5UR3oteYn88UXMh21G2pKJG2jK2C+GF2BfWPTlD2dYWt5Ilt9dprr0m+4ZyQb9df\nf71Zc801IykbC94hgVRWBCjGMlOCyHyEQLH06OCQ8K7IXnvtJYr56aeflnc7cASogMwfZDSCDwxx\nnGlbzz77rFRUP6UBh4NpV95B4He99daTuPSUMYcYB4SeMoADiANCpSb/03VI4l+qIuyEE04oOoeE\nMuKlUeotUw1xssPiRYnqkBQPXNEKoqYPkB0o2n333dfstNNOwh122EHeXfPvFiqTI3Lcv9y+4447\nmu22206mQEetFzXKRJazAtR+++0n+o8PTUK2jzjiiFIdcMqVEzuBl9gbN24s7ZqPJPpe/rD4ynBi\nF2MTUgdp13QsR9XGqhIOSTwxng866CAZIcB4DotT1Rg/ZYseQhqsb7ReQOKQ4JigdPzx+Ia9MoeE\nCnLGGWfIetbYJ5ARFT/VimsyusKUL++QYCSg2NQhWTkpH97DIU/j58tGneqQFA9c0QqiqA+QHzgf\nyBVPfZk9PSKPNB8rR/IwsT5CdexSJ9O1fP5h28XbLMrkScexz0fqZlTzsaAdEjK1X79+8oITFRfD\nmATRk3/44YeHnlMVGe+Q4CTEH/MVj6/tXnjhhXKcvEJo9ujRI2b8r8whoTLT63jjjTea/v37iwHN\nKhesBIXCgi+//LIsYeu/AfPuu++Kka0OSfmkLHCayaeLL75YRkZ8flI+YedEheqQFA9c0QoKrYNK\nqVQqlYXBgnZIMNqYdsS7Exh0rBCCUYxx3rdv3zLGeVUlDgkjFryYHpZmHBCG3hlGZiUV8grn5IYb\nbpB3QIhz5JFHyndM4h0SHAZWtaCC3HHHHWI480EdvnGCc4KTw+gL8RkZwWlhOPCiiy6SKXPMU+Tr\n7HjkvKvC+bzEjkOCo8J+vENCGI5ksTgkjCLxchn5cP7550u5kHdsf/XVV7F8iSLVIUkLG7nfgoIr\nWoE6JEqlUqnMBgt+yhaGM98e4RsakJ57DNqo9zBnkhQiy8TCsOOQ/GAkifwhn/hwJE6EN3pZ95uF\nAOLPYSQEh4V4zOHkJTNeIsXpYdSEJYWZqsX9ic/oiC8Hev4ZTWGVG++wsHKXLxviMkLjz/XnM+oS\nH1aViSPJamjki883SBlFfXUgdUjSAvlTcE6JK1qBOiRKpVKpzAYL3iFRZpc4ExjIdevWlW+ZMC0O\nh4KKw4gLoyXF4kAoS6gOSVqQ0TDLgnJKXNEKVB8olUqlMhtUh0RZIRnVYOrVuuuua5544gnz6KOP\nyugI06t69+4d6alFyuxQHZK04B2SgnJKXNEKVB8olUqlMhtUh0RZIXE4mKbFeyCMkkC+/Dl27Nii\neU9HWZrqkKSFeIekYJwSV7QC1QdKpVKpzAbVIVEqlSlTHZK0kOiQwMg7Ja5oBbyzR9krlUqlUplJ\nol8WLVrktE0AdUiUSuVKiVwIg7NhFeFIdEY8I+2UuKIVLFu2TKlUKpXKrBC/Ix7qkCiVypVSHZK0\nkOiIxDOyTokrWoVCoVAocopSDglDKMzrUiqVSk/kQhicDasIR6ITkshIOiWuaBUKhUKhyClUASkU\nirTgbNgoI9EJiBoj55S4olUoFAqFIqdQBaRQKNKCs2GjjEQHIIqMlFPiilahUCgUipxCFZBCoUgL\nzoaNMhKN/6gyMk6JK1qFQqFQKHIKVUAKhSItOBs2ykg0/KPMSDglrmgVCoVCocgpVAEpFIq04GzY\nKCPR6I868+6UuKJVKBQKhSKnUAWkUCjSgrNho4xEg78QmFenxBWtQqFQKBQ5hSoghUKRFpwNG2Uk\nGvuFwrw5Ja5oCwL//fef+emnn8x3331nli9f7kIVqeCLL74wrVq1Mp999pkZPHiwC1WkgqVLl5bK\nR77dpEgftOeBAweW+WieIjlQ/1q3bh2rj9TPQoE6JAqFIi04GzbKSDT0C4l5cUpc0RYEXnjhhVh+\nLVy40IUqksX1119vNttsM/PJJ5+Yhx56yKy33nrmm2++cUcVyWD27NnmtNNOM8ccc4z56KOPzCWX\nXGJ23XVX069fPxdDkQrOPPNMac/XXHNNQRnSUcG4cePMjjvuaB544AHz3nvvmcMOO8wcfPDBBSMf\n1SFRKBRpAQM24ogZrAXIoZY5hyvayGPKlCmmUaNG5txzzzU1atRQhyRFvP/++1LP4oFTsu2225o5\nc+a4EEVFuP/++812221Xqjf/uOOOMzvttJPbUySDoUOHmm222cZsuOGG4tSpQ5I6lixZYi6//HJz\n0UUXxfJu3rx5ZoMNNhAHpRBQJRwSHdpTKHKPwISNNOIN/EJiXpwR4Io28rjpppvMlVdeaZ566il1\nSNLAWWedJYZLPHr27Glq165tevXq5UIUK8P8+fPN0UcfbZ5//nkXEuDdd98166yzjpk8ebILUVSE\ndu3amccff1y2qZvqkKSOuXPnilPHNNZ4nHDCCWbrrbd2e9FGwTokbdq0iSnwYsOMGTPMpZdeaho2\nbChD7ptvvrmpW7eu+fvvv5NyzqZPny7nfv311y5EkQt069bN1KtXz2yxxRbyu/3225tp06a5o4UH\nsWCjjZiMKCDmzRkBrmgjjZEjR5patWqZYcOGmRdffFEdkhTBNKP99tvP3HnnnS4kwKxZs6QOolsV\nFYO5+hh6H3zwgQsJ8Mcff5iaNWuKvFekBuyXs88+Wx2SNIBdSPvt0KGDCwlwzz33FIyMLDhr3hvc\nhxxyiAiDAtGhGQO9Mg0aNDAXX3yxCwnQsmVLyYvOnTu7kJWPHDVu3LhMxU0XqYxQpRK3KoF0M+fd\nT4fgJVyG+plvXKgv5Nr6FnVImygg5tUZAa5oIw3kPtOLwHPPPacOSYrAcNljjz1EHiWCKtC2bVu3\np1gZJk2aZDbeeGN5CTsevXv3Fofkr7/+ciGKZIGeVIckPdC5SftlpDMeTz/9tFl99dXNokWLXEh0\nUZDW/EsvvWSOOOII6eEpEB2aMTz66KNmt912k96sRDCFoVmzZqUqHo7bWmutJQKS6Q2APFt11VVl\neL5OnTrmmWeekXDw5ptvShg9+LvssouZMGGCO2LMq6++ak488UTZ5gXINddc0xx55JGy73HggQea\nFi1auD1jhgwZIvOSBwwY4EKMadq0qdyb+aKLFy8uWifl5ptvltEtVgsqRNh6FHVIXS8Q5t0ZAa5o\nIwvf8eKhDknqwCHZa6+9zB133OFCSkDeqkOSHHBINt100zIjJEx5U4ckPahDkj68Q9KxY0cXEgC7\nTx2SLIJMp3f/kUceKaWcigE777yzufXWW91eabDUG1O3cFaofLxcd84557ijxjRp0kR+aej0zP/2\n22+y70F+ImC9gYwDQv6iwAAVm6le66+/vrxABajoF1xwgWwD5jAyjcKjf//+Mr3Cz0s+5ZRThGD0\n6NHmxhtvlO1ixJZbbimrYBQqbN2IOqT+FgAj4YwAV7SRBAqXKar33nuvad++vfn999/NFVdcIZ0r\nTD9lyqqiYqAb6NCjQyQew4cPF3netWtXF6JYGfxIE++MxAM9TCcgKx4pUoM6JOlj5syZ0kmMXIwH\ns2moj4WAgrPmTzrppFgvPStcRFyHZhz0BvqRjkR06dJF8gNBOXHiRBmFCFPSOBw4NvFDzYxUcG78\neyW8JIXj4qdHMPRXrVq1Uuusf/zxxzLSMXbsWNmvyCFh9ZHEnrliGSHBiWvevLmMSKHIWCpywYIF\n7mjhAQM24pA6HXFGxhkBrmgjCTpaPv30U5EvyCJ+jz/+eFO9enXpTNHvaCQPVuNh6i/w8veNN96Q\nke9Cfq/t/+ydBbhU1dfGP8XAwEaxW0RssLu7sTv+dncnit0YGGB3dzctoBggGIRKiHRIuL/zW7P3\neO7MmZszw9yZ9314n+HOydln7b1X7XWKCQw7dBHK/oLQjjgBWR8o1B0ySOoPKmqh1911111VdCqq\nEbLmuDGgUWnzhKJIJwre+Uo0SPi98RSrOIJBgsHBWpJll13WjRw50m/9D0kGCftj7MRTqwAGIN40\n0KFDB4uOxMH+PJPvvvvO/q7JIOnYsaPdI9GaENKuFINk5syZJsO0NV400rUocdhYET3HUofJWgmz\npIwR4B9towEVjhi3wpwg1A7kmfO4iYKDwYMHm2HXqVMn+1uoHXghIu34xRdf2N/vvPOO/d29e3f7\nW6g7jjjiiCqZE5WiH+QDIaslAF2RvxuLcddoJiAWc5PewvqE0LhUDwiNXylv6qWcICUvk0C0gpfi\nkE/Ny65Ir6JefyaSDBJe6kS4PtPLSHoVL9YBGCR40OJgjUhdDJIA0sl4dueff77/pvJArjZtQMnD\nxojo3ksd1r4lypIzRoB/tI0GePVxvDSG/OhSw5AhQ2ytA5F0It9vvfWW3yLUBcyjtCGkCylVq+7A\nSYe+gi6x+OKLm57D/0lrFuoGUlpJ3ULvatWqVeJ641JFo5mAKFV42mmnue22285tscUWtngaAWYA\n4LvPP//c71neuPjiiy3dJ57qEzwIbdq0MWMF46xHjx4mlEkGCSldpE7FyzuSP0xb4uEJYJJn8eNl\nl11mf5MqxiARn/w5B3ndITUMg+iWW26x/wPW+nDeTIMEBO9SpYI24/djPDZGRPcu5IY92xwsSWME\n+EfbKCDPqTC7IRksPNTGtUdjb6tGrQ1W4qL2UaNGmdWbmRPIy2+aNm1qlT8AVjHeBazlgJNPPtle\n1kR6w6abbpq1loOKWTvssIP/y7lnnnnGvI9hzUgI/2200Ub2N4YPxkj8Xs4777x0RIXOQVoSx2CQ\nsOiqffv2tg1ceeWVrkWLFv6v8gaG4emnn+7/SoG2ZrFZY32BFgqskBMm9wksWWME+EcrCIIgCEVF\no56A4ilblQYiHFSXIe+XNrjiiiv8lv+AEoxBwX7sgzEQLOj33nvPzT333OnvAaleRKH4jvNyDcL6\nAaRsEU5lXQr7QIycOIi+UNaXFIAll1wyHc4OtbFJseB+2M69VdICyjPOOMN+N23LJ8UAGjOi5y/k\nRrqPxFjSxgjwj1YQBEEQiopGOwHFQ1OVFtLL9XsL3Q5Ja0hAba9bac8pjtn1zAoJr8MKyWh0xgjw\nj1YQBEEQigpNQEKtkcsgESoTXocVktHojBHgH60gCIIgFBWagIRagzU7VGURBOB1WCEZjc4YAf7R\nCoIgCEJRoQlIqDMqOfVK+A9ehxWS0eiMEeAfrSAIgiAUFZqABEGoF7wOKySj0RkjwD9aQRAEQSgq\nNAEJglAveB1WKCP4R2ugYh6lukVRFEUxn+QF3ZlvkJdBIghCveB1WKGM4B+tgffj8A4iURRFUcwn\neTH0P//842ebFKpMQLyHQhRFMZNJ8DqsUEbwj9Ygg0QURVEsBKs1SFiozFu+2UkURTEwvP0/E16H\nFcoI/tEaZJCIoiiKhSC6RbUGiSYgURQzybiQBK/DCmUE/2gNmg9EURTFQlAGiSiKdaYMksqBf7QG\nzQeiKIpiISiDRBTFOlMGSeXAP1qD5gNRFEWxEJRBIopinSmDpHLgH61B84EoiqJYCMogEUWxzpRB\nUjnwj9ag+UAURVEsBGWQiKJYZ8ogqRz4R2soxfmAim+9e/d2PXr0cN27d09z0KBBbsSIEYnHiMkc\nPXp0uv26devmfvzxRzdq1KjEfcVkInP0k3g7/vLLL27kyJGJ+4vJpB2HDRuW7te042+//aY+XQ/S\nZij7tOPAgQMT9ykFyiARRbHOlEFSOfCP1lBq8wFKXp8+fZA7t9NOO7kDDzzQtWvXzh100EHu5Zdf\nNgU76Tgxmxged999t5trrrnc4Ycf7rbffnu3+uqruw8++MD99ddficeIVYnih9J87rnnupVWWskd\ndthhbt1113U777yz+/rrr2Xc1ZK0488//+xOPPFEt84667hDDz3UtWzZ0u23336uf//+Mu7qQPru\nr7/+6s4++2wbJ/ks1XFRBomYxaFDhxrD3wyweCHj+4iVTRkklQP/aA2laJD07dvXzTnnnK5Xr15u\n/PjxbsyYMUYpLbUnCkrnzp1NYfn222/T7Yhh0rp1a5sDko4TqxLl7+qrr3ZLLbWUeaLHjRtnyvWG\nG27oNt10U3uhbNJxYjYvv/xyM0J++ukna0eU1RVXXNHttddebvLkyYnHiFWJ7H300UfmWNhss83c\nqquu6s4//3wZJIUiVvSTTz7p7rnnHvPuBOKN0ISUTISU9unYsaP74YcfqmxDUPEw7rjjjmaUMKgy\nSV144YV2XHxfMX+kbV9//XX30EMPua5du5a87MogqRz4R2soZYPkq6++kge6nvz7778turT33nvb\n//mOueCNN95wCy64oPv44481/teCGG5EQy6++OK0LI4dO9bdcMMNbrHFFrP5Vu1YM/Hoo4Ncd911\n6e8wkok8NWvWzHQTtWPNpI1uvvlmazfkcbXVVnPnnXeeDJJCkMYmJ26eeeZxN910k3vwwQfdAw88\nYGSSkkGSzAkTJljoE10Db058G4J61FFHuX333dc6PTmcTz/9tLVzfD8xv/zmm2/c0ksvbc/kqquu\nskksab9SoQySyoF/tIZSNEhCytYdd9zhXnjhBffss8+al19pRrXn4MGD3cYbb+xOO+00i4zwHW1L\negxt++ijj2o+rQWRO7zQOEVDeyGHyCR6yptvvql2rAW///57mw/R6cJ3GMo4UWnHTz75RM6HWpJ2\nI8KEbieDpIDEIGHB0/zzz28DgTp6zaSNmHyYZAiHMnjGPQ2ZBkn4LrNt+S4wvo1z8R2fDBhhn/ix\nYlXSCQmjEpmaY4453LXXXpv2UpYqZZBUDvyjNZTafMA4Q5Qch9Rll13mrrzySlv7sPbaa9sakqBc\ni9WTxevk6tOGcUOOsRsR6NKli+bXWhDjmHQt0t/CvEobsg4HRfqdd95RO9aC6HMLLLCAe/7559Pf\n0Zcx7Oaee2732WefySCpI5FDGSQFJB0eg2S++eazARWvsnKHqyeWMlERwp5M2KQ6vPvuu+nOjaDG\nDRKMlxYtWrjbb7/dtiMw/fr1c/POO68J9yKLLGL7k+fJREYlDCYwrrHVVlu5VVZZxS2xxBLurbfe\nqnIfYorI8HfffecWXXRR9+GHH9qCUhkkQinBP1pDqc4HjPmQcQyut956rm3btlbdKGl/sSqZP1l8\nfcYZZyQaJCjYmldrJgYJ8+W9996bbq+4QfL222+rHWvBYJA8/PDD6e/iBsmnn34qg6SOlEFSYKLM\noQA3adLE7brrrm7//fc3RfqLL76wfMOkY8Q/bYETC8OYeNZYYw2rTjNlyhTblmmQ4H1kHwZYtn/5\n5ZeWU/zcc8+Z4LA/3ki8ktOmTTMDkQlsu+22s2NZfHbOOee4jTbayPJC4/ch/ummT5/uNtlkE3fs\nscdaeyPLMkiEUoJ/tIZSng8C6TuXXHKJLYDFeZK0j1iVjM1bbrmlO/nkk6ukbDG/4iR57bXXpEjX\ngqwRWWuttSx9MLQX7fnII4+YE5DUZ7VjzcRAxpl53333pb/D4cx6CBzQpHSpHetGGSRFINWfyHNl\nzQhCSuoLCjPrSMLAKqZIB2bhNEovE8zEiRNtAiJKEkrp1WSQ3HLLLW6bbbaxwYHjZ8yYYfmytHmI\nWKHDUGggeNree+89t9xyy7kBAwZUuZ9KJ/JJSJr2woAeMmSIDBKh5OAfraEU5wPGori3FOfKcccd\nZ44XRUhqR8Z+UkZbtWpllaBQDIim33///RYFpwhKSEEScxPDDucc5X6RS9oReTz++OPd8ssvb2sy\n1Y41k36LnnHqqaem+zaKKo7UlVde2b5TO9aNMkhmA1GQGVQpVUjnT9qnUsmkc+aZZ1q1j0mTJplC\nTDQJnYMSewygNRkkVA9h/4UWWijNxRdf3PKPEaZgkDzzzDNpoSdcLYMkm7Qt4X2MZwwS5BWDpH37\n9qYMJB1TKpRBUjnwj9ZQavMBY9YJJ5xgFbYY+wHRW26bBbClbtiXChmrg3OE9CyAgw+vPhUW42lc\nYm4yx956663Wjp9//rm14/vvv29/UxhA8lg7Ymx06NDBnKU4mwHOVNqRTzmba0/6LtkrM2fOtHXD\n6HqzZs0yhzI6W9Ixs4tlZ5CgyJ100kkW7mNdQ9I+lUraA8MB4wArmVQqDBQ6OfnDPOuaDBKEGc8F\nCjSen0BSs/BayCCpHZm4iDbRVpdeeqm9rIj8bQbgrbfe2pQp2j/p2FKgDJLKgX+0hlKbD5hsicaS\nVkRBCMgts63UJttSJ+M3YzXtF9rxrrvuSo/jYu2IsnznnXdWaUeyBOTRrxtpRypOxtuxd+/eifuK\nyUTmcDDg6AxtGLjbbruZUZJ03OxiozdIUIyD94Yfwz2jXO+www6yomNEMKnwwcI6JnD+D/He4NFh\noRgLz2nL6gwSjsVrxiQVJnzamVLCKNkySGpPFrPT/jwHCguEdDoM6p49e5Z035NBUjnwj9ZQijLJ\nOEOaUSDewKT9xJqJUUL7hXbUHFo/0m5xeQxpR2LtiX5BRCnejugYSfuKuYlOF9owTnS2UnPaNGqD\nhJs/5JBDXKdOnSx3k3tlTUTTpk3tvQ4S3v+IUB555JEW3cjMq6adMFQIzTNwYpDss88+aYOEXOxg\nkLDYrE2bNm6DDTaw42jz66+/3vYnSiKDpH7EYNQaEqEU4R+toZTnA1EURbHxslEbJCjE5A7vscce\nVmJ2iy22cHvuuacs6QTisSHqgfGWFIKnjj8FAXjeREx4QyoFA3jzLB77V1991fajXYlKcS6qsmy2\n2WbuxBNPNCFCqWZxPAvPPvroo7RXCG//McccY8ZN/Jrif6TtWENCu8YLApQqZZBUDvyjNcggEUVR\nFAvBRm2QQBRkFjZC1o/wmbSfmFoAmmSMQAyWEJ5HGY6H6vHWZ4ac420e9+ajWPNd3CDk/+zDtvCd\nmMzqnlEpUQZJ5cA/WoMMElEURbEQbPQGiSiKxacMksqBf7QGzQeiKIpiISiDRBTFOlMGSeXAP1qD\n5gNRFEWxEJRBIopinSmDpHLgH61B84EoiqJYCMogEUWxzpRBUjnwj9ag+UAURVEsBJlfZJCIolgn\nyiCpHPhHa6B2vSiKoigWgrxRPo4qBgmlXwmjiKIoBjIuJMHrsEIZwT9aQRAEQSgqqkxA06dPdzNm\nzBBFUUyTcSEJXocVygj+0QqCIAhCUaEJSBCEesHrsEIZwT9aQRAEQSgqNAEJglAveB1WKCP4RysI\ngiAIRYUmIEEQ6gWvwwplBP9oBUEQBKGo0AQkCEK94HVYoYzgH60gCIIgFBWagARBqBe8DiuUEfyj\nFQRBEISiQhOQIAj1gtdhhTKCf7SCIAiCUFRoAhIEoV7wOqxQRvCPVhAEQRCKCk1AgiDUC16HFcoI\n/tGWNKZNm+aefPJJt8wyy7hWrVoZjzrqKDdr1iy/h1Ab9O/fnz7s1llnHbfsssu6bbfd1o0aNcpv\nFWoDZO7pp5928847r7Xjggsu6M4880x7C7VQd3z66acmkyeccELO918JufHPP/+4M844w6288squ\ndevW1paPPPJI1hvRSxVlY5DwpvlKQa7fWkltUC5ozM8MBVYoL/hHW9I47LDDzAiR0ld/fPnll6as\ndO/e3X/j3M477+y22morGXZ1wGOPPeYWXnhh9/PPP9vfkyZNcquuuqo78MAD7W+hdhg5cqQ7++yz\nTSY322wzd/rpp9tLeYXag357/fXXu3XXXdecNuD777+3Nn3mmWfs71JHozVIFlpoIfOQwYMOOsh/\nWzl44YUX3FJLLWW/f8kll3Q77bST31Izxo0b5/bZZx/32Wef+W+EYmHppZc2tmjRwv3vf/+z7xqr\nUYICK5QX/KMtWfz00082wfbs2dN/I9QHjD277bab/yuFjz/+2JTrH3/80X8jVAc8+AcccIC75JJL\n/Dcp3HLLLa558+YymOuAZ5991h166KHujz/+cIcffrg75ZRTZJDUEZMnT3Ybbrihe+655/w3KRD5\n3GijjfxfpY1GZ5C89dZbFT0hEZKjw2bqDm3atHErrLCC++uvv/w3ufH333+7tdde29pSKA7wXqy2\n2mqWJgFQrOaee263xx572N+NESiwQnnBP9qSRadOndJjH15VUowY8xQdrj3w4m+99dbuwgsv9N+k\nMGzYMGvbV155xX8jVAdkb6211nJdunTx36Tw5ptvuqZNm1aJPgm1A/34kEMOkUFSD6DXoVOQ9hYH\nKVx8P3XqVP9N6aJRGSRjxoyxXM2nnnrKf1N5+OCDD9ycc85pUY4wCYfPNddc05133nlVQu6vv/66\ne+CBB1zHjh3dN998Y9916NDBIiuERR988EH39ddf2/dgwIAB7v7777fv8VqE0B9AmX733Xft/w89\n9JCdN9Ooee2116p42MaOHWvhQp5dwKuvvuruu+8+y22sRITnddxxx7nll1++Sts0JpgGK5QV/KMt\nWZx44olu8cUXd9dcc415+Pfff38bD1EKZZTUDow3eFJpw0wgAozhQs0YPny4zaPPP/+8/yaFvn37\nunnmmUdRvHpABkn9MXr0aOu/Xbt29d+kcPPNN5s8xnW5UkWjMkjwPCyyyCL+r8rE9ttv74499lj/\nV1XgPSRKQqiYRUz33HOPW3/99c1oeOmll1zLli1tv86dO9t+V1xxhRksAwcOtO8J2a+xxhpmvGA0\nEEJt27atbQPXXnutefm5h8cff9wMDTrAo48+6vdwpmATsg5gcJ5jjjnSgzP3RI7jhx9+aPtdffXV\n9n0lIShO2223neUbN1agwArlBf9oSxannnqqW2yxxdyff/7pv0mlr5JqhDNFqBnBILnyyiv9N/8B\nEVCEpHYgooRBkpmfHwwSRUjqDhkk9UcwSFgfFkcwSKZMmeK/KV00KoME5ZXUJBbu0PAQ71hjsPzy\nBXJT+f1J+OKLL6xNCN2NGDHCzT///OkUIRAiJyFlCwMvDo699957/V/ODRkyxAyMYHAg2Ozzzjvv\n2N8ALxuRmVCdBYPljjvusP8DojLzzTef6927t/29wQYbVIlwVapX86abbrK2zBw8GhOi+xfKDP7R\nlixOPvlkt9xyy/m/UiAVgVv/5JNP/DdCdSDXnLzyzJQtxvsmTZpobWEtgQLIPJqZsoWTr1mzZm7Q\noEH+G6G2kEFSf6DXkSqYmbJ12mmn2feNAY3KILnuuuts4qHDB5CDv9JKK9kgWwng96PMJiFUTkEw\nv/vuO/PeJJVxTDJI+vTpY8ZdZpiZtAgW7gFSvVhAH0e3bt2qGD41GSQUIKBzkCZG2lklAQ8FUSrK\n8a2yyioWcWrMC0gjWRPKDP7RlizuvPNOG+NYPxIwfvx4+w6HjFA7MA4ToY2DSDoZCFKkawfW4lBM\n5vzzz/ffpHDVVVdZGWW2C3WDDJL6g8wYnDWZEbstttjCbbPNNv6v0kajMkhIMSLUHAeDJwpvv379\n/DflDSbeyy+/3P9VFW+//balM0ycONHyWhFOIiWZSDJI2J+wXmbaA8bI5ptvbv/HIFliiSXs/wGk\ne9XFIOF+WH9CigULAsOalErEMcccY2uivv32W/9N4wIKrFBe8I+2ZBHSEigRGrDXXnu5jTfeuGKc\nUvkAaW60Y69evexvokykj6IICrUHaceM4b///rv9PXToUEtRvvvuu+1voe448sgjq/TvSs2iqCtI\n0z/33HOtgmdAKAJF+d/GgEZlkLD+IHPO/O2336yCQBhYyx277767LeRMemkQCz733Xdf24aiT7pV\n3JMYEAyS+IJ09idc/9VXX/lvUsADxFoSgEFCylgchPeJmvzyyy/2N5MaXswAIjUYLMEgCWACvOGG\nG2zwrlSQIoE8P/HEE/6bxoXo3oUyg3+0JY1ff/3Vxg0mXhwk++23n0qs1hEoeawT5JETSWfsb9++\nvV5GV0egBNJuOPNoR9rzxRdf1Ltc6ggyLXi3EK8yoA0h/0dPEWoPokrobLRdYGNKZW1UBgmLxBg4\n44o06xoQ5MZaqaiuIBKEgv/555/7b1LgxUx04vfee8/+ZtEnf8fzCZnImXBIlWKxe9xwAKRSxSuv\nYEzwzgwGCxDWkFCBK4BFprxMK6zj2Xvvve27gPCyo2CQBE8SQBHneVaCMkG7YzzHgZcSxaqx5r5H\nz1UoM/hHKwiCUBJQhKRy0OgmIErNMm+SvsUCRxZJB4W5EkDnfOONNyxSwaJEyvxefPHFti6B6lkB\neG74e8UVV7T9qEW94447WsoDoEAAa2/YRilhgPFCviHlaDkvb+6NV8xi7coCCyxg244++mh3xBFH\nWIpWvGww61j4joVUZ511lnkveV49evQwo+WEE06w47nuOuusY+V/KwGk0fEG2gsuuMB+P2l3tEvm\ngsjGBBRYobzgH60gCIIgFBWNcgLCc8/aBKIFpB9VIkiRYu0BrK4deAFf0j6kTP3www/WjvG0LhbB\n8x3HDB48uIp3Ir6GhNKG7Je5aJ79SUXieM7PdVhnwicgOsM2juXeKgnxtuWzsS8e9TqsUEbwj1YQ\nBEEQigpNQEKtkbSoXahceB1WKCP4RysIgiAIRYUmIKHWoEwtFbMEAXgdVigj+EcrCIIgCEWFJiCh\n1mBRvF6aJQR4HVYoI/hHKwiCIAhFhSYgQRDqBa/DCmUE/2gFQRAEoajQBCQIQr3gdVihjOAfrSAI\ngiAUFZqABEGoF7wOK5QR/KM1UKb7n3/+EUVRFMW8kvkl8wWiMkgEQagXvA4rlBH8ozUMHz7c/fHH\nH6IoiqKYV/KSbAyTONITEO+PYAJiJ1EUxUDGhSR4HVYoI/hHa+DZ//nnn6IoiqKYV2KUVGuQaAIS\nRTGTjAtJ8DqsUEbwj9ag+UAURVEsBGWQiKJYZ8ogqRz4R2vQfCCKoigWgjJIRFGsM2WQVA78ozVo\nPhBFURQLQRkkoijWmTJIKgf+0Ro0H4iiKIqFoAwSURTrTBkklQP/aA2aD0RRFMVCUAaJKIp1pgyS\nyoF/tAbNB6IoimIhKINEFMU6UwZJ5cA/WkOpzQdMYOPGjXOTJ0/O4sSJE92oUaMSjxOTSXvG23Ds\n2LH2XdK+Ym6OHDmySjv+9ddfifuJ1ZP+G2/H0aNHJ+4n1kxkcsqUKe7vv/9O3F4KlEEiZnH8+PFu\nwoQJ9v8RI0aUvBCLxacMksqBf7SGUpsPGKv22msvN/fcc7t55pknTW57oYUWcu+9956NYUnHitnk\n/UK03bzzzmuf++67r7Wf2rD2RPF7++23q7TjVVddlbivmJu041NPPVWlHW+//XYZyHVk6Lsff/yx\nteG5555bsoZdozdI8DzgIcsknh0NormJQDKZ0+kzvz/xxBPdoYce6oYOHeoGDRrkVlxxRXfjjTeq\nPQtMjD5ktzF4gWSQVA78ozWU2nzABIbzZNq0aWmCo48+2q255pru119/TTxOrEo80V988YUpLB07\ndrQ2ZLxfffXV3Y477qhIUy3J2P3kk0+aUfzWW29ZO37zzTdu4YUXdieccIJF7ZKOE6sS2XvkkUes\n3T777DNrxx49epjj4ZJLLjHdJek4MZs//PCDO/vss60t4YUXXiiDpBDk5rH2Fl98cbfkkksaW7Ro\n4eacc063yCKLuK5duyYeV+lE8aVTM/k8/PDDVbYhqEcddZR5xjBIfv75Z9e2bVt39913V9lPzC9/\n++03t/baa9szufzyy82gTtqvVCiDpHLgH62h1B1UKM49e/Z0c801l7vuuutKvh+VCmmn4447zm2w\nwQYWEWduZZ54+umnTYnp1q2bHFK1IP1jn332MYM4ZBVghFxwwQWmpzCnqh1r5pAhQ9zee+/tzjzz\nzLTyTMoW7brEEkvYd2rHmkkb3XLLLW777bc3h0OrVq3ceeedJ4OkUGQgjecYgo022shtttlmEtgE\n0iYII4PjfPPNZ4Iaj5JkGiTsD0IKF0RoaGsmLjhmzJj0tpCnyHkYiPn/pEmTsiIxYlU+9NBDbv31\n1zeD5Nprry35FDkZJJUD/2gNpT4fME6ddNJJ5pT65ZdfNO7UkkSSttxyS3fKKaekx3Parnv37jYm\nPfbYY2rLWvD77793a621lrvzzjvT7UV7du7c2TVt2tS98847asda8Mcff3Qrr7yyu//++9Pfoevd\neuutlr71+eefK2pXS5KGCdHJVlttNRkkxSKN