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empet/Cities-Live_work.ipynb

Last active August 29, 2015 14:04
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The best cities the live and work remotely
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Cities-Live_work.ipynb
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"worksheets": [
{
"cells": [
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"<center>The best cities to live and work remotely</center>"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"At [http://www.nomadlist.io](http://www.nomadlist.io) is displayed the so called NomadList<sup>TM</sup>, a ranking list of the best cities to live and work remotely.\n",
"This ranking is based on NomadScore<sup>TM</sup>, a score computed\n",
"from the monthly cost of living/work, internet speed, weather and level of safety.\n",
"\n",
"NomadCost<sup>TM</sup> is based on staying in a hostel, hotel or apartment in the center, working in a coworking space and having a basic meal three times a day.\n",
"\n",
"The NomadList<sup>TM</sup> automatically refreshes once a day. \n",
"\n",
"In this IPython Notebook we visualize the NomadList<sup>TM</sup> through a plotly bubble chart, based on data\n",
"displayed today, August 5, 2014."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\n",
"import numpy as np"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"xl = pd.ExcelFile('NomadList.xls')\n",
"\n",
"df = xl.parse(\"Sheet1\")\n",
"df"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>City</th>\n",
" <th>Country</th>\n",
" <th>Region</th>\n",
" <th>NomadCost</th>\n",
" <th>Temperature</th>\n",
" <th>InternetSpeed</th>\n",
" <th>Pts</th>\n",
" <th>Safety</th>\n",
" <th>English</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> Chiang May</td>\n",
" <td> Thailand</td>\n",
" <td> SEAsia</td>\n",
" <td> 657</td>\n",
" <td> 29</td>\n",
" <td> 20</td>\n",
" <td> 150</td>\n",
" <td> 5</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> Taipei</td>\n",
" <td> Taiwan</td>\n",
" <td> EastAsia</td>\n",
" <td> 1100</td>\n",
" <td> 32</td>\n",
" <td> 40</td>\n",
" <td> 83</td>\n",
" <td> 5</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> Sofia</td>\n",
" <td> Bulgaria</td>\n",
" <td> Europe</td>\n",
" <td> 1527</td>\n",
" <td> 24</td>\n",
" <td> 40</td>\n",
" <td> 56</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> Timisoara</td>\n",
" <td> Romania</td>\n",
" <td> Europe</td>\n",
" <td> 2125</td>\n",
" <td> 28</td>\n",
" <td> 60</td>\n",
" <td> 33</td>\n",
" <td> 5</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> Kosice</td>\n",
" <td> Slovakia</td>\n",
" <td> Europe</td>\n",
" <td> 1503</td>\n",
" <td> 26</td>\n",
" <td> 20</td>\n",
" <td> 30</td>\n",
" <td> 3</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> Ubud, Bali</td>\n",
" <td> Indonesia</td>\n",
" <td> SEAsia</td>\n",
" <td> 1196</td>\n",
" <td> 29</td>\n",
" <td> 5</td>\n",
" <td> 28</td>\n",
" <td> 5</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> Penang</td>\n",
" <td> Malaysia</td>\n",
" <td> SEAsia</td>\n",
" <td> 1059</td>\n",
" <td> 29</td>\n",
" <td> 10</td>\n",
" <td> 28</td>\n",
" <td> 3</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> Belgrade</td>\n",
" <td> Serbia</td>\n",
" <td> Europe</td>\n",
" <td> 1410</td>\n",
