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simgeekiz/yüzdeyazdırma.ipynb

Created October 8, 2014 12:05
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yüzdeyazdırma.ipynb
{
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"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"from pandas import DataFrame, read_csv\n",
"from numpy import random\n",
"import numpy.numarray as na\n",
"from pylab import *\n",
"from __future__ import division\n",
"%matplotlib inline"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 1
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = read_csv('../dosya/olimpiyat/countries_TUR__medals.csv')\n",
"df=df[df['Games'].str.contains('Summer')] # Yaz olimpiyatlar\u0131n\u0131 se\u00e7iyoruz.\n",
"df['Games']=df['Games'].str.slice(0,4) # Games kolonuna sadece y\u0131llar\u0131 alacak \u015fekilde ilk 4 digitini al\u0131yoruz.\n",
"tri = df[['Games','Men','Women']] #istedi\u011fimiz kolonlar\u0131 se\u00e7erek tri ad\u0131nda yeni bir dataframe olu\u015fturuyoruz.\n",
"tri=tri.sort_index(by='Games') #indexini k\u00fc\u00e7\u00fckten b\u00fcy\u00fc\u011fe s\u0131ral\u0131yoruz.\n",
"tri"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Games</th>\n",
" <th>Men</th>\n",
" <th>Women</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> 1906</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td> 1912</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td> 1924</td>\n",
" <td> 22</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td> 1928</td>\n",
" <td> 31</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td> 1932</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> 1936</td>\n",
" <td> 46</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> 1948</td>\n",
" <td> 57</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> 1952</td>\n",
" <td> 51</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> 1956</td>\n",
" <td> 13</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> 1960</td>\n",
" <td> 46</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> 1964</td>\n",
" <td> 23</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> 1968</td>\n",
" <td> 29</td>\n",
" <td> 0</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> 1972</td>\n",
" <td> 42</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 1976</td>\n",
" <td> 26</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 1984</td>\n",
" <td> 45</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 1988</td>\n",
" <td> 36</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 1992</td>\n",
" <td> 36</td>\n",
" <td> 5</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 1996</td>\n",
" <td> 44</td>\n",
" <td> 9</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 2000</td>\n",
" <td> 42</td>\n",
" <td> 15</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 2004</td>\n",
" <td> 44</td>\n",
" <td> 20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 2008</td>\n",
" <td> 48</td>\n",
" <td> 19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 2012</td>\n",
" <td> 48</td>\n",
" <td> 64</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>22 rows \u00d7 3 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 2,
"text": [
" Games Men Women\n",
"38 1906 2 0\n",
"37 1912 2 0\n",
"36 1924 22 0\n",
"35 1928 31 0\n",
"34 1932 1 0\n",
"32 1936 46 2\n",
"30 1948 57 1\n",
"29 1952 51 0\n",
"26 1956 13 0\n",
"24 1960 46 3\n",
"22 1964 23 0\n",
"20 1968 29 0\n",
"19 1972 42 1\n",
"17 1976 26 1\n",
"15 1984 45 1\n",
"13 1988 36 5\n",
"11 1992 36 5\n",
"9 1996 44 9\n",
"7 2000 42 15\n",
"5 2004 44 20\n",
"3 2008 48 19\n",
"1 2012 48 64\n",
"\n",
"[22 rows x 3 columns]"
]
}
],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#Total ad\u0131nda yeni bir kolon ekliyoruz ve bu kolonda bayan ve erken kat\u0131l\u0131mc\u0131lar\u0131n toplam say\u0131s\u0131 bulunuyor.\n",
