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simgeekiz/ders2.ipynb

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ders2.ipynb
{
"metadata": {
"name": "",
"signature": "sha256:911d57b09a01e3ce9939bc1483335c849f62bb13d165095ba8d91e3ff2aade0c"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"# The usual preamble\n",
"import pandas as pd\n",
"pd.set_option('display.mpl_style', 'default') \n",
"pd.set_option('display.line_width', 5000) \n",
"pd.set_option('display.max_columns', 60) \n",
"\n",
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 1
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Biz b\u00fcy\u00fck veri setleri ile nas\u0131l ba\u015fa \u00e7\u0131k\u0131ld\u0131\u011f\u0131n\u0131 g\u00f6stermek i\u00e7in, burada yeni bir veri k\u00fcmesi kullan\u0131yoruz. Bu veri gelen 311 hizmet taleplerinin bir alt k\u00fcmesidir."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataframe = pd.read_csv('dosya/deneme.csv')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"\u00c7ok geni\u015f data setlerinde bize datay\u0131 g\u00f6stermek yerine \u00f6zetini veriyor. \n",
"Kolonlar\u0131 ve her kolonda ka\u00e7 tane bo\u015f de\u011fer oldu\u011funu g\u00f6steriyor."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataframe\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>Rk</th>\n",
" <th>Athlete</th>\n",
" <th>Gender</th>\n",
" <th>Age</th>\n",
" <th>Sport</th>\n",
" <th>Gold</th>\n",
" <th>Silver</th>\n",
" <th>Bronze</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> 1</td>\n",
" <td> Halil Mutlu</td>\n",
" <td> Male</td>\n",
" <td> 27</td>\n",
" <td> Weightlifting</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 2</td>\n",
" <td> H\u00fcseyin \u00d6zkan</td>\n",
" <td> Male</td>\n",
" <td> 28</td>\n",
" <td> Judo</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 3</td>\n",
" <td> Hamza Yerlikaya</td>\n",
" <td> Male</td>\n",
" <td> 24</td>\n",
" <td> Wrestling</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 4</td>\n",
" <td> Adem Bereket</td>\n",
" <td> Male</td>\n",
" <td> 27</td>\n",
" <td> Wrestling</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 5</td>\n",
" <td> Hamide B\u0131k\u00e7\u0131n</td>\n",
" <td> Female</td>\n",
" <td> 22</td>\n",
" <td> Taekwondo</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 6</td>\n",
" <td> Ali Enver Adakan</td>\n",
" <td> Male</td>\n",
" <td> 23</td>\n",
" <td> Sailing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> 7</td>\n",
" <td> \u00d6zdemir Akbal</td>\n",
" <td> Male</td>\n",
" <td> 23</td>\n",
" <td> Archery</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 8</td>\n",
" <td> \u0130lknur Akdo\u011fan</td>\n",
" <td> Female</td>\n",
" <td> 31</td>\n",
" <td> Sailing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 9</td>\n",
" <td> Serap Akta\u015f</td>\n",
" <td> Female</td>\n",
" <td> 28</td>\n",
" <td> Athletics</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 10</td>\n",
" <td> Abdul Aziz Alpak</td>\n",
" <td> Male</td>\n",
" <td> 25</td>\n",
" <td> Weightlifting</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 11</td>\n",
" <td> Elif Alt\u0131nkaynak</td>\n",
" <td> Female</td>\n",
" <td> 26</td>\n",
" <td> Archery</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 12</td>\n",
" <td> Yasin Arslan</td>\n",
" <td> Male</td>\n",
" <td> 22</td>\n",
" <td> Weightlifting</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 13</td>\n",
" <td> Nazmi Avluca</td>\n",
" <td> Male</td>\n",
" <td> 23</td>\n",
