simgeekiz/anotateyüzdeyazdırma.ipynb

## anotateyüzdeyazdırma.ipynb
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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import DataFrame, read_csv\n",
      "from numpy import random\n",
      " \n",
      "# General syntax to import a library but no functions: \n",
      "##import (library) as (give the library a nickname/alias)\n",
      "import matplotlib.pyplot as plt\n",
      "import pandas as pd\n",
      "\n",
      "# Enable inline plotting\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."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[:3]"
     ],
     "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>Rk</th>\n",
        "      <th>Games</th>\n",
        "      <th>Flag Bearer</th>\n",
        "      <th>Participants</th>\n",
        "      <th>Men</th>\n",
        "      <th>Women</th>\n",
        "      <th>Sports</th>\n",
        "      <th>Gold</th>\n",
        "      <th>Silver</th>\n",
        "      <th>Bronze</th>\n",
        "      <th>Total</th>\n",
        "      <th>Top Medalist(s)</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 2</td>\n",
        "      <td> 2012</td>\n",
        "      <td>  Neslihan Darnel</td>\n",
        "      <td> 112</td>\n",
        "      <td> 48</td>\n",
        "      <td> 64</td>\n",
        "      <td> 16</td>\n",
        "      <td> 2</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td>  5</td>\n",
        "      <td>      5 athletes with 1 medal</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 4</td>\n",
        "      <td> 2008</td>\n",
        "      <td>      Mehmet \u00d6zal</td>\n",
        "      <td>  67</td>\n",
        "      <td> 48</td>\n",
        "      <td> 19</td>\n",
        "      <td> 12</td>\n",
        "      <td> 1</td>\n",
        "      <td> 4</td>\n",
        "      <td> 3</td>\n",
        "      <td>  8</td>\n",
        "      <td> Elvan Abeylegesse (2 medals)</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 6</td>\n",
        "      <td> 2004</td>\n",
        "      <td> Ali Enver Adakan</td>\n",
        "      <td>  64</td>\n",
        "      <td> 44</td>\n",
        "      <td> 20</td>\n",
        "      <td> 10</td>\n",
        "      <td> 3</td>\n",
        "      <td> 3</td>\n",
        "      <td> 5</td>\n",
        "      <td> 11</td>\n",
        "      <td>     11 athletes with 1 medal</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 12 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "   Rk Games       Flag Bearer  Participants  Men  Women  Sports  Gold  Silver  \\\n",
        "1   2  2012   Neslihan Darnel           112   48     64      16     2       2   \n",
        "3   4  2008       Mehmet \u00d6zal            67   48     19      12     1       4   \n",
        "5   6  2004  Ali Enver Adakan            64   44     20      10     3       3   \n",
        "\n",
        "   Bronze  Total               Top Medalist(s)  \n",
        "1       1      5       5 athletes with 1 medal  \n",
