simgeekiz/pandasproject1.ipynb

## pandasproject1.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:641a76668346b223ad76e2854b342a832eb8f0a54e5ea2f6e5cae8ab4c783d0e"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "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",
      "import numpy as np\n",
      "import numpy.numarray as na\n",
      "from pylab import *\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') # csv dosyam\u0131z\u0131 y\u00fckl\u00fcyoruz.\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df=df[df['Games'].str.contains('Summer')] #sadece yaz olimpiyatlar\u0131 olanlar\u0131 incelemek istedi\u011fimizden i\u00e7inde \n",
      "# summer ge\u00e7en Games kolonunu al\u0131yoruz.\n",
      "df['Games']=df['Games'].str.slice(0,4) # sadece y\u0131llar\u0131 almak istiyoruz o y\u00fczden ilk 4 digiti al\u0131p geri kalan\u0131n\u0131 at\u0131yoruz."
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df = df.set_index('Games')\n",
      "df = df.sort_index(ascending = True)# y\u0131llar\u0131 d\u00fczenli bir \u015fekilde s\u0131ralamak istedi\u011fimizde (k\u00fc\u00e7\u00fckten b\u00fcy\u00fc\u011fe)\n",
      "df = df[['Men','Women']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "sum = [df['Men'] + df['Women']]\n",
      "df['Total'] =df['Men'] + df['Women']\n",
      "df"
     ],
     "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>Total</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>   2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1912</th>\n",
        "      <td>  2</td>\n",
        "      <td>  0</td>\n",
        "      <td>   2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1924</th>\n",
        "      <td> 22</td>\n",
        "      <td>  0</td>\n",
        "      <td>  22</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1928</th>\n",
        "      <td> 31</td>\n",
        "      <td>  0</td>\n",
        "      <td>  31</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1932</th>\n",
        "      <td>  1</td>\n",
        "      <td>  0</td>\n",
        "      <td>   1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1936</th>\n",
        "      <td> 46</td>\n",
        "      <td>  2</td>\n",
        "      <td>  48</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1948</th>\n",
        "      <td> 57</td>\n",
        "      <td>  1</td>\n",
        "      <td>  58</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1952</th>\n",
        "      <td> 51</td>\n",
        "      <td>  0</td>\n",
        "      <td>  51</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1956</th>\n",
        "      <td> 13</td>\n",
        "      <td>  0</td>\n",
        "      <td>  13</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1960</th>\n",
        "      <td> 46</td>\n",
        "      <td>  3</td>\n",
        "      <td>  49</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1964</th>\n",
        "      <td> 23</td>\n",
        "      <td>  0</td>\n",
        "      <td>  23</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1968</th>\n",
        "      <td> 29</td>\n",
        "      <td>  0</td>\n",
        "      <td>  29</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1972</th>\n",
        "      <td> 42</td>\n",
        "      <td>  1</td>\n",
        "      <td>  43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1976</th>\n",
        "      <td> 26</td>\n",
        "      <td>  1</td>\n",
        "      <td>  27</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1984</th>\n",
        "      <td> 45</td>\n",
        "      <td>  1</td>\n",
        "      <td>  46</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1988</th>\n",
        "      <td> 36</td>\n",
        "      <td>  5</td>\n",
        "      <td>  41</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1992</th>\n",
        "      <td> 36</td>\n",
        "      <td>  5</td>\n",
        "      <td>  41</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1996</th>\n",
        "      <td> 44</td>\n",
        "      <td>  9</td>\n",
        "      <td>  53</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2000</th>\n",
        "      <td> 42</td>\n",
        "      <td> 15</td>\n",
        "      <td>  57</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2004</th>\n",
        "      <td> 44</td>\n",
        "      <td> 20</td>\n",
        "      <td>  64</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2008</th>\n",
        "      <td> 48</td>\n",
        "      <td> 19</td>\n",
        "      <td>  67</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012</th>\n",
        "      <td> 48</td>\n",
        "      <td> 64</td>\n",
        "      <td> 112</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>22 rows \u00d7 3 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "       Men  Women  Total\n",
        "Games                   \n",
        "1906     2      0      2\n",
        "1912     2      0      2\n",
        "1924    22      0     22\n",
        "1928    31      0     31\n",
        "1932     1      0      1\n",
        "1936    46      2     48\n",
        "1948    57      1     58\n",
        "1952    51      0     51\n",
        "1956    13      0     13\n",
        "1960    46      3     49\n",
        "1964    23      0     23\n",
        "1968    29      0     29\n",
        "1972    42      1     43\n",
        "1976    26      1     27\n",
        "1984    45      1     46\n",
        "1988    36      5     41\n",
        "1992    36      5     41\n",
        "1996    44      9     53\n",
        "2000    42     15     57\n",
        "2004    44     20     64\n",
        "2008    48     19     67\n",
        "2012    48     64    112\n",
        "\n",
        "[22 rows x 3 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "df[['Men','Women']].plot(kind='bar',figsize=(15,6),stacked=True)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7fa8e0142e90>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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jYhPE7IrYBHbliNgEduWI2AQxuyI2gV1ZAq1BW26K42803P/Chvv/uPcmSZIkScrUzxTH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\nqOUxzJFKBzRIpQMWiXleQcR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       "text": [
        "<matplotlib.figure.Figure at 0x7fa8e0142790>"
       ]
      }
     ],
     "prompt_number": 6
    }
   ],
   "metadata": {}
  }
 ]
}