pandasexample

assignment1.ipynb

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  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from pandas import DataFrame, read_csv, concat\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": [
      "#ayr\u0131 olan csv dosyalar\u0131n\u0131 ayr\u0131 dataframeler olarak y\u00fckl\u00fcyoruz\n",
      "df1936 = read_csv('dosya/olimpiyat/1936.csv')\n",
      "df1948 = read_csv('dosya/olimpiyat/1948.csv')\n",
      "df1952 = read_csv('dosya/olimpiyat/1952.csv')\n",
      "df1956 = read_csv('dosya/olimpiyat/1956.csv')\n",
      "df1960 = read_csv('dosya/olimpiyat/1960.csv')\n",
      "df1964 = read_csv('dosya/olimpiyat/1964.csv')\n",
      "df1968 = read_csv('dosya/olimpiyat/1968.csv')\n",
      "df1972 = read_csv('dosya/olimpiyat/1972.csv')\n",
      "df1984 = read_csv('dosya/olimpiyat/1984.csv')\n",
      "df1988 = read_csv('dosya/olimpiyat/1988.csv')\n",
      "df1992 = read_csv('dosya/olimpiyat/1992.csv')\n",
      "df1996 = read_csv('dosya/olimpiyat/1996.csv')\n",
      "df2000 = read_csv('dosya/olimpiyat/2000.csv')\n",
      "df2004 = read_csv('dosya/olimpiyat/2004.csv')\n",
      "df2008 = read_csv('dosya/olimpiyat/2008.csv')\n",
      "df2012 = read_csv('dosya/olimpiyat/2012.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# ve bu dataframelere y\u0131llar\u0131n\u0131 kolon olarak ekleyelim.\n",
      "df1936['Games']='1936'\n",
      "df1948['Games']='1948'\n",
      "df1952['Games']='1952'\n",
      "df1956['Games']='1956'\n",
      "df1960['Games']='1960'\n",
      "df1964['Games']='1964'\n",
      "df1968['Games']='1968'\n",
      "df1972['Games']='1972'\n",
      "df1984['Games']='1984'\n",
      "df1988['Games']='1988'\n",
      "df1992['Games']='1992'\n",
      "df1996['Games']='1996'\n",
      "df2000['Games']='2000'\n",
      "df2004['Games']='2004'\n",
      "df2008['Games']='2008'\n",
      "df2012['Games']='2012'\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# concat ile 1936 dan g\u00fcn\u00fcm\u00fcze kadar olan yaz olimpiyatlar\u0131n\u0131 bir dataframede birle\u015ftirelim.\n",
      "total = concat([df1936,df1948,df1952,df1956,df1960,df1964,df1968,df1972,df1984,df1984,df1992,df1996,df2000,df2004,df2008,df2012])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total[: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>Age</th>\n",
        "      <th>Athlete</th>\n",
        "      <th>Bronze</th>\n",
        "      <th>Games</th>\n",
        "      <th>Gender</th>\n",
        "      <th>Gold</th>\n",
        "      <th>Rk</th>\n",
        "      <th>Silver</th>\n",
        "      <th>Sport</th>\n",
        "      <th>Total</th>\n",
        "      <th>\ufeffRk</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 25</td>\n",
        "      <td>   Ya\u015far Erkan</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1936</td>\n",
        "      <td> Male</td>\n",
        "      <td>  1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Wrestling</td>\n",
        "      <td>  1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 21</td>\n",
        "      <td> Ahmet Kire\u00e7\u00e7i</td>\n",
        "      <td>  1</td>\n",
        "      <td> 1936</td>\n",
        "      <td> Male</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 2</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Wrestling</td>\n",
        "      <td>  1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 20</td>\n",
        "      <td>    Orhan Ada\u015f</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1936</td>\n",
        "      <td> Male</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 3</td>\n",
        "      <td>NaN</td>\n",
        "      <td>   Fencing</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 25</td>\n",
        "      <td>   L\u00fcft\u00fc Aksoy</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1936</td>\n",
        "      <td> Male</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 4</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  Football</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 25</td>\n",
        "      <td>    Hakk\u0131 Ala\u00e7</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 1936</td>\n",
        "      <td> Male</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 5</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  Football</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 11 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "   Age        Athlete  Bronze Games Gender  Gold  Rk  Silver      Sport  \\\n",
        "0   25    Ya\u015far Erkan     NaN  1936   Male     1   1     NaN  Wrestling   \n",
        "1   21  Ahmet Kire\u00e7\u00e7i       1  1936   Male   NaN   2     NaN  Wrestling   \n",
        "2   20     Orhan Ada\u015f     NaN  1936   Male   NaN   3     NaN    Fencing   \n",
        "3   25    L\u00fcft\u00fc Aksoy     NaN  1936   Male   NaN   4     NaN   Football   \n",
        "4   25     Hakk\u0131 Ala\u00e7     NaN  1936   Male   NaN   5     NaN   Football   \n",
        "\n",
        "   Total  \ufeffRk  \n",
        "0      1  NaN  \n",
        "1      1  NaN  \n",
        "2    NaN  NaN  \n",
        "3    NaN  NaN  \n",
        "4    NaN  NaN  \n",
        "\n",
        "[5 rows x 11 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#ve bunu csv dosyas\u0131na yazd\u0131ral\u0131m\n",
      "total.to_csv('dosya/olimpiyat/total_countries_TUR__medals.csv')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total=total[['Bronze','Games','Gender','Gold','Silver','Sport']]\n",
      "total[: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>Bronze</th>\n",
