{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "by [@tozCSS](https://twitter.com/tozCSS)\n", "\n", "\n", "This notebook contains some maps and plots regarding the last two general election results in Turkey (held in Jun 7, 2015 and Nov 1, 2015).\n", "\n", "I also estimate the vote transition rates (the image on the right) using a recursive method developed by [Andreadis](http://www.tandfonline.com/doi/abs/10.1080/17457280902799089) (An R script and the instructions are also made [available](http://www.polres.gr/en/vtr)). To get the exact transition values you can mouse-over the links in the interactive Sankey diagram at the bottom of this notebook. \n", "\n", "Finally, I respond to the fraud claims due to the high increase in the number of registered voters (and valid votes) in 5 months by looking at the correlation between the changes in party shares and the valid votes.\n", "\n", "## Data Source\n", "Both Jun 7 and Nov 1 election results are scraped from the Nov 1, 2015 [election results page](http://www.yenisafak.com/secim-2015-kasim/secim-sonuclari) of the Yenisafak daily. Scraped datasets are available in two files as [Nov 1,2015](data/nov.csv) and [Jun 7,2015](data/jun.csv) in csv format.\n", "\n", "[UPDATE] : Town level data added. [Nov 1,2015](data/nov_towns.csv) and [Jun 7,2015](data/jun_towns.csv) \n", "\n", "## Election Results Maps\n", "I created maps for the last two general election results of Turkey for four parties with different legends. If you click on any of the cities in those maps (below), vote shares of the four parties are shown for that particular election.\n", "\n", "The colors in these maps represent the party vote share in those cities. Since one of the more interesting questions regarding the Nov 1, 2015 election is in which cities AKP increased its votes, I also created its Jun 7, 2015 election density map to facilitate us seeing the change.\n", "\n", "## Vote Share Scatter Plots of Each Party per City for the Last Two Elections\n", "For all of the parties, their vote shares in the last two elections is compared in a scatter plot where the dots represent the cities and their colors represent the geographic region they belong to. One interesting pattern for example is that unlike other parties, CHP increased its vote share in some cities and lost some in others. On the other hand we see that AKP increased its vote share in every city, whereas MHP and HDP lost their shares almost in every single city." ] }, { "cell_type": "code", "execution_count": 1, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import json\n", "import pandas as pd\n", "from unidecode import unidecode\n", "import folium\n", "from IPython.display import HTML\n", "from os import chdir\n", "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "plt.style.use('ggplot')\n", "\n", "import plotly.plotly as py\n", "import cufflinks as cf\n", "import plotly.graph_objs as go\n", "cf.set_config_file(offline=False, world_readable=True, theme='ggplot')" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#read YSK data\n", "df = pd.read_csv('data/ysk.csv')\n", "df = df.rename(columns={'AK PARTİ':'AKP','İL ADI':'il','İLÇE ADI':'ilce','OY KULLANAN SEÇMEN SAYISI':'kullanan','SEÇMEN SAYISI':'kayitli','GEÇERLİ OY TOPLAMI':'gecerli','GEÇERSİZ OY TOPLAMI':'gecersiz','MAHALLE/KÖY':'mahalle','SANDIK NO':'sandik','İTİRAZSIZ GEÇERLİ OY SAYISI':'itirazsiz','İTİRAZLI GEÇERLİ OY SAYISI':'itirazli'})\n", "df = df.fillna(0)" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": false }, "outputs": [], "source": [ "parties = ['AKP','CHP','MHP','HDP','OTHERS']\n", "vals = ['il','ilce','mahalle','sandik','kayitli','kullanan','itirazsiz','itirazli','gecerli','gecersiz']\n", "others = set(df.columns) - set(parties+vals)\n", "df['OTHERS'] = df[list(others)].sum(axis=1)\n", "df = df[parties+vals]\n", "rates = [p+'R' for p in parties]\n", "df[rates] = df[parties].div(df.gecerli,axis='index')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#df.pivot_table(index='il')[rates].plot(kind='barh',stacked=True,figsize=(10,25),xlim=(0,1),color=['orange','r','g','purple','grey'])\n", "il = df.pivot_table(index='il')[rates]\n", "il.iplot(kind='bar',barmode='stack', filename='ysk/il',colors=['orange','red','green','purple','grey'],legend=False)" ] }, { "cell_type": "code", "execution_count": 2, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#read the data\n", "nov = pd.read_csv('data/nov.csv')\n", "jun = pd.read_csv('data/jun.csv')\n", "nt = pd.read_csv('data/nov_towns.csv')\n", "jt = pd.read_csv('data/jun_towns.csv')\n", "nt = nt[nt.AKP > 0] #this is to filter some towns\n", "jt = jt[jt.AKP > 0] #with all zeros in Yenisafak site\n", "# see for example row of ondokuzmayis here: http://www.yenisafak.com/secim-2015-kasim/samsun-ili-secim-sonuclari" ] }, { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "def create_map(is_nov,threshold_scale,fill_color,party):\n", " chdir('maps/')\n", " mapname = party+'_share_nov' if is_nov else party+'_share_jun'\n", " electiondate = 'Nov 1, 2015' if is_nov else 'Jun 7, 2015'\n", " tr_geo = 'tr_nov.geojson' if is_nov else 'tr_jun.geojson'\n", " df = nov if is_nov else jun\n", " tr = folium.Map(location=[39.5, 35], zoom_start=6, tiles='Mapbox Bright')\n", " tr.geo_json(geo_path=tr_geo, data=df, data_out=mapname+'.json',\n", " columns=['city', party],\n", " threshold_scale=threshold_scale,\n", " key_on='feature.properties.name',\n", " fill_color=fill_color, fill_opacity=0.7, line_opacity=0.2,\n", " legend_name=party+' Vote Shares (%), '+electiondate+' Turkish General Elections')\n", " tr.create_map(path=mapname+'.html')\n", " chdir('..')\n", " return HTML('')" ] }, { "cell_type": "code", "execution_count": 4, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# AKP Vote Shares (%), Nov 1, 2015 Turkish General Elections\n", "create_map(True,[15,25,35,45,55,65],'YlOrRd','AKP')" ] }, { "cell_type": "code", "execution_count": 5, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# Vote Shares (%), Jun 7, 2015 Turkish General Elections\n", "create_map(False,[15,25,35,45,55,65],'YlOrRd','AKP')" ] }, { "cell_type": "code", "execution_count": 6, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 6, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# CHP Vote Shares (%), Nov 1, 2015 Turkish General Elections\n", "create_map(True,[10,15,20,25,30,35],'PuRd','CHP')" ] }, { "cell_type": "code", "execution_count": 7, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# CHP Vote Shares (%), Jun 7, 2015 Turkish General Elections\n", "create_map(False,[10,15,20,25,30,35],'PuRd','CHP')" ] }, { "cell_type": "code", "execution_count": 8, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 8, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# MHP Vote Shares (%), Nov 1, 2015 Turkish General Elections\n", "create_map(True,[5,10,15,20,25,30],'GnBu','MHP')" ] }, { "cell_type": "code", "execution_count": 9, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 9, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# MHP Vote Shares (%), Jun 7, 2015 Turkish General Elections\n", "create_map(False,[5,10,15,20,25,30],'GnBu','MHP')" ] }, { "cell_type": "code", "execution_count": 10, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 10, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# HDP Vote Shares (%), Nov 1, 2015 Turkish General Elections\n", "create_map(True,[5,10,15,20,25,30],'BuPu','HDP')" ] }, { "cell_type": "code", "execution_count": 11, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 11, "metadata": {}, "output_type": "execute_result" } ], "source": [ "# HDP Vote Shares (%), Jun 7, 2015 Turkish General Elections\n", "create_map(False,[5,10,15,20,25,30],'BuPu','HDP')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Scatter plot\n", "import plotly.plotly as py\n", "from plotly.graph_objs import *\n", "from palettable.colorbrewer.qualitative import Dark2_7 as colmap\n", "\n", "il = pd.read_csv('data/city_meta.csv',usecols=['il','bolge'])\n", "colors = dict(zip(il['bolge'].unique().tolist(),colmap.hex_colors))\n", "parties = ['AKP', 'CHP','MHP','HDP']\n", "jun['bolge'] = il['bolge']\n", "nov['bolge'] = il['bolge']\n", "\n", "for p in parties:\n", " lim = max(nov[p].max(),jun[p].max())\n", " #each region is a trace, otherwise they do not show up in the legend\n", " traces = [Scatter(x=jun[jun.bolge==b][p],y=nov[nov.bolge==b][p],\n", " mode='markers', text=il[il.bolge==b]['il'], name=b,\n", " marker=Marker(color=v)) for b,v in colors.items()]\n", " traces.append(Scatter(x=[0,lim],y=[0,lim],mode='lines', name='y=x',\n", " line=Line(color='grey',dash='dash')))\n", " data = Data(traces)\n", " layout = Layout(title=p+' Nov 1, 2015 vs Jun 7, 2015 Vote Shares',\n", " autosize=True,\n", " xaxis=XAxis(title=p+\" Jun 7, 2015 Vote Shares\",zeroline=False),\n", " yaxis=YAxis(title=p+\" Nov 1, 2015 Vote Shares\",zeroline=False),\n", " legend=Legend(x=.01,y=1))\n", " fig = Figure(data=data,layout=layout)\n", " url = py.plot(fig,filename= p+' Nov 1, 2015 vs Jun 7, 2015 Vote Shares')" ] }, { "cell_type": "code", "execution_count": 12, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\"AKP
