clustering.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 56,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from pyspark.sql import SQLContext\n",
    "from pyspark.ml.clustering import KMeans\n",
    "from pyspark.ml.feature import VectorAssembler\n",
    "from pyspark.ml.feature import StandardScaler\n",
    "from notebooks import utils\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "sqlContext = SQLContext(sc)\n",
    "df = sqlContext.read.load('file:///home/cloudera/Downloads/big-data-4/minute_weather.csv', \n",
    "                          format='com.databricks.spark.csv', \n",
    "                          header='true',inferSchema='true')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1587257"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158726"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filteredDF = df.filter((df.rowID % 10) == 0)\n",
    "filteredDF.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "158679"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempDF = filteredDF.na.drop()\n",
    "tempDF.count()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9708620631891018"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tempDF.stat.corr('min_wind_speed', 'avg_wind_speed')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>summary</th>\n",
       "      <td>count</td>\n",
       "      <td>mean</td>\n",
       "      <td>stddev</td>\n",
       "      <td>min</td>\n",
       "      <td>max</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rowID</th>\n",
       "      <td>158726</td>\n",
       "      <td>793625.0</td>\n",
       "      <td>458203.9375103623</td>\n",
       "      <td>0</td>\n",
       "      <td>1587250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>air_pressure</th>\n",
       "      <td>158726</td>\n",
       "      <td>916.8301614102434</td>\n",
       "      <td>3.051716552830638</td>\n",
       "      <td>905.0</td>\n",
       "      <td>929.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>air_temp</th>\n",
       "      <td>158726</td>\n",
       "      <td>61.851589153636304</td>\n",
       "      <td>11.833569210641757</td>\n",
       "      <td>31.64</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_wind_direction</th>\n",
       "      <td>158680</td>\n",
       "      <td>162.15610032770354</td>\n",
       "      <td>95.27820101905898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_wind_speed</th>\n",
       "      <td>158680</td>\n",
       "      <td>2.775214897907747</td>\n",
       "      <td>2.057623969742642</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_wind_direction</th>\n",
       "      <td>158680</td>\n",
       "      <td>163.46214393748426</td>\n",
       "      <td>92.45213853838689</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_wind_speed</th>\n",
       "      <td>158680</td>\n",
       "      <td>3.400557726241518</td>\n",
       "      <td>2.4188016208098886</td>\n",
       "      <td>0.1</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_wind_direction</th>\n",
       "      <td>158680</td>\n",
       "      <td>166.77401688933702</td>\n",
       "      <td>97.44110914784567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_wind_speed</th>\n",
       "      <td>158680</td>\n",
       "      <td>2.1346641038568754</td>\n",
       "      <td>1.7421125052424393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rain_accumulation</th>\n",
       "      <td>158725</td>\n",
       "      <td>3.178453299732825E-4</td>\n",
       "      <td>0.011235979086039813</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3.12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rain_duration</th>\n",
       "      <td>158725</td>\n",
       "      <td>0.4096267128681682</td>\n",
       "      <td>8.665522693479772</td>\n",
       "      <td>0.0</td>\n",
       "      <td>2960.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relative_humidity</th>\n",
       "      <td>158726</td>\n",
       "      <td>47.609469778108206</td>\n",
       "      <td>26.214408535062027</td>\n",
       "      <td>0.9</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0                     1                     2      3  \\\n",
       "summary              count                  mean                stddev    min   \n",
       "rowID               158726              793625.0     458203.9375103623      0   \n",
       "air_pressure        158726     916.8301614102434     3.051716552830638  905.0   \n",
       "air_temp            158726    61.851589153636304    11.833569210641757  31.64   \n",
       "avg_wind_direction  158680    162.15610032770354     95.27820101905898    0.0   \n",
       "avg_wind_speed      158680     2.775214897907747     2.057623969742642    0.0   \n",
       "max_wind_direction  158680    163.46214393748426     92.45213853838689    0.0   \n",
       "max_wind_speed      158680     3.400557726241518    2.4188016208098886    0.1   \n",
       "min_wind_direction  158680    166.77401688933702     97.44110914784567    0.0   \n",
       "min_wind_speed      158680    2.1346641038568754    1.7421125052424393    0.0   \n",
       "rain_accumulation   158725  3.178453299732825E-4  0.011235979086039813    0.0   \n",
       "rain_duration       158725    0.4096267128681682     8.665522693479772    0.0   \n",
       "relative_humidity   158726    47.609469778108206    26.214408535062027    0.9   \n",
       "\n",
       "                          4  \n",
       "summary                 max  \n",
       "rowID               1587250  \n",
       "air_pressure          929.5  \n",
       "air_temp               99.5  \n",
       "avg_wind_direction    359.0  \n",
