From bda4f5d15030f4fb4993a2ec1c39c4059fc1fdfb Mon Sep 17 00:00:00 2001 From: Pratool Gadtaula Date: Thu, 25 Feb 2016 21:12:04 -0500 Subject: [PATCH 01/24] Added proposal and edited readme --- README.md | 6 ++++-- proposal.pdf | Bin 0 -> 72755 bytes proposal.tex | 43 +++++++++++++++++++++++++++++++++++++++++++ 3 files changed, 47 insertions(+), 2 deletions(-) create mode 100644 proposal.pdf create mode 100644 proposal.tex diff --git a/README.md b/README.md index a8435e9..ebb035d 100644 --- a/README.md +++ b/README.md @@ -1,2 +1,4 @@ -# DataScience16CTW -This is the base repo for the "Change the World" project for Data Science at Olin College, Spring 2016. +# Sexual Assault and Immigration +# Misinterpreted Data and Lies About Immigrants in Sweden + +In an exploration of data visualization and data analysis, we hope to elucidate the meaning of the data behind the recent relation behind sexual assault and immigration in Sweden. diff --git a/proposal.pdf b/proposal.pdf new file mode 100644 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looking into immigration with an initial intent of the variation of immigration laws over time all over the world, we came across the idea to reflect the type and frequency of crimes that immigrants commit or don’t commit as compared to the the rest of the non-immigrant population in that area. Sexual violence and immigration have been linked together for years in the media and politics in particularly negative ways. With the motivation to uncover the truth, we quickly uncovered the massive amount of visualizations of data surrounding sexual violence statistics in Sweden. Especially in the case of Sweden, sexual violence statistics have been misconstrued to try and prove that immigrants of certain backgrounds, in this case Muslims, are bringing problems that did not previously exist on a large scale. At a time when racial and religious tensions are rampant in society, it is important to work to combat unjustified prejudices and disprove harmful assumptions. + +\section{Main Ideas} + In order to change people’s minds and prove that biases against people exist and should be confronted instead of scapegoating. In order to change people’s minds, we want present a dramatic visualization that strikes the viewer with empathy and a sense of humanity. We imagine this manifesting in a visualization that represents the conclusions we were able to gather. This visualization would be part of a non-scientific article presented to a broad audience that describes and digs into our insights. As a stretch goal, we would like this visualization to be interactive and also tell a story - explaining people’s experiences and livelihoods not as a simple series of numbers. + +\section{Learning Goals} + We would mainly like to work on learning how to use data in a persuasive way, either to prove a point or bring awareness to a more overlooked problem. In doing this, it would also be really interesting to figure out how to go about structuring our project and determining what information we need. Additionally, we both want to learn how to work with very technical information and data and present it in a format for a more general audience. + +\section{Data Sources} + Most of our data is available through government statistics sites, which house information on immigration data and crime statistics through the years, starting from around the 1990’s to 2014. If we have time, it would also be interesting to see if we can incorporate anecdotal information from news articles to provide a framework for our work. + +\section{Deliverables and Assessment} + Our final deliverable as of now is going to be an article that depicts the story of the issue at hand, and our approach to disproving some common misassumptions through with a potentially interactive data visualization (or multiple visualizations). We will get started quickly by learning the basics of D3 (as we are very interested in making our final visualization interactive). Then we will mostly pair-program and understand and parse the data together in meetings. We may also try generating insights for our written report individually. We plan to have a solid start on figuring out D3 visualizations, and have collected all of our data. Hopefully, we will also have gotten to the point where we can plan out our article or even start writing. For assessment, we believe that we could be fairly assessed based on the thoroughness of our insights -- we expect it to be journalistic in nature. We also want to be assessed on our bias and the meaningfulness of our visualization(s). + +\newpage + +\section*{Sources} +\texttt{https://www.dhs.gov/data-statistics\\ +https://www.bra.se/bra/bra-in-english/home/crime-and-statistics/crime-statistics.html } + +\end{document} \ No newline at end of file From b111b64767cad2506543320fb0a5045345f06b2c Mon Sep 17 00:00:00 2001 From: Pratool Gadtaula Date: Thu, 25 Feb 2016 21:12:31 -0500 Subject: [PATCH 02/24] Changed heading size in README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index ebb035d..b253d5e 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ # Sexual Assault and Immigration -# Misinterpreted Data and Lies About Immigrants in Sweden +## Misinterpreted Data and Lies About Immigrants in Sweden In an exploration of data visualization and data analysis, we hope to elucidate the meaning of the data behind the recent relation behind sexual assault and immigration in Sweden. From a1ba033b0982975457e80be9696f6c80faf54932 Mon Sep 17 00:00:00 2001 From: Kiki Date: Thu, 3 Mar 2016 00:59:00 -0500 Subject: [PATCH 03/24] added immigration graph by country and nature --- 2005_immigration_data.csv | 77 ++ immigration_plots.ipynb | 1662 +++++++++++++++++++++++++++++++++++++ 2 files changed, 1739 insertions(+) create mode 100644 2005_immigration_data.csv create mode 100644 immigration_plots.ipynb diff --git a/2005_immigration_data.csv b/2005_immigration_data.csv new file mode 100644 index 0000000..1375f50 --- /dev/null +++ b/2005_immigration_data.csv @@ -0,0 +1,77 @@ +Citizenship 2005_refugees 2005_fam_reun 2005_labour 2005_students 2005_adopted + +EUROPE of which 1648 8366 2085 752 86 +Bulgaria - + +Former Yugoslavia there off: 1299 2111 +Bosnia H. 197 543 +Kosovo 5) .. +Serbia and Montenegro 6) 1059 1262 + +Poland - 634 * * - +Romania 4 301 414 - +Russia 157 700 376 168 41 +United Kingdom 641 * +Turkey 76 886 86 207 +Ukraine 547 +Estonia * * +Croatia 140 +Lithuania * * +Belarus 88 +Germany 829 * +France * +Greece * +Latvia * +Netherlands * + + +AFRICA of which 1991 2864 259 1155 62 +Eritrea 475 190 +Ethiopia 37 178 60 43 +Somalia 841 770 +Uganda 9 +Egypt 18 +Cameroon 329 +Nigeria 301 + + +AMERICA of which 428 2238 1175 998 11 +Chile 6 356 - +Colombia 332 103 - +Cuba 15 +Brazil 143 54 - +Bolivia 10 +Guatemala - +Canada 241 363 +USA 522 629 213 +Mexico 168 + + +ASIA of which 3711 7733 2136 3636 645 +Afghanistan 387 +Bangladesh 44 307 +Philippines 373 8 +India 175 760 592 38 +Iraq 1692 1705 +Iran 230 626 44 266 +China 36 498 479 934 450 +Japan 152 151 +Pakistan 35 624 +Lebanon 97 277 +Sri Lanka 5 85 1 +Syria 178 427 32 +Thailand 2095 329 35 +Vietnam 3 283 86 +Korea, Republic 9 + + +OCEANIA of which 235 316 280 +Australia 265 + + +OTHERS of which 1081 472 14 16 1 + + +TOTAL of which 8859 21908 5985 6837 805 +Females 4185 12511 1512 2367 583 +Males 4674 9397 4473 4470 222 diff --git a/immigration_plots.ipynb b/immigration_plots.ipynb new file mode 100644 index 0000000..ce30951 --- /dev/null +++ b/immigration_plots.ipynb @@ -0,0 +1,1662 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.9.6\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning:\n", + "\n", + "Matplotlib is building the font cache using fc-list. 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" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly\n", + "print plotly.__version__ # version 1.9.4 required\n", + "plotly.offline.init_notebook_mode() # run at the start of every notebook\n", + "plotly.offline.iplot({\n", + "\"data\": [{\n", + " \"x\": [1, 2, 3],\n", + " \"y\": [4, 2, 5]\n", + "}],\n", + "\"layout\": {\n", + " \"title\": \"hello world\"\n", + "}\n", + "})\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning:\n", + "\n", + "axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", + "\n" + ] + } + ], + "source": [ + "import plotly.plotly as py\n", + "import seaborn\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 92, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Citizenship2005_refugees2005_fam_reun2005_labour2005_students2005_adopted
0NaNNaNNaNNaNNaNNaN
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2Bulgaria-NaNNaNNaNNaN
3NaNNaNNaNNaNNaNNaN
4Former Yugoslavia there off:12992111NaNNaNNaN
5Bosnia H.197543NaNNaNNaN
6Kosovo 5)..NaNNaNNaNNaN
7Serbia and Montenegro 6)10591262NaNNaNNaN
8NaNNaNNaNNaNNaNNaN
9Poland-634**-
10Romania4301414NaN-
11Russia15770037616841
12United KingdomNaN641NaN*NaN
13Turkey7688686207NaN
14UkraineNaNNaN547NaNNaN
15EstoniaNaNNaN**NaN
16CroatiaNaNNaN140NaNNaN
17LithuaniaNaNNaN**NaN
18BelarusNaNNaN88NaNNaN
19GermanyNaN829NaN*NaN
20FranceNaNNaNNaN*NaN
21GreeceNaNNaNNaN*NaN
22LatviaNaNNaNNaN*NaN
23NetherlandsNaNNaNNaN*NaN
24NaNNaNNaNNaNNaNNaN
25NaNNaNNaNNaNNaNNaN
26AFRICA of which19912864259115562
27Eritrea475190NaNNaNNaN
28Ethiopia37178NaN6043
29Somalia841770NaNNaNNaN
.....................
46NaNNaNNaNNaNNaNNaN
47NaNNaNNaNNaNNaNNaN
48ASIA of which3711773321363636645
49Afghanistan387NaNNaNNaNNaN
50Bangladesh44NaNNaN307NaN
51PhilippinesNaN373NaNNaN8
52IndiaNaN17576059238
53Iraq16921705NaNNaNNaN
54Iran23062644266NaN
55China36498479934450
56JapanNaNNaN152151NaN
57PakistanNaNNaN35624NaN
58Lebanon97277NaNNaNNaN
59Sri Lanka585NaNNaN1
60Syria17842732NaNNaN
61ThailandNaN2095329NaN35
62Vietnam3283NaNNaN86
63Korea, RepublicNaNNaNNaNNaN9
64NaNNaNNaNNaNNaNNaN
65NaNNaNNaNNaNNaNNaN
66OCEANIA of whichNaN235316280NaN
67AustraliaNaNNaN265NaNNaN
68NaNNaNNaNNaNNaNNaN
69NaNNaNNaNNaNNaNNaN
70OTHERS of which108147214161
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72NaNNaNNaNNaNNaNNaN
73TOTAL of which88592190859856837805
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75Males4674939744734470222
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Citizenship2005_refugees2005_fam_reun2005_labour2005_students2005_adopted
2Bulgaria00000
5Bosnia H.197543000
6Kosovo 5)00000
7Serbia and Montenegro 6)10591262000
9Poland0634000
10Romania430141400
11Russia15770037616841
12United Kingdom0641000
13Turkey76886862070
14Ukraine0054700
15Estonia00000
16Croatia0014000
17Lithuania00000
18Belarus008800
19Germany0829000
20France00000
21Greece00000
22Latvia00000
23Netherlands00000
27Eritrea475190000
28Ethiopia3717806043
29Somalia841770000
30Uganda90000
31Egypt000180
32Cameroon0003290
33Nigeria0003010
37Chile6356000
38Colombia332103000
39Cuba150000
40Brazil00143540
41Bolivia000010
42Guatemala00000
43Canada002413630
44USA05226292130
45Mexico0001680
49Afghanistan3870000
50Bangladesh44003070
51Philippines0373008
52India017576059238
53Iraq16921705000
54Iran230626442660
55China36498479934450
56Japan001521510
57Pakistan00356240
58Lebanon97277000
59Sri Lanka585001
60Syria1784273200
61Thailand02095329035
62Vietnam32830086
63Korea, Republic00009
67Australia0026500
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" + ], + "text/plain": [ + " Citizenship 2005_refugees 2005_fam_reun 2005_labour \\\n", + "2 Bulgaria 0 0 0 \n", + "5 Bosnia H. 197 543 0 \n", + "6 Kosovo 5) 0 0 0 \n", + "7 Serbia and Montenegro 6) 1059 1262 0 \n", + "9 Poland 0 634 0 \n", + "10 Romania 4 301 414 \n", + "11 Russia 157 700 376 \n", + "12 United Kingdom 0 641 0 \n", + "13 Turkey 76 886 86 \n", + "14 Ukraine 0 0 547 \n", + "15 Estonia 0 0 0 \n", + "16 Croatia 0 0 140 \n", + "17 Lithuania 0 0 0 \n", + "18 Belarus 0 0 88 \n", + "19 Germany 0 829 0 \n", + "20 France 0 0 0 \n", + "21 Greece 0 0 0 \n", + "22 Latvia 0 0 0 \n", + "23 Netherlands 0 0 0 \n", + "27 Eritrea 475 190 0 \n", + "28 Ethiopia 37 178 0 \n", + "29 Somalia 841 770 0 \n", + "30 Uganda 9 0 0 \n", + "31 Egypt 0 0 0 \n", + "32 Cameroon 0 0 0 \n", + "33 Nigeria 0 0 0 \n", + "37 Chile 6 356 0 \n", + "38 Colombia 332 103 0 \n", + "39 Cuba 15 0 0 \n", + "40 Brazil 0 0 143 \n", + "41 Bolivia 0 0 0 \n", + "42 Guatemala 0 0 0 \n", + "43 Canada 0 0 241 \n", + "44 USA 0 522 629 \n", + "45 Mexico 0 0 0 \n", + "49 Afghanistan 387 0 0 \n", + "50 Bangladesh 44 0 0 \n", + "51 Philippines 0 373 0 \n", + "52 India 0 175 760 \n", + "53 Iraq 1692 1705 0 \n", + "54 Iran 230 626 44 \n", + "55 China 36 498 479 \n", + "56 Japan 0 0 152 \n", + "57 Pakistan 0 0 35 \n", + "58 Lebanon 97 277 0 \n", + "59 Sri Lanka 5 85 0 \n", + "60 Syria 178 427 32 \n", + "61 Thailand 0 2095 329 \n", + "62 Vietnam 3 283 0 \n", + "63 Korea, Republic 0 0 0 \n", + "67 Australia 0 0 265 \n", + "\n", + " 2005_students 2005_adopted \n", + "2 0 0 \n", + "5 0 0 \n", + "6 0 0 \n", + "7 0 0 \n", + "9 0 0 \n", + "10 0 0 \n", + "11 168 41 \n", + "12 0 0 \n", + "13 207 0 \n", + "14 0 0 \n", + "15 0 0 \n", + "16 0 0 \n", + "17 0 0 \n", + "18 0 0 \n", + "19 0 0 \n", + "20 0 0 \n", + "21 0 0 \n", + "22 0 0 \n", + "23 0 0 \n", + "27 0 0 \n", + "28 60 43 \n", + "29 0 0 \n", + "30 0 0 \n", + "31 18 0 \n", + "32 329 0 \n", + "33 301 0 \n", + "37 0 0 \n", + "38 0 0 \n", + "39 0 0 \n", + "40 54 0 \n", + "41 0 10 \n", + "42 0 0 \n", + "43 363 0 \n", + "44 213 0 \n", + "45 168 0 \n", + "49 0 0 \n", + "50 307 0 \n", + "51 0 8 \n", + "52 592 38 \n", + "53 0 0 \n", + "54 266 0 \n", + "55 934 450 \n", + "56 151 0 \n", + "57 624 0 \n", + "58 0 0 \n", + "59 0 1 \n", + "60 0 0 \n", + "61 0 35 \n", + "62 0 86 \n", + "63 0 9 \n", + "67 0 0 " + ] + }, + "execution_count": 144, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import plotly.graph_objs as go\n", + "\n", + "def clean(x):\n", + " try:\n", + " return int(x)\n", + " except:\n", + " return 0\n", + "\n", + "data['2005_refugees'] = data['2005_refugees'].apply(lambda x: clean(x))\n", + "data['2005_fam_reun'] = data['2005_fam_reun'].apply(lambda x: clean(x))\n", + "data['2005_labour'] = data['2005_labour'].apply(lambda x: clean(x))\n", + "data['2005_students'] = data['2005_students'].apply(lambda x: clean(x))\n", + "data['2005_adopted'] = data['2005_adopted'].apply(lambda x: clean(x))\n", + "total = data[data.Citizenship == 'TOTAL of which']\n", + "del total['Citizenship']\n", + "countries = data[data['Citizenship'].str.contains(\"which|off|which|ales\") == False]\n", + "countries\n" + ] + }, + { + "cell_type": "code", + "execution_count": 154, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 154, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref = countries['2005_refugees']\n", + "fam = countries['2005_fam_reun']\n", + "labour = countries['2005_labour']\n", + "students = countries['2005_students']\n", + "adopt = countries['2005_adopted']\n", + "country_names = countries['Citizenship']\n", + "\n", + "df = pd.DataFrame({'x': country_names, 'y': ref, 'y2':fam, 'y3':labour, 'y4':students, 'y5':adopt})\n", + "df.head()\n", + "\n", + "dat = [\n", + " go.Bar(\n", + " x=df['x'], # assign x as the dataframe column 'x'\n", + " y=df['y'],\n", + " name='refugees'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y2'],\n", + " name='family reunited'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y3'],\n", + " name='labourers'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y4'],\n", + " name='students'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y5'],\n", + " name='adopted'\n", + " )\n", + "]\n", + "\n", + "layout = go.Layout(\n", + " barmode='stack',\n", + " title='Total Immigration by Country in 2005'\n", + ")\n", + "\n", + "fig = go.Figure(data=dat, layout=layout)\n", + "py.iplot(fig, filename='tot_country')" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total = total.transpose().reset_index()\n", + "dat = [\n", + " go.Bar(\n", + " x=total['index'], # assign x as the dataframe column 'x'\n", + " y=total[73]\n", + " )\n", + "]\n", + "py.iplot(dat, filename='total_bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "# Learn about API authentication here: https://plot.ly/pandas/getting-started\n", + "# Find your api_key here: https://plot.ly/settings/ap\n", + "\n", + "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_us_cities.csv')\n", + "df.head()\n", + "\n", + "df['text'] = df['name'] + '
Population ' + (df['pop']/1e6).astype(str)+' million'\n", + "limits = [(0,2),(3,10),(11,20),(21,50),(50,3000)]\n", + "colors = [\"rgb(0,116,217)\",\"rgb(255,65,54)\",\"rgb(133,20,75)\",\"rgb(255,133,27)\",\"rgb(255,220,0)\"]\n", + "cities = []\n", + "scale = 50000\n", + "\n", + "for i in range(len(limits)):\n", + " lim = limits[i]\n", + " df_sub = df[lim[0]:lim[1]]\n", + " city = dict(\n", + " type = 'scattergeo',\n", + " locationmode = 'USA-states',\n", + " lon = df_sub['lon'],\n", + " lat = df_sub['lat'],\n", + " text = df_sub['text'],\n", + " sizemode = 'diameter',\n", + " marker = dict( \n", + " size = df_sub['pop']/scale, \n", + " color = colors[i],\n", + " line = dict(width = 2,color = 'black')\n", + " ),\n", + " name = '{0} - {1}'.format(lim[0],lim[1]) )\n", + " cities.append(city)\n", + "\n", + "layout = dict(\n", + " title = '2014 US city populations
(Click legend to toggle traces)',\n", + " showlegend = True,\n", + " geo = dict(\n", + " scope='usa',\n", + " projection=dict( type='albers usa' ),\n", + " showland = True,\n", + " landcolor = 'rgb(217, 217, 217)', \n", + " subunitwidth=1,\n", + " countrywidth=1,\n", + " subunitcolor=\"rgb(255, 255, 255)\",\n", + " countrycolor=\"rgb(255, 255, 255)\" \n", + " ), \n", + " )\n", + " \n", + "fig = dict( data=cities, layout=layout )\n", + "url = py.plot( fig, validate=False, filename='d3-bubble-map-populations' )" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 986ef64f7b17232935c52cd3ed3707fc293f5796 Mon Sep 17 00:00:00 2001 From: Kiki Date: Thu, 3 Mar 2016 01:00:31 -0500 Subject: [PATCH 04/24] added html with graph --- immigration.html | 11 +++++++++++ 1 file changed, 11 insertions(+) create mode 100644 immigration.html diff --git a/immigration.html b/immigration.html new file mode 100644 index 0000000..bddc91e --- /dev/null +++ b/immigration.html @@ -0,0 +1,11 @@ + + + + Basic Map + + + + + \ No newline at end of file From 72ff82ab6a6d2664253408fefc5202327f0314e2 Mon Sep 17 00:00:00 2001 From: Kiki Date: Thu, 3 Mar 2016 04:05:32 -0500 Subject: [PATCH 05/24] made world map with immigrant data --- .../immigration_plots-checkpoint.ipynb | 3597 +++++++++++++++++ 2005_immigration_data.csv | 154 +- immigration.html | 2 + immigration_plots.ipynb | 2475 ++++++++++-- 4 files changed, 5881 insertions(+), 347 deletions(-) create mode 100644 .ipynb_checkpoints/immigration_plots-checkpoint.ipynb diff --git a/.ipynb_checkpoints/immigration_plots-checkpoint.ipynb b/.ipynb_checkpoints/immigration_plots-checkpoint.ipynb new file mode 100644 index 0000000..971b638 --- /dev/null +++ b/.ipynb_checkpoints/immigration_plots-checkpoint.ipynb @@ -0,0 +1,3597 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.9.6\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning:\n", + "\n", + "Matplotlib is building the font cache using fc-list. This may take a moment.\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly\n", + "print plotly.__version__ # version 1.9.4 required\n", + "plotly.offline.init_notebook_mode() # run at the start of every notebook\n", + "plotly.offline.iplot({\n", + "\"data\": [{\n", + " \"x\": [1, 2, 3],\n", + " \"y\": [4, 2, 5]\n", + "}],\n", + "\"layout\": {\n", + " \"title\": \"hello world\"\n", + "}\n", + "})\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning:\n", + "\n", + "axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", + "\n" + ] + } + ], + "source": [ + "import plotly.plotly as py\n", + "import seaborn\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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CitizenshipCode2005_refugees2005_fam_reun2005_labour2005_students2005_adopted
0NaNNaNNaNNaNNaNNaNNaN
1EUROPE of whichNaN16488366208575286
2BulgariaBGR-NaNNaNNaNNaN
3NaNNaNNaNNaNNaNNaNNaN
4Former Yugoslavia there off:NaN12992111NaNNaNNaN
5BosniaBIH197543NaNNaNNaN
6KosovoKSV..NaNNaNNaNNaN
7Serbia and MontenegroSRB10591262NaNNaNNaN
8NaNNaNNaNNaNNaNNaNNaN
9PolandPOL-634**-
10RomaniaROU4301414NaN-
11RussiaRUS15770037616841
12United KingdomGBRNaN641NaN*NaN
13TurkeyTUR7688686207NaN
14UkraineUKRNaNNaN547NaNNaN
15EstoniaESTNaNNaN**NaN
16CroatiaHRVNaNNaN140NaNNaN
17LithuaniaLTUNaNNaN**NaN
18BelarusBLRNaNNaN88NaNNaN
19GermanyDEUNaN829NaN*NaN
20FranceFRANaNNaNNaN*NaN
21GreeceGRCNaNNaNNaN*NaN
22LatviaLVANaNNaNNaN*NaN
23NetherlandsNLDNaNNaNNaN*NaN
24NaNNaNNaNNaNNaNNaNNaN
25NaNNaNNaNNaNNaNNaNNaN
26AFRICA of whichNaN19912864259115562
27EritreaERI475190NaNNaNNaN
28EthiopiaETH37178NaN6043
29SomaliaSOM841770NaNNaNNaN
........................