" <td> Wrestling</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 14</td>\n",
" <td> S\u00fcreyya Ayhan</td>\n",
" <td> Female</td>\n",
" <td> 22</td>\n",
" <td> Athletics</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 15</td>\n",
" <td> Fatih Bakir</td>\n",
" <td> Male</td>\n",
" <td> 23</td>\n",
" <td> Wrestling</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> 16</td>\n",
" <td> Ramazan Ballio\u011flu</td>\n",
" <td> Male</td>\n",
" <td> 21</td>\n",
" <td> Boxing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> 17</td>\n",
" <td> Hakk\u0131 Ba\u015far</td>\n",
" <td> Male</td>\n",
" <td> 30</td>\n",
" <td> Wrestling</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> 18</td>\n",
" <td> Derya B\u00fcy\u00fckuncu</td>\n",
" <td> Male</td>\n",
" <td> 24</td>\n",
" <td> Swimming</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> 19</td>\n",
" <td> Ayhan Cicek</td>\n",
" <td> Male</td>\n",
" <td> 22</td>\n",
" <td> Weightlifting</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> 20</td>\n",
" <td> Ayse Diker</td>\n",
" <td> Female</td>\n",
" <td> 16</td>\n",
" <td> Swimming</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> 21</td>\n",
" <td> \u0130lkay Dikmen</td>\n",
" <td> Female</td>\n",
" <td> 19</td>\n",
" <td> Swimming</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> 22</td>\n",
" <td> Harun Do\u011fan</td>\n",
" <td> Male</td>\n",
" <td> 24</td>\n",
" <td> Wrestling</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> 23</td>\n",
" <td> Ahmet Do\u011fu</td>\n",
" <td> Male</td>\n",
" <td> 26</td>\n",
" <td> Wrestling</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> 24</td>\n",
" <td> \u015eadan Derya Erke</td>\n",
" <td> Female</td>\n",
" <td> 16</td>\n",
" <td> Swimming</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> 25</td>\n",
" <td> \u015eeref Ero\u011flu</td>\n",
" <td> Male</td>\n",
" <td> 24</td>\n",
" <td> Wrestling</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> 26</td>\n",
" <td> D\u00f6nd\u00fc G\u00fcvenc</td>\n",
" <td> Female</td>\n",
" <td> 22</td>\n",
" <td> Taekwondo</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> 27</td>\n",
" <td> Ertu\u011frul \u0130\u00e7ingir</td>\n",
" <td> Male</td>\n",
" <td> 24</td>\n",
" <td> Sailing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> 28</td>\n",
" <td> F\u0131rat Karag\u00f6ll\u00fc</td>\n",
" <td> Male</td>\n",
" <td> 22</td>\n",
" <td> Boxing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> 29</td>\n",
" <td> Ebru Kavakl\u0131o\u011flu</td>\n",
" <td> Female</td>\n",
" <td> 30</td>\n",
" <td> Athletics</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> 30</td>\n",
" <td> Zekiye Keskin \u015eat\u0131r</td>\n",
" <td> Female</td>\n",
" <td> 24</td>\n",
" <td> Archery</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> 31</td>\n",
" <td> Ay\u015fe Kil</td>\n",
" <td> Female</td>\n",
" <td> 28</td>\n",
" <td> Shooting</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td> 32</td>\n",
" <td> Hakan Kiper</td>\n",
" <td> Male</td>\n",
" <td> 27</td>\n",
" <td> Swimming</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> 33</td>\n",
" <td> Ak\u0131n Kulo\u011flu</td>\n",
" <td> Male</td>\n",
" <td> 28</td>\n",
" <td> Boxing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td> 34</td>\n",
" <td> Oksana Mert</td>\n",
" <td> Female</td>\n",
" <td> 27</td>\n",
" <td> Athletics</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td> 35</td>\n",
" <td> Aytekin Mindan</td>\n",
" <td> Male</td>\n",
" <td> 19</td>\n",
