simgeekiz/turkiye_greece_katilimyuzdelerikarsilastirmasi

## turkiye_greece_katilimyuzdelerikarsilastirmasi
{
 "metadata": {
  "name": "",
  "signature": "sha256:c27ec7b5cde425df540a968c0e9cc6e3e5ee4fcbaeff337bf3cecd7f4d9db671"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "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')]\n",
      "df['Games']=df['Games'].str.slice(0,4)\n",
      "tri = df[['Games','Men','Women']]\n",
      "tri=tri.sort_index(by='Games')\n",
      "yillartr = tri['Games']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dfgre = read_csv('../dosya/greece/Greece.csv')\n",
      "dfgre=dfgre[dfgre['Games'].str.contains('Summer')]\n",
      "dfgre['Games']=dfgre['Games'].str.slice(0,4)\n",
      "trigre = dfgre[['Games','Men','Women']]\n",
      "trigre=trigre.sort_index(by='Games')\n",
      "yillargre = trigre['Games']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tri['Total'] =tri['Men'] + tri['Women']\n",
      "tri['Men_ratio'] = tri['Men']/ tri['Total']\n",
      "tri['Women_ratio'] = tri['Women']/ tri['Total']\n",
      "#tri['Men_ratio']=str(tri['Men_ratio']*100)[:4]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "trigre['Total'] =trigre['Men'] + trigre['Women']\n",
      "trigre['Men_ratio'] = trigre['Men']/ trigre['Total']\n",
      "trigre['Women_ratio'] = trigre['Women']/ trigre['Total']\n",
      "#tri['Men_ratio']=str(tri['Men_ratio']*100)[:4]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "rcParams['figure.figsize'] = 15, 12"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#!/usr/bin/env python\n",
      "# a bar plot with errorbars\n",
      "\n",
      "n_group = len(trigre['Men'])\n",
      "menMeansgre = trigre['Men']\n",
      "\n",
      "indexgre = 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",
      "rects1gre = ax.bar(indexgre, menMeansgre, width, color='r')\n",
      "#fig.set_size_inches(18.5,6.5)\n",
      "\n",
      "womenMeansgre =  trigre['Women']\n",
      "rects2gre = ax.bar(indexgre, womenMeansgre, width, color='y',bottom=menMeansgre)\n",
      "\n",
      "# add some\n",
      "# ax.set_xticklabels( ('G1', 'G2', 'G3', 'G4', 'G5') )\n",
      "\n",
      "ax.legend( (rects1gre[0], rects2gre[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(indexgre+width, (list(trigre['Games'])))\n",
      "for label in ax.xaxis.get_ticklabels():\n",
      "    label.set_rotation(45)\n",
      "    \"\"\"\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(rects1gre)):\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(rects1gre,rects2gre)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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URKQBAAAURKQBAAAURKQBAAAURKQBAAAURKQBAAAURKQBAAAURKQBAAAURKQBAAAURKQB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\nAACAgog0AACAgog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9YHq4zjgUsTVbby6XNwcAAKjRjs6kLU1yQJIXkgxL8mJ1+aIkIzus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\nBK4AAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f527cab7910>"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "n_group = len(tri['Men'])\n",
      "n_groupgre = len(trigre['Men'])\n",
      "menMeans = tri['Men']\n",
      "menMeansgre = trigre['Men']\n",
      "index = np.arange(n_group)  # the x locations for the groups\n",
      "indexgre = np.arange(n_groupgre)\n",
      "width = 0.95       # the width of the bars\n",
      "\n",
      "plt.figure(1)\n",
      "ax = plt.subplot(2,1,1)\n",
      "ax2 = plt.subplot(2,1,2)\n",
      "\n",
      "womenMeans =  tri['Women']\n",
      "womenMeansgre = trigre['Women']\n",
      "\n",
      "\n",
      "# add some\n",
      "# ax.set_xticklabels( ('G1', 'G2', 'G3', 'G4', 'G5') )\n",
      "rects1 = ax.bar(index, menMeans, width, color='r')\n",
      "rects2 = ax.bar(index, womenMeans, width, color='y',bottom=menMeans)\n",
      "ax.set_title('Turkiye')\n",
      "\n",
      "rects1gre = ax2.bar(indexgre, menMeansgre, width, color='r')\n",
      "rects2gre = ax2.bar(indexgre, womenMeansgre, width, color='y',bottom=menMeansgre)\n",
      "ax2.set_title(\"Yunanistan\")\n",
      "\n",
      "ax.legend( (rects1[0], rects2[0]), ('Men', 'Women') )\n",
      "ax2.legend( (rects1[0], rects2[0]), ('Men', 'Women') )\n",
      "\n",
      "#plt.ylabel('Scores')\n",
      "#plt.title('Scores by group and gender')\n",
      "#ax.set_xlim(list(tri['Games']))\n",
      "ax.set_xticks(index+width/2)\n",
      "#ax2.set_xticklabels(list(trigre['Games']))\n",
      "ax.set_xticklabels(list(tri['Games']))\n",
      "plt.xticks(indexgre+width/2, (list(trigre['Games'])))\n",
      "\n",
      "\n",
      "\n",
      "for label in ax.xaxis.get_ticklabels():\n",
      "    label.set_rotation(45)\n",
      "for label in ax2.xaxis.get_ticklabels():\n",
      "    label.set_rotation(45)\n",
      "\n",
      "    \n",
      "\n",
      "def autolabel3(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",
      "        ax2.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",
      "            ax2.text(rects2[j].get_x()+rects2[j].get_width()/2., 1.04*height2,'%.1f'%float(height/height2*100) ,ha='center', va='bottom')    \n",
      "        \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",
      "autolabel3(rects1gre,rects2gre)\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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TJ07c5bVf/vKXDB06lCFDhvCFL3yBV155JYEI1ZRWrFjBvvvuy8UXX8yRRx7J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\ntPPKyjYwZ07mt1NSsiHzG5EkSZKaOUvSJEmSJClGdlSS1gZ4Btgd2A1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BmgQaPpNTSb9Y773QoUOGefJn7DG3AqJk352s5q/E7OUBrW/zDmo/YgdwPqcsELNvI19/35KGDO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\nrkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f527c36b1d0>"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16,\n",
        "       17, 18, 19, 20, 21])"
       ]
      }
     ],
     "prompt_number": 9
    }
   ],
   "metadata": {}
  }
 ]
}