46NaNNaNNaNNaNNaNNaNNaN
47NaNNaNNaNNaNNaNNaNNaN
48ASIA of whichNaN3711773321363636645
49AfghanistanAFG387NaNNaNNaNNaN
50BangladeshBGD44NaNNaN307NaN
51PhilippinesPHLNaN373NaNNaN8
52IndiaINDNaN17576059238
53IraqIRQ16921705NaNNaNNaN
54IranIRN23062644266NaN
55ChinaCHN36498479934450
56JapanJPNNaNNaN152151NaN
57PakistanPAKNaNNaN35624NaN
58LebanonLBN97277NaNNaNNaN
59Sri LankaLKA585NaNNaN1
60SyriaSYR17842732NaNNaN
61ThailandTHANaN2095329NaN35
62VietnamNaN3283NaNNaN86
63Korea, RepublicPRKNaNNaNNaNNaN9
64NaNNaNNaNNaNNaNNaNNaN
65NaNNaNNaNNaNNaNNaNNaN
66OCEANIA of whichNaNNaN235316280NaN
67AustraliaAUSNaNNaN265NaNNaN
68NaNNaNNaNNaNNaNNaNNaN
69NaNNaNNaNNaNNaNNaNNaN
70OTHERS of whichNaN108147214161
71NaNNaNNaNNaNNaNNaNNaN
72NaNNaNNaNNaNNaNNaNNaN
73TOTAL of whichNaN88592190859856837805
74FemalesNaN41851251115122367583
75MalesNaN4674939744734470222
\n", + "

76 rows × 7 columns

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CitizenshipCode2005_refugees2005_fam_reun2005_labour2005_students2005_adopted
2BulgariaBGR00000
5BosniaBIH197543000
6KosovoKSV00000
7Serbia and MontenegroSRB10591262000
9PolandPOL0634000
10RomaniaROU430141400
11RussiaRUS15770037616841
12United KingdomGBR0641000
13TurkeyTUR76886862070
14UkraineUKR0054700
15EstoniaEST00000
16CroatiaHRV0014000
17LithuaniaLTU00000
18BelarusBLR008800
19GermanyDEU0829000
20FranceFRA00000
21GreeceGRC00000
22LatviaLVA00000
23NetherlandsNLD00000
27EritreaERI475190000
28EthiopiaETH3717806043
29SomaliaSOM841770000
30UgandaUGA90000
31EgyptEGY000180
32CameroonCMR0003290
33NigeriaNGA0003010
37ChileCHL6356000
38ColombiaCOL332103000
39CubaCUB150000
40BrazilBRA00143540
41BoliviaBOL000010
42GuatemalaGTM00000
43CanadaCAN002413630
44USAUSA05226292130
45MexicoMEX0001680
49AfghanistanAFG3870000
50BangladeshBGD44003070
51PhilippinesPHL0373008
52IndiaIND017576059238
53IraqIRQ16921705000
54IranIRN230626442660
55ChinaCHN36498479934450
56JapanJPN001521510
57PakistanPAK00356240
58LebanonLBN97277000
59Sri LankaLKA585001
60SyriaSYR1784273200
61ThailandTHA02095329035
62VietnamNaN32830086
63Korea, RepublicPRK00009
67AustraliaAUS0026500
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" + ], + "text/plain": [ + " Citizenship Code 2005_refugees 2005_fam_reun 2005_labour \\\n", + "2 Bulgaria BGR 0 0 0 \n", + "5 Bosnia BIH 197 543 0 \n", + "6 Kosovo KSV 0 0 0 \n", + "7 Serbia and Montenegro SRB 1059 1262 0 \n", + "9 Poland POL 0 634 0 \n", + "10 Romania ROU 4 301 414 \n", + "11 Russia RUS 157 700 376 \n", + "12 United Kingdom GBR 0 641 0 \n", + "13 Turkey TUR 76 886 86 \n", + "14 Ukraine UKR 0 0 547 \n", + "15 Estonia EST 0 0 0 \n", + "16 Croatia HRV 0 0 140 \n", + "17 Lithuania LTU 0 0 0 \n", + "18 Belarus BLR 0 0 88 \n", + "19 Germany DEU 0 829 0 \n", + "20 France FRA 0 0 0 \n", + "21 Greece GRC 0 0 0 \n", + "22 Latvia LVA 0 0 0 \n", + "23 Netherlands NLD 0 0 0 \n", + "27 Eritrea ERI 475 190 0 \n", + "28 Ethiopia ETH 37 178 0 \n", + "29 Somalia SOM 841 770 0 \n", + "30 Uganda UGA 9 0 0 \n", + "31 Egypt EGY 0 0 0 \n", + "32 Cameroon CMR 0 0 0 \n", + "33 Nigeria NGA 0 0 0 \n", + "37 Chile CHL 6 356 0 \n", + "38 Colombia COL 332 103 0 \n", + "39 Cuba CUB 15 0 0 \n", + "40 Brazil BRA 0 0 143 \n", + "41 Bolivia BOL 0 0 0 \n", + "42 Guatemala GTM 0 0 0 \n", + "43 Canada CAN 0 0 241 \n", + "44 USA USA 0 522 629 \n", + "45 Mexico MEX 0 0 0 \n", + "49 Afghanistan AFG 387 0 0 \n", + "50 Bangladesh BGD 44 0 0 \n", + "51 Philippines PHL 0 373 0 \n", + "52 India IND 0 175 760 \n", + "53 Iraq IRQ 1692 1705 0 \n", + "54 Iran IRN 230 626 44 \n", + "55 China CHN 36 498 479 \n", + "56 Japan JPN 0 0 152 \n", + "57 Pakistan PAK 0 0 35 \n", + "58 Lebanon LBN 97 277 0 \n", + "59 Sri Lanka LKA 5 85 0 \n", + "60 Syria SYR 178 427 32 \n", + "61 Thailand THA 0 2095 329 \n", + "62 Vietnam NaN 3 283 0 \n", + "63 Korea, Republic PRK 0 0 0 \n", + "67 Australia AUS 0 0 265 \n", + "\n", + " 2005_students 2005_adopted \n", + "2 0 0 \n", + "5 0 0 \n", + "6 0 0 \n", + "7 0 0 \n", + "9 0 0 \n", + "10 0 0 \n", + "11 168 41 \n", + "12 0 0 \n", + "13 207 0 \n", + "14 0 0 \n", + "15 0 0 \n", + "16 0 0 \n", + "17 0 0 \n", + "18 0 0 \n", + "19 0 0 \n", + "20 0 0 \n", + "21 0 0 \n", + "22 0 0 \n", + "23 0 0 \n", + "27 0 0 \n", + "28 60 43 \n", + "29 0 0 \n", + "30 0 0 \n", + "31 18 0 \n", + "32 329 0 \n", + "33 301 0 \n", + "37 0 0 \n", + "38 0 0 \n", + "39 0 0 \n", + "40 54 0 \n", + "41 0 10 \n", + "42 0 0 \n", + "43 363 0 \n", + "44 213 0 \n", + "45 168 0 \n", + "49 0 0 \n", + "50 307 0 \n", + "51 0 8 \n", + "52 592 38 \n", + "53 0 0 \n", + "54 266 0 \n", + "55 934 450 \n", + "56 151 0 \n", + "57 624 0 \n", + "58 0 0 \n", + "59 0 1 \n", + "60 0 0 \n", + "61 0 35 \n", + "62 0 86 \n", + "63 0 9 \n", + "67 0 0 " + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import plotly.graph_objs as go\n", + "\n", + "def clean(x):\n", + " try:\n", + " return int(x)\n", + " except:\n", + " return 0\n", + "\n", + "data['2005_refugees'] = data['2005_refugees'].apply(lambda x: clean(x))\n", + "data['2005_fam_reun'] = data['2005_fam_reun'].apply(lambda x: clean(x))\n", + "data['2005_labour'] = data['2005_labour'].apply(lambda x: clean(x))\n", + "data['2005_students'] = data['2005_students'].apply(lambda x: clean(x))\n", + "data['2005_adopted'] = data['2005_adopted'].apply(lambda x: clean(x))\n", + "total = data[data.Citizenship == 'TOTAL of which']\n", + "del total['Citizenship']\n", + "countries = data[data['Citizenship'].str.contains(\"which|off|which|ales\") == False]\n", + "countries\n" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ref = countries['2005_refugees']\n", + "fam = countries['2005_fam_reun']\n", + "labour = countries['2005_labour']\n", + "students = countries['2005_students']\n", + "adopt = countries['2005_adopted']\n", + "country_names = countries['Citizenship']\n", + "\n", + "df = pd.DataFrame({'x': country_names, 'y': ref, 'y2':fam, 'y3':labour, 'y4':students, 'y5':adopt})\n", + "df.head()\n", + "\n", + "dat = [\n", + " go.Bar(\n", + " x=df['x'], # assign x as the dataframe column 'x'\n", + " y=df['y'],\n", + " name='refugees'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y2'],\n", + " name='family reunited'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y3'],\n", + " name='labourers'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y4'],\n", + " name='students'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y5'],\n", + " name='adopted'\n", + " )\n", + "]\n", + "\n", + "layout = go.Layout(\n", + " barmode='stack',\n", + " title='Total Immigration by Country in 2005'\n", + ")\n", + "\n", + "fig = go.Figure(data=dat, layout=layout)\n", + "py.iplot(fig, filename='tot_country')" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kiki/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "\n" + ] + }, + { + "data": { + "text/html": [ + "
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CitizenshipCode2005_refugees2005_fam_reun2005_labour2005_students2005_adoptedTotal
2BulgariaBGR000000
5BosniaBIH197543000740
6KosovoKSV000000
7Serbia and MontenegroSRB105912620002321
9PolandPOL0634000634
10RomaniaROU430141400719
11RussiaRUS157700376168411442
12United KingdomGBR0641000641
13TurkeyTUR768868620701255
14UkraineUKR0054700547
15EstoniaEST000000
16CroatiaHRV0014000140
17LithuaniaLTU000000
18BelarusBLR00880088
19GermanyDEU0829000829
20FranceFRA000000
21GreeceGRC000000
22LatviaLVA000000
23NetherlandsNLD000000
27EritreaERI475190000665
28EthiopiaETH3717806043318
29SomaliaSOM8417700001611
30UgandaUGA900009
31EgyptEGY00018018
32CameroonCMR0003290329
33NigeriaNGA0003010301
37ChileCHL6356000362
38ColombiaCOL332103000435
39CubaCUB15000015
40BrazilBRA00143540197
41BoliviaBOL00001010
42GuatemalaGTM000000
43CanadaCAN002413630604
44USAUSA052262921301364
45MexicoMEX0001680168
49AfghanistanAFG3870000387
50BangladeshBGD44003070351
51PhilippinesPHL0373008381
52IndiaIND0175760592381565
53IraqIRQ169217050003397
54IranIRN2306264426601166
55ChinaCHN364984799344502397
56JapanJPN001521510303
57PakistanPAK00356240659
58LebanonLBN97277000374
59Sri LankaLKA58500191
60SyriaSYR1784273200637
61ThailandTHA020953290352459
62VietnamNaN32830086372
63Korea, RepublicPRK000099
67AustraliaAUS0026500265
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" + ], + "text/plain": [ + " Citizenship Code 2005_refugees 2005_fam_reun 2005_labour \\\n", + "2 Bulgaria BGR 0 0 0 \n", + "5 Bosnia BIH 197 543 0 \n", + "6 Kosovo KSV 0 0 0 \n", + "7 Serbia and Montenegro SRB 1059 1262 0 \n", + "9 Poland POL 0 634 0 \n", + "10 Romania ROU 4 301 414 \n", + "11 Russia RUS 157 700 376 \n", + "12 United Kingdom GBR 0 641 0 \n", + "13 Turkey TUR 76 886 86 \n", + "14 Ukraine UKR 0 0 547 \n", + "15 Estonia EST 0 0 0 \n", + "16 Croatia HRV 0 0 140 \n", + "17 Lithuania LTU 0 0 0 \n", + "18 Belarus BLR 0 0 88 \n", + "19 Germany DEU 0 829 0 \n", + "20 France FRA 0 0 0 \n", + "21 Greece GRC 0 0 0 \n", + "22 Latvia LVA 0 0 0 \n", + "23 Netherlands NLD 0 0 0 \n", + "27 Eritrea ERI 475 190 0 \n", + "28 Ethiopia ETH 37 178 0 \n", + "29 Somalia SOM 841 770 0 \n", + "30 Uganda UGA 9 0 0 \n", + "31 Egypt EGY 0 0 0 \n", + "32 Cameroon CMR 0 0 0 \n", + "33 Nigeria NGA 0 0 0 \n", + "37 Chile CHL 6 356 0 \n", + "38 Colombia COL 332 103 0 \n", + "39 Cuba CUB 15 0 0 \n", + "40 Brazil BRA 0 0 143 \n", + "41 Bolivia BOL 0 0 0 \n", + "42 Guatemala GTM 0 0 0 \n", + "43 Canada CAN 0 0 241 \n", + "44 USA USA 0 522 629 \n", + "45 Mexico MEX 0 0 0 \n", + "49 Afghanistan AFG 387 0 0 \n", + "50 Bangladesh BGD 44 0 0 \n", + "51 Philippines PHL 0 373 0 \n", + "52 India IND 0 175 760 \n", + "53 Iraq IRQ 1692 1705 0 \n", + "54 Iran IRN 230 626 44 \n", + "55 China CHN 36 498 479 \n", + "56 Japan JPN 0 0 152 \n", + "57 Pakistan PAK 0 0 35 \n", + "58 Lebanon LBN 97 277 0 \n", + "59 Sri Lanka LKA 5 85 0 \n", + "60 Syria SYR 178 427 32 \n", + "61 Thailand THA 0 2095 329 \n", + "62 Vietnam NaN 3 283 0 \n", + "63 Korea, Republic PRK 0 0 0 \n", + "67 Australia AUS 0 0 265 \n", + "\n", + " 2005_students 2005_adopted Total \n", + "2 0 0 0 \n", + "5 0 0 740 \n", + "6 0 0 0 \n", + "7 0 0 2321 \n", + "9 0 0 634 \n", + "10 0 0 719 \n", + "11 168 41 1442 \n", + "12 0 0 641 \n", + "13 207 0 1255 \n", + "14 0 0 547 \n", + "15 0 0 0 \n", + "16 0 0 140 \n", + "17 0 0 0 \n", + "18 0 0 88 \n", + "19 0 0 829 \n", + "20 0 0 0 \n", + "21 0 0 0 \n", + "22 0 0 0 \n", + "23 0 0 0 \n", + "27 0 0 665 \n", + "28 60 43 318 \n", + "29 0 0 1611 \n", + "30 0 0 9 \n", + "31 18 0 18 \n", + "32 329 0 329 \n", + "33 301 0 301 \n", + "37 0 0 362 \n", + "38 0 0 435 \n", + "39 0 0 15 \n", + "40 54 0 197 \n", + "41 0 10 10 \n", + "42 0 0 0 \n", + "43 363 0 604 \n", + "44 213 0 1364 \n", + "45 168 0 168 \n", + "49 0 0 387 \n", + "50 307 0 351 \n", + "51 0 8 381 \n", + "52 592 38 1565 \n", + "53 0 0 3397 \n", + "54 266 0 1166 \n", + "55 934 450 2397 \n", + "56 151 0 303 \n", + "57 624 0 659 \n", + "58 0 0 374 \n", + "59 0 1 91 \n", + "60 0 0 637 \n", + "61 0 35 2459 \n", + "62 0 86 372 \n", + "63 0 9 9 \n", + "67 0 0 265 " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries['Total'] = countries['2005_refugees']+countries['2005_fam_reun']+countries['2005_labour']+countries['2005_students']+countries['2005_adopted']\n", + "countries" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total = total.transpose().reset_index()\n", + "dat = [\n", + " go.Bar(\n", + " x=total['index'], # assign x as the dataframe column 'x'\n", + " y=total[73]\n", + " )\n", + "]\n", + "py.iplot(dat, filename='total_bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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2005_adopted2005_fam_reun2005_labour2005_refugees2005_studentsCODECOUNTRYCitizenshipCodeGDP (BILLIONS)Total
000000NaNNaNBulgariaBGRNaN0
1054301970NaNNaNBosniaBIHNaN740
200000NaNNaNKosovoKSVNaN0
301262010590NaNNaNSerbia and MontenegroSRBNaN2321
40634000NaNNaNPolandPOLNaN634
5030141440NaNNaNRomaniaROUNaN719
641700376157168NaNNaNRussiaRUSNaN1442
70641000NaNNaNUnited KingdomGBRNaN641
808868676207NaNNaNTurkeyTURNaN1255
90054700NaNNaNUkraineUKRNaN547
1000000NaNNaNEstoniaESTNaN0
110014000NaNNaNCroatiaHRVNaN140
1200000NaNNaNLithuaniaLTUNaN0
13008800NaNNaNBelarusBLRNaN88
140829000NaNNaNGermanyDEUNaN829
1500000NaNNaNFranceFRANaN0
1600000NaNNaNGreeceGRCNaN0
1700000NaNNaNLatviaLVANaN0
1800000NaNNaNNetherlandsNLDNaN0
19019004750NaNNaNEritreaERINaN665
204317803760NaNNaNEthiopiaETHNaN318
21077008410NaNNaNSomaliaSOMNaN1611
2200090NaNNaNUgandaUGANaN9
23000018NaNNaNEgyptEGYNaN18
240000329NaNNaNCameroonCMRNaN329
250000301NaNNaNNigeriaNGANaN301
260356060NaNNaNChileCHLNaN362
27010303320NaNNaNColombiaCOLNaN435
28000150NaNNaNCubaCUBNaN15
2900143054NaNNaNBrazilBRANaN197
....................................
192NaNNaNNaNNaNNaNSVNSloveniaSloveniaSVN49.930
193NaNNaNNaNNaNNaNSLBSolomon IslandsSolomon IslandsSLB1.160
194NaNNaNNaNNaNNaNZAFSouth AfricaSouth AfricaZAF341.200
195NaNNaNNaNNaNNaNSSDSouth SudanSouth SudanSSD11.890
196NaNNaNNaNNaNNaNESPSpainSpainESP1400.000
197NaNNaNNaNNaNNaNSDNSudanSudanSDN70.030
198NaNNaNNaNNaNNaNSURSurinameSurinameSUR5.270
199NaNNaNNaNNaNNaNSWZSwazilandSwazilandSWZ3.840
200NaNNaNNaNNaNNaNSWESwedenSwedenSWE559.100
201NaNNaNNaNNaNNaNCHESwitzerlandSwitzerlandCHE679.000
202NaNNaNNaNNaNNaNTWNTaiwanTaiwanTWN529.500
203NaNNaNNaNNaNNaNTJKTajikistanTajikistanTJK9.160
204NaNNaNNaNNaNNaNTZATanzaniaTanzaniaTZA36.620
205NaNNaNNaNNaNNaNTLSTimor-LesteTimor-LesteTLS4.510
206NaNNaNNaNNaNNaNTGOTogoTogoTGO4.840
207NaNNaNNaNNaNNaNTONTongaTongaTON0.490
208NaNNaNNaNNaNNaNTTOTrinidad and TobagoTrinidad and TobagoTTO29.630
209NaNNaNNaNNaNNaNTUNTunisiaTunisiaTUN49.120
210NaNNaNNaNNaNNaNTKMTurkmenistanTurkmenistanTKM43.500
211NaNNaNNaNNaNNaNTUVTuvaluTuvaluTUV0.040
212NaNNaNNaNNaNNaNAREUnited Arab EmiratesUnited Arab EmiratesARE416.400
213NaNNaNNaNNaNNaNURYUruguayUruguayURY55.600
214NaNNaNNaNNaNNaNUZBUzbekistanUzbekistanUZB63.080
215NaNNaNNaNNaNNaNVUTVanuatuVanuatuVUT0.820
216NaNNaNNaNNaNNaNVENVenezuelaVenezuelaVEN209.200
217NaNNaNNaNNaNNaNVNMVietnamVietnamVNM187.800
218NaNNaNNaNNaNNaNWBGWest BankWest BankWBG6.640
219NaNNaNNaNNaNNaNYEMYemenYemenYEM45.450
220NaNNaNNaNNaNNaNZMBZambiaZambiaZMB25.610
221NaNNaNNaNNaNNaNZWEZimbabweZimbabweZWE13.740
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222 rows × 11 columns

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NaN NaN \n", + "198 NaN NaN NaN NaN NaN \n", + "199 NaN NaN NaN NaN NaN \n", + "200 NaN NaN NaN NaN NaN \n", + "201 NaN NaN NaN NaN NaN \n", + "202 NaN NaN NaN NaN NaN \n", + "203 NaN NaN NaN NaN NaN \n", + "204 NaN NaN NaN NaN NaN \n", + "205 NaN NaN NaN NaN NaN \n", + "206 NaN NaN NaN NaN NaN \n", + "207 NaN NaN NaN NaN NaN \n", + "208 NaN NaN NaN NaN NaN \n", + "209 NaN NaN NaN NaN NaN \n", + "210 NaN NaN NaN NaN NaN \n", + "211 NaN NaN NaN NaN NaN \n", + "212 NaN NaN NaN NaN NaN \n", + "213 NaN NaN NaN NaN NaN \n", + "214 NaN NaN NaN NaN NaN \n", + "215 NaN NaN NaN NaN NaN \n", + "216 NaN NaN NaN NaN NaN \n", + "217 NaN NaN NaN NaN NaN \n", + "218 NaN NaN NaN NaN NaN \n", + "219 NaN NaN NaN NaN NaN \n", + "220 NaN NaN NaN NaN NaN \n", + "221 NaN NaN NaN NaN NaN \n", + "\n", + " CODE COUNTRY Citizenship Code GDP (BILLIONS) \\\n", + "0 NaN NaN Bulgaria BGR NaN \n", + "1 NaN NaN Bosnia BIH NaN \n", + "2 NaN NaN Kosovo KSV NaN \n", + "3 NaN NaN Serbia and Montenegro SRB NaN \n", + "4 NaN NaN Poland POL NaN \n", + "5 NaN NaN Romania ROU NaN \n", + "6 NaN NaN Russia RUS NaN \n", + "7 NaN NaN United Kingdom GBR NaN \n", + "8 NaN NaN Turkey TUR NaN \n", + "9 NaN NaN Ukraine UKR NaN \n", + "10 NaN NaN Estonia EST NaN \n", + "11 NaN NaN Croatia HRV NaN \n", + "12 NaN NaN Lithuania LTU NaN \n", + "13 NaN NaN Belarus BLR NaN \n", + "14 NaN NaN Germany DEU NaN \n", + "15 NaN NaN France FRA NaN \n", + "16 NaN NaN Greece GRC NaN \n", + "17 NaN NaN Latvia LVA NaN \n", + "18 NaN NaN Netherlands NLD NaN \n", + "19 NaN NaN Eritrea ERI NaN \n", + "20 NaN NaN Ethiopia ETH NaN \n", + "21 NaN NaN Somalia SOM NaN \n", + "22 NaN NaN Uganda UGA NaN \n", + "23 NaN NaN Egypt EGY NaN \n", + "24 NaN NaN Cameroon CMR NaN \n", + "25 NaN NaN Nigeria NGA NaN \n", + "26 NaN NaN Chile CHL NaN \n", + "27 NaN NaN Colombia COL NaN \n", + "28 NaN NaN Cuba CUB NaN \n", + "29 NaN NaN Brazil BRA NaN \n", + ".. ... ... ... ... ... \n", + "192 SVN Slovenia Slovenia SVN 49.93 \n", + "193 SLB Solomon Islands Solomon Islands SLB 1.16 \n", + "194 ZAF South Africa South Africa ZAF 341.20 \n", + "195 SSD South Sudan South Sudan SSD 11.89 \n", + "196 ESP Spain Spain ESP 1400.00 \n", + "197 SDN Sudan Sudan SDN 70.03 \n", + "198 SUR Suriname Suriname SUR 5.27 \n", + "199 SWZ Swaziland Swaziland SWZ 3.84 \n", + "200 SWE Sweden Sweden SWE 559.10 \n", + "201 CHE Switzerland Switzerland CHE 679.00 \n", + "202 TWN Taiwan Taiwan TWN 529.50 \n", + "203 TJK Tajikistan Tajikistan TJK 9.16 \n", + "204 TZA Tanzania Tanzania TZA 36.62 \n", + "205 TLS Timor-Leste Timor-Leste TLS 4.51 \n", + "206 TGO Togo Togo TGO 4.84 \n", + "207 TON Tonga Tonga TON 0.49 \n", + "208 TTO Trinidad and Tobago Trinidad and Tobago TTO 29.63 \n", + "209 TUN Tunisia Tunisia TUN 49.12 \n", + "210 TKM Turkmenistan Turkmenistan TKM 43.50 \n", + "211 TUV Tuvalu Tuvalu TUV 0.04 \n", + "212 ARE United Arab Emirates United Arab Emirates ARE 416.40 \n", + "213 URY Uruguay Uruguay URY 55.60 \n", + "214 UZB Uzbekistan Uzbekistan UZB 63.08 \n", + "215 VUT Vanuatu Vanuatu VUT 0.82 \n", + "216 VEN Venezuela Venezuela VEN 209.20 \n", + "217 VNM Vietnam Vietnam VNM 187.80 \n", + "218 WBG West Bank West Bank WBG 6.64 \n", + "219 YEM Yemen Yemen YEM 45.45 \n", + "220 ZMB Zambia Zambia ZMB 25.61 \n", + "221 ZWE Zimbabwe Zimbabwe ZWE 13.74 \n", + "\n", + " Total \n", + "0 0 \n", + "1 740 \n", + "2 0 \n", + "3 2321 \n", + "4 634 \n", + "5 719 \n", + "6 1442 \n", + "7 641 \n", + "8 1255 \n", + "9 547 \n", + "10 0 \n", + "11 140 \n", + "12 0 \n", + "13 88 \n", + "14 829 \n", + "15 0 \n", + "16 0 \n", + "17 0 \n", + "18 0 \n", + "19 665 \n", + "20 318 \n", + "21 1611 \n", + "22 9 \n", + "23 18 \n", + "24 329 \n", + "25 301 \n", + "26 362 \n", + "27 435 \n", + "28 15 \n", + "29 197 \n", + ".. ... \n", + "192 0 \n", + "193 0 \n", + "194 0 \n", + "195 0 \n", + "196 0 \n", + "197 0 \n", + "198 0 \n", + "199 0 \n", + "200 0 \n", + "201 0 \n", + "202 0 \n", + "203 0 \n", + "204 0 \n", + "205 0 \n", + "206 0 \n", + "207 0 \n", + "208 0 \n", + "209 0 \n", + "210 0 \n", + "211 0 \n", + "212 0 \n", + "213 0 \n", + "214 0 \n", + "215 0 \n", + "216 0 \n", + "217 0 \n", + "218 0 \n", + "219 0 \n", + "220 0 \n", + "221 0 \n", + "\n", + "[222 rows x 11 columns]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')\n", + "df['Citizenship'] = df['COUNTRY']\n", + "df['Code'] = df['CODE']\n", + "world = pd.concat([countries,df]).drop_duplicates(['Code']).reset_index(drop=True)\n", + "world['Total'].fillna(0,inplace=True)\n", + "world" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "PlotlyError", + "evalue": "Request throttled. You've created/updated more charts than your allowed limit of 50/day. You may either wait one day or upgrade your account. Visit https://plot.ly/settings/subscription/ to upgrade.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mPlotlyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlayout\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 35\u001b[0m \u001b[1;31m# url = py.plot(fig, filename='d3-world-map')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/home/kiki/anaconda/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'auto_open'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 150\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/home/kiki/anaconda/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[0mplot_options\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_plot_option_logic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_send_to_plotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/home/kiki/anaconda/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36m_send_to_plotly\u001b[1;34m(figure, **plot_options)\u001b[0m\n\u001b[0;32m 1402\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1403\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'error'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1404\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1405\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1406\u001b[0m \u001b[1;31m# Check if the url needs a secret key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mPlotlyError\u001b[0m: Request throttled. You've created/updated more charts than your allowed limit of 50/day. You may either wait one day or upgrade your account. Visit https://plot.ly/settings/subscription/ to upgrade." + ] + } + ], + "source": [ + "data = [ dict(\n", + " type = 'choropleth',\n", + " locations = countries['Code'],\n", + " z = countries['Total'],\n", + " text = world['Citizenship'],\n", + " colorscale = [[0,\"rgb(154, 7, 31)\"],[0.35,\"rgb(201, 58, 6)\"],[0.5,\"rgb(239, 125, 7)\"],\\\n", + " [0.6,\"rgb(255, 204, 103)\"],[0.7,\"rgb(255, 240, 75)\"],[1,\"rgb(255, 251, 235)\"]],\n", + " autocolorscale = False,\n", + " reversescale = True,\n", + " marker = dict(\n", + " line = dict (\n", + " color = 'rgb(180,180,180)',\n", + " width = 0.5\n", + " )\n", + " ),\n", + " zmin = 0,\n", + " colorbar = dict(\n", + " title = '# of Immigrants'\n", + " ),\n", + " ) ]\n", + "\n", + "layout = dict(\n", + " title = 'Immigration to Sweden in 2005',\n", + " geo = dict(\n", + " showframe = False,\n", + " showcoastlines = False,\n", + " projection = dict(\n", + " type = 'Equirectangular'\n", + " )\n", + " )\n", + ")\n", + "\n", + "fig = dict( data=data, layout=layout )\n", + "py.iplot(fig)\n", + "# url = py.plot(fig, filename='d3-world-map')" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/2005_immigration_data.csv b/2005_immigration_data.csv index 1375f50..32110b5 100644 --- a/2005_immigration_data.csv +++ b/2005_immigration_data.csv @@ -1,77 +1,77 @@ -Citizenship 2005_refugees 2005_fam_reun 2005_labour 2005_students 2005_adopted - -EUROPE of which 1648 8366 2085 752 86 -Bulgaria - - -Former Yugoslavia there off: 1299 2111 -Bosnia H. 197 543 -Kosovo 5) .. -Serbia and Montenegro 6) 1059 1262 - -Poland - 634 * * - -Romania 4 301 414 - -Russia 157 700 376 168 41 -United Kingdom 641 * -Turkey 76 886 86 207 -Ukraine 547 -Estonia * * -Croatia 140 -Lithuania * * -Belarus 88 -Germany 829 * -France * -Greece * -Latvia * -Netherlands * - - -AFRICA of which 1991 2864 259 1155 62 -Eritrea 475 190 -Ethiopia 37 178 60 43 -Somalia 841 770 -Uganda 9 -Egypt 18 -Cameroon 329 -Nigeria 301 - - -AMERICA of which 428 2238 1175 998 11 -Chile 6 356 - -Colombia 332 103 - -Cuba 15 -Brazil 143 54 - -Bolivia 10 -Guatemala - -Canada 241 363 -USA 522 629 213 -Mexico 168 - - -ASIA of which 3711 7733 2136 3636 645 -Afghanistan 387 -Bangladesh 44 307 -Philippines 373 8 -India 175 760 592 38 -Iraq 1692 1705 -Iran 230 626 44 266 -China 36 498 479 934 450 -Japan 152 151 -Pakistan 35 624 -Lebanon 97 277 -Sri Lanka 5 85 1 -Syria 178 427 32 -Thailand 2095 329 35 -Vietnam 3 283 86 -Korea, Republic 9 - - -OCEANIA of which 235 316 280 -Australia 265 - - -OTHERS of which 1081 472 14 16 1 - - -TOTAL of which 8859 21908 5985 6837 805 -Females 4185 12511 1512 2367 583 -Males 4674 9397 4473 4470 222 +Citizenship Code 2005_refugees 2005_fam_reun 2005_labour 2005_students 2005_adopted + +EUROPE of which 1648 8366 2085 752 86 +Bulgaria BGR - + +Former Yugoslavia there off: 1299 2111 +Bosnia BIH 197 543 +Kosovo KSV .. +Serbia and Montenegro SRB 1059 1262 + +Poland POL - 634 * * - +Romania ROU 4 301 414 - +Russia RUS 157 700 376 168 41 +United Kingdom GBR 641 * +Turkey TUR 76 886 86 207 +Ukraine UKR 547 +Estonia EST * * +Croatia HRV 140 +Lithuania LTU * * +Belarus BLR 88 +Germany DEU 829 * +France FRA * +Greece GRC * +Latvia LVA * +Netherlands NLD * + + +AFRICA of which 1991 2864 259 1155 62 +Eritrea ERI 475 190 +Ethiopia ETH 37 178 60 43 +Somalia SOM 841 770 +Uganda UGA 9 +Egypt EGY 18 +Cameroon CMR 329 +Nigeria NGA 301 + + +AMERICA of which 428 2238 1175 998 11 +Chile CHL 6 356 - +Colombia COL 332 103 - +Cuba CUB 15 +Brazil BRA 143 54 - +Bolivia BOL 10 +Guatemala GTM - +Canada CAN 241 363 +USA USA 522 629 213 +Mexico MEX 168 + + +ASIA of which 3711 7733 2136 3636 645 +Afghanistan AFG 387 +Bangladesh BGD 44 307 +Philippines PHL 373 8 +India IND 175 760 592 38 +Iraq IRQ 1692 1705 +Iran IRN 230 626 44 266 +China CHN 36 498 479 934 450 +Japan JPN 152 151 +Pakistan PAK 35 624 +Lebanon LBN 97 277 +Sri Lanka LKA 5 85 1 +Syria SYR 178 427 32 +Thailand THA 2095 329 35 +Vietnam 3 283 86 +Korea, Republic PRK 9 + + +OCEANIA of which 235 316 280 +Australia AUS 265 + + +OTHERS of which 1081 472 14 16 1 + + +TOTAL of which 8859 21908 5985 6837 805 +Females 4185 12511 1512 2367 583 +Males 4674 9397 4473 4470 222 diff --git a/immigration.html b/immigration.html index bddc91e..639a7e5 100644 --- a/immigration.html +++ b/immigration.html @@ -4,6 +4,8 @@ Basic Map + diff --git a/immigration_plots.ipynb b/immigration_plots.ipynb index ce30951..971b638 100644 --- a/immigration_plots.ipynb +++ b/immigration_plots.ipynb @@ -82,7 +82,7 @@ { "data": { "text/html": [ - "
" + "