       "avg_wind_speed         31.9  \n",
       "max_wind_direction    359.0  \n",
       "max_wind_speed         36.0  \n",
       "min_wind_direction    359.0  \n",
       "min_wind_speed         31.6  \n",
       "rain_accumulation      3.12  \n",
       "rain_duration        2960.0  \n",
       "relative_humidity      93.0  "
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filteredDF.describe().toPandas().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157812"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filteredDF.filter(filteredDF.rain_accumulation == 0.0).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "157237"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filteredDF.filter(filteredDF.rain_duration == 0.0).count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>summary</th>\n",
       "      <td>count</td>\n",
       "      <td>mean</td>\n",
       "      <td>stddev</td>\n",
       "      <td>min</td>\n",
       "      <td>max</td>\n",
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       "      <th>rowID</th>\n",
       "      <td>158726</td>\n",
       "      <td>793625.0</td>\n",
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       "      <td>0</td>\n",
       "      <td>1587250</td>\n",
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       "    <tr>\n",
       "      <th>air_pressure</th>\n",
       "      <td>158726</td>\n",
       "      <td>916.8301614102434</td>\n",
       "      <td>3.051716552830638</td>\n",
       "      <td>905.0</td>\n",
       "      <td>929.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>air_temp</th>\n",
       "      <td>158726</td>\n",
       "      <td>61.851589153636304</td>\n",
       "      <td>11.833569210641757</td>\n",
       "      <td>31.64</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_wind_direction</th>\n",
       "      <td>158680</td>\n",
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       "      <td>95.27820101905898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_wind_speed</th>\n",
       "      <td>158680</td>\n",
       "      <td>2.775214897907747</td>\n",
       "      <td>2.057623969742642</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.9</td>\n",
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       "    <tr>\n",
       "      <th>max_wind_direction</th>\n",
       "      <td>158680</td>\n",
       "      <td>163.46214393748426</td>\n",
       "      <td>92.45213853838689</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_wind_speed</th>\n",
       "      <td>158680</td>\n",
       "      <td>3.400557726241518</td>\n",
       "      <td>2.4188016208098886</td>\n",
       "      <td>0.1</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_wind_direction</th>\n",
       "      <td>158680</td>\n",
       "      <td>166.77401688933702</td>\n",
       "      <td>97.44110914784567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_wind_speed</th>\n",
       "      <td>158680</td>\n",
       "      <td>2.1346641038568754</td>\n",
       "      <td>1.7421125052424393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relative_humidity</th>\n",
       "      <td>158726</td>\n",
       "      <td>47.609469778108206</td>\n",
       "      <td>26.214408535062027</td>\n",
       "      <td>0.9</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0                   1                   2      3  \\\n",
       "summary              count                mean              stddev    min   \n",
       "rowID               158726            793625.0   458203.9375103623      0   \n",
       "air_pressure        158726   916.8301614102434   3.051716552830638  905.0   \n",
       "air_temp            158726  61.851589153636304  11.833569210641757  31.64   \n",
       "avg_wind_direction  158680  162.15610032770354   95.27820101905898    0.0   \n",
       "avg_wind_speed      158680   2.775214897907747   2.057623969742642    0.0   \n",
       "max_wind_direction  158680  163.46214393748426   92.45213853838689    0.0   \n",
       "max_wind_speed      158680   3.400557726241518  2.4188016208098886    0.1   \n",
       "min_wind_direction  158680  166.77401688933702   97.44110914784567    0.0   \n",
       "min_wind_speed      158680  2.1346641038568754  1.7421125052424393    0.0   \n",
       "relative_humidity   158726  47.609469778108206  26.214408535062027    0.9   \n",
       "\n",
       "                          4  \n",
       "summary                 max  \n",
       "rowID               1587250  \n",
       "air_pressure          929.5  \n",
       "air_temp               99.5  \n",
       "avg_wind_direction    359.0  \n",
       "avg_wind_speed         31.9  \n",
       "max_wind_direction    359.0  \n",
       "max_wind_speed         36.0  \n",
       "min_wind_direction    359.0  \n",
       "min_wind_speed         31.6  \n",
       "relative_humidity      93.0  "
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The weather measurements in this dataset were collected during a drought in San Diego and because of that both rain_accumulation\n",
    "# and rain_duration have 0 values in most samples. Hence, we can drop these two columns.\n",
    "# We can also drop hpwren_timestamp column since we don't use it.\n",
    "workingDF = filteredDF.drop('rain_accumulation').drop('rain_duration').drop('hpwren_timestamp')\n",
    "workingDF.describe().toPandas().transpose()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "46"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "before = workingDF.count()\n",
    "#Drop rows with missing values\n",
    "workingDF = workingDF.na.drop()\n",
    "after = workingDF.count()\n",
    "before - after"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['rowID',\n",
