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yehjames/[資料分析&機器學習] 第4.2講 共享單車需求預測.ipynb

Last active April 23, 2018 07:32
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[資料分析&機器學習] 第4.2講 共享單車需求預測.ipynb
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"from sklearn.ensemble import RandomForestRegressor\n",
"from datetime import datetime\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"import seaborn as sns\n",
"import calendar\n",
"pd.options.mode.chained_assignment = None\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"%matplotlib inline"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"train = pd.read_csv('BikeSharingDemand/train.csv')\n",
"test = pd.read_csv('BikeSharingDemand/test.csv')\n",
"submit = pd.read_csv('BikeSharingDemand/sampleSubmission.csv')"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## 都沒有空值，真的hen棒?"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"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>datetime</th>\n",
" <th>season</th>\n",
" <th>holiday</th>\n",
" <th>workingday</th>\n",
" <th>weather</th>\n",
" <th>temp</th>\n",
" <th>atemp</th>\n",
" <th>humidity</th>\n",
" <th>windspeed</th>\n",
" <th>casual</th>\n",
" <th>registered</th>\n",
" <th>count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>2011-01-01 00:00:00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>9.84</td>\n",
" <td>14.395</td>\n",
" <td>81</td>\n",
" <td>0.0</td>\n",
" <td>3</td>\n",
" <td>13</td>\n",
" <td>16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2011-01-01 01:00:00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>9.02</td>\n",
" <td>13.635</td>\n",
" <td>80</td>\n",
" <td>0.0</td>\n",
" <td>8</td>\n",
" <td>32</td>\n",
" <td>40</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>2011-01-01 02:00:00</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>9.02</td>\n",
" <td>13.635</td>\n",
" <td>80</td>\n",
" <td>0.0</td>\n",
" <td>5</td>\n",
" <td>27</td>\n",
" <td>32</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" datetime season holiday workingday weather temp atemp \\\n",
"0 2011-01-01 00:00:00 1 0 0 1 9.84 14.395 \n",
"1 2011-01-01 01:00:00 1 0 0 1 9.02 13.635 \n",
"2 2011-01-01 02:00:00 1 0 0 1 9.02 13.635 \n",
"\n",
" humidity windspeed casual registered count \n",
"0 81 0.0 3 13 16 \n",
"1 80 0.0 8 32 40 \n",
"2 80 0.0 5 27 32 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.head(3)"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 10886 entries, 0 to 10885\n",
"Data columns (total 12 columns):\n",
"datetime 10886 non-null object\n",
"season 10886 non-null int64\n",
"holiday 10886 non-null int64\n",
"workingday 10886 non-null int64\n",
"weather 10886 non-null int64\n",
"temp 10886 non-null float64\n",
"atemp 10886 non-null float64\n",
"humidity 10886 non-null int64\n",
"windspeed 10886 non-null float64\n",
"casual 10886 non-null int64\n",
"registered 10886 non-null int64\n",
"count 10886 non-null int64\n",
"dtypes: float64(3), int64(8), object(1)\n",
"memory usage: 1020.6+ KB\n"
]
}
],
"source": [
"train.info()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"<class 'pandas.core.frame.DataFrame'>\n",
"RangeIndex: 6493 entries, 0 to 6492\n",
"Data columns (total 9 columns):\n",
"datetime 6493 non-null object\n",
"season 6493 non-null int64\n",
"holiday 6493 non-null int64\n",
"workingday 6493 non-null int64\n",
"weather 6493 non-null int64\n",
"temp 6493 non-null float64\n",
"atemp 6493 non-null float64\n",
"humidity 6493 non-null int64\n",
"windspeed 6493 non-null float64\n",
"dtypes: float64(3), int64(5), object(1)\n",
"memory usage: 456.6+ KB\n"
]
}
],
"source": [
"test.info()"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\n",
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" text-align: left;\n",
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"\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>season</th>\n",
" <th>holiday</th>\n",
" <th>workingday</th>\n",
" <th>weather</th>\n",
" <th>temp</th>\n",
" <th>atemp</th>\n",
" <th>humidity</th>\n",
" <th>windspeed</th>\n",
" <th>casual</th>\n",
" <th>registered</th>\n",
" <th>count</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>10886.000000</td>\n",
" <td>10886.000000</td>\n",
" <td>10886.000000</td>\n",
" <td>10886.000000</td>\n",
" <td>10886.00000</td>\n",
" <td>10886.000000</td>\n",
" <td>10886.000000</td>\n",
" <td>10886.000000</td>\n",
" <td>10886.000000</td>\n",
" <td>10886.000000</td>\n",
" <td>10886.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2.506614</td>\n",
" <td>0.028569</td>\n",
" <td>0.680875</td>\n",
" <td>1.418427</td>\n",
" <td>20.23086</td>\n",
" <td>23.655084</td>\n",
" <td>61.886460</td>\n",
" <td>12.799395</td>\n",
" <td>36.021955</td>\n",
" <td>155.552177</td>\n",
" <td>191.574132</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.116174</td>\n",
" <td>0.166599</td>\n",
" <td>0.466159</td>\n",
" <td>0.633839</td>\n",
" <td>7.79159</td>\n",
" <td>8.474601</td>\n",
" <td>19.245033</td>\n",
" <td>8.164537</td>\n",
" <td>49.960477</td>\n",
" <td>151.039033</td>\n",
" <td>181.144454</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.82000</td>\n",
" <td>0.760000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>13.94000</td>\n",
" <td>16.665000</td>\n",
" <td>47.000000</td>\n",
" <td>7.001500</td>\n",
" <td>4.000000</td>\n",
" <td>36.000000</td>\n",
" <td>42.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>20.50000</td>\n",
" <td>24.240000</td>\n",
" <td>62.000000</td>\n",
" <td>12.998000</td>\n",
" <td>17.000000</td>\n",
" <td>118.000000</td>\n",
" <td>145.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>4.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>26.24000</td>\n",
" <td>31.060000</td>\n",
" <td>77.000000</td>\n",
" <td>16.997900</td>\n",
" <td>49.000000</td>\n",
" <td>222.000000</td>\n",
" <td>284.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>4.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>4.000000</td>\n",
" <td>41.00000</td>\n",
" <td>45.455000</td>\n",
" <td>100.000000</td>\n",
" <td>56.996900</td>\n",
" <td>367.000000</td>\n",
" <td>886.000000</td>\n",
" <td>977.000000</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" season holiday workingday weather temp \\\n",
"count 10886.000000 10886.000000 10886.000000 10886.000000 10886.00000 \n",
"mean 2.506614 0.028569 0.680875 1.418427 20.23086 \n",
"std 1.116174 0.166599 0.466159 0.633839 7.79159 \n",
"min 1.000000 0.000000 0.000000 1.000000 0.82000 \n",
"25% 2.000000 0.000000 0.000000 1.000000 13.94000 \n",
"50% 3.000000 0.000000 1.000000 1.000000 20.50000 \n",
"75% 4.000000 0.000000 1.000000 2.000000 26.24000 \n",
"max 4.000000 1.000000 1.000000 4.000000 41.00000 \n",
"\n",
" atemp humidity windspeed casual registered \\\n",
"count 10886.000000 10886.000000 10886.000000 10886.000000 10886.000000 \n",
"mean 23.655084 61.886460 12.799395 36.021955 155.552177 \n",
"std 8.474601 19.245033 8.164537 49.960477 151.039033 \n",
"min 0.760000 0.000000 0.000000 0.000000 0.000000 \n",
"25% 16.665000 47.000000 7.001500 4.000000 36.000000 \n",