" ], "text/plain": [ "" @@ -109,7 +109,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 2, "metadata": { "collapsed": false }, @@ -134,7 +134,7 @@ }, { "cell_type": "code", - "execution_count": 92, + "execution_count": 34, "metadata": { "collapsed": false }, @@ -148,6 +148,7 @@ " \n", " \n", " Citizenship\n", + " Code\n", " 2005_refugees\n", " 2005_fam_reun\n", " 2005_labour\n", @@ -164,10 +165,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", " \n", " \n", " 1\n", " EUROPE of which\n", + " NaN\n", " 1648\n", " 8366\n", " 2085\n", @@ -177,6 +180,7 @@ " \n", " 2\n", " Bulgaria\n", + " BGR\n", " -\n", " NaN\n", " NaN\n", @@ -191,10 +195,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", " \n", " \n", " 4\n", " Former Yugoslavia there off:\n", + " NaN\n", " 1299\n", " 2111\n", " NaN\n", @@ -203,7 +209,8 @@ " \n", " \n", " 5\n", - " Bosnia H.\n", + " Bosnia\n", + " BIH\n", " 197\n", " 543\n", " NaN\n", @@ -212,7 +219,8 @@ " \n", " \n", " 6\n", - " Kosovo 5)\n", + " Kosovo\n", + " KSV\n", " ..\n", " NaN\n", " NaN\n", @@ -221,7 +229,8 @@ " \n", " \n", " 7\n", - " Serbia and Montenegro 6)\n", + " Serbia and Montenegro\n", + " SRB\n", " 1059\n", " 1262\n", " NaN\n", @@ -236,10 +245,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", " \n", " \n", " 9\n", " Poland\n", + " POL\n", " -\n", " 634\n", " *\n", @@ -249,6 +260,7 @@ " \n", " 10\n", " Romania\n", + " ROU\n", " 4\n", " 301\n", " 414\n", @@ -258,6 +270,7 @@ " \n", " 11\n", " Russia\n", + " RUS\n", " 157\n", " 700\n", " 376\n", @@ -267,6 +280,7 @@ " \n", " 12\n", " United Kingdom\n", + " GBR\n", " NaN\n", " 641\n", " NaN\n", @@ -276,6 +290,7 @@ " \n", " 13\n", " Turkey\n", + " TUR\n", " 76\n", " 886\n", " 86\n", @@ -285,6 +300,7 @@ " \n", " 14\n", " Ukraine\n", + " UKR\n", " NaN\n", " NaN\n", " 547\n", @@ -294,6 +310,7 @@ " \n", " 15\n", " Estonia\n", + " EST\n", " NaN\n", " NaN\n", " *\n", @@ -303,6 +320,7 @@ " \n", " 16\n", " Croatia\n", + " HRV\n", " NaN\n", " NaN\n", " 140\n", @@ -312,6 +330,7 @@ " \n", " 17\n", " Lithuania\n", + " LTU\n", " NaN\n", " NaN\n", " *\n", @@ -321,6 +340,7 @@ " \n", " 18\n", " Belarus\n", + " BLR\n", " NaN\n", " NaN\n", " 88\n", @@ -330,6 +350,7 @@ " \n", " 19\n", " Germany\n", + " DEU\n", " NaN\n", " 829\n", " NaN\n", @@ -339,6 +360,7 @@ " \n", " 20\n", " France\n", + " FRA\n", " NaN\n", " NaN\n", " NaN\n", @@ -348,6 +370,7 @@ " \n", " 21\n", " Greece\n", + " GRC\n", " NaN\n", " NaN\n", " NaN\n", @@ -357,6 +380,7 @@ " \n", " 22\n", " Latvia\n", + " LVA\n", " NaN\n", " NaN\n", " NaN\n", @@ -366,6 +390,7 @@ " \n", " 23\n", " Netherlands\n", + " NLD\n", " NaN\n", " NaN\n", " NaN\n", @@ -380,6 +405,7 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", " \n", " \n", " 25\n", @@ -389,10 +415,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", " \n", " \n", " 26\n", " AFRICA of which\n", + " NaN\n", " 1991\n", " 2864\n", " 259\n", @@ -402,6 +430,7 @@ " \n", " 27\n", " Eritrea\n", + " ERI\n", " 475\n", " 190\n", " NaN\n", @@ -411,6 +440,7 @@ " \n", " 28\n", " Ethiopia\n", + " ETH\n", " 37\n", " 178\n", " NaN\n", @@ -420,6 +450,7 @@ " \n", " 29\n", " Somalia\n", + " SOM\n", " 841\n", " 770\n", " NaN\n", @@ -434,6 +465,7 @@ " ...\n", " ...\n", " ...\n", + " ...\n", " \n", " \n", " 46\n", @@ -443,6 +475,7 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", " \n", " \n", " 47\n", @@ -452,10 +485,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", " \n", " \n", " 48\n", " ASIA of which\n", + " NaN\n", " 3711\n", " 7733\n", " 2136\n", @@ -465,6 +500,7 @@ " \n", " 49\n", " Afghanistan\n", + " AFG\n", " 387\n", " NaN\n", " NaN\n", @@ -474,6 +510,7 @@ " \n", " 50\n", " Bangladesh\n", + " BGD\n", " 44\n", " NaN\n", " NaN\n", @@ -483,6 +520,7 @@ " \n", " 51\n", " Philippines\n", + " PHL\n", " NaN\n", " 373\n", " NaN\n", @@ -492,6 +530,7 @@ " \n", " 52\n", " India\n", + " IND\n", " NaN\n", " 175\n", " 760\n", @@ -501,6 +540,7 @@ " \n", " 53\n", " Iraq\n", + " IRQ\n", " 1692\n", " 1705\n", " NaN\n", @@ -510,6 +550,7 @@ " \n", " 54\n", " Iran\n", + " IRN\n", " 230\n", " 626\n", " 44\n", @@ -519,6 +560,7 @@ " \n", " 55\n", " China\n", + " CHN\n", " 36\n", " 498\n", " 479\n", @@ -528,6 +570,7 @@ " \n", " 56\n", " Japan\n", + " JPN\n", " NaN\n", " NaN\n", " 152\n", @@ -537,6 +580,7 @@ " \n", " 57\n", " Pakistan\n", + " PAK\n", " NaN\n", " NaN\n", " 35\n", @@ -546,6 +590,7 @@ " \n", " 58\n", " Lebanon\n", + " LBN\n", " 97\n", " 277\n", " NaN\n", @@ -555,6 +600,7 @@ " \n", " 59\n", " Sri Lanka\n", + " LKA\n", " 5\n", " 85\n", " NaN\n", @@ -564,6 +610,7 @@ " \n", " 60\n", " Syria\n", + " SYR\n", " 178\n", " 427\n", " 32\n", @@ -573,6 +620,7 @@ " \n", " 61\n", " Thailand\n", + " THA\n", " NaN\n", " 2095\n", " 329\n", @@ -582,6 +630,7 @@ " \n", " 62\n", " Vietnam\n", + " NaN\n", " 3\n", " 283\n", " NaN\n", @@ -591,6 +640,7 @@ " \n", " 63\n", " Korea, Republic\n", + " PRK\n", " NaN\n", " NaN\n", " NaN\n", @@ -605,6 +655,7 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", " \n", " \n", " 65\n", @@ -614,11 +665,13 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", " \n", " \n", " 66\n", " OCEANIA of which\n", " NaN\n", + " NaN\n", " 235\n", " 316\n", " 280\n", @@ -627,6 +680,7 @@ " \n", " 67\n", " Australia\n", + " AUS\n", " NaN\n", " NaN\n", " 265\n", @@ -641,6 +695,7 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", " \n", " \n", " 69\n", @@ -650,10 +705,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", " \n", " \n", " 70\n", " OTHERS of which\n", + " NaN\n", " 1081\n", " 472\n", " 14\n", @@ -668,6 +725,7 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", " \n", " \n", " 72\n", @@ -677,10 +735,12 @@ " NaN\n", " NaN\n", " NaN\n", + " NaN\n", " \n", " \n", " 73\n", " TOTAL of which\n", + " NaN\n", " 8859\n", " 21908\n", " 5985\n", @@ -690,6 +750,7 @@ " \n", " 74\n", " Females\n", + " NaN\n", " 4185\n", " 12511\n", " 1512\n", @@ -699,6 +760,7 @@ " \n", " 75\n", " Males\n", + " NaN\n", " 4674\n", " 9397\n", " 4473\n", @@ -707,140 +769,140 @@ " \n", " \n", "\n", - "

76 rows × 6 columns

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76 rows × 7 columns

\n", "" ], "text/plain": [ - " Citizenship 2005_refugees 2005_fam_reun 2005_labour \\\n", - "0 NaN NaN NaN NaN \n", - "1 EUROPE of which 1648 8366 2085 \n", - "2 Bulgaria - NaN NaN \n", - "3 NaN NaN NaN NaN \n", - "4 Former Yugoslavia there off: 1299 2111 NaN \n", - "5 Bosnia H. 197 543 NaN \n", - "6 Kosovo 5) .. NaN NaN \n", - "7 Serbia and Montenegro 6) 1059 1262 NaN \n", - "8 NaN NaN NaN NaN \n", - "9 Poland - 634 * \n", - "10 Romania 4 301 414 \n", - "11 Russia 157 700 376 \n", - "12 United Kingdom NaN 641 NaN \n", - "13 Turkey 76 886 86 \n", - "14 Ukraine NaN NaN 547 \n", - "15 Estonia NaN NaN * \n", - "16 Croatia NaN NaN 140 \n", - "17 Lithuania NaN NaN * \n", - "18 Belarus NaN NaN 88 \n", - "19 Germany NaN 829 NaN \n", - "20 France NaN NaN NaN \n", - "21 Greece NaN NaN NaN \n", - "22 Latvia NaN NaN NaN \n", - "23 Netherlands NaN NaN NaN \n", - "24 NaN NaN NaN NaN \n", - "25 NaN NaN NaN NaN \n", - "26 AFRICA of which 1991 2864 259 \n", - "27 Eritrea 475 190 NaN \n", - "28 Ethiopia 37 178 NaN \n", - "29 Somalia 841 770 NaN \n", - ".. ... ... ... ... \n", - "46 NaN NaN NaN NaN \n", - "47 NaN NaN NaN NaN \n", - "48 ASIA of which 3711 7733 2136 \n", - "49 Afghanistan 387 NaN NaN \n", - "50 Bangladesh 44 NaN NaN \n", - "51 Philippines NaN 373 NaN \n", - "52 India NaN 175 760 \n", - "53 Iraq 1692 1705 NaN \n", - "54 Iran 230 626 44 \n", - "55 China 36 498 479 \n", - "56 Japan NaN NaN 152 \n", - "57 Pakistan NaN NaN 35 \n", - "58 Lebanon 97 277 NaN \n", - "59 Sri Lanka 5 85 NaN \n", - "60 Syria 178 427 32 \n", - "61 Thailand NaN 2095 329 \n", - "62 Vietnam 3 283 NaN \n", - "63 Korea, Republic NaN NaN NaN \n", - "64 NaN NaN NaN NaN \n", - "65 NaN NaN NaN NaN \n", - "66 OCEANIA of which NaN 235 316 \n", - "67 Australia NaN NaN 265 \n", - "68 NaN NaN NaN NaN \n", - "69 NaN NaN NaN NaN \n", - "70 OTHERS of which 1081 472 14 \n", - "71 NaN NaN NaN NaN \n", - "72 NaN NaN NaN NaN \n", - "73 TOTAL of which 8859 21908 5985 \n", - "74 Females 4185 12511 1512 \n", - "75 Males 4674 9397 4473 \n", + " Citizenship Code 2005_refugees 2005_fam_reun \\\n", + "0 NaN NaN NaN NaN \n", + "1 EUROPE of which NaN 1648 8366 \n", + "2 Bulgaria BGR - NaN \n", + "3 NaN NaN NaN NaN \n", + "4 Former Yugoslavia there off: NaN 1299 2111 \n", + "5 Bosnia BIH 197 543 \n", + "6 Kosovo KSV .. NaN \n", + "7 Serbia and Montenegro SRB 1059 1262 \n", + "8 NaN NaN NaN NaN \n", + "9 Poland POL - 634 \n", + "10 Romania ROU 4 301 \n", + "11 Russia RUS 157 700 \n", + "12 United Kingdom GBR NaN 641 \n", + "13 Turkey TUR 76 886 \n", + "14 Ukraine UKR NaN NaN \n", + "15 Estonia EST NaN NaN \n", + "16 Croatia HRV NaN NaN \n", + "17 Lithuania LTU NaN NaN \n", + "18 Belarus BLR NaN NaN \n", + "19 Germany DEU NaN 829 \n", + "20 France FRA NaN NaN \n", + "21 Greece GRC NaN NaN \n", + "22 Latvia LVA NaN NaN \n", + "23 Netherlands NLD NaN NaN \n", + "24 NaN NaN NaN NaN \n", + "25 NaN NaN NaN NaN \n", + "26 AFRICA of which NaN 1991 2864 \n", + "27 Eritrea ERI 475 190 \n", + "28 Ethiopia ETH 37 178 \n", + "29 Somalia SOM 841 770 \n", + ".. ... ... ... ... \n", + "46 NaN NaN NaN NaN \n", + "47 NaN NaN NaN NaN \n", + "48 ASIA of which NaN 3711 7733 \n", + "49 Afghanistan AFG 387 NaN \n", + "50 Bangladesh BGD 44 NaN \n", + "51 Philippines PHL NaN 373 \n", + "52 India IND NaN 175 \n", + "53 Iraq IRQ 1692 1705 \n", + "54 Iran IRN 230 626 \n", + "55 China CHN 36 498 \n", + "56 Japan JPN NaN NaN \n", + "57 Pakistan PAK NaN NaN \n", + "58 Lebanon LBN 97 277 \n", + "59 Sri Lanka LKA 5 85 \n", + "60 Syria SYR 178 427 \n", + "61 Thailand THA NaN 2095 \n", + "62 Vietnam NaN 3 283 \n", + "63 Korea, Republic PRK NaN NaN \n", + "64 NaN NaN NaN NaN \n", + "65 NaN NaN NaN NaN \n", + "66 OCEANIA of which NaN NaN 235 \n", + "67 Australia AUS NaN NaN \n", + "68 NaN NaN NaN NaN \n", + "69 NaN NaN NaN NaN \n", + "70 OTHERS of which NaN 1081 472 \n", + "71 NaN NaN NaN NaN \n", + "72 NaN NaN NaN NaN \n", + "73 TOTAL of which NaN 8859 21908 \n", + "74 Females NaN 4185 12511 \n", + "75 Males NaN 4674 9397 \n", "\n", - " 2005_students 2005_adopted \n", - "0 NaN NaN \n", - "1 752 86 \n", - "2 NaN NaN \n", - "3 NaN NaN \n", - "4 NaN NaN \n", - "5 NaN NaN \n", - "6 NaN NaN \n", - "7 NaN NaN \n", - "8 NaN NaN \n", - "9 * - \n", - "10 NaN - \n", - "11 168 41 \n", - "12 * NaN \n", - "13 207 NaN \n", - "14 NaN NaN \n", - "15 * NaN \n", - "16 NaN NaN \n", - "17 * NaN \n", - "18 NaN NaN \n", - "19 * NaN \n", - "20 * NaN \n", - "21 * NaN \n", - "22 * NaN \n", - "23 * NaN \n", - "24 NaN NaN \n", - "25 NaN NaN \n", - "26 1155 62 \n", - "27 NaN NaN \n", - "28 60 43 \n", - "29 NaN NaN \n", - ".. ... ... \n", - "46 NaN NaN \n", - "47 NaN NaN \n", - "48 3636 645 \n", - "49 NaN NaN \n", - "50 307 NaN \n", - "51 NaN 8 \n", - "52 592 38 \n", - "53 NaN NaN \n", - "54 266 NaN \n", - "55 934 450 \n", - "56 151 NaN \n", - "57 624 NaN \n", - "58 NaN NaN \n", - "59 NaN 1 \n", - "60 NaN NaN \n", - "61 NaN 35 \n", - "62 NaN 86 \n", - "63 NaN 9 \n", - "64 NaN NaN \n", - "65 NaN NaN \n", - "66 280 NaN \n", - "67 NaN NaN \n", - "68 NaN NaN \n", - "69 NaN NaN \n", - "70 16 1 \n", - "71 NaN NaN \n", - "72 NaN NaN \n", - "73 6837 805 \n", - "74 2367 583 \n", - "75 4470 222 \n", + " 2005_labour 2005_students 2005_adopted \n", + "0 NaN NaN NaN \n", + "1 2085 752 86 \n", + "2 NaN NaN NaN \n", + "3 NaN NaN NaN \n", + "4 NaN NaN NaN \n", + "5 NaN NaN NaN \n", + "6 NaN NaN NaN \n", + "7 NaN NaN NaN \n", + "8 NaN NaN NaN \n", + "9 * * - \n", + "10 414 NaN - \n", + "11 376 168 41 \n", + "12 NaN * NaN \n", + "13 86 207 NaN \n", + "14 547 NaN NaN \n", + "15 * * NaN \n", + "16 140 NaN NaN \n", + "17 * * NaN \n", + "18 88 NaN NaN \n", + "19 NaN * NaN \n", + "20 NaN * NaN \n", + "21 NaN * NaN \n", + "22 NaN * NaN \n", + "23 NaN * NaN \n", + "24 NaN NaN NaN \n", + "25 NaN NaN NaN \n", + "26 259 1155 62 \n", + "27 NaN NaN NaN \n", + "28 NaN 60 43 \n", + "29 NaN NaN NaN \n", + ".. ... ... ... \n", + "46 NaN NaN NaN \n", + "47 NaN NaN NaN \n", + "48 2136 3636 645 \n", + "49 NaN NaN NaN \n", + "50 NaN 307 NaN \n", + "51 NaN NaN 8 \n", + "52 760 592 38 \n", + "53 NaN NaN NaN \n", + "54 44 266 NaN \n", + "55 479 934 450 \n", + "56 152 151 NaN \n", + "57 35 624 NaN \n", + "58 NaN NaN NaN \n", + "59 NaN NaN 1 \n", + "60 32 NaN NaN \n", + "61 329 NaN 35 \n", + "62 NaN NaN 86 \n", + "63 NaN NaN 9 \n", + "64 NaN NaN NaN \n", + "65 NaN NaN NaN \n", + "66 316 280 NaN \n", + "67 265 NaN NaN \n", + "68 NaN NaN NaN \n", + "69 NaN NaN NaN \n", + "70 14 16 1 \n", + "71 NaN NaN NaN \n", + "72 NaN NaN NaN \n", + "73 5985 6837 805 \n", + "74 1512 2367 583 \n", + "75 4473 4470 222 \n", "\n", - "[76 rows x 6 columns]" + "[76 rows x 7 columns]" ] }, - "execution_count": 92, + "execution_count": 34, "metadata": {}, "output_type": "execute_result" } @@ -852,7 +914,7 @@ }, { "cell_type": "code", - "execution_count": 144, + "execution_count": 35, "metadata": { "collapsed": false }, @@ -866,6 +928,7 @@ " \n", " \n", " Citizenship\n", + " Code\n", " 2005_refugees\n", " 2005_fam_reun\n", " 2005_labour\n", @@ -877,6 +940,7 @@ " \n", " 2\n", " Bulgaria\n", + " BGR\n", " 0\n", " 0\n", " 0\n", @@ -885,7 +949,8 @@ " \n", " \n", " 5\n", - " Bosnia H.\n", + " Bosnia\n", + " BIH\n", " 197\n", " 543\n", " 0\n", @@ -894,7 +959,8 @@ " \n", " \n", " 6\n", - " Kosovo 5)\n", + " Kosovo\n", + " KSV\n", " 0\n", " 0\n", " 0\n", @@ -903,7 +969,8 @@ " \n", " \n", " 7\n", - " Serbia and Montenegro 6)\n", + " Serbia and Montenegro\n", + " SRB\n", " 1059\n", " 1262\n", " 0\n", @@ -913,6 +980,7 @@ " \n", " 9\n", " Poland\n", + " POL\n", " 0\n", " 634\n", " 0\n", @@ -922,6 +990,7 @@ " \n", " 10\n", " Romania\n", + " ROU\n", " 4\n", " 301\n", " 414\n", @@ -931,6 +1000,7 @@ " \n", " 11\n", " Russia\n", + " RUS\n", " 157\n", " 700\n", " 376\n", @@ -940,6 +1010,7 @@ " \n", " 12\n", " United Kingdom\n", + " GBR\n", " 0\n", " 641\n", " 0\n", @@ -949,6 +1020,7 @@ " \n", " 13\n", " Turkey\n", + " TUR\n", " 76\n", " 886\n", " 86\n", @@ -958,6 +1030,7 @@ " \n", " 14\n", " Ukraine\n", + " UKR\n", " 0\n", " 0\n", " 547\n", @@ -967,6 +1040,7 @@ " \n", " 15\n", " Estonia\n", + " EST\n", " 0\n", " 0\n", " 0\n", @@ -976,6 +1050,7 @@ " \n", " 16\n", " Croatia\n", + " HRV\n", " 0\n", " 0\n", " 140\n", @@ -985,6 +1060,7 @@ " \n", " 17\n", " Lithuania\n", + " LTU\n", " 0\n", " 0\n", " 0\n", @@ -994,6 +1070,7 @@ " \n", " 18\n", " Belarus\n", + " BLR\n", " 0\n", " 0\n", " 88\n", @@ -1003,6 +1080,7 @@ " \n", " 19\n", " Germany\n", + " DEU\n", " 0\n", " 829\n", " 0\n", @@ -1012,6 +1090,7 @@ " \n", " 20\n", " France\n", + " FRA\n", " 0\n", " 0\n", " 0\n", @@ -1021,6 +1100,7 @@ " \n", " 21\n", " Greece\n", + " GRC\n", " 0\n", " 0\n", " 0\n", @@ -1030,6 +1110,7 @@ " \n", " 22\n", " Latvia\n", + " LVA\n", " 0\n", " 0\n", " 0\n", @@ -1039,6 +1120,7 @@ " \n", " 23\n", " Netherlands\n", + " NLD\n", " 0\n", " 0\n", " 0\n", @@ -1048,6 +1130,7 @@ " \n", " 27\n", " Eritrea\n", + " ERI\n", " 475\n", " 190\n", " 0\n", @@ -1057,6 +1140,7 @@ " \n", " 28\n", " Ethiopia\n", + " ETH\n", " 37\n", " 178\n", " 0\n", @@ -1066,6 +1150,7 @@ " \n", " 29\n", " Somalia\n", + " SOM\n", " 841\n", " 770\n", " 0\n", @@ -1075,6 +1160,7 @@ " \n", " 30\n", " Uganda\n", + " UGA\n", " 9\n", " 0\n", " 0\n", @@ -1084,6 +1170,7 @@ " \n", " 31\n", " Egypt\n", + " EGY\n", " 0\n", " 0\n", " 0\n", @@ -1093,6 +1180,7 @@ " \n", " 32\n", " Cameroon\n", + " CMR\n", " 0\n", " 0\n", " 0\n", @@ -1102,6 +1190,7 @@ " \n", " 33\n", " Nigeria\n", + " NGA\n", " 0\n", " 0\n", " 0\n", @@ -1111,6 +1200,7 @@ " \n", " 37\n", " Chile\n", + " CHL\n", " 6\n", " 356\n", " 0\n", @@ -1120,6 +1210,7 @@ " \n", " 38\n", " Colombia\n", + " COL\n", " 332\n", " 103\n", " 0\n", @@ -1129,6 +1220,7 @@ " \n", " 39\n", " Cuba\n", + " CUB\n", " 15\n", " 0\n", " 0\n", @@ -1138,6 +1230,7 @@ " \n", " 40\n", " Brazil\n", + " BRA\n", " 0\n", " 0\n", " 143\n", @@ -1147,6 +1240,7 @@ " \n", " 41\n", " Bolivia\n", + " BOL\n", " 0\n", " 0\n", " 0\n", @@ -1156,6 +1250,7 @@ " \n", " 42\n", " Guatemala\n", + " GTM\n", " 0\n", " 0\n", " 0\n", @@ -1165,6 +1260,7 @@ " \n", " 43\n", " Canada\n", + " CAN\n", " 0\n", " 0\n", " 241\n", @@ -1174,6 +1270,7 @@ " \n", " 44\n", " USA\n", + " USA\n", " 0\n", " 522\n", " 629\n", @@ -1183,6 +1280,7 @@ " \n", " 45\n", " Mexico\n", + " MEX\n", " 0\n", " 0\n", " 0\n", @@ -1192,6 +1290,7 @@ " \n", " 49\n", " Afghanistan\n", + " AFG\n", " 387\n", " 0\n", " 0\n", @@ -1201,6 +1300,7 @@ " \n", " 50\n", " Bangladesh\n", + " BGD\n", " 44\n", " 0\n", " 0\n", @@ -1210,6 +1310,7 @@ " \n", " 51\n", " Philippines\n", + " PHL\n", " 0\n", " 373\n", " 0\n", @@ -1219,6 +1320,7 @@ " \n", " 52\n", " India\n", + " IND\n", " 0\n", " 175\n", " 760\n", @@ -1228,6 +1330,7 @@ " \n", " 53\n", " Iraq\n", + " IRQ\n", " 1692\n", " 1705\n", " 0\n", @@ -1237,6 +1340,7 @@ " \n", " 54\n", " Iran\n", + " IRN\n", " 230\n", " 626\n", " 44\n", @@ -1246,6 +1350,7 @@ " \n", " 55\n", " China\n", + " CHN\n", " 36\n", " 498\n", " 479\n", @@ -1255,6 +1360,7 @@ " \n", " 56\n", " Japan\n", + " JPN\n", " 0\n", " 0\n", " 152\n", @@ -1264,6 +1370,7 @@ " \n", " 57\n", " Pakistan\n", + " PAK\n", " 0\n", " 0\n", " 35\n", @@ -1273,6 +1380,7 @@ " \n", " 58\n", " Lebanon\n", + " LBN\n", " 97\n", " 277\n", " 0\n", @@ -1282,6 +1390,7 @@ " \n", " 59\n", " Sri Lanka\n", + " LKA\n", " 5\n", " 85\n", " 0\n", @@ -1291,6 +1400,7 @@ " \n", " 60\n", " Syria\n", + " SYR\n", " 178\n", " 427\n", " 32\n", @@ -1300,6 +1410,7 @@ " \n", " 61\n", " Thailand\n", + " THA\n", " 0\n", " 2095\n", " 329\n", @@ -1309,6 +1420,7 @@ " \n", " 62\n", " Vietnam\n", + " NaN\n", " 3\n", " 283\n", " 0\n", @@ -1318,6 +1430,7 @@ " \n", " 63\n", " Korea, Republic\n", + " PRK\n", " 0\n", " 0\n", " 0\n", @@ -1327,6 +1440,7 @@ " \n", " 67\n", " Australia\n", + " AUS\n", " 0\n", " 0\n", " 265\n", @@ -1338,58 +1452,58 @@ "" ], "text/plain": [ - " Citizenship 2005_refugees 2005_fam_reun 2005_labour \\\n", - "2 Bulgaria 0 0 0 \n", - "5 Bosnia H. 197 543 0 \n", - "6 Kosovo 5) 0 0 0 \n", - "7 Serbia and Montenegro 6) 1059 1262 0 \n", - "9 Poland 0 634 0 \n", - "10 Romania 4 301 414 \n", - "11 Russia 157 700 376 \n", - "12 United Kingdom 0 641 0 \n", - "13 Turkey 76 886 86 \n", - "14 Ukraine 0 0 547 \n", - "15 Estonia 0 0 0 \n", - "16 Croatia 0 0 140 \n", - "17 Lithuania 0 0 0 \n", - "18 Belarus 0 0 88 \n", - "19 Germany 0 829 0 \n", - "20 France 0 0 0 \n", - "21 Greece 0 0 0 \n", - "22 Latvia 0 0 0 \n", - "23 Netherlands 0 0 0 \n", - "27 Eritrea 475 190 0 \n", - "28 Ethiopia 37 178 0 \n", - "29 Somalia 841 770 0 \n", - "30 Uganda 9 0 0 \n", - "31 Egypt 0 0 0 \n", - "32 Cameroon 0 0 0 \n", - "33 Nigeria 0 0 0 \n", - "37 Chile 6 356 0 \n", - "38 Colombia 332 103 0 \n", - "39 Cuba 15 0 0 \n", - "40 Brazil 0 0 143 \n", - "41 Bolivia 0 0 0 \n", - "42 Guatemala 0 0 0 \n", - "43 Canada 0 0 241 \n", - "44 USA 0 522 629 \n", - "45 Mexico 0 0 0 \n", - "49 Afghanistan 387 0 0 \n", - "50 Bangladesh 44 0 0 \n", - "51 Philippines 0 373 0 \n", - "52 India 0 175 760 \n", - "53 Iraq 1692 1705 0 \n", - "54 Iran 230 626 44 \n", - "55 China 36 498 479 \n", - "56 Japan 0 0 152 \n", - "57 Pakistan 0 0 35 \n", - "58 Lebanon 97 277 0 \n", - "59 Sri Lanka 5 85 0 \n", - "60 Syria 178 427 32 \n", - "61 Thailand 0 2095 329 \n", - "62 Vietnam 3 283 0 \n", - "63 Korea, Republic 0 0 0 \n", - "67 Australia 0 0 265 \n", + " Citizenship Code 2005_refugees 2005_fam_reun 2005_labour \\\n", + "2 Bulgaria BGR 0 0 0 \n", + "5 Bosnia BIH 197 543 0 \n", + "6 Kosovo KSV 0 0 0 \n", + "7 Serbia and Montenegro SRB 1059 1262 0 \n", + "9 Poland POL 0 634 0 \n", + "10 Romania ROU 4 301 414 \n", + "11 Russia RUS 157 700 376 \n", + "12 United Kingdom GBR 0 641 0 \n", + "13 Turkey TUR 76 886 86 \n", + "14 Ukraine UKR 0 0 547 \n", + "15 Estonia EST 0 0 0 \n", + "16 Croatia HRV 0 0 140 \n", + "17 Lithuania LTU 0 0 0 \n", + "18 Belarus BLR 0 0 88 \n", + "19 Germany DEU 0 829 0 \n", + "20 France FRA 0 0 0 \n", + "21 Greece GRC 0 0 0 \n", + "22 Latvia LVA 0 0 0 \n", + "23 Netherlands NLD 0 0 0 \n", + "27 Eritrea ERI 475 190 0 \n", + "28 Ethiopia ETH 37 178 0 \n", + "29 Somalia SOM 841 770 0 \n", + "30 Uganda UGA 9 0 0 \n", + "31 Egypt EGY 0 0 0 \n", + "32 Cameroon CMR 0 0 0 \n", + "33 Nigeria NGA 0 0 0 \n", + "37 Chile CHL 6 356 0 \n", + "38 Colombia COL 332 103 0 \n", + "39 Cuba CUB 15 0 0 \n", + "40 Brazil BRA 0 0 143 \n", + "41 Bolivia BOL 0 0 0 \n", + "42 Guatemala GTM 0 0 0 \n", + "43 Canada CAN 0 0 241 \n", + "44 USA USA 0 522 629 \n", + "45 Mexico MEX 0 0 0 \n", + "49 Afghanistan AFG 387 0 0 \n", + "50 Bangladesh BGD 44 0 0 \n", + "51 Philippines PHL 0 373 0 \n", + "52 India IND 0 175 760 \n", + "53 Iraq IRQ 1692 1705 0 \n", + "54 Iran IRN 230 626 44 \n", + "55 China CHN 36 498 479 \n", + "56 Japan JPN 0 0 152 \n", + "57 Pakistan PAK 0 0 35 \n", + "58 Lebanon LBN 97 277 0 \n", + "59 Sri Lanka LKA 5 85 0 \n", + "60 Syria SYR 178 427 32 \n", + "61 Thailand THA 0 2095 329 \n", + "62 Vietnam NaN 3 283 0 \n", + "63 Korea, Republic PRK 0 0 0 \n", + "67 Australia AUS 0 0 265 \n", "\n", " 2005_students 2005_adopted \n", "2 0 0 \n", @@ -1445,7 +1559,7 @@ "67 0 0 " ] }, - "execution_count": 144, + "execution_count": 35, "metadata": {}, "output_type": "execute_result" } @@ -1472,7 +1586,7 @@ }, { "cell_type": "code", - "execution_count": 154, + "execution_count": 36, "metadata": { "collapsed": false }, @@ -1486,7 +1600,7 @@ "" ] }, - "execution_count": 154, + "execution_count": 36, "metadata": {}, "output_type": "execute_result" } @@ -1541,91 +1655,1912 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 37, "metadata": { "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kiki/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning:\n", + "\n", + "\n", + "A value is trying to be set on a copy of a slice from a DataFrame.\n", + "Try using .loc[row_indexer,col_indexer] = value instead\n", + "\n", + "See the caveats in the documentation: http://pandas.pydata.org/pandas-docs/stable/indexing.html#indexing-view-versus-copy\n", + "\n" + ] + }, { "data": { "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "total = total.transpose().reset_index()\n", - "dat = [\n", - " go.Bar(\n", - " x=total['index'], # assign x as the dataframe column 'x'\n", - " y=total[73]\n", - " )\n", - "]\n", - "py.iplot(dat, filename='total_bar')" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "# Learn about API authentication here: https://plot.ly/pandas/getting-started\n", - "# Find your api_key here: https://plot.ly/settings/ap\n", - "\n", - "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_us_cities.csv')\n", - "df.head()\n", - "\n", - "df['text'] = df['name'] + '
Population ' + (df['pop']/1e6).astype(str)+' million'\n", - "limits = [(0,2),(3,10),(11,20),(21,50),(50,3000)]\n", - "colors = [\"rgb(0,116,217)\",\"rgb(255,65,54)\",\"rgb(133,20,75)\",\"rgb(255,133,27)\",\"rgb(255,220,0)\"]\n", - "cities = []\n", - "scale = 50000\n", - "\n", - "for i in range(len(limits)):\n", - " lim = limits[i]\n", - " df_sub = df[lim[0]:lim[1]]\n", - " city = dict(\n", - " type = 'scattergeo',\n", - " locationmode = 'USA-states',\n", - " lon = df_sub['lon'],\n", - " lat = df_sub['lat'],\n", - " text = df_sub['text'],\n", - " sizemode = 'diameter',\n", - " marker = dict( \n", - " size = df_sub['pop']/scale, \n", - " color = colors[i],\n", - " line = dict(width = 2,color = 'black')\n", - " ),\n", - " name = '{0} - {1}'.format(lim[0],lim[1]) )\n", - " cities.append(city)\n", - "\n", - "layout = dict(\n", - " title = '2014 US city populations
(Click legend to toggle traces)',\n", - " showlegend = True,\n", - " geo = dict(\n", - " scope='usa',\n", - " projection=dict( type='albers usa' ),\n", - " showland = True,\n", - " landcolor = 'rgb(217, 217, 217)', \n", - " subunitwidth=1,\n", - " countrywidth=1,\n", - " subunitcolor=\"rgb(255, 255, 255)\",\n", - " countrycolor=\"rgb(255, 255, 255)\" \n", - " ), \n", - " )\n", - " \n", - "fig = dict( data=cities, layout=layout )\n", - "url = py.plot( fig, validate=False, filename='d3-bubble-map-populations' )" + "
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CitizenshipCode2005_refugees2005_fam_reun2005_labour2005_students2005_adoptedTotal
2BulgariaBGR000000
5BosniaBIH197543000740
6KosovoKSV000000
7Serbia and MontenegroSRB105912620002321
9PolandPOL0634000634
10RomaniaROU430141400719
11RussiaRUS157700376168411442
12United KingdomGBR0641000641
13TurkeyTUR768868620701255
14UkraineUKR0054700547
15EstoniaEST000000
16CroatiaHRV0014000140
17LithuaniaLTU000000
18BelarusBLR00880088
19GermanyDEU0829000829
20FranceFRA000000
21GreeceGRC000000
22LatviaLVA000000
23NetherlandsNLD000000
27EritreaERI475190000665
28EthiopiaETH3717806043318
29SomaliaSOM8417700001611
30UgandaUGA900009
31EgyptEGY00018018
32CameroonCMR0003290329
33NigeriaNGA0003010301
37ChileCHL6356000362
38ColombiaCOL332103000435
39CubaCUB15000015
40BrazilBRA00143540197
41BoliviaBOL00001010
42GuatemalaGTM000000
43CanadaCAN002413630604
44USAUSA052262921301364
45MexicoMEX0001680168
49AfghanistanAFG3870000387
50BangladeshBGD44003070351
51PhilippinesPHL0373008381
52IndiaIND0175760592381565
53IraqIRQ169217050003397
54IranIRN2306264426601166
55ChinaCHN364984799344502397
56JapanJPN001521510303
57PakistanPAK00356240659
58LebanonLBN97277000374
59Sri LankaLKA58500191
60SyriaSYR1784273200637
61ThailandTHA020953290352459
62VietnamNaN32830086372
63Korea, RepublicPRK000099
67AustraliaAUS0026500265
\n", + "
" + ], + "text/plain": [ + " Citizenship Code 2005_refugees 2005_fam_reun 2005_labour \\\n", + "2 Bulgaria BGR 0 0 0 \n", + "5 Bosnia BIH 197 543 0 \n", + "6 Kosovo KSV 0 0 0 \n", + "7 Serbia and Montenegro SRB 1059 1262 0 \n", + "9 Poland POL 0 634 0 \n", + "10 Romania ROU 4 301 414 \n", + "11 Russia RUS 157 700 376 \n", + "12 United Kingdom GBR 0 641 0 \n", + "13 Turkey TUR 76 886 86 \n", + "14 Ukraine UKR 0 0 547 \n", + "15 Estonia EST 0 0 0 \n", + "16 Croatia HRV 0 0 140 \n", + "17 Lithuania LTU 0 0 0 \n", + "18 Belarus BLR 0 0 88 \n", + "19 Germany DEU 0 829 0 \n", + "20 France FRA 0 0 0 \n", + "21 Greece GRC 0 0 0 \n", + "22 Latvia LVA 0 0 0 \n", + "23 Netherlands NLD 0 0 0 \n", + "27 Eritrea ERI 475 190 0 \n", + "28 Ethiopia ETH 37 178 0 \n", + "29 Somalia SOM 841 770 0 \n", + "30 Uganda UGA 9 0 0 \n", + "31 Egypt EGY 0 0 0 \n", + "32 Cameroon CMR 0 0 0 \n", + "33 Nigeria NGA 0 0 0 \n", + "37 Chile CHL 6 356 0 \n", + "38 Colombia COL 332 103 0 \n", + "39 Cuba CUB 15 0 0 \n", + "40 Brazil BRA 0 0 143 \n", + "41 Bolivia BOL 0 0 0 \n", + "42 Guatemala GTM 0 0 0 \n", + "43 Canada CAN 0 0 241 \n", + "44 USA USA 0 522 629 \n", + "45 Mexico MEX 0 0 0 \n", + "49 Afghanistan AFG 387 0 0 \n", + "50 Bangladesh BGD 44 0 0 \n", + "51 Philippines PHL 0 373 0 \n", + "52 India IND 0 175 760 \n", + "53 Iraq IRQ 1692 1705 0 \n", + "54 Iran IRN 230 626 44 \n", + "55 China CHN 36 498 479 \n", + "56 Japan JPN 0 0 152 \n", + "57 Pakistan PAK 0 0 35 \n", + "58 Lebanon LBN 97 277 0 \n", + "59 Sri Lanka LKA 5 85 0 \n", + "60 Syria SYR 178 427 32 \n", + "61 Thailand THA 0 2095 329 \n", + "62 Vietnam NaN 3 283 0 \n", + "63 Korea, Republic PRK 0 0 0 \n", + "67 Australia AUS 0 0 265 \n", + "\n", + " 2005_students 2005_adopted Total \n", + "2 0 0 0 \n", + "5 0 0 740 \n", + "6 0 0 0 \n", + "7 0 0 2321 \n", + "9 0 0 634 \n", + "10 0 0 719 \n", + "11 168 41 1442 \n", + "12 0 0 641 \n", + "13 207 0 1255 \n", + "14 0 0 547 \n", + "15 0 0 0 \n", + "16 0 0 140 \n", + "17 0 0 0 \n", + "18 0 0 88 \n", + "19 0 0 829 \n", + "20 0 0 0 \n", + "21 0 0 0 \n", + "22 0 0 0 \n", + "23 0 0 0 \n", + "27 0 0 665 \n", + "28 60 43 318 \n", + "29 0 0 1611 \n", + "30 0 0 9 \n", + "31 18 0 18 \n", + "32 329 0 329 \n", + "33 301 0 301 \n", + "37 0 0 362 \n", + "38 0 0 435 \n", + "39 0 0 15 \n", + "40 54 0 197 \n", + "41 0 10 10 \n", + "42 0 0 0 \n", + "43 363 0 604 \n", + "44 213 0 1364 \n", + "45 168 0 168 \n", + "49 0 0 387 \n", + "50 307 0 351 \n", + "51 0 8 381 \n", + "52 592 38 1565 \n", + "53 0 0 3397 \n", + "54 266 0 1166 \n", + "55 934 450 2397 \n", + "56 151 0 303 \n", + "57 624 0 659 \n", + "58 0 0 374 \n", + "59 0 1 91 \n", + "60 0 0 637 \n", + "61 0 35 2459 \n", + "62 0 86 372 \n", + "63 0 9 9 \n", + "67 0 0 265 " + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "countries['Total'] = countries['2005_refugees']+countries['2005_fam_reun']+countries['2005_labour']+countries['2005_students']+countries['2005_adopted']\n", + "countries" + ] + }, + { + "cell_type": "code", + "execution_count": 98, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 98, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total = total.transpose().reset_index()\n", + "dat = [\n", + " go.Bar(\n", + " x=total['index'], # assign x as the dataframe column 'x'\n", + " y=total[73]\n", + " )\n", + "]\n", + "py.iplot(dat, filename='total_bar')" + ] + }, + { + "cell_type": "code", + "execution_count": 59, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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2005_adopted2005_fam_reun2005_labour2005_refugees2005_studentsCODECOUNTRYCitizenshipCodeGDP (BILLIONS)Total
000000NaNNaNBulgariaBGRNaN0
1054301970NaNNaNBosniaBIHNaN740
200000NaNNaNKosovoKSVNaN0
301262010590NaNNaNSerbia and MontenegroSRBNaN2321
40634000NaNNaNPolandPOLNaN634
5030141440NaNNaNRomaniaROUNaN719
641700376157168NaNNaNRussiaRUSNaN1442
70641000NaNNaNUnited KingdomGBRNaN641
808868676207NaNNaNTurkeyTURNaN1255
90054700NaNNaNUkraineUKRNaN547
1000000NaNNaNEstoniaESTNaN0
110014000NaNNaNCroatiaHRVNaN140
1200000NaNNaNLithuaniaLTUNaN0
13008800NaNNaNBelarusBLRNaN88
140829000NaNNaNGermanyDEUNaN829
1500000NaNNaNFranceFRANaN0
1600000NaNNaNGreeceGRCNaN0
1700000NaNNaNLatviaLVANaN0
1800000NaNNaNNetherlandsNLDNaN0
19019004750NaNNaNEritreaERINaN665
204317803760NaNNaNEthiopiaETHNaN318
21077008410NaNNaNSomaliaSOMNaN1611
2200090NaNNaNUgandaUGANaN9
23000018NaNNaNEgyptEGYNaN18
240000329NaNNaNCameroonCMRNaN329
250000301NaNNaNNigeriaNGANaN301
260356060NaNNaNChileCHLNaN362
27010303320NaNNaNColombiaCOLNaN435
28000150NaNNaNCubaCUBNaN15
2900143054NaNNaNBrazilBRANaN197
....................................