" ], "text/plain": [ "" ] }, "execution_count": 12, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML(\"\"\"
\"AKP
\"\"\")" ] }, { "cell_type": "code", "execution_count": 13, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\"CHP
" ], "text/plain": [ "" ] }, "execution_count": 13, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML(\"\"\"
\"CHP
\"\"\")" ] }, { "cell_type": "code", "execution_count": 14, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\"MHP
" ], "text/plain": [ "" ] }, "execution_count": 14, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML(\"\"\"
\"MHP
\"\"\")" ] }, { "cell_type": "code", "execution_count": 15, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
\"HDP
" ], "text/plain": [ "" ] }, "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML(\"\"\"
\"HDP
\"\"\")" ] }, { "cell_type": "code", "execution_count": 16, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "" ], "text/plain": [ "" ] }, "execution_count": 16, "metadata": {}, "output_type": "execute_result" } ], "source": [ "HTML(\"\"\"\"\"\")" ] }, { "cell_type": "code", "execution_count": 17, "metadata": { "collapsed": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Total increase in the registered voters: 1555664.0\n", "Total increase in the valid votes: 1769286.0\n" ] } ], "source": [ "print('Total increase in the registered voters:',nov.registered.sum() - jun.registered.sum())\n", "print('Total increase in the valid votes:',nov.valid.sum() - jun.valid.sum())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some claimed that it is not normal to have such high number of registered voters (or valid votes) increase in five months. It smells fraud to many. However, I couldn't find any support to these claims in the city level data: there is no significant positive or negative correlation between the changes in the valid votes and changes in any of the party shares. [See the output of the following cell]" ] }, { "cell_type": "code", "execution_count": 18, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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AKPCHPMHPHDPothersregistered
AKP1.000000-0.250031-0.345152-0.348651-0.170495-0.120211
CHP-0.2500311.0000000.004348-0.253937-0.0306820.002432
MHP-0.3451520.0043481.000000-0.465819-0.3344730.215103
HDP-0.348651-0.253937-0.4658191.0000000.0615520.049348
others-0.170495-0.030682-0.3344730.0615521.000000-0.156149
registered-0.1202110.0024320.2151030.049348-0.1561491.000000
\n", "
" ], "text/plain": [ " AKP CHP MHP HDP others registered\n", "AKP 1.000000 -0.250031 -0.345152 -0.348651 -0.170495 -0.120211\n", "CHP -0.250031 1.000000 0.004348 -0.253937 -0.030682 0.002432\n", "MHP -0.345152 0.004348 1.000000 -0.465819 -0.334473 0.215103\n", "HDP -0.348651 -0.253937 -0.465819 1.000000 0.061552 0.049348\n", "others -0.170495 -0.030682 -0.334473 0.061552 1.000000 -0.156149\n", "registered -0.120211 0.002432 0.215103 0.049348 -0.156149 1.000000" ] }, "execution_count": 18, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#check the correlation of change in registered voters with the change in the party shares\n", "parties = ['AKP', 'CHP','MHP','HDP','others']\n", "change = nov[parties] - jun[parties]\n", "change['registered'] = (nov['registered'] - jun['registered'])/nov['registered']\n", "change.index = nov['city']\n", "change.corr() #change in registered voters vs change in party shares" ] }, { "cell_type": "code", "execution_count": 19, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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citytownAKP_nCHP_nMHP_nHDP_nothers_nAKP_jCHP_jMHP_jHDP_jothers_j
0AdanaALADAĞ57.0211.9127.670.351.0250.0911.5033.470.511.75
1AdanaCEYHAN33.1027.7021.8215.430.4825.8425.8427.2218.271.19
2AdanaÇUKUROVA28.0643.1921.555.350.5723.1540.8025.797.291.47
3AdanaFEKE54.6915.1425.560.271.0047.0115.3231.490.672.09
4AdanaİMAMOĞLU53.0918.2224.421.681.0045.9222.1725.522.372.20
5AdanaKARAİSALI59.739.4527.770.410.7555.309.3630.950.621.63
6AdanaKARATAŞ27.1740.1526.174.940.3423.2241.6626.376.690.78
7AdanaKOZAN50.1318.2528.120.610.9942.9020.7130.721.292.37
8AdanaPOZANTI52.7120.4322.921.000.9845.3821.2227.081.812.42
9AdanaSAİMBEYLİ54.6211.0630.830.350.7948.3611.8634.820.551.40
10AdanaSARIÇAM44.6916.1932.973.870.6736.6416.3538.784.981.78
11AdanaSEYHAN32.2432.9514.5718.320.6325.8831.4218.0621.431.49
12AdanaTUFANBEYLİ47.3119.7328.930.610.8935.3618.9938.951.012.61
13AdanaYUMURTALIK37.2626.5931.621.530.8032.9625.9835.662.500.99