"50% 24.240000 62.000000 12.998000 17.000000 118.000000 \n",
"75% 31.060000 77.000000 16.997900 49.000000 222.000000 \n",
"max 45.455000 100.000000 56.996900 367.000000 886.000000 \n",
"\n",
" count \n",
"count 10886.000000 \n",
"mean 191.574132 \n",
"std 181.144454 \n",
"min 1.000000 \n",
"25% 42.000000 \n",
"50% 145.000000 \n",
"75% 284.000000 \n",
"max 977.000000 "
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"train.describe()"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"<div>\n",
"<style>\n",
" .dataframe thead tr:only-child th {\n",
" text-align: right;\n",
" }\n",
"\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>season</th>\n",
" <th>holiday</th>\n",
" <th>workingday</th>\n",
" <th>weather</th>\n",
" <th>temp</th>\n",
" <th>atemp</th>\n",
" <th>humidity</th>\n",
" <th>windspeed</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>count</th>\n",
" <td>6493.000000</td>\n",
" <td>6493.000000</td>\n",
" <td>6493.000000</td>\n",
" <td>6493.000000</td>\n",
" <td>6493.000000</td>\n",
" <td>6493.000000</td>\n",
" <td>6493.000000</td>\n",
" <td>6493.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>mean</th>\n",
" <td>2.493300</td>\n",
" <td>0.029108</td>\n",
" <td>0.685815</td>\n",
" <td>1.436778</td>\n",
" <td>20.620607</td>\n",
" <td>24.012865</td>\n",
" <td>64.125212</td>\n",
" <td>12.631157</td>\n",
" </tr>\n",
" <tr>\n",
" <th>std</th>\n",
" <td>1.091258</td>\n",
" <td>0.168123</td>\n",
" <td>0.464226</td>\n",
" <td>0.648390</td>\n",
" <td>8.059583</td>\n",
" <td>8.782741</td>\n",
" <td>19.293391</td>\n",
" <td>8.250151</td>\n",
" </tr>\n",
" <tr>\n",
" <th>min</th>\n",
" <td>1.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>0.820000</td>\n",
" <td>0.000000</td>\n",
" <td>16.000000</td>\n",
" <td>0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25%</th>\n",
" <td>2.000000</td>\n",
" <td>0.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>13.940000</td>\n",
" <td>16.665000</td>\n",
" <td>49.000000</td>\n",
" <td>7.001500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50%</th>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>21.320000</td>\n",
" <td>25.000000</td>\n",
" <td>65.000000</td>\n",
" <td>11.001400</td>\n",
" </tr>\n",
" <tr>\n",
" <th>75%</th>\n",
" <td>3.000000</td>\n",
" <td>0.000000</td>\n",
" <td>1.000000</td>\n",
" <td>2.000000</td>\n",
" <td>27.060000</td>\n",
" <td>31.060000</td>\n",
" <td>81.000000</td>\n",
" <td>16.997900</td>\n",
" </tr>\n",
" <tr>\n",
" <th>max</th>\n",
" <td>4.000000</td>\n",
" <td>1.000000</td>\n",
" <td>1.000000</td>\n",
" <td>4.000000</td>\n",
" <td>40.180000</td>\n",
" <td>50.000000</td>\n",
" <td>100.000000</td>\n",
" <td>55.998600</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" season holiday workingday weather temp \\\n",
"count 6493.000000 6493.000000 6493.000000 6493.000000 6493.000000 \n",
"mean 2.493300 0.029108 0.685815 1.436778 20.620607 \n",
"std 1.091258 0.168123 0.464226 0.648390 8.059583 \n",
"min 1.000000 0.000000 0.000000 1.000000 0.820000 \n",
"25% 2.000000 0.000000 0.000000 1.000000 13.940000 \n",
"50% 3.000000 0.000000 1.000000 1.000000 21.320000 \n",
"75% 3.000000 0.000000 1.000000 2.000000 27.060000 \n",
"max 4.000000 1.000000 1.000000 4.000000 40.180000 \n",
"\n",
" atemp humidity windspeed \n",
"count 6493.000000 6493.000000 6493.000000 \n",
"mean 24.012865 64.125212 12.631157 \n",
"std 8.782741 19.293391 8.250151 \n",
"min 0.000000 16.000000 0.000000 \n",
"25% 16.665000 49.000000 7.001500 \n",
"50% 25.000000 65.000000 11.001400 \n",
"75% 31.060000 81.000000 16.997900 \n",
"max 50.000000 100.000000 55.998600 "
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"test.describe()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Feature Engineering"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Shape Of The Before Ouliers: (10886, 12)\n",
"Shape Of The After Ouliers: (10739, 12)\n"
]
}
],
"source": [
"print (\"Shape Of The Before Ouliers: \",train.shape)\n",
"train = train[np.abs(train[\"count\"]-train[\"count\"].mean())<=(3*train[\"count\"].std())] \n",
"print (\"Shape Of The After Ouliers: \",train.shape)"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"data = train.append(test)\n",
"data.reset_index(inplace=True)\n",
"data.drop('index',inplace=True,axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [],
"source": [
"data[\"date\"] = data.datetime.apply(lambda x : x.split()[0])\n",
"data[\"hour\"] = data.datetime.apply(lambda x : x.split()[1].split(\":\")[0]).astype(\"int\")\n",
"data[\"year\"] = data.datetime.apply(lambda x : x.split()[0].split(\"-\")[0])\n",
"data[\"weekday\"] = data.date.apply(lambda dateString : datetime.strptime(dateString,\"%Y-%m-%d\").weekday())\n",
"data[\"month\"] = data.date.apply(lambda dateString : datetime.strptime(dateString,\"%Y-%m-%d\").month)"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"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>atemp</th>\n",
" <th>casual</th>\n",
" <th>count</th>\n",
" <th>datetime</th>\n",
" <th>holiday</th>\n",
" <th>humidity</th>\n",
" <th>registered</th>\n",
" <th>season</th>\n",
" <th>temp</th>\n",
" <th>weather</th>\n",
" <th>windspeed</th>\n",
" <th>workingday</th>\n",
" <th>date</th>\n",
" <th>hour</th>\n",
" <th>year</th>\n",
" <th>weekday</th>\n",
" <th>month</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>14.395</td>\n",
" <td>3.0</td>\n",
" <td>16.0</td>\n",
" <td>2011-01-01 00:00:00</td>\n",
" <td>0</td>\n",
" <td>81</td>\n",
" <td>13.0</td>\n",
" <td>1</td>\n",
" <td>9.84</td>\n",
" <td>1</td>\n",
" <td>0.0000</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>0</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>13.635</td>\n",
" <td>8.0</td>\n",
" <td>40.0</td>\n",
" <td>2011-01-01 01:00:00</td>\n",
" <td>0</td>\n",
" <td>80</td>\n",
" <td>32.0</td>\n",
" <td>1</td>\n",
" <td>9.02</td>\n",
" <td>1</td>\n",
" <td>0.0000</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>13.635</td>\n",
" <td>5.0</td>\n",
" <td>32.0</td>\n",
" <td>2011-01-01 02:00:00</td>\n",
" <td>0</td>\n",
" <td>80</td>\n",
" <td>27.0</td>\n",
" <td>1</td>\n",
" <td>9.02</td>\n",
" <td>1</td>\n",
" <td>0.0000</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>2</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>14.395</td>\n",
" <td>3.0</td>\n",
" <td>13.0</td>\n",
" <td>2011-01-01 03:00:00</td>\n",
" <td>0</td>\n",
" <td>75</td>\n",
" <td>10.0</td>\n",
" <td>1</td>\n",
" <td>9.84</td>\n",
" <td>1</td>\n",
" <td>0.0000</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>3</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>14.395</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2011-01-01 04:00:00</td>\n",
" <td>0</td>\n",
" <td>75</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>9.84</td>\n",
" <td>1</td>\n",
" <td>0.0000</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>4</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td>12.880</td>\n",
" <td>0.0</td>\n",
" <td>1.0</td>\n",
" <td>2011-01-01 05:00:00</td>\n",
" <td>0</td>\n",
" <td>75</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>9.84</td>\n",
" <td>2</td>\n",