       " 'air_pressure',\n",
       " 'air_temp',\n",
       " 'avg_wind_direction',\n",
       " 'avg_wind_speed',\n",
       " 'max_wind_direction',\n",
       " 'max_wind_speed',\n",
       " 'min_wind_direction',\n",
       " 'min_wind_speed',\n",
       " 'relative_humidity']"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Normalize the data with 0 mean and 1 SD\n",
    "workingDF.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "#Combine columns into a single vector; \n",
    "featuresUsed = ['air_pressure',\n",
    " 'air_temp',\n",
    " 'avg_wind_direction',\n",
    " 'avg_wind_speed',\n",
    " 'max_wind_direction',\n",
    " 'max_wind_speed',\n",
    " 'relative_humidity']\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "      <td>1587250</td>\n",
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       "    <tr>\n",
       "      <th>air_pressure</th>\n",
       "      <td>158680</td>\n",
       "      <td>916.8304071086608</td>\n",
       "      <td>3.0516872463866034</td>\n",
       "      <td>905.0</td>\n",
       "      <td>929.5</td>\n",
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       "    <tr>\n",
       "      <th>air_temp</th>\n",
       "      <td>158680</td>\n",
       "      <td>61.85578258129617</td>\n",
       "      <td>11.83251581712244</td>\n",
       "      <td>31.64</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_wind_direction</th>\n",
       "      <td>158680</td>\n",
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       "      <td>95.27820101905898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_wind_speed</th>\n",
       "      <td>158680</td>\n",
       "      <td>2.775214897907747</td>\n",
       "      <td>2.057623969742642</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_wind_direction</th>\n",
       "      <td>158680</td>\n",
       "      <td>163.46214393748426</td>\n",
       "      <td>92.45213853838689</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_wind_speed</th>\n",
       "      <td>158680</td>\n",
       "      <td>3.400557726241518</td>\n",
       "      <td>2.4188016208098886</td>\n",
       "      <td>0.1</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_wind_direction</th>\n",
       "      <td>158680</td>\n",
       "      <td>166.77401688933702</td>\n",
       "      <td>97.44110914784567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_wind_speed</th>\n",
       "      <td>158680</td>\n",
       "      <td>2.1346641038568754</td>\n",
       "      <td>1.7421125052424393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relative_humidity</th>\n",
       "      <td>158680</td>\n",
       "      <td>47.59692021678852</td>\n",
       "      <td>26.20783114367006</td>\n",
       "      <td>0.9</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0                   1                   2      3  \\\n",
       "summary              count                mean              stddev    min   \n",
       "rowID               158680   793627.3355810436   458198.0363632447      0   \n",
       "air_pressure        158680   916.8304071086608  3.0516872463866034  905.0   \n",
       "air_temp            158680   61.85578258129617   11.83251581712244  31.64   \n",
       "avg_wind_direction  158680  162.15610032770354   95.27820101905898    0.0   \n",
       "avg_wind_speed      158680   2.775214897907747   2.057623969742642    0.0   \n",
       "max_wind_direction  158680  163.46214393748426   92.45213853838689    0.0   \n",
       "max_wind_speed      158680   3.400557726241518  2.4188016208098886    0.1   \n",
       "min_wind_direction  158680  166.77401688933702   97.44110914784567    0.0   \n",
       "min_wind_speed      158680  2.1346641038568754  1.7421125052424393    0.0   \n",
       "relative_humidity   158680   47.59692021678852   26.20783114367006    0.9   \n",
       "\n",
       "                          4  \n",
       "summary                 max  \n",
       "rowID               1587250  \n",
       "air_pressure          929.5  \n",
       "air_temp               99.5  \n",
       "avg_wind_direction    359.0  \n",
       "avg_wind_speed         31.9  \n",
       "max_wind_direction    359.0  \n",
       "max_wind_speed         36.0  \n",
       "min_wind_direction    359.0  \n",
       "min_wind_speed         31.6  \n",
       "relative_humidity      93.0  "
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "assembler = VectorAssembler(inputCols = featuresUsed, outputCol = \"features_unscaled\")\n",
    "assembled = assembler.transform(workingDF)\n",
    "assembled.describe().toPandas().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "scaler = StandardScaler(inputCol=\"features_unscaled\", outputCol = \"features\", withStd = True, withMean = True)\n",
    "scalerModel = scaler.fit(assembled)\n",
    "scaledData = scalerModel.transform(assembled)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>summary</th>\n",
       "      <td>count</td>\n",
       "      <td>mean</td>\n",
       "      <td>stddev</td>\n",
       "      <td>min</td>\n",
       "      <td>max</td>\n",
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       "    <tr>\n",
       "      <th>rowID</th>\n",
       "      <td>158680</td>\n",
       "      <td>793627.3355810436</td>\n",
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       "      <td>0</td>\n",
       "      <td>1587250</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>air_pressure</th>\n",
       "      <td>158680</td>\n",
       "      <td>916.8304071086608</td>\n",
       "      <td>3.0516872463866034</td>\n",
       "      <td>905.0</td>\n",
       "      <td>929.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>air_temp</th>\n",