"tri['Total'] =tri['Men'] + tri['Women']\n",
"# Men_ratio ad\u0131nda kolon ekliyoruz ve bu kolonda erkek kat\u0131l\u0131mc\u0131lar\u0131n toplam say\u0131ya oran\u0131 bulunuyor.\n",
"tri['Men_ratio'] = tri['Men']/ tri['Total'] \n",
"# Women_ratio ad\u0131nda kolon ekliyoruz ve bu kolonda bayan kat\u0131l\u0131mc\u0131lar\u0131n toplam say\u0131ya oran\u0131 bulunuyor.\n",
"tri['Women_ratio'] = tri['Women']/ tri['Total']\n",
"###tri['Men_ratio']=str(tri['Men_ratio']*100)[:4]\n",
"tri"
],
"language": "python",
"metadata": {},
"outputs": [
{
"html": [
"<div style=\"max-height:1000px;max-width:1500px;overflow:auto;\">\n",
"<table border=\"1\" class=\"dataframe\">\n",
" <thead>\n",
" <tr style=\"text-align: right;\">\n",
" <th></th>\n",
" <th>Games</th>\n",
" <th>Men</th>\n",
" <th>Women</th>\n",
" <th>Total</th>\n",
" <th>Men_ratio</th>\n",
" <th>Women_ratio</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> 1906</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td> 1912</td>\n",
" <td> 2</td>\n",
" <td> 0</td>\n",
" <td> 2</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td> 1924</td>\n",
" <td> 22</td>\n",
" <td> 0</td>\n",
" <td> 22</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td> 1928</td>\n",
" <td> 31</td>\n",
" <td> 0</td>\n",
" <td> 31</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td> 1932</td>\n",
" <td> 1</td>\n",
" <td> 0</td>\n",
" <td> 1</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> 1936</td>\n",
" <td> 46</td>\n",
" <td> 2</td>\n",
" <td> 48</td>\n",
" <td> 0.958333</td>\n",
" <td> 0.041667</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> 1948</td>\n",
" <td> 57</td>\n",
" <td> 1</td>\n",
" <td> 58</td>\n",
" <td> 0.982759</td>\n",
" <td> 0.017241</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> 1952</td>\n",
" <td> 51</td>\n",
" <td> 0</td>\n",
" <td> 51</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> 1956</td>\n",
" <td> 13</td>\n",
" <td> 0</td>\n",
" <td> 13</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> 1960</td>\n",
" <td> 46</td>\n",
" <td> 3</td>\n",
" <td> 49</td>\n",
" <td> 0.938776</td>\n",
" <td> 0.061224</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> 1964</td>\n",
" <td> 23</td>\n",
" <td> 0</td>\n",
" <td> 23</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> 1968</td>\n",
" <td> 29</td>\n",
" <td> 0</td>\n",
" <td> 29</td>\n",
" <td> 1.000000</td>\n",
" <td> 0.000000</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> 1972</td>\n",
" <td> 42</td>\n",
" <td> 1</td>\n",
" <td> 43</td>\n",
" <td> 0.976744</td>\n",
" <td> 0.023256</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 1976</td>\n",
" <td> 26</td>\n",
" <td> 1</td>\n",
" <td> 27</td>\n",
" <td> 0.962963</td>\n",
" <td> 0.037037</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 1984</td>\n",
" <td> 45</td>\n",
" <td> 1</td>\n",
" <td> 46</td>\n",
" <td> 0.978261</td>\n",
" <td> 0.021739</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 1988</td>\n",
" <td> 36</td>\n",
" <td> 5</td>\n",
" <td> 41</td>\n",
" <td> 0.878049</td>\n",
" <td> 0.121951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 1992</td>\n",
" <td> 36</td>\n",
" <td> 5</td>\n",
" <td> 41</td>\n",
" <td> 0.878049</td>\n",
" <td> 0.121951</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 1996</td>\n",
" <td> 44</td>\n",
" <td> 9</td>\n",
" <td> 53</td>\n",
" <td> 0.830189</td>\n",
" <td> 0.169811</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 2000</td>\n",
" <td> 42</td>\n",
" <td> 15</td>\n",
" <td> 57</td>\n",
" <td> 0.736842</td>\n",
" <td> 0.263158</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 2004</td>\n",
" <td> 44</td>\n",
" <td> 20</td>\n",
" <td> 64</td>\n",
" <td> 0.687500</td>\n",
" <td> 0.312500</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 2008</td>\n",