192NaNNaNNaNNaNNaNSVNSloveniaSloveniaSVN49.930
193NaNNaNNaNNaNNaNSLBSolomon IslandsSolomon IslandsSLB1.160
194NaNNaNNaNNaNNaNZAFSouth AfricaSouth AfricaZAF341.200
195NaNNaNNaNNaNNaNSSDSouth SudanSouth SudanSSD11.890
196NaNNaNNaNNaNNaNESPSpainSpainESP1400.000
197NaNNaNNaNNaNNaNSDNSudanSudanSDN70.030
198NaNNaNNaNNaNNaNSURSurinameSurinameSUR5.270
199NaNNaNNaNNaNNaNSWZSwazilandSwazilandSWZ3.840
200NaNNaNNaNNaNNaNSWESwedenSwedenSWE559.100
201NaNNaNNaNNaNNaNCHESwitzerlandSwitzerlandCHE679.000
202NaNNaNNaNNaNNaNTWNTaiwanTaiwanTWN529.500
203NaNNaNNaNNaNNaNTJKTajikistanTajikistanTJK9.160
204NaNNaNNaNNaNNaNTZATanzaniaTanzaniaTZA36.620
205NaNNaNNaNNaNNaNTLSTimor-LesteTimor-LesteTLS4.510
206NaNNaNNaNNaNNaNTGOTogoTogoTGO4.840
207NaNNaNNaNNaNNaNTONTongaTongaTON0.490
208NaNNaNNaNNaNNaNTTOTrinidad and TobagoTrinidad and TobagoTTO29.630
209NaNNaNNaNNaNNaNTUNTunisiaTunisiaTUN49.120
210NaNNaNNaNNaNNaNTKMTurkmenistanTurkmenistanTKM43.500
211NaNNaNNaNNaNNaNTUVTuvaluTuvaluTUV0.040
212NaNNaNNaNNaNNaNAREUnited Arab EmiratesUnited Arab EmiratesARE416.400
213NaNNaNNaNNaNNaNURYUruguayUruguayURY55.600
214NaNNaNNaNNaNNaNUZBUzbekistanUzbekistanUZB63.080
215NaNNaNNaNNaNNaNVUTVanuatuVanuatuVUT0.820
216NaNNaNNaNNaNNaNVENVenezuelaVenezuelaVEN209.200
217NaNNaNNaNNaNNaNVNMVietnamVietnamVNM187.800
218NaNNaNNaNNaNNaNWBGWest BankWest BankWBG6.640
219NaNNaNNaNNaNNaNYEMYemenYemenYEM45.450
220NaNNaNNaNNaNNaNZMBZambiaZambiaZMB25.610
221NaNNaNNaNNaNNaNZWEZimbabweZimbabweZWE13.740
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222 rows × 11 columns

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NaN NaN \n", + "198 NaN NaN NaN NaN NaN \n", + "199 NaN NaN NaN NaN NaN \n", + "200 NaN NaN NaN NaN NaN \n", + "201 NaN NaN NaN NaN NaN \n", + "202 NaN NaN NaN NaN NaN \n", + "203 NaN NaN NaN NaN NaN \n", + "204 NaN NaN NaN NaN NaN \n", + "205 NaN NaN NaN NaN NaN \n", + "206 NaN NaN NaN NaN NaN \n", + "207 NaN NaN NaN NaN NaN \n", + "208 NaN NaN NaN NaN NaN \n", + "209 NaN NaN NaN NaN NaN \n", + "210 NaN NaN NaN NaN NaN \n", + "211 NaN NaN NaN NaN NaN \n", + "212 NaN NaN NaN NaN NaN \n", + "213 NaN NaN NaN NaN NaN \n", + "214 NaN NaN NaN NaN NaN \n", + "215 NaN NaN NaN NaN NaN \n", + "216 NaN NaN NaN NaN NaN \n", + "217 NaN NaN NaN NaN NaN \n", + "218 NaN NaN NaN NaN NaN \n", + "219 NaN NaN NaN NaN NaN \n", + "220 NaN NaN NaN NaN NaN \n", + "221 NaN NaN NaN NaN NaN \n", + "\n", + " CODE COUNTRY Citizenship Code GDP (BILLIONS) \\\n", + "0 NaN NaN Bulgaria BGR NaN \n", + "1 NaN NaN Bosnia BIH NaN \n", + "2 NaN NaN Kosovo KSV NaN \n", + "3 NaN NaN Serbia and Montenegro SRB NaN \n", + "4 NaN NaN Poland POL NaN \n", + "5 NaN NaN Romania ROU NaN \n", + "6 NaN NaN Russia RUS NaN \n", + "7 NaN NaN United Kingdom GBR NaN \n", + "8 NaN NaN Turkey TUR NaN \n", + "9 NaN NaN Ukraine UKR NaN \n", + "10 NaN NaN Estonia EST NaN \n", + "11 NaN NaN Croatia HRV NaN \n", + "12 NaN NaN Lithuania LTU NaN \n", + "13 NaN NaN Belarus BLR NaN \n", + "14 NaN NaN Germany DEU NaN \n", + "15 NaN NaN France FRA NaN \n", + "16 NaN NaN Greece GRC NaN \n", + "17 NaN NaN Latvia LVA NaN \n", + "18 NaN NaN Netherlands NLD NaN \n", + "19 NaN NaN Eritrea ERI NaN \n", + "20 NaN NaN Ethiopia ETH NaN \n", + "21 NaN NaN Somalia SOM NaN \n", + "22 NaN NaN Uganda UGA NaN \n", + "23 NaN NaN Egypt EGY NaN \n", + "24 NaN NaN Cameroon CMR NaN \n", + "25 NaN NaN Nigeria NGA NaN \n", + "26 NaN NaN Chile CHL NaN \n", + "27 NaN NaN Colombia COL NaN \n", + "28 NaN NaN Cuba CUB NaN \n", + "29 NaN NaN Brazil BRA NaN \n", + ".. ... ... ... ... ... \n", + "192 SVN Slovenia Slovenia SVN 49.93 \n", + "193 SLB Solomon Islands Solomon Islands SLB 1.16 \n", + "194 ZAF South Africa South Africa ZAF 341.20 \n", + "195 SSD South Sudan South Sudan SSD 11.89 \n", + "196 ESP Spain Spain ESP 1400.00 \n", + "197 SDN Sudan Sudan SDN 70.03 \n", + "198 SUR Suriname Suriname SUR 5.27 \n", + "199 SWZ Swaziland Swaziland SWZ 3.84 \n", + "200 SWE Sweden Sweden SWE 559.10 \n", + "201 CHE Switzerland Switzerland CHE 679.00 \n", + "202 TWN Taiwan Taiwan TWN 529.50 \n", + "203 TJK Tajikistan Tajikistan TJK 9.16 \n", + "204 TZA Tanzania Tanzania TZA 36.62 \n", + "205 TLS Timor-Leste Timor-Leste TLS 4.51 \n", + "206 TGO Togo Togo TGO 4.84 \n", + "207 TON Tonga Tonga TON 0.49 \n", + "208 TTO Trinidad and Tobago Trinidad and Tobago TTO 29.63 \n", + "209 TUN Tunisia Tunisia TUN 49.12 \n", + "210 TKM Turkmenistan Turkmenistan TKM 43.50 \n", + "211 TUV Tuvalu Tuvalu TUV 0.04 \n", + "212 ARE United Arab Emirates United Arab Emirates ARE 416.40 \n", + "213 URY Uruguay Uruguay URY 55.60 \n", + "214 UZB Uzbekistan Uzbekistan UZB 63.08 \n", + "215 VUT Vanuatu Vanuatu VUT 0.82 \n", + "216 VEN Venezuela Venezuela VEN 209.20 \n", + "217 VNM Vietnam Vietnam VNM 187.80 \n", + "218 WBG West Bank West Bank WBG 6.64 \n", + "219 YEM Yemen Yemen YEM 45.45 \n", + "220 ZMB Zambia Zambia ZMB 25.61 \n", + "221 ZWE Zimbabwe Zimbabwe ZWE 13.74 \n", + "\n", + " Total \n", + "0 0 \n", + "1 740 \n", + "2 0 \n", + "3 2321 \n", + "4 634 \n", + "5 719 \n", + "6 1442 \n", + "7 641 \n", + "8 1255 \n", + "9 547 \n", + "10 0 \n", + "11 140 \n", + "12 0 \n", + "13 88 \n", + "14 829 \n", + "15 0 \n", + "16 0 \n", + "17 0 \n", + "18 0 \n", + "19 665 \n", + "20 318 \n", + "21 1611 \n", + "22 9 \n", + "23 18 \n", + "24 329 \n", + "25 301 \n", + "26 362 \n", + "27 435 \n", + "28 15 \n", + "29 197 \n", + ".. ... \n", + "192 0 \n", + "193 0 \n", + "194 0 \n", + "195 0 \n", + "196 0 \n", + "197 0 \n", + "198 0 \n", + "199 0 \n", + "200 0 \n", + "201 0 \n", + "202 0 \n", + "203 0 \n", + "204 0 \n", + "205 0 \n", + "206 0 \n", + "207 0 \n", + "208 0 \n", + "209 0 \n", + "210 0 \n", + "211 0 \n", + "212 0 \n", + "213 0 \n", + "214 0 \n", + "215 0 \n", + "216 0 \n", + "217 0 \n", + "218 0 \n", + "219 0 \n", + "220 0 \n", + "221 0 \n", + "\n", + "[222 rows x 11 columns]" + ] + }, + "execution_count": 59, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('https://raw.githubusercontent.com/plotly/datasets/master/2014_world_gdp_with_codes.csv')\n", + "df['Citizenship'] = df['COUNTRY']\n", + "df['Code'] = df['CODE']\n", + "world = pd.concat([countries,df]).drop_duplicates(['Code']).reset_index(drop=True)\n", + "world['Total'].fillna(0,inplace=True)\n", + "world" + ] + }, + { + "cell_type": "code", + "execution_count": 78, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "ename": "PlotlyError", + "evalue": "Request throttled. You've created/updated more charts than your allowed limit of 50/day. You may either wait one day or upgrade your account. Visit https://plot.ly/settings/subscription/ to upgrade.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mPlotlyError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlayout\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 35\u001b[0m \u001b[1;31m# url = py.plot(fig, filename='d3-world-map')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/home/kiki/anaconda/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'auto_open'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 150\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/home/kiki/anaconda/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[0mplot_options\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_plot_option_logic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_send_to_plotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/home/kiki/anaconda/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36m_send_to_plotly\u001b[1;34m(figure, **plot_options)\u001b[0m\n\u001b[0;32m 1402\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1403\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'error'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1404\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1405\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1406\u001b[0m \u001b[1;31m# Check if the url needs a secret key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mPlotlyError\u001b[0m: Request throttled. You've created/updated more charts than your allowed limit of 50/day. You may either wait one day or upgrade your account. Visit https://plot.ly/settings/subscription/ to upgrade." + ] + } + ], + "source": [ + "data = [ dict(\n", + " type = 'choropleth',\n", + " locations = countries['Code'],\n", + " z = countries['Total'],\n", + " text = world['Citizenship'],\n", + " colorscale = [[0,\"rgb(154, 7, 31)\"],[0.35,\"rgb(201, 58, 6)\"],[0.5,\"rgb(239, 125, 7)\"],\\\n", + " [0.6,\"rgb(255, 204, 103)\"],[0.7,\"rgb(255, 240, 75)\"],[1,\"rgb(255, 251, 235)\"]],\n", + " autocolorscale = False,\n", + " reversescale = True,\n", + " marker = dict(\n", + " line = dict (\n", + " color = 'rgb(180,180,180)',\n", + " width = 0.5\n", + " )\n", + " ),\n", + " zmin = 0,\n", + " colorbar = dict(\n", + " title = '# of Immigrants'\n", + " ),\n", + " ) ]\n", + "\n", + "layout = dict(\n", + " title = 'Immigration to Sweden in 2005',\n", + " geo = dict(\n", + " showframe = False,\n", + " showcoastlines = False,\n", + " projection = dict(\n", + " type = 'Equirectangular'\n", + " )\n", + " )\n", + ")\n", + "\n", + "fig = dict( data=data, layout=layout )\n", + "py.iplot(fig)\n", + "# url = py.plot(fig, filename='d3-world-map')" ] }, { From 9d6d8119ca7464c0f49e24d1480d38d90d59fae7 Mon Sep 17 00:00:00 2001 From: Pratool Gadtaula Date: Thu, 3 Mar 2016 18:06:24 -0500 Subject: [PATCH 06/24] Have mid-project checkin deliverables --- .gitignore | 3 + crime_distribution_by_origin.txt | 9 ++ immigration_plots.ipynb | 116 ++++++++---------- mid_project_checkin.md | 14 +++ ..._and_parents_origin_overrepresentation.txt | 5 + ...ndent_birth_country_overrepresentation.txt | 16 +++ 6 files changed, 97 insertions(+), 66 deletions(-) create mode 100644 .gitignore create mode 100644 crime_distribution_by_origin.txt create mode 100644 mid_project_checkin.md create mode 100644 respondent_and_parents_origin_overrepresentation.txt create mode 100644 respondent_birth_country_overrepresentation.txt diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..ae4ecad --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +*.pyc +.ipynb_checkpoints/* +*.ods diff --git a/crime_distribution_by_origin.txt b/crime_distribution_by_origin.txt new file mode 100644 index 0000000..44ed2a9 --- /dev/null +++ b/crime_distribution_by_origin.txt @@ -0,0 +1,9 @@ +type of crime|both parents born in Sweden|one parent born in sweden|both parents foreign born|foreign born +crimes against persons|21|19|20|29 +theft|26|28|27|24 +fraud|11|10|10|10 +damage|4|4|4|3 +driving offenses|19|19|19|15 +drug offenses|4|5|5|4 +other crimes|15|14|14|15 +number of violations|880525|187127|100847|348138 diff --git a/immigration_plots.ipynb b/immigration_plots.ipynb index 971b638..dda4a23 100644 --- a/immigration_plots.ipynb +++ b/immigration_plots.ipynb @@ -14,16 +14,6 @@ "1.9.6\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning:\n", - "\n", - "Matplotlib is building the font cache using fc-list. This may take a moment.\n", - "\n" - ] - }, { "data": { "text/html": [ @@ -82,7 +72,7 @@ { "data": { "text/html": [ - "
" + "
" ], "text/plain": [ "" @@ -113,18 +103,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning:\n", - "\n", - "axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import plotly.plotly as py\n", "import seaborn\n", @@ -134,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -902,7 +881,7 @@ "[76 rows x 7 columns]" ] }, - "execution_count": 34, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -914,7 +893,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -1559,7 +1538,7 @@ "67 0 0 " ] }, - "execution_count": 35, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1586,23 +1565,25 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" + "ename": "PlotlyLocalCredentialsError", + "evalue": "\nCouldn't find a 'username', 'api-key' pair for you on your local machine. To sign in temporarily (until you stop running Python), run:\n>>> import plotly.plotly as py\n>>> py.sign_in('username', 'api_key')\n\nEven better, save your credentials permanently using the 'tools' module:\n>>> import plotly.tools as tls\n>>> tls.set_credentials_file(username='username', api_key='api-key')\n\nFor more help, see https://plot.ly/python.\n", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mPlotlyLocalCredentialsError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 44\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlayout\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 45\u001b[1;33m \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'tot_country'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'auto_open'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 150\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[0mplot_options\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_plot_option_logic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_send_to_plotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36m_send_to_plotly\u001b[1;34m(figure, **plot_options)\u001b[0m\n\u001b[0;32m 1374\u001b[0m cls=utils.PlotlyJSONEncoder)\n\u001b[0;32m 1375\u001b[0m \u001b[0mcredentials\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_credentials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1376\u001b[1;33m \u001b[0mvalidate_credentials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcredentials\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1377\u001b[0m \u001b[0musername\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'username'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1378\u001b[0m \u001b[0mapi_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'api_key'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mvalidate_credentials\u001b[1;34m(credentials)\u001b[0m\n\u001b[0;32m 1323\u001b[0m \u001b[0mapi_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'api_key'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1324\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0musername\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mapi_key\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1325\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyLocalCredentialsError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1327\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mPlotlyLocalCredentialsError\u001b[0m: \nCouldn't find a 'username', 'api-key' pair for you on your local machine. To sign in temporarily (until you stop running Python), run:\n>>> import plotly.plotly as py\n>>> py.sign_in('username', 'api_key')\n\nEven better, save your credentials permanently using the 'tools' module:\n>>> import plotly.tools as tls\n>>> tls.set_credentials_file(username='username', api_key='api-key')\n\nFor more help, see https://plot.ly/python.\n" + ] } ], "source": [ @@ -1655,7 +1636,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -1664,7 +1645,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/kiki/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning:\n", + "-c:1: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", @@ -2366,7 +2347,7 @@ "67 0 0 265 " ] }, - "execution_count": 37, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -2378,23 +2359,25 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" + "ename": "PlotlyLocalCredentialsError", + "evalue": "\nCouldn't find a 'username', 'api-key' pair for you on your local machine. To sign in temporarily (until you stop running Python), run:\n>>> import plotly.plotly as py\n>>> py.sign_in('username', 'api_key')\n\nEven better, save your credentials permanently using the 'tools' module:\n>>> import plotly.tools as tls\n>>> tls.set_credentials_file(username='username', api_key='api-key')\n\nFor more help, see https://plot.ly/python.\n", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mPlotlyLocalCredentialsError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 6\u001b[0m )\n\u001b[0;32m 7\u001b[0m ]\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdat\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfilename\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'total_bar'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'auto_open'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 150\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[0mplot_options\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_plot_option_logic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_send_to_plotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36m_send_to_plotly\u001b[1;34m(figure, **plot_options)\u001b[0m\n\u001b[0;32m 1374\u001b[0m cls=utils.PlotlyJSONEncoder)\n\u001b[0;32m 1375\u001b[0m \u001b[0mcredentials\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_credentials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1376\u001b[1;33m \u001b[0mvalidate_credentials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcredentials\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1377\u001b[0m \u001b[0musername\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'username'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1378\u001b[0m \u001b[0mapi_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'api_key'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mvalidate_credentials\u001b[1;34m(credentials)\u001b[0m\n\u001b[0;32m 1323\u001b[0m \u001b[0mapi_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'api_key'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1324\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0musername\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mapi_key\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1325\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyLocalCredentialsError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1327\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mPlotlyLocalCredentialsError\u001b[0m: \nCouldn't find a 'username', 'api-key' pair for you on your local machine. To sign in temporarily (until you stop running Python), run:\n>>> import plotly.plotly as py\n>>> py.sign_in('username', 'api_key')\n\nEven better, save your credentials permanently using the 'tools' module:\n>>> import plotly.tools as tls\n>>> tls.set_credentials_file(username='username', api_key='api-key')\n\nFor more help, see https://plot.ly/python.\n" + ] } ], "source": [ @@ -2410,7 +2393,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -3489,7 +3472,7 @@ "[222 rows x 11 columns]" ] }, - "execution_count": 59, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -3505,23 +3488,24 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { - "ename": "PlotlyError", - "evalue": "Request throttled. You've created/updated more charts than your allowed limit of 50/day. You may either wait one day or upgrade your account. Visit https://plot.ly/settings/subscription/ to upgrade.", + "ename": "PlotlyLocalCredentialsError", + "evalue": "\nCouldn't find a 'username', 'api-key' pair for you on your local machine. To sign in temporarily (until you stop running Python), run:\n>>> import plotly.plotly as py\n>>> py.sign_in('username', 'api_key')\n\nEven better, save your credentials permanently using the 'tools' module:\n>>> import plotly.tools as tls\n>>> tls.set_credentials_file(username='username', api_key='api-key')\n\nFor more help, see https://plot.ly/python.\n", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mPlotlyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlayout\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 35\u001b[0m \u001b[1;31m# url = py.plot(fig, filename='d3-world-map')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/home/kiki/anaconda/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'auto_open'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 150\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/home/kiki/anaconda/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[0mplot_options\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_plot_option_logic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_send_to_plotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/home/kiki/anaconda/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36m_send_to_plotly\u001b[1;34m(figure, **plot_options)\u001b[0m\n\u001b[0;32m 1402\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1403\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'error'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1404\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1405\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1406\u001b[0m \u001b[1;31m# Check if the url needs a secret key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mPlotlyError\u001b[0m: Request throttled. You've created/updated more charts than your allowed limit of 50/day. You may either wait one day or upgrade your account. Visit https://plot.ly/settings/subscription/ to upgrade." + "\u001b[1;31mPlotlyLocalCredentialsError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlayout\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 35\u001b[0m \u001b[1;31m# url = py.plot(fig, filename='d3-world-map')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'auto_open'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 150\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[0mplot_options\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_plot_option_logic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_send_to_plotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36m_send_to_plotly\u001b[1;34m(figure, **plot_options)\u001b[0m\n\u001b[0;32m 1374\u001b[0m cls=utils.PlotlyJSONEncoder)\n\u001b[0;32m 1375\u001b[0m \u001b[0mcredentials\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_credentials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1376\u001b[1;33m \u001b[0mvalidate_credentials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcredentials\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1377\u001b[0m \u001b[0musername\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'username'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1378\u001b[0m \u001b[0mapi_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'api_key'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mvalidate_credentials\u001b[1;34m(credentials)\u001b[0m\n\u001b[0;32m 1323\u001b[0m \u001b[0mapi_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'api_key'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1324\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0musername\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mapi_key\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1325\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyLocalCredentialsError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1327\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mPlotlyLocalCredentialsError\u001b[0m: \nCouldn't find a 'username', 'api-key' pair for you on your local machine. To sign in temporarily (until you stop running Python), run:\n>>> import plotly.plotly as py\n>>> py.sign_in('username', 'api_key')\n\nEven better, save your credentials permanently using the 'tools' module:\n>>> import plotly.tools as tls\n>>> tls.set_credentials_file(username='username', api_key='api-key')\n\nFor more help, see https://plot.ly/python.\n" ] } ], @@ -3576,7 +3560,7 @@ "metadata": { "kernelspec": { "display_name": "Python 2", - "language": "python", + "language": "python2", "name": "python2" }, "language_info": { diff --git a/mid_project_checkin.md b/mid_project_checkin.md new file mode 100644 index 0000000..db9771f --- /dev/null +++ b/mid_project_checkin.md @@ -0,0 +1,14 @@ +# Mid Project Check-In +## Kiki Chandra and Pratool Gadtaula + +Our goals for the mid-project check-in were to have a solid start on figuring out D3 visualizations, and have collected all of our data. We also planned to have gotten to the point where we could plan out our article or potentially begin writing it. + +After talking to Paul, we decided to scale back on the discussion and conclusion of our data analysis and avoid having to prove that the statistic on Muslim immigrants committing crime in Sweden was entirely false. Instead we will provide context on why there seems to be inflated data and other statistics that are often overlooked when sensationalizing controversial topics. + +One of our goals for this check-in was to have scaffolded the article we plan to write for our final deliverable. However, after discussion we decided that it would make more sense to focus on using the data we managed to collect to create significant plots, and frame the story around them once they are more finalized. + +During the first week of the project, we did a lot of background reading to get a better sense of what statistics were available for us to use. We found that many of the statistics we were looking for, such as breakdowns of immigration by country to specific regions, and crime statistics by country of origin, were no longer reported by the Swedish government. Thus, we decided to focus on an earlier time period, the mid-2000s, rather than looking at current-day data. We gathered most of the data we thought are most relevant to the project, including overrepresentation of foreigners in crime statistics, breakdowns of immigration by country, and types of crimes committed by groups of different backgrounds. In this step of the project we met with some difficulty, because most of the tables and reports we found were in Swedish, so it took a bit longer than expected. We did manage to generate some plots, including interactive ones, that demonstrate some of the context that we would like the outstanding statistics to be seen with. These are available in our ipython notebooks and the html file named “immigration.html”. Rather than directly using D3, some of our plots are statically generated by Matplotlib and Seaborn, and our other interactive plots are generated using the Python library Plotly, which interfaces with Pandas very easily. + +By next Friday, we will have generated all of our visualizations and have embedded them into some kind of web document (that may be HTML or markdown or some other easy-to-use format). This web document will contain analysis and a brief explanation on each of our visualizations. Some of these textual components may be sparse. The MVP that we will have created will closely resemble the article that we would like to be able to easily distribute online. + + diff --git a/respondent_and_parents_origin_overrepresentation.txt b/respondent_and_parents_origin_overrepresentation.txt new file mode 100644 index 0000000..b8d7782 --- /dev/null +++ b/respondent_and_parents_origin_overrepresentation.txt @@ -0,0 +1,5 @@ +|percentage of suspects|over representation +Both parents were born in Sweden|58.9|0.8 +One parent born in Sweden|10.4|1.1 +Both parents born abroad|5.2|1.6 +Respondent born abroad|25.6|2.0 diff --git a/respondent_birth_country_overrepresentation.txt b/respondent_birth_country_overrepresentation.txt new file mode 100644 index 0000000..d9a2196 --- /dev/null +++ b/respondent_birth_country_overrepresentation.txt @@ -0,0 +1,16 @@ +Country of birth|percentage of suspects|over representation +Nordic countries except Sweden|4.7|1.4 +EU15 Excluding Denmark, Finland, Sweden|1.2|1.1 +New EU 10 countries|1.6|1.8 +Other European countries including Turkey and Cyprus|5.4|2.1 +USA, Canada, Australia, New Zealand|0.2|0.9 +Other North America, Central America, etc.|0.3|2.6 +South America|2.0|2.6 +West Asia|3.4|3.0 +South Central Asia|3.0|2.5 +Southeast Asia|0.8|1.7 +East Asia|0.2|0.8 +North Africa|0.7|3.7 +East Africa|1.5|2.8 +Other Africa|0.6|4.2 +Unclassified|0.0|1.2 From 03ae730f6c0d0f9aeda57a7641bc35226be16310 Mon Sep 17 00:00:00 2001 From: Kiki Date: Fri, 4 Mar 2016 00:28:50 -0500 Subject: [PATCH 07/24] Added immigration to counties and ran ipynb --- 2007_immigration_to_counties.csv | 333 ++++++++++++++++++++++++++++ immigration_to_countries_notes.docx | Bin 0 -> 4543 bytes 2 files changed, 333 insertions(+) create mode 100644 2007_immigration_to_counties.csv create mode 100644 immigration_to_countries_notes.docx diff --git a/2007_immigration_to_counties.csv b/2007_immigration_to_counties.csv new file mode 100644 index 0000000..1d689f4 --- /dev/null +++ b/2007_immigration_to_counties.csv @@ -0,0 +1,333 @@ +Place Municipality Population Women Men 0-17 18-64 65.0 Married Single Foreign Background Foreign Born Foreign National Average Age County +Country 9182927 50.3 49.7 21.0 61.4 17.5 34.1 9.4 17.3 13.4 5.7 41.0 -2 +Stockholms county 1949516 50.7 49.3 22.0 63.9 14.2 31.9 10.3 26.3 19.7 8.6 39.0 1 +Botkyrka 2 79031 49.8 50.2 24.8 63.6 11.6 35.7 11.1 50.8 35.2 14.7 36.8 1 +Danderyd 2 30789 51.7 48.3 26.3 55.4 18.2 40.5 7.6 16.1 13.4 6.3 40.2 1 +Ekerö 2 24687 50.1 49.9 28.0 59.3 12.7 37.8 7.8 11.1 8.8 3.8 37.7 1 +Haninge 2 73698 49.9 50.1 24.0 63.6 12.4 33.4 11.0 27.1 19.8 9.1 37.8 1 +Huddinge 2 91827 50.1 49.9 25.3 63.0 11.7 34.2 9.8 31.7 23.7 10.8 37.0 1 +Järfälla 2 63427 50.2 49.8 23.2 61.4 15.4 35.9 10.3 28.9 20.9 7.7 39.4 1 +Lidingö 2 42710 52.2 47.8 23.9 57.8 18.3 36.9 9.7 16.7 13.8 6.3 41.0 1 +Nacka 2 84303 50.9 49.1 25.4 61.4 13.2 34.7 9.5 21.9 16.7 7.4 38.1 1 +Norrtälje 7 55225 50.0 50.0 20.5 58.9 20.6 35.4 10.3 11.3 9.1 4.0 43.3 1 +Nykvarn 4 8926 49.2 50.8 27.3 60.3 12.5 36.3 7.9 15.2 10.8 5.2 37.6 1 +Nynäshamn 4 25353 49.7 50.3 22.4 60.7 16.9 35.4 11.2 14.3 11.5 5.3 41.1 1 +Salem 2 15065 50.8 49.2 28.1 58.5 13.4 36.3 8.2 19.2 13.9 6.3 37.0 1 +Sigtuna 4 37793 50.2 49.8 23.9 62.5 13.6 33.9 10.7 28.4 20.9 8.8 38.1 1 +Sollentuna 2 61387 50.5 49.5 26.0 60.2 13.8 37.1 9.0 23.6 17.7 6.8 38.1 1 +Solna 2 63318 51.2 48.8 15.6 68.0 16.4 27.4 11.2 28.8 22.3 9.8 40.7 1 +Stockholm 1 795163 51.2 48.8 18.9 66.9 14.2 27.6 11.0 27.6 21.0 9.1 39.4 1 +Sundbyberg 2 35078 50.6 49.4 18.3 68.2 13.4 25.6 12.1 31.3 23.1 9.7 39.0 1 +Södertälje 3 83642 49.7 50.3 22.7 62.5 14.8 33.4 9.9 40.0 28.5 12.9 38.8 1 +Tyresö 2 42047 50.3 49.7 26.7 60.1 13.3 34.1 9.9 18.3 13.6 6.1 37.5 1 +Täby 2 61633 50.8 49.2 24.9 58.8 16.3 39.8 8.6 17.1 13.6 5.4 39.8 1 +Upplands Väsby 2 38055 50.2 49.8 22.9 63.6 13.5 33.7 10.6 29.3 21.6 9.0 38.7 1 +Upplands-Bro 2 22221 50.3 49.7 24.1 63.8 12.0 35.1 10.1 25.8 19.1 8.7 38.1 1 +Vallentuna 2 28382 49.9 50.1 27.0 60.5 12.5 36.1 8.6 14.2 11.0 4.8 37.2 1 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61.2 14.4 36.4 9.3 20.8 15.7 6.6 38.8 22 +Större städer 3 2528609 50.5 49.5 20.6 62.4 16.9 33.4 9.1 16.5 12.7 5.1 40.4 22 +Pendlingskommuner 4 592864 49.8 50.2 22.9 59.7 17.4 37.0 9.0 11.8 9.2 4.2 41.0 22 +Glesbygdskommuner 5 298858 49.2 50.8 19.4 57.1 23.5 32.8 8.4 6.9 6.1 3.7 44.9 22 +Varuprod. kommuner 6 586400 49.5 50.5 21.2 58.4 20.4 37.9 7.9 13.9 11.1 4.7 42.7 22 +Övriga, > 25 000 inv. 7 1256796 50.3 49.7 20.6 59.5 19.9 35.5 9.4 11.5 9.1 3.9 42.5 22 +Övr, 12 500 - 25 000 inv. 8 647470 49.9 50.1 20.0 58.3 21.7 35.7 9.1 10.3 8.4 4.0 43.8 22 +Övriga, < 12 500 inv. 9 265714 49.6 50.4 19.9 57.7 22.4 36.1 9.2 11.6 9.7 5.8 44.5 22 + +Distribution Measurements + Max 795163 52.2 52.5 28.6 68.2 30.2 44.5 12.4 50.8 39.2 28.2 48.2 -1 + 90 % 63329 50.8 51.1 24.3 61.6 24.4 39.4 10.2 22.1 16.9 7.2 45.5 -1 + 75 % 32839 50.3 50.7 22.2 60.0 22.0 37.8 9.7 14.5 11.5 5.3 44.2 -1 + Median (50 %) 15285 49.8 50.2 20.9 58.8 20.3 36.2 8.9 10.7 8.6 4.0 42.8 -1 + 25 % 9976 49.3 49.7 19.5 57.8 17.6 34.2 8.0 7.4 6.3 3.1 41.0 -1 + 10 % 6520 48.9 49.2 18.7 56.5 14.7 31.7 7.4 5.6 5.0 2.4 39.0 -1 + Min 2549 47.5 47.8 15.6 50.7 10.3 25.6 5.6 3.8 3.4 1.3 36.1 -1 diff --git a/immigration_to_countries_notes.docx b/immigration_to_countries_notes.docx new file mode 100644 index 0000000000000000000000000000000000000000..2eff3e9abed813a4d49881ee5b51f11ed84b5085 GIT binary patch literal 4543 zcmaJ^bzGBs``#F%k?!u2Qu-wvUBZThh=fQwIs}9XNC^XRgg80`MM9)Ox(1>&BNPk- z1e8vx-*(RHk>l?@=iX=g?2p}hp6mMF_w`&C)Br?C4s))m29+7eK@9%zApNFHZuPPtzP2&Zo_ZI zGSc49GI*SfO)$##yj4$8-ZQm(k3%oy=17lYZ}nQeBB(jkJ(8EZ!&u25Dd=QVbp1t@ zf&_z6ki)QDK~S;x!7Z6oia=|ZFem{=j!3?D-r1K zruD$!T!$2e=3XZ6CMWIH^~ zpW3*0l;i!jyi_?Btfcb8qp8tDoowL7+qF5Yknw5SD_pwieVx{_@>u;)4821;OL3)G z8hv$-PZeDn57R2_iP4p6u|=$06e_nYjcdW7Nbu$os(M+0c$b){DOzXjN=KpGWWDdDGwB(Zdgq^v^LsT_)?0qK?|j z=6y3;WJZlD^3e1W+W|;t)wau#_6gq!2E|R%R!5EobCoQ`j-pxiwSgq;N9RwBMy}*yni%p*XVPkS0(V1l`G{v1$ zxODH(n1~)E_|A;rsQh(Us(egqg}XI8imhZog_RZcG2DYAS~sNyLc9JfxfiG^p?3H* 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Sweden|both parents foreign born|foreign born crimes against persons|21|19|20|29 theft|26|28|27|24 fraud|11|10|10|10 @@ -6,4 +6,3 @@ damage|4|4|4|3 driving offenses|19|19|19|15 drug offenses|4|5|5|4 other crimes|15|14|14|15 -number of violations|880525|187127|100847|348138 diff --git a/immigration_plots.ipynb b/immigration_plots.ipynb index dda4a23..2a5e850 100644 --- a/immigration_plots.ipynb +++ b/immigration_plots.ipynb @@ -3547,6 +3547,342 @@ "# url = py.plot(fig, filename='d3-world-map')" ] }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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country of birthpercentage of suspectsover representation