14AdanaYÜREĞİR42.7524.6515.4815.100.6033.7824.1518.5819.631.37
15AdıyamanBESNİ67.4316.544.208.831.0352.4224.034.6715.171.42
16AdıyamanÇELİKHAN65.639.441.4920.741.1957.569.162.0027.142.74
17AdıyamanGERGER85.123.360.967.671.0270.915.001.3817.442.70
18AdıyamanGÖLBAŞI58.1225.118.035.990.6348.5027.8210.959.141.29
19AdıyamanKAHTA82.782.240.6412.520.6668.591.710.9425.761.45
20AdıyamanMerkez64.1911.972.9119.000.6454.499.664.5127.802.28
21AdıyamanSAMSAT86.192.852.325.811.9771.212.712.8016.325.75
22AdıyamanSİNCİK95.940.850.601.550.4290.711.001.075.121.04
23AdıyamanTUT66.2120.465.514.750.6056.2424.518.066.571.80
24AfyonkarahisarBAŞMAKÇI48.9329.8318.190.320.7041.2631.4222.560.650.95
25AfyonkarahisarBAYAT62.0317.6214.990.651.8951.4618.6322.112.051.90
26AfyonkarahisarBOLVADİN71.5910.1315.710.430.6762.5610.5622.440.981.80
27AfyonkarahisarÇAY54.1223.2419.360.540.6645.4522.7326.830.941.60
28AfyonkarahisarÇOBANLAR64.6111.8921.550.180.5553.0514.3829.110.531.50
29AfyonkarahisarDAZKIRI51.7224.2420.770.360.6943.3727.7525.060.531.40
.......................................
940VanSARAY11.840.470.6385.650.554.350.230.6294.260.14
941VanTUŞBA36.221.420.9759.620.5022.721.262.0171.370.73
942YalovaALTINOVA53.6229.998.055.510.7744.9729.9813.907.442.02
943YalovaARMUTLU53.5423.3914.605.081.4146.1122.2021.065.622.95
944YalovaÇINARCIK42.8238.5510.365.640.5834.5136.1318.547.371.45
945YalovaÇİFTLİKKÖY49.6032.2610.984.220.9640.9930.1919.865.122.05
946YalovaMerkez48.4831.299.527.590.8238.2627.9620.369.961.88
947YalovaTERMAL60.6523.1610.151.242.3249.7120.4422.421.983.63
948YozgatAKDAĞMADENİ62.034.047.820.1721.8561.027.2226.330.493.46
949YozgatAYDINCIK70.698.658.130.448.9964.0110.1021.560.991.78
950YozgatBOĞAZLIYAN51.5320.8514.810.649.3643.4022.8529.471.011.72
951YozgatÇANDIR50.5520.4723.200.183.0944.2017.9834.370.591.21
952YozgatÇAYIRALAN55.8015.9312.620.2711.3150.8517.6327.520.471.60
953YozgatÇEKEREK75.198.456.370.406.1868.019.1517.580.633.05
954YozgatKADIŞEHRİ77.027.135.330.097.5672.718.4614.360.322.58
955YozgatMerkez63.666.2315.140.3912.0253.916.4235.330.902.44
956YozgatSARAYKENT75.740.697.290.1012.8672.971.5122.740.191.32
957YozgatSARIKAYA69.722.9113.470.3110.3564.794.9525.750.991.93
958YozgatSORGUN74.803.939.470.338.4268.695.9020.310.623.43
959YozgatŞEFAATLİ50.326.3415.830.2723.8046.7510.7239.580.501.23
960YozgatYENİFAKILI48.6621.5019.420.147.4142.2823.9330.230.761.28
961YozgatYERKÖY57.815.9418.182.0012.7050.848.8533.073.212.63
962ZonguldakALAPLI59.9027.848.150.611.2348.6129.1716.920.932.53
963ZonguldakÇAYCUMA47.4639.518.100.541.5635.1944.8113.350.753.28
964ZonguldakDEVREK56.9429.228.220.511.8545.6231.9114.020.764.46
965ZonguldakEREĞLİ51.0035.269.630.910.8339.3437.4517.801.382.03
966ZonguldakGÖKÇEBEY48.9538.576.950.431.6636.3143.5412.420.763.46
967ZonguldakKİLİMLİ41.6242.2811.740.382.2431.3344.7416.810.724.77
968ZonguldakKOZLU52.1731.6010.830.921.8839.3934.7817.811.394.82
969ZonguldakMerkez42.3941.7810.931.111.7032.4842.6517.931.683.57
\n", "

970 rows × 12 columns

\n", "
" ], "text/plain": [ " city town AKP_n CHP_n MHP_n HDP_n others_n AKP_j \\\n", "0 Adana ALADAĞ 57.02 11.91 27.67 0.35 1.02 50.09 \n", "1 Adana CEYHAN 33.10 27.70 21.82 15.43 0.48 25.84 \n", "2 Adana ÇUKUROVA 28.06 43.19 21.55 5.35 0.57 23.15 \n", "3 Adana FEKE 54.69 15.14 25.56 0.27 1.00 47.01 \n", "4 Adana İMAMOĞLU 53.09 18.22 24.42 1.68 1.00 45.92 \n", "5 Adana KARAİSALI 59.73 9.45 27.77 0.41 0.75 55.30 \n", "6 Adana KARATAŞ 27.17 40.15 26.17 4.94 0.34 23.22 \n", "7 Adana KOZAN 50.13 18.25 28.12 0.61 0.99 42.90 \n", "8 Adana POZANTI 52.71 20.43 22.92 1.00 0.98 45.38 \n", "9 Adana SAİMBEYLİ 54.62 11.06 30.83 0.35 0.79 48.36 \n", "10 Adana SARIÇAM 44.69 16.19 32.97 3.87 0.67 36.64 \n", "11 Adana SEYHAN 32.24 32.95 14.57 18.32 0.63 25.88 \n", "12 Adana TUFANBEYLİ 47.31 19.73 28.93 0.61 0.89 35.36 \n", "13 Adana YUMURTALIK 37.26 26.59 31.62 1.53 0.80 32.96 \n", "14 Adana YÜREĞİR 42.75 24.65 15.48 15.10 0.60 33.78 \n", "15 Adıyaman BESNİ 67.43 16.54 4.20 8.83 1.03 52.42 \n", "16 Adıyaman ÇELİKHAN 65.63 9.44 1.49 20.74 1.19 57.56 \n", "17 Adıyaman GERGER 85.12 3.36 0.96 7.67 1.02 70.91 \n", "18 Adıyaman GÖLBAŞI 58.12 25.11 8.03 5.99 0.63 48.50 \n", "19 Adıyaman KAHTA 82.78 2.24 0.64 12.52 0.66 68.59 \n", "20 Adıyaman Merkez 64.19 11.97 2.91 19.00 0.64 54.49 \n", "21 Adıyaman SAMSAT 86.19 2.85 2.32 5.81 1.97 71.21 \n", "22 Adıyaman SİNCİK 95.94 0.85 0.60 1.55 0.42 90.71 \n", "23 Adıyaman TUT 66.21 20.46 5.51 4.75 0.60 56.24 \n", "24 Afyonkarahisar BAŞMAKÇI 48.93 29.83 18.19 0.32 0.70 41.26 \n", "25 Afyonkarahisar BAYAT 62.03 