/MYbb9gAimdHC8mySZuQlznHHHO49u3b2ych5TBI\nZhokTFQYLV26dLHtwUAhlL/YYou5BRdc0AYNUo04x9dff+1WWWUV98EHH9hxGD6bb765GzBggAzE\nasjgy6TVpEkTGSRCScE/WkMpzweML3379rXouNI66kbSOtZdd1131llnVZk3mQ8QAcZ/KdI1s0+f\nPpalQdpbfE5lPiSNSwZJ7fjtt9+6BRZYwD366KPp75DL5557ztK2SOOSQVI3IocySIpIlDjCpXj+\np0+fnrhPpRMPIgYCoXmsZtaHkPaGQcH2TIOElK011ljD3Xvvvbb9p59+ci1btrR82JkzZ9rAQXtf\nffXVdm7yPJnAWFD6ySefWHSEHOR27dq5YcOGVbkXMSWzpJa0adPGDRw4UAaJUHLwj9ZQ6gZJhw4d\nbPxBwZbiV3vSXuuss467+OKLEw0SnCVqz5qJQbLUUkvZ+ofggIsbJCx2VzvWzGCQPPPMM+nviDQF\ng+TTTz+VQVJHyiApIun85MjNP//8Fl5WdCSbtBG5wLQReYUYELvuuqtr1qyZDZJsr8kgYc0JRgwp\nWxwP8EYSjiZFKxgk99xzT9pD+dJLL7nlllvOFO74/VQ6GVApGkB7EYKmr8kgEUoN/tEaSnk+GDx4\nsKUdHXnkkVJW6kjG+U033dSddtppVVK2+vXrZ1H0F198UYp0Ldi/f3/LHmDNZWgvdBEyNlCw0VHU\njjUTAxmd4cEHH0x/x5yIXoEDlEio2rFulEFSRPJDWAyMZx5FOmmfSidCeMMNN5jSS3SEiSeU1SM0\nysBZk0FCG5PDSZSEKjbky7LIjJxtZCUYJM8++2xa6D/88EMbXEjbit9PpZP23mqrrdzhhx9ua21Y\n2C6DRCg1+EdrKOX5gIgst4v3NGm7mJvMn7vttpvbfffd04UAGL9feeUVc1jRpsHjL+YmDiYyEFjE\nHoxi2vOaa66xeVKpy7Uj7chaYDIvQnvh4DzjjDMsJRP9Re1YN9KfZZAUiax1oILAFVdckbhd/NMW\nee6www5VyjhicDCJb7HFFhbxqMkgYaBlbUgYEOLknMEgIdQahJ5wtQySqqS9iOQxSdFudERkGIOE\nySt4KUuVMkgqB/7RGkp1PqC/7Lfffm7rrbe2SEnSPmJu0n5EzXncREWIgPP9JptsYlF0RZxqR8Z1\nSqwuuuiiJoekLDPv4ig99dRTVfa3lmScocLWCiusYPMi7RayCXini9aH1Z70XVLykUWid6Rl8v9S\n1DHKwiChwfHII6wsdkraR/zTKqbg7dpwww1tASOkstN6661nC/Eok4zXvjqDhJDpSiutZIITzkv7\ncxyfMkhqRyYuolJUgwvPghxu0iN4FpTgJGKSdGwpUAZJ5cA/WkMpzgeMO6Ea1JVXXml/J+0n5ibj\nEeMN7UdKL06rVVdd1R1yyCFW8UjpMbUj7Uhq8mGHHWbzJA5ACrvw7gfmU7YnHSdWJe2E3O2xxx6W\nhYETFN2FQjxJ+4vJpB0p9LTtttsaGSNZ40Rmxvnnn59eO1wqLAuDhB+AJc0gqg6fTCZpPO9YyHgc\niIZAPA94chBU3oJaU4SENSiETC+66CKTkVmzZpnAs46E88kgqT1pn/hz4G8iJDfddJOlcCUdUyqU\nQVI58I/WUKrzARFbFEHGrKTtYu3I82WchiiEFCLRnFo30l60W7wdG4MeVWqkHXkfSbwd445QsXZk\nTAxtyBgZ/o9uV2p9u9EbJHjmX331VVOCWUjG30n7VTqxhFkMxlvYg6EQ5y677GJWM8+bt7STSxwM\nEhax33XXXel9GRhYM0K1C9odzz7fc97gqWRtSrjO+++/75o3b27HhXOIVcnAgIeStiMkHfK4S5XI\nSRKi+xfKDP7RGqRYiaIoioVgWURIUHzxKMsYyU0eNHnBuRRdcjLx1PN/jJeQo4miTL5hZtvS3nDq\n1Knm3Q+eC0L7fB83eojOcI5Ss8ZLkbRdqS9ohzJIKgf+0RpkkIiiKIqFYFkYJKIoFpcySCoH/tEa\nNB+IoiiKhaAMElEU60wZJJUD/2gNmg9EURTFQlAGiSiKdaYMksqBf7QGzQeiKIpiISiDRBTFOlMG\nSeXAP1oD65tYhyaKoiiK+STzy4wZM/xsk0IVg4TFzLxARRRFMZBxIQlehxXKCP7R2nxw2WWX2bsA\nRFEURTGf5D1IvJYijvQEROjk+uuvd7fddpsoimKajAu8gyYTXocVygj+0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e8XP1/8e5h5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MfTk4oYrFKqiVU0V6WUUkoppZRqGXW3RbiTIKWUUkoNmooYrFKqiVU0V6WUUkoppZT6T8hJ\nkFJKKaUGTUUMVinVxCqaq1JKKaWUUkr9J+QkSCmllFKDpiIGq5RqYhXNVSmllFJKKaX+A0rpX+Hl\nXkAbu3NwAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 220,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 900
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='images/onehot.png', width = 900, height = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's see how one-hot encoding works on the gender variable. First we will transform the categories \"Male\", \"Female\" and \"NaN\" to integers 0, 1, 2. Usually one would either impute or remove missing values, however in some cases the pattern of missing values might give you valuable insights and thus you can treat them as a separate category. The pattern of missing values for gender is probably not meaningful in this case, but for the sake of example, we will treat \"NaN\"s as another category."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 221,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seq_id</th>\n",
       "      <th>hubway_id</th>\n",
       "      <th>status</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>strt_statn</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_statn</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subsc_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>check_out_hour</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Closed</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Closed</td>\n",
       "      <td>220.0</td>\n",
       "      <td>7/28/2011 10:21:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:25:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>Closed</td>\n",
       "      <td>56.0</td>\n",
       "      <td>7/28/2011 10:33:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:34:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>Closed</td>\n",
       "      <td>64.0</td>\n",
       "      <td>7/28/2011 10:35:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:36:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>Closed</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>13</td>\n",
       "      <td>Closed</td>\n",
       "      <td>19.0</td>\n",
       "      <td>7/28/2011 10:39:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:39:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02021</td>\n",
       "      <td>1951.0</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>14</td>\n",