0Nordic countries except Sweden4.71.4
1EU15 Excluding Denmark, Finland, Sweden1.21.1
2New EU 10 countries1.61.8
3Other European countries including Turkey and ...5.42.1
4USA, Canada, Australia, New Zealand0.20.9
\n", + "
" + ], + "text/plain": [ + " country of birth percentage of suspects \\\n", + "0 Nordic countries except Sweden 4.7 \n", + "1 EU15 Excluding Denmark, Finland, Sweden 1.2 \n", + "2 New EU 10 countries 1.6 \n", + "3 Other European countries including Turkey and ... 5.4 \n", + "4 USA, Canada, Australia, New Zealand 0.2 \n", + "\n", + " over representation \n", + "0 1.4 \n", + "1 1.1 \n", + "2 1.8 \n", + "3 2.1 \n", + "4 0.9 " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "\n", + "overrep_df = pd.read_table('respondent_birth_country_overrepresentation.txt', sep='|')\n", + "overrep_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+SXPtRo0aUaxYsTT3M2TIEO7fv09gYGCWLi3LiMFgoFWrVtSsWZOSJUvSqVMn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\nRESeOEu/20m1arULO8YT79SpX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set_context(\"poster\")\n", + "sns.barplot(y=\"country of birth\", x=\"percentage of suspects\", data=overrep_df);\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SJNX1DB48mHv37hEYGPhMl6ilx2Qy0bJlS2rUqEHx4sXp2LEjd+/e5fDhw2m2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\nTFQsEhERERERERERk0KWDkBER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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.barplot(y=\"country of birth\", x=\"over representation\", data=overrep_df);\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From these two bar graphs, it is evident that people who have an African heritage have a much greater representation as suspects in crime than any other foreign-born residents. Another visualization that I hope to accomplish is to relate each of the regions specified with a map. I will also put these bar graphs side by side, as to make it easier to compare each category." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " type of crime both parents born in Sweden \\\n", + "0 crimes against persons 21 \n", + "1 theft 26 \n", + "2 fraud 11 \n", + "3 damage 4 \n", + "4 driving offenses 19 \n", + "\n", + " one parent born in Sweden both parents foreign born foreign born \n", + "0 19 20 29 \n", + "1 28 27 24 \n", + "2 10 10 10 \n", + "3 4 4 3 \n", + "4 19 19 15 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crime_dist_df = pd.read_table('crime_distribution_by_origin.txt', sep='|')\n", + "crime_dist_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def recode_crime_dist(df):\n", + " committers = []\n", + " for committer in df.columns.unique():\n", + " if committer != \"type of crime\":\n", + " committers.append(committer)\n", + " crime_types = []\n", + " for i in range(4*len(df[\"type of crime\"])):\n", + " crime_types.append(df[\"type of crime\"][i%4])\n", + " percent_col = []\n", + " for committer in committers:\n", + " percent_col.append(df[committer])\n", + " percent_col = np.squeeze(np.hstack(tuple(percent_col)))\n", + " data = {\"type of crime\": pd.Series(crime_types),\n", + " \"origin\": pd.Series(committers*4),\n", + " \"percent\": pd.Series(percent_col)}\n", + " data = pd.DataFrame(data)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " origin percent type of crime\n", + "0 both parents born in Sweden 21 crimes against persons\n", + "1 one parent born in Sweden 26 theft\n", + "2 both parents foreign born 11 fraud\n", + "3 foreign born 4 damage\n", + "4 both parents born in Sweden 19 crimes against persons" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recoded_crime_dist = recode_crime_dist(crime_dist_df)\n", + "recoded_crime_dist.head()" + ] + }, { "cell_type": "code", "execution_count": null, diff --git a/respondent_birth_country_overrepresentation.txt b/respondent_birth_country_overrepresentation.txt index d9a2196..8fc9729 100644 --- a/respondent_birth_country_overrepresentation.txt +++ b/respondent_birth_country_overrepresentation.txt @@ -1,4 +1,4 @@ -Country of birth|percentage of suspects|over representation +country of birth|percentage of suspects|over representation Nordic countries except Sweden|4.7|1.4 EU15 Excluding Denmark, Finland, Sweden|1.2|1.1 New EU 10 countries|1.6|1.8 From f1ff6ed297cc8f199bc99613d00e7b028d07b55b Mon Sep 17 00:00:00 2001 From: Pratool Gadtaula Date: Fri, 4 Mar 2016 10:38:12 -0500 Subject: [PATCH 09/24] Added breakdown of crime committers by origin plot --- .gitignore | 2 +- .../immigration_plots-checkpoint.ipynb | 521 +++++++++++++++--- immigration_plots.ipynb | 69 ++- 3 files changed, 521 insertions(+), 71 deletions(-) diff --git a/.gitignore b/.gitignore index ae4ecad..5060aa5 100644 --- a/.gitignore +++ b/.gitignore @@ -1,3 +1,3 @@ *.pyc -.ipynb_checkpoints/* +.ipynb_checkpoints* *.ods diff --git a/.ipynb_checkpoints/immigration_plots-checkpoint.ipynb b/.ipynb_checkpoints/immigration_plots-checkpoint.ipynb index 971b638..8504ac0 100644 --- a/.ipynb_checkpoints/immigration_plots-checkpoint.ipynb +++ b/.ipynb_checkpoints/immigration_plots-checkpoint.ipynb @@ -14,16 +14,6 @@ "1.9.6\n" ] }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/font_manager.py:273: UserWarning:\n", - "\n", - "Matplotlib is building the font cache using fc-list. This may take a moment.\n", - "\n" - ] - }, { "data": { "text/html": [ @@ -82,7 +72,7 @@ { "data": { "text/html": [ - "
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" ], "text/plain": [ "" @@ -113,18 +103,7 @@ "metadata": { "collapsed": false }, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning:\n", - "\n", - "axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import plotly.plotly as py\n", "import seaborn\n", @@ -134,7 +113,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -902,7 +881,7 @@ "[76 rows x 7 columns]" ] }, - "execution_count": 34, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -914,7 +893,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -1559,7 +1538,7 @@ "67 0 0 " ] }, - "execution_count": 35, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -1586,23 +1565,25 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 5, "metadata": { "collapsed": false }, "outputs": [ { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" + "ename": "PlotlyLocalCredentialsError", + "evalue": "\nCouldn't find a 'username', 'api-key' pair for you on your local machine. To sign in temporarily (until you stop running Python), run:\n>>> import plotly.plotly as py\n>>> py.sign_in('username', 'api_key')\n\nEven better, save your credentials permanently using the 'tools' module:\n>>> import plotly.tools as tls\n>>> tls.set_credentials_file(username='username', api_key='api-key')\n\nFor more help, see https://plot.ly/python.\n", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mPlotlyLocalCredentialsError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 44\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mgo\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mFigure\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdat\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[0m_plot_option_logic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_send_to_plotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36m_send_to_plotly\u001b[1;34m(figure, **plot_options)\u001b[0m\n\u001b[0;32m 1374\u001b[0m cls=utils.PlotlyJSONEncoder)\n\u001b[0;32m 1375\u001b[0m \u001b[0mcredentials\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_credentials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1376\u001b[1;33m \u001b[0mvalidate_credentials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcredentials\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1377\u001b[0m \u001b[0musername\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'username'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1378\u001b[0m \u001b[0mapi_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'api_key'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mvalidate_credentials\u001b[1;34m(credentials)\u001b[0m\n\u001b[0;32m 1323\u001b[0m \u001b[0mapi_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'api_key'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1324\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0musername\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mapi_key\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1325\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyLocalCredentialsError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1327\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mPlotlyLocalCredentialsError\u001b[0m: \nCouldn't find a 'username', 'api-key' pair for you on your local machine. To sign in temporarily (until you stop running Python), run:\n>>> import plotly.plotly as py\n>>> py.sign_in('username', 'api_key')\n\nEven better, save your credentials permanently using the 'tools' module:\n>>> import plotly.tools as tls\n>>> tls.set_credentials_file(username='username', api_key='api-key')\n\nFor more help, see https://plot.ly/python.\n" + ] } ], "source": [ @@ -1655,7 +1636,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -1664,7 +1645,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "/home/kiki/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning:\n", + "-c:1: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", @@ -2366,7 +2347,7 @@ "67 0 0 265 " ] }, - "execution_count": 37, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -2378,23 +2359,25 @@ }, { "cell_type": "code", - "execution_count": 98, + "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { - "data": { - "text/html": [ - "" - ], - "text/plain": [ - "" - ] - }, - "execution_count": 98, - "metadata": {}, - "output_type": "execute_result" + "ename": "PlotlyLocalCredentialsError", + "evalue": "\nCouldn't find a 'username', 'api-key' pair for you on your local machine. To sign in temporarily (until you stop running Python), run:\n>>> import plotly.plotly as py\n>>> py.sign_in('username', 'api_key')\n\nEven better, save your credentials permanently using the 'tools' module:\n>>> import plotly.tools as tls\n>>> tls.set_credentials_file(username='username', api_key='api-key')\n\nFor more help, see https://plot.ly/python.\n", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mPlotlyLocalCredentialsError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 6\u001b[0m )\n\u001b[0;32m 7\u001b[0m ]\n\u001b[1;32m----> 8\u001b[1;33m \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdat\u001b[0m\u001b[1;33m,\u001b[0m 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\u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[0mplot_options\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_plot_option_logic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_send_to_plotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36m_send_to_plotly\u001b[1;34m(figure, **plot_options)\u001b[0m\n\u001b[0;32m 1374\u001b[0m cls=utils.PlotlyJSONEncoder)\n\u001b[0;32m 1375\u001b[0m \u001b[0mcredentials\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_credentials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1376\u001b[1;33m \u001b[0mvalidate_credentials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcredentials\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1377\u001b[0m \u001b[0musername\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'username'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1378\u001b[0m \u001b[0mapi_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'api_key'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mvalidate_credentials\u001b[1;34m(credentials)\u001b[0m\n\u001b[0;32m 1323\u001b[0m \u001b[0mapi_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'api_key'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1324\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0musername\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mapi_key\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1325\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyLocalCredentialsError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1327\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mPlotlyLocalCredentialsError\u001b[0m: \nCouldn't find a 'username', 'api-key' pair for you on your local machine. To sign in temporarily (until you stop running Python), run:\n>>> import plotly.plotly as py\n>>> py.sign_in('username', 'api_key')\n\nEven better, save your credentials permanently using the 'tools' module:\n>>> import plotly.tools as tls\n>>> tls.set_credentials_file(username='username', api_key='api-key')\n\nFor more help, see https://plot.ly/python.\n" + ] } ], "source": [ @@ -2410,7 +2393,7 @@ }, { "cell_type": "code", - "execution_count": 59, + "execution_count": 8, "metadata": { "collapsed": false }, @@ -3489,7 +3472,7 @@ "[222 rows x 11 columns]" ] }, - "execution_count": 59, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -3505,23 +3488,24 @@ }, { "cell_type": "code", - "execution_count": 78, + "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { - "ename": "PlotlyError", - "evalue": "Request throttled. You've created/updated more charts than your allowed limit of 50/day. You may either wait one day or upgrade your account. Visit https://plot.ly/settings/subscription/ to upgrade.", + "ename": "PlotlyLocalCredentialsError", + "evalue": "\nCouldn't find a 'username', 'api-key' pair for you on your local machine. To sign in temporarily (until you stop running Python), run:\n>>> import plotly.plotly as py\n>>> py.sign_in('username', 'api_key')\n\nEven better, save your credentials permanently using the 'tools' module:\n>>> import plotly.tools as tls\n>>> tls.set_credentials_file(username='username', api_key='api-key')\n\nFor more help, see https://plot.ly/python.\n", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mPlotlyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlayout\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 35\u001b[0m \u001b[1;31m# url = py.plot(fig, filename='d3-world-map')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/home/kiki/anaconda/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'auto_open'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 150\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/home/kiki/anaconda/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[0mplot_options\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_plot_option_logic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_send_to_plotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m/home/kiki/anaconda/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36m_send_to_plotly\u001b[1;34m(figure, **plot_options)\u001b[0m\n\u001b[0;32m 1402\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1403\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'error'\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mr\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1404\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1405\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1406\u001b[0m \u001b[1;31m# Check if the url needs a secret key\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mPlotlyError\u001b[0m: Request throttled. You've created/updated more charts than your allowed limit of 50/day. You may either wait one day or upgrade your account. Visit https://plot.ly/settings/subscription/ to upgrade." + "\u001b[1;31mPlotlyLocalCredentialsError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m()\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 33\u001b[0m \u001b[0mfig\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m(\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlayout\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlayout\u001b[0m \u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 34\u001b[1;33m \u001b[0mpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfig\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 35\u001b[0m \u001b[1;31m# url = py.plot(fig, filename='d3-world-map')\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36miplot\u001b[1;34m(figure_or_data, **plot_options)\u001b[0m\n\u001b[0;32m 149\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;34m'auto_open'\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 150\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mFalse\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 151\u001b[1;33m \u001b[0murl\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mplot\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 152\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 153\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure_or_data\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mplot\u001b[1;34m(figure_or_data, validate, **plot_options)\u001b[0m\n\u001b[0;32m 239\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 240\u001b[0m \u001b[0mplot_options\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_plot_option_logic\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 241\u001b[1;33m \u001b[0mres\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0m_send_to_plotly\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfigure\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mplot_options\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 242\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mres\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'error'\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;34m''\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 243\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mplot_options\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'auto_open'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36m_send_to_plotly\u001b[1;34m(figure, **plot_options)\u001b[0m\n\u001b[0;32m 1374\u001b[0m cls=utils.PlotlyJSONEncoder)\n\u001b[0;32m 1375\u001b[0m \u001b[0mcredentials\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mget_credentials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1376\u001b[1;33m \u001b[0mvalidate_credentials\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcredentials\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1377\u001b[0m \u001b[0musername\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'username'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1378\u001b[0m \u001b[0mapi_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'api_key'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m/usr/lib/python2.7/site-packages/plotly/plotly/plotly.pyc\u001b[0m in \u001b[0;36mvalidate_credentials\u001b[1;34m(credentials)\u001b[0m\n\u001b[0;32m 1323\u001b[0m \u001b[0mapi_key\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcredentials\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'api_key'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1324\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0musername\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mapi_key\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1325\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mexceptions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mPlotlyLocalCredentialsError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1326\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1327\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mPlotlyLocalCredentialsError\u001b[0m: \nCouldn't find a 'username', 'api-key' pair for you on your local machine. To sign in temporarily (until you stop running Python), run:\n>>> import plotly.plotly as py\n>>> py.sign_in('username', 'api_key')\n\nEven better, save your credentials permanently using the 'tools' module:\n>>> import plotly.tools as tls\n>>> tls.set_credentials_file(username='username', api_key='api-key')\n\nFor more help, see https://plot.ly/python.\n" ] } ], @@ -3565,18 +3549,419 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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country of birthpercentage of suspectsover representation
0Nordic countries except Sweden4.71.4
1EU15 Excluding Denmark, Finland, Sweden1.21.1
2New EU 10 countries1.61.8
3Other European countries including Turkey and ...5.42.1
4USA, Canada, Australia, New Zealand0.20.9
\n", + "
" + ], + "text/plain": [ + " country of birth percentage of suspects \\\n", + "0 Nordic countries except Sweden 4.7 \n", + "1 EU15 Excluding Denmark, Finland, Sweden 1.2 \n", + "2 New EU 10 countries 1.6 \n", + "3 Other European countries including Turkey and ... 5.4 \n", + "4 USA, Canada, Australia, New Zealand 0.2 \n", + "\n", + " over representation \n", + "0 1.4 \n", + "1 1.1 \n", + "2 1.8 \n", + "3 2.1 \n", + "4 0.9 " + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "\n", + "overrep_df = pd.read_table('respondent_birth_country_overrepresentation.txt', sep='|')\n", + "overrep_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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+SXPtRo0aUaxYsTT3M2TIEO7fv09gYGCWLi3LiMFgoFWrVtSsWZOSJUvSqVMn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\nRESeOEu/20m1arULO8YT79SpX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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set_context(\"poster\")\n", + "sns.barplot(y=\"country of birth\", x=\"percentage of suspects\", data=overrep_df);\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SJNX1DB48mHv37hEYGPhMl6ilx2Qy0bJlS2rUqEHx4sXp2LEjd+/e5fDhw2m2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\nTFQsEhERERERERERk0KWDkBER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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.barplot(y=\"country of birth\", x=\"over representation\", data=overrep_df);\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From these two bar graphs, it is evident that people who have an African heritage have a much greater representation as suspects in crime than any other foreign-born residents. Another visualization that I hope to accomplish is to relate each of the regions specified with a map. I will also put these bar graphs side by side, as to make it easier to compare each category." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " type of crime both parents born in Sweden \\\n", + "0 crimes against persons 21 \n", + "1 theft 26 \n", + "2 fraud 11 \n", + "3 damage 4 \n", + "4 driving offenses 19 \n", + "\n", + " one parent born in Sweden both parents foreign born foreign born \n", + "0 19 20 29 \n", + "1 28 27 24 \n", + "2 10 10 10 \n", + "3 4 4 3 \n", + "4 19 19 15 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crime_dist_df = pd.read_table('crime_distribution_by_origin.txt', sep='|')\n", + "crime_dist_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def recode_crime_dist(df):\n", + " committers = []\n", + " for committer in df.columns.unique():\n", + " if committer != \"type of crime\":\n", + " committers.append(committer)\n", + " crime_types = []\n", + " for i in range(4*len(df[\"type of crime\"])):\n", + " crime_types.append(df[\"type of crime\"][i%4])\n", + " percent_col = []\n", + " for committer in committers:\n", + " percent_col.append(df[committer])\n", + " percent_col = np.squeeze(np.hstack(tuple(percent_col)))\n", + " data = {\"type of crime\": pd.Series(crime_types),\n", + " \"origin\": pd.Series(committers*4),\n", + " \"percent\": pd.Series(percent_col)}\n", + " data = pd.DataFrame(data)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " origin percent type of crime\n", + "0 both parents born in Sweden 21 crimes against persons\n", + "1 one parent born in Sweden 26 theft\n", + "2 both parents foreign born 11 fraud\n", + "3 foreign born 4 damage\n", + "4 both parents born in Sweden 19 crimes against persons" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recoded_crime_dist = recode_crime_dist(crime_dist_df)\n", + "recoded_crime_dist.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "def grouped_histogram(df):\n", + " col_names = ['both parents born in Sweden', 'one parent born in Sweden',\\\n", + " 'both parents foreign born', 'foreign born']\n", + " col1 = df[col_names[0]]\n", + " col2 = df[col_names[1]]\n", + " col3 = df[col_names[2]]\n", + " col4 = df[col_names[3]]\n", + " N = len(col1)\n", + "\n", + " ind = np.arange(N) # the x locations for the groups\n", + " width = 0.15 # the width of the bars\n", + "\n", + " fig, ax = plt.subplots()\n", + " rects1 = ax.bar(ind, col1, width, color='#9b59b6')\n", + " rects2 = ax.bar(ind + width, col2, width, color='#3498db')\n", + " rects3 = ax.bar(ind + 2*width, col3, width, color='#95a5a6')\n", + " rects4 = ax.bar(ind + 3*width, col4, width, color='#e74c3c')\n", + "\n", + " # add some text for labels, title and axes ticks\n", + " ax.set_ylabel('Percent of Crime Committers')\n", + " ax.set_title('Breakdown of Crime Committers by Origin')\n", + " ax.set_xticks(ind + width)\n", + " ax.set_xticklabels(df[\"type of crime\"])\n", + " ax.set_xlabel('Types of Crime')\n", + "\n", + " ax.legend((rects1[0], rects2[0], rects3[0], rects4[0]), col_names)\n", + "\n", + "\n", + " def autolabel(rects):\n", + " # attach some text labels\n", + " for rect in rects:\n", + " height = rect.get_height()\n", + " ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,\n", + " '%d' % int(height),\n", + " ha='center', va='bottom')\n", + "\n", + " #autolabel(rects1)\n", + " #autolabel(rects2)\n", + " #autolabel(rects3)\n", + " #autolabel(rects4)\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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wSyH+7eTNhhD/QZ6enmo3t/Hx8URGRhIcHMyWLVtYtmwZRkZGqv2GhoY5Jozeu3cPpVKJ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\nV8ybNw9hY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grouped_histogram(crime_dist_df)" + ] } ], "metadata": { "kernelspec": { "display_name": "Python 2", - "language": "python", + "language": "python2", "name": "python2" }, "language_info": { diff --git a/immigration_plots.ipynb b/immigration_plots.ipynb index 2a5e850..8504ac0 100644 --- a/immigration_plots.ipynb +++ b/immigration_plots.ipynb @@ -3885,12 +3885,77 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": true }, "outputs": [], - "source": [] + "source": [ + "def grouped_histogram(df):\n", + " col_names = ['both parents born in Sweden', 'one parent born in Sweden',\\\n", + " 'both parents foreign born', 'foreign born']\n", + " col1 = df[col_names[0]]\n", + " col2 = df[col_names[1]]\n", + " col3 = df[col_names[2]]\n", + " col4 = df[col_names[3]]\n", + " N = len(col1)\n", + "\n", + " ind = np.arange(N) # the x locations for the groups\n", + " width = 0.15 # the width of the bars\n", + "\n", + " fig, ax = plt.subplots()\n", + " rects1 = ax.bar(ind, col1, width, color='#9b59b6')\n", + " rects2 = ax.bar(ind + width, col2, width, color='#3498db')\n", + " rects3 = ax.bar(ind + 2*width, col3, width, color='#95a5a6')\n", + " rects4 = ax.bar(ind + 3*width, col4, width, color='#e74c3c')\n", + "\n", + " # add some text for labels, title and axes ticks\n", + " ax.set_ylabel('Percent of Crime Committers')\n", + " ax.set_title('Breakdown of Crime Committers by Origin')\n", + " ax.set_xticks(ind + width)\n", + " ax.set_xticklabels(df[\"type of crime\"])\n", + " ax.set_xlabel('Types of Crime')\n", + "\n", + " ax.legend((rects1[0], rects2[0], rects3[0], rects4[0]), col_names)\n", + "\n", + "\n", + " def autolabel(rects):\n", + " # attach some text labels\n", + " for rect in rects:\n", + " height = rect.get_height()\n", + " ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,\n", + " '%d' % int(height),\n", + " ha='center', va='bottom')\n", + "\n", + " #autolabel(rects1)\n", + " #autolabel(rects2)\n", + " #autolabel(rects3)\n", + " #autolabel(rects4)\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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wSyH+7eTNhhD/QZ6enmo3t/Hx8URGRhIcHMyWLVtYtmwZRkZGqv2GhoY5Jozeu3cPpVKJ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\nV8ybNw9hY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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grouped_histogram(crime_dist_df)" + ] } ], "metadata": { From 4748b0c7c677202157ea1c7fa964607d7d6e1b11 Mon Sep 17 00:00:00 2001 From: Pratool Gadtaula Date: Sat, 5 Mar 2016 14:12:37 -0500 Subject: [PATCH 10/24] restructuring directories --- .../immigration_plots-checkpoint.ipynb | 160 ++++- Bokeh Tutorial.ipynb | 320 +++++++++ crime_representation.ipynb | 606 ++++++++++++++++++ .../2005_immigration_data.csv | 0 .../2007_immigration_to_counties.csv | 0 .../crime_distribution_by_origin.txt | 0 data/crime_on_distribution_by_origin.txt | 24 + .../immigration_to_countries_notes.docx | Bin ..._and_parents_origin_overrepresentation.txt | 0 ...ndent_birth_country_overrepresentation.txt | 0 data/world-110m2.json | 1 + data/world-country-names.tsv | 253 ++++++++ immigration_interactive.html | 127 ++++ immigration_plots.ipynb | 160 ++++- interactive-map.html | 46 ++ 15 files changed, 1695 insertions(+), 2 deletions(-) create mode 100644 Bokeh Tutorial.ipynb create mode 100644 crime_representation.ipynb rename 2005_immigration_data.csv => data/2005_immigration_data.csv (100%) rename 2007_immigration_to_counties.csv => data/2007_immigration_to_counties.csv (100%) rename crime_distribution_by_origin.txt => data/crime_distribution_by_origin.txt (100%) create mode 100644 data/crime_on_distribution_by_origin.txt rename immigration_to_countries_notes.docx => data/immigration_to_countries_notes.docx (100%) rename respondent_and_parents_origin_overrepresentation.txt => data/respondent_and_parents_origin_overrepresentation.txt (100%) rename respondent_birth_country_overrepresentation.txt => data/respondent_birth_country_overrepresentation.txt (100%) create mode 100644 data/world-110m2.json create mode 100644 data/world-country-names.tsv create mode 100644 immigration_interactive.html create mode 100644 interactive-map.html diff --git a/.ipynb_checkpoints/immigration_plots-checkpoint.ipynb b/.ipynb_checkpoints/immigration_plots-checkpoint.ipynb index 8504ac0..20994cf 100644 --- a/.ipynb_checkpoints/immigration_plots-checkpoint.ipynb +++ b/.ipynb_checkpoints/immigration_plots-checkpoint.ipynb @@ -3939,7 +3939,8 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false + "collapsed": false, + "scrolled": true }, "outputs": [ { @@ -3956,6 +3957,163 @@ "source": [ "grouped_histogram(crime_dist_df)" ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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type of crimeborn in Sweden with Swedish-born parentsimmigrant childrenforeign born