        "3       3      8  Elvan Abeylegesse (2 medals)  \n",
        "5       5     11      11 athletes with 1 medal  \n",
        "\n",
        "[3 rows x 12 columns]"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "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\n",
      "\n",
      "'''tri = tri[['Men','Women']]\n",
      "tri = tri.sort_index(ascending = True)'''"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 4,
       "text": [
        "\"tri = tri[['Men','Women']]\\ntri = tri.sort_index(ascending = True)\""
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tri = tri.set_index('Games')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tri[:3]\n"
     ],
     "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>Men</th>\n",
        "      <th>Women</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Games</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1906</th>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1912</th>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1924</th>\n",
        "      <td> 22</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "       Men  Women\n",
        "Games            \n",
        "1906     2      0\n",
        "1912     2      0\n",
        "1924    22      0\n",
        "\n",
        "[3 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tri['index1'] = tri.index\n",
      "tri[:5]"
     ],
     "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>Men</th>\n",
        "      <th>Women</th>\n",
        "      <th>index1</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Games</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1906</th>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1906</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1912</th>\n",
        "      <td>  2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1912</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1924</th>\n",
        "      <td> 22</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1924</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1928</th>\n",
        "      <td> 31</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1928</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1932</th>\n",
        "      <td>  1</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1932</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "       Men  Women index1\n",
        "Games                   \n",
        "1906     2      0   1906\n",
        "1912     2      0   1912\n",
        "1924    22      0   1924\n",
        "1928    31      0   1928\n",
        "1932     1      0   1932\n",
        "\n",
        "[5 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sum = [tri['Men'] + tri['Women']]\n",
      "tri['Total'] =tri['Men'] + tri['Women']\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 8
    },
    {
     "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]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "s1= tri['Men_ratio']\n",