17.62 14.99 0.65 1.89 51.46 \n", "26 Afyonkarahisar BOLVADİN 71.59 10.13 15.71 0.43 0.67 62.56 \n", "27 Afyonkarahisar ÇAY 54.12 23.24 19.36 0.54 0.66 45.45 \n", "28 Afyonkarahisar ÇOBANLAR 64.61 11.89 21.55 0.18 0.55 53.05 \n", "29 Afyonkarahisar DAZKIRI 51.72 24.24 20.77 0.36 0.69 43.37 \n", ".. ... ... ... ... ... ... ... ... \n", "940 Van SARAY 11.84 0.47 0.63 85.65 0.55 4.35 \n", "941 Van TUŞBA 36.22 1.42 0.97 59.62 0.50 22.72 \n", "942 Yalova ALTINOVA 53.62 29.99 8.05 5.51 0.77 44.97 \n", "943 Yalova ARMUTLU 53.54 23.39 14.60 5.08 1.41 46.11 \n", "944 Yalova ÇINARCIK 42.82 38.55 10.36 5.64 0.58 34.51 \n", "945 Yalova ÇİFTLİKKÖY 49.60 32.26 10.98 4.22 0.96 40.99 \n", "946 Yalova Merkez 48.48 31.29 9.52 7.59 0.82 38.26 \n", "947 Yalova TERMAL 60.65 23.16 10.15 1.24 2.32 49.71 \n", "948 Yozgat AKDAĞMADENİ 62.03 4.04 7.82 0.17 21.85 61.02 \n", "949 Yozgat AYDINCIK 70.69 8.65 8.13 0.44 8.99 64.01 \n", "950 Yozgat BOĞAZLIYAN 51.53 20.85 14.81 0.64 9.36 43.40 \n", "951 Yozgat ÇANDIR 50.55 20.47 23.20 0.18 3.09 44.20 \n", "952 Yozgat ÇAYIRALAN 55.80 15.93 12.62 0.27 11.31 50.85 \n", "953 Yozgat ÇEKEREK 75.19 8.45 6.37 0.40 6.18 68.01 \n", "954 Yozgat KADIŞEHRİ 77.02 7.13 5.33 0.09 7.56 72.71 \n", "955 Yozgat Merkez 63.66 6.23 15.14 0.39 12.02 53.91 \n", "956 Yozgat SARAYKENT 75.74 0.69 7.29 0.10 12.86 72.97 \n", "957 Yozgat SARIKAYA 69.72 2.91 13.47 0.31 10.35 64.79 \n", "958 Yozgat SORGUN 74.80 3.93 9.47 0.33 8.42 68.69 \n", "959 Yozgat ŞEFAATLİ 50.32 6.34 15.83 0.27 23.80 46.75 \n", "960 Yozgat YENİFAKILI 48.66 21.50 19.42 0.14 7.41 42.28 \n", "961 Yozgat YERKÖY 57.81 5.94 18.18 2.00 12.70 50.84 \n", "962 Zonguldak ALAPLI 59.90 27.84 8.15 0.61 1.23 48.61 \n", "963 Zonguldak ÇAYCUMA 47.46 39.51 8.10 0.54 1.56 35.19 \n", "964 Zonguldak DEVREK 56.94 29.22 8.22 0.51 1.85 45.62 \n", "965 Zonguldak EREĞLİ 51.00 35.26 9.63 0.91 0.83 39.34 \n", "966 Zonguldak GÖKÇEBEY 48.95 38.57 6.95 0.43 1.66 36.31 \n", "967 Zonguldak KİLİMLİ 41.62 42.28 11.74 0.38 2.24 31.33 \n", "968 Zonguldak KOZLU 52.17 31.60 10.83 0.92 1.88 39.39 \n", "969 Zonguldak Merkez 42.39 41.78 10.93 1.11 1.70 32.48 \n", "\n", " CHP_j MHP_j HDP_j others_j \n", "0 11.50 33.47 0.51 1.75 \n", "1 25.84 27.22 18.27 1.19 \n", "2 40.80 25.79 7.29 1.47 \n", "3 15.32 31.49 0.67 2.09 \n", "4 22.17 25.52 2.37 2.20 \n", "5 9.36 30.95 0.62 1.63 \n", "6 41.66 26.37 6.69 0.78 \n", "7 20.71 30.72 1.29 2.37 \n", "8 21.22 27.08 1.81 2.42 \n", "9 11.86 34.82 0.55 1.40 \n", "10 16.35 38.78 4.98 1.78 \n", "11 31.42 18.06 21.43 1.49 \n", "12 18.99 38.95 1.01 2.61 \n", "13 25.98 35.66 2.50 0.99 \n", "14 24.15 18.58 19.63 1.37 \n", "15 24.03 4.67 15.17 1.42 \n", "16 9.16 2.00 27.14 2.74 \n", "17 5.00 1.38 17.44 2.70 \n", "18 27.82 10.95 9.14 1.29 \n", "19 1.71 0.94 25.76 1.45 \n", "20 9.66 4.51 27.80 2.28 \n", "21 2.71 2.80 16.32 5.75 \n", "22 1.00 1.07 5.12 1.04 \n", "23 24.51 8.06 6.57 1.80 \n", "24 31.42 22.56 0.65 0.95 \n", "25 18.63 22.11 2.05 1.90 \n", "26 10.56 22.44 0.98 1.80 \n", "27 22.73 26.83 0.94 1.60 \n", "28 14.38 29.11 0.53 1.50 \n", "29 27.75 25.06 0.53 1.40 \n", ".. ... ... ... ... \n", "940 0.23 0.62 94.26 0.14 \n", "941 1.26 2.01 71.37 0.73 \n", "942 29.98 13.90 7.44 2.02 \n", "943 22.20 21.06 5.62 2.95 \n", "944 36.13 18.54 7.37 1.45 \n", "945 30.19 19.86 5.12 2.05 \n", "946 27.96 20.36 9.96 1.88 \n", "947 20.44 22.42 1.98 3.63 \n", "948 7.22 26.33 0.49 3.46 \n", "949 10.10 21.56 0.99 1.78 \n", "950 22.85 29.47 1.01 1.72 \n", "951 17.98 34.37 0.59 1.21 \n", "952 17.63 27.52 0.47 1.60 \n", "953 9.15 17.58 0.63 3.05 \n", "954 8.46 14.36 0.32 2.58 \n", "955 6.42 35.33 0.90 2.44 \n", "956 1.51 22.74 0.19 1.32 \n", "957 4.95 25.75 0.99 1.93 \n", "958 5.90 20.31 0.62 3.43 \n", "959 10.72 39.58 0.50 1.23 \n", "960 23.93 30.23 0.76 1.28 \n", "961 8.85 33.07 3.21 2.63 \n", "962 29.17 16.92 0.93 2.53 \n", "963 44.81 13.35 0.75 3.28 \n", "964 31.91 14.02 0.76 4.46 \n", "965 37.45 17.80 1.38 2.03 \n", "966 43.54 12.42 0.76 3.46 \n", "967 44.74 16.81 0.72 4.77 \n", "968 34.78 17.81 1.39 4.82 \n", "969 42.65 17.93 1.68 3.57 \n", "\n", "[970 rows x 12 columns]" ] }, "execution_count": 19, "metadata": {}, "output_type": "execute_result" } ], "source": [ "nt.merge(jt,on=['city','town'],suffixes=('_n','_j')) #nov and jun combined" ] }, { "cell_type": "code", "execution_count": 20, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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AKPCHPMHPHDPothers
KÖPRÜKÖY, Erzurum34.980.04-6.89-26.60-0.66
AKÇAKALE, Şanlıurfa30.52-2.87-19.40-3.14-0.45
HARRAN, Şanlıurfa28.53-0.98-24.85-1.19-0.48