0crimes against life and health1.402.504.10
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4trespassing0.250.460.50
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" + ], + "text/plain": [ + " type of crime \\\n", + "0 crimes against life and health \n", + "1 lethal violence and attempted murder and mansl... \n", + "2 aggravated assault \n", + "3 crimes against freedom and peace \n", + "4 trespassing \n", + "\n", + " born in Sweden with Swedish-born parents immigrant children foreign born \n", + "0 1.40 2.50 4.10 \n", + "1 0.04 0.09 0.15 \n", + "2 1.40 2.40 4.10 \n", + "3 1.10 1.90 3.40 \n", + "4 0.25 0.46 0.50 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crime_on_dist = pd.read_table('crime_on_distribution_by_origin.txt', sep='|')\n", + "crime_on_dist.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def grouped_histogram2(df):\n", + " col_names = ['born in Sweden with Swedish-born parents', 'immigrant children',\\\n", + " 'foreign born']\n", + " col1 = df[col_names[0]]\n", + " col2 = df[col_names[1]]\n", + " col3 = df[col_names[2]]\n", + " N = len(col1)\n", + "\n", + " ind = np.arange(N) # the x locations for the groups\n", + " width = 0.2 # the width of the bars\n", + "\n", + " fig, ax = plt.subplots()\n", + " rects1 = ax.bar(ind, col1, width, color='#9b59b6')\n", + " rects2 = ax.bar(ind + width, col2, width, color='#3498db')\n", + " rects3 = ax.bar(ind + 2*width, col3, width, color='#95a5a6')\n", + "\n", + " # add some text for labels, title and axes ticks\n", + " ax.set_ylabel('Percent of Crime Committers')\n", + " ax.set_title('Breakdown of People that Crimes are Committed Against')\n", + " ax.set_xticks(ind + width)\n", + " ax.set_xticklabels(df[\"type of crime\"])\n", + " ax.set_xlabel('Types of Crime')\n", + "\n", + " ax.legend((rects1[0], rects2[0], rects3[0]), col_names)\n", + "\n", + "\n", + " def autolabel(rects):\n", + " # attach some text labels\n", + " for rect in rects:\n", + " height = rect.get_height()\n", + " ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,\n", + " '%d' % int(height),\n", + " ha='center', va='bottom')\n", + "\n", + " #autolabel(rects1)\n", + " #autolabel(rects2)\n", + " #autolabel(rects3)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SmD9/vsbyZs6cSceOHRk9ejTbt2/HysqKNWvWMHfuXKZOnYqOjg41atRg6dKl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\nFKXy4osvql+/fg5jS5Ys0caNGzV//vzij2f5vY4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Q//79De4UAAAAgCfiiqgJZWZm6pZb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\n05qG2YMHpRgAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grouped_histogram2(crime_on_dist);" + ] } ], "metadata": { diff --git a/Bokeh Tutorial.ipynb b/Bokeh Tutorial.ipynb new file mode 100644 index 0000000..d563c25 --- /dev/null +++ b/Bokeh Tutorial.ipynb @@ -0,0 +1,320 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Bokeh Tutorial" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from bokeh.plotting import figure\n", + "from bokeh.models import Range1d\n", + "from bokeh.embed import components\n", + "\n", + "# create some data\n", + "x1 = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n", + "y1 = [0, 8, 2, 4, 6, 9, 5, 6, 25, 28, 4, 7]\n", + "x2 = [2, 5, 7, 15, 18, 19, 25, 28, 9, 10, 4]\n", + "y2 = [2, 4, 6, 9, 15, 18, 0, 8, 2, 25, 28]\n", + "x3 = [0, 1, 0, 8, 2, 4, 6, 9, 7, 8, 9]\n", + "y3 = [0, 8, 4, 6, 9, 15, 18, 19, 19, 25, 28]\n", + "\n", + "# select the tools we want\n", + "TOOLS=\"pan,wheel_zoom,box_zoom,reset,save\"\n", + "\n", + "# the red and blue graphs will share this data range\n", + "xr1 = Range1d(start=0, end=30)\n", + "yr1 = Range1d(start=0, end=30)\n", + "\n", + "# only the green will use this data range\n", + "xr2 = Range1d(start=0, end=30)\n", + "yr2 = Range1d(start=0, end=30)\n", + "\n", + "# build our figures\n", + "p1 = figure(x_range=xr1, y_range=yr1, tools=TOOLS, plot_width=300, plot_height=300)\n", + "p1.scatter(x1, y1, size=12, color=\"red\", alpha=0.5)\n", + "\n", + "p2 = figure(x_range=xr1, y_range=yr1, tools=TOOLS, plot_width=300, plot_height=300)\n", + "p2.scatter(x2, y2, size=12, color=\"blue\", alpha=0.5)\n", + "\n", + "p3 = figure(x_range=xr2, y_range=yr2, tools=TOOLS, plot_width=300, plot_height=300)\n", + "p3.scatter(x3, y3, size=12, color=\"green\", alpha=0.5)\n", + "\n", + "# plots can be a single PlotObject, a list/tuple, or even a dictionary\n", + "plots = {'Red': p1, 'Blue': p2, 'Green': p3}" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "from bokeh.plotting import figure\n", + "from bokeh.embed import notebook_div\n", + "\n", + "plot = figure()\n", + "plot.circle([1,2], [3,4])\n", + "\n", + "div = notebook_div(plot)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
\n", + " \n", + " Loading BokehJS ...\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/javascript": [ + "\n", + "(function(global) {\n", + " function now() {\n", + " return new Date();\n", + " }\n", + "\n", + " if (typeof (window._bokeh_onload_callbacks) === \"undefined\") {\n", + " window._bokeh_onload_callbacks = [];\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " window._bokeh_onload_callbacks.forEach(function(callback) { callback() });\n", + " delete window._bokeh_onload_callbacks\n", + " console.info(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(js_urls, callback) {\n", + " window._bokeh_onload_callbacks.push(callback);\n", + " if (window._bokeh_is_loading > 0) {\n", + " console.log(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.log(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " window._bokeh_is_loading = js_urls.length;\n", + " for (var i = 0; i < js_urls.length; i++) {\n", + " var url = js_urls[i];\n", + " var s = document.createElement('script');\n", + " s.src = url;\n", + " s.async = false;\n", + " s.onreadystatechange = s.onload = function() {\n", + " window._bokeh_is_loading--;\n", + " if (window._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: all BokehJS libraries loaded\");\n", + " run_callbacks()\n", + " }\n", + " };\n", + " s.onerror = function() {\n", + " console.warn(\"failed to load library \" + url);\n", + " };\n", + " console.log(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.getElementsByTagName(\"head\")[0].appendChild(s);\n", + " }\n", + " };\n", + "\n", + " var js_urls = ['https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.js', 'https://cdn.pydata.org/bokeh/release/bokeh-compiler-0.11.1.min.js'];\n", + "\n", + " var inline_js = [\n", + " function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + " \n", + " function(Bokeh) {\n", + " Bokeh.$(\"#25833594-5904-4187-bbcd-be0c76d8643e\").text(\"BokehJS successfully loaded\");\n", + " },\n", + " function(Bokeh) {\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.11.1.min.css\");\n", + " console.log(\"Bokeh: injecting CSS: https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n", + " Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-widgets-0.11.1.min.css\");\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " for (var i = 0; i < inline_js.length; i++) {\n", + " inline_js[i](window.Bokeh);\n", + " }\n", + " }\n", + "\n", + " if (window._bokeh_is_loading === 0) {\n", + " console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(js_urls, function() {\n", + " console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(this));" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "\n", + "\n", + "
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<Bokeh Notebook handle for In[8]>

" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from bokeh.io import output_notebook, show\n", + "output_notebook()\n", + "show(plot)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python2", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/crime_representation.ipynb b/crime_representation.ipynb new file mode 100644 index 0000000..b8d3230 --- /dev/null +++ b/crime_representation.ipynb @@ -0,0 +1,606 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Crime Representation of Minorities in Sweden" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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country of birthpercentage of suspectsover representation
0Nordic countries except Sweden4.71.4
1EU15 Excluding Denmark, Finland, Sweden1.21.1
2New EU 10 countries1.61.8
3Other European countries including Turkey and ...5.42.1
4USA, Canada, Australia, New Zealand0.20.9
\n", + "
" + ], + "text/plain": [ + " country of birth percentage of suspects \\\n", + "0 Nordic countries except Sweden 4.7 \n", + "1 EU15 Excluding Denmark, Finland, Sweden 1.2 \n", + "2 New EU 10 countries 1.6 \n", + "3 Other European countries including Turkey and ... 5.4 \n", + "4 USA, Canada, Australia, New Zealand 0.2 \n", + "\n", + " over representation \n", + "0 1.4 \n", + "1 1.1 \n", + "2 1.8 \n", + "3 2.1 \n", + "4 0.9 " + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "%matplotlib inline\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "import pandas as pd\n", + "\n", + "overrep_df = pd.read_table('respondent_birth_country_overrepresentation.txt', sep='|')\n", + "overrep_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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TalPjd8Td3yGCgz1Sv14kpqRtmQsI1fS9ERERERGRJlJVXa1/i4s0hhTgmLfw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\nX7Hp3OGIE8rP3SqDH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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.set_context(\"poster\")\n", + "sns.barplot(y=\"country of birth\", x=\"percentage of suspects\", data=overrep_df);\n", + "plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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q2+a5QFFd3xsREREREamwdjU1+n90kZaQAh/zFl4tn5aNAV5198Mq17M5n5nd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\n+Uh1zn2BM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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "sns.barplot(y=\"country of birth\", x=\"over representation\", data=overrep_df);\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "From these two bar graphs, it is evident that people who have an African heritage have a much greater representation as suspects in crime than any other foreign-born residents. Another visualization that I hope to accomplish is to relate each of the regions specified with a map. I will also put these bar graphs side by side, as to make it easier to compare each category." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " type of crime both parents born in Sweden \\\n", + "0 crimes against persons 21 \n", + "1 theft 26 \n", + "2 fraud 11 \n", + "3 damage 4 \n", + "4 driving offenses 19 \n", + "\n", + " one parent born in Sweden both parents foreign born foreign born \n", + "0 19 20 29 \n", + "1 28 27 24 \n", + "2 10 10 10 \n", + "3 4 4 3 \n", + "4 19 19 15 " + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crime_dist_df = pd.read_table('crime_distribution_by_origin.txt', sep='|')\n", + "crime_dist_df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "def recode_crime_dist(df):\n", + " committers = []\n", + " for committer in df.columns.unique():\n", + " if committer != \"type of crime\":\n", + " committers.append(committer)\n", + " crime_types = []\n", + " for i in range(4*len(df[\"type of crime\"])):\n", + " crime_types.append(df[\"type of crime\"][i%4])\n", + " percent_col = []\n", + " for committer in committers:\n", + " percent_col.append(df[committer])\n", + " percent_col = np.squeeze(np.hstack(tuple(percent_col)))\n", + " data = {\"type of crime\": pd.Series(crime_types),\n", + " \"origin\": pd.Series(committers*4),\n", + " \"percent\": pd.Series(percent_col)}\n", + " data = pd.DataFrame(data)\n", + " return data" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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4both parents born in Sweden19crimes against persons
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" + ], + "text/plain": [ + " origin percent type of crime\n", + "0 both parents born in Sweden 21 crimes against persons\n", + "1 one parent born in Sweden 26 theft\n", + "2 both parents foreign born 11 fraud\n", + "3 foreign born 4 damage\n", + "4 both parents born in Sweden 19 crimes against persons" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "recoded_crime_dist = recode_crime_dist(crime_dist_df)\n", + "recoded_crime_dist.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def grouped_histogram(df):\n", + " col_names = ['both parents born in Sweden', 'one parent born in Sweden',\\\n", + " 'both parents foreign born', 'foreign born']\n", + " col1 = df[col_names[0]]\n", + " col2 = df[col_names[1]]\n", + " col3 = df[col_names[2]]\n", + " col4 = df[col_names[3]]\n", + " N = len(col1)\n", + "\n", + " ind = np.arange(N) # the x locations for the groups\n", + " width = 0.15 # the width of the bars\n", + "\n", + " fig, ax = plt.subplots()\n", + " rects1 = ax.bar(ind, col1, width, color='#9b59b6')\n", + " rects2 = ax.bar(ind + width, col2, width, color='#3498db')\n", + " rects3 = ax.bar(ind + 2*width, col3, width, color='#95a5a6')\n", + " rects4 = ax.bar(ind + 3*width, col4, width, color='#e74c3c')\n", + "\n", + " # add some text for labels, title and axes ticks\n", + " ax.set_ylabel('Percent of Crime Committers')\n", + " ax.set_title('Breakdown of Crime Committers by Origin')\n", + " ax.set_xticks(ind + width)\n", + " ax.set_xticklabels(df[\"type of crime\"])\n", + " ax.set_xlabel('Types of Crime')\n", + "\n", + " ax.legend((rects1[0], rects2[0], rects3[0], rects4[0]), col_names)\n", + "\n", + "\n", + " def autolabel(rects):\n", + " # attach some text labels\n", + " for rect in rects:\n", + " height = rect.get_height()\n", + " ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,\n", + " '%d' % int(height),\n", + " ha='center', va='bottom')\n", + "\n", + " #autolabel(rects1)\n", + " #autolabel(rects2)\n", + " #autolabel(rects3)\n", + " #autolabel(rects4)\n", + "\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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CxJTp+GBmO2rA9dp1t9fbNq6mLaZ7Vp56caET+2fXXUWhY5mtC+Q2oJlSyqVLmFKqlFJq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type of crimeborn in Sweden with Swedish-born parentsimmigrant childrenforeign born
0crimes against life and health1.402.504.10
1lethal violence and attempted murder and mansl...0.040.090.15
2aggravated assault1.402.404.10
3crimes against freedom and peace1.101.903.40
4trespassing0.250.460.50
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" + ], + "text/plain": [ + " type of crime \\\n", + "0 crimes against life and health \n", + "1 lethal violence and attempted murder and mansl... \n", + "2 aggravated assault \n", + "3 crimes against freedom and peace \n", + "4 trespassing \n", + "\n", + " born in Sweden with Swedish-born parents immigrant children foreign born \n", + "0 1.40 2.50 4.10 \n", + "1 0.04 0.09 0.15 \n", + "2 1.40 2.40 4.10 \n", + "3 1.10 1.90 3.40 \n", + "4 0.25 0.46 0.50 " + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crime_on_dist = pd.read_table('crime_on_distribution_by_origin.txt', sep='|')\n", + "crime_on_dist.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def grouped_histogram2(df):\n", + " col_names = ['born in Sweden with Swedish-born parents', 'immigrant children',\\\n", + " 'foreign born']\n", + " col1 = df[col_names[0]]\n", + " col2 = df[col_names[1]]\n", + " col3 = df[col_names[2]]\n", + " N = len(col1)\n", + "\n", + " ind = np.arange(N) # the x locations for the groups\n", + " width = 0.2 # the width of the bars\n", + "\n", + " fig, ax = plt.subplots()\n", + " rects1 = ax.bar(ind, col1, width, color='#9b59b6')\n", + " rects2 = ax.bar(ind + width, col2, width, color='#3498db')\n", + " rects3 = ax.bar(ind + 2*width, col3, width, color='#95a5a6')\n", + "\n", + " # add some text for labels, title and axes ticks\n", + " ax.set_ylabel('Percent of Crime Committers')\n", + " ax.set_title('Breakdown of People that Crimes are Committed Against')\n", + " ax.set_xticks(ind + width)\n", + " ax.set_xticklabels(df[\"type of crime\"])\n", + " ax.set_xlabel('Types of Crime')\n", + "\n", + " ax.legend((rects1[0], rects2[0], rects3[0]), col_names)\n", + "\n", + "\n", + " def autolabel(rects):\n", + " # attach some text labels\n", + " for rect in rects:\n", + " height = rect.get_height()\n", + " ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,\n", + " '%d' % int(height),\n", + " ha='center', va='bottom')\n", + "\n", + " #autolabel(rects1)\n", + " #autolabel(rects2)\n", + " #autolabel(rects3)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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atWtL7dqPU79+TYYM6c/p06ed38+bN4unn27E33//xcsvt6FWrWo8/3xzfv75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Qzvy5PFvYgCiLrch2h0cbo/jcwP/iul+21R5hg0RqGEl56+KPPB4n8QLV9PHts2orny/U3JMgao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\n4chPpFju0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+ "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grouped_histogram2(crime_on_dist);" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python2", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} diff --git a/2005_immigration_data.csv b/data/2005_immigration_data.csv similarity index 100% rename from 2005_immigration_data.csv rename to data/2005_immigration_data.csv diff --git a/2007_immigration_to_counties.csv b/data/2007_immigration_to_counties.csv similarity index 100% rename from 2007_immigration_to_counties.csv rename to data/2007_immigration_to_counties.csv diff --git a/crime_distribution_by_origin.txt b/data/crime_distribution_by_origin.txt similarity index 100% rename from crime_distribution_by_origin.txt rename to data/crime_distribution_by_origin.txt diff --git a/data/crime_on_distribution_by_origin.txt b/data/crime_on_distribution_by_origin.txt new file mode 100644 index 0000000..77c7908 --- /dev/null +++ b/data/crime_on_distribution_by_origin.txt @@ -0,0 +1,24 @@ +type of crime|born in Sweden with Swedish-born parents|immigrant children|foreign born +crimes against life and health|1.40|2.50|4.10 +lethal violence and attempted murder and manslaughter|0.04|0.09|0.15 +aggravated assault|1.40|2.40|4.10 +crimes against freedom and peace|1.10|1.90|3.40 +trespassing|0.25|0.46|0.50 +unlawful threats|0.70|1.40|2.70 +molestation|0.32|0.54|0.89 +sexual crimes|0.15|0.23|0.49 +rape / attempted rape|0.04|0.08|0.22 +theft, robbery, and other acquisitive crime|1.50|2.80|4.20 +robbery|0.09|0.28|0.35 +theft of car|0.29|0.63|0.43 +burglary of residence|0.10|0.20|0.16 +theft in stores|0.70|1.30|2.70 +fraud, embezzlement, breach of trust|1.00|1.70|2.50 +vandalism|0.60|1.10|1.00 +counterfeiting|0.17|0.30|0.42 +violence against officials and violent resistance|0.44|0.90|1.30 +breach of weapons law|0.17|0.39|0.35 +breach of the law on the prohibition of knives|0.22|0.51|0.45 +traffic offense act except drunk driving|0.70|1.30|1.90 +drunk driving|0.80|1.20|1.10 +crimes against drug criminals law|0.50|1.10|1.00 diff --git a/immigration_to_countries_notes.docx b/data/immigration_to_countries_notes.docx similarity index 100% rename from immigration_to_countries_notes.docx rename to data/immigration_to_countries_notes.docx diff --git a/respondent_and_parents_origin_overrepresentation.txt b/data/respondent_and_parents_origin_overrepresentation.txt similarity index 100% rename from respondent_and_parents_origin_overrepresentation.txt rename to data/respondent_and_parents_origin_overrepresentation.txt diff --git a/respondent_birth_country_overrepresentation.txt b/data/respondent_birth_country_overrepresentation.txt similarity index 100% rename from respondent_birth_country_overrepresentation.txt rename to data/respondent_birth_country_overrepresentation.txt diff --git a/data/world-110m2.json b/data/world-110m2.json new file mode 100644 index 0000000..598d1eb --- /dev/null +++ b/data/world-110m2.json @@ -0,0 +1 @@ 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\ No newline at end of file diff --git a/data/world-country-names.tsv b/data/world-country-names.tsv new file mode 100644 index 0000000..c4c9f4e --- /dev/null +++ b/data/world-country-names.tsv @@ -0,0 +1,253 @@ +id name +-1 Northern Cyprus +-2 Kosovo +-3 Somaliland +4 Afghanistan +8 Albania +10 Antarctica +12 Algeria +16 American Samoa +20 Andorra +24 Angola +28 Antigua and Barbuda +31 Azerbaijan +32 Argentina +36 Australia +40 Austria +44 Bahamas +48 Bahrain +50 Bangladesh +51 Armenia +52 Barbados +56 Belgium +60 Bermuda +64 Bhutan +68 Bolivia, Plurinational State of +70 Bosnia and Herzegovina +72 Botswana +74 Bouvet Island +76 Brazil +84 Belize +86 British Indian Ocean Territory +90 Solomon Islands +92 Virgin Islands, British +96 Brunei Darussalam +100 Bulgaria +104 Myanmar +108 Burundi +112 Belarus +116 Cambodia +120 Cameroon +124 Canada +132 Cape Verde +136 Cayman Islands +140 Central African Republic +144 Sri Lanka +148 Chad +152 Chile +156 China +158 Taiwan, Province of China +162 Christmas Island +166 Cocos (Keeling) Islands +170 Colombia +174 Comoros +175 Mayotte +178 Congo +180 Congo, the Democratic Republic of the +184 Cook Islands +188 Costa Rica +191 Croatia +192 Cuba +196 Cyprus +203 Czech Republic +204 Benin +208 Denmark +212 Dominica +214 Dominican Republic +218 Ecuador +222 El Salvador +226 Equatorial Guinea +231 Ethiopia +232 Eritrea +233 Estonia +234 Faroe Islands +238 Falkland Islands (Malvinas) +239 South Georgia and the South Sandwich Islands +242 Fiji +246 Finland +248 Åland Islands +250 France +254 French Guiana +258 French Polynesia +260 French Southern Territories +262 Djibouti +266 Gabon +268 Georgia +270 Gambia +275 Palestinian Territory, Occupied +276 Germany +288 Ghana +292 Gibraltar +296 Kiribati +300 Greece +304 Greenland +308 Grenada +312 Guadeloupe +316 Guam +320 Guatemala +324 Guinea +328 Guyana +332 Haiti +334 Heard Island and McDonald Islands +336 Holy See (Vatican City State) +340 Honduras +344 Hong Kong +348 Hungary +352 Iceland +356 India +360 Indonesia +364 Iran, Islamic Republic of +368 Iraq +372 Ireland +376 Israel +380 Italy +384 Côte d'Ivoire +388 Jamaica +392 Japan +398 Kazakhstan +400 Jordan +404 Kenya +408 Korea, Democratic People's Republic of +410 Korea, Republic of +414 Kuwait +417 Kyrgyzstan +418 Lao People's Democratic Republic +422 Lebanon +426 Lesotho +428 Latvia +430 Liberia +434 Libya +438 Liechtenstein +440 Lithuania +442 Luxembourg +446 Macao +450 Madagascar +454 Malawi +458 Malaysia +462 Maldives +466 Mali +470 Malta +474 Martinique +478 Mauritania +480 Mauritius +484 Mexico +492 Monaco +496 Mongolia +498 Moldova, Republic of +499 Montenegro +500 Montserrat +504 Morocco +508 Mozambique +512 Oman +516 Namibia +520 Nauru +524 Nepal +528 Netherlands +531 Curaçao +533 Aruba +534 Sint Maarten (Dutch part) +535 Bonaire, Sint Eustatius and Saba +540 New Caledonia +548 Vanuatu +554 New Zealand +558 Nicaragua +562 Niger +566 Nigeria +570 Niue +574 Norfolk Island +578 Norway +580 Northern Mariana Islands +581 United States Minor Outlying Islands +583 Micronesia, Federated States of +584 Marshall Islands +585 Palau +586 Pakistan +591 Panama +598 Papua New Guinea +600 Paraguay +604 Peru +608 Philippines +612 Pitcairn +616 Poland +620 Portugal +624 Guinea-Bissau +626 Timor-Leste +630 Puerto Rico +634 Qatar +638 Réunion +642 Romania +643 Russian Federation +646 Rwanda +652 Saint Barthélemy +654 Saint Helena, Ascension and Tristan da Cunha +659 Saint Kitts and Nevis +660 Anguilla +662 Saint Lucia +663 Saint Martin (French part) +666 Saint Pierre and Miquelon +670 Saint Vincent and the Grenadines +674 San Marino +678 Sao Tome and Principe +682 Saudi Arabia +686 Senegal +688 Serbia +690 Seychelles +694 Sierra Leone +702 Singapore +703 Slovakia +704 Viet Nam +705 Slovenia +706 Somalia +710 South Africa +716 Zimbabwe +724 Spain +728 South Sudan +729 Sudan +732 Western Sahara +740 Suriname +744 Svalbard and Jan Mayen +748 Swaziland +752 Sweden +756 Switzerland +760 Syrian Arab Republic +762 Tajikistan +764 Thailand +768 Togo +772 Tokelau +776 Tonga +780 Trinidad and Tobago +784 United Arab Emirates +788 Tunisia +792 Turkey +795 Turkmenistan +796 Turks and Caicos Islands +798 Tuvalu +800 Uganda +804 Ukraine +807 Macedonia, the former Yugoslav Republic of +818 Egypt +826 United Kingdom +831 Guernsey +832 Jersey +833 Isle of Man +834 Tanzania, United Republic of +840 United States +850 Virgin Islands, U.S. +854 Burkina Faso +858 Uruguay +860 Uzbekistan +862 Venezuela, Bolivarian Republic of +876 Wallis and Futuna +882 Samoa +887 Yemen +894 Zambia diff --git a/immigration_interactive.html b/immigration_interactive.html new file mode 100644 index 0000000..fd76168 --- /dev/null +++ b/immigration_interactive.html @@ -0,0 +1,127 @@ + + + Interactive Swedish Immigration Statistics + + + + + + + + \ No newline at end of file diff --git a/immigration_plots.ipynb b/immigration_plots.ipynb index 8504ac0..20994cf 100644 --- a/immigration_plots.ipynb +++ b/immigration_plots.ipynb @@ -3939,7 +3939,8 @@ "cell_type": "code", "execution_count": 10, "metadata": { - "collapsed": false + "collapsed": false, + "scrolled": true }, "outputs": [ { @@ -3956,6 +3957,163 @@ "source": [ "grouped_histogram(crime_dist_df)" ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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type of crimeborn in Sweden with Swedish-born parentsimmigrant childrenforeign born