       "      <td>Closed</td>\n",
       "      <td>24.0</td>\n",
       "      <td>7/28/2011 10:47:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:47:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02140</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>15</td>\n",
       "      <td>Closed</td>\n",
       "      <td>7.0</td>\n",
       "      <td>7/28/2011 10:48:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:48:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02140</td>\n",
       "      <td>1971.0</td>\n",
       "      <td>0</td>\n",
       "      <td>40.0</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>16</td>\n",
       "      <td>Closed</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7/28/2011 11:01:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 11:01:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>17</td>\n",
       "      <td>Closed</td>\n",
       "      <td>1108.0</td>\n",
       "      <td>7/28/2011 11:55:00</td>\n",
       "      <td>47.0</td>\n",
       "      <td>7/28/2011 12:13:00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>B00550</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'01867</td>\n",
       "      <td>1994.0</td>\n",
       "      <td>1</td>\n",
       "      <td>17.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>18</td>\n",
       "      <td>Closed</td>\n",
       "      <td>1055.0</td>\n",
       "      <td>7/28/2011 11:55:00</td>\n",
       "      <td>47.0</td>\n",
       "      <td>7/28/2011 12:13:00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>B00580</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'01867</td>\n",
       "      <td>1956.0</td>\n",
       "      <td>1</td>\n",
       "      <td>55.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>19</td>\n",
       "      <td>Closed</td>\n",
       "      <td>1042.0</td>\n",
       "      <td>7/28/2011 11:55:00</td>\n",
       "      <td>47.0</td>\n",
       "      <td>7/28/2011 12:12:00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>B00539</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'01867</td>\n",
       "      <td>1959.0</td>\n",
       "      <td>0</td>\n",
       "      <td>52.0</td>\n",
       "      <td>11</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>23</td>\n",
       "      <td>Closed</td>\n",
       "      <td>994.0</td>\n",
       "      <td>7/28/2011 12:00:00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7/28/2011 12:16:00</td>\n",
       "      <td>47.0</td>\n",
       "      <td>B00368</td>\n",
       "      <td>Casual</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2</td>\n",
       "      <td>29.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>25</td>\n",
       "      <td>Closed</td>\n",
       "      <td>15.0</td>\n",
       "      <td>7/28/2011 12:00:00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>7/28/2011 12:00:00</td>\n",
       "      <td>22.0</td>\n",
       "      <td>B00442</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02446</td>\n",
       "      <td>1982.0</td>\n",
       "      <td>1</td>\n",
       "      <td>29.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>27</td>\n",
       "      <td>Closed</td>\n",
       "      <td>952.0</td>\n",
       "      <td>7/28/2011 12:00:00</td>\n",
       "      <td>40.0</td>\n",
       "      <td>7/28/2011 12:16:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00556</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02128</td>\n",
       "      <td>1944.0</td>\n",
       "      <td>1</td>\n",
       "      <td>67.0</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    seq_id  hubway_id  status  duration          start_date  strt_statn  \\\n",
       "0        1          8  Closed       9.0  7/28/2011 10:12:00        23.0   \n",
       "1        2          9  Closed     220.0  7/28/2011 10:21:00        23.0   \n",
       "2        3         10  Closed      56.0  7/28/2011 10:33:00        23.0   \n",
       "3        4         11  Closed      64.0  7/28/2011 10:35:00        23.0   \n",
       "4        5         12  Closed      12.0  7/28/2011 10:37:00        23.0   \n",
       "5        6         13  Closed      19.0  7/28/2011 10:39:00        23.0   \n",
       "6        7         14  Closed      24.0  7/28/2011 10:47:00        23.0   \n",
       "7        8         15  Closed       7.0  7/28/2011 10:48:00        23.0   \n",
       "8        9         16  Closed       8.0  7/28/2011 11:01:00        23.0   \n",
       "9       10         17  Closed    1108.0  7/28/2011 11:55:00        47.0   \n",
       "10      11         18  Closed    1055.0  7/28/2011 11:55:00        47.0   \n",
       "11      12         19  Closed    1042.0  7/28/2011 11:55:00        47.0   \n",