ELEŞKİRT, Ağrı27.88-0.08-1.10-24.54-1.32
SİVEREK, Şanlıurfa24.10-5.661.18-7.66-0.28
HİLVAN, Şanlıurfa22.28-7.432.64-4.70-0.69
Merkez, Iğdır21.20-1.68-16.85-2.86-0.03
HAMUR, Ağrı20.830.99-0.15-19.98-1.31
PASİNLER, Erzurum19.77-0.02-12.80-4.79-1.65
ÇERMİK, Diyarbakır19.620.81-0.41-13.52-4.93
HORASAN, Erzurum19.42-0.43-6.59-10.55-0.58
SOLHAN, Bingöl18.95-0.20-0.49-11.28-4.68
ADAKLI, Bingöl18.910.930.28-16.73-0.34
KARAKOYUNLU, Iğdır18.83-2.69-9.84-6.930.46
YEŞİLLİ, Mardin18.53-2.77-0.22-6.78-0.29
PALANDÖKEN, Erzurum18.310.23-12.64-4.24-1.84
ARTUKLU, Mardin18.230.22-0.50-8.52-0.70
KARLIOVA, Bingöl18.050.25-0.51-10.52-5.45
GENÇ, Bingöl18.03-0.22-0.96-11.13-3.46
KAZIMKARABEKİR, Karaman17.682.94-19.75-0.45-0.34
PALU, Elazığ17.64-5.78-1.24-7.32-3.01
ARICAK, Elazığ17.62-1.40-1.13-15.43-0.24
MUTKİ, Bitlis17.523.51-1.72-16.73-0.67
TUZLUCA, Iğdır17.52-1.84-8.80-7.480.41
Merkez, Kilis17.345.06-18.97-2.06-0.71
\n", "
" ], "text/plain": [ " AKP CHP MHP HDP others\n", "KÖPRÜKÖY, Erzurum 34.98 0.04 -6.89 -26.60 -0.66\n", "AKÇAKALE, Şanlıurfa 30.52 -2.87 -19.40 -3.14 -0.45\n", "HARRAN, Şanlıurfa 28.53 -0.98 -24.85 -1.19 -0.48\n", "ELEŞKİRT, Ağrı 27.88 -0.08 -1.10 -24.54 -1.32\n", "SİVEREK, Şanlıurfa 24.10 -5.66 1.18 -7.66 -0.28\n", "HİLVAN, Şanlıurfa 22.28 -7.43 2.64 -4.70 -0.69\n", "Merkez, Iğdır 21.20 -1.68 -16.85 -2.86 -0.03\n", "HAMUR, Ağrı 20.83 0.99 -0.15 -19.98 -1.31\n", "PASİNLER, Erzurum 19.77 -0.02 -12.80 -4.79 -1.65\n", "ÇERMİK, Diyarbakır 19.62 0.81 -0.41 -13.52 -4.93\n", "HORASAN, Erzurum 19.42 -0.43 -6.59 -10.55 -0.58\n", "SOLHAN, Bingöl 18.95 -0.20 -0.49 -11.28 -4.68\n", "ADAKLI, Bingöl 18.91 0.93 0.28 -16.73 -0.34\n", "KARAKOYUNLU, Iğdır 18.83 -2.69 -9.84 -6.93 0.46\n", "YEŞİLLİ, Mardin 18.53 -2.77 -0.22 -6.78 -0.29\n", "PALANDÖKEN, Erzurum 18.31 0.23 -12.64 -4.24 -1.84\n", "ARTUKLU, Mardin 18.23 0.22 -0.50 -8.52 -0.70\n", "KARLIOVA, Bingöl 18.05 0.25 -0.51 -10.52 -5.45\n", "GENÇ, Bingöl 18.03 -0.22 -0.96 -11.13 -3.46\n", "KAZIMKARABEKİR, Karaman 17.68 2.94 -19.75 -0.45 -0.34\n", "PALU, Elazığ 17.64 -5.78 -1.24 -7.32 -3.01\n", "ARICAK, Elazığ 17.62 -1.40 -1.13 -15.43 -0.24\n", "MUTKİ, Bitlis 17.52 3.51 -1.72 -16.73 -0.67\n", "TUZLUCA, Iğdır 17.52 -1.84 -8.80 -7.48 0.41\n", "Merkez, Kilis 17.34 5.06 -18.97 -2.06 -0.71" ] }, "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#let's look at the towns who changed their party preference in favor of AKP the most\n", "df = nt[parties]-jt[parties]\n", "df.index = nt.town + ', ' + nt.city\n", "df = df.sort('AKP',ascending=False)\n", "df.ix[:25]" ] }, { "cell_type": "code", "execution_count": 21, "metadata": { "collapsed": false }, "outputs": [ { "data": { "image/png": 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Oy5cvL/HuBPkOHz6s48ePF/q7/Omnn9SxY8dStwcAlD0KeADAFRs7dqy2b9+uMWPG6Oef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\nWZ+f3hJNMTExQmmnT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"text/plain": [ "" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "ax = df.ix[:25].plot(kind='bar',figsize=(17,5),title='Towns of New AKP Converts (Top 25)')\n", "ax.set_ylabel(\"Change in vote shares (%)\");" ] }, { "cell_type": "code", "execution_count": 22, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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AKPCHPMHPHDPothers
SAMANDAĞ, Hatay0.3313.490.196.88-0.63
HANAK, Ardahan2.2612.453.94-4.25-10.58
PEHLİVANKÖY, Kırklareli0.1111.83-11.08-0.190.06
PÜLÜMÜR, Tunceli-0.4210.20-1.08-7.82-0.38
PERTEK, Tunceli1.839.22-2.98-7.97-0.25
SARAY, Tekirdağ6.038.93-13.57-0.84-0.54
NAZIMİYE, Tunceli-0.748.87-0.43-7.110.02
Merkez, Tunceli-1.098.86-4.22-3.21-0.38
BABAESKİ, Kırklareli1.048.60-8.69-0.31-0.03
Merkez, Ardahan7.758.321.76-7.15-9.12
NURHAK, Kahramanmaraş6.368.18-3.75-7.11-2.63
Merkez, Kırklareli7.777.94-14.51-0.58-0.40
ŞAVŞAT, Artvin3.627.89-9.99-0.53-0.77
OVACIK, Tunceli-0.637.64-1.68-4.51-0.30
SUSUZ, Kars3.507.110.50-9.82-0.30
YEDİSU, Bingöl7.937.06-0.65-14.400.09
SELİM, Kars9.476.98-3.52-10.87-0.44
PAZARYERİ, Bilecik10.386.55-15.51-1.33-0.53
HOPA, Artvin4.336.48-9.13-1.61-0.61
Merkez, Bilecik9.545.94-13.96-0.74-0.95
Merkez, Artvin5.655.72-9.93-0.67-0.75
DATÇA, Muğla-0.235.67-2.31-1.26-1.26
ÇILDIR, Ardahan11.025.232.32-1.88-13.84
HOZAT, Tunceli0.275.16-1.80-2.95-0.15
Merkez, Kilis17.345.06-18.97-2.06-0.71
\n", "