" <td>6.0032</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>5</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td>13.635</td>\n",
" <td>2.0</td>\n",
" <td>2.0</td>\n",
" <td>2011-01-01 06:00:00</td>\n",
" <td>0</td>\n",
" <td>80</td>\n",
" <td>0.0</td>\n",
" <td>1</td>\n",
" <td>9.02</td>\n",
" <td>1</td>\n",
" <td>0.0000</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>6</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td>12.880</td>\n",
" <td>1.0</td>\n",
" <td>3.0</td>\n",
" <td>2011-01-01 07:00:00</td>\n",
" <td>0</td>\n",
" <td>86</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>8.20</td>\n",
" <td>1</td>\n",
" <td>0.0000</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>7</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td>14.395</td>\n",
" <td>1.0</td>\n",
" <td>8.0</td>\n",
" <td>2011-01-01 08:00:00</td>\n",
" <td>0</td>\n",
" <td>75</td>\n",
" <td>7.0</td>\n",
" <td>1</td>\n",
" <td>9.84</td>\n",
" <td>1</td>\n",
" <td>0.0000</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>8</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td>17.425</td>\n",
" <td>8.0</td>\n",
" <td>14.0</td>\n",
" <td>2011-01-01 09:00:00</td>\n",
" <td>0</td>\n",
" <td>76</td>\n",
" <td>6.0</td>\n",
" <td>1</td>\n",
" <td>13.12</td>\n",
" <td>1</td>\n",
" <td>0.0000</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>9</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td>19.695</td>\n",
" <td>12.0</td>\n",
" <td>36.0</td>\n",
" <td>2011-01-01 10:00:00</td>\n",
" <td>0</td>\n",
" <td>76</td>\n",
" <td>24.0</td>\n",
" <td>1</td>\n",
" <td>15.58</td>\n",
" <td>1</td>\n",
" <td>16.9979</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>10</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td>16.665</td>\n",
" <td>26.0</td>\n",
" <td>56.0</td>\n",
" <td>2011-01-01 11:00:00</td>\n",
" <td>0</td>\n",
" <td>81</td>\n",
" <td>30.0</td>\n",
" <td>1</td>\n",
" <td>14.76</td>\n",
" <td>1</td>\n",
" <td>19.0012</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>11</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td>21.210</td>\n",
" <td>29.0</td>\n",
" <td>84.0</td>\n",
" <td>2011-01-01 12:00:00</td>\n",
" <td>0</td>\n",
" <td>77</td>\n",
" <td>55.0</td>\n",
" <td>1</td>\n",
" <td>17.22</td>\n",
" <td>1</td>\n",
" <td>19.0012</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>12</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td>22.725</td>\n",
" <td>47.0</td>\n",
" <td>94.0</td>\n",
" <td>2011-01-01 13:00:00</td>\n",
" <td>0</td>\n",
" <td>72</td>\n",
" <td>47.0</td>\n",
" <td>1</td>\n",
" <td>18.86</td>\n",
" <td>2</td>\n",
" <td>19.9995</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>13</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td>22.725</td>\n",
" <td>35.0</td>\n",
" <td>106.0</td>\n",
" <td>2011-01-01 14:00:00</td>\n",
" <td>0</td>\n",
" <td>72</td>\n",
" <td>71.0</td>\n",
" <td>1</td>\n",
" <td>18.86</td>\n",
" <td>2</td>\n",
" <td>19.0012</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>14</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td>21.970</td>\n",
" <td>40.0</td>\n",
" <td>110.0</td>\n",
" <td>2011-01-01 15:00:00</td>\n",
" <td>0</td>\n",
" <td>77</td>\n",
" <td>70.0</td>\n",
" <td>1</td>\n",
" <td>18.04</td>\n",
" <td>2</td>\n",
" <td>19.9995</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>15</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td>21.210</td>\n",
" <td>41.0</td>\n",
" <td>93.0</td>\n",
" <td>2011-01-01 16:00:00</td>\n",
" <td>0</td>\n",
" <td>82</td>\n",
" <td>52.0</td>\n",
" <td>1</td>\n",
" <td>17.22</td>\n",
" <td>2</td>\n",
" <td>19.9995</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>16</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td>21.970</td>\n",
" <td>15.0</td>\n",
" <td>67.0</td>\n",
" <td>2011-01-01 17:00:00</td>\n",
" <td>0</td>\n",
" <td>82</td>\n",
" <td>52.0</td>\n",
" <td>1</td>\n",
" <td>18.04</td>\n",
" <td>2</td>\n",
" <td>19.0012</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>17</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td>21.210</td>\n",
" <td>9.0</td>\n",
" <td>35.0</td>\n",
" <td>2011-01-01 18:00:00</td>\n",
" <td>0</td>\n",
" <td>88</td>\n",
" <td>26.0</td>\n",
" <td>1</td>\n",
" <td>17.22</td>\n",
" <td>3</td>\n",
" <td>16.9979</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>18</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td>21.210</td>\n",
" <td>6.0</td>\n",
" <td>37.0</td>\n",
" <td>2011-01-01 19:00:00</td>\n",
" <td>0</td>\n",
" <td>88</td>\n",
" <td>31.0</td>\n",
" <td>1</td>\n",
" <td>17.22</td>\n",
" <td>3</td>\n",
" <td>16.9979</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>19</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td>20.455</td>\n",
" <td>11.0</td>\n",
" <td>36.0</td>\n",
" <td>2011-01-01 20:00:00</td>\n",
" <td>0</td>\n",
" <td>87</td>\n",
" <td>25.0</td>\n",
" <td>1</td>\n",
" <td>16.40</td>\n",
" <td>2</td>\n",
" <td>16.9979</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>20</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td>20.455</td>\n",
" <td>3.0</td>\n",
" <td>34.0</td>\n",
" <td>2011-01-01 21:00:00</td>\n",
" <td>0</td>\n",
" <td>87</td>\n",
" <td>31.0</td>\n",
" <td>1</td>\n",
" <td>16.40</td>\n",
" <td>2</td>\n",
" <td>12.9980</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>21</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td>20.455</td>\n",
" <td>11.0</td>\n",
" <td>28.0</td>\n",
" <td>2011-01-01 22:00:00</td>\n",
" <td>0</td>\n",
" <td>94</td>\n",
" <td>17.0</td>\n",
" <td>1</td>\n",
" <td>16.40</td>\n",
" <td>2</td>\n",
" <td>15.0013</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>22</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td>22.725</td>\n",
" <td>15.0</td>\n",
" <td>39.0</td>\n",
" <td>2011-01-01 23:00:00</td>\n",
" <td>0</td>\n",
" <td>88</td>\n",
" <td>24.0</td>\n",
" <td>1</td>\n",
" <td>18.86</td>\n",
" <td>2</td>\n",
" <td>19.9995</td>\n",
" <td>0</td>\n",
" <td>2011-01-01</td>\n",
" <td>23</td>\n",
" <td>2011</td>\n",
" <td>5</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td>22.725</td>\n",
" <td>4.0</td>\n",
" <td>17.0</td>\n",
" <td>2011-01-02 00:00:00</td>\n",
" <td>0</td>\n",
" <td>88</td>\n",
" <td>13.0</td>\n",
" <td>1</td>\n",
" <td>18.86</td>\n",
" <td>2</td>\n",
" <td>19.9995</td>\n",
" <td>0</td>\n",
" <td>2011-01-02</td>\n",
" <td>0</td>\n",
" <td>2011</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td>21.970</td>\n",
" <td>1.0</td>\n",
" <td>17.0</td>\n",
" <td>2011-01-02 01:00:00</td>\n",
" <td>0</td>\n",
" <td>94</td>\n",
" <td>16.0</td>\n",
" <td>1</td>\n",
" <td>18.04</td>\n",
" <td>2</td>\n",
" <td>16.9979</td>\n",
" <td>0</td>\n",
" <td>2011-01-02</td>\n",
" <td>1</td>\n",
" <td>2011</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td>21.210</td>\n",
" <td>1.0</td>\n",
" <td>9.0</td>\n",
" <td>2011-01-02 02:00:00</td>\n",
" <td>0</td>\n",
" <td>100</td>\n",
" <td>8.0</td>\n",
" <td>1</td>\n",
" <td>17.22</td>\n",
" <td>2</td>\n",
" <td>19.0012</td>\n",
" <td>0</td>\n",
" <td>2011-01-02</td>\n",
" <td>2</td>\n",