       "12      13         23  Closed     994.0  7/28/2011 12:00:00        40.0   \n",
       "13      14         25  Closed      15.0  7/28/2011 12:00:00        22.0   \n",
       "14      15         27  Closed     952.0  7/28/2011 12:00:00        40.0   \n",
       "\n",
       "              end_date  end_statn bike_nr  subsc_type zip_code  birth_date  \\\n",
       "0   7/28/2011 10:12:00       23.0  B00468  Registered   '97217      1976.0   \n",
       "1   7/28/2011 10:25:00       23.0  B00554  Registered   '02215      1966.0   \n",
       "2   7/28/2011 10:34:00       23.0  B00456  Registered   '02108      1943.0   \n",
       "3   7/28/2011 10:36:00       23.0  B00554  Registered   '02116      1981.0   \n",
       "4   7/28/2011 10:37:00       23.0  B00554  Registered   '97214      1983.0   \n",
       "5   7/28/2011 10:39:00       23.0  B00456  Registered   '02021      1951.0   \n",
       "6   7/28/2011 10:47:00       23.0  B00554  Registered   '02140      1971.0   \n",
       "7   7/28/2011 10:48:00       23.0  B00554  Registered   '02140      1971.0   \n",
       "8   7/28/2011 11:01:00       23.0  B00554  Registered   '97214      1983.0   \n",
       "9   7/28/2011 12:13:00       40.0  B00550  Registered   '01867      1994.0   \n",
       "10  7/28/2011 12:13:00       40.0  B00580  Registered   '01867      1956.0   \n",
       "11  7/28/2011 12:12:00       40.0  B00539  Registered   '01867      1959.0   \n",
       "12  7/28/2011 12:16:00       47.0  B00368      Casual      NaN         NaN   \n",
       "13  7/28/2011 12:00:00       22.0  B00442  Registered   '02446      1982.0   \n",
       "14  7/28/2011 12:16:00       23.0  B00556  Registered   '02128      1944.0   \n",
       "\n",
       "    gender   age  check_out_hour  \n",
       "0        1  35.0              10  \n",
       "1        1  45.0              10  \n",
       "2        1  68.0              10  \n",
       "3        0  30.0              10  \n",
       "4        0  28.0              10  \n",
       "5        1  60.0              10  \n",
       "6        0  40.0              10  \n",
       "7        0  40.0              10  \n",
       "8        0  28.0              11  \n",
       "9        1  17.0              11  \n",
       "10       1  55.0              11  \n",
       "11       0  52.0              11  \n",
       "12       2  29.0              12  \n",
       "13       1  29.0              12  \n",
       "14       1  67.0              12  "
      ]
     },
     "execution_count": 221,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hubway_data['gender'] = hubway_data['gender'].fillna('None') # replace nans by another name so that python doesn't recognize it as a nan\n",
    "le = preprocessing.LabelEncoder() # specify paramets for encoder\n",
    "hubway_data['gender'] = le.fit_transform(hubway_data['gender']) # fit the model and transform the gender variable\n",
    "hubway_data.head(15) # number in paranthesis specifies the numbers of lines to show"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Now we will encode integer categories 0, 1, and 2 as one-hot vectors."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seq_id</th>\n",
       "      <th>hubway_id</th>\n",
       "      <th>status</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>strt_statn</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_statn</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subsc_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>check_out_hour</th>\n",
       "      <th>g1</th>\n",
       "      <th>g2</th>\n",
       "      <th>g3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Closed</td>\n",
       "      <td>9.0</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97217</td>\n",
       "      <td>1976.0</td>\n",
       "      <td>1</td>\n",
       "      <td>35.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Closed</td>\n",
       "      <td>220.0</td>\n",
       "      <td>7/28/2011 10:21:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:25:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02215</td>\n",
       "      <td>1966.0</td>\n",
       "      <td>1</td>\n",
       "      <td>45.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>Closed</td>\n",
       "      <td>56.0</td>\n",
       "      <td>7/28/2011 10:33:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:34:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02108</td>\n",
       "      <td>1943.0</td>\n",
       "      <td>1</td>\n",
       "      <td>68.0</td>\n",
       "      <td>10</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>Closed</td>\n",
       "      <td>64.0</td>\n",
       "      <td>7/28/2011 10:35:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:36:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02116</td>\n",