      "s2= tri['Women_ratio']\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tri[['Men','Women']].plot(kind='bar', figsize=(25,10), stacked=True)\n",
      "a=22.55\n",
      "b=21.55\n",
      "for ii  in  range(0, 21):\n",
      "    iii=21-ii\n",
      "    a=a-1.1\n",
      "    b=b-1.1\n",
      "    plt.annotate(str(s2[iii]*100)[:4], xy=(a,12), xytext=(b, 18),\n",
      "                 arrowprops=dict(facecolor='black', shrink=0.05),\n",
      "            )\n",
      "for ii  in  range(0, 21):\n",
      "    iii=21-ii\n",
      "    a=a-1.1\n",
      "    b=b-1.1\n",
      "    plt.annotate(str(s2[iii]*100)[:4], xy=(a, 85), xytext=(b, 85),\n",
      "                 arrowprops=dict(facecolor='black', shrink=0.05),\n",
      "            )     \n",
      "   "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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VBihD3zoH9K13Q7UBCjFUG6AMfdu6odoAZehb53pr1zXm\npc51xVBtgCIM1QYoxFBtgEIM1QYowlBtgEIM1QYoxFBtQFyQhz2CIAiCIAiCIAiCIAiCIAiCIAgp\njJzZIwiCnNkjCGGiax5gndG5znXVrnPuZ53RNd4FPZF41w+dxzad411X7RLv+tU5oK92iXedtcuZ\nPYIgCIIgCIIgCIIgCIIgCIIgCGmHPOwRBEEZOue9Fu06Yqg2QBn61jmgb70bqg1QiKHaAGXo29YN\n1QYoQ98611u7rjEvda4rhmoDFGGoNkAhhmoDFGKoNkARhmoDFGKoNkAhhmoD4oI87BEEQRAEQRAE\nQRAEQRAEQRAEQUhh5MweQRDkzB5BCBNd8wDrjM51rqt2nXM/64yu8a4zhYUdUFa2R9n1CwqKUFq6\nW8m1Jd71Q+exTed411W7xLt+dQ7oq13iXWft1s91shNriiAIgiAIgiAIgiCoxXzQo+4GQVmZrLsU\nBEEQBEEQYoukcRMEQRk6570W7TpiqDZAGfrWOaBvvRuqDVCIodoAZejb1g3VBihD3zoHdK53XbVL\nvOuKodoARRiqDVCIodoAhRiqDVCEodoAhRiqDVCIodqAuCAPewRBEARBEARBEARBEARBEARBEFIY\n2TsuCIKc2SMIYaJrHmCd0bnOddWuc+5nndE13nVG57Yu8a4fEu96xruu2iXe9atzQF/tEu86a7d+\nriM7ewRBEARBEARBEARBEARBEARBEFIYedgjCIIydM57Ldp1xFBtgDL0rXNA33o3VBugEEO1AcrQ\nt60bqg1Qhr51Duhc77pql3jXFUO1AYowVBugEEO1AQoxVBugCEO1AQoxVBugEEO1AXFBHvYIgiAI\ngiAIgiAIgiAIgiAIgiCkMHJmjyAIcmaPIISJrnmAdUbnOtdVu865n3VG13jXGZ3busS7fki86xnv\numqXeNevzgF9tUu866zd+rlOdmJNEQRBEARBEARBEARBSDyF7QtRtq9MybUL2hWgdG+pkmsLgiAI\ngqAH8rBHEARlGIYBn8+n2gwliHafajMUYADwKbZBDfrWOaBvvRvQUzegs3Z927oBqXMdMaBrvaey\n9rJ9ZcBf2/jmTQAOjuLaf1XzkCk2GEjVOo8eA3pqN6CnbkC0+xTboAIDeuoGRLtPsQ2xR87sEQRB\nEARBEARBEARBEARBEARBSGHkzB5BEOTMHkEIE13zAOuMznWuq3adcz/rjK7xrjM6t3Wd4z0jI6Pt\nO3ui5a/QtM4BiXdFV9dUu8S7fnUO6Ktd4l1n7dbPdWRnjyAIgiAIgiAIgiAIgiAIgiAIQgojZ/YI\ngqAMnfO8i3afajMUYCAd88GGg751Duhb7wb01A3orF3ftm5A6lxHDOha7ymtPRPqdvak9FJbAylb\n51FjQE/tBvTUDYh2n2IbVGBAT92AaPcptiH2pPR0QxAEQRAEQRAEQRAEISz8gJnypS1lcRTvZeDa\ngiAIgiAI8UPO7BEEQc7sEYQw0TUPsM7oXOe6atc597PO6BrvOqNzW9c53nXVLvGuX50D+mqXeNev\nzgF9tUu866xdzuwRBEEQBEEQBEEQBEEQBEEQBEFIO+RhjyAIyjAMQ7UJyhDtOmKoNkAZ+tY5oG+9\nG6oNUIih2gBl6NvWDdUGKEPfOgd0rnd9tRuqDVCIodoAhRiqDVCEodoAhRiqDVCIodoARRiqDVCI\nodoAhRiqDYgL8rBHEARBEARBEARBEARBEARBEAQhhZEzewRBkDN7BCFMdM0DrDM617mu2nXO/awz\nusa7zujc1nWOd121S7zrV+eAvtol3vWrc0Bf7RLvOmuXM3sEQRAEQRAEQRAEQRAEQRAEQRDSDnnY\nIwiCMnTO8y7adcRQbYAy9K1zQN96N1QboBBDtQHK0LetG6oNUIa+dQ7oXO/6ajdUG6AQQ7UBCjFU\nG6AIQ7UBCjFUG6AQQ7UBijBUG6AQQ7UBCjFUGxAX5GGPIAiCIAiCIAiCIAiCIAiCIAhCCiNn9giC\nIGf2CEKY6JoHWGd0rnNdteuc+1lndI13ndG5resc77pql3jXr84BfbVLvOtX