       "      <td>158680</td>\n",
       "      <td>61.85578258129617</td>\n",
       "      <td>11.83251581712244</td>\n",
       "      <td>31.64</td>\n",
       "      <td>99.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_wind_direction</th>\n",
       "      <td>158680</td>\n",
       "      <td>162.15610032770354</td>\n",
       "      <td>95.27820101905898</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>avg_wind_speed</th>\n",
       "      <td>158680</td>\n",
       "      <td>2.775214897907747</td>\n",
       "      <td>2.057623969742642</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_wind_direction</th>\n",
       "      <td>158680</td>\n",
       "      <td>163.46214393748426</td>\n",
       "      <td>92.45213853838689</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max_wind_speed</th>\n",
       "      <td>158680</td>\n",
       "      <td>3.400557726241518</td>\n",
       "      <td>2.4188016208098886</td>\n",
       "      <td>0.1</td>\n",
       "      <td>36.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_wind_direction</th>\n",
       "      <td>158680</td>\n",
       "      <td>166.77401688933702</td>\n",
       "      <td>97.44110914784567</td>\n",
       "      <td>0.0</td>\n",
       "      <td>359.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min_wind_speed</th>\n",
       "      <td>158680</td>\n",
       "      <td>2.1346641038568754</td>\n",
       "      <td>1.7421125052424393</td>\n",
       "      <td>0.0</td>\n",
       "      <td>31.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>relative_humidity</th>\n",
       "      <td>158680</td>\n",
       "      <td>47.59692021678852</td>\n",
       "      <td>26.20783114367006</td>\n",
       "      <td>0.9</td>\n",
       "      <td>93.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                         0                   1                   2      3  \\\n",
       "summary              count                mean              stddev    min   \n",
       "rowID               158680   793627.3355810436   458198.0363632447      0   \n",
       "air_pressure        158680   916.8304071086608  3.0516872463866034  905.0   \n",
       "air_temp            158680   61.85578258129617   11.83251581712244  31.64   \n",
       "avg_wind_direction  158680  162.15610032770354   95.27820101905898    0.0   \n",
       "avg_wind_speed      158680   2.775214897907747   2.057623969742642    0.0   \n",
       "max_wind_direction  158680  163.46214393748426   92.45213853838689    0.0   \n",
       "max_wind_speed      158680   3.400557726241518  2.4188016208098886    0.1   \n",
       "min_wind_direction  158680  166.77401688933702   97.44110914784567    0.0   \n",
       "min_wind_speed      158680  2.1346641038568754  1.7421125052424393    0.0   \n",
       "relative_humidity   158680   47.59692021678852   26.20783114367006    0.9   \n",
       "\n",
       "                          4  \n",
       "summary                 max  \n",
       "rowID               1587250  \n",
       "air_pressure          929.5  \n",
       "air_temp               99.5  \n",
       "avg_wind_direction    359.0  \n",
       "avg_wind_speed         31.9  \n",
       "max_wind_direction    359.0  \n",
       "max_wind_speed         36.0  \n",
       "min_wind_direction    359.0  \n",
       "min_wind_speed         31.6  \n",
       "relative_humidity      93.0  "
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaledData.describe().toPandas().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "scaledData = scaledData.select(\"features\", \"rowID\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>summary</th>\n",
       "      <td>count</td>\n",
       "      <td>mean</td>\n",
       "      <td>stddev</td>\n",
       "      <td>min</td>\n",
       "      <td>max</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rowID</th>\n",
       "      <td>158680</td>\n",
       "      <td>793627.3355810436</td>\n",
       "      <td>458198.0363632447</td>\n",
       "      <td>0</td>\n",
       "      <td>1587250</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              0                  1                  2    3        4\n",
       "summary   count               mean             stddev  min      max\n",
       "rowID    158680  793627.3355810436  458198.0363632447    0  1587250"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaledData.describe().toPandas().transpose()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Elbown method is very compute-intensive; hence reduce the data size\n",
    "elbowset =scaledData.filter((scaledData.rowID % 3) == 0).select(\"features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[features: vector]"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Request Spark to keep the data in memory if possible\n",
    "elbowset.persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Training for cluster size 2 \n",
      "......................WSSE = 114993.13181214454 \n",
      "Training for cluster size 3 \n",
      "......................WSSE = 104181.0978581738 \n",
      "Training for cluster size 4 \n",
      "......................WSSE = 94577.27151288436 \n",
      "Training for cluster size 5 \n",
      "......................WSSE = 87993.46098415818 \n",
      "Training for cluster size 6 \n",
      "......................WSSE = 85084.23922296542 \n",
      "Training for cluster size 7 \n",
      "......................WSSE = 81664.96024487517 \n",
      "Training for cluster size 8 \n",
      "......................WSSE = 78397.76745113655 \n",
      "Training for cluster size 9 \n",
      "......................WSSE = 76599.60815784344 \n",