        "      <th>Games</th>\n",
        "      <th>Gender</th>\n",
        "      <th>Gold</th>\n",
        "      <th>Silver</th>\n",
        "      <th>Sport</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 1936</td>\n",
        "      <td> Male</td>\n",
        "      <td>  1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Wrestling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  1</td>\n",
        "      <td> 1936</td>\n",
        "      <td> Male</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Wrestling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 1936</td>\n",
        "      <td> Male</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>   Fencing</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 1936</td>\n",
        "      <td> Male</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  Football</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 1936</td>\n",
        "      <td> Male</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  Football</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "   Bronze Games Gender  Gold  Silver      Sport\n",
        "0     NaN  1936   Male     1     NaN  Wrestling\n",
        "1       1  1936   Male   NaN     NaN  Wrestling\n",
        "2     NaN  1936   Male   NaN     NaN    Fencing\n",
        "3     NaN  1936   Male   NaN     NaN   Football\n",
        "4     NaN  1936   Male   NaN     NaN   Football\n",
        "\n",
        "[5 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2=total.groupby(['Games','Gender']).aggregate('sum')\n",
      "df2[:10]"
     ],
     "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></th>\n",
        "      <th>Bronze</th>\n",
        "      <th>Gold</th>\n",
        "      <th>Silver</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Games</th>\n",
        "      <th>Gender</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">1936</th>\n",
        "      <th>Female</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Male</th>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">1948</th>\n",
        "      <th>Female</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Male</th>\n",
        "      <td>  2</td>\n",
        "      <td>  6</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1952</th>\n",
        "      <th>Male</th>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1956</th>\n",
        "      <th>Male</th>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th rowspan=\"2\" valign=\"top\">1960</th>\n",
        "      <th>Female</th>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Male</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  7</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1964</th>\n",
        "      <th>Male</th>\n",
        "      <td>  1</td>\n",
        "      <td>  2</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1968</th>\n",
        "      <th>Male</th>\n",
        "      <td>NaN</td>\n",
        "      <td>  2</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>10 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "              Bronze  Gold  Silver\n",
        "Games Gender                      \n",
        "1936  Female     NaN   NaN     NaN\n",
        "      Male         1     1     NaN\n",
        "1948  Female     NaN   NaN     NaN\n",
        "      Male         2     6       4\n",
        "1952  Male         1     2     NaN\n",
        "1956  Male         2     3       2\n",
        "1960  Female     NaN   NaN     NaN\n",
        "      Male       NaN     7       2\n",
        "1964  Male         1     2       3\n",
        "1968  Male       NaN     2     NaN\n",
        "\n",
        "[10 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df3 =total.groupby(['Games']).aggregate('sum')\n",
      "df3[: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>Bronze</th>\n",
        "      <th>Gold</th>\n",
        "      <th>Silver</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>1936</th>\n",
        "      <td> 1</td>\n",
        "      <td> 1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1948</th>\n",
        "      <td> 2</td>\n",
        "      <td> 6</td>\n",
        "      <td>  4</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1952</th>\n",
        "      <td> 1</td>\n",
        "      <td> 2</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "       Bronze  Gold  Silver\n",
        "Games                      \n",
        "1936        1     1     NaN\n",
        "1948        2     6       4\n",
        "1952        1     2     NaN\n",
        "\n",
        "[3 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Y\u0131l ve Cinsiyete G\u00f6re Madalya Da\u011f\u0131l\u0131mlar\u0131"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df2.plot(kind='bar',stacked=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f9665080350>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2Teb2W2+l+Y4VaZcs5sPOnl6I3Zi9Wf7GlEFSvaD6dUuLx7ls7emJ/yrFdOIu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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f966507b990>"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Y\u0131la g\u00f6re Madalya Da\u011f\u0131l\u0131m\u0131"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df3.plot(kind='bar')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f966503a710>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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wtV87UzvGNiuWt4BpSfOHMdbT61j2jEc/4J+Bt52pCziAuQQY60J3HvAhplX4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\ngg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9665044690>"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "Sporlara G\u00f6re Madalya Da\u011f\u0131l\u0131mlar\u0131"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "total[: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>Bronze</th>\n",