54C+2iXeddYuZ/YI\ngiAIgiAIgiAIgiAIgiAIgiCkHfKwRxAEZeic512064ih2gBl6FvngL71bqg2QCGGagOUoW9bN1Qb\noAx96xzQud711W6oNkAhhmoDFGKoNkARhmoDFGKoNkAhhmoDFGGoNkAhhmoDFGKoNiAuyMMeQRAE\nQRAEQRAEQRAEQRAEQRCEFEbO7BEEQc7sEYQw0TUPsM7oXOe6atc597PO6BrvhYUdUFa2R8m1AaCg\noAilpbuVXFvntq5rvAP6apd416/OAX21S7zrV+eAvtol3nXWbv1cJzuxpgiCIAiCIAiCIAjJgPmg\nR92H5LIyWXsoCIIgCIIgCLFC0rgJgqAMnfO8i3YdMVQboAx96xzQt94N1QYoxFBtgDL0beuGagMU\nYriOuFsAACAASURBVKg2QCGGagMUYqg2QBGGagMUYqg2QCGGagMUYag2QCGGagMUYqg2QBGGagMU\nYqg2QCGGagPigjzsEQRBEARBEARBEARBEARBEARBSGFk37wgCHJmjxARKvP7q8ztD6jOCSvneKhA\n5zrXVbvOuZ91RuJdFaJdyZU1jXdAX+0S7/rVOaCvdol3/eoc0Fe7xLvO2uXMHkEQBCEGqMzvL7n9\nBUEQBEEQBEEQBEEQBKE1ksZNEARl6JvbX2/t6ZoXNTSGagOUIfGuI4ZqAxRiqDZAGfq2dUO1AQox\nVBugEEO1AQoxVBugCEO1AQoxVBugEEO1AYowVBugEEO1AQoxVBugCEO1AQoxVBugEEO1AXFBHvYI\ngiAIgiAIgiAIgiAIgiAIgiCkMJIPRxAEObNHiAhdc+ECemvXFZ3rXFftOud+1hmJd1WIdiVX1jTe\nAX21S7zrV+eAvtol3vWrc0Bf7RLvOmu3fq4jO3sEIT3oBmAxgK8AfAng6sDvOwB4B8BaAG8DaK/E\nOkEQBEEQBEEQBEEQBEEQBCFuyMMeQUgPagFcC2AAgBEArgDQD8DNMB/29AbwbuDnpEHf3P56a0/X\nvKihMVQboAyJdx0xVBugEEO1AcrQt60bqg1QiKHaAIUYqg1QiKHaAEUYqg1QiKHaAIUYqg1QhKHa\nAIUYqg1QiKHaAEUYqg1QiKHaAIUYqg2IC/KwRxDSgx8AfBb4vhzA1wC6AhgD4LnA758DMDbxpgmC\nIAiCIAiCIAiCIAiCIAjxRM7sEYT0oweA9wAMBLAVQFHg9xkAdjf5OYic2SNEhK65cAG9teuKznWu\nq3adcz/rjMS7KkS7kitrGu+Avtol3vWrc0Bf7RLv+tU5oK92iXedtcuZPYKgA/kAXgNwDYCyFn8j\n1PaCgiAIgiAIgiAIgiAIgiAIQhzIVm2AIAgxIwfmg54XAMwO/G4ngGKYad4OBPCj1Rsvuugi9OjR\nAwDQvn17DB48GD6fD0Bj/v14/Nw0t38irpdMPwd/lyz2tMV+k+DPvgh+/gzAlDa+37RBlf5Gm8K1\nt+nPwe/DfX3rn5Ol/iXefRH8LPGeavHeSGT2ttYczfuTJ/4j+fmzzz7DlClTksaeSH42MSDxHqn9\nwd9F8/5U1R9N/978+qkV7wDwdwCD2/x+9e09Mnsl3g1IvEu8p0p9S7wHbYjU3uDPEu9tf7/Eu27x\nHrQpEXoNw8DMmTMBoOH+rSAIqcVJAL4BsA7ATTaveTjw988BDAHwPIAHW7zmvibvvxnANIv/Q1Us\nXrw47NfOnz+fffr0Ya9evTht2jTL11x11VXs1asXBw0axFWrVsXIyvgQiXY7VPkEAAFGURZH8d7Q\n8VpXV8fBgwfztNNOa/W3r7/+miNGjGBeXh7/93//t9nf9uzZw7POOot9+/Zlv379uHz58hhrj0Z3\neNpbMmnSJHbp0oUDBw60/Pv06dM5ePBgDh48mAMHDmRWVhb37NnDrVu30ufzsX///hwwYAAfeuih\niK/dlFjEe6yItN0ke7zH0yfNtV9FoBeBQQRWJWW8xwpT9x4CZxHoS6AfgeUt7PuawAgCeQT+t8nv\ntxIYTKA/gQEEHkop7U6E03bGjRuXMuNwS9rW1ucT6EOgK4FpNq8Jp+2ojvdY9XFbCfhCxP/sgC8G\nExjaoD3W407itduVKgJHEDgs0Jfc3Kre7cZiMrx2l7za7fu4UHVtN1/75ptvGnw1ePBgFhYWRhwr\nyTiXs5qv3XDDDezbty8HDRrEcePGce/evWG/N/a646fdjlj6RHW8xwq7un744YfZt29fDhgwgH/8\n4x+b/a1R+98JDKTZN/89Keu8LdiNHabuh2nO4wYQ+KOFfU79c/Jrt8PUbqXr/2iOz5kEPrGxO9p5\nrDrdjdrtdFnNTz4kMDyguQ+BjyKMk+TQ3vY+rjuBQ2nOU4cHfndOwB+DCfQIfLV67yQCXWj2K6mo\nPV3auiAIqUIWgPUwz97JgfmYvV+L15wCYF7g+yMBfAXAH3jtp4FyEoAOABYCWAvgbQDtLa6nrHMK\nl7q6Ovbs2ZObNm1iTU0NDzvsMK5Zs6bZa9566y2efPLJJMkVK1bwyCOPVGFqwlDpE0Q9oMZ3ML3/\n/vt53nnn8fTTT2/1tx9//JEff/wxb7311lYPey644AI+/fTTJMna2lrLD5DJrr0lS5Ys4apVq0Le\nACDJuXPn8vjjjydJ7tixg59++ilJsqysjL17924VX6lIW9pNqtV5LH3SqP0tAicHvl9B4Mi00G6H\nqfsCAk8HbKklsLeFfT8S+JjArWz+sGcHgU8D35cR6E1gTcpot0OHcTjytl5HoCeBTQRqaN4EaFnX\n4bYd1fEeq3YbTvyXN/l+dYN2FeNO4vr3iiZ9yZEEltrWe9OxOJx2l/zareM9VF07zdeC1NfXs7i4\nmFu3bk0p7VZYzdfefvtt1tfXkyRvuukm3nTTTWG/N/l022u3I5Y+ScY6bwtWPlm0aBFHjx7Nmpoa\nkmbbaYqp/QuaN2SraI5dowmsTyntdtiNHabu