      "Training for cluster size 10 \n",
      "......................WSSE = 74023.93930498546 \n",
      "Training for cluster size 11 \n",
      "......................WSSE = 72772.61056038193 \n",
      "Training for cluster size 12 \n",
      "......................WSSE = 70281.81723912072 \n",
      "Training for cluster size 13 \n",
      "......................WSSE = 69473.53243631397 \n",
      "Training for cluster size 14 \n",
      "......................WSSE = 68756.12329160626 \n",
      "Training for cluster size 15 \n",
      "......................WSSE = 67394.28524204041 \n",
      "Training for cluster size 16 \n",
      "......................WSSE = 66698.44744917285 \n",
      "Training for cluster size 17 \n",
      "......................WSSE = 64559.11686276019 \n",
      "Training for cluster size 18 \n",
      "......................WSSE = 63205.19662076986 \n",
      "Training for cluster size 19 \n",
      "......................WSSE = 62368.48817165655 \n",
      "Training for cluster size 20 \n",
      "......................WSSE = 62444.64399663452 \n",
      "Training for cluster size 21 \n",
      "......................WSSE = 61265.25028685173 \n",
      "Training for cluster size 22 \n",
      "......................WSSE = 60936.7345412291 \n",
      "Training for cluster size 23 \n",
      "......................WSSE = 60109.64682994062 \n",
      "Training for cluster size 24 \n",
      "......................WSSE = 60087.31889119306 \n",
      "Training for cluster size 25 \n",
      "......................WSSE = 59084.887257386996 \n",
      "Training for cluster size 26 \n",
      "......................WSSE = 58498.072941068385 \n",
      "Training for cluster size 27 \n",
      "......................WSSE = 58215.79330982371 \n",
      "Training for cluster size 28 \n",
      "......................WSSE = 58132.840856721 \n",
      "Training for cluster size 29 \n",
      "......................WSSE = 57426.52665309544 \n",
      "Training for cluster size 30 \n",
      "......................WSSE = 57099.24349435484 \n"
     ]
    }
   ],
   "source": [
    "clusters = range(2, 31)\n",
    "wsseList = utils.elbow(elbowset, clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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qrLvSYK6UNDOlNJgrJc1MKS9sCEvEIYfAe+/Bo49mXYkkSZKkcuEewhIybtwC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\nD6F6vIED4cEH4YgjYOlS+Pz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O8N//DZWVWVckSZJ6MvcQKnXOuisNpZqrQYPgsccKewz3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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb380f47f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.elbow_plot(wsseList, clusters)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#Let's select k = 12\n",
    "scaledDataFeat = scaledData.select(\"features\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "DataFrame[features: vector]"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "scaledDataFeat.persist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "kmeans = KMeans(k=12, seed=1)\n",
    "#kmeans = KMeans(k=20, seed=1)\n",
    "model = kmeans.fit(scaledDataFeat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "transformed = model.transform(scaledDataFeat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 119,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "centers = model.clusterCenters()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([-0.13720796,  0.6061152 ,  0.22970948, -0.62174454,  0.40604553,\n",
       "        -0.63465994, -0.42215364]),\n",
       " array([ 1.42238994, -0.10953198, -1.10891543, -0.07335197, -0.96904335,\n",
       "        -0.05226062, -0.99615617]),\n",
       " array([-0.63637648,  0.01435705, -1.1038928 , -0.58676582, -0.96998758,\n",
       "        -0.61362174,  0.33603011]),\n",
       " array([-0.22385278, -1.06643622,  0.5104215 , -0.24620591,  0.68999967,\n",
       "        -0.24399706,  1.26206479]),\n",
       " array([ 1.17896517, -0.25134204, -1.15089838,  2.11902126, -1.04950228,\n",
       "         2.23439263, -1.12861666]),\n",
       " array([-1.14087425, -0.979473  ,  0.42483303,  1.68904662,  0.52550171,\n",
       "         1.65795704,  1.03863542]),\n",
       " array([ 0.50746307, -1.08840683, -1.20882766, -0.57604727, -1.0367013 ,\n",
       "        -0.58206904,  0.97099067]),\n",
       " array([ 0.14064028,  0.83834618,  1.89291279, -0.62970435, -1.54598923,\n",
       "        -0.55625032, -0.75082891]),\n",
       " array([-0.0339489 ,  0.98719067, -1.33032244, -0.57824562, -1.18095582,\n",
       "        -0.58893358, -0.81187427]),\n",
       " array([-0.22747944,  0.59239924,  0.40531475,  0.6721331 ,  0.51459992,\n",
       "         0.61355559, -0.15474261]),\n",
       " array([ 0.3334222 , -0.99822761,  1.8584392 , -0.68367089, -1.53246714,\n",
       "        -0.59099434,  0.91004892]),\n",
       " array([ 0.3051367 ,  0.67973831,  1.36434828, -0.63793718,  1.631528  ,\n",