" <td> 27</td>\n",
" <td> 10</td>\n",
" <td> 27</td>\n",
" <td> 5</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> Bangkok</td>\n",
" <td> Thailand</td>\n",
" <td> SEAsia</td>\n",
" <td> 1386</td>\n",
" <td> 32</td>\n",
" <td> 20</td>\n",
" <td> 26</td>\n",
" <td> 5</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> Wroklav</td>\n",
" <td> Poland</td>\n",
" <td> Europe</td>\n",
" <td> 1840</td>\n",
" <td> 28</td>\n",
" <td> 20</td>\n",
" <td> 25</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> Ljubljana</td>\n",
" <td> Slovenia</td>\n",
" <td> Europe</td>\n",
" <td> 2217</td>\n",
" <td> 23</td>\n",
" <td> 20</td>\n",
" <td> 24</td>\n",
" <td> 5</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> Budapest</td>\n",
" <td> Hungary</td>\n",
" <td> Europe</td>\n",
" <td> 2270</td>\n",
" <td> 27</td>\n",
" <td> 30</td>\n",
" <td> 23</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> Lisbon</td>\n",
" <td> Portugal</td>\n",
" <td> Europe</td>\n",
" <td> 2168</td>\n",
" <td> 21</td>\n",
" <td> 40</td>\n",
" <td> 21</td>\n",
" <td> 3</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> Las Vegas</td>\n",
" <td> USA</td>\n",
" <td> NorthAmerica</td>\n",
" <td> 2148</td>\n",
" <td> 30</td>\n",
" <td> 30</td>\n",
" <td> 16</td>\n",
" <td> 3</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> Davao</td>\n",
" <td> Philippines</td>\n",
" <td> SEAsia</td>\n",
" <td> 1061</td>\n",
" <td> 31</td>\n",
" <td> 5</td>\n",
" <td> 14</td>\n",
" <td> 5</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> Montreal</td>\n",
" <td> Canada</td>\n",
" <td> NorthAmerica</td>\n",
" <td> 2334</td>\n",
" <td> 20</td>\n",
" <td> 20</td>\n",
" <td> 9</td>\n",
" <td> 5</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> Lima</td>\n",
" <td> Peru</td>\n",
" <td> SouthAmerica</td>\n",
" <td> 1121</td>\n",
" <td> 17</td>\n",
" <td> 10</td>\n",
" <td> 7</td>\n",
" <td> 3</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> Kuala Lumpur</td>\n",
" <td> Malaysia</td>\n",
" <td> SEAsia</td>\n",
" <td> 1199</td>\n",
" <td> 32</td>\n",
" <td> 10</td>\n",
" <td> 6</td>\n",
" <td> 1</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> Park City</td>\n",
" <td> USA</td>\n",
" <td> NorthAmerica</td>\n",
" <td> 2862</td>\n",
" <td> 25</td>\n",
" <td> 30</td>\n",
" <td> 5</td>\n",
" <td> 5</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> Orlando</td>\n",
" <td> USA</td>\n",
" <td> NorthAmerica</td>\n",
" <td> 2436</td>\n",
" <td> 27</td>\n",
" <td> 20</td>\n",
" <td> 4</td>\n",
" <td> 5</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> Singapore</td>\n",
" <td> Singapore</td>\n",
" <td> SEAsia</td>\n",
" <td> 2697</td>\n",
" <td> 33</td>\n",
" <td> 70</td>\n",
" <td> 3</td>\n",
" <td> 5</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> Santa Monica</td>\n",
" <td> USA</td>\n",
" <td> NorthAmerica</td>\n",
" <td> 2601</td>\n",
" <td> 26</td>\n",
" <td> 30</td>\n",
" <td> 3</td>\n",
" <td> 5</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> Vancouver</td>\n",
" <td> Canada</td>\n",
" <td> NorthAmerica</td>\n",
" <td> 2879</td>\n",
" <td> 22</td>\n",
" <td> 20</td>\n",
" <td> 2</td>\n",
" <td> 5</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> Hong Kong</td>\n",
" <td> Hong Kong</td>\n",
" <td> EastAsia</td>\n",