       "      <td>1981.0</td>\n",
       "      <td>0</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>Closed</td>\n",
       "      <td>12.0</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97214</td>\n",
       "      <td>1983.0</td>\n",
       "      <td>0</td>\n",
       "      <td>28.0</td>\n",
       "      <td>10</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seq_id  hubway_id  status  duration          start_date  strt_statn  \\\n",
       "0       1          8  Closed       9.0  7/28/2011 10:12:00        23.0   \n",
       "1       2          9  Closed     220.0  7/28/2011 10:21:00        23.0   \n",
       "2       3         10  Closed      56.0  7/28/2011 10:33:00        23.0   \n",
       "3       4         11  Closed      64.0  7/28/2011 10:35:00        23.0   \n",
       "4       5         12  Closed      12.0  7/28/2011 10:37:00        23.0   \n",
       "\n",
       "             end_date  end_statn bike_nr  subsc_type zip_code  birth_date  \\\n",
       "0  7/28/2011 10:12:00       23.0  B00468  Registered   '97217      1976.0   \n",
       "1  7/28/2011 10:25:00       23.0  B00554  Registered   '02215      1966.0   \n",
       "2  7/28/2011 10:34:00       23.0  B00456  Registered   '02108      1943.0   \n",
       "3  7/28/2011 10:36:00       23.0  B00554  Registered   '02116      1981.0   \n",
       "4  7/28/2011 10:37:00       23.0  B00554  Registered   '97214      1983.0   \n",
       "\n",
       "   gender   age  check_out_hour   g1   g2   g3  \n",
       "0       1  35.0              10  0.0  1.0  0.0  \n",
       "1       1  45.0              10  0.0  1.0  0.0  \n",
       "2       1  68.0              10  0.0  1.0  0.0  \n",
       "3       0  30.0              10  1.0  0.0  0.0  \n",
       "4       0  28.0              10  1.0  0.0  0.0  "
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "enc = preprocessing.OneHotEncoder()\n",
    "new_columns = enc.fit_transform(hubway_data['gender'].values.reshape(-1, 1)).toarray()\n",
    "hubway_data[['g1', 'g2', 'g3']] = pd.DataFrame(new_columns, index = hubway_data.index)\n",
    "hubway_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# FEATURE NORMALIZATION\n",
    "* Range of values for each feature might vary dramatically\n",
    "* Often in machine learning it is critical to rescale or standardize features before fitting a model\n",
    "* Example: if a classifier is computing a distance between two points\n",
    "    * If one of the features has a broad range of values, the distance will be dominated by this particular feature\n",
    "    * Normalize each feature to be within the same range so that each feature contributes approximately proportionately to the final distance\n",
    "* Some algorithms such as gradient descent converge much faster with feature normalization\n",
    "* Standardization $$x_{new} = \\frac{x - \\bar{x}}{\\sigma}$$\n",
    "* Rescaling $$x_{new} = \\frac{x - \\min(x)}{\\max(x) - \\min(x)}$$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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++efFzeDixYspffr04nlSpEhB36rkchCyuXLlEu8di+vQjzp16kTvv/9+RJ8C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DpU2wpAULhtv3r11tF586xk4+4ie74ISRNu+rp6OdBc+gQWadOm2eZApvFmo6CyFyR5EIqGXL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ZpRzGVmoSfy1hve97wX+PnwLrni8XfoPPO+4ueG15nvMfflZEBskQAlhEjq0N8+dgzmu9SW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SJDULA6i8ebniZN99940///nPufO0sTF37tz47LPPcuf6k6Nbt27Rvn373Dl/7qS/EQyg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\nRK5CYII=\n",
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 186,
     "metadata": {
      "image/png": {
       "height": 500,
       "width": 900
      }
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Image(filename='images/rescaling.png', width = 900, height = 500)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "collapsed": true,
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "Let's standardize the duration, birth date and age variables"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>seq_id</th>\n",
       "      <th>hubway_id</th>\n",
       "      <th>status</th>\n",
       "      <th>duration</th>\n",
       "      <th>start_date</th>\n",
       "      <th>strt_statn</th>\n",
       "      <th>end_date</th>\n",
       "      <th>end_statn</th>\n",
       "      <th>bike_nr</th>\n",
       "      <th>subsc_type</th>\n",
       "      <th>zip_code</th>\n",
       "      <th>birth_date</th>\n",
       "      <th>gender</th>\n",
       "      <th>age</th>\n",
       "      <th>check_out_hour</th>\n",
       "      <th>g1</th>\n",
       "      <th>g2</th>\n",
       "      <th>g3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>8</td>\n",
       "      <td>Closed</td>\n",