       "        -0.58807924, -0.67531539])]"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "centers"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 121,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "p = utils.pd_centers(featuresUsed, centers)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 123,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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InTtL994rTZyYfgVnnLDSiVjgiaGZN0865hgbAZ6d7TqaaCJXSm/+fMuv8eOl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\nDWnr3t11JACQHn76ybpEpk2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B4J98X3rvPem446zQvO46afZs21LD2bhAtNBei8j5/ntb\npVy5Uho+XKpVa9v/zMZtKl99JR1/vK2eVq8ecrBIO7Q0IRGu82XVKunpp+1849q1bX/aySczdCiK\nXOdKJvF9acIEa6NdsUK6805b1UynQpN8QVBxaa+l6EQk+b6tfOblSb17S+3bb/1D1vqbd3Gxtde0\nbWvHsgCb4i96JCIq+bJ6tfTCC9bF4XlWfLZqJW23nevIsF5UciXOfF96/30rNr//3orNdF3VJF8Q\nFEVnyCg6IUlz5tiQoQMOsKf9u+22+e9bf/N+6CHpjTfsCSj7OLA5/EWPREQtX3zfJn/37SvNnWst\nhVdeWboBbEiuqOVK3EycaIOBvv12Q7GZjHO+XSFfEFRcik4+liPSate2oRo1akh169rejS1ZuFC6\n917pmWcoOAHEk+dJLVrY0JS337YHc/vvL3XtakewAHHzwQd2ju3ll0sdOtjDltat07vgBDJRSj+a\ne553ged5sz3PK/Y8r0Eq3xvpa/vtpX79pOeeky691A5S//vv//2+jh1tANEBB6Q+RgBItXr1pKFD\n7fzBChWkhg2t5XbKFNeRAWX34YdS06ZWaF52mc1raNuWYhNIV6leD5ol6VxJH6T4fREDzZvbBMeC\nAunII+1pZ2HhYrVunSdJmj07T2eeudhxlACQWvvsI91/v1RYaNNvzzvPPqz/5z92BiiQTj76SGrW\nzArNSy+Vvv7afqbYBNKbkz2dnue9L6mb7/vTtvI97OnEZvm+tdDedNNiVajwmFauzJNUSdLvqlmz\np8aN66TsbMbWYvPYR4NEpGO+rFkjjRxp+z7//NOGDrVuLe2wg+vI4i0dcyVKPv7Y9mwuWiTdcYfl\nbIUKrqMKD/mCoNjTCTjieba34/jjB68rOCuu+52KKijIU48egx1GBwBuVahgA9imTrUp4K+9JmVn\nS/fcI/34o+vogH/65BPppJOsdTY311Y2L7ss3gUnkImS3qzged44SXtu/CVJvqTbfd9/K5HXateu\nnWrUqCFJqlKliurVq6ecnBxJ0sSJEyWJ6wy+XrLkv9pQcErSREk5Wr68JBLxcc0111y7vm7WTMrK\nmqjCQunjj3N0wAFSTs5EXXCBlJvrPr44Xa8XlXiifr3ddjnq2VOaNWui2rSR3nknRxUqRCc+8oVr\nV9czZszQzz//LElaFKMJcbTXIm21bp2n4cNvlBWe659tFCk3t6+GDevpNjhEFi1NSETc8uXbb6XH\nH7cjqE44wVpvjzrKdVTxELdcCctnn9k5m/PmWRtt27aZed4s+YKgaK8tu7T/w4Nb+fntVLNmT0lF\n675SpJo1eyo/v52zmAAgyvbe246WKiy0ojM3VzrmGGvBLS52HR3i7PPPpVNOsQnLF1wgzZ9vU+cz\nseAEMlFKi07P887xPG+ppCaS/uN53jupfH/ES3Z2dY0b10m5uX0lSbm5fRkiBAABVKokdepkH/yv\nv17q3Vs6+GCpf3/pjz9cR4c4+eIL6dRTpZYtbbLy/Pk2l4FiE8gsTtprg6C9FomgTQVBkStIRKbk\ni+/bQJe+faVPP5Wuvlq69lrp3/92HVn6yJRcCWrSJGujnT3bztC+7DIKzY2RLwiK9loAABALnicd\ne6z0+ut2TuL330sHHSRdeaXtvQOCmjRJOv10a6E96yxpwQLLIwpOILNRdAIAgP930EHSgAFWbO69\nt3TccVY8fPihrYgCmzNlinTGGdL559vPCxZIV10lbb+968gARAHttYgF2lQQFLmCRJAvtsdz6FDp\nwQelKlWkG2+0vXnlk37oWnrL1FyZOtXaaKdPl7p3t+FAFJrblqn5gsTFpb2WohOxwM0bQZErSAT5\nskFJifTWW7bv85tvpK5dpfbtbSgRMi9Xpk2zYnPaNOnWW63Y3GEH11Glj0zLF5ReXIpO2msBAMA2\nZWVJZ59tez5feMF+rlFDuu02O/8TmWH6dMuDM8+UmjeXFi6UrruOghPA1lF0AgCAhDRpIo0cacdh\n/PabVLu2TSedPdt1ZAjLjBnSOefYfs0TT5QKCuzYHYpNAEFQdAIAgFKpWVN67DFb7apVy1a+Tj1V\nGj+eoUNxMWOGdO650mmnSU2b2n/rzp0pNgEkhj2diAX2RiAocgWJIF8S89df0ogRtu9z++1t6NBF\nF0kVKriOLHxxy5WZM6W8POmzz6RbbrFjT3bc0XVU8RG3fEF44rKnk6ITscDNG0GRK0gE+VI6JSXS\nmDFWfC5cKHXpYoNmdtnFdWThiUuufPmlFZuffirdfLMVmzvt5Dqq+IlLviB8cSk6aa8FAABJlZVl\n7ZgTJkivvWbHauy/v618Ll3qOjpszqxZ0oUXSi1aSMccY3s2u3al4ASQHBSdAAAgNA0bWsvt9Om2\nz7NuXal1a7uGe7NnWwt08+Y2IGrhQumGGyg2ASQXRScAAAjdfvtJ/fpJ//2vFZ5nnimddJK14dJl\nmNbFRDgAABo0SURBVHpz5kgtW9p/g8aNbWWzWzepYkXXkQGII/Z0IhbYG4GgyBUkgnwJz+rV0ksv\n2b7PkhIreFq1sgFE6ShdcmXuXOnuu6X337d252uuodB0IV3yBe6xpxMAAKCUtttOatPGjuR48EHp\nhRek7GypVy9p1SrX0cXP3LlW1DdtKtWvbyubN91EwQkgNSg6AQCAM55n+wnHjrVW23nz7PzPLl2k\nwkLX0aW/r76SLrlEysmxtuaCAjsCpVIl15EByCQUnQAAIBIOP1waPNgmqe64o9Soke07nDzZdWTp\n5+uvpdxc6YQTpDp1rNi89VaKTQBuUHQCAIBIqVZN6t3bVjqPOkq64AIrnt56y/Z/YsvmzbPpwMcf\nL9WubcVm9+5S5cquIwOQyRgkhFhgQz6CIleQCPIlGtaulV55xYYO/f67HenRpo2thkaF61yZP1/K\nz7cW5euvlzp1knbe2Vk42AbX+YL0wSAhAACAFChfXrr4YmuzfeopW/HMzrYprCtXuo7OrQULpLZt\npWOOkQ46yFY2b7+dghNAtFB0AgCAtOB5G9ps339fWrpUqlXLjv1YsMB1dKm1cKF06aXS0Ufbn8HC\nhdIdd1BsAogmik4AAJB2DjlEGjjQprPutpsVX+edJ336qevIwrVwodSundSkiU35XbBA6tFD2mUX\n15EBwJZRdAIAgLS11162l3HRIunEE22v51FHSa++KhUXu44ueQoKpMsus2IzO9uKzzvvlKpUcR0Z\nAGwbg4QQC2zIR1DkChJBvqSf4mLpjTekBx6QVqyQuna1lcGKFcN937By5b//le65R3rzTem662xI\nEIVm+uPegqAYJAQAABAx5cpZm+1nn0lDh0rjx0s1algL6vffu44uuMJCqWNHqXFjad99rY32rrso\nOAGkJ4pOAAAQS0cfLY0aZfs8f/xROvhg6fLLbR9oVC1aZDE2aiRVrWpHoeTlSbvu6joyACg9ik4A\nABBrtWpJTz5pBdy++0o5OdKZZ0offCBFpcNx0SLpiiukhg2lPfe0WO++W/rXv1xHBgBlR9EJAAAy\nwh572PCdRYuks86SrrzSVhRffFFau9ZNTIsXWxwNG1p88+fbHk6KTQBxQtEJAAAyyo47Wgvr3LlS\nz55S//7SAQdIDz8s/fZbamJYskS66iqpQQM78mX+fOnee+3XABA3FJ0AACAjZWVtaLN9+WUbPpSd\nLd16q7RsWTjvuWSJdPXVUv36tk9z3jzpvvsoNgHEG0UnAADIeI0bSy+9JE2eLP31l1Snjh21MmtW\ncl5/6VLpmmus2NxlFys2e/WSdt89Oa8PAFFG0QkAALBOdra12RYU2LTbFi3sx7hxpRs69M030rXX\nSvXqSZUrS19/LfXuTbEJILNQdAIAAGxi112tzbawUGrVSura1QrH55+XVq/e9j+/bJl03XXS4YdL\nFStasdmnjw0LAoBMQ9EJAACwBdtvv6HNtk8facgQaf/9pQcekH75RSosXKzWrfMkSa1b5+mzzxar\nUydrz91xRys277+fYhNAZvP8qBxQtQnP8/yoxobo8TxP5AuCIFeQCPIFmzN9utSvn/TWW4vleY/p\nl1/yJFWS9LuysnqqfftOuuee6tpzT9eRIqq4tyCodbniuY6jrFjpBAAASED9+tKwYVKzZoPXFZwV\n1/1ORZWU5OnPPwdTcALARig6AQAASuHnn0u0oeBcr6KWLy9xEQ4ARBZFJwAAQClUq5YlqWiTrxap\nalU+XgHAxrgrAgAAlEJ+fjvVrNlTGwrPItWs2VP5+e2cxQQAUUTRCQAAUArZ2dU1blwn5eb2lSTl\n5vbVuHGdlJ1d3XFkABAtTK9FLDAFDkGRK0gE+YKgyBUkgnxBUEyvBQAAAABgGyg6AQAAAAChoegE\nAAAAAISGohMAAAAAEBqKTgAAAABAaCg6AQAAAAChoegEAAAAAISGohMAAAAAEBqKTgAAAABAaCg6\nAQAAAAChoegEAAAAAISGohMAAAAAEBqKTgAAAABAaCg6AQAAAAChoegEgP9r787DJanKO45/fyCb\nwIjggkYBiShBUeK+EFBRY1xw36IG8Alxi4i7iAnGDcUE17gjKogaFxQ3GMQZVkcYYBYREIKQRyP6\nuIyOGgGdkz/Oaaa43r63586tnpnm+3me+8yp6uo61X3ePlVv1akaSZIk9cakU5IkSZLUG5NOSZIk\nSVJvTDolSZIkSb0x6ZQkSZIk9cakU5IkSZLUG5NOSZIkSVJvTDolSZIkSb0x6ZQkSZIk9cakU5Ik\nSZLUG5NOSZIkSVJvxpp0JjkmyaVJliX5YpIF46xfkiRJkjRe477SuRC4RyllH+AK4Igx1y9JkiRJ\nGqOxJp2llG+VUta0ySXAncZZvyRJkiRpvDbkPZ3PB765AeuXJEmSJPXsFvO9wiSnA7fvzgIKcGQp\n5attmSOBG0opJ820roMPPpjddtsNgB122IF99tmHhz3sYQAsXrwYwGmnGVi8ePFGsz1OO+20007f\nvKYHNpbtcXrjnh7YWLbH6Y1netmyZaxatQqAq6++mkmRUsp4K0wOBg4FHlFKuW6G5cq4t02briQY\nLxqFsaJ1YbxoVMaK1oXxolG1WMmG3o71Ne9XOmeS5DHAq4H9Zko4JUmSJEmTYaxXOpNcAWwJ/KLN\nWlJKefGQZb3SqZF5xlCjMla0LowXjcpY0bowXjQqr3TOQSllj3HWJ0mSJEnasDbb0BsgSZIkSZpc\nJp2SJEmSpN6YdEqSJEmSemPSKUmSJEnqjUmnJEmSJKk3Jp2SJEmSpN6YdEqSJEmSemPSKUmSJEnq\njUmnJEmSJKk3Jp2SJEmSpN6YdEqSJEmSemPSKUmSJEnqjUmnJEmSJKk3Jp2SJEmSpN6YdEqSJEmS\nemPSKUmSJEnqjUmnJEmSJKk3Jp2SJEmSpN6YdEqSJEmSemPSKUmSJEnqjUmnJEmSJKk3Jp2SJEmS\npN6YdEqSJEmSemPSKUmSJEnqjUmnJEmSJKk3Jp2SJEmSpN6YdEqSJEmSemPSKUmSJEnqjUmnJEmS\nJKk3Jp2SJEmSpN6YdEqSJEmSemPSKUmSJEnqjUmnJEmSJKk3Jp2SJEmSpN6YdEqSJEmSemPSKUmS\nJEnqjUmnJEmSJKk3Jp2SJEmSpN6YdEqSJEmSemPSKUmSJEnqjUmnJEmSJKk3Jp2SJEmSpN6YdEqS\nJEmSemPSKUmSJEnqjUmnJEmSJKk3Jp2SJEmSpN6YdEqSJEmSemPSKUmSJEnqjUmnJEmSJKk3Jp2S\nJEmSpN6YdEqSJEmSemPSKUmSJEnqjUmnJEmSJKk3Jp2SJEmSpN6YdEqSJEmSemPSKUmSJEnqjUmn\nJEmSJKk3Jp2SJEmSpN6YdEqSJEmSemPSKUmSJEnqjUmnJEmSJKk3Jp2SJEmSpN6YdEqSJEmSemPS\nKUmSJEnqjUmnJEmSJKk3Jp2SJEmSpN6MNelM8qYky5NcnOTUJDuPs35JkiRJ0nillDK+ypLtSim/\nbeWXAnuVUl40ZNkyzm3Tpi0JxotGYaxoXRgvGpWxonVhvGhULVayobdjfY31Sucg4Wy2BdaMs35J\nkiRJ0njdYtwVJnkL8A/AKuDh465fkiRJkjQ+8z68NsnpwO27s4ACHFlK+WpnudcC25RS3jhkPY45\nkCRJknSzNgnDa8d6T+dNKk7uDHyjlLL3BtkASZIkSVLvxv302rt2Jp8EXDrO+iVJkiRJ4zXup9d+\nAbgb9QFC1wAvLKX8ZGwbIEmSJEkaqw02vFaSJEmSNPnGOrxWkiRJknTzMuekM8nXkiyYz43Rzde6\nxlOSI/rcHm28krwgyXPX8T2LktxnxGUPSvLeudY1w3qPmDJ9znysV3PTdxz1LcnxSZ6yobdj0iS5\nb5J3r+N7jkryihGX3TXJyrnWNcN6D0qyc2f6I0n2nI91a276jqW+tZh634bejg1tlH4/ycuSbN2Z\nnrccqdtnzLck/5bkEdPM3z/JV1v5CUle08pPXJ9+Zc7/T2cp5fHTzU+SMscxu0k2L6X8aa7btLHW\npdnNIZ5eDxzd71ZpY1RK+fCGrmuO/cdNYraUsu/6bJvWzzjjSJuOUsqFwIV9VzNTXXPsXw4Gvgdc\n29b9T+u5jVpPY4qlvt1s7sFbn/wFOBw4AfgDDD+mXQ+9tEMp5ajZ6mz/3eXgv7x8EvA14LK51DfS\nlc4kJye5IMnKJP/Y5v0wyY4tA78sySdbJn6nIetYneTYJN9LcnqSndr8RUneleQC4LAkt0nyhSTf\nbX8Pbsvtn+TiJBcluTDJtkl2TnJmm7ciyUMHdXXqfWqS41v5+CQfTLIEeEeSWyY5LsmSts4nzOVL\n1LpZ33hKcjSwTWv3E9q857R4uai1cdr81UmOaXG3MMn9W8xdmeTxbZmDkny5zb88yb+O79vYdE1p\nx0NTrxwd03m9e8XwX1q7npXkpGFncpPcNsnSVr53kjVJ7tSmr0yydTpnglubvb21/WWdPmDrJJ9J\nckmSLwFbT1dfp95DWtsvAR7amT+1rpn6qoe05bZN8vHWJy1L8uQhMdvtp97ZvsflSZ7R5u3f6vx8\nkksH75s0kxJHSTZL3cesaO34ss663526/1qR5P5t/tT9z4Gd9RzTtmVZkkM7dby/xcJC4Hbr871v\nbFL7/kvbd3h5khOTHJDknDZ9v9T++7z2fZ2TZI/23sOTHNfKe7dYmratWhssaOWfp13tTt3nHJCb\nnuE/qrXRYJ/x0s56jmzbdRZw91k+231bW14MvKQzf2pdn0odAfGpWeLgte1zXJzkbUmeCtwPOLH1\nMVunc3UmybPb8iuSvL2zntVJ3tLWf16S265Dk220JjyWDkvtj5YlOamz7k+1z3N52nFVe+1VSc5v\nyx/VmT/smOmQTLMvnET58+PN57XvcGmSzyW55TTv+UD7PlcOvs/WlncEFiU5o80bHNMeneTFnfd3\n9zvTts0Qt0gdvfC9JKcm2aqto/s73ynJD1v5oNR968IkVyV5SZKXt/Y+L8kObbkbR8wkeUz73SwF\nbhxF09b1vtR87EDgmLae3ZNc2Fnurt3paZVSZv0Ddmj/bg2sBHYErmr/7gr8Ebj/LOtYAzyrlf8F\neG8rLwLe31nu08BDWvnOwPdb+RTgwa18S2Bz4BXAEW1egG1b+Ted9T0V+HgrHw+c0nntrcDft/Kt\ngMuBbUb5Tvyb+988xVO3jfds8bF5m/5P4LmduHt0K38JOJV6suVewMVt/kHAj4EdOtt0nw39PW3s\nf9O0422BKzqvfwN4CPVg6CJgC2A74AfAK2ZY78q23EuA7wLPBnYBzm2vHzV4f+s/3tnKfwec3sov\nBz7WynsDNwxrU2Bn6tO0d6SO/jin0z9NrWuUvurtwLGd5W41NWa7062POq2Vb9e25fbA/sCvgDtQ\n+7fzBvVN0t8ExdF9gIWd6QWddX+4lf8GWNnK0+5/gEOB17f5WwIXUPvFJ3fi5A4tNp6yodtvHuNg\nV+B6YK82vbTz3R8InNzac7M27wDgC60cYDH1LPwFwINmqOcDrY3v0eJi0DY/aN///rTjhBYj51D7\nhZ2An1OPPe4LLAe2ArYHrpglFpcDD23lY4AVrTy1rguALdv0sDh4TNumrab8fr4N/HWnzkUtJu/A\n2v5tM+AM4MC2zBrgsa38jkF9m/rfhMfSj4EtWnlBZ90XtzjZCfgf6n7tUZ1tCvVq1b4MOWZihn3h\nJP7ROd5s39uZtBwAeA3whlZeROv3O7+3zdr8e7bpq4Bbd9Y9OKbdB1jcmX8J8BfD2maG7bwB2LtN\nf461+47utu0EXNXKB7U4vCVwG2AVcGh77VjgsFY+nppgbtXiZvdOHad01vXe7vKdbTsDuFcrvxV4\nyUzf+ajDaw9P8qRWvhOwx5TXrymlXDDLOv4E/Fcrnwh8sfPa5zrlRwJ/NTjrAmzXzjacC7wryaeB\nL5VSfpx6xeG4JFsAXymlLB/hs3y+U3408IQkr27TW1IPSi4fYT2au/mIp64DqDvXC1rcbE0bYgRc\nX0pZ2MorgT+UUta0s1q7dtZxeillFUDqFY19qQe4Gm5qO+4O/HeSBwBXAncvpZyXesXnK6WUG4Ab\nBmd+Z3Ae9fvfD3gbdae+GXD2kOW/1P69kLVtuh/wHoBSysokM/UNDwQWlVJ+CZDkc/x5TA7M1ldt\n2+Y/c7BQKeXXM9QN9WzyZ9qyP0uymLoTXA2cX9p/K5VkGbAb9fuZJJMSR1cBd0nyHmqivLDz2qB9\nz06yfbs6Mmz/82hg7yRPb/MXUONxv856fpLk2zN//E3SD0sp32/lS6gHNFD77l2pJwY/lXpVqtBu\nESqllCSHACuAD5VSlsxQxznUZOAa4EPAoUnuCPyylPJ/a3/ON/p6KeWPwC+S/JR6Qmhf4ORSynXA\ndUlOGVZZkltRTzyd22adQE0cp3NKKeX6Vh4WB48Ejm91M9hvUQ9c/2zjqX1Jt3/7NDWWTqHuH7/R\nlruwrXtSTFwsNcuBk5J8GfhyZ/5XWuz8ovUND6Ce5HpUkotoF2aoMXRvarLbPWb6Keu2L5wU15RS\nLkjyOGAv4Nz2nWzB9PvaZ6WOOrgFNUnfizqsfervLwCllGWpI292pp5U/mXLXw5n+rYZ9qyHq0op\ng/s6L6QeC8xmUSnl98Dvk6yiDouF+hvYe8qye7Y6rmrTJ1JPfM3mOOCQJK+kHvfcf6aFZ006k+wP\nPAJ4YCnluiSL+PMhRr8bYcOmKkPen1bXDVOWf0eSrwGPowbFo9sOfL827xNJ/qOUcuKU9822rU8t\npVwxh+3XHMxjPE39cX+ylHLkNMtd3ymvAQY76pKkG/+Fm5o6rY4h7bgV8Flqx3MZ9WzyXJxN3Vnu\nUkr5SpLXUdvu60OWv679+yeG92nTHYyty+sDs/ZVSYbFzqh1dJe7rlOe6fNtkiYpjkopq5LcG/hb\n4IXA04HBMLfp+pcwzf6nHfC8tJRy+pT5jxtW9wTpxvuazvQa6kHgm4Fvl1KekmRX6ln+gbtRT9Tc\ncZY6zqJe/b4zcCT1CvLTGH4yYpy/wan9y3RxMCxhncmwuO32XZPWv0xqLD2OetLgQODIJPds87t9\nTDrTR5dSPtpdQZJ/Bj4x9ZgpyRMZfT81KQa/uVBHqjxn2IJJdgNeCdy3lPKb1Fv3Zrx1p/k8dX+w\nM2tPXIdp2mYGU2NnUO8fWXur5NRt6b6ncNPfwHSxN5e2/yL1SvsiYGkp5VczLTzKPZ23An7VDgj2\nBB40zcaNsqGbU3+MAM9heDa/EHjZjSuuO3GS7F5KuaSUcgx1yMOeSXYBflZKOQ74GPVqF8C1Se6e\nZDNqJzDMacBhnbr2GeFzaP3MVzxdn2TzVj4DeFra/ShJbp3kziOsq/vao5LskGQb6rCac4e8R9Ww\ndvwy8ETgWdTEAep3+YQkWyXZDpjtBvuzqUN9BgfjvwQey/A+YzpnUfsZ2k75XjMs+11gvxY3W1B3\nDqOYtq8CTuem923t0IrXTznRMYi/s4Fnpt7DdVtqonT+iNuwqZuYOEp9TsHmpZSTgTewdn8E7cp3\nkn2BX5dSVjN8/3Ma8OJBrCTZo432OYu1cXIH4OHr8Dk2FbP1/QuowwsBDrnxTfVq4nuoB+M7pd7j\nOK1Syo+ow832KKVcTY2HV1G/31G37yzgSS0WtweGPg+ijXRYlXbPNzUmRzEsDk6nXlnYps2/dVv+\nN9TvZ6rzqf3bjm2f+Wzq8NFJN3Gx1E5I7VJKORN4XfsM27WXn5hky9YP7U89Tl4IPD91BA5J7tj2\nMdMdM+3C3PeFm7JBOywBHprkL+HGe+6nXuVdAPwWWJ3k9tTRMwPDfn9QR3k+i3orzWC05WlM3zaz\nbedUV1NvPYH1a6/LgF2T3KVNP3vIcqvpfM52hf404IPUobczGiXpPBXYIskl1CFKg8vN3bMqo1wV\n+h3wgNRhjQ8D3jTkvS8D7pf6IIbvAS9o8w9Pe9AG9erVN9t6lrfL08+gDYMCjqCezT4H+N8ZtvMt\n7bOtaNv1JtS3+YqnjwArk5xQSrmUep/wwhYfC6n3scy2ru5r51OH1y0DPl9KcWjtzKa243fgxqFe\nl1J3jEvbvKXUoVzLqb/LFcDQIaellGta8cz27znAqiHDVIe17wepw10vAd5IvadnWH3XtmWWUBOV\n7w9bdMr0sL7qrcCOrb+6mNpPQY3ZFVn7QKDS6j+Z+p0sB74FvLqU8rMR6p8EExNH1Pt0Frc2P4F6\nUDjwh7af+gDw/DbvzUy///kYNQYvavM/xNpk9krqUMFPMHnDrGHm/UCh3g/59tSHVXSPX44F3ldK\nuZJ6dfnoJLeZoZ4lrL2N5mzqFa1RTkYMfrMXUw8kV1BjcbaTRM8HPtBiYNTf8bA4OI36O1ja1vfK\ntvwngQ+lPUios63XUmNxMfW+v6WllMEwu0nsUwYmMZY2pz4sajl1iOV7Sim/aa+toLbxecCbSinX\ntqvkJwHfSbKCmvBs146Z3sBNj5l2Xod94SQZtMPPqU+A/kz7Ts5j7UOdBsusoB4jXkodftpt548C\np6Y9SIhOzLVh3tsDPyql/LTNm7ZtZtvOafw78KIWxzvO4f2Dz3Yd9RjmG6kPEvrpkOU/C7w69QFc\ngwT109SrrwuHvOdGKWU8fU6S1aWU7cdSmbQOkhxEHS5x2KwLa06SbFtK+V07O38W9Yb2ZRt6u7Rp\n2RTjKHXI8Cs9kSWpD6lPPl1dSjl2Q2+Lbn5S7+dcUGb+71eA8Y7fn+QzapJm9pEke1Hv1/vExp4o\naKO1KcaR+z5J0sRJffDm7tRnM8y+/Hxf6Uz9v322HExSd7jPK6VcMq8V6WbBeJpcSd5PfXLr4KEq\nhTpc6JM91mk8TRjjSNNJcjB1CHz3IOfcUspLp3/HvNQ59lhU/4wljUOSHan32w7ibNDuB8z2gJ5N\nxdiG10qSJEmSbn5GeZCQJEmSJElzYtIpSZIkSeqNSackSZIkqTcmnZIkSZKk3vw/1RxufoBalEMA\nAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb28a12588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.parallel_plot(p.iloc[[10]], p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 