" <td> 48</td>\n",
" <td> 19</td>\n",
" <td> 67</td>\n",
" <td> 0.716418</td>\n",
" <td> 0.283582</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 2012</td>\n",
" <td> 48</td>\n",
" <td> 64</td>\n",
" <td> 112</td>\n",
" <td> 0.428571</td>\n",
" <td> 0.571429</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>22 rows \u00d7 6 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
" Games Men Women Total Men_ratio Women_ratio\n",
"38 1906 2 0 2 1.000000 0.000000\n",
"37 1912 2 0 2 1.000000 0.000000\n",
"36 1924 22 0 22 1.000000 0.000000\n",
"35 1928 31 0 31 1.000000 0.000000\n",
"34 1932 1 0 1 1.000000 0.000000\n",
"32 1936 46 2 48 0.958333 0.041667\n",
"30 1948 57 1 58 0.982759 0.017241\n",
"29 1952 51 0 51 1.000000 0.000000\n",
"26 1956 13 0 13 1.000000 0.000000\n",
"24 1960 46 3 49 0.938776 0.061224\n",
"22 1964 23 0 23 1.000000 0.000000\n",
"20 1968 29 0 29 1.000000 0.000000\n",
"19 1972 42 1 43 0.976744 0.023256\n",
"17 1976 26 1 27 0.962963 0.037037\n",
"15 1984 45 1 46 0.978261 0.021739\n",
"13 1988 36 5 41 0.878049 0.121951\n",
"11 1992 36 5 41 0.878049 0.121951\n",
"9 1996 44 9 53 0.830189 0.169811\n",
"7 2000 42 15 57 0.736842 0.263158\n",
"5 2004 44 20 64 0.687500 0.312500\n",
"3 2008 48 19 67 0.716418 0.283582\n",
"1 2012 48 64 112 0.428571 0.571429\n",
"\n",
"[22 rows x 6 columns]"
]
}
],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"rcParams['figure.figsize'] = 15, 8"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#!/usr/bin/env python\n",
"# a bar plot with errorbars\n",
"\n",
"n_group = len(tri['Men'])\n",
"menMeans = tri['Men']\n",
"\n",
"index = np.arange(n_group) # the x locations for the groups\n",
"width = 0.35 # the width of the bars\n",
"\n",
"fig, ax = plt.subplots()\n",
"rects1 = ax.bar(index, menMeans, width, color='r')\n",
"\n",
"womenMeans = tri['Women']\n",
"rects2 = ax.bar(index, womenMeans, width, color='y',bottom=menMeans)\n",
"\n",
"# add some\n",
"# ax.set_xticklabels( ('G1', 'G2', 'G3', 'G4', 'G5') )\n",
"\n",
"ax.legend( (rects1[0], rects2[0]), ('Men', 'Women') )\n",
"#plt.legend( (rects1[0], rects2[0]), ('Men', 'Women') )\n",
"\n",
"#plt.ylabel('Scores')\n",
"#plt.title('Scores by group and gender')\n",
"plt.xticks(index+width, (list(tri['Games'])))\n",
"for label in ax.xaxis.get_ticklabels():\n",
" label.set_rotation(45)\n",
"def autolabel(rects):\n",
" # attach some text labels\n",
" for menrect in rects:\n",
" height = menrect.get_height()\n",
" ax.text(menrect.get_x()+menrect.get_width()/2., 0.85*height, '%d'%int(height),\n",
" ha='center', va='bottom')\n",
"\n",
"def autolabel2(rects1,rects2):\n",
" # attach some text labels\n",
" for i in range(len(rects1)):\n",
" height2 = rects2[i].get_height() +rects1[i].get_height()\n",
" height = rects1[i].get_height()\n",
" \n",
" ax.text(rects1[i].get_x()+rects1[i].get_width()/2., 0.50*height,'%.1f'%float(height/height2*100),ha='center', va='bottom')\n",
" for j in range(len(rects2)):\n",
" height2 = rects2[j].get_height() +rects1[j].get_height() \n",
" height = rects2[j].get_height()\n",
" \n",
" if height>0:\n",
" ax.text(rects2[j].get_x()+rects2[j].get_width()/2., 1.04*height2,'%.1f'%float(height/height2*100) ,ha='center', va='bottom') \n",
"#autolabel(rects1)\n",
"autolabel2(rects1,rects2)\n",
"\n",
"plt.show()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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97W9z6aWX8sc//pGioiI+/vjjMo936dKF+++/nwULFiSpQkmS1JQ4giZJldi9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\nEEIIIYQQQgghhBBvhf8Hv+MHkWltYPkAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7fbcd7335f10>"
]
}
],
"prompt_number": 5
},
{
"cell_type": "code",
"collapsed": false,
"input": [],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
}
],
"metadata": {}
}
]
}
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