       "      <td>-0.044894</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:12:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00468</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97217</td>\n",
       "      <td>-0.026848</td>\n",
       "      <td>1</td>\n",
       "      <td>0.781431</td>\n",
       "      <td>-0.784748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>9</td>\n",
       "      <td>Closed</td>\n",
       "      <td>-0.036942</td>\n",
       "      <td>7/28/2011 10:21:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:25:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02215</td>\n",
       "      <td>-0.935962</td>\n",
       "      <td>1</td>\n",
       "      <td>2.494727</td>\n",
       "      <td>-0.784748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>Closed</td>\n",
       "      <td>-0.043123</td>\n",
       "      <td>7/28/2011 10:33:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:34:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00456</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02108</td>\n",
       "      <td>-3.026923</td>\n",
       "      <td>1</td>\n",
       "      <td>6.435310</td>\n",
       "      <td>-0.784748</td>\n",
       "      <td>0.0</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>11</td>\n",
       "      <td>Closed</td>\n",
       "      <td>-0.042821</td>\n",
       "      <td>7/28/2011 10:35:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:36:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'02116</td>\n",
       "      <td>0.427709</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.075218</td>\n",
       "      <td>-0.784748</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>12</td>\n",
       "      <td>Closed</td>\n",
       "      <td>-0.044781</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>7/28/2011 10:37:00</td>\n",
       "      <td>23.0</td>\n",
       "      <td>B00554</td>\n",
       "      <td>Registered</td>\n",
       "      <td>'97214</td>\n",
       "      <td>0.609531</td>\n",
       "      <td>0</td>\n",
       "      <td>-0.417877</td>\n",
       "      <td>-0.784748</td>\n",
       "      <td>1.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   seq_id  hubway_id  status  duration          start_date  strt_statn  \\\n",
       "0       1          8  Closed -0.044894  7/28/2011 10:12:00        23.0   \n",
       "1       2          9  Closed -0.036942  7/28/2011 10:21:00        23.0   \n",
       "2       3         10  Closed -0.043123  7/28/2011 10:33:00        23.0   \n",
       "3       4         11  Closed -0.042821  7/28/2011 10:35:00        23.0   \n",
       "4       5         12  Closed -0.044781  7/28/2011 10:37:00        23.0   \n",
       "\n",
       "             end_date  end_statn bike_nr  subsc_type zip_code  birth_date  \\\n",
       "0  7/28/2011 10:12:00       23.0  B00468  Registered   '97217   -0.026848   \n",
       "1  7/28/2011 10:25:00       23.0  B00554  Registered   '02215   -0.935962   \n",
       "2  7/28/2011 10:34:00       23.0  B00456  Registered   '02108   -3.026923   \n",
       "3  7/28/2011 10:36:00       23.0  B00554  Registered   '02116    0.427709   \n",
       "4  7/28/2011 10:37:00       23.0  B00554  Registered   '97214    0.609531   \n",
       "\n",
       "   gender       age  check_out_hour   g1   g2   g3  \n",
       "0       1  0.781431       -0.784748  0.0  1.0  0.0  \n",
       "1       1  2.494727       -0.784748  0.0  1.0  0.0  \n",
       "2       1  6.435310       -0.784748  0.0  1.0  0.0  \n",
       "3       0 -0.075218       -0.784748  1.0  0.0  0.0  \n",
       "4       0 -0.417877       -0.784748  1.0  0.0  0.0  "
      ]
     },
     "execution_count": 188,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = hubway_data[['duration', 'birth_date', 'age', 'check_out_hour']]\n",
    "hubway_data[['duration', 'birth_date', 'age', 'check_out_hour']] = (x - np.mean(x))/np.std(x)\n",
    "hubway_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "subslide"
    }
   },
   "source": [
    "# Exercise: Now it's your turn!!!\n",
    "* Open up wids_workshop_exercise.ipynb and complete the code"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "# [Haversine distance](https://en.wikipedia.org/wiki/Haversine_formula)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<img src=\"http://giventocode.com/Media/Default/Windows-Live-Writer/Geotag-your-Entities-using-Azure-Mobile-_FFE5/image_4.png\" width=\"200\" height=\"200\"/>"
      ],
      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "Image(url='http://giventocode.com/Media/Default/Windows-Live-Writer/Geotag-your-Entities-using-Azure-Mobile-_FFE5/image_4.png', width = 200, height = 200)"
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  }
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