0crimes against life and health1.402.504.10
1lethal violence and attempted murder and mansl...0.040.090.15
2aggravated assault1.402.404.10
3crimes against freedom and peace1.101.903.40
4trespassing0.250.460.50
\n", + "
" + ], + "text/plain": [ + " type of crime \\\n", + "0 crimes against life and health \n", + "1 lethal violence and attempted murder and mansl... \n", + "2 aggravated assault \n", + "3 crimes against freedom and peace \n", + "4 trespassing \n", + "\n", + " born in Sweden with Swedish-born parents immigrant children foreign born \n", + "0 1.40 2.50 4.10 \n", + "1 0.04 0.09 0.15 \n", + "2 1.40 2.40 4.10 \n", + "3 1.10 1.90 3.40 \n", + "4 0.25 0.46 0.50 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crime_on_dist = pd.read_table('crime_on_distribution_by_origin.txt', sep='|')\n", + "crime_on_dist.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "def grouped_histogram2(df):\n", + " col_names = ['born in Sweden with Swedish-born parents', 'immigrant children',\\\n", + " 'foreign born']\n", + " col1 = df[col_names[0]]\n", + " col2 = df[col_names[1]]\n", + " col3 = df[col_names[2]]\n", + " N = len(col1)\n", + "\n", + " ind = np.arange(N) # the x locations for the groups\n", + " width = 0.2 # the width of the bars\n", + "\n", + " fig, ax = plt.subplots()\n", + " rects1 = ax.bar(ind, col1, width, color='#9b59b6')\n", + " rects2 = ax.bar(ind + width, col2, width, color='#3498db')\n", + " rects3 = ax.bar(ind + 2*width, col3, width, color='#95a5a6')\n", + "\n", + " # add some text for labels, title and axes ticks\n", + " ax.set_ylabel('Percent of Crime Committers')\n", + " ax.set_title('Breakdown of People that Crimes are Committed Against')\n", + " ax.set_xticks(ind + width)\n", + " ax.set_xticklabels(df[\"type of crime\"])\n", + " ax.set_xlabel('Types of Crime')\n", + "\n", + " ax.legend((rects1[0], rects2[0], rects3[0]), col_names)\n", + "\n", + "\n", + " def autolabel(rects):\n", + " # attach some text labels\n", + " for rect in rects:\n", + " height = rect.get_height()\n", + " ax.text(rect.get_x() + rect.get_width()/2., 1.05*height,\n", + " '%d' % int(height),\n", + " ha='center', va='bottom')\n", + "\n", + " #autolabel(rects1)\n", + " #autolabel(rects2)\n", + " #autolabel(rects3)\n", + " plt.show()" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "image/png": 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SmD9/vsbyZs6cSceOHRk9ejTbt2/HysqKNWvWMHfuXKZOnYqOjg41atRg6dKl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\nFKXy4osvql+/fg5jS5Ys0caNGzV//vzij2f5vY4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Q//79De4UAAAAgCfiiqgJZWZm6pZb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\n05qG2YMHpRgAAAAASUVORK5CYII=\n", + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "grouped_histogram2(crime_on_dist);" + ] } ], "metadata": { diff --git a/interactive-map.html b/interactive-map.html new file mode 100644 index 0000000..fca628d --- /dev/null +++ b/interactive-map.html @@ -0,0 +1,46 @@ + + + Interactive Map + + + + + + + + \ No newline at end of file From 2d30021c77284b8fb78e1a495d9ba05c75177728 Mon Sep 17 00:00:00 2001 From: Kiki Date: Sat, 5 Mar 2016 14:24:03 -0500 Subject: [PATCH 11/24] updated filepaths --- immigration_plots.ipynb | 130 +++++++++++++++++++++++----------------- 1 file changed, 74 insertions(+), 56 deletions(-) diff --git a/immigration_plots.ipynb b/immigration_plots.ipynb index 20994cf..86df8d1 100644 --- a/immigration_plots.ipynb +++ b/immigration_plots.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": 3, "metadata": { "collapsed": false }, @@ -68,42 +68,32 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
" - ], - "text/plain": [ - "" - ] - }, - "metadata": {}, - "output_type": "display_data" } ], "source": [ "import plotly\n", "print plotly.__version__ # version 1.9.4 required\n", - "plotly.offline.init_notebook_mode() # run at the start of every notebook\n", - "plotly.offline.iplot({\n", - "\"data\": [{\n", - " \"x\": [1, 2, 3],\n", - " \"y\": [4, 2, 5]\n", - "}],\n", - "\"layout\": {\n", - " \"title\": \"hello world\"\n", - "}\n", - "})\n" + "plotly.offline.init_notebook_mode() # run at the start of every notebook\n" ] }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 4, "metadata": { "collapsed": false }, - "outputs": [], + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning:\n", + "\n", + "axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", + "\n" + ] + } + ], "source": [ "import plotly.plotly as py\n", "import seaborn\n", @@ -113,7 +103,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -881,19 +871,19 @@ "[76 rows x 7 columns]" ] }, - "execution_count": 3, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "data = pd.read_table('2005_immigration_data.csv')\n", + "data = pd.read_table('data/2005_immigration_data.csv')\n", "data" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 7, "metadata": { "collapsed": false }, @@ -1538,7 +1528,7 @@ "67 0 0 " ] }, - "execution_count": 4, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -1563,6 +1553,22 @@ "countries\n" ] }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [ + "ref = countries['2005_refugees']\n", + "fam = countries['2005_fam_reun']\n", + "labour = countries['2005_labour']\n", + "students = countries['2005_students']\n", + "adopt = countries['2005_adopted']\n", + "country_names = countries['Citizenship']" + ] + }, { "cell_type": "code", "execution_count": 5, @@ -1587,13 +1593,6 @@ } ], "source": [ - "ref = countries['2005_refugees']\n", - "fam = countries['2005_fam_reun']\n", - "labour = countries['2005_labour']\n", - "students = countries['2005_students']\n", - "adopt = countries['2005_adopted']\n", - "country_names = countries['Citizenship']\n", - "\n", "df = pd.DataFrame({'x': country_names, 'y': ref, 'y2':fam, 'y3':labour, 'y4':students, 'y5':adopt})\n", "df.head()\n", "\n", @@ -1636,7 +1635,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 9, "metadata": { "collapsed": false }, @@ -1645,7 +1644,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "-c:1: SettingWithCopyWarning:\n", + "/home/kiki/anaconda/lib/python2.7/site-packages/IPython/kernel/__main__.py:1: SettingWithCopyWarning:\n", "\n", "\n", "A value is trying to be set on a copy of a slice from a DataFrame.\n", @@ -2347,7 +2346,7 @@ "67 0 0 265 " ] }, - "execution_count": 6, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -2393,7 +2392,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 10, "metadata": { "collapsed": false }, @@ -3472,7 +3471,7 @@ "[222 rows x 11 columns]" ] }, - "execution_count": 8, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -3549,7 +3548,7 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 11, "metadata": { "collapsed": false }, @@ -3618,7 +3617,7 @@ "4 0.9 " ] }, - "execution_count": 1, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -3629,22 +3628,32 @@ "import seaborn as sns\n", "import pandas as pd\n", "\n", - "overrep_df = pd.read_table('respondent_birth_country_overrepresentation.txt', sep='|')\n", + "overrep_df = pd.read_table('data/respondent_birth_country_overrepresentation.txt', sep='|')\n", "overrep_df.head()" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:892: UserWarning:\n", + "\n", + "axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", + "\n" + ] + }, { "data": { - "image/png": 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+SXPtRo0aUaxYsTT3M2TIEO7fv09gYGCWLi3LiMFgoFWrVtSsWZOSJUvSqVMn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\nRESeOEu/20m1arULO8YT79SpX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RaYMUFBIRERERERERaYNUU0hERFqktz/7tNpNEBEREWmQtz/7lJWq3QiREhQUEhGRFmnt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kiS2u/DQGbm/o0DdUbN62Tm+cD5ETEfpW7Q8cDJlDpDfYqIlYD1KTOX7mxpPqAe7y0p1JeplGVg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\nkiRJktSBT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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3659,16 +3668,16 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { - "image/png": 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SJNX1DB48mHv37hEYGPhMl6ilx2Qy0bJlS2rUqEHx4sXp2LEjd+/e5fDhw2m2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\nTFQsEhERERERERERk0KWDkBER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REZESChiJiIiIiIiIiEgJBYxERERERERERKSEahiJiEib9MbHH1W7CyJzlTc+/ohVq90J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\nRsRTvR/A4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"text/plain": [ - "" + "" ] }, "metadata": {}, @@ -3776,7 +3785,7 @@ } ], "source": [ - "crime_dist_df = pd.read_table('crime_distribution_by_origin.txt', sep='|')\n", + "crime_dist_df = pd.read_table('data/crime_distribution_by_origin.txt', sep='|')\n", "crime_dist_df.head()" ] }, @@ -3960,7 +3969,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": { "collapsed": false }, @@ -4035,13 +4044,13 @@ "4 0.25 0.46 0.50 " ] }, - "execution_count": 11, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "crime_on_dist = pd.read_table('crime_on_distribution_by_origin.txt', sep='|')\n", + "crime_on_dist = pd.read_table('data/crime_on_distribution_by_origin.txt', sep='|')\n", "crime_on_dist.head()" ] }, @@ -4114,12 +4123,21 @@ "source": [ "grouped_histogram2(crime_on_dist);" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": true + }, + "outputs": [], + "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", - "language": "python2", + "language": "python", "name": "python2" }, "language_info": { From 3b630c5d1230b561c822192b0f2f1dd2af5d3bc5 Mon Sep 17 00:00:00 2001 From: Kiki Date: Sat, 5 Mar 2016 16:00:38 -0500 Subject: [PATCH 12/24] Started working on counties --- County_Data.ipynb | 1443 +++++++++++++++++++++++++++++++++++++++++++++ 1 file changed, 1443 insertions(+) create mode 100644 County_Data.ipynb diff --git a/County_Data.ipynb b/County_Data.ipynb new file mode 100644 index 0000000..c229128 --- /dev/null +++ b/County_Data.ipynb @@ -0,0 +1,1443 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.9.6\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import plotly\n", + "print plotly.__version__ # version 1.9.4 required\n", + "plotly.offline.init_notebook_mode() # run at the start of every notebook\n", + "import plotly.plotly as py\n", + "import plotly.graph_objs as go\n", + "import seaborn\n", + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PlaceMunicipalityPopulationWomenMen0-1718-6465.0MarriedSingleForeign BackgroundForeign BornForeign NationalAverage AgeCountyLatLong
1Stockholms countyNaN194951650.749.322.063.914.231.910.326.319.78.639.0159.602518.1384
2Botkyrka27903149.850.224.863.611.635.711.150.835.214.736.81NaNNaN
3Danderyd23078951.748.326.355.418.240.57.616.113.46.340.21NaNNaN
4Ekerö22468750.149.928.059.312.737.87.811.18.83.837.71NaNNaN
5Haninge27369849.950.124.063.612.433.411.027.119.89.137.81NaNNaN
6Huddinge29182750.149.925.363.011.734.29.831.723.710.837.01NaNNaN
7Järfälla26342750.249.823.261.415.435.910.328.920.97.739.41NaNNaN
8Lidingö24271052.247.823.957.818.336.99.716.713.86.341.01NaNNaN
9Nacka28430350.949.125.461.413.234.79.521.916.77.438.11NaNNaN
10Norrtälje75522550.050.020.558.920.635.410.311.39.14.043.31NaNNaN
11Nykvarn4892649.250.827.360.312.536.37.915.210.85.237.61NaNNaN
12Nynäshamn42535349.750.322.460.716.935.411.214.311.55.341.11NaNNaN
13Salem21506550.849.228.158.513.436.38.219.213.96.337.01NaNNaN
14Sigtuna43779350.249.823.962.513.633.910.728.420.98.838.11NaNNaN
15Sollentuna26138750.549.526.060.213.837.19.023.617.76.838.11NaNNaN
16Solna26331851.248.815.668.016.427.411.228.822.39.840.71NaNNaN
17Stockholm179516351.248.818.966.914.227.611.027.621.09.139.41NaNNaN
18Sundbyberg23507850.649.418.368.213.425.612.131.323.19.739.01NaNNaN
19Södertälje38364249.750.322.762.514.833.49.940.028.512.938.81NaNNaN
20Tyresö24204750.349.726.760.113.334.19.918.313.66.137.51NaNNaN
21Täby26163350.849.224.958.816.339.88.617.113.65.439.81NaNNaN
22Upplands Väsby23805550.249.822.963.613.533.710.629.321.69.038.71NaNNaN
23Upplands-Bro22222150.349.724.163.812.035.110.125.819.18.738.11NaNNaN
24Vallentuna22838249.950.127.060.512.536.18.614.211.04.837.21NaNNaN
25Vaxholm21060050.649.427.059.313.737.88.910.78.73.338.61NaNNaN
26Värmdö23687049.450.627.660.811.534.89.013.610.35.437.21NaNNaN
27Österåker23828650.149.926.160.713.237.39.014.511.34.938.11NaNNaN
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" + ], + "text/plain": [ + " Place Municipality Population Women Men 0-17 18-64 \\\n", + "1 Stockholms county NaN 1949516 50.7 49.3 22.0 63.9 \n", + "2 Botkyrka 2 79031 49.8 50.2 24.8 63.6 \n", + "3 Danderyd 2 30789 51.7 48.3 26.3 55.4 \n", + "4 Ekerö 2 24687 50.1 49.9 28.0 59.3 \n", + "5 Haninge 2 73698 49.9 50.1 24.0 63.6 \n", + "6 Huddinge 2 91827 50.1 49.9 25.3 63.0 \n", + "7 Järfälla 2 63427 50.2 49.8 23.2 61.4 \n", + "8 Lidingö 2 42710 52.2 47.8 23.9 57.8 \n", + "9 Nacka 2 84303 50.9 49.1 25.4 61.4 \n", + "10 Norrtälje 7 55225 50.0 50.0 20.5 58.9 \n", + "11 Nykvarn 4 8926 49.2 50.8 27.3 60.3 \n", + "12 Nynäshamn 4 25353 49.7 50.3 22.4 60.7 \n", + "13 Salem 2 15065 50.8 49.2 28.1 58.5 \n", + "14 Sigtuna 4 37793 50.2 49.8 23.9 62.5 \n", + "15 Sollentuna 2 61387 50.5 49.5 26.0 60.2 \n", + "16 Solna 2 63318 51.2 48.8 15.6 68.0 \n", + "17 Stockholm 1 795163 51.2 48.8 18.9 66.9 \n", + "18 Sundbyberg 2 35078 50.6 49.4 18.3 68.2 \n", + "19 Södertälje 3 83642 49.7 50.3 22.7 62.5 \n", + "20 Tyresö 2 42047 50.3 49.7 26.7 60.1 \n", + "21 Täby 2 61633 50.8 49.2 24.9 58.8 \n", + "22 Upplands Väsby 2 38055 50.2 49.8 22.9 63.6 \n", + "23 Upplands-Bro 2 22221 50.3 49.7 24.1 63.8 \n", + "24 Vallentuna 2 28382 49.9 50.1 27.0 60.5 \n", + "25 Vaxholm 2 10600 50.6 49.4 27.0 59.3 \n", + "26 Värmdö 2 36870 49.4 50.6 27.6 60.8 \n", + "27 Österåker 2 38286 50.1 49.9 26.1 60.7 \n", + "\n", + " 65.0 Married Single Foreign Background Foreign Born Foreign National \\\n", + "1 14.2 31.9 10.3 26.3 19.7 8.6 \n", + "2 11.6 35.7 11.1 50.8 35.2 14.7 \n", + "3 18.2 40.5 7.6 16.1 13.4 6.3 \n", + "4 12.7 37.8 7.8 11.1 8.8 3.8 \n", + "5 12.4 33.4 11.0 27.1 19.8 9.1 \n", + "6 11.7 34.2 9.8 31.7 23.7 10.8 \n", + "7 15.4 35.9 10.3 28.9 20.9 7.7 \n", + "8 18.3 36.9 9.7 16.7 13.8 6.3 \n", + "9 13.2 34.7 9.5 21.9 16.7 7.4 \n", + "10 20.6 35.4 10.3 11.3 9.1 4.0 \n", + "11 12.5 36.3 7.9 15.2 10.8 5.2 \n", + "12 16.9 35.4 11.2 14.3 11.5 5.3 \n", + "13 13.4 36.3 8.2 19.2 13.9 6.3 \n", + "14 13.6 33.9 10.7 28.4 20.9 8.8 \n", + "15 13.8 37.1 9.0 23.6 17.7 6.8 \n", + "16 16.4 27.4 11.2 28.8 22.3 9.8 \n", + "17 14.2 27.6 11.0 27.6 21.0 9.1 \n", + "18 13.4 25.6 12.1 31.3 23.1 9.7 \n", + "19 14.8 33.4 9.9 40.0 28.5 12.9 \n", + "20 13.3 34.1 9.9 18.3 13.6 6.1 \n", + "21 16.3 39.8 8.6 17.1 13.6 5.4 \n", + "22 13.5 33.7 10.6 29.3 21.6 9.0 \n", + "23 12.0 35.1 10.1 25.8 19.1 8.7 \n", + "24 12.5 36.1 8.6 14.2 11.0 4.8 \n", + "25 13.7 37.8 8.9 10.7 8.7 3.3 \n", + "26 11.5 34.8 9.0 13.6 10.3 5.4 \n", + "27 13.2 37.3 9.0 14.5 11.3 4.9 \n", + "\n", + " Average Age County Lat Long \n", + "1 39.0 1 59.6025 18.1384 \n", + "2 36.8 1 NaN NaN \n", + "3 40.2 1 NaN NaN \n", + "4 37.7 1 NaN NaN \n", + "5 37.8 1 NaN NaN \n", + "6 37.0 1 NaN NaN \n", + "7 39.4 1 NaN NaN \n", + "8 41.0 1 NaN NaN \n", + "9 38.1 1 NaN NaN \n", + "10 43.3 1 NaN NaN \n", + "11 37.6 1 NaN NaN \n", + "12 41.1 1 NaN NaN \n", + "13 37.0 1 NaN NaN \n", + "14 38.1 1 NaN NaN \n", + "15 38.1 1 NaN NaN \n", + "16 40.7 1 NaN NaN \n", + "17 39.4 1 NaN NaN \n", + "18 39.0 1 NaN NaN \n", + "19 38.8 1 NaN NaN \n", + "20 37.5 1 NaN NaN \n", + "21 39.8 1 NaN NaN \n", + "22 38.7 1 NaN NaN \n", + "23 38.1 1 NaN NaN \n", + "24 37.2 1 NaN NaN \n", + "25 38.6 1 NaN NaN \n", + "26 37.2 1 NaN NaN \n", + "27 38.1 1 NaN NaN " + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "imm_counties = pd.read_table('data/2007_immigration_to_counties.csv')\n", + "\n", + "def clean(x):\n", + " try:\n", + " val = int(x)\n", + " if val == 22:\n", + " return -3\n", + " else:\n", + " return val\n", + " except:\n", + " return -3\n", + " \n", + "imm_counties['County'] = imm_counties['County'].apply(lambda x: clean(x))\n", + "counties = imm_counties[imm_counties.County > 0]\n", + "stockholm = counties[counties.County == 1]\n", + "stockholm" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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PlaceMunicipalityPopulationWomenMen0-1718-6465.0MarriedSingleForeign BackgroundForeign BornForeign NationalAverage AgeCountyLatLong
1Stockholms countyNaN194951650.749.322.063.914.231.910.326.319.78.639.0159.602518.1384
28Uppsala countyNaN32327050.449.621.663.215.333.48.915.712.24.939.6259.858317.6500
37Södermanlands countyNaN26519050.349.721.259.719.235.99.917.413.25.442.1359.033616.7519
47Östergötlands countyNaN42080950.050.020.961.118.035.08.513.710.64.141.1458.345415.5198
61Jönköpings countyNaN33361050.149.922.059.518.538.27.315.211.74.241.1557.370814.3439
75Kronobergs countyNaN18078749.750.320.960.219.036.67.614.712.15.441.6656.718314.4115
84Kalmar countyNaN23383450.249.819.859.221.035.98.79.57.83.443.3757.235016.1849
97Gotlands countyNaN5712250.649.420.060.619.532.29.65.44.41.742.8857.468418.4867
99Blekinge countyNaN15190049.550.520.059.520.536.68.811.29.44.442.7956.278415.0180
105Skåne countyNaN119935750.649.420.861.617.735.79.820.816.27.040.91055.990313.5958
139Hallands countyNaN29139350.349.722.259.518.337.68.512.39.63.441.21156.896712.8034
146Västra Götalands countyNaN154729850.249.821.161.817.234.59.217.913.65.540.71258.252813.0596
196Värmlands countyNaN27382650.249.819.959.720.433.09.610.48.84.943.01359.729413.2354
213Örebro countyNaN27606750.549.520.860.618.634.19.515.011.44.441.71459.535015.0066
226Västmanlands countyNaN24919350.249.820.860.418.835.09.920.215.15.841.81559.671416.2159
237Dalarnas countyNaN27561850.050.020.359.420.333.39.69.47.53.643.01661.091714.6664
253Gävleborgs countyNaN27555650.149.919.959.920.232.19.89.17.43.543.01761.301216.1534
264Västernorrlands countyNaN24344950.149.920.059.320.733.99.07.66.33.243.11863.427617.7292
272Jämtlands countyNaN12693750.149.920.059.920.029.38.86.15.43.042.81963.171214.9592
281Västerbottens countyNaN25759349.950.120.461.518.132.87.47.66.53.341.02065.333716.5162
297Norrbottens countyNaN25060249.350.719.860.519.633.48.910.38.54.742.62160.128218.6435
\n", + "
" + ], + "text/plain": [ + " Place Municipality Population Women Men 0-17 \\\n", + "1 Stockholms county NaN 1949516 50.7 49.3 22.0 \n", + "28 Uppsala county NaN 323270 50.4 49.6 21.6 \n", + "37 Södermanlands county NaN 265190 50.3 49.7 21.2 \n", + "47 Östergötlands county NaN 420809 50.0 50.0 20.9 \n", + "61 Jönköpings county NaN 333610 50.1 49.9 22.0 \n", + "75 Kronobergs county NaN 180787 49.7 50.3 20.9 \n", + "84 Kalmar county NaN 233834 50.2 49.8 19.8 \n", + "97 Gotlands county NaN 57122 50.6 49.4 20.0 \n", + "99 Blekinge county NaN 151900 49.5 50.5 20.0 \n", + "105 Skåne county NaN 1199357 50.6 49.4 20.8 \n", + "139 Hallands county NaN 291393 50.3 49.7 22.2 \n", + "146 Västra Götalands county NaN 1547298 50.2 49.8 21.1 \n", + "196 Värmlands county NaN 273826 50.2 49.8 19.9 \n", + "213 Örebro county NaN 276067 50.5 49.5 20.8 \n", + "226 Västmanlands county NaN 249193 50.2 49.8 20.8 \n", + "237 Dalarnas county NaN 275618 50.0 50.0 20.3 \n", + "253 Gävleborgs county NaN 275556 50.1 49.9 19.9 \n", + "264 Västernorrlands county NaN 243449 50.1 49.9 20.0 \n", + "272 Jämtlands county NaN 126937 50.1 49.9 20.0 \n", + "281 Västerbottens county NaN 257593 49.9 50.1 20.4 \n", + "297 Norrbottens county NaN 250602 49.3 50.7 19.8 \n", + "\n", + " 18-64 65.0 Married Single Foreign Background Foreign Born \\\n", + "1 63.9 14.2 31.9 10.3 26.3 19.7 \n", + "28 63.2 15.3 33.4 8.9 15.7 12.2 \n", + "37 59.7 19.2 35.9 9.9 17.4 13.2 \n", + "47 61.1 18.0 35.0 8.5 13.7 10.6 \n", + "61 59.5 18.5 38.2 7.3 15.2 11.7 \n", + "75 60.2 19.0 36.6 7.6 14.7 12.1 \n", + "84 59.2 21.0 35.9 8.7 9.5 7.8 \n", + "97 60.6 19.5 32.2 9.6 5.4 4.4 \n", + "99 59.5 20.5 36.6 8.8 11.2 9.4 \n", + "105 61.6 17.7 35.7 9.8 20.8 16.2 \n", + "139 59.5 18.3 37.6 8.5 12.3 9.6 \n", + "146 61.8 17.2 34.5 9.2 17.9 13.6 \n", + "196 59.7 20.4 33.0 9.6 10.4 8.8 \n", + "213 60.6 18.6 34.1 9.5 15.0 11.4 \n", + "226 60.4 18.8 35.0 9.9 20.2 15.1 \n", + "237 59.4 20.3 33.3 9.6 9.4 7.5 \n", + "253 59.9 20.2 32.1 9.8 9.1 7.4 \n", + "264 59.3 20.7 33.9 9.0 7.6 6.3 \n", + "272 59.9 20.0 29.3 8.8 6.1 5.4 \n", + "281 61.5 18.1 32.8 7.4 7.6 6.5 \n", + "297 60.5 19.6 33.4 8.9 10.3 8.5 \n", + "\n", + " Foreign National Average Age County Lat Long \n", + "1 8.6 39.0 1 59.6025 18.1384 \n", + "28 4.9 39.6 2 59.8583 17.6500 \n", + "37 5.4 42.1 3 59.0336 16.7519 \n", + "47 4.1 41.1 4 58.3454 15.5198 \n", + "61 4.2 41.1 5 57.3708 14.3439 \n", + "75 5.4 41.6 6 56.7183 14.4115 \n", + "84 3.4 43.3 7 57.2350 16.1849 \n", + "97 1.7 42.8 8 57.4684 18.4867 \n", + "99 4.4 42.7 9 56.2784 15.0180 \n", + "105 7.0 40.9 10 55.9903 13.5958 \n", + "139 3.4 41.2 11 56.8967 12.8034 \n", + "146 5.5 40.7 12 58.2528 13.0596 \n", + "196 4.9 43.0 13 59.7294 13.2354 \n", + "213 4.4 41.7 14 59.5350 15.0066 \n", + "226 5.8 41.8 15 59.6714 16.2159 \n", + "237 3.6 43.0 16 61.0917 14.6664 \n", + "253 3.5 43.0 17 61.3012 16.1534 \n", + "264 3.2 43.1 18 63.4276 17.7292 \n", + "272 3.0 42.8 19 63.1712 14.9592 \n", + "281 3.3 41.0 20 65.3337 16.5162 \n", + "297 4.7 42.6 21 60.1282 18.6435 " + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "county_loc = counties[counties.Lat.notnull()]\n", + "county_loc" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "county_loc.head()\n", + "\n", + "colors = ['rgb(239,243,255)','rgb(189,215,231)','rgb(107,174,214)','rgb(33,113,181)']\n", + "\n", + "for i in range(1,22)[::-1]:\n", + " cases.append(go.Scattergeo(\n", + " lon = county_loc[county_loc['County'] == i]['Long'], #-(max(range(6,10))-i),\n", + " lat = county_loc[county_loc['County'] == i]['Lat'],\n", + " text = county_loc[county_loc['County'] == i]['Place'],\n", + " name = county_loc[county_loc['County'] == i]['Place'],\n", + " marker = dict(\n", + " size = 1/50,\n", + " color = colors[2],\n", + " line = dict(width = 0)\n", + " ),\n", + " ) )\n", + "\n", + "inset = [\n", + " go.Choropleth(\n", + " locationmode = 'country names',\n", + " locations = 'sweden',\n", + " z = 1,\n", + " text = 'test',\n", + " colorscale = [[0,'rgb(0, 0, 0)'],[1,'rgb(0, 0, 0)']],\n", + " autocolorscale = False,\n", + " showscale = False,\n", + " geo = 'geo2'\n", + " ),\n", + " go.Scattergeo(\n", + " lat = [59.35],\n", + " lon = [18.0667],\n", + " text = ['Sweden'],\n", + " mode = 'text',\n", + " showlegend = False,\n", + " geo = 'geo2'\n", + " )\n", + "]\n", + "\n", + "layout = go.Layout(\n", + " title = 'Ebola cases reported by month in West Africa 2014
\\\n", + "Source: \\\n", + "HDX',\n", + " geo = dict(\n", + " resolution = 100,\n", + " scope = 'sweden',\n", + " showframe = True,\n", + " showcoastlines = True,\n", + " showland = True,\n", + " showcountries = True,\n", + " landcolor = \"rgb(229, 229, 229)\",\n", + " countrycolor = \"rgb(255, 255, 255)\" ,\n", + " coastlinecolor = \"rgb(255, 255, 255)\",\n", + " projection = dict(\n", + " type = 'Mercator'\n", + " ),\n", + " lataxis = dict( range= [ 58.928611, 65.710833 ] ),\n", + " lonaxis = dict( range= [13.3594, 20.5486 ] ),\n", + " domain = dict(\n", + " x = [ 0, 1 ],\n", + " y = [ 0, 1 ]\n", + " )\n", + " ),\n", + " geo2 = dict(\n", + " scope = 'europe',\n", + " showframe = True,\n", + " showland = True,\n", + " landcolor = \"rgb(229, 229, 229)\",\n", + " showcountries = False,\n", + " domain = dict(\n", + " x = [ 0, 0.3 ],\n", + " y = [ 0, 0.3 ]\n", + " ),\n", + " bgcolor = 'rgba(255, 255, 255, 0.0)',\n", + " ),\n", + " legend = dict(\n", + " traceorder = 'reversed'\n", + " )\n", + ")\n", + "\n", + "fig = go.Figure(layout=layout, data=cases+inset)\n", + "py.iplot(fig, validate=False, filename='West Africa Ebola cases 2014')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 49ee0bbdfdabf0d5bc6f3a57d680135f2f36cc50 Mon Sep 17 00:00:00 2001 From: Pratool Gadtaula Date: Sat, 5 Mar 2016 16:01:01 -0500 Subject: [PATCH 13/24] Added map selection by country name --- interactive-map.html | 76 +++++++++++++++++++++++++++++++++----------- 1 file changed, 58 insertions(+), 18 deletions(-) diff --git a/interactive-map.html b/interactive-map.html index fca628d..ffbb6fc 100644 --- a/interactive-map.html +++ b/interactive-map.html @@ -12,35 +12,75 @@ #32 { fill: red; } + + .country { + fill: #ccc; + stroke: #fff; + stroke-width: .5px; + stroke-linejoin: round; + } + + path[title="Afghanistan"] { + fill: black; + } + - +
+ + \ No newline at end of file From 9d8b7bdfbc209f41feb70c5ae57e7e89e03700b1 Mon Sep 17 00:00:00 2001 From: Pratool Gadtaula Date: Sat, 5 Mar 2016 22:56:08 -0500 Subject: [PATCH 14/24] Added region to country mapping data and have interactivity between bar graph and map --- data/region_country_mapping.json | 200 +++++++++++++++++++++++++++++++ interactive-map.html | 169 +++++++++++++++++++++++--- 2 files changed, 354 insertions(+), 15 deletions(-) create mode 100644 data/region_country_mapping.json diff --git a/data/region_country_mapping.json b/data/region_country_mapping.json new file mode 100644 index 0000000..810f34d --- /dev/null +++ b/data/region_country_mapping.json @@ -0,0 +1,200 @@ +{ + "Nordic countries except Sweden": [ + "Denmark", + "Finland", + "Iceland", + "Norway", + "Greenland", + "Faroe Islands"], + "EU15 Excluding Denmark, Finland, Sweden": [ + "Austria", + "Belgium", + "France", + "Germany", + "Greece", + "Ireland", + "Italy", + "Luxembourg", + "Netherlands", + "Portugal", + "Spain", + "United Kingdom"], + "New EU 10 countries": [ + "Estonia", + "Hungary", + "Poland", + "Czech Republic", + "Slovenia", + "Latvia", + "Lithuania", + "Malta", + "Slovakia"], + "Other European countries including Turkey and Cyprus": [ + "Albania", + "Andorra", + "Belarus", + "Bosnia and Herzogovina", + "Bulgaria", + "Croatia", + "Cyprus", + "Liechtenstein", + "Macedonia", + "Moldova", + "Monaco", + "Montenegro", + "Romania", + "Russia", + "San Marino", + "Serbia", + "Switzerland", + "Turkey", + "Ukraine", + "Vatican City"], + "USA, Canada, Australia, New Zealand": [ + "United States", + "Canada", + "Australia", + "New Zealand"], + "South America": [ + "Argentina", + "Bolivia", + "Brazil", + "Chile", + "Colombia", + "Ecuador", + "French Guiana", + "Guyana", + "Paraguay", + "Peru", + "Suriname", + "Uruguay", + "Venezuela"], + "West Asia": [ + "Azerbaijan", + "Armenia", + "Georgia", + "Bahrain", + "Iraq", + "Israel", + "Jordan", + "Kuwait", + "Lebanon", + "Oman", + "Palestine", + "Qatar", + "Saudi Arabia", + "Syria", + "United Arab Emirates", + "Yemen"], + "South Central Asia": [ + "Kazakhstan", + "Uzbekistan", + "Turkmenistan", + "Afghanistan", + "Pakistan", + "India", + "Sri Lanka", + "Nepal", + "Tajikistan", + "Kyrgyzstan", + "Bhutan", + "Bangladesh"], + "Southeast Asia": [ + "Brunei", + "Cambodia", + "East Timor", + "Indonesia", + "Laos", + "Malaysia", + "Myanmar", + "Philippines", + "Singapore", + "Thailand", + "Vietnam"], + "East Asia": [ + "China", + "Hong Kong", + "Taiwan", + "Mongolia", + "Japan", + "North Korea", + "South Korea"], + "North Africa": [ + "Algeria", + "Egypt", + "Libya", + "Morocco", + "Sudan", + "Tunisia", + "Western Sahara"], + "East Africa": [ + "Tanzania", + "Kenya", + "Uganda", + "Rwanda", + "Burundi", + "Djibouti", + "Eritrea", + "Ethiopia", + "Somalia", + "Comoros", + "Mauritius", + "Seychelles", + "Réunion", + "Mayotte", + "Mozambique", + "Madagascar", + "Malawi", + "Zambia", + "Zimbabwe"], + "Other Africa": [ + "Angola", + "Cameroon", + "Central African Republic", + "Chad", + "Republic of the Congo", + "Congo, the Democratic Republic of the", + "Equatorial Guinea", + "Gabon", + "Sao Tome and Principe", + "South Sudan"], + "Other North America, Central America, etc.": [ + "Anguilla", + "Antigua and Barbuda", + "Aruba", + "Barbados", + "Bonaire, Sint Eustatius and Saba", + "Virgin Islands, British", + "Cayman Islands", + "Cuba", + "Curaçao", + "Dominica", + "Dominican Republic", + "Grenada", + "Guadeloupe", + "Haiti", + "Jamaica", + "Martinique", + "Montserrat", + "Peurto Rico", + "Saint Barthélemy", + "Saint Kitts and Nevis", + "Saint Lucia", + "Saint Martin", + "Sant Vincent and the Grenadines", + "Sint Maarten (Dutch part)", + "Trinidad and Tobago", + "Virgin Islands, United States", + "Belize", + "Costa Rica", + "Clipperton Island", + "El Salvador", + "Guatemala", + "Honduras", + "Mexico", + "Nicaragua", + "Panama"], + "Unclassified": [ + "Saint Pierre and Miquelon" + ] +} diff --git a/interactive-map.html b/interactive-map.html index ffbb6fc..ba99362 100644 --- a/interactive-map.html +++ b/interactive-map.html @@ -2,28 +2,54 @@ Interactive Map @@ -43,26 +69,66 @@ var path = d3.geo.path() .projection(projection); - var svg = d3.select(".map").append("svg") + var svg2 = d3.select(".map").append("svg") .attr("width", width) .attr("height", height) .attr("class", "map"); - var g = svg.append("g"); + var g = svg2.append("g"); + + var NorthAfrica = [ + "Algeria", + "Egypt", + "Libya", + "Morocco", + "Sudan", + "Tunisia", + "Western Sahara" + ]; + + // Bar charts + var outerWidth = 750; + var outerHeight = 500; + var margin = {top: 20, right: 30, bottom: 30, left: 275}; + var innerWidth = outerWidth - margin.left - margin.right; + var innerHeight = outerHeight - margin.top - margin.bottom; + + var x = d3.scale.linear() + .range([0, innerWidth]); + + var