" <td> Swimming</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td> 36</td>\n",
" <td> A\u011fas\u0131 M\u0259mm\u0259dov</td>\n",
" <td> Male</td>\n",
" <td> 20</td>\n",
" <td> Boxing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td> 37</td>\n",
" <td> Natalia Nasaridze-\u00c7akir</td>\n",
" <td> Female</td>\n",
" <td> 27</td>\n",
" <td> Archery</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td> 38</td>\n",
" <td> U\u011fur Orel Oral</td>\n",
" <td> Male</td>\n",
" <td> 20</td>\n",
" <td> Swimming</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> 39</td>\n",
" <td> Hasan Orbay</td>\n",
" <td> Male</td>\n",
" <td> 21</td>\n",
" <td> Archery</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td> 40</td>\n",
" <td> Ali \u00d6zen</td>\n",
" <td> Male</td>\n",
" <td> 29</td>\n",
" <td> Wrestling</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td> 41</td>\n",
" <td> Ramazan Paliani</td>\n",
" <td> Male</td>\n",
" <td> 27</td>\n",
" <td> Boxing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td> 42</td>\n",
" <td> Selim Palyani</td>\n",
" <td> Male</td>\n",
" <td> 24</td>\n",
" <td> Boxing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td> 43</td>\n",
" <td> Ayd\u0131n Polat\u00e7\u0131</td>\n",
" <td> Male</td>\n",
" <td> 23</td>\n",
" <td> Wrestling</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td> 44</td>\n",
" <td> Y\u00fcksel \u015eanl\u0131</td>\n",
" <td> Male</td>\n",
" <td> 26</td>\n",
" <td> Wrestling</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td> 45</td>\n",
" <td> Serdar \u015eatir</td>\n",
" <td> Male</td>\n",
" <td> 22</td>\n",
" <td> Archery</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td> 46</td>\n",
" <td> Ne\u015fe \u015eensoy Y\u0131ld\u0131z</td>\n",
" <td> Female</td>\n",
" <td> 26</td>\n",
" <td> Judo</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td> 47</td>\n",
" <td> B\u00fcnyamin Suda\u015f</td>\n",
" <td> Male</td>\n",
" <td> 25</td>\n",
" <td> Weightlifting</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td> 48</td>\n",
" <td> Naim S\u00fcleymano\u011flu</td>\n",
" <td> Male</td>\n",
" <td> 33</td>\n",
" <td> Weightlifting</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td> 49</td>\n",
" <td> Nurhan S\u00fcleymano\u011flu</td>\n",
" <td> Male</td>\n",
" <td> 29</td>\n",
" <td> Boxing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td> 50</td>\n",
" <td> Selim Tataro\u011flu</td>\n",
" <td> Male</td>\n",
" <td> 28</td>\n",
" <td> Judo</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td> 51</td>\n",
" <td> Ali Kemal T\u00fcfek\u00e7i</td>\n",
" <td> Male</td>\n",
" <td> 27</td>\n",
" <td> Sailing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td> 52</td>\n",
" <td> Halil \u0130brahim Turan</td>\n",
" <td> Male</td>\n",
" <td> 20</td>\n",
" <td> Boxing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td> 53</td>\n",
" <td> O\u011fuzhan T\u00fcz\u00fcn</td>\n",
" <td> Male</td>\n",
" <td> 17</td>\n",
" <td> Shooting</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td> 54</td>\n",
" <td> B\u00fclent Ulusoy</td>\n",
" <td> Male</td>\n",
" <td> 22</td>\n",
" <td> Boxing</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td> 55</td>\n",
" <td> Mesut Yava\u015f</td>\n",
" <td> Male</td>\n",
" <td> 22</td>\n",
" <td> Athletics</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td> 56</td>\n",
" <td> Ercan Y\u0131ld\u0131z</td>\n",
" <td> Male</td>\n",
" <td> 26</td>\n",
" <td> Wrestling</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td> 57</td>\n",