" <td>2011</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td>22.725</td>\n",
" <td>2.0</td>\n",
" <td>6.0</td>\n",
" <td>2011-01-02 03:00:00</td>\n",
" <td>0</td>\n",
" <td>94</td>\n",
" <td>4.0</td>\n",
" <td>1</td>\n",
" <td>18.86</td>\n",
" <td>2</td>\n",
" <td>12.9980</td>\n",
" <td>0</td>\n",
" <td>2011-01-02</td>\n",
" <td>3</td>\n",
" <td>2011</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td>22.725</td>\n",
" <td>2.0</td>\n",
" <td>3.0</td>\n",
" <td>2011-01-02 04:00:00</td>\n",
" <td>0</td>\n",
" <td>94</td>\n",
" <td>1.0</td>\n",
" <td>1</td>\n",
" <td>18.86</td>\n",
" <td>2</td>\n",
" <td>12.9980</td>\n",
" <td>0</td>\n",
" <td>2011-01-02</td>\n",
" <td>4</td>\n",
" <td>2011</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td>21.210</td>\n",
" <td>0.0</td>\n",
" <td>2.0</td>\n",
" <td>2011-01-02 06:00:00</td>\n",
" <td>0</td>\n",
" <td>77</td>\n",
" <td>2.0</td>\n",
" <td>1</td>\n",
" <td>17.22</td>\n",
" <td>3</td>\n",
" <td>19.9995</td>\n",
" <td>0</td>\n",
" <td>2011-01-02</td>\n",
" <td>6</td>\n",
" <td>2011</td>\n",
" <td>6</td>\n",
" <td>1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>...</th>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" <td>...</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17202</th>\n",
" <td>10.605</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-30 18:00:00</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>9.84</td>\n",
" <td>2</td>\n",
" <td>19.9995</td>\n",
" <td>0</td>\n",
" <td>2012-12-30</td>\n",
" <td>18</td>\n",
" <td>2012</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17203</th>\n",
" <td>18.180</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-30 19:00:00</td>\n",
" <td>0</td>\n",
" <td>61</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>13.94</td>\n",
" <td>1</td>\n",
" <td>0.0000</td>\n",
" <td>0</td>\n",
" <td>2012-12-30</td>\n",
" <td>19</td>\n",
" <td>2012</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17204</th>\n",
" <td>9.850</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-30 20:00:00</td>\n",
" <td>0</td>\n",
" <td>47</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>9.02</td>\n",
" <td>1</td>\n",
" <td>22.0028</td>\n",
" <td>0</td>\n",
" <td>2012-12-30</td>\n",
" <td>20</td>\n",
" <td>2012</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17205</th>\n",
" <td>10.605</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-30 21:00:00</td>\n",
" <td>0</td>\n",
" <td>51</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>8.20</td>\n",
" <td>1</td>\n",
" <td>11.0014</td>\n",
" <td>0</td>\n",
" <td>2012-12-30</td>\n",
" <td>21</td>\n",
" <td>2012</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17206</th>\n",
" <td>9.850</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-30 22:00:00</td>\n",
" <td>0</td>\n",
" <td>55</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>8.20</td>\n",
" <td>1</td>\n",
" <td>12.9980</td>\n",
" <td>0</td>\n",
" <td>2012-12-30</td>\n",
" <td>22</td>\n",
" <td>2012</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17207</th>\n",
" <td>9.850</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-30 23:00:00</td>\n",
" <td>0</td>\n",
" <td>51</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>8.20</td>\n",
" <td>1</td>\n",
" <td>15.0013</td>\n",
" <td>0</td>\n",
" <td>2012-12-30</td>\n",
" <td>23</td>\n",
" <td>2012</td>\n",
" <td>6</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17208</th>\n",
" <td>9.090</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 00:00:00</td>\n",
" <td>0</td>\n",
" <td>55</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>7.38</td>\n",
" <td>1</td>\n",
" <td>12.9980</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>0</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17209</th>\n",
" <td>9.090</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 01:00:00</td>\n",
" <td>0</td>\n",
" <td>55</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>7.38</td>\n",
" <td>1</td>\n",
" <td>12.9980</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>1</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17210</th>\n",
" <td>8.335</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 02:00:00</td>\n",
" <td>0</td>\n",
" <td>59</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>6.56</td>\n",
" <td>1</td>\n",
" <td>11.0014</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>2</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17211</th>\n",
" <td>9.090</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 03:00:00</td>\n",
" <td>0</td>\n",
" <td>59</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>6.56</td>\n",
" <td>1</td>\n",
" <td>7.0015</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>3</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17212</th>\n",
" <td>8.335</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 04:00:00</td>\n",
" <td>0</td>\n",
" <td>69</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>5.74</td>\n",
" <td>1</td>\n",
" <td>7.0015</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>4</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17213</th>\n",
" <td>7.575</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 05:00:00</td>\n",
" <td>0</td>\n",
" <td>64</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>6.56</td>\n",
" <td>1</td>\n",
" <td>12.9980</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>5</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17214</th>\n",
" <td>8.335</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 06:00:00</td>\n",
" <td>0</td>\n",
" <td>64</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>6.56</td>\n",
" <td>1</td>\n",
" <td>11.0014</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>6</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17215</th>\n",
" <td>9.090</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 07:00:00</td>\n",
" <td>0</td>\n",
" <td>64</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>6.56</td>\n",
" <td>1</td>\n",
" <td>8.9981</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>7</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17216</th>\n",
" <td>7.575</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 08:00:00</td>\n",
" <td>0</td>\n",
" <td>69</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>5.74</td>\n",
" <td>1</td>\n",
" <td>8.9981</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>8</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17217</th>\n",
" <td>10.605</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 09:00:00</td>\n",
" <td>0</td>\n",
" <td>64</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>7.38</td>\n",
" <td>2</td>\n",
" <td>7.0015</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>9</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17218</th>\n",
" <td>10.605</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 10:00:00</td>\n",
" <td>0</td>\n",
" <td>69</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>8.20</td>\n",
" <td>2</td>\n",
" <td>8.9981</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>10</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17219</th>\n",