" ], "text/plain": [ " AKP CHP MHP HDP others\n", "SAMANDAĞ, Hatay 0.33 13.49 0.19 6.88 -0.63\n", "HANAK, Ardahan 2.26 12.45 3.94 -4.25 -10.58\n", "PEHLİVANKÖY, Kırklareli 0.11 11.83 -11.08 -0.19 0.06\n", "PÜLÜMÜR, Tunceli -0.42 10.20 -1.08 -7.82 -0.38\n", "PERTEK, Tunceli 1.83 9.22 -2.98 -7.97 -0.25\n", "SARAY, Tekirdağ 6.03 8.93 -13.57 -0.84 -0.54\n", "NAZIMİYE, Tunceli -0.74 8.87 -0.43 -7.11 0.02\n", "Merkez, Tunceli -1.09 8.86 -4.22 -3.21 -0.38\n", "BABAESKİ, Kırklareli 1.04 8.60 -8.69 -0.31 -0.03\n", "Merkez, Ardahan 7.75 8.32 1.76 -7.15 -9.12\n", "NURHAK, Kahramanmaraş 6.36 8.18 -3.75 -7.11 -2.63\n", "Merkez, Kırklareli 7.77 7.94 -14.51 -0.58 -0.40\n", "ŞAVŞAT, Artvin 3.62 7.89 -9.99 -0.53 -0.77\n", "OVACIK, Tunceli -0.63 7.64 -1.68 -4.51 -0.30\n", "SUSUZ, Kars 3.50 7.11 0.50 -9.82 -0.30\n", "YEDİSU, Bingöl 7.93 7.06 -0.65 -14.40 0.09\n", "SELİM, Kars 9.47 6.98 -3.52 -10.87 -0.44\n", "PAZARYERİ, Bilecik 10.38 6.55 -15.51 -1.33 -0.53\n", "HOPA, Artvin 4.33 6.48 -9.13 -1.61 -0.61\n", "Merkez, Bilecik 9.54 5.94 -13.96 -0.74 -0.95\n", "Merkez, Artvin 5.65 5.72 -9.93 -0.67 -0.75\n", "DATÇA, Muğla -0.23 5.67 -2.31 -1.26 -1.26\n", "ÇILDIR, Ardahan 11.02 5.23 2.32 -1.88 -13.84\n", "HOZAT, Tunceli 0.27 5.16 -1.80 -2.95 -0.15\n", "Merkez, Kilis 17.34 5.06 -18.97 -2.06 -0.71" ] }, "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort('CHP',ascending=False).ix[:25]" ] }, { "cell_type": "code", "execution_count": 23, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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AKPCHPMHPHDPothers
HANAK, Ardahan2.2612.453.94-4.25-10.58
TAŞOVA, Amasya2.54-4.183.04-0.35-0.58
HİLVAN, Şanlıurfa22.28-7.432.64-4.70-0.69
ÇILDIR, Ardahan11.025.232.32-1.88-13.84
HEKİMHAN, Malatya3.40-4.001.95-0.76-0.73
Merkez, Ardahan7.758.321.76-7.15-9.12
GÜLNAR, Mersin7.26-8.271.29-0.33-0.61
SİVEREK, Şanlıurfa24.10-5.661.18-7.66-0.28
YENİCE, Karabük10.91-10.061.15-0.15-1.43
SÖKE, Aydın4.31-1.460.61-2.84-0.43
SARAYKÖY, Denizli1.12-0.550.58-1.32-0.20
SUSUZ, Kars3.507.110.50-9.82-0.30
YENİPAZAR, Aydın1.70-1.340.50-0.32-0.42
KIRIKHAN, Hatay8.71-5.940.48-1.57-1.35
BAYKAN, Siirt12.250.790.39-13.00-0.38
ADAKLI, Bingöl18.910.930.28-16.73-0.34
DAMAL, Ardahan2.862.890.21-1.69-3.32
SAMANDAĞ, Hatay0.3313.490.196.88-0.63
SARAY, Van7.490.240.01-8.610.41
ERGANİ, Diyarbakır9.75-0.02-0.03-6.85-2.45
Merkez, Kars6.383.64-0.05-8.72-0.47
HASKÖY, Muş10.851.06-0.06-11.42-0.28
BAŞKALE, Van1.630.22-0.07-1.72-0.07
KORKUT, Muş9.620.33-0.08-10.29-0.01
KOCAKÖY, Diyarbakır1.940.32-0.12-2.09-0.05
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" ], "text/plain": [ " AKP CHP MHP HDP others\n", "HANAK, Ardahan 2.26 12.45 3.94 -4.25 -10.58\n", "TAŞOVA, Amasya 2.54 -4.18 3.04 -0.35 -0.58\n", "HİLVAN, Şanlıurfa 22.28 -7.43 2.64 -4.70 -0.69\n", "ÇILDIR, Ardahan 11.02 5.23 2.32 -1.88 -13.84\n", "HEKİMHAN, Malatya 3.40 -4.00 1.95 -0.76 -0.73\n", "Merkez, Ardahan 7.75 8.32 1.76 -7.15 -9.12\n", "GÜLNAR, Mersin 7.26 -8.27 1.29 -0.33 -0.61\n", "SİVEREK, Şanlıurfa 24.10 -5.66 1.18 -7.66 -0.28\n", "YENİCE, Karabük 10.91 -10.06 1.15 -0.15 -1.43\n", "SÖKE, Aydın 4.31 -1.46 0.61 -2.84 -0.43\n", "SARAYKÖY, Denizli 1.12 -0.55 0.58 -1.32 -0.20\n", "SUSUZ, Kars 3.50 7.11 0.50 -9.82 -0.30\n", "YENİPAZAR, Aydın 1.70 -1.34 0.50 -0.32 -0.42\n", "KIRIKHAN, Hatay 8.71 -5.94 0.48 -1.57 -1.35\n", "BAYKAN, Siirt 12.25 0.79 0.39 -13.00 -0.38\n", "ADAKLI, Bingöl 18.91 0.93 0.28 -16.73 -0.34\n", "DAMAL, Ardahan 2.86 2.89 0.21 -1.69 -3.32\n", "SAMANDAĞ, Hatay 0.33 13.49 0.19 6.88 -0.63\n", "SARAY, Van 7.49 0.24 0.01 -8.61 0.41\n", "ERGANİ, Diyarbakır 9.75 -0.02 -0.03 -6.85 -2.45\n", "Merkez, Kars 6.38 3.64 -0.05 -8.72 -0.47\n", "HASKÖY, Muş 10.85 1.06 -0.06 -11.42 -0.28\n", "BAŞKALE, Van 1.63 0.22 -0.07 -1.72 -0.07\n", "KORKUT, Muş 9.62 0.33 -0.08 -10.29 -0.01\n", "KOCAKÖY, Diyarbakır 1.94 0.32 -0.12 -2.09 -0.05" ] }, "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort('MHP',ascending=False).ix[:25]" ] }, { "cell_type": "code", "execution_count": 24, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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AKPCHPMHPHDPothers
SAMANDAĞ, Hatay0.3313.490.196.88-0.63
DEFNE, Hatay0.290.83-0.373.57-0.06
GÜÇLÜKONAK, Şırnak0.44-0.43-2.512.54-0.18
BEYTÜŞŞEBAP, Şırnak0.070.76-1.821.430.00
CİZRE, Şırnak0.630.03-0.661.27-0.87
ULUDERE, Şırnak1.230.20-2.271.06-0.19
ARSUZ, Hatay2.50-0.40-0.460.34-0.35
YAYLADAĞI, Hatay5.60-2.02-2.100.25-1.00
ÇUKURCA, Hakkari1.83-0.35-1.560.18-0.22
BOĞAZKALE, Çorum7.70-0.32-6.850.18-1.23
ANTAKYA, Hatay4.910.74-3.400.12-1.24
KANGAL, Sivas11.79-0.56-9.300.12-2.26
KOFÇAZ, Kırklareli4.513.14-6.150.10-0.50
DEMİRÖZÜ, Bayburt14.30-1.55-10.650.09-2.07
DEREBUCAK, Konya6.001.22-7.460.08-0.62
PAZARYOLU, Erzurum4.92-0.21-2.540.08-2.64
KORGUN, Çankırı10.28-0.64-6.850.07-2.32