" <td> 2633</td>\n",
" <td> 37</td>\n",
" <td> 90</td>\n",
" <td> 1</td>\n",
" <td> 5</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> Bordeaux</td>\n",
" <td> France</td>\n",
" <td> Europe</td>\n",
" <td> 3155</td>\n",
" <td> 23</td>\n",
" <td> 30</td>\n",
" <td> 1</td>\n",
" <td> 5</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> Ho Chi Minh</td>\n",
" <td> Vietnam</td>\n",
" <td> SEAsia</td>\n",
" <td> 1328</td>\n",
" <td> 33</td>\n",
" <td> 20</td>\n",
" <td> 1</td>\n",
" <td> 3</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
" City Country Region NomadCost Temperature \\\n",
"0 Chiang May Thailand SEAsia 657 29 \n",
"1 Taipei Taiwan EastAsia 1100 32 \n",
"2 Sofia Bulgaria Europe 1527 24 \n",
"3 Timisoara Romania Europe 2125 28 \n",
"4 Kosice Slovakia Europe 1503 26 \n",
"5 Ubud, Bali Indonesia SEAsia 1196 29 \n",
"6 Penang Malaysia SEAsia 1059 29 \n",
"7 Belgrade Serbia Europe 1410 27 \n",
"8 Bangkok Thailand SEAsia 1386 32 \n",
"9 Wroklav Poland Europe 1840 28 \n",
"10 Ljubljana Slovenia Europe 2217 23 \n",
"11 Budapest Hungary Europe 2270 27 \n",
"12 Lisbon Portugal Europe 2168 21 \n",
"13 Las Vegas USA NorthAmerica 2148 30 \n",
"14 Davao Philippines SEAsia 1061 31 \n",
"15 Montreal Canada NorthAmerica 2334 20 \n",
"16 Lima Peru SouthAmerica 1121 17 \n",
"17 Kuala Lumpur Malaysia SEAsia 1199 32 \n",
"18 Park City USA NorthAmerica 2862 25 \n",
"19 Orlando USA NorthAmerica 2436 27 \n",
"20 Singapore Singapore SEAsia 2697 33 \n",
"21 Santa Monica USA NorthAmerica 2601 26 \n",
"22 Vancouver Canada NorthAmerica 2879 22 \n",
"23 Hong Kong Hong Kong EastAsia 2633 37 \n",
"24 Bordeaux France Europe 3155 23 \n",
"25 Ho Chi Minh Vietnam SEAsia 1328 33 \n",
"\n",
" InternetSpeed Pts Safety English \n",
"0 20 150 5 2 \n",
"1 40 83 5 3 \n",
"2 40 56 3 3 \n",
"3 60 33 5 4 \n",
"4 20 30 3 0 \n",
"5 5 28 5 4 \n",
"6 10 28 3 4 \n",
"7 10 27 5 4 \n",
"8 20 26 5 3 \n",
"9 20 25 3 3 \n",
"10 20 24 5 5 \n",
"11 30 23 3 3 \n",
"12 40 21 3 4 \n",
"13 30 16 3 5 \n",
"14 5 14 5 5 \n",
"15 20 9 5 4 \n",
"16 10 7 3 3 \n",
"17 10 6 1 5 \n",
"18 30 5 5 5 \n",
"19 20 4 5 5 \n",
"20 70 3 5 5 \n",
"21 30 3 5 5 \n",
"22 20 2 5 5 \n",
"23 90 1 5 5 \n",
"24 30 1 5 2 \n",
"25 20 1 3 2 "
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df.columns"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"Index([u'City', u'Country', u'Region', u'NomadCost', u'Temperature', u'InternetSpeed', u'Pts', u'Safety', u'English'], dtype='object')"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"L=len(df)\n",
"print L"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"26\n"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"import plotly.plotly as py \n",
"import plotly.tools as tls \n",
"from plotly.graph_objs import *\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 7
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"colors = dict(\n",
" EastAsia='rgba(31,119,180, 0.7)',\n",
" Europe='rgba(214,39,40, 0.7)', \n",
" SEAsia='rgba(44,160,44, 0.7)',\n",
" NorthAmerica='rgba(255,127,14, 0.7)',\n",
" SouthAmerica='rgba(153,0, 153, 0.7)'\n",
" )"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 8
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"sizemode='area' \n",
"sizeref=df['Pts'].max()/75\n",
"\n",
"cities=list(df['City']) \n",