" <td>11.365</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 11:00:00</td>\n",
" <td>0</td>\n",
" <td>60</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>9.02</td>\n",
" <td>2</td>\n",
" <td>12.9980</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>11</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17220</th>\n",
" <td>11.365</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 12:00:00</td>\n",
" <td>0</td>\n",
" <td>56</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>9.84</td>\n",
" <td>2</td>\n",
" <td>12.9980</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>12</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17221</th>\n",
" <td>12.880</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 13:00:00</td>\n",
" <td>0</td>\n",
" <td>44</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>10.66</td>\n",
" <td>2</td>\n",
" <td>11.0014</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>13</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17222</th>\n",
" <td>13.635</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 14:00:00</td>\n",
" <td>0</td>\n",
" <td>45</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>11.48</td>\n",
" <td>2</td>\n",
" <td>15.0013</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>14</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17223</th>\n",
" <td>14.395</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 15:00:00</td>\n",
" <td>0</td>\n",
" <td>45</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>11.48</td>\n",
" <td>2</td>\n",
" <td>8.9981</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>15</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17224</th>\n",
" <td>12.880</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 16:00:00</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>10.66</td>\n",
" <td>2</td>\n",
" <td>12.9980</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>16</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17225</th>\n",
" <td>14.395</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 17:00:00</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>10.66</td>\n",
" <td>2</td>\n",
" <td>6.0032</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>17</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17226</th>\n",
" <td>13.635</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 18:00:00</td>\n",
" <td>0</td>\n",
" <td>48</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>10.66</td>\n",
" <td>2</td>\n",
" <td>8.9981</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>18</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17227</th>\n",
" <td>12.880</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 19:00:00</td>\n",
" <td>0</td>\n",
" <td>60</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>10.66</td>\n",
" <td>2</td>\n",
" <td>11.0014</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>19</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17228</th>\n",
" <td>12.880</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 20:00:00</td>\n",
" <td>0</td>\n",
" <td>60</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>10.66</td>\n",
" <td>2</td>\n",
" <td>11.0014</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>20</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17229</th>\n",
" <td>12.880</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 21:00:00</td>\n",
" <td>0</td>\n",
" <td>60</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>10.66</td>\n",
" <td>1</td>\n",
" <td>11.0014</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>21</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17230</th>\n",
" <td>13.635</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 22:00:00</td>\n",
" <td>0</td>\n",
" <td>56</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>10.66</td>\n",
" <td>1</td>\n",
" <td>8.9981</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>22</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17231</th>\n",
" <td>13.635</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>2012-12-31 23:00:00</td>\n",
" <td>0</td>\n",
" <td>65</td>\n",
" <td>NaN</td>\n",
" <td>1</td>\n",
" <td>10.66</td>\n",
" <td>1</td>\n",
" <td>8.9981</td>\n",
" <td>1</td>\n",
" <td>2012-12-31</td>\n",
" <td>23</td>\n",
" <td>2012</td>\n",
" <td>0</td>\n",
" <td>12</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>17232 rows × 17 columns</p>\n",
"</div>"
],
"text/plain": [
" atemp casual count datetime holiday humidity \\\n",
"0 14.395 3.0 16.0 2011-01-01 00:00:00 0 81 \n",
"1 13.635 8.0 40.0 2011-01-01 01:00:00 0 80 \n",
"2 13.635 5.0 32.0 2011-01-01 02:00:00 0 80 \n",
"3 14.395 3.0 13.0 2011-01-01 03:00:00 0 75 \n",
"4 14.395 0.0 1.0 2011-01-01 04:00:00 0 75 \n",
"5 12.880 0.0 1.0 2011-01-01 05:00:00 0 75 \n",
"6 13.635 2.0 2.0 2011-01-01 06:00:00 0 80 \n",
"7 12.880 1.0 3.0 2011-01-01 07:00:00 0 86 \n",
"8 14.395 1.0 8.0 2011-01-01 08:00:00 0 75 \n",
"9 17.425 8.0 14.0 2011-01-01 09:00:00 0 76 \n",
"10 19.695 12.0 36.0 2011-01-01 10:00:00 0 76 \n",
"11 16.665 26.0 56.0 2011-01-01 11:00:00 0 81 \n",
"12 21.210 29.0 84.0 2011-01-01 12:00:00 0 77 \n",
"13 22.725 47.0 94.0 2011-01-01 13:00:00 0 72 \n",
"14 22.725 35.0 106.0 2011-01-01 14:00:00 0 72 \n",
"15 21.970 40.0 110.0 2011-01-01 15:00:00 0 77 \n",
"16 21.210 41.0 93.0 2011-01-01 16:00:00 0 82 \n",
"17 21.970 15.0 67.0 2011-01-01 17:00:00 0 82 \n",
"18 21.210 9.0 35.0 2011-01-01 18:00:00 0 88 \n",
"19 21.210 6.0 37.0 2011-01-01 19:00:00 0 88 \n",
"20 20.455 11.0 36.0 2011-01-01 20:00:00 0 87 \n",
"21 20.455 3.0 34.0 2011-01-01 21:00:00 0 87 \n",
"22 20.455 11.0 28.0 2011-01-01 22:00:00 0 94 \n",
"23 22.725 15.0 39.0 2011-01-01 23:00:00 0 88 \n",
"24 22.725 4.0 17.0 2011-01-02 00:00:00 0 88 \n",
"25 21.970 1.0 17.0 2011-01-02 01:00:00 0 94 \n",
"26 21.210 1.0 9.0 2011-01-02 02:00:00 0 100 \n",
"27 22.725 2.0 6.0 2011-01-02 03:00:00 0 94 \n",
"28 22.725 2.0 3.0 2011-01-02 04:00:00 0 94 \n",
"29 21.210 0.0 2.0 2011-01-02 06:00:00 0 77 \n",
"... ... ... ... ... ... ... \n",
"17202 10.605 NaN NaN 2012-12-30 18:00:00 0 44 \n",
"17203 18.180 NaN NaN 2012-12-30 19:00:00 0 61 \n",
"17204 9.850 NaN NaN 2012-12-30 20:00:00 0 47 \n",
"17205 10.605 NaN NaN 2012-12-30 21:00:00 0 51 \n",
"17206 9.850 NaN NaN 2012-12-30 22:00:00 0 55 \n",
"17207 9.850 NaN NaN 2012-12-30 23:00:00 0 51 \n",
"17208 9.090 NaN NaN 2012-12-31 00:00:00 0 55 \n",
"17209 9.090 NaN NaN 2012-12-31 01:00:00 0 55 \n",
"17210 8.335 NaN NaN 2012-12-31 02:00:00 0 59 \n",
"17211 9.090 NaN NaN 2012-12-31 03:00:00 0 59 \n",
"17212 8.335 NaN NaN 2012-12-31 04:00:00 0 69 \n",
"17213 7.575 NaN NaN 2012-12-31 05:00:00 0 64 \n",
"17214 8.335 NaN NaN 2012-12-31 06:00:00 0 64 \n",
"17215 9.090 NaN NaN 2012-12-31 07:00:00 0 64 \n",
"17216 7.575 NaN NaN 2012-12-31 08:00:00 0 69 \n",
"17217 10.605 NaN NaN 2012-12-31 09:00:00 0 64 \n",
"17218 10.605 NaN NaN 2012-12-31 10:00:00 0 69 \n",
"17219 11.365 NaN NaN 2012-12-31 11:00:00 0 60 \n",
"17220 11.365 NaN NaN 2012-12-31 12:00:00 0 56 \n",
"17221 12.880 NaN NaN 2012-12-31 13:00:00 0 44 \n",