        "      <th>Games</th>\n",
        "      <th>Gender</th>\n",
        "      <th>Gold</th>\n",
        "      <th>Silver</th>\n",
        "      <th>Sport</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 1936</td>\n",
        "      <td> Male</td>\n",
        "      <td>  1</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Wrestling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  1</td>\n",
        "      <td> 1936</td>\n",
        "      <td> Male</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td> Wrestling</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>NaN</td>\n",
        "      <td> 1936</td>\n",
        "      <td> Male</td>\n",
        "      <td>NaN</td>\n",
        "      <td>NaN</td>\n",
        "      <td>   Fencing</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 6 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "   Bronze Games Gender  Gold  Silver      Sport\n",
        "0     NaN  1936   Male     1     NaN  Wrestling\n",
        "1       1  1936   Male   NaN     NaN  Wrestling\n",
        "2     NaN  1936   Male   NaN     NaN    Fencing\n",
        "\n",
        "[3 rows x 6 columns]"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df4 = total.groupby(['Sport']).aggregate('sum')\n",
      "df4 = df4.dropna(axis=0,how='all') #null value olanlardan kurtulak i\u00e7in dropna fonksiyonunu kulland\u0131k.\n",
      "df4"
     ],
     "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>Bronze</th>\n",
        "      <th>Gold</th>\n",
        "      <th>Silver</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sport</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Athletics</th>\n",
        "      <td>  2</td>\n",
        "      <td>  1</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Boxing</th>\n",
        "      <td>  5</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Judo</th>\n",
        "      <td>  1</td>\n",
        "      <td>  1</td>\n",
        "      <td>NaN</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Taekwondo</th>\n",
        "      <td>  2</td>\n",
        "      <td>  1</td>\n",
        "      <td>  3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Weightlifting</th>\n",
        "      <td>  2</td>\n",
        "      <td>  7</td>\n",
        "      <td>  1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Wrestling</th>\n",
        "      <td> 15</td>\n",
        "      <td> 28</td>\n",
        "      <td> 15</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>6 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "               Bronze  Gold  Silver\n",
        "Sport                              \n",
        "Athletics           2     1       3\n",
        "Boxing              5   NaN       2\n",
        "Judo                1     1     NaN\n",
        "Taekwondo           2     1       3\n",
        "Weightlifting       2     7       1\n",
        "Wrestling          15    28      15\n",
        "\n",
        "[6 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# df6.plot(kind='bar',figsize=(15,10))\n",
      "df4.sort_index(by='Gold',ascending=False).plot(kind='bar',figsize=(15,5),colors=['gray','gold','pink'])"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "/usr/lib/python2.7/dist-packages/pandas/tools/plotting.py:847: UserWarning: 'colors' is being deprecated. Please use 'color'instead of 'colors'\n",
        "  warnings.warn((\"'colors' is being deprecated. Please use 'color'\"\n"
       ]
      },
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f9664a85ed0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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KubqK2AukiHmhiO8jipgXipgX2RiJqYmVEXgOSZIkSaobI3FE7BfAb4BPDT8c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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9664a85350>"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "B\u00fct\u00fcn s\u00fctunlar dahil"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dft = total.groupby(['Sport']).aggregate('sum')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "dft.sort_index(by='Gold',ascending=False).plot(kind='bar',figsize=(15,5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 16,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f966499cd50>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PmCRJUoexR0xqGo8esfWJQRiNj+uX/D2SJEmS1HGmjMHv6GOIyYoFCxbQ3d0N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\nAAAAAAAAAAAAAAAAAAB8sf8B4QFqYo7TuywAAAAASUVORK5CYII=\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f9664b1bf10>"
       ]
      }
     ],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df5 = total.groupby(['Sport','Gender']).aggregate('sum')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df5.plot(kind='bar',figsize=(15,5))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 18,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f9664a1b850>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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JkiRJUj3YR0ySJElS49hHTJIkSZIaprIVMduummFGNXLMaF5GrhwzmpeRK8eM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\nJF//KcqzbXrLoGyfE7LXP0eOF0txrLLv33qeMh/NTVdnrwejusfxyDF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       "text": [
        "<matplotlib.figure.Figure at 0x7f96645f7610>"
       ]
      }
     ],
     "prompt_number": 18
    }
   ],
   "metadata": {}
  }
 ]
}