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bike-sharing-neural-network/Bike-Sharing-Dataset/Untitled.ipynb

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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## This is a title\n",
"dsjksd\n",
"sd\n",
"gsf;sfag\n"
]
},
{
"cell_type": "code",
"execution_count": 2,
"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>instant</th>\n",
" <th>dteday</th>\n",
" <th>season</th>\n",
" <th>yr</th>\n",
" <th>mnth</th>\n",
" <th>holiday</th>\n",
" <th>weekday</th>\n",
" <th>workingday</th>\n",
" <th>weathersit</th>\n",
" <th>temp</th>\n",
" <th>atemp</th>\n",
" <th>hum</th>\n",
" <th>windspeed</th>\n",
" <th>casual</th>\n",
" <th>registered</th>\n",
" <th>cnt</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td>1</td>\n",
" <td>2011-01-01</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>6</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0.344167</td>\n",
" <td>0.363625</td>\n",
" <td>0.805833</td>\n",
" <td>0.160446</td>\n",
" <td>331</td>\n",
" <td>654</td>\n",
" <td>985</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td>2</td>\n",
" <td>2011-01-02</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>0.363478</td>\n",
" <td>0.353739</td>\n",
" <td>0.696087</td>\n",
" <td>0.248539</td>\n",
" <td>131</td>\n",
" <td>670</td>\n",
" <td>801</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td>3</td>\n",
" <td>2011-01-03</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.196364</td>\n",
" <td>0.189405</td>\n",
" <td>0.437273</td>\n",
" <td>0.248309</td>\n",
" <td>120</td>\n",
" <td>1229</td>\n",
" <td>1349</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td>4</td>\n",
" <td>2011-01-04</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>2</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.200000</td>\n",
" <td>0.212122</td>\n",
" <td>0.590435</td>\n",
" <td>0.160296</td>\n",
" <td>108</td>\n",
" <td>1454</td>\n",
" <td>1562</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td>5</td>\n",
" <td>2011-01-05</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>1</td>\n",
" <td>0</td>\n",
" <td>3</td>\n",
" <td>1</td>\n",
" <td>1</td>\n",
" <td>0.226957</td>\n",
" <td>0.229270</td>\n",
" <td>0.436957</td>\n",
" <td>0.186900</td>\n",
" <td>82</td>\n",
" <td>1518</td>\n",
" <td>1600</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"text/plain": [
" instant dteday season yr mnth holiday weekday workingday \\\n",
"0 1 2011-01-01 1 0 1 0 6 0 \n",
"1 2 2011-01-02 1 0 1 0 0 0 \n",
"2 3 2011-01-03 1 0 1 0 1 1 \n",
"3 4 2011-01-04 1 0 1 0 2 1 \n",
"4 5 2011-01-05 1 0 1 0 3 1 \n",
"\n",
" weathersit temp atemp hum windspeed casual registered \\\n",
"0 2 0.344167 0.363625 0.805833 0.160446 331 654 \n",
"1 2 0.363478 0.353739 0.696087 0.248539 131 670 \n",
"2 1 0.196364 0.189405 0.437273 0.248309 120 1229 \n",
"3 1 0.200000 0.212122 0.590435 0.160296 108 1454 \n",
"4 1 0.226957 0.229270 0.436957 0.186900 82 1518 \n",
"\n",
" cnt \n",
"0 985 \n",
"1 801 \n",
"2 1349 \n",
"3 1562 \n",
"4 1600 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"%matplotlib inline\n",
"import pandas as pd\n",
"\n",
"data = pd.read_csv('day.csv')\n",
"\n",
"data.head()"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {
"collapsed": false
},
"outputs": [
{
"data": {
"text/plain": [
"<matplotlib.axes._subplots.AxesSubplot at 0x7f7638803198>"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
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"text/plain": [
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"data[24*10:].plot(x='dteday', y='cnt')"
]
}
],
"metadata": {
"hide_input": false,
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
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"file_extension": ".py",
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