0TTHZ9Kct1nZaNc/J792O0ztVrq+JvAtzYcedg97\nop3HqtPdqD0SXccSWBD4/byAb9oaJ6rrvC1x2oPALoe/X0/gLpu/LaG5iCnVH/akelu3JtPuD4Ig\nKOMImA97NgOoBTALwBktXjMGwHOB7z+E+YDoQJj7D4cEygIAuwGMBtAbwIkA9sbX9Pjw0UcfoVev\nXujRowdycnIwYcIEvPHGG81eM2fOHFx44YUAgCOPPBJ79+7Fzp07VZibEMQn1mzbtg3z5s3DpZde\nCnP8a07nzp0xbNgw5OTkNPv9vn37sHTpUlx88cUAgOzsbLRr1y4hNseTY445BkVFRWG99l//+hfO\nPfdcAEBxcTEGDx4MAMjPz0e/fv3w/fffx83ORNGWdmOSvu0mHJ8AcwBcGPj+SJhDSfr6xGQpgIsD\n32cDaNkfdAYwDOaajKYUwxyKAfMYvX4A9Gw76T/mfASgFxrX5kwAoHvbCSf+vU2+L298Z5qOOyae\nwNcaAPUw12JZ03QsDq9/Tk1C1bXdfK0pCxcuRM+ePdGtW7e42poIrOZrJ5xwAjIzzds1Rx55JLZt\n2xb2e9MB8UlrrHQ98cQTuOWWWxraSufOnS3e+Q3MMcgF87bBsQD+E19jE4TV2LF9+/bAX29B4zzN\nyi9AJP1zamGlqy/M20JOpOc81lrXdpi30PYFfr8XQFeb96drnAD2zwsI4P8AnGvz92MApF8/my7I\nwx5BSD66Aviuyc/b0HrUsXpNSZztijmt84tas3379mYf5EpKSppM4uxfY/cBIBkIV7sdqe0TI27/\n+dprr8X06dMbPgiGy6ZNm9C5c2dMmjQJQ4cOxWWXXYbKysoYW2fE+P/FjsrKSvz3v//FWWed1epv\nmzdvxqefftrkwUfkRBvvsaIt7cYkmnZjRPHe+BOOT8wPQ019UoLQPjFiYp86OgOYBGAogMsARNIf\nGIGvm2FutG1720kWwm07P/30U7PXJMeYEy+atgsDZruIRdtJNQyb32+GffzPhnmj5WTrd8Zg3EkM\nRpiv88O8yXQAgF8D6G/5qpZjcXj9syqMmPyXaOp61qxZOO+882JiR/gYCb6eyTPPPINTTjlFybUb\nMRRfvzmJ9YmRoOtEzrp167BkyRKMGDECPp8PK1eutHjVQJiLWHbDnM+8hfDGIiN2hiaA1v3JEgAj\nYJ6tYeUXwL5/NuJnaEIIb9yxxgh83Yx0mcc2ZzNMXSNgnnJwPYCDAFwN4G8274nGn8lMBsy14X0A\nzGjxt6Uw9fZMtFEJxlBtQFyQhz2CkHzYbsVrQUYb35dyZGS0lGpNy50c4b4vFRGftObNN99Ely5d\nMGTIEMtdPU7U1dVh1apVuPzyy7Fq1Sp4vV5Mm2Z1xFV6MnfuXIwaNQrt2zfP9FheXo7x48fjoYce\nQn5+viLrYkdb203r7jZ9CL9P0McnJqsAXB746oX1kXdOlAMYD+AhmCsIUxsZc6yQtmNPqPgfC+Br\nAHNbvzPNxh2TTJiZlrfBvPloWL6q5Vic3u0nurquqanB3LlzcfbZZ8fJuuRh6tSpyM3NVfBgK3kR\nnzRSV1eHPXv2YMWKFZg+fTrOOecci1f1hXmM74kwH7IPQbrdCmzanxQUFAR+uwfACgDTAVj5BQi3\nf049otWVXvPYRlrqugTmUdhbAVyBxl39LUnXOPkA5oOvewE8BvMBT5CXAUgfm6qkVw8vCOlBy2Wg\n3dB66Y3VUtFkWeoXNj6fL6zXde3aFd9917iR6bvvvkNJSYnja7Zt24auXe224aonXO12pLZPfHH5\nr8uWLcOcOXNw8MEH49xzz8WiRYtwwQUXhPXekpISlJSUYPjw4QCA8ePHY9WqVTG20Bfj/xc7Zs2a\n1ZA2JkhtbS3OOussnH/++Rg7dmxU/z/aeI8VbWk3gd9GcVVfFO+NP+H4JLwNpy3xxcQ+dZQAGB74\nfjzMhz7hcjSAswCcD/OmduoTbttpmj4mecaceNG0XfgC38ei7aQavhY/1yL8+D8GALBr1y7znTEc\ndxKDL8LXtwNwKuxWmLcci8Prn1Xhi+rd0db1/Pnzcfjhh9ukrIonvoRebebMmZg3bx5eeumlhF7X\nGp9qAwCo8okvgdeKjJKSEpx55pkAgOHDhyMzM7OhT23OxTD7nvdgHuPbJ4z/7ouVmXHFvj85M/B1\nOMxbn1Z+CdKyf/bF3E41OI871qTfPNbEan7yEYBxge//EvjZibb4M5k5MPB1LEw/BPXXAXgdwG9V\nGJVgfKoNiAvysEcQko+VAH4JMwl8Lswedk6L18wBELyLPQJpngR+2LBhWLduHTZv3oyamhq88sor\nGDNmTLPXjBkzBs8//zwAYMWKFWjfvj0OOOAAFeYmBPFJa+655x5899132LRpE2bNmoXjjjuuQX9L\nWq4+Ly4uRrdu3bB27VoAZh74AQMGxN3mZGDfvn1YsmQJzjij8WgwkrjkkkvQv39/TJkyRaF1saUt\n7cYkfdtNOD4xj4kLtqUVMG8SpK9PTLoBWBv4fiEAu/6g5a4Nwlwl2B+A3m0n3ccc88ymdTDTgdQA\neAVmW2mKbm0nnPjfgMZ2Yz5E7dixY9qOO8DPaDwyswrAOzBX1TfHaiwOr39OTcKta7ud2i+//HKr\nRSrpxoIFCzB9+nS88cYbcLlcqs1JCsQnrRk7diwWLVoEAFi7di1qamrQsWNHi1f+GPi6FeZN3PRY\nse88diwKfF0Lc5xu6Zfw+ufUJJQup3Na0m8ea6+rF8wHoIAZL1ZnGqVrnFQCKAt8XwHgbQCHBn5e\nCDPd7i8U2CUIgpC+nAzgWwDrYZ4sCAC/C5Qgjwb+/jnMQwXaClWxePHisF87b9489u7dmz179uQ9\n99xDknzyySf55JNPNrzmiiuuYM+ePTlo0CB+8sknsTY3pkSi3Q5VPgFAgFGUxVG8N7x4NQyDp59+\neiuf7NixgyUlJSwsLGT79u3ZrVs3lpWVkSQ/++wzDhs2jIMGDeK4ceO4d+/eGGuPRnf42psyYcIE\nHnjggczJyWFJSQmffvrpVjEyc+ZMnnvuuc3et3TpUmZkZPCwww7j4MGDOXjwYM6fPz/i6weJRbzH\nikjbTSrEe7x80lz7FQR6EhhE4JOkjPdYYer+jMCwgN5xBPYQeDJQSGAHgRIChQTaE+hGoIzAUgIZ\nBA4jMDhQ5qeMdifCaTtjx45NmXG4JW1r6/MI9CbwCwL3BH7XNE7CbTuq4z1WfZxV/M9r4ZN7CQwI\n/G1Ug/ZYjzuJ125XVhMYEvDJoQTuaxInjfVuNRaT1u0udbTb93Et63revHlhz9fKy8vZsWNHlpaW\nKtAejW77tm41X+vVqxcPOuigBh/94Q9/IElu376dp5xySqv35ubmsqSkhM8880wS1rm9djti6RPV\n8R4rrOq6pqaG559/PgcOHMihQ4c2zLmDPmnUfgyB/jT7okVJWedtwW7sMHWfT2AggaFNtGwncErg\n+89p3T+nhnY7TO1Wuv5Dc+7qInAAgZMsfBLtPFad7kbtVnbZzU8+JnBE4Pf9CayKME6SQ3vb+riN\nAU2HEejBxnksCVxE4B8tXt/UJyQwgcCBBHIJwHbsSU7tqdXPOWu3Jr0TAQuCEA6BfiLxGIaRNOmd\nEk0qazdzyEcTMwbavl02A6riFYhWu4Hotgmr1R4NEu++tl5d4l0BauscUF3v0aBvWzeQqnWuc7yL\ndhnbIseA1HmbLdBUu8S7CiTeJd4jx4DUeZstEO0KCJztaPlcR9K4CUL6kQvz1LibAbgV2+JIom4I\nVVVV4eijj8b999+P6urqhFwzFKpvhpWXl2PEiBF4+OGHUVNTk+Cr+xJ8veh58MEHceWVV0b5X3yx\nMCUpuP3223H99dejoqIirNerjvfYsgNm+qbnANSH8XpfXK1JDrbC9MnLAPyB3/mUWaMen8Pf/gsz\nL/aWxJiSYJza+po1azBs2DDMnj07ZR9m2eNr4/s+R/BsqNT1iS8hV3nhhRdw/vnn48cffwz94oTh\nU3r11atX4+STT8aXX36p4Oo+BdcMzZw5czB+/Hhs3x6vo0x9cfq/8aG+vh5nnnkm/vGPf8Tgv/li\n8D+SgVqYKTafRfg3CH1xsyZ5mAzgHpj+CeJTY0pS4APwCMzEKnudX5p2+Bz+NgXAnwEkxz2V2OJr\n4/vuQuqnvPPF7D+RZmrFe++9F7W1taHfoByfagMEQRDC4iqYCTjLYSYYnQQgy+H1CdxoqIZ77rmH\nLpeLXq+XnTt35gsvvMD6+nrVZinl9ttvZ15eHr1eLw844AC+/PLLYfsEUW+VTa1tst9++y3dbjdd\nLldA+2xttFuxcuVKut1uut1udurUia+//jr9fr9qs+JG63i/iEA2AS+Bgwm8RcCflnUefls/h0BW\nwCe/JPB2SmuPXx9XRqBTwFceAlMJ1CSV9nhy/PHHMyMjg16vl4ceeiiXLl2q2qRmqBnbjgpcFxw6\ndCiXL1+uie7I4v2HH36g1+tlTk4O8/Pz+cQTT8RkHpcK2u2oq6vjgAEDmJGRQbfbzWuvvZbl5eUp\noj0+fdyePXvYvn17Zmdn0+Px8IEHHmBtba0W2u145JFH6PF46PV6A7o/1UZ7U5rX+b0E3DTnLEMI\nfJG2ultrtytzaM5LgnPbpSmvPfp2viEQJ3kE2hF4iZHN91NZu11ZFIgTD4GuBBYmle74arcrnwfi\nxLxP8MYbb2ii277eX3/99YZx55BDDuEHH3ygjfZEE/gMIQiCBhQA2Aez0QdLOczTg0+H9RY/ZZ1T\nIs7x2L17d5MPOGbxer3s2bMn58+fr+wmtcozTHbu3EmPx9PKJ7179+Y777wT8v2IekBdnDKDqd/v\n5xFHHMHMzMwm/vISOIHA1gTqVjuRCFJXV8f+/fszIyOjWez4fD5u2LDB9n3JdGZPpDSP928ZnMw3\nj4fDCaxI+Xh31m5XVtP8kNPSJ/0JrExJ7fHr465p4Su7myrq23pbsWvrS5cubTXueDweHnfccfzy\nyy8Ta6QN0dV7W9r524EYaO6T3/zmN/zmm29SRHe0fVx48T5u3Djm5uay6bhz6KGH8rPPPkt77XY8\n+uijzea3brebXbp04ZtvvpkC2uPTx11yySVNFuY0zm0/+uijGGpXV+eREnxI2nx8dhP4PYF9aa29\nJY11/h3NG9VBf2QEfHINzQUZqRPvkWu3K+UEOreIEw/N8ziiXeCWDHXeluKnec5eVhOfeGku0Fib\n5trt4n0/zfN+WsbJWJrnWKrXHb32SNt5fSBOGj8Xezwejh49mlu2bEkh3dH2cY31XlZWxo4dOzaL\nE7fbzYkTJ3LXrl1prV0FAR8LgqABd8Pc1UOLUg7gUwAjW7xHWeeUiBvAU6ZMafbBr2nxer0cPnx4\nqw+BiUDlze/JkyczLy/P0icej4dHHXWU48HaUDqgJjZen3/+eYsPyqC5s8ND4H4CdWk/kQjyxBNP\nWPojKyuLbrebf/nLX7h///5W70ufhz0ns/kHv5Y3UE4i8E3KxruzdrviI9D0YWjLGyhjCKxPKe3x\n6eO+ZOsHhU1j51wCPynXHi1Wbd3v93PQoEGW2jMzM+lyuThhwgRu3bo18QY3Ibp6j7Sd+wn0tvRJ\nVlYWXS4XJ06cyO3btye57viPbYsWLaLb3fKBMhp2tFxxxRUsLS1NS+127Ny502ZuYs7jTjrpJH73\n3XdJrD32fdzKlStt5/tut5uXXHIJ9+7dGwPtauq8LYwfP545OTkWPnERKCIwi5HtVEgd7S1prPNT\nac7hrcbhjjQPsLfySXLFe9u025Ub2HrRDmjuaHHTPKS9PuW0R9fOX6P1nC0z4JNbaT78SEftdvF+\nJ5s/KA2WXJoPwh6l+Zk42eM9ln3cU2y5aAdAw+7Sv/3tb6ypqUkB3dH2cY3xfvXVV