"17222 13.635 NaN NaN 2012-12-31 14:00:00 0 45 \n",
"17223 14.395 NaN NaN 2012-12-31 15:00:00 0 45 \n",
"17224 12.880 NaN NaN 2012-12-31 16:00:00 0 48 \n",
"17225 14.395 NaN NaN 2012-12-31 17:00:00 0 48 \n",
"17226 13.635 NaN NaN 2012-12-31 18:00:00 0 48 \n",
"17227 12.880 NaN NaN 2012-12-31 19:00:00 0 60 \n",
"17228 12.880 NaN NaN 2012-12-31 20:00:00 0 60 \n",
"17229 12.880 NaN NaN 2012-12-31 21:00:00 0 60 \n",
"17230 13.635 NaN NaN 2012-12-31 22:00:00 0 56 \n",
"17231 13.635 NaN NaN 2012-12-31 23:00:00 0 65 \n",
"\n",
" registered season temp weather windspeed workingday date \\\n",
"0 13.0 1 9.84 1 0.0000 0 2011-01-01 \n",
"1 32.0 1 9.02 1 0.0000 0 2011-01-01 \n",
"2 27.0 1 9.02 1 0.0000 0 2011-01-01 \n",
"3 10.0 1 9.84 1 0.0000 0 2011-01-01 \n",
"4 1.0 1 9.84 1 0.0000 0 2011-01-01 \n",
"5 1.0 1 9.84 2 6.0032 0 2011-01-01 \n",
"6 0.0 1 9.02 1 0.0000 0 2011-01-01 \n",
"7 2.0 1 8.20 1 0.0000 0 2011-01-01 \n",
"8 7.0 1 9.84 1 0.0000 0 2011-01-01 \n",
"9 6.0 1 13.12 1 0.0000 0 2011-01-01 \n",
"10 24.0 1 15.58 1 16.9979 0 2011-01-01 \n",
"11 30.0 1 14.76 1 19.0012 0 2011-01-01 \n",
"12 55.0 1 17.22 1 19.0012 0 2011-01-01 \n",
"13 47.0 1 18.86 2 19.9995 0 2011-01-01 \n",
"14 71.0 1 18.86 2 19.0012 0 2011-01-01 \n",
"15 70.0 1 18.04 2 19.9995 0 2011-01-01 \n",
"16 52.0 1 17.22 2 19.9995 0 2011-01-01 \n",
"17 52.0 1 18.04 2 19.0012 0 2011-01-01 \n",
"18 26.0 1 17.22 3 16.9979 0 2011-01-01 \n",
"19 31.0 1 17.22 3 16.9979 0 2011-01-01 \n",
"20 25.0 1 16.40 2 16.9979 0 2011-01-01 \n",
"21 31.0 1 16.40 2 12.9980 0 2011-01-01 \n",
"22 17.0 1 16.40 2 15.0013 0 2011-01-01 \n",
"23 24.0 1 18.86 2 19.9995 0 2011-01-01 \n",
"24 13.0 1 18.86 2 19.9995 0 2011-01-02 \n",
"25 16.0 1 18.04 2 16.9979 0 2011-01-02 \n",
"26 8.0 1 17.22 2 19.0012 0 2011-01-02 \n",
"27 4.0 1 18.86 2 12.9980 0 2011-01-02 \n",
"28 1.0 1 18.86 2 12.9980 0 2011-01-02 \n",
"29 2.0 1 17.22 3 19.9995 0 2011-01-02 \n",
"... ... ... ... ... ... ... ... \n",
"17202 NaN 1 9.84 2 19.9995 0 2012-12-30 \n",
"17203 NaN 1 13.94 1 0.0000 0 2012-12-30 \n",
"17204 NaN 1 9.02 1 22.0028 0 2012-12-30 \n",
"17205 NaN 1 8.20 1 11.0014 0 2012-12-30 \n",
"17206 NaN 1 8.20 1 12.9980 0 2012-12-30 \n",
"17207 NaN 1 8.20 1 15.0013 0 2012-12-30 \n",
"17208 NaN 1 7.38 1 12.9980 1 2012-12-31 \n",
"17209 NaN 1 7.38 1 12.9980 1 2012-12-31 \n",
"17210 NaN 1 6.56 1 11.0014 1 2012-12-31 \n",
"17211 NaN 1 6.56 1 7.0015 1 2012-12-31 \n",
"17212 NaN 1 5.74 1 7.0015 1 2012-12-31 \n",
"17213 NaN 1 6.56 1 12.9980 1 2012-12-31 \n",
"17214 NaN 1 6.56 1 11.0014 1 2012-12-31 \n",
"17215 NaN 1 6.56 1 8.9981 1 2012-12-31 \n",
"17216 NaN 1 5.74 1 8.9981 1 2012-12-31 \n",
"17217 NaN 1 7.38 2 7.0015 1 2012-12-31 \n",
"17218 NaN 1 8.20 2 8.9981 1 2012-12-31 \n",
"17219 NaN 1 9.02 2 12.9980 1 2012-12-31 \n",
"17220 NaN 1 9.84 2 12.9980 1 2012-12-31 \n",
"17221 NaN 1 10.66 2 11.0014 1 2012-12-31 \n",
"17222 NaN 1 11.48 2 15.0013 1 2012-12-31 \n",
"17223 NaN 1 11.48 2 8.9981 1 2012-12-31 \n",
"17224 NaN 1 10.66 2 12.9980 1 2012-12-31 \n",
"17225 NaN 1 10.66 2 6.0032 1 2012-12-31 \n",
"17226 NaN 1 10.66 2 8.9981 1 2012-12-31 \n",
"17227 NaN 1 10.66 2 11.0014 1 2012-12-31 \n",
"17228 NaN 1 10.66 2 11.0014 1 2012-12-31 \n",
"17229 NaN 1 10.66 1 11.0014 1 2012-12-31 \n",
"17230 NaN 1 10.66 1 8.9981 1 2012-12-31 \n",
"17231 NaN 1 10.66 1 8.9981 1 2012-12-31 \n",
"\n",
" hour year weekday month \n",
"0 0 2011 5 1 \n",
"1 1 2011 5 1 \n",
"2 2 2011 5 1 \n",
"3 3 2011 5 1 \n",
"4 4 2011 5 1 \n",
"5 5 2011 5 1 \n",
"6 6 2011 5 1 \n",
"7 7 2011 5 1 \n",
"8 8 2011 5 1 \n",
"9 9 2011 5 1 \n",
"10 10 2011 5 1 \n",
"11 11 2011 5 1 \n",
"12 12 2011 5 1 \n",
"13 13 2011 5 1 \n",
"14 14 2011 5 1 \n",
"15 15 2011 5 1 \n",
"16 16 2011 5 1 \n",
"17 17 2011 5 1 \n",
"18 18 2011 5 1 \n",
"19 19 2011 5 1 \n",
"20 20 2011 5 1 \n",
"21 21 2011 5 1 \n",
"22 22 2011 5 1 \n",
"23 23 2011 5 1 \n",
"24 0 2011 6 1 \n",
"25 1 2011 6 1 \n",
"26 2 2011 6 1 \n",
"27 3 2011 6 1 \n",
"28 4 2011 6 1 \n",
"29 6 2011 6 1 \n",
"... ... ... ... ... \n",
"17202 18 2012 6 12 \n",
"17203 19 2012 6 12 \n",
"17204 20 2012 6 12 \n",
"17205 21 2012 6 12 \n",
"17206 22 2012 6 12 \n",
"17207 23 2012 6 12 \n",
"17208 0 2012 0 12 \n",
"17209 1 2012 0 12 \n",
"17210 2 2012 0 12 \n",
"17211 3 2012 0 12 \n",
"17212 4 2012 0 12 \n",
"17213 5 2012 0 12 \n",
"17214 6 2012 0 12 \n",
"17215 7 2012 0 12 \n",
"17216 8 2012 0 12 \n",
"17217 9 2012 0 12 \n",
"17218 10 2012 0 12 \n",
"17219 11 2012 0 12 \n",
"17220 12 2012 0 12 \n",
"17221 13 2012 0 12 \n",
"17222 14 2012 0 12 \n",
"17223 15 2012 0 12 \n",
"17224 16 2012 0 12 \n",
"17225 17 2012 0 12 \n",
"17226 18 2012 0 12 \n",
"17227 19 2012 0 12 \n",
"17228 20 2012 0 12 \n",
"17229 21 2012 0 12 \n",
"17230 22 2012 0 12 \n",
"17231 23 2012 0 12 \n",
"\n",
"[17232 rows x 17 columns]"
]
},
"execution_count": 12,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"data"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[<matplotlib.text.Text at 0x118251f98>, <matplotlib.text.Text at 0x11be875c0>]"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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uU0REQivYhHk/sAv4rq+GuWzMtnPASmNMFtCNtxzjK0A/8HljzN8BhUAK3iR6Qm1tvUGG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8RicZkrX2RG02hakuRYYHtsfyI5L5MR5ousqHdjs5o50TW3D63T3fXZubU5U2EJsWRkhFnMiz47\nzDsnB1i7rIKta2qyHY4oAmuXVTAeinKuT2rBFhtfGk1LkpLtsRcyJaPUbsVqkctjKpvVzMoGN+f6\n/IyHItkOR4glJyPMYs7i8Tg/efEkAB+9dQ0mGV0Ws5hpXvFcR5nWLqvglQPd6HMjrGiQBabFZC6L\n/mxWM1aLef5l5cYmpAbzDNatqOR45yjvnhrIdihCLDn5CC3m7K1jfZzp9nH1ujpWSVc/sUTUcmMe\nsz47nOVIxFIb9ac/JcNkMlFeZptspT0X8Xgc//iEzF+ewdXr6wHYc6Qvy5EIsfRkhFnMSSQa49GX\nTmExm/jwLauyHY4oIjXlTqo9dk50jhKLx2Xe/BwopczAt4AtQAj4jNb6ZMrzdwN/AUSAh7TWDyql\nbMBDQCtgBx7QWj++1LEDjPjDlFjNkwv6LsdTVsK5Pj/xeHxOd8DGQxGisbi0xZ5Bc00ZLbUuDp4e\nRK2owG6bvU25EIVERpjFnLy4v4v+kSC3bmue7MAmxFJZu6wC//gE3QOBbIeSb+4FHFrr64A/A76e\nfCKRGH8DeC9wC/A5pVQ98HFgUGt9E3An8M0ljzphJBCiwmVPO/ktL7MTicYZm+Nc28mmJTLCPKMd\nG+qIxuKc7ZWa6KK4SMIs0jYWjPDEa+047RY+cENrtsMRRShZXk7aZM/ZjcAzAFrr3cBVKc+tB05q\nrYe11mHgVeBm4KfAVxLbmDBGn5dcNBbDGwhT7kp/1NdTZiS8c134JzWYL29HYlrGmW5JmEVxuez9\nrXneypt2H6XUNuBJ4ERi9/+rtf6XTP5AYvE8/WYH/vEJPnzLqrRa1AqRaetWVAJwpH2Y91zZkuVo\n8ooHSC2gG1VKWbXWkWme8wHlWms/gFLKDfwM+HI6L1RZWYrVmrlb9YOj48TjUF9dRm3tNIs9Tw7i\ndjkueqihNg50Y7JZp99nBqd7/Yn93RftN/X4czHd6093vHS2S/dYC4ljum1S962tdbNuRSW6Yxiz\n1UKZw3bRc+m8xlz+T+ZisY67WPIp3nyKFRYn3nQmhE3eylNKXYtxK+8euOhW3tVAAHhNKfU4cMMM\n+2wH/lZr/fVpXkfksCFvkOfeOkel2469xCId1URW1FeWUlfh5Ej7EJFoLNvh5BMvkHoFMSeS5eme\ncwMjAEqpZcBjwLe01j9K54WGh8cWHm2K9h4vAE6bhf7+6Uc1ff6L2zVbiQNwtmuEBo897dfqSryW\nOR676LWmHn8upsZcW+ue9njT/WxTt0tnm3TjSGdft8uBzx+8ZN/ta2s51jHMviM9bFtbO+fXmOn/\ncSFqa92LctzFkk/x5lOssLB4Z0u005mSMZ9beTPtsx14v1LqZaXUPyVGLkQe+NdXzjARifGhm1ZJ\nfVKRVZtWVRMMR6Xj2Ny8BvwaQGIQ42DKc0eBNqVUlVKqBOMc/kZiHvNzwH/RWj+01AEnjfiMaRUV\n7vTvapW7jCR5rqXlklMyXDIlY1Y3bm7EabdwtGN4XjWZpaW9yEfpjDDP+VbeTPsAe4Dvaq33KaW+\nBHwV+E8zvXCmb+0tVL7dkki1kNjbu728dqib1kYPH7y1jV++2ZHByC5vIbdDs61QY5/p/TSXn3cu\nx0jd9sYrW3jh7U5O9fhoqC6b07GL2GPAHUqp1zHmI9+vlPoY4NJaf0cp9cfAsxiDKA9prbuUUv8b\nqAS+opRKzmW+S2s9vpSBj/hDAFSUpT9S7Cmd3xzm5GulU++5mNltFjavruHNI70cODXIjg312Q5