" <td> Mehmet Y\u0131lmaz</td>\n",
" <td> Male</td>\n",
" <td> 26</td>\n",
" <td> Weightlifting</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>57 rows \u00d7 9 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 3,
"text": [
" Rk Athlete Gender Age Sport Gold Silver Bronze Total\n",
"0 1 Halil Mutlu Male 27 Weightlifting 1 NaN NaN 1\n",
"1 2 H\u00fcseyin \u00d6zkan Male 28 Judo 1 NaN NaN 1\n",
"2 3 Hamza Yerlikaya Male 24 Wrestling 1 NaN NaN 1\n",
"3 4 Adem Bereket Male 27 Wrestling NaN NaN 1 1\n",
"4 5 Hamide B\u0131k\u00e7\u0131n Female 22 Taekwondo NaN NaN 1 1\n",
"5 6 Ali Enver Adakan Male 23 Sailing NaN NaN NaN NaN\n",
"6 7 \u00d6zdemir Akbal Male 23 Archery NaN NaN NaN NaN\n",
"7 8 \u0130lknur Akdo\u011fan Female 31 Sailing NaN NaN NaN NaN\n",
"8 9 Serap Akta\u015f Female 28 Athletics NaN NaN NaN NaN\n",
"9 10 Abdul Aziz Alpak Male 25 Weightlifting NaN NaN NaN NaN\n",
"10 11 Elif Alt\u0131nkaynak Female 26 Archery NaN NaN NaN NaN\n",
"11 12 Yasin Arslan Male 22 Weightlifting NaN NaN NaN NaN\n",
"12 13 Nazmi Avluca Male 23 Wrestling NaN NaN NaN NaN\n",
"13 14 S\u00fcreyya Ayhan Female 22 Athletics NaN NaN NaN NaN\n",
"14 15 Fatih Bakir Male 23 Wrestling NaN NaN NaN NaN\n",
"15 16 Ramazan Ballio\u011flu Male 21 Boxing NaN NaN NaN NaN\n",
"16 17 Hakk\u0131 Ba\u015far Male 30 Wrestling NaN NaN NaN NaN\n",
"17 18 Derya B\u00fcy\u00fckuncu Male 24 Swimming NaN NaN NaN NaN\n",
"18 19 Ayhan Cicek Male 22 Weightlifting NaN NaN NaN NaN\n",
"19 20 Ayse Diker Female 16 Swimming NaN NaN NaN NaN\n",
"20 21 \u0130lkay Dikmen Female 19 Swimming NaN NaN NaN NaN\n",
"21 22 Harun Do\u011fan Male 24 Wrestling NaN NaN NaN NaN\n",
"22 23 Ahmet Do\u011fu Male 26 Wrestling NaN NaN NaN NaN\n",
"23 24 \u015eadan Derya Erke Female 16 Swimming NaN NaN NaN NaN\n",
"24 25 \u015eeref Ero\u011flu Male 24 Wrestling NaN NaN NaN NaN\n",
"25 26 D\u00f6nd\u00fc G\u00fcvenc Female 22 Taekwondo NaN NaN NaN NaN\n",
"26 27 Ertu\u011frul \u0130\u00e7ingir Male 24 Sailing NaN NaN NaN NaN\n",
"27 28 F\u0131rat Karag\u00f6ll\u00fc Male 22 Boxing NaN NaN NaN NaN\n",
"28 29 Ebru Kavakl\u0131o\u011flu Female 30 Athletics NaN NaN NaN NaN\n",
"29 30 Zekiye Keskin \u015eat\u0131r Female 24 Archery NaN NaN NaN NaN\n",
"30 31 Ay\u015fe Kil Female 28 Shooting NaN NaN NaN NaN\n",
"31 32 Hakan Kiper Male 27 Swimming NaN NaN NaN NaN\n",
"32 33 Ak\u0131n Kulo\u011flu Male 28 Boxing NaN NaN NaN NaN\n",
"33 34 Oksana Mert Female 27 Athletics NaN NaN NaN NaN\n",
"34 35 Aytekin Mindan Male 19 Swimming NaN NaN NaN NaN\n",
"35 36 A\u011fas\u0131 M\u0259mm\u0259dov Male 20 Boxing NaN NaN NaN NaN\n",
"36 37 Natalia Nasaridze-\u00c7akir Female 27 Archery NaN NaN NaN NaN\n",
"37 38 U\u011fur Orel Oral Male 20 Swimming NaN NaN NaN NaN\n",
"38 39 Hasan Orbay Male 21 Archery NaN NaN NaN NaN\n",
"39 40 Ali \u00d6zen Male 29 Wrestling NaN NaN NaN NaN\n",
"40 41 Ramazan Paliani Male 27 Boxing NaN NaN NaN NaN\n",
"41 42 Selim Palyani Male 24 Boxing NaN NaN NaN NaN\n",
"42 43 Ayd\u0131n Polat\u00e7\u0131 Male 23 Wrestling NaN NaN NaN NaN\n",
"43 44 Y\u00fcksel \u015eanl\u0131 Male 26 Wrestling NaN NaN NaN NaN\n",
"44 45 Serdar \u015eatir Male 22 Archery NaN NaN NaN NaN\n",
"45 46 Ne\u015fe \u015eensoy Y\u0131ld\u0131z Female 26 Judo NaN NaN NaN NaN\n",
"46 47 B\u00fcnyamin Suda\u015f Male 25 Weightlifting NaN NaN NaN NaN\n",
"47 48 Naim S\u00fcleymano\u011flu Male 33 Weightlifting NaN NaN NaN NaN\n",
"48 49 Nurhan S\u00fcleymano\u011flu Male 29 Boxing NaN NaN NaN NaN\n",
"49 50 Selim Tataro\u011flu Male 28 Judo NaN NaN NaN NaN\n",