"countries=list(df['Country'])\n",
"Y=list(df['InternetSpeed'])\n",
"T=list(df['Temperature'])\n",
"livingcost=list(df['NomadCost'])\n",
"safety=list(df['Safety'])\n",
"sizeb=(df['Pts'].values)*50\n",
"\n",
"m, M= min(livingcost), max(livingcost)"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 9
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"def make_trace(x, y, text, sizeb, color): \n",
" return Scatter(\n",
" x=x,\n",
" y=y,\n",
" text=text, \n",
" mode='markers', \n",
" marker= Marker(\n",
" color=color, \n",
" size=sizeb, # bubble size\n",
" sizeref=sizeref, \n",
" sizemode=sizemode, \n",
" line= Line(width=1.0) # width of marker border\n",
" )\n",
" )"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"text=[]\n",
"color=[]\n",
"for k in df.index: \n",
" txt='Rank '+str(k+1)+': '+ cities[k]+ ' '+'<br>Country:'+ countries[k]+' '\\\n",
" +'<br>Temperat 08.05.14: '+str(T[k])+'C'+'<br>Safety Level:'+str(safety[k])\n",
" text.append(txt) \n",
" color.append(colors[df['Region'][k]]) \n",
" "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"data=Data([make_trace(livingcost,Y, text, sizeb, color)])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"ftitle = ' The best cities to live and work remotely,'+\\\n",
"'<br>based on cost of living, internet speed, weather, and safety'\n",
"xtitle = 'Monthly living cost in dollars'\n",
"ytitle = 'Internet speed (download) in MBPS'\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"axis_style = dict( \n",
" zeroline=False, \n",
" gridcolor='#FFFFFF', \n",
" ticks='outside', \n",
" ticklen=6, \n",
" tickwidth=1 \n",
")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"anno_txt1='$\\\\text{Bubble area is proportional to the } \\\\text{NomadScore}^{\\\\text{TM}}$'\n",
"anno_txt2=\"Data source: <a href='http://nomadlist.io/'> [1]</a>, August 5, 2014\"\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"layout = Layout(\n",
" title=ftitle, \n",
" plot_bgcolor='#EFECEA', # set background color to grey\n",
" font=Font(family='Raleway, sans-serif'),\n",
" hovermode='closest',\n",
" showlegend=False, \n",
" autosize=False,\n",
" width=500,\n",
" height=500,\n",
" xaxis=XAxis(axis_style, \n",
" range=[m-400, M+50],\n",
" nticks=18, \n",
" tickangle=-45,\n",
" title=xtitle \n",
" ),\n",
" yaxis=YAxis(axis_style, \n",
" range=[-3, 100],\n",
" nticks=11, \n",
" title=ytitle \n",
" ),\n",
" margin=Margin(\n",
" l=80,\n",
" r=80,\n",
" b=125,\n",
" t=90,\n",
" pad=2\n",
" ),\n",
" annotations=Annotations([\n",
" Annotation(\n",
" showarrow=False, \n",
" text=anno_txt1, \n",
" xref='paper', \n",
" yref='paper', \n",
" x=-0.16, \n",
" y=-0.3, \n",
" xanchor='left', \n",
" yanchor='bottom', \n",
" font=Font(\n",
" size=12 \n",
" )\n",
" ),\n",
" \n",
" Annotation(\n",
" showarrow=False, \n",
" text=anno_txt2, \n",
" xref='paper', \n",
" yref='paper', \n",
" x=-0.16, \n",
" y=-0.41, \n",
" xanchor='left', \n",
" yanchor='bottom', \n",
" font=Font(\n",
" size=12 \n",
" )\n",
" ), \n",
" ]), \n",
")"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"fig = Figure(data=data, layout=layout)\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"py.sign_in('empet', 'my_api_key')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 18