1vO2fLy8lhY\nWMhnn3025outk0W7CgL+tUTO7BGE9MTqIAkvgMMAvAPgbQD9EmqRBYk4xyMzMxMZGRnIzGzd3VVU\nVGDlypXw+Xw45ZRTsHbt2rjbE0TlGSZZWVkNfmlJZWUlVqxYgVGjRmHs2LHYuHFjHCzwxeF/xofM\nzEyQRFZWy0yIdTCfq/4ZwEAAn4Tx33wxti7xZGVlwe/3t/JHfX09qqqqMH36dPTs2RMLFy5s9vf0\nObMnF0Cezd+qYHavgwFcBPNsHyAd6t2ZXAAui98Tpk/mwWwjlwHYmUC7VOKz+F01zGP0rHxVBeA1\nAIcA+Gf8zEoAVm2dJNxuN7xeb6u/+f1+7N+/H6+99hr69OmDKVOmYPfu3QmwNNb4Inx9HcxpWWuf\n1NfXY//+/XjllVfQq1cv3Hjjjdi3b18MbIwXvrj+9+CYk5ub2+z3JFFVVYWnn34aBx98MP7zn//A\n/NybSHwJvp7J/v37UVRUBI/H0+pvlZWVWLhwIfr06YMHHngAdXV1cbLCF6f/2zZqa2uRl5eHvLzW\nY3RVVRVeeukl9OjRA7NmzYrySr4o3584CgsLkZOTY/GX/QD2ALgEwCgA4X7+8cXIMpUUwpy3tKQK\nwC4AEwH8GkDLzz+++JqllOC8pOXnwmqYfrkOwBAAqxNplGII81Zpdovf+2H65EEAPQG8m2C7EoXP\n4ncumH5pGSc1ACoA3ARgUHzNiju+CF+fBTMmmn8urqurQ2VlJe666y707dsXy5Yti5F98cQXk/9i\nNdcHgOrqapSWluLKK6/EEUccgTVr1sTkerHBp9oAQRCEsOgH82FOBczRx2IFRsOd6hegePVFIli9\nejV9Pl+rFDJNS3Al7UUXXcTvv/9etclxZ9WqVRw5cqSjT7Kzs+l2uzl58mTu3Lmz4b2IevVEaq2c\n2LhxI4855hjbVbRmcRP4A51TQKSedivWrVvHUaNGOfrD4/FwzJgxCVmVHm9ax/sSAofSaiVXY8kN\nxMSNBPambJ2H39bfJdA3hE+CK0T/RKA0qbXHr4/bR2AyrVfQBovpwy+++EKZ/njg9/v51ltvsUeP\nHo59R/B8valTp7KysjKhNiZ+bPPTTJHTzSEeTJ/k5+fzvvvuY1VVVRrojryt79y5k2effbbjnCWY\nUnTjxo1ppd2O/fv387bbbqPb7W6Rara5T/r06WO5gz0d53K7d+/mhRdeaLmquKlPzO/DTcWUGtrt\nWLx4MQ866CCHthPcqXAzgcq00t6U5vH+NoFf0HqXAmju4HYT+AtD795Ibt2ttduVT2im37Ubn4O7\nta9keHM49dqj7+O20tyxbz/uWKcxSwftduULAoNDxAn4u9/9jvv27Usz7XZlHYGjHXwS/zRmanTb\nx/vHH3/MPn362M73MzMz6Xa7ef3117OioiKttCeagE8FQdCMkTDTtpXDZuCBuWSH1157LXfv3p3w\nzinRqZ0Mw+CAAQMcbzTl5ubS7XbzpptuiuskJVnSWr3zzjuOg3HQJx6Ph7feeitLS0tjMKAuTrnB\n1O/385VXXglot0vF5KKZAuKNOOhWO5Foid/v56uvvsoOHTrY3lTJycmhx+Ph/fffz4ULF6o2uc1Y\nx7ufwOs0b9KGegjoITCdbbthoLbOI2vrfgL/R+DAMHxSSOAhAtVJqT3+fVyomyrmB8MpU6ZEdOB6\nMhBqbKurq+PMmTPZqVOnkA+MO3TowBkzZlgesB4Poqv3aPr3WgJgUVFRyIcZnTp14syZM1lXV5ck\nuhM7ti1evJjdunWz9VMwpeidd97J6urqtNJux9q1a3nUUUc5tqeWaczUa49etxPLli1jz549w1ik\ncxsjH5vV13mkVFdX8+677w7otkphFrxpXUxgXlppD9I63qsI3B6IA7v0vE0PoU/eeI9cu12po5mK\ny0tz0ZJdu+lA4FWGlwIwmeq8rX3cXAJdaL9Qp2kas6bp7tJBu1WpJzCDQIFtnLhcLhYVFfGVV15J\n+PnIauZyfgKvNGi38kkwjdnMmTPj4pNkm8/U1dXxwQcfpNfrtUknGvlZg6miPZEEfCkIgoZkADgd\nwGYAZbDoZIHGVaPTpk2Ly6pRO1Q88PD7/XzttdfYtWtX5ufnW/oDgcGnoKCA999/v+UZJNGSLA97\nSLK+vp6zZs1icXFxyJsF7dq1C/zsdJM2ngOqusGUDA6ok+i8Mt9D4Dc0D4FNj4mEHaWlpbzyyivp\ndruZkZFh6Q+v18uuXbty+fLlqs1tE3CcQNbSzGVeROeVf16ah98+z/DPeFJf587a7UoNG28WhPLJ\nAQRepnUueNUT53h/aKgj8EjAD/Yfgjp16sQ33nhDmS8iJdyxraqqivfddx/z8/NtPxgH+49u3bpx\n9uzZcb9hEF29R9+/V1RUcOrUqfR6vSF9cvDBB/Ott96KiU8SE++x6+eqq6t5xx130O12MyvL+iat\nx+PhQQcdRMMw0kq7HX6/ny+++CLbtWtneyajy+Vi+/btG26+qdUeG91O1NbW8r777qPH42F2+Vi4\nTgAAIABJREFUttMDjuDN/NSq87Zgah7N0DsVTmbrOWw6aLeyaT2BX9F5kYqH5sr971NOt7N2u/ID\ngTNDxImXwLEENiSt9tj2ceUErqP5+c/6847pkwEEPk0z7XblJ5rnTTrvpBw1ahTXr1+fItqj7+Mm\nT54ccnfpsGHDuGbNmiTSHRvtVmzfvp2nn36642Imj8fD3/zmN20+uzNZtSeCgA8FQdCYLACTYCYj\ntt3p4/V62bFjRz7zzDMxXTWajNTW1vKJJ55g+/btQ66k7dy5M1988UXW19erNjuuVFdX8+GHH2a7\ndu0cJylmKaZ5sGtbD+xMvcGUbDqZWEbgYNp/OMym+SHpAUZ2cz95tTuxevVqDh48OOTDwokTJ/Ln\nn39WbW5ENNa5U6kgcFcgHqxvtJnFS+AQmitnk3tFZPja7UoZgT8H2kEon/Qm8E7SaI9Od6RlB80U\nIM4fgkaPHs3Nmzcr80m82LNnD6+//nq63W7m5tqtJDbH4kGDBvH999+Pmy2JrXf7tr5r1y5eddVV\ndLvdtqshgz45/PDDuWLFihTW3fa2vmHDBh577LEhd4idffbZzVLRpoN2O3bv3s2LLroo5I2mY445\nJmniPd5s3bqVJ554ouNc3+x/z6R5kzt9tLeksc7n0HmnQnAOey/NBRzppN2q+Am8RnN3vp1PcmjO\nVx6kudAnNXSH1u5U3iVQwtDp7v5K+x1yyVrnbS1fEhjC0OnuLk9D7XZlKYEetj4J7rj985//HJcF\ntGq1W8f7xx9/zL59+zqmMXO5XLzxxhtjlrI42ecz//3vf3nggQfajsXZ2dn0eDy87777It7Rn+za\n40nAf4IgCHAD+CPMXT5VsOhoAfODYI8ePTh37tyEb71NNBUVFbzzzjvp8XhsV0MGfdKzZ08uWLAg\n7X1SVlbG2267jR6Px/Hmmzmp68PIVkSm7mBKtpxM1BC4m84pILwE+hFYlfLaQ1FfX89nnnmGhYWF\ntm0puIV9xowZKfPwtHmdhyo/08xn7qZ9qpRgXAwj8GFS13lk2u3KjwR+H4ZPPASOopniLBkmzon+\noLCQTmcIBD8ETZ06lTU1Ncp8Ey+2bdvGiRMn0uVy2e7WAMyb98cffzy/+uqrmNugpt7t2/qWLVv4\n29/+1vFMFsB8kH7SSSfxm2++SUHd0bX14G5tp5Siubm5zM/P5xNPPNF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       "text": [
        "<matplotlib.figure.Figure at 0x7ffd82996250>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tri[['Men','Women']].plot(kind='bar', figsize=(25,10), stacked=True)\n",
      "a=22.4\n",
      "b=21.2\n",
      "for ii  in  range(0, 22):\n",
      "    iii=21-ii\n",
      "    a=a-1.0\n",
      "    b=b-1.0\n",
      "    plt.annotate('MEN: ' + str(s1[iii]*100)[:4] +'\\nWOMEN: ' + str(s2[iii]*100)[:4] , xy=(a,60), xytext=(b, 85),\n",
      "        arrowprops=dict(facecolor='black', shrink=0.05),bbox=dict(boxstyle=\"round4\", fc=\"w\"),\n",
      "            )"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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57/7umR/R0dHWa3n26572Nx20896X+ev34fzsEszJU8/yavrZxTM/IiMjrX1F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\n58bGxnTCCSdsGifUh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       "text": [
        "<matplotlib.figure.Figure at 0x7ffd82876190>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "tri[:2]"
     ],
     "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>Men</th>\n",
        "      <th>Women</th>\n",
        "      <th>index1</th>\n",
        "      <th>Total</th>\n",
        "      <th>Men_ratio</th>\n",
        "      <th>Women_ratio</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Games</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>1906</th>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1906</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1912</th>\n",
        "      <td> 2</td>\n",
        "      <td> 0</td>\n",
        "      <td> 1912</td>\n",
        "      <td> 2</td>\n",
        "      <td> 1</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>2 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "       Men  Women index1  Total  Men_ratio  Women_ratio\n",
        "Games                                                  \n",
        "1906     2      0   1906      2          1            0\n",
        "1912     2      0   1912      2          1            0\n",
        "\n",
        "[2 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 13
    }
   ],
   "metadata": {}
  }
 ]
}