y = d3.scale.ordinal() + .rangeRoundBands([0, innerHeight], .25); + + var xAxis = d3.svg.axis() + .scale(x) + .orient("bottom") + .ticks(10); + + var yAxis = d3.svg.axis() + .scale(y) + .orient("left"); + + var svg = d3.select("body").append("svg") + .attr("width", outerWidth) + .attr("height", outerHeight) + .append("g") + .attr("transform", "translate(" + margin.left + "," + margin.top + ")"); /*queue() .defer(d3.json, "./data/world-110m2.json") .defer(d3.tsv, "./data/world-country-names.tsv") .await(ready);*/ + var psv = d3.dsv("|", "text/plain"); + queue() .defer(d3.json, "https://raw.githubusercontent.com/mbostock/topojson/master/examples/world-110m.json") .defer(d3.tsv, "https://raw.githubusercontent.com/KoGor/Maps.GeoInfo/master/world-country-names.tsv") + .defer(psv, "https://raw.githubusercontent.com/skchandra/DataScience16CTW/master/data/respondent_birth_country_overrepresentation.txt") .await(ready); - function ready (error, world, names) { + function ready (error, world, names, overrepresentation) { var countries = topojson.object(world, world.objects.countries).geometries; - //var neighbors = topojson.neighbors(world, countries); var i = -1; var n = countries.length; @@ -73,14 +139,87 @@ } }); - var country = svg.selectAll(".country").data(countries); + var country = svg2.selectAll(".country").data(countries); country.enter() .insert("path") - //.append("path") .attr("class", "country") .attr("title", function(d,i) { return d.name; }) .attr("d", path); + + render(overrepresentation); + } + + function highlight(countryNames) { + countryNames.forEach(function(d) { + d3.select("path[title=\"" + d + "\"]") + .attr("style", "fill: black"); + }); + } + + function unhighlight(countryNames) { + countryNames.forEach(function(d) { + d3.select("path[title=\"" + d + "\"]") + .attr("style", "fill: #ccc"); + }); + } + + function render (data) { + // Renders the bar charts + x.domain([0, d3.max(data, function (d) { return d["percentage of suspects"] })]); + y.domain(data.map( function (d) { return d["country of birth"]; })); + + svg.append("g") + .attr("class", "x axis") + .attr("transform", "translate(0," + innerHeight + ")") + .call(xAxis); + + svg.append("g") + .attr("class", "y axis") + .call(yAxis) + .append("text") + .attr("class", "y axis label") + .attr("transform", "rotate(-90)") + .attr("x", -innerHeight/2) + .attr("y", -margin.left/2) + .style("text-anchor", "middle") + .text("Country of Birth"); + + var bars = svg.selectAll(".bar").data(data); + bars.enter().append("rect") + .attr("class", "bar") + .attr("x", 0) + .attr("height", 20) + .attr("onmouseover", "highlight(NorthAfrica)") + .attr("onmouseout", "unhighlight(NorthAfrica)"); + bars + .attr("width", function (d) { return x(d["percentage of suspects"]); }) + .attr("y", function (d) { return y(d["country of birth"]); }); + + var bartext = svg.selectAll(".bartext").data(data); + bartext.enter().append("text") + .attr("class", "bartext") + .attr("x", 5) + .attr("y", 15); + bartext + .text( function (d) { return d["percentage of suspects"]; }) + .attr("transform", function (d) { + return "translate("+ x(d["percentage of suspects"]) + "," + + y(d["country of birth"]) + ")"; + }); + } + + function type (d) { + d["percentage of suspects"] = +d["percentage of suspects"]; + return d; } + + /*var parser = d3.dsv("|", "text/plain"); + parser("./respondent_birth_country_overrepresentation.txt", type, function (err, data) { + if (err) throw err; + render(data); + });*/ + + North Africa \ No newline at end of file From dd3301d9bb51b9bfe4960f08561e7560d0155728 Mon Sep 17 00:00:00 2001 From: Pratool Gadtaula Date: Sat, 5 Mar 2016 23:30:05 -0500 Subject: [PATCH 15/24] Added accurate interactively between regions and bars --- interactive-map.html | 95 +++++++++++++++++++++++--------------------- 1 file changed, 49 insertions(+), 46 deletions(-) diff --git a/interactive-map.html b/interactive-map.html index ba99362..3c04b64 100644 --- a/interactive-map.html +++ b/interactive-map.html @@ -125,9 +125,10 @@ .defer(d3.json, "https://raw.githubusercontent.com/mbostock/topojson/master/examples/world-110m.json") .defer(d3.tsv, "https://raw.githubusercontent.com/KoGor/Maps.GeoInfo/master/world-country-names.tsv") .defer(psv, "https://raw.githubusercontent.com/skchandra/DataScience16CTW/master/data/respondent_birth_country_overrepresentation.txt") + .defer(d3.json, "https://raw.githubusercontent.com/skchandra/DataScience16CTW/master/data/region_country_mapping.json") .await(ready); - function ready (error, world, names, overrepresentation) { + function ready (error, world, names, overrepresentation, regionMappings) { var countries = topojson.object(world, world.objects.countries).geometries; var i = -1; var n = countries.length; @@ -146,6 +147,53 @@ .attr("title", function(d,i) { return d.name; }) .attr("d", path); + render = function (data) { + // Renders the bar charts + x.domain([0, d3.max(data, function (d) { return d["percentage of suspects"] })]); + y.domain(data.map( function (d) { return d["country of birth"]; })); + + svg.append("g") + .attr("class", "x axis") + .attr("transform", "translate(0," + innerHeight + ")") + .call(xAxis); + + svg.append("g") + .attr("class", "y axis") + .call(yAxis) + .append("text") + .attr("class", "y axis label") + .attr("transform", "rotate(-90)") + .attr("x", -innerHeight/2) + .attr("y", -margin.left/2) + .style("text-anchor", "middle") + .text("Country of Birth"); + + var bars = svg.selectAll(".bar").data(data); + bars.enter().append("rect") + .attr("class", "bar") + .attr("x", 0) + .attr("height", 20); + bars + .attr("width", function (d) { return x(d["percentage of suspects"]); }) + .attr("y", function (d) { return y(d["country of birth"]); }) + .attr("onmouseover", function (d) { + return 'highlight([\"' + String(regionMappings[d["country of birth"]]).replace(/,/g, "\",\"") + '\"])'; }) + .attr("onmouseout", function (d) { + return 'unhighlight([\"' + String(regionMappings[d["country of birth"]]).replace(/,/g, "\",\"") + '\"])'; + }); + + var bartext = svg.selectAll(".bartext").data(data); + bartext.enter().append("text") + .attr("class", "bartext") + .attr("x", 5) + .attr("y", 15); + bartext + .text( function (d) { return d["percentage of suspects"]; }) + .attr("transform", function (d) { + return "translate("+ x(d["percentage of suspects"]) + "," + + y(d["country of birth"]) + ")"; + }); + } render(overrepresentation); } @@ -163,51 +211,6 @@ }); } - function render (data) { - // Renders the bar charts - x.domain([0, d3.max(data, function (d) { return d["percentage of suspects"] })]); - y.domain(data.map( function (d) { return d["country of birth"]; })); - - svg.append("g") - .attr("class", "x axis") - .attr("transform", "translate(0," + innerHeight + ")") - .call(xAxis); - - svg.append("g") - .attr("class", "y axis") - .call(yAxis) - .append("text") - .attr("class", "y axis label") - .attr("transform", "rotate(-90)") - .attr("x", -innerHeight/2) - .attr("y", -margin.left/2) - .style("text-anchor", "middle") - .text("Country of Birth"); - - var bars = svg.selectAll(".bar").data(data); - bars.enter().append("rect") - .attr("class", "bar") - .attr("x", 0) - .attr("height", 20) - .attr("onmouseover", "highlight(NorthAfrica)") - .attr("onmouseout", "unhighlight(NorthAfrica)"); - bars - .attr("width", function (d) { return x(d["percentage of suspects"]); }) - .attr("y", function (d) { return y(d["country of birth"]); }); - - var bartext = svg.selectAll(".bartext").data(data); - bartext.enter().append("text") - .attr("class", "bartext") - .attr("x", 5) - .attr("y", 15); - bartext - .text( function (d) { return d["percentage of suspects"]; }) - .attr("transform", function (d) { - return "translate("+ x(d["percentage of suspects"]) + "," - + y(d["country of birth"]) + ")"; - }); - } - function type (d) { d["percentage of suspects"] = +d["percentage of suspects"]; return d; From 3fe7fae2af3d9f9fc45db6b909bd07c1c0cc66f8 Mon Sep 17 00:00:00 2001 From: Pratool Gadtaula Date: Sun, 6 Mar 2016 02:40:55 -0500 Subject: [PATCH 16/24] cleaned up interaction between map and bar graph --- interactive-map.html | 8 -------- 1 file changed, 8 deletions(-) diff --git a/interactive-map.html b/interactive-map.html index 3c04b64..2541352 100644 --- a/interactive-map.html +++ b/interactive-map.html @@ -215,14 +215,6 @@ d["percentage of suspects"] = +d["percentage of suspects"]; return d; } - - /*var parser = d3.dsv("|", "text/plain"); - parser("./respondent_birth_country_overrepresentation.txt", type, function (err, data) { - if (err) throw err; - render(data); - });*/ - - North Africa \ No newline at end of file From bb04cbb97bde11119d2161b5fa1ce1c71e632cd1 Mon Sep 17 00:00:00 2001 From: Pratool Gadtaula Date: Tue, 8 Mar 2016 01:23:33 -0500 Subject: [PATCH 17/24] added second bar chart --- interactive-map.html | 113 ++++++++++++++++++++++++++++++++++--------- 1 file changed, 89 insertions(+), 24 deletions(-) diff --git a/interactive-map.html b/interactive-map.html index 2541352..b6ace19 100644 --- a/interactive-map.html +++ b/interactive-map.html @@ -2,10 +2,8 @@ Interactive Map @@ -57,41 +66,6 @@ - + + + \ No newline at end of file diff --git a/map_visualilzer.js b/map_visualilzer.js index e481a79..3e41f90 100644 --- a/map_visualilzer.js +++ b/map_visualilzer.js @@ -39,5 +39,12 @@ var mapVisualization = function() { .attr("class", "country") .attr("title", function(d,i) { return d.name; }) .attr("d", path); + + svg.append("text") + .attr("class", "regionName") + .attr("text-anchor", "end") + .attr("x", width) + .attr("y", height-30) + .text("what"); } } \ No newline at end of file From 915a8656d7c363466d030d305a182dc6658e586c Mon Sep 17 00:00:00 2001 From: Pratool Gadtaula Date: Fri, 11 Mar 2016 02:58:09 -0500 Subject: [PATCH 22/24] Removed unnecessary files --- data.csv | 7 -- immigration.html | 13 ---- immigration_interactive.html | 127 ----------------------------------- 3 files changed, 147 deletions(-) delete mode 100644 data.csv delete mode 100644 immigration.html delete mode 100644 immigration_interactive.html diff --git a/data.csv b/data.csv deleted file mode 100644 index ac84883..0000000 --- a/data.csv +++ /dev/null @@ -1,7 +0,0 @@ -State,Under 5 Years,5 to 13 Years,14 to 17 Years,18 to 24 Years,25 to 44 Years,45 to 64 Years,65 Years and Over -CA,2704659,4499890,2159981,3853788,10604510,8819342,4114496 -TX,2027307,3277946,1420518,2454721,7017731,5656528,2472223 -NY,1208495,2141490,1058031,1999120,5355235,5120254,2607672 -FL,1140516,1938695,925060,1607297,4782119,4746856,3187797 -IL,894368,1558919,725973,1311479,3596343,3239173,1575308 -PA,737462,1345341,679201,1203944,3157759,3414001,1910571 \ No newline at end of file diff --git a/immigration.html b/immigration.html deleted file mode 100644 index 639a7e5..0000000 --- a/immigration.html +++ /dev/null @@ -1,13 +0,0 @@ - - - - Basic Map - - - - - - \ No newline at end of file diff --git a/immigration_interactive.html b/immigration_interactive.html deleted file mode 100644 index fd76168..0000000 --- a/immigration_interactive.html +++ /dev/null @@ -1,127 +0,0 @@ - - - Interactive Swedish Immigration Statistics - - - - - - - - \ No newline at end of file From e90306d544bc3b3088dac093be46ae2f485a1359 Mon Sep 17 00:00:00 2001 From: Kiki Date: Fri, 11 Mar 2016 03:30:54 -0500 Subject: [PATCH 23/24] commented notebooks --- County_Data.ipynb | 110 +++-- Economy_employment.ipynb | 929 +++++++++++++++++++++++++++++++++++++++ 2 files changed, 994 insertions(+), 45 deletions(-) create mode 100644 Economy_employment.ipynb diff --git a/County_Data.ipynb b/County_Data.ipynb index fe84b6d..f607edb 100644 --- a/County_Data.ipynb +++ b/County_Data.ipynb @@ -1,5 +1,13 @@ { "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shivali Chandra
\n", + "This notebook is for viewing and organizing the data we have relating to counties, and making a mock-up map of population in regions before we create the final version in javascript. " + ] + }, { "cell_type": "code", "execution_count": 1, @@ -78,32 +86,30 @@ }, "metadata": {}, "output_type": "display_data" - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/kiki/anaconda/lib/python2.7/site-packages/matplotlib/__init__.py:872: UserWarning:\n", - "\n", - "axes.color_cycle is deprecated and replaced with axes.prop_cycle; please use the latter.\n", - "\n" - ] } ], "source": [ - "import plotly\n", + "#import needed libraries\n", + "\n", + "import plotly \n", "print plotly.__version__ # version 1.9.4 required\n", "plotly.offline.init_notebook_mode() # run at the start of every notebook\n", "import plotly.plotly as py\n", "import plotly.graph_objs as go\n", - "import seaborn\n", "import pandas as pd\n", "import numpy as np" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We found data related to nationality and region. This included a breakdown of the percentage of the total population that has a foreign background, is foreign born, or is a foreign national. Because there was no way for us to ascertain if these percentages overlapped, we decided to solely stick to representing foreign born groups. We also decided to only use counties rather than the neighborhoods and cities within these counties as well, mostly due to time constraints. It also was not clear how much value we could add to our project by breaking this down further into neighborhoods, unless we also had corresponding information on the neighborhoods, such as safety, crime, and income rates. " + ] + }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 4, "metadata": { "collapsed": false }, @@ -769,7 +775,7 @@ "27 38.1 1 NaN NaN " ] }, - "execution_count": 20, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -793,9 +799,16 @@ "stockholm" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Some basic cleaning of the data: " + ] + }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 5, "metadata": { "collapsed": false }, @@ -1353,7 +1366,7 @@ "297 4.7 42.6 21 60.1282 18.6435 " ] }, - "execution_count": 21, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -1371,9 +1384,16 @@ "county_loc" ] }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Using the total populations of the counties and the percentage of foreign born people, we got a pretty decent estimate for the foreign population in that area. " + ] + }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 6, "metadata": { "collapsed": false }, @@ -1382,28 +1402,28 @@ "name": "stdout", "output_type": "stream", "text": [ - " Place foreign_pop\n", - "97 Gotlands county 2513.368\n", - "272 Jämtlands county 6854.598\n", - "99 Blekinge county 14278.600\n", - "264 Västernorrlands county 15337.287\n", - "281 Västerbottens county 16743.545\n", - "84 Kalmar county 18239.052\n", - "253 Gävleborgs county 20391.144\n", - "237 Dalarnas county 20671.350\n", - "297 Norrbottens county 21301.170\n", - "75 Kronobergs county 21875.227\n", - "196 Värmlands county 24096.688\n", - "139 Hallands county 27973.728\n", - "213 Örebro county 31471.638\n", - "37 Södermanlands county 35005.080\n", - "226 Västmanlands county 37628.143\n", - "61 Jönköpings county 39032.370\n", - "28 Uppsala county 39438.940\n", - "47 Östergötlands county 44605.754\n", - "105 Skåne county 194295.834\n", - "146 Västra Götalands county 210432.528\n", - "1 Stockholms county 384054.652\n" + " Place foreign_pop Foreign Born\n", + "97 Gotlands county 2513.368 4.4\n", + "272 Jämtlands county 6854.598 5.4\n", + "264 Västernorrlands county 15337.287 6.3\n", + "281 Västerbottens county 16743.545 6.5\n", + "253 Gävleborgs county 20391.144 7.4\n", + "237 Dalarnas county 20671.350 7.5\n", + "84 Kalmar county 18239.052 7.8\n", + "297 Norrbottens county 21301.170 8.5\n", + "196 Värmlands county 24096.688 8.8\n", + "99 Blekinge county 14278.600 9.4\n", + "139 Hallands county 27973.728 9.6\n", + "47 Östergötlands county 44605.754 10.6\n", + "213 Örebro county 31471.638 11.4\n", + "61 Jönköpings county 39032.370 11.7\n", + "75 Kronobergs county 21875.227 12.1\n", + "28 Uppsala county 39438.940 12.2\n", + "37 Södermanlands county 35005.080 13.2\n", + "146 Västra Götalands county 210432.528 13.6\n", + "226 Västmanlands county 37628.143 15.1\n", + "105 Skåne county 194295.834 16.2\n", + "1 Stockholms county 384054.652 19.7\n" ] }, { @@ -1427,8 +1447,8 @@ ], "source": [ "county_loc['foreign_pop'] = (county_loc['Foreign Born']/100)*county_loc['Population']\n", - "foreign = county_loc[['Place','foreign_pop']]\n", - "print foreign.sort(columns=\"foreign_pop\") " + "foreign = county_loc[['Place','foreign_pop','Foreign Born']]\n", + "print foreign.sort(columns=\"Foreign Born\") " ] }, { @@ -1536,13 +1556,13 @@ ] }, { - "cell_type": "code", - "execution_count": null, + "cell_type": "markdown", "metadata": { "collapsed": true }, - "outputs": [], - "source": [] + "source": [ + "In the figure above we made a pretty rough visualization depicting the areas with higher concentrations of immigrants. As you can see, this map isn't very intuitively broken into regions, nor is it very clear what to conclude. Thus we looked into other methods of graphing, and decided to use amcharts (https://www.amcharts.com/). This allows us to, through javascript, plot interactive maps very easily. " + ] } ], "metadata": { diff --git a/Economy_employment.ipynb b/Economy_employment.ipynb new file mode 100644 index 0000000..72c55e9 --- /dev/null +++ b/Economy_employment.ipynb @@ -0,0 +1,929 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Shivali Chandra\n", + "The purpose of this notebook was to look at some interesting statistics on group representation in certain topics, such as labor breakdown and income parity. We used this data in order to create a better socio-economic story around factors that contribute to higher crime rates. " + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.9.6\n" + ] + }, + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "#import needed libraries\n", + "\n", + "import plotly\n", + "print plotly.__version__ # version 1.9.4 required\n", + "import plotly.plotly as py\n", + "import seaborn\n", + "import pandas as pd\n", + "import numpy as np\n", + "import plotly.graph_objs as go\n", + "\n", + "plotly.offline.init_notebook_mode() # run at the start of every notebook" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The data we used was from a report made in 2007 (cited in our article). " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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Unnamed: 0Swedish_bornScandinavia_bornEurope_bornOther_born
0SexNaNNaNNaNNaN
1Men44.534.244.549.5
2Women55.865.855.550.5
3Missing % (n)NaNNaNNaNNaN
4AgeNaNNaNNaNNaN
520–3437.213.620.037.0
635–4931.133.934.047.0
750–6531.552.546.016.1
8Missing % (n)NaNNaNNaNNaN
9Annual household incomeNaNNaNNaNNaN
10Less than 100,000 SEK6.94.110.220.7
11100,000–149,000 SEK6.08.912.618.8
12150,000–199,000 SEK10.816.211.723.4
13200,000–299,000 SEK20.423.425.219.2
14More than 300,000 SEK55.947.140.217.9
15Missing % (n)NaNNaNNaNNaN
16Economic securityNaNNaNNaNNaN
17High65.655.154.531.1
18Rather high21.026.425.430.1
19Rather low7.611.09.917.1
20Low5.87.510.221.7
21Missing % (n)NaNNaNNaNNaN
22Level of educationNaNNaNNaNNaN
23High29.223.829.626.8
24Medium33.822.131.730.1
25Low37.154.138.643.1
26Missing % (n)NaNNaNNaNNaN
27Not cohabiting with adult partner29.331.225.728.7
28Missing % (n)NaNNaNNaNNaN
29Poor social support network24.231.437.854.9
30Missing % (n)NaNNaNNaNNaN
31Labour market positionNaNNaNNaNNaN
32Blue collar worker17.923.619.333.1
33White collar worker63.950.750.933.6
34Self-employed6.86.69.28.3
35Student5.53.14.310.1
36Unemployed2.42.84.08.8
37Disability pension2.511.110.75.5
38Early retirement1.02.11.50.7
39Missing % (n)NaNNaNNaNNaN
40Depression7.36.913.722.2
41Missing % (n)NaNNaNNaNNaN
42Low SWB13.413.220.125.7
43Missing % (n)NaNNaNNaNNaN
\n", + "
" + ], + "text/plain": [ + " Unnamed: 0 Swedish_born Scandinavia_born \\\n", + "0 Sex NaN NaN \n", + "1 Men 44.5 34.2 \n", + "2 Women 55.8 65.8 \n", + "3 Missing % (n) NaN NaN \n", + "4 Age NaN NaN \n", + "5 20–34 37.2 13.6 \n", + "6 35–49 31.1 33.9 \n", + "7 50–65 31.5 52.5 \n", + "8 Missing % (n) NaN NaN \n", + "9 Annual household income NaN NaN \n", + "10 Less than 100,000 SEK 6.9 4.1 \n", + "11 100,000–149,000 SEK 6.0 8.9 \n", + "12 150,000–199,000 SEK 10.8 16.2 \n", + "13 200,000–299,000 SEK 20.4 23.4 \n", + "14 More than 300,000 SEK 55.9 47.1 \n", + "15 Missing % (n) NaN NaN \n", + "16 Economic security NaN NaN \n", + "17 High 65.6 55.1 \n", + "18 Rather high 21.0 26.4 \n", + "19 Rather low 7.6 11.0 \n", + "20 Low 5.8 7.5 \n", + "21 Missing % (n) NaN NaN \n", + "22 Level of education NaN NaN \n", + "23 High 29.2 23.8 \n", + "24 Medium 33.8 22.1 \n", + "25 Low 37.1 54.1 \n", + "26 Missing % (n) NaN NaN \n", + "27 Not cohabiting with adult partner 29.3 31.2 \n", + "28 Missing % (n) NaN NaN \n", + "29 Poor social support network 24.2 31.4 \n", + "30 Missing % (n) NaN NaN \n", + "31 Labour market position NaN NaN \n", + "32 Blue collar worker 17.9 23.6 \n", + "33 White collar worker 63.9 50.7 \n", + "34 Self-employed 6.8 6.6 \n", + "35 Student 5.5 3.1 \n", + "36 Unemployed 2.4 2.8 \n", + "37 Disability pension 2.5 11.1 \n", + "38 Early retirement 1.0 2.1 \n", + "39 Missing % (n) NaN NaN \n", + "40 Depression 7.3 6.9 \n", + "41 Missing % (n) NaN NaN \n", + "42 Low SWB 13.4 13.2 \n", + "43 Missing % (n) NaN NaN \n", + "\n", + " Europe_born Other_born \n", + "0 NaN NaN \n", + "1 44.5 49.5 \n", + "2 55.5 50.5 \n", + "3 NaN NaN \n", + "4 NaN NaN \n", + "5 20.0 37.0 \n", + "6 34.0 47.0 \n", + "7 46.0 16.1 \n", + "8 NaN NaN \n", + "9 NaN NaN \n", + "10 10.2 20.7 \n", + "11 12.6 18.8 \n", + "12 11.7 23.4 \n", + "13 25.2 19.2 \n", + "14 40.2 17.9 \n", + "15 NaN NaN \n", + "16 NaN NaN \n", + "17 54.5 31.1 \n", + "18 25.4 30.1 \n", + "19 9.9 17.1 \n", + "20 10.2 21.7 \n", + "21 NaN NaN \n", + "22 NaN NaN \n", + "23 29.6 26.8 \n", + "24 31.7 30.1 \n", + "25 38.6 43.1 \n", + "26 NaN NaN \n", + "27 25.7 28.7 \n", + "28 NaN NaN \n", + "29 37.8 54.9 \n", + "30 NaN NaN \n", + "31 NaN NaN \n", + "32 19.3 33.1 \n", + "33 50.9 33.6 \n", + "34 9.2 8.3 \n", + "35 4.3 10.1 \n", + "36 4.0 8.8 \n", + "37 10.7 5.5 \n", + "38 1.5 0.7 \n", + "39 NaN NaN \n", + "40 13.7 22.2 \n", + "41 NaN NaN \n", + "42 20.1 25.7 \n", + "43 NaN NaN " + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = pd.read_table('data/2007_statistics.csv')\n", + "df1" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "What stood out immediately was the disparity in income distribution. We isolated this as a new dataframe and plotted it: " + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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1011121314
Swedish_born6.9610.820.455.9
Scandinavia_born4.18.916.223.447.1
Europe_born10.212.611.725.240.2
Other_born20.718.823.419.217.9
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" + ], + "text/plain": [ + " 10 11 12 13 14\n", + "Swedish_born 6.9 6 10.8 20.4 55.9\n", + "Scandinavia_born 4.1 8.9 16.2 23.4 47.1\n", + "Europe_born 10.2 12.6 11.7 25.2 40.2\n", + "Other_born 20.7 18.8 23.4 19.2 17.9" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "money = df1[df1['Unnamed: 0'].str.contains(\"SEK\")==True].transpose()\n", + "\n", + "money.drop(money.index[:1], inplace=True)\n", + "money" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame({'x': ['Swedish','Scandinavian','European','Other'], 'y': money[10], 'y2':money[11], 'y3':money[12], 'y4':money[13], 'y5':money[14]})\n", + "df.head()\n", + "\n", + "dat = [\n", + " go.Bar(\n", + " x=df['x'], # assign x as the dataframe column 'x'\n", + " y=df['y'],\n", + " name='Less than 100,000 SEK'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y2'],\n", + " name='100,000 - 149,000 SEK'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y3'],\n", + " name='150,000 - 199,000 SEK'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y4'],\n", + " name='200,000 - 299,000 SEK'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y5'],\n", + " name='More than 300,000 SEK'\n", + " )\n", + "]\n", + "\n", + "layout = go.Layout(\n", + " barmode='stack',\n", + " title='Income Breakdown by Birthplace in 2007'\n", + ")\n", + "\n", + "fig = go.Figure(data=dat, layout=layout)\n", + "py.iplot(fig, filename='tot_income_country')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "As we expected, Swedish and Scandinavian groups tended to have relatively higher incomes. What was surprising was that for the \"other\" group was almost evenly split between all income levels. It wasn't too clear what could cause this, but having a further breakdown of ethnicities within this group could perhaps shed more light. Next, we also looked at the breakdown of labor. " + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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32333435363738
Swedish_born17.963.96.85.52.42.51
Scandinavia_born23.650.76.63.12.811.12.1
Europe_born19.350.99.24.3410.71.5
Other_born33.133.68.310.18.85.50.7
\n", + "
" + ], + "text/plain": [ + " 32 33 34 35 36 37 38\n", + "Swedish_born 17.9 63.9 6.8 5.5 2.4 2.5 1\n", + "Scandinavia_born 23.6 50.7 6.6 3.1 2.8 11.1 2.1\n", + "Europe_born 19.3 50.9 9.2 4.3 4 10.7 1.5\n", + "Other_born 33.1 33.6 8.3 10.1 8.8 5.5 0.7" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "labor = df1.loc[df1.index[32:39]].transpose()\n", + "labor = labor.drop(labor.index[:1])\n", + "labor" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": false + }, + "outputs": [ + { + "data": { + "text/html": [ + "" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.DataFrame({'x': ['Swedish','Scandinavian','European','Other'], 'y':labor[35], 'y2':labor[37], 'y3':labor[38], 'y4':labor[34], 'y5':labor[33], 'y6':labor[32], 'y7':labor[36]})\n", + "df.head()\n", + "\n", + "dat = [\n", + " go.Bar(\n", + " x=df['x'], # assign x as the dataframe column 'x'\n", + " y=df['y'],\n", + " name='Student'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y2'],\n", + " name='Disability Pension'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y3'],\n", + " name='Early retirement'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y4'],\n", + " name='Self-Employed'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y5'],\n", + " name='White Collar Worker'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y6'],\n", + " name='Blue Collar Worker'\n", + " ),\n", + " go.Bar(\n", + " x=df['x'],\n", + " y=df['y7'],\n", + " name='Unemployed'\n", + " )\n", + "]\n", + "\n", + "layout = go.Layout(\n", + " barmode='bar',\n", + " title='Employment Breakdown by Birthplace in 2007'\n", + ")\n", + "\n", + "fig = go.Figure(data=dat, layout=layout)\n", + "py.iplot(fig, filename='tot_employ')" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Here, as well, we found some interesting information. Though we found in the same PDF that we got this data from that each group had about the same percentages of educated people, the types of jobs that were available to the groups differed greatly. For the \"other\" group, especially, the unemployment rate is significantly higher than those of the other groups. Additionally, many of them go into blue collar jobs, even though they are overqualified. " + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 2", + "language": "python", + "name": "python2" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 2 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython2", + "version": "2.7.11" + } + }, + "nbformat": 4, + "nbformat_minor": 0 +} From 992c962ad049893cc467544110bcb7a02f7fd366 Mon Sep 17 00:00:00 2001 From: Pratool Gadtaula Date: Sat, 12 Mar 2016 00:13:57 -0500 Subject: [PATCH 24/24] Added adjustment to README and final reflection --- README.md | 2 +- reflection.pdf | Bin 0 -> 59137 bytes reflection.tex | 32 ++++++++++++++++++++++++++++++++ 3 files changed, 33 insertions(+), 1 deletion(-) create mode 100644 reflection.pdf create mode 100644 reflection.tex diff --git a/README.md b/README.md index b253d5e..f01c530 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ # Sexual Assault and Immigration ## Misinterpreted Data and Lies About Immigrants in Sweden -In an exploration of data visualization and data analysis, we hope to elucidate the meaning of the data behind the recent relation behind sexual assault and immigration in Sweden. +In an exploration of data visualization and data analysis, we hope to elucidate the meaning of the data behind the recent relation behind sexual assault and immigration in Sweden. For the article written around the data gathered in this repository, [click here!](http://skchandra.github.io/DataScience16CTW/) diff --git a/reflection.pdf b/reflection.pdf new file mode 100644 index 0000000000000000000000000000000000000000..9058d9e2859635ac22ac5c0c44f8f8eece9eb719 GIT binary patch literal 59137 zcma&MLy#~`(4g72ZQHhO+qP}KZQHhO+qP}n-TUpv{)dT~!{i|=GwM*6JUkiokSd6X z(K6DpLXpldkE}y66EG0i8(Bf|@IcYanA(}USP(FAvTzXme*r}=W@+PM>O?>pyjyk%{v3KUOT3hUPNs*H6>+g>aZ)WZudL$n0>&!m)e_8B#kOYnYl#x)(+WcO>*%JB{};9jja{-; zs%g+sHLX@jeVe9XflbzRrLAkLWQ(Q~8Xb6m)h-D%)zW?er?63Y%D&pSSd-J<5>??oCpX<=V zw*={&a^MG<{946g>$I&BFF#gFWO8Z63^>7Ioo32)Vz0_iz9Wu{Pg0NiU{$ifspP|0 zl$QO2iZZ_f=1sxICE?gDgM_Z^);F5mf|d^@QZLN3q=S?iQ9{fsG< z{cDrh)$%c3*+I&aghg;uCk)q|ZrMNiF&-QAc@Qe)0^T_Hw~H)$>1~Adtt_`6r}D#g|(ql zl<8_LPvI=DOw3gxdoRlt**+^mC^!yw&N<4dQNHe6RdQKkB;?9)GKbRP5V8mSM$^$4 zq&CfO?Y&02l^ag$n1LnmWtD$yZEKt!T=7%RoQ1)xpA$9Xusji4B&#{X<APPg>eVF9~>`1YkLt zJ4avVo3?J&Q?i5uZ~8(0>C7&S)zdmWGY*Ypg@CRb%vNpCwD=jviO@FEB_B^}{Sch4- 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