"50 51 Ali Kemal T\u00fcfek\u00e7i Male 27 Sailing NaN NaN NaN NaN\n",
"51 52 Halil \u0130brahim Turan Male 20 Boxing NaN NaN NaN NaN\n",
"52 53 O\u011fuzhan T\u00fcz\u00fcn Male 17 Shooting NaN NaN NaN NaN\n",
"53 54 B\u00fclent Ulusoy Male 22 Boxing NaN NaN NaN NaN\n",
"54 55 Mesut Yava\u015f Male 22 Athletics NaN NaN NaN NaN\n",
"55 56 Ercan Y\u0131ld\u0131z Male 26 Wrestling NaN NaN NaN NaN\n",
"56 57 Mehmet Y\u0131lmaz Male 26 Weightlifting NaN NaN NaN NaN\n",
"\n",
"[57 rows x 9 columns]"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Selecting columns and rows"
]
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Sadece bir kolonu se\u00e7mek istedi\u011fimizde o kolonun ad\u0131yla indexliyoruz\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataframe['Athlete']"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
"0 Halil Mutlu\n",
"1 H\u00fcseyin \u00d6zkan\n",
"2 Hamza Yerlikaya\n",
"3 Adem Bereket\n",
"4 Hamide B\u0131k\u00e7\u0131n\n",
"5 Ali Enver Adakan\n",
"6 \u00d6zdemir Akbal\n",
"7 \u0130lknur Akdo\u011fan\n",
"8 Serap Akta\u015f\n",
"9 Abdul Aziz Alpak\n",
"10 Elif Alt\u0131nkaynak\n",
"11 Yasin Arslan\n",
"12 Nazmi Avluca\n",
"13 S\u00fcreyya Ayhan\n",
"14 Fatih Bakir\n",
"15 Ramazan Ballio\u011flu\n",
"16 Hakk\u0131 Ba\u015far\n",
"17 Derya B\u00fcy\u00fckuncu\n",
"18 Ayhan Cicek\n",
"19 Ayse Diker\n",
"20 \u0130lkay Dikmen\n",
"21 Harun Do\u011fan\n",
"22 Ahmet Do\u011fu\n",
"23 \u015eadan Derya Erke\n",
"24 \u015eeref Ero\u011flu\n",
"25 D\u00f6nd\u00fc G\u00fcvenc\n",
"26 Ertu\u011frul \u0130\u00e7ingir\n",
"27 F\u0131rat Karag\u00f6ll\u00fc\n",
"28 Ebru Kavakl\u0131o\u011flu\n",
"29 Zekiye Keskin \u015eat\u0131r\n",
"30 Ay\u015fe Kil\n",
"31 Hakan Kiper\n",
"32 Ak\u0131n Kulo\u011flu\n",
"33 Oksana Mert\n",
"34 Aytekin Mindan\n",
"35 A\u011fas\u0131 M\u0259mm\u0259dov\n",
"36 Natalia Nasaridze-\u00c7akir\n",
"37 U\u011fur Orel Oral\n",
"38 Hasan Orbay\n",
"39 Ali \u00d6zen\n",
"40 Ramazan Paliani\n",
"41 Selim Palyani\n",
"42 Ayd\u0131n Polat\u00e7\u0131\n",
"43 Y\u00fcksel \u015eanl\u0131\n",
"44 Serdar \u015eatir\n",
"45 Ne\u015fe \u015eensoy Y\u0131ld\u0131z\n",
"46 B\u00fcnyamin Suda\u015f\n",
"47 Naim S\u00fcleymano\u011flu\n",
"48 Nurhan S\u00fcleymano\u011flu\n",
"49 Selim Tataro\u011flu\n",
"50 Ali Kemal T\u00fcfek\u00e7i\n",
"51 Halil \u0130brahim Turan\n",
"52 O\u011fuzhan T\u00fcz\u00fcn\n",
"53 B\u00fclent Ulusoy\n",
"54 Mesut Yava\u015f\n",
"55 Ercan Y\u0131ld\u0131z\n",
"56 Mehmet Y\u0131lmaz\n",
"Name: Athlete, Length: 57, dtype: object"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Datan\u0131n ilk be\u015f sat\u0131r\u0131n\u0131 g\u00f6rmek istersek df[:5] \n",
"\u0130\u00e7indekilere bakmak ve bu veri k\u00fcmesi i\u00e7in bir fikir almak i\u00e7in harika bir yoldur."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataframe[: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>Rk</th>\n",
" <th>Athlete</th>\n",
" <th>Gender</th>\n",
" <th>Age</th>\n",
" <th>Sport</th>\n",
" <th>Gold</th>\n",
" <th>Silver</th>\n",
" <th>Bronze</th>\n",
" <th>Total</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> 1</td>\n",
" <td> Halil Mutlu</td>\n",
" <td> Male</td>\n",
" <td> 27</td>\n",
" <td> Weightlifting</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> H\u00fcseyin \u00d6zkan</td>\n",
" <td> Male</td>\n",
" <td> 28</td>\n",
" <td> Judo</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> Hamza Yerlikaya</td>\n",
" <td> Male</td>\n",
" <td> 24</td>\n",
" <td> Wrestling</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> Adem Bereket</td>\n",