J\niEWXTsI8n1t50+6jlHpMa51crfMY8A+zvXCmb+0tRL7dkki10Nj/8dF3icfh3htXMjToX9DtyblK\n3hLMR4Uce7q3xmczl2OkbttQbsdqMfPmoW7es336ecy58ruaK4m71joGfH7Kw8dSnn8CeGLKPn8I\n/OHiRze7yYR5Tov+jG3nOsI87DNeq8qTvx90l0pbSzlH2oc4fm6EDa2VuGVdiyhw6dxbn/OtvFn2\neVYpdU3i69uAfQv+CcSiOnh6kENnhtjQWsmmVVXZDkcI7DYL65ZX0NkfIBCcyHY4YpGN+JNTMtIf\nYS5PjEbPdYR5yBvCajFJ45I0mM0mtrbVEI/D7sO9xOPxbIckxKJKZ4R5PrfyLtkncazfAf5BKTUB\n9ACfy/DPI+ZpujlksVicF9/uwgT8+/e0SQtskTM2rarm0JkhzvcHaEuUmhOFKTnCXD6HKRlOuwWr\nxTznhHnYF6TCZZemOGlqbXBz+ryXrv4ARzuGuXWbVK4RheuyCfM8b+VNtw9a67cxKmiIPHCyc5Su\ngQA3b2lkWZ0r2+EIMWnT6mp+/MIJOiVhLngj/hAlNjNOe/rrWebTHjsSjTHqD9PWUj6fMIuSyWTi\n+o0NPPFaO2/rAc71+eVaIQqWlDsQ0wpHorxzcgC7zcKHbpIW2CK31Fc6aakto6s/QCgczXY4YhGN\n+MNz6vKX5Cmz4w2E054qMOoPE0fmL8+V027l+o0NxOJxvvnzA4wm7ggIUWgkYRbTOnR6iGA4yq9d\nu3yyRJMQucJkMnFd4iLd3pMbC/xE5kVjMXyBMBXzqFpRXlYyp/bYyQV/lXOYKy0MLXUuNq+upn8k\nyDd+8u68Ss0JkeskYRaX8I9PcKR9mFKHlfdeszzb4QgxrWs3NGACTp+XesyFyhuYIM7cFvwlJdtj\nj/rTm5Yx5DOqs0jCPD9b1lSzc2sTZ/v8/N1P35UFuaLgpLPoTxSZ/cf7icXibGur4Y3DPdkOR4hp\nVbrtNFSX0j04hjcQltq5BehCSbm5J7FVbmNqxZA3SFPN9PW6U10YYZYpGfNhMpn4+HsVY6EIe472\n8dff38cffXTLgo453WL0nVubF3RMIeZLRpjFRfpHxjnT7aPaY2dVkyfb4Qgxq9XNxgKtU+e9WY5E\nLIaFJMy1FU7AOKelY8ibrMEsI8zzZTab+NwHr+DOa5bTMzTGA4/s5fxAINthCZERkjCLSfF4nL3H\n+gC4al2dlJETOW9ZnQurxcSprlGiMakDW2iSNZjL59C0JKmmwhgp7h9Nr5nOcGJKRpVMyVgQs8nE\nR9+zhk/eqQiGIzy/t5N3TgwQkzrNIs9Jwiwmne310z8SZHm9i/qq0myHI8Rl2axm2loqGAtGOCOj\nzAVnxDf/EeaacmOEeSDNEeZhXwiL2YRbpvZkxC1bm/nix7fjcto4cGqQ5/d2ymJAkdckYRaAsRp9\nn+7HbIIr19ZmOxwh0rZhZSVmk4mDpwdlFKvAzKctdlK5qwSrxUz/SHojzEO+kDQtybCVjR7ef/0K\nWupc9AyO8eTr7XT1yxQNkZ8kYRYAHD4zjH98ArW8UhZPibxS5rCxutmDb2yC9m4pMVdIRhOd+uYz\nwmw2maitcDAwevkR5mgsxog/JPOXF4HdZuHWbU1cqWoJhqO8sK+TVw90EwzLaLPIL5IwC/pGxjl4\nahCn3cKWNdXZDkeIOdu4qgqTCQ6eHpS5zAVk2BfCbrPgKEm/y1+qmnIngWCEseDsydmoP0w8LiXl\nFovJZGLjyiref90Kqj12Tp/38tjLZzhwapCJSCzb4QmRFikrV+Ti8Tg/+uVxorE4V6k6SmzzuzCJ\n4jJduadscpeWsKrJw6kuL8/v6eDK1fLBL9/F4nF6h8eoryyd9wLk5MK/gdFxljvcM26XLClXJSXl\nFlWVx8Fd165Anx3hwKlB3jkxgD47zJY1Ndy0uRGLWcbwRO6Sd2eR239igAOnBmmoKqW1ceYLihC5\nbltbLVaLie8/dVSaJhSAYW+I8ESMxur5L0CuLU+vtNxkDWaZkrHozGYT61sr+dAtK9m0upqJSIzd\nh3v56kNvcfzcSLbDE2JGkjAXsVA4yo+eP47FbGLHBikjJ/JbqcPK5tXVeANhfvHKmWyHIxaoZ2gM\ngIYFVOypTZaWu8zCvyGvlJRbaiVWC9vaarj3plWsaSmneyDA//jh2zz89FH84/KBV+QeSZiL2OOv\nn2HIG+LOHcspn8eiGiFyzfrWShpryvjV21109vuzHY5YgO5Bo5pCwwJGmCdLy11m4d+QdPnLmlKH\nles3NvDFT2ynpbaMl9/t5ksP7uaNQz3EpeqNyCGSMBeproEAz+05R7XHwQeub812OEJkhMVs5rP3\nbCQWj/Pj50/IBTePdSdGmBurLt/Weia1k3OYZx9hHkyMMMuiv+xZ01zOX3zqaj5y62pC4SgPPnmE\n5/acm2woI0S2ScJchKKxGN976ijRWJyP3dGGXRb6iQJy9YYGNq+u5mjHMPt0f7bDEfPUM7jwKRml\nDhtlDutl5zB39vkpc1jnVe9ZZI7VYuauHSt44DM72NZWQ+/wOE++1sGeI72EJqLZDk8UOUmYi9Az\nb57l9HkvOzbUs61NmpSIwvPrt7VhMZv4l1+dkAttnuoeDFDtsWOfZ0m5pJpyJwOjwRnvNoyHIvQO\nj7O83i3rOHJETYWT3//wZm7b3oKr1MaxsyP868tnOHFuZNrmRLve6eKZN9rZ9U7X5B8hMk0S5iJz\nrs/Pv75yhnJXCb95x9pshyPEomioKuW9Vy9j0BvimTfPZjscMUfjoQgj/jAN1fOfjpFUU+FgIhKb\nbIIy1dleo9nNinqpEpRrmmvL+OCNrVy5toZoLMYbh3t54JG96LPDMt1KLDmpw1xEQuEo33n8MNFY\nnPvvWo/Lact2SEIsmg9c38rrh3p4ancHN2xqmFwAJnJfz+T85flPx0hKlpYbGAlO2zGwo9dYHLq8\nwbXg1xKZZzGb2biqmlVNHvbqftq7fXztR/tZXufihk2NLK93EQxHkY87YrFJwlwk4vE4339W0zUQ\n4LbtLWyWxg6iwDntVj5y62q+++RRfvKrk/zuhzZlOySRpsn5ywuokJFUW2kkzN2DAda0lF/yfEeP\njDDng1KHjZu3NPGx21w899ZZ3j4+wI9fOJHyvDEHfWWjh9YG97TTMnZubV7KkEWBkYS5SLxyoJs3\nDvewstHDv3/PmmyHI8SSuPaKBl7c38Ve3c/R9iHWt1ZlOySRhu4ho6RcJkaYVzd5ADh+boSbtjRd\n8vzZXh/2Egv1GXgtsfjWtJSzpmUTQ94g+uwInQN+DpwaxBsIc35gjPMDY+w91s+Va2tY01Iu89JF\nxkjCXAQOnhrgn5/VlDmsbGur4dWD3dkOSYglYTaZ+Njta3ngkb386PkTfPX+q7FaZOlGruueHGFe\n+BzmljoXLqeNY4l5r6kJVGgiyvnBAGuayzFLYpVXqjwOrtvYAEBthRO3y0F3nw99boTj50Z443Av\nPUNjXHtFAzar/M6LhZN3UYE7PxDgr7+3B4DfvXcjrlKZtyyKy8pGDzdtaaJrIMCv3pbV8/mgZ2gM\nR4klI2XezCYTalkFg94Q/VPqMXf2+4nHZTpGoXCV2tiuarn7hlZqyh2c6fbx9O4O6RwoMkIS5gI2\nMDLON37yLoHxCT511zq5HS2K1odvWUWZw8ovXj3NqD+U7XDELCYiUXqHxmioKs3Y7fR1KyoBONYx\nfNHjZxPzl5dLwlxQXE4b79uxHLW8ghF/mKd3dzAwGryo7JyUnxNzJQlzgeobHuNrP3qbQW+QT9y1\nnhs2NWY7JCGyxl1awn03r2I8FOVfXjyZ7XDELI60DxOJxlm3vDJjx5wpYe5IlpRrkIS50FjMJnZs\nqOfqdXWMh6I8s/ssR9qHpBydmDdJmAvQ2V6j7M6gN8R9N6/io7dLvWUhbtnaTGuDm92He3nnxEC2\nwxEz2J/4v9m2tiZjx2yqLsVTVsLRlPq98Xick11erBYzjRmoxiFy0/rWSm7b3kyJzczeY/08v7fz\nsq3ShZiOJMwFZu+xPv7bD/Yx7Avx0VvX8IHrW7MdkhA5wWw28en3r8dqMfHIM8dkXmMOisXjvHNy\nAHepjdVNl5aAmy+TycS65RWM+sOTNZ7fOTHA+YEAW9dUy0LQAtdc6+LuG1pprimje3CMp97o4Jdv\nnePMeS/jocgl28vUDTEdqZJRIELhKD976RQv7OvEXmLh9+7bhHcszK53unC7HJAbpewAABCESURB\nVPj88olaiJZaF/fcuJJHXzrND57T/PYHr5CyUznk9Hkv3kCYGzc3YjZn9v9l3fJK9hzt4+3j/dy1\no5RHXz6NyQT33rQqo68jcpPTbuU925vpGRrj4KkhugfH6B4cY/eRXta2lLNuRSXrV1SxQhrYiBlI\nwlwAfvzCcXYf7sU3NkF5WQk3b23COzZ9G1ghit2dO5az/8QAe472saLezV3Xrsh2SCJh/4l+ALa1\nZW46RtKWNTXYXzzJoy+d5kTnKOcHAty4uZGmmoWXrhP5wWQy0VhdRmN1GSP+EO3dPoa8QQ63D3O4\nfRg4jdNupcpjp67SSV2Fk5pyBxa5AyGQhDmvdfX7+dmuU7x7ahCADa2VbG2rkduLQszCYjbzhQ9t\n4oHv7+Wnu05RV+lku6rLdlgCY5pEidXMhkWo6FPptvPF37yS//2zAxw4NYjVYuaeG1Zm/HVEfqhw\n2dnaZmfn1mZGA2H02WGOdgxztH2Yrv4AXf1G8xyzyUR1uYNRf5iNK6tY2ejJ+N0PkR8kYc4zsXgc\nfXaE5/acnUyU6yudXLWujupyR5ajEyI/VLrt/OG/28x//8HbfOeJI/xWLM416+uzHVZRO9oxTPfg\nGNvaarDbLIvyGsvr3Xzlk1fxw+eOs761Us6ZAoDyshKuWV8/eQ54ancH/SPj9A0bfwZGxvnFq2f4\nxatnKLVb2dBaSVtLBSsbPTTVlFHqmDmVkhbdheOyCbNSygx8C9gChIDPaK1Ppjx/N/AXQAR4SGv9\n4Ez7KKXWAA8DceAQ8AWtdSyzP9LieOaN9kvmAS/Vmz4ej3O218/+E/28fqhncoXvmpZyfu3aFQz7\ngjIPU4g5Wl7v5gv3beT/PHaIb//iMGd7/dx708qCvEOTyfP4YsQ37Avxj48fxmI2LfoUmQqXnS/c\nt2lRX0Pkt1KHlRUN7slyg+GJKNUeB4fODHHo9BB7dT97df/k9iU2szGVw22nxGrBXmKhxGbBbjMz\nMBrEYjZhtZhxlFhw2q2c7BolZrEQjcSkC2EeSWeE+V7AobW+Til1LfB14B4ApZQN+AZwNRAAXlNK\nPQ7cMMM+fwt8WWu9Syn17cRjj2X6h5qvYDjCiD+Mf2wC37jxt398gkg0xvmhcaKRKPYSC44SK/YS\nC4OjQTxlJRl9w8fjcbxjE/QOjdHZ7+dk5yj63AjDPqPZgtViYnWzh7aWCuoqnYz4Q5IsCzFPG1dW\n8+VPbOcfHj3IU7s72HO0l/dft4IdG+pxlBTUDbhMnsczKhKN8e1fHMIbCPMbt7Wxpjlz1TGEyIQS\nm4Xtqo7tqo54PE7/yDinz3t55UA33kAY3/gE46EI58YjRKKXHwN86Z3zKcc2U2q30lRTRnmZnQp3\nCeVldkrtVhwlRvJtt1kmv3bYLiTk0s59aaVzRbgReAZAa71bKXVVynPrgZNa62EApdSrwM3AdTPs\nsx14KfH108B7yXDCHI3F6OwLEIvHMZtMxOJxorE4oXCUYDhCMBwlGI7iGwsz5Asx7Asx4gsx5AtN\nW15mNk+90QFAmcNKhctOhauEcpedclcJZQ4bNosZm9X4YzGbiMbixGJGPNFYnLFQhLHgBIHxCIHg\nBEO+EH3DY4yHohe9jstp47or6rFZzTTXuuQTqRAZ1Fzr4iufuopfvHKGXe+c55FnND947jhtLeW0\nNniorXCwaVU1NRXObIe6EJk8j2fUiXMjnOgc5ep1ddx+VctivIQQGWMymairLKWuspTgxMXX6p1b\nm4nF4oQmooQnooQiMV472E00GmMiGiMYjjIeijAeijARBW8gxHgwwlgwwpH24RlecWZTE2mn3Uqp\nw/hT5rBht1mwWs3YLGYsFhM2ixmrxYzVako8ZsZsMmEygSnxs5H82vhhMZug1xtidHQ88fMb2xnP\ngwlj/8v/u6WxDQv/ABAMRzh8bpR4JMLGldULPl6qdBJmDzCa8n1UKWXVWkemec4HlM+0D2DSWsen\nbJtRT+8+y89fPj2nfUoTq2IrXR4q3HY8pSW4nLbJPzarmeNdXnyBIMFwNJF8Ryl3lTDqDzPiNxLv\nroHAgmK3WszUVzqpX1FKaCJKeVkJtRVOPGU2GUUWBWcutU0Xuw5qmcPGx+5Yy13XruDF/V0cPD3I\nsbMjHDs7AsDalnL+7OPbFzWGRZax83hin4xpW1bB7967kc2rq+U8J/Ke2WzCabfitBvpVaXbPu12\nU8u9Xn9FA6MBI58Y9YcJhqMcOjPIRDRGJBIjEo0zETES70jiseRz4Yko3oCxjzBYLSa+9ce3ZHSK\nXToJsxdI7RtqTjlhTn3ODYzMtI9SKjbNtjOqrXXP+ex5/z2buP+ezM9P25nxIwohFktt7fxaHdfW\nulm7KvMlzXJAxs7jl3uh+Zy3GxvmP3Zy5zz/r+fiI3esy8rx0tluIbEtxb65/hqL/bpziW3qqqgP\npb2nWArppN6vAb8GkJjHdjDluaNAm1KqSilVgnEb741Z9tmvlNqZ+Pou4JWF/gBCCCEuK5PncSGE\nKDqmeDw+6wYpK6U3Y0xruR+4EnBprb+TsrrajLG6+v9Mt4/W+phSai3wIFCCcZL+rNZa7iEIIcQi\nyuR5PCs/gBBCZNllE2YhhBBCCCGKmZRbEEIIIYQQYhaSMAshhBBCCDELSZiFEEIIIYSYRUG1ssq0\npWwNmymJrl0PAa2AHXgAOEIetSRXStUB+4A7MFr1PkwexK6U+iLwQYxFrd/CaNLzMDkee+I98wjG\neyYKfJY8+HdXSu0Avqa13qmUWsM08SqlPgv8NsbP84DW+smsBSwyKl/Oz+m8T7MZX1I+XTuUUhaM\nAgIKI7bPA0FyMNakfLquKaXexigrCXAG+GtyNN6lvO7KCPPsJtvJAn+G0Ro2130cGNRa3wTcCXyT\nCy3Jb8JY7Z7x9raZkjhp/yMwnngoL2JPlEu8HqOd8C3AMvIkdozSYVat9fXAX2GcHHM6dqXUnwLf\nBRyJhy6JVynVAPwBxv/J+4D/rpSavouAyEc5f35O532ardimkU/XjrsBtNY3AF8mx89Z+XRdU0o5\nMJrM7Uz8uZ8cjXepr7uSMM/uonaywKK0hs2wnwJfSXxtwvgkO7Ul+e1ZiCtdfwN8Gzif+D5fYn8f\nRp3ax4AngCfJn9iPA9bEiJ0HmCD3Yz8F3Jfy/XTxXgO8prUOaa1HgZMYJdJEYciH83M679NckTfX\nDq31vwKfS3y7AqPRTk7GmpBP17UtQKlS6jml1K8SNdhzNd4lve5Kwjy7mVp85yyttV9r7VNKuYGf\nYXz6XvSW5JmglPoU0K+1fjbl4byIHajBuGB/BOP24A8xOqPlQ+x+jNuwxzBuc/49Of7vrrV+FCOx\nT5ou3plaPovCkPPn5zTfpzkh364die7BjwD/gHG+zclY8/C6NoaR4L+PC9eyXI13Sa+7kjDPbl6t\nYbNNKbUMeBH4Z631j4A5tSTPok8DdyildgFbge8DdSnP53Lsg8CzWuuw1lpjzKdL/UXN5dj/I0bs\nazFGFx7BmA+WlMuxJ033Hp+p5bMoDPl4fs7pc3G+XTu01p8Ekg3RnClP5VKs+XZdOw78QGsd11of\nx7i21ac8n0vxLul1VxLm2eVda1ilVD3wHPBftNYPJR7Oi5bkWuubtda3aK13Au8A/wF4Oh9iB14F\n7lRKmZRSTUAZ8EKexD7MhZG6IcBGnrxnUkwX7x7gJqWUQylVDqzHWAQiCkPenZ/J4d+rfLp2KKU+\nkVjsBcaIaAzYm4ux5uF17dMk1gMkrmUe4LkcjXdJr7s5dfsqBz2G8cnwdS60k811fw5UAl9RSiXn\no/0h8PdKqWRL8p9lK7h5+BPgwVyPXWv9pFLqZowkzQx8AWN1cc7HDnwDeEgp9QrGyPKfA3vJj9iT\nLnmfaK2jSqm/xzhhmoEvaa2D2QxSZFQ+np9z+XyWT9eOnwPfU0q9jPEB/48w4svVf9upcvl98E/A\nw0qpVzEqTXwaGCAH413q6660xhZCCCGEEGIWMiVDCCGEEEKIWUjCLIQQQgghxCwkYRZCCCGEEGIW\nkjALIYQQQggxC0mYhRBCCCGEmIUkzCKvKKV2JgrAZ/KY31VKXdJWVyn1sFLqU0qpJqXUU4nH7lZK\n/XEmX18IIQqZUuoqpdR357jPkpfwUkq1KqXal/p1RX6QOsyi6GmtP3OZ58+TaJCA0adeCCFEmrTW\ne4FZz7NC5DpJmEU+qk2M+K4GNPCfMdpjtgIopf4SQGv9l0qpHuAJ4CagG/gW8AdAC/AprfVLiRHr\nvwRewuhw9AHgPGABdimlWoFdGEnz5xOvcQ74CvBerfVxpVQZcAxok+YYQohio5Q6CHxUa31UKfVD\nwKu1/p1EF8ZfAXu01sk7hHswzsm1wO9rrZ9OnGd/ALiA3SnHvQ34nxhNNIaB30hs8wRwCmgDOoCP\na62HlFJ3An+F0dDkDPBZrfWgUupqjCZNpRiNOH5ba31GKbUNo1kHwLuL9M8jCoBMyRD5aDlGR5/1\nQANw+yzb1gNPaq3XJb7/kNb6JowE+Y+mbPthYBtwBfARYE3qk1rrI8C3gW9rrf8JeAT4eMq+T0qy\nLIQoUv8G3Jb4ejNwY+Lru4D/NGXbEq31dcB/BB5IPPZN4GGt9VaMtudJXwY+r7W+CiNJvjLx+Ebg\n77TWV2B0dPtLpVQt8D+A92mttwHPAl9LdH37LvAxrfWVGAMjDyaO833gTxOPn17IP4AobJIwi3z0\nrtb6jNY6hnGirLnM9k8n/u7AGOlIfl05ZbudwM+11hNa637gqcsc93vAxxJffxJ4+PKhCyFEQfo3\n4Dal1AbgMBBVStVhJMz+Kds+k/j7EFCV+Hon8C+Jr38ITCS+fhx4TCn1TeCo1vq5xOPHtda7El8/\nArwH2IExoPKiUuod4PcwRqDXYtyRfDzx+NeAVUqpGqBJa/184jgPz/unFwVPEmaRjyIpXycXhphS\nHrOlbqy1Ds+w71RxLv6dmG1btNbtQIdS6j6gXmv95mzbCyFEAXsd2Ipxx28XxhS3fweUAGenbJu8\nExfnwrk79fwbB2IAWutvYCTTJ4H/qZT6UmKb1POzOfG9BXhVa701MVJ9dSIGC3A65fHtGCPgqa8/\n9ZhCXEQSZlEIRoBKpVStUsoO3DnP4zwPfEQpZVdKVc5wnAgXz/1/CPh74J/n+ZpCCJH3tNZR4E2M\nNSK7MO7mfYnL36lLep4LU9zuA+wASqk3AbfW+u8w5iAnp2QopdTWxNf3Y9xJfBO4Tim1NvH4V4D/\nhbG+pEopdVPi8U8DP9JaD2IMerw/8XjyjqEQl5CEWRSCUYyT4lsYJ9098zmI1voXGCf6Qxi3AY9M\ns9nLwG8qpX4/8f3PgWokYRZCiH8DyrTWxzBGmOuBJ9Pc9/eADyulDmAssPYlHv9z4GGl1D7gc8BX\nE48PAf9VKXUYqAMe0Fr3YCTDP0ksQrwS+BOtdQhjXcrXE8f/JPBbieN8HPiqUmo/xrQNIaZliseX\nvNShEAVBKWXCmJ/3ea31B7MdjxBCFINk5aJkZSQhloKUlRNi/r4B3I2RNAshhBCiQMkIsxBCCCGE\nELOQOcxCCCGEEELMQhJmIYQQQgghZiEJsxBCCCGEELOQhFkIIYQQQohZSMIshBBCCCHELCRhFkII\nIYQQYhb/Hz6GDsunuKCcAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11818a550>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"fig, axes = plt.subplots(nrows=2,ncols=2)\n",
"fig.set_size_inches(12, 10)\n",
"sns.distplot(data[\"temp\"],ax=axes[0][0])\n",
"sns.distplot(data[\"atemp\"],ax=axes[0][1])\n",
"sns.distplot(data[\"humidity\"],ax=axes[1][0])\n",
"sns.distplot(data[\"windspeed\"],ax=axes[1][1])\n",
"\n",
"axes[0][0].set(xlabel='temp',title=\"distribution of temp\")\n",
"axes[0][1].set(xlabel='atemp',title=\"distribution of atemp\")\n",
"axes[1][0].set(xlabel='humidity',title=\"distribution of humidity\")\n",
"axes[1][1].set(xlabel='windspeed',title=\"distribution of windspeed\")"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [],
"source": [
"dataWind0 = data[data[\"windspeed\"]==0]\n",
"dataWindNot0 = data[data[\"windspeed\"]!=0]\n",
"rfModel_wind = RandomForestRegressor(n_estimators=1000,random_state=42)\n",
"windColumns = [\"season\",\"weather\",\"humidity\",\"month\",\"temp\",\"year\",\"atemp\"]\n",
"rfModel_wind.fit(dataWindNot0[windColumns], dataWindNot0[\"windspeed\"])\n",
"\n",
"wind0Values = rfModel_wind.predict(X= dataWind0[windColumns])\n",
"dataWind0.loc[:,\"windspeed\"] = wind0Values\n",
"data = dataWindNot0.append(dataWind0)\n",
"data.reset_index(inplace=True)\n",
"data.drop('index',inplace=True,axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 15,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11818b358>"
]
},
"execution_count": 15,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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ZZZxo72Tf4RaOHm/nt7sb+O3uBnLCIc6bVkphfg6TyguZWBbVuH+RIQh00tdN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\nAAAASUVORK5CYII=\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x11818bb70>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(data[\"windspeed\"])"
]
},
{
"cell_type": "code",
"execution_count": 16,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"dataTrain = data[pd.notnull(data['count'])].sort_values(by=[\"datetime\"])\n",
"dataTest = data[~pd.notnull(data['count'])].sort_values(by=[\"datetime\"])\n",
"datetimecol = dataTest[\"datetime\"]\n",
"yLabels = dataTrain[\"count\"]\n",
"yLabelsLog = np.log(yLabels)"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"dropFeatures = ['casual',\"count\",\"datetime\",\"date\",\"registered\"]\n",
"dataTrain = dataTrain.drop(dropFeatures,axis=1)\n",
"dataTest = dataTest.drop(dropFeatures,axis=1)"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x11cb48630>"
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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GbR09gx8A9c2dnGvtYtfBc5QXh1llUVZZBQtmFulvR6akVEL/SeB2M9sGBIB7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\n9EVEfOT/A40Ckpqm1IcKAAAAAElFTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x118586588>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(yLabels)"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x153c61a90>"
]
},
"execution_count": 19,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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WglpnRCzN53XSPzJJMDRNrS//lyFIJ9zvAw4BaK2PzYZ5KifwceCvMzjnBj6f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\nTkSuQmCC\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x153c73d30>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.distplot(yLabelsLog)"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [],
"source": [
"rfModel = RandomForestRegressor(n_estimators=1000,random_state=42)\n",
"yLabelsLog = np.log(yLabels)\n",
"rfModel.fit(dataTrain,yLabelsLog)\n",
"preds = rfModel.predict(X= dataTrain)"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"predsTest = rfModel.predict(X= dataTest)\n",
"submission = pd.DataFrame({\n",
" \"datetime\": datetimecol,\n",
" \"count\": [max(0, x) for x in np.exp(predsTest)]\n",
" })\n",
"submission.to_csv('bike_predictions_RF.csv', index=False)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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