131,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TD31obo44Ivnwh/fMhz6U3Hln/7RvPG8v0y/tsd0/2/PmzcuiRYuSJAsWLMig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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb287be400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.parallel_plot(p.iloc[[0]], p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Oe+45VI0aDVadOs01YIDUv7/UsqXvShElcXx3+d57pQcekGbNkvbd13c12SWO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\npO6SZmT4vgAAAAAAD2pn8mZBECyWJGO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G/FXSXsATwE5mNke+4nOXma0CVhUzvzWYg5f/\nSOACvFHvAcwsCT8p/j5ER52OBC4BMLNFkmrpho8C08zsJQBJt/JGmSyop6s2C/+vFIHM7F810gaf\nTb45wr4gaTreCC4H5ln8WylJC4Ht8fJpJppFjpYCO0i6BB8otyT3ivqdKal/rI6UtT8HACMkfSn8\nB+DyODKJ5x+S7qv9+t2SJ83skXAvxjs04Lp7KD4xeL18VcqILUJmZpLGAu3ABDObWyONWfhg4Glg\nAnCspG2Bl8zsvx2f8xr+YGavAf+U9Dw+IbQvcIeZrQBWSJpclpikzfGJp9nhdQM+cKzGZDNbGe4y\nOdgPuCbSpmi38I7rGzKP65JUv92Iy9JkvH28O8I9FHE3C00nS0EbcJOkO4E7E/+7Qnb+GbphL3yS\na39JDxMLM7gM7YYPdtM+0/OsW1vYLDxtZvMlHQTsDMyOMulF9bb2cLnVwTvwQfrOuFl75fcnADNb\nKLe82RqfVH4pxi8nUb1uys56WGpmxb7Oh/C+QD2mmdl/gP9IWoabxYJ/AyMqwg6PNJbG9UR84qse\nVwNjJZ2K93v2rBW47qBT0ijg08BHzWyFpGm80cTo1QYyVomVPK9Ia1VF+B9L+j1wEC4UB0QDPjL8\nrpX0MzObWPFcvbweamaPdyL/mU7QhfJU+XFfZ2ZnVQm3MnGvBoqG2iSl8m+sTeV1JqGkHvsAt+CK\n51F8NrkzzMQbyyFmdpekM/C6+0NJ+BXx93XKdVq1zti63C+oq6sklclOo2mk4VYk7lrv1y1pJjky\ns2WSdgM+A3wT+BJQmLlV0y+iSvsTHZ7jzWxqhf9BZWk3Eam8r06uV+OdwPOA+8zsi5KG4rP8Be/H\nJ2q2rZPGDHz1ezvgLHwF+TDKJyM25DdYqV+qyUHZgLUWZXKb6q5m0y/NKksH4ZMGY4CzJO0S/qmO\nUXJ9oZldmUYg6TvAtZV9JkkH03g71SwU35xwS5UjywJK2h44FdjdzF6Rb92ruXUnuA1vD7amY+Ja\nVKmbGlTKTpHua3RslazMS/qMsfY3UE32OlP3t+Mr7dOAVjN7uVbgRvZ0bg68HB2C4cDHqmSukYz2\nxD9GgCMpH823ACeuidgbcSQNM7PFZjYON3kYLmkI8IKZXQ1cha92ATwnaSdJPXAlUMYU4IQkrQ81\n8B6ZN0dXydNKST3DfS9wmGI/iqQtJG3XQFzpvf0lDZS0CW5WM7vkmYxTVo93AgcDh+MDB/Cy/Lyk\nPpL6AfU22M/ETX2KzvhLwIGU64xqzMD1DNEo71oj7IPAyJCbXnjj0AhVdRUwlbX3bQ0M58qKiY5C\n/mYCX5Hv4doKHyjNazAP3Z2mkSP5OQU9zewO4Gw62iOIlW9J+wL/MrPllLc/U4BvF7Iiacew9plB\nh5xsA3xqHd6ju1BP9w/AzQsBxq55yFcTL8E744PlexyrYmbP4OZmO5rZU7g8nIaXb6P5mwEcErLY\nHyg9DyIsHZYp9nzjMtkIZXIwFV9Z2CT8t4jwr+DlU8k8XL8NijbzCNx8tNlpOlmKCakhZnY/cEa8\nQ7+4fbCk3qGHRuH95BbgGLkFDpK2jTamWp9pCJ1vC7szRT3MBfaR9F5Ys+e+cpV3APBvYLmkd+HW\nMwVl3x+4lefh+FaawtpyCtXrpl4+K3kK33oCb66+HgWGStohro8oCbec5D1jhX4KcAVueluTRgad\n9wC9JC3GTZSK5eZ0VqWRVaFXgb3kZo2fBM4tefZEYA/5QQx/Br4R/icpDtrAV6/+GPG0xfL0lwkz\nKOBMfDZ7FvD3Gvn8Ubxbe+TrXDLrm66Sp18DiyTdYGZL8H3CLSEfLfg+lnpxpffm4eZ1C4HbzCyb\n1tamsh4fgDWmXkvwhrE1/FpxU642/LtsB0pNTs3s6XDeH39nActKzFTL6vcK3Nx1MfBDfE9PWXrP\nRZi5+EDlkbKgFddluup8YFDoqwW4ngKX2XZ1HAhkkf4deJm0AX8CTjezFxpIvxloGjnC9+lMjzq/\nAe8UFvwv2qnxwDHhdx7V25+rcBl8OPwn0DGYfQI3FbyW5jOzhtrtgOH7IS+SH1aR9l8uBi4zsyfw\n1eULJW1ZI525dGyjmYmvaDUyGVF8swvwjmQ7Lov1JomOAcaHDDT6HZfJwRT8O2iN+E6N8NcBExQH\nCSV5fQ6Xxen4vr9WMyvM7JpRpxQ0oyz1xA+LasNNLC8xs1fiXjtex3OAc83suVglvwl4QFI7PuDp\nF32ms1m7z7T1OrSFzURRDy/iJ0DfHGUyh45DnYow7XgfcQlufprW85XAPYqDhEhkLsy8+wPPmNnz\n4Ve1burlswo/Bb4VcjyoE88X77YC78PcLT9I6PmS8LcAp8sP4CoGqDfiq68tJc+sQWYbRudIWm5m\n/TdIYpnMOiDpaNxc4oS6gTOdQtJmZvZqzM7PwDe0L3yr85XpXnRHOZKbDJ+aJ7Iymcz6QH7y6XIz\nu/itzkvm7Yd8P+cAq/3vV4ANa7/fzDNqmUymNr+WtDO+X+/ajX2gkNlo6Y5ylNu+TCaTyTQd8oM3\nh+FnM9QP39UrnfL/7dO7uMQb3KPMbHGXJpR5W5DlqXmRdDl+cmtxqIrh5kLXrcc0szw1GVmOMtWQ\n9HXcBD7t5Mw2s+OrP9ElaW5wWcysf7IsZTYEkgbh+20LOSvqfXS9A3q6CxvMvDaTyWQymUwmk8lk\nMm8/GjlIKJPJZDKZTCaTyWQymU6RB52ZTCaTyWQymUwmk1lv5EFnJpPJZDKZTCaTyWTWG3nQmclk\nMplMJpPJZDKZ9cb/Ad6WXnzvSk1cAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2891f8d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.parallel_plot(p.iloc[[11]], p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 127,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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CwmDzZtPeJXY3UnTqzJaiE7T9FUaOhBkztMJTPqBxL6YdvLMp+mI0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\nYmCafQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb28b0b9b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.parallel_plot(p[p['relative_humidity'] < - 0.5], p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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q1XL81su1cvAgNGwIP/wA1avnc4fFXejcItZSrYgtVC/WKyjvlaevXuvt7A6IiOTJtm3w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\n/kuGqu7wSQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb2892a358>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.parallel_plot(p[p['air_temp'] > 0.5], p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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KbjduNBdr01TOLWGurAxeftmM/sfFmUW4rrzS7IUrgalq1UP7fW7cCA8/DH/8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\niIiIiIgERqFTREREREREAvP/AUpbyHmVwM9vAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb289ffba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.parallel_plot(p[(p['relative_humidity'] > 0.5) & (p['air_temp'] < 0.5)], p)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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ZtGjxgFsETBVnnVXX1vyHf6ij8y67LDniCIGTyW2kejovv7z2aJ5ySh1eOGtW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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fcb28b08748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "utils.parallel_plot(p.iloc[[2]], p)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}