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"py.iplot(fig, filename='Where-live-workNScore1', width=500, height=500)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<iframe id=\"igraph\" scrolling=\"no\" style=\"border:none;\"seamless=\"seamless\" src=\"https://plot.ly/~empet/43/475/475\" height=\"500\" width=\"500\"></iframe>"
],
"metadata": {},
"output_type": "display_data",
"text": [
"<IPython.core.display.HTML at 0x1e26cc0>"
]
}
],
"prompt_number": 19
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"from IPython.core.display import HTML\n",
"def css_styling():\n",
" styles = open(\"./styles/custom.css\", \"r\").read()\n",
" return HTML(styles)\n",
"css_styling()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<style>\n",
" div.cell{\n",
" width:800px;\n",
" margin-left:16% !important;\n",
" margin-right:auto;\n",
" }\n",
" h1 {\n",
" font-family: 'Alegreya Sans', sans-serif;\n",
" }\n",
" \n",
" h2 {\n",
" font-family: 'Fenix', serif;\n",
" text-indent:1em;\n",
" }\n",
" .text_cell_render h2 {\n",
" font-weight: 200;\n",
" font-size: 20pt;\n",
" line-height: 100%;\n",
" margin-bottom: 1.5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
" }\n",
" h3 {\n",
" font-family: 'Fenix', serif;\n",
" %margin-top:12px;\n",
" %margin-bottom: 3px;\n",
" }\n",
" .text_cell_render h3 {\n",
" font-weight: 300;\n",
" font-size: 18pt;\n",
" line-height: 100%;\n",
" margin-bottom: 0.5em;\n",
" margin-top: 2em;\n",
" display: block;\n",
" }\n",
" h4 {\n",
" font-family: 'Fenix', serif;\n",
" }\n",
" .text_cell_render h4 {\n",
" font-weight: 300;\n",
" font-size: 16pt;\n",
" margin-bottom: 0.5em;\n",
" margin-top: 0.5em;\n",
" display: block;\n",
" }\n",
" h5 {\n",
" font-family: 'Alegreya Sans', sans-serif;\n",
" }\n",
" .text_cell_render h5 {\n",
" font-weight: 300;\n",
" font-style: normal;\n",
" font-size: 16pt;\n",
" margin-bottom: 0em;\n",
" margin-top: 1.5em;\n",
" display: block;\n",
" }\n",
" \n",
" div.text_cell_render{\n",
" %font-family: Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
" font-family: 'Alegreya Sans',Computer Modern, \"Helvetica Neue\", Arial, Helvetica, Geneva, sans-serif;\n",
" line-height: 145%;\n",
" font-size: 130%;\n",
" width:800px;\n",
" margin-left:auto;\n",
" margin-right:auto;\n",
" }\n",
" blockquote{\n",
" display:block;\n",
" background: #f3f3f3;\n",
" font-family: \"Open sans\",verdana,arial,sans-serif;\n",
" width:610px;\n",
" padding: 15px 15px 15px 15px;\n",
" text-align:justify;\n",
" text-justify:inter-word;\n",
" }\n",
" blockquote p {\n",
" margin-bottom: 0;\n",
" line-height: 125%;\n",
" font-size: 100%;\n",
" }\n",
" \n",
" .prompt{\n",
" display: None;\n",
" }\n",
" .warning{\n",
" color: rgb( 240, 20, 20 )\n",
" } \n",
"</style>\n",
"<script>\n",
" MathJax.Hub.Config({\n",
" TeX: {\n",
" extensions: [\"AMSmath.js\"]\n",
" },\n",
" tex2jax: {\n",
" inlineMath: [ ['$','$'], [\"\\\\(\",\"\\\\)\"] ],\n",
" displayMath: [ ['$$','$$'], [\"\\\\[\",\"\\\\]\"] ]\n",
" },\n",
" displayAlign: 'center', // Change this to 'center' to center equations.\n",
" \"HTML-CSS\": {\n",
" styles: {'.MathJax_Display': {\"margin\": 4}}\n",
" }\n",
" });\n",
"</script>\n"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 21,
"text": [
"<IPython.core.display.HTML at 0xc0e4438>"
]
}
],
"prompt_number": 21
}
],
"metadata": {}
}
]
}
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