SARAYDÜZÜ, Sinop7.821.51-7.470.06-1.08
ORTAKÖY, Çorum8.24-1.24-6.270.05-1.27
YAPRAKLI, Çankırı5.25-0.26-4.170.04-0.54
FELAHİYE, Kayseri7.990.06-8.110.02-1.00
UĞURLUDAĞ, Çorum9.14-1.32-6.630.01-1.15
YENİŞARBADEMLİ, Isparta2.922.53-5.890.01-0.51
AĞLI, Kastamonu13.18-1.26-10.400.00-0.52
AYDINTEPE, Bayburt9.35-0.18-7.92-0.02-1.38
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" ], "text/plain": [ " AKP CHP MHP HDP others\n", "SAMANDAĞ, Hatay 0.33 13.49 0.19 6.88 -0.63\n", "DEFNE, Hatay 0.29 0.83 -0.37 3.57 -0.06\n", "GÜÇLÜKONAK, Şırnak 0.44 -0.43 -2.51 2.54 -0.18\n", "BEYTÜŞŞEBAP, Şırnak 0.07 0.76 -1.82 1.43 0.00\n", "CİZRE, Şırnak 0.63 0.03 -0.66 1.27 -0.87\n", "ULUDERE, Şırnak 1.23 0.20 -2.27 1.06 -0.19\n", "ARSUZ, Hatay 2.50 -0.40 -0.46 0.34 -0.35\n", "YAYLADAĞI, Hatay 5.60 -2.02 -2.10 0.25 -1.00\n", "ÇUKURCA, Hakkari 1.83 -0.35 -1.56 0.18 -0.22\n", "BOĞAZKALE, Çorum 7.70 -0.32 -6.85 0.18 -1.23\n", "ANTAKYA, Hatay 4.91 0.74 -3.40 0.12 -1.24\n", "KANGAL, Sivas 11.79 -0.56 -9.30 0.12 -2.26\n", "KOFÇAZ, Kırklareli 4.51 3.14 -6.15 0.10 -0.50\n", "DEMİRÖZÜ, Bayburt 14.30 -1.55 -10.65 0.09 -2.07\n", "DEREBUCAK, Konya 6.00 1.22 -7.46 0.08 -0.62\n", "PAZARYOLU, Erzurum 4.92 -0.21 -2.54 0.08 -2.64\n", "KORGUN, Çankırı 10.28 -0.64 -6.85 0.07 -2.32\n", "SARAYDÜZÜ, Sinop 7.82 1.51 -7.47 0.06 -1.08\n", "ORTAKÖY, Çorum 8.24 -1.24 -6.27 0.05 -1.27\n", "YAPRAKLI, Çankırı 5.25 -0.26 -4.17 0.04 -0.54\n", "FELAHİYE, Kayseri 7.99 0.06 -8.11 0.02 -1.00\n", "UĞURLUDAĞ, Çorum 9.14 -1.32 -6.63 0.01 -1.15\n", "YENİŞARBADEMLİ, Isparta 2.92 2.53 -5.89 0.01 -0.51\n", "AĞLI, Kastamonu 13.18 -1.26 -10.40 0.00 -0.52\n", "AYDINTEPE, Bayburt 9.35 -0.18 -7.92 -0.02 -1.38" ] }, "execution_count": 24, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort('HDP',ascending=False).ix[:25]" ] }, { "cell_type": "code", "execution_count": 25, "metadata": { "collapsed": false }, "outputs": [ { "data": { "text/html": [ "
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AKPCHPMHPHDPothers
DİCLE, Diyarbakır-5.570.24-0.61-10.5616.75
Merkez, Tunceli-1.098.86-4.22-3.21-0.38
NAZIMİYE, Tunceli-0.748.87-0.43-7.110.02
OVACIK, Tunceli-0.637.64-1.68-4.51-0.30
PÜLÜMÜR, Tunceli-0.4210.20-1.08-7.82-0.38
DATÇA, Muğla-0.235.67-2.31-1.26-1.26
MAZGİRT, Tunceli-0.032.63-0.75-2.180.35
BEYTÜŞŞEBAP, Şırnak0.070.76-1.821.430.00
PEHLİVANKÖY, Kırklareli0.1111.83-11.08-0.190.06
HOZAT, Tunceli0.275.16-1.80-2.95-0.15
DEFNE, Hatay0.290.83-0.373.57-0.06
SAMANDAĞ, Hatay0.3313.490.196.88-0.63
GÜÇLÜKONAK, Şırnak0.44-0.43-2.512.54-0.18
LİCE, Diyarbakır0.60-0.01-0.28-0.19-0.04
CİZRE, Şırnak0.630.03-0.661.27-0.87
YÜKSEKOVA, Hakkari0.850.01-0.70-0.350.03
AKDAĞMADENİ, Yozgat1.01-3.18-18.51-0.3218.39
BABAESKİ, Kırklareli1.048.60-8.69-0.31-0.03
SARAYKÖY, Denizli1.12-0.550.58-1.32-0.20
VARTO, Muş1.161.34-0.24-2.510.07
ENEZ, Edirne1.181.57-2.57-0.19-0.44
ULUDERE, Şırnak1.230.20-2.271.06-0.19
GÜNEY, Denizli1.26-0.42-1.43-0.29-0.02
BAŞKALE, Van1.630.22-0.07-1.72-0.07
YENİPAZAR, Aydın1.70-1.340.50-0.32-0.42
\n", "
" ], "text/plain": [ " AKP CHP MHP HDP others\n", "DİCLE, Diyarbakır -5.57 0.24 -0.61 -10.56 16.75\n", "Merkez, Tunceli -1.09 8.86 -4.22 -3.21 -0.38\n", "NAZIMİYE, Tunceli -0.74 8.87 -0.43 -7.11 0.02\n", "OVACIK, Tunceli -0.63 7.64 -1.68 -4.51 -0.30\n", "PÜLÜMÜR, Tunceli -0.42 10.20 -1.08 -7.82 -0.38\n", "DATÇA, Muğla -0.23 5.67 -2.31 -1.26 -1.26\n", "MAZGİRT, Tunceli -0.03 2.63 -0.75 -2.18 0.35\n", "BEYTÜŞŞEBAP, Şırnak 0.07 0.76 -1.82 1.43 0.00\n", "PEHLİVANKÖY, Kırklareli 0.11 11.83 -11.08 -0.19 0.06\n", "HOZAT, Tunceli 0.27 5.16 -1.80 -2.95 -0.15\n", "DEFNE, Hatay 0.29 0.83 -0.37 3.57 -0.06\n", "SAMANDAĞ, Hatay 0.33 13.49 0.19 6.88 -0.63\n", "GÜÇLÜKONAK, Şırnak 0.44 -0.43 -2.51 2.54 -0.18\n", "LİCE, Diyarbakır 0.60 -0.01 -0.28 -0.19 -0.04\n", "CİZRE, Şırnak 0.63 0.03 -0.66 1.27 -0.87\n", "YÜKSEKOVA, Hakkari 0.85 0.01 -0.70 -0.35 0.03\n", "AKDAĞMADENİ, Yozgat 1.01 -3.18 -18.51 -0.32 18.39\n", "BABAESKİ, Kırklareli 1.04 8.60 -8.69 -0.31 -0.03\n", "SARAYKÖY, Denizli 1.12 -0.55 0.58 -1.32 -0.20\n", "VARTO, Muş 1.16 1.34 -0.24 -2.51 0.07\n", "ENEZ, Edirne 1.18 1.57 -2.57 -0.19 -0.44\n", "ULUDERE, Şırnak 1.23 0.20 -2.27 1.06 -0.19\n", "GÜNEY, Denizli 1.26 -0.42 -1.43 -0.29 -0.02\n", "BAŞKALE, Van 1.63 0.22 -0.07 -1.72 -0.07\n", "YENİPAZAR, Aydın 1.70 -1.34 0.50 -0.32 -0.42" ] }, "execution_count": 25, "metadata": {}, "output_type": "execute_result" } ], "source": [ "df.sort('AKP').ix[:25] #bottom 25 towns among 970 in turning to AKP" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.4.2" } }, "nbformat": 4, "nbformat_minor": 0 }