" <td> Male</td>\n",
" <td> 27</td>\n",
" <td> Wrestling</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> Hamide B\u0131k\u00e7\u0131n</td>\n",
" <td> Female</td>\n",
" <td> 22</td>\n",
" <td> Taekwondo</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 rows \u00d7 9 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
" Rk Athlete Gender Age Sport Gold Silver Bronze Total\n",
"0 1 Halil Mutlu Male 27 Weightlifting 1 NaN NaN 1\n",
"1 2 H\u00fcseyin \u00d6zkan Male 28 Judo 1 NaN NaN 1\n",
"2 3 Hamza Yerlikaya Male 24 Wrestling 1 NaN NaN 1\n",
"3 4 Adem Bereket Male 27 Wrestling NaN NaN 1 1\n",
"4 5 Hamide B\u0131k\u00e7\u0131n Female 22 Taekwondo NaN NaN 1 1\n",
"\n",
"[5 rows x 9 columns]"
]
}
],
"prompt_number": 5
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"Bir kolonun ilk 5 sat\u0131r\u0131 i\u00e7in :\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataframe['Age'][:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
"0 27\n",
"1 28\n",
"2 24\n",
"3 27\n",
"4 22\n",
"Name: Age, dtype: int64"
]
}
],
"prompt_number": 6
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Selecting multiple columns"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"birden fazla kolonu se\u00e7mek i\u00e7in istedi\u011fimiz kolonlar\u0131n isimlerini indexliyoruz.\n",
"b\u00fcy\u00fck datalarda bize \u00f6zeti veriyor."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataframe[['Athlete', 'Age']]"
],
"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>Athlete</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0 </th>\n",
" <td> Halil Mutlu</td>\n",
" <td> 27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> H\u00fcseyin \u00d6zkan</td>\n",
" <td> 28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> Hamza Yerlikaya</td>\n",
" <td> 24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> Adem Bereket</td>\n",
" <td> 27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> Hamide B\u0131k\u00e7\u0131n</td>\n",
" <td> 22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> Ali Enver Adakan</td>\n",
" <td> 23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6 </th>\n",
" <td> \u00d6zdemir Akbal</td>\n",
" <td> 23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> \u0130lknur Akdo\u011fan</td>\n",
" <td> 31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> Serap Akta\u015f</td>\n",
" <td> 28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> Abdul Aziz Alpak</td>\n",
" <td> 25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> Elif Alt\u0131nkaynak</td>\n",
" <td> 26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> Yasin Arslan</td>\n",
" <td> 22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> Nazmi Avluca</td>\n",
" <td> 23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> S\u00fcreyya Ayhan</td>\n",
" <td> 22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> Fatih Bakir</td>\n",
" <td> 23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>15</th>\n",
" <td> Ramazan Ballio\u011flu</td>\n",
" <td> 21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>16</th>\n",
" <td> Hakk\u0131 Ba\u015far</td>\n",
" <td> 30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>17</th>\n",
" <td> Derya B\u00fcy\u00fckuncu</td>\n",
" <td> 24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>18</th>\n",
" <td> Ayhan Cicek</td>\n",
" <td> 22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>19</th>\n",
" <td> Ayse Diker</td>\n",
" <td> 16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>20</th>\n",
" <td> \u0130lkay Dikmen</td>\n",
" <td> 19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>21</th>\n",
" <td> Harun Do\u011fan</td>\n",
" <td> 24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>22</th>\n",
" <td> Ahmet Do\u011fu</td>\n",
" <td> 26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>23</th>\n",
" <td> \u015eadan Derya Erke</td>\n",
" <td> 16</td>\n",
" </tr>\n",
" <tr>\n",
" <th>24</th>\n",
" <td> \u015eeref Ero\u011flu</td>\n",
" <td> 24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>25</th>\n",
" <td> D\u00f6nd\u00fc G\u00fcvenc</td>\n",
" <td> 22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>26</th>\n",
" <td> Ertu\u011frul \u0130\u00e7ingir</td>\n",
" <td> 24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>27</th>\n",
" <td> F\u0131rat Karag\u00f6ll\u00fc</td>\n",
" <td> 22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>28</th>\n",
" <td> Ebru Kavakl\u0131o\u011flu</td>\n",
" <td> 30</td>\n",
" </tr>\n",
" <tr>\n",
" <th>29</th>\n",
" <td> Zekiye Keskin \u015eat\u0131r</td>\n",
" <td> 24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>30</th>\n",
" <td> Ay\u015fe Kil</td>\n",
" <td> 28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>31</th>\n",
" <td> Hakan Kiper</td>\n",
" <td> 27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>32</th>\n",
" <td> Ak\u0131n Kulo\u011flu</td>\n",
" <td> 28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>33</th>\n",
" <td> Oksana Mert</td>\n",
" <td> 27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>34</th>\n",
" <td> Aytekin Mindan</td>\n",
" <td> 19</td>\n",
" </tr>\n",
" <tr>\n",
" <th>35</th>\n",
" <td> A\u011fas\u0131 M\u0259mm\u0259dov</td>\n",
" <td> 20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>36</th>\n",
" <td> Natalia Nasaridze-\u00c7akir</td>\n",
" <td> 27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>37</th>\n",
" <td> U\u011fur Orel Oral</td>\n",
" <td> 20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>38</th>\n",
" <td> Hasan Orbay</td>\n",
" <td> 21</td>\n",
" </tr>\n",
" <tr>\n",
" <th>39</th>\n",
" <td> Ali \u00d6zen</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>40</th>\n",
" <td> Ramazan Paliani</td>\n",
" <td> 27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>41</th>\n",
" <td> Selim Palyani</td>\n",
" <td> 24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>42</th>\n",
" <td> Ayd\u0131n Polat\u00e7\u0131</td>\n",
" <td> 23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>43</th>\n",
" <td> Y\u00fcksel \u015eanl\u0131</td>\n",
" <td> 26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>44</th>\n",
" <td> Serdar \u015eatir</td>\n",
" <td> 22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>45</th>\n",
" <td> Ne\u015fe \u015eensoy Y\u0131ld\u0131z</td>\n",
" <td> 26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>46</th>\n",
" <td> B\u00fcnyamin Suda\u015f</td>\n",
" <td> 25</td>\n",
" </tr>\n",
" <tr>\n",
" <th>47</th>\n",
" <td> Naim S\u00fcleymano\u011flu</td>\n",
" <td> 33</td>\n",
" </tr>\n",
" <tr>\n",
" <th>48</th>\n",
" <td> Nurhan S\u00fcleymano\u011flu</td>\n",
" <td> 29</td>\n",
" </tr>\n",
" <tr>\n",
" <th>49</th>\n",
" <td> Selim Tataro\u011flu</td>\n",
" <td> 28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>50</th>\n",
" <td> Ali Kemal T\u00fcfek\u00e7i</td>\n",
" <td> 27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>51</th>\n",
" <td> Halil \u0130brahim Turan</td>\n",
" <td> 20</td>\n",
" </tr>\n",
" <tr>\n",
" <th>52</th>\n",
" <td> O\u011fuzhan T\u00fcz\u00fcn</td>\n",
" <td> 17</td>\n",
" </tr>\n",
" <tr>\n",
" <th>53</th>\n",
" <td> B\u00fclent Ulusoy</td>\n",
" <td> 22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>54</th>\n",
" <td> Mesut Yava\u015f</td>\n",
" <td> 22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>55</th>\n",
" <td> Ercan Y\u0131ld\u0131z</td>\n",
" <td> 26</td>\n",
" </tr>\n",
" <tr>\n",
" <th>56</th>\n",
" <td> Mehmet Y\u0131lmaz</td>\n",
" <td> 26</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>57 rows \u00d7 2 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
" Athlete Age\n",
"0 Halil Mutlu 27\n",
"1 H\u00fcseyin \u00d6zkan 28\n",
"2 Hamza Yerlikaya 24\n",
"3 Adem Bereket 27\n",
"4 Hamide B\u0131k\u00e7\u0131n 22\n",
"5 Ali Enver Adakan 23\n",
"6 \u00d6zdemir Akbal 23\n",
"7 \u0130lknur Akdo\u011fan 31\n",
"8 Serap Akta\u015f 28\n",
"9 Abdul Aziz Alpak 25\n",
"10 Elif Alt\u0131nkaynak 26\n",
"11 Yasin Arslan 22\n",
"12 Nazmi Avluca 23\n",
"13 S\u00fcreyya Ayhan 22\n",
"14 Fatih Bakir 23\n",
"15 Ramazan Ballio\u011flu 21\n",
"16 Hakk\u0131 Ba\u015far 30\n",
"17 Derya B\u00fcy\u00fckuncu 24\n",
"18 Ayhan Cicek 22\n",
"19 Ayse Diker 16\n",
"20 \u0130lkay Dikmen 19\n",
"21 Harun Do\u011fan 24\n",
"22 Ahmet Do\u011fu 26\n",
"23 \u015eadan Derya Erke 16\n",
"24 \u015eeref Ero\u011flu 24\n",
"25 D\u00f6nd\u00fc G\u00fcvenc 22\n",
"26 Ertu\u011frul \u0130\u00e7ingir 24\n",
"27 F\u0131rat Karag\u00f6ll\u00fc 22\n",
"28 Ebru Kavakl\u0131o\u011flu 30\n",
"29 Zekiye Keskin \u015eat\u0131r 24\n",
"30 Ay\u015fe Kil 28\n",
"31 Hakan Kiper 27\n",
"32 Ak\u0131n Kulo\u011flu 28\n",
"33 Oksana Mert 27\n",
"34 Aytekin Mindan 19\n",
"35 A\u011fas\u0131 M\u0259mm\u0259dov 20\n",
"36 Natalia Nasaridze-\u00c7akir 27\n",
"37 U\u011fur Orel Oral 20\n",
"38 Hasan Orbay 21\n",
"39 Ali \u00d6zen 29\n",
"40 Ramazan Paliani 27\n",
"41 Selim Palyani 24\n",
"42 Ayd\u0131n Polat\u00e7\u0131 23\n",
"43 Y\u00fcksel \u015eanl\u0131 26\n",
"44 Serdar \u015eatir 22\n",
"45 Ne\u015fe \u015eensoy Y\u0131ld\u0131z 26\n",
"46 B\u00fcnyamin Suda\u015f 25\n",
"47 Naim S\u00fcleymano\u011flu 33\n",
"48 Nurhan S\u00fcleymano\u011flu 29\n",
"49 Selim Tataro\u011flu 28\n",
"50 Ali Kemal T\u00fcfek\u00e7i 27\n",
"51 Halil \u0130brahim Turan 20\n",
"52 O\u011fuzhan T\u00fcz\u00fcn 17\n",
"53 B\u00fclent Ulusoy 22\n",
"54 Mesut Yava\u015f 22\n",
"55 Ercan Y\u0131ld\u0131z 26\n",
"56 Mehmet Y\u0131lmaz 26\n",
"\n",
"[57 rows x 2 columns]"
]
}
],
"prompt_number": 7
},
{
"cell_type": "heading",
"level": 6,
"metadata": {},
"source": [
"\u0130ndexledi\u011fimiz kolonlar\u0131n ilk 10 sat\u0131r\u0131na bakabiliriz.\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataframe[['Athlete', 'Age']][: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>Athlete</th>\n",
" <th>Age</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>0</th>\n",
" <td> Halil Mutlu</td>\n",
" <td> 27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> H\u00fcseyin \u00d6zkan</td>\n",
" <td> 28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> Hamza Yerlikaya</td>\n",
" <td> 24</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> Adem Bereket</td>\n",
" <td> 27</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> Hamide B\u0131k\u00e7\u0131n</td>\n",
" <td> 22</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5</th>\n",
" <td> Ali Enver Adakan</td>\n",
" <td> 23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>6</th>\n",
" <td> \u00d6zdemir Akbal</td>\n",
" <td> 23</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7</th>\n",
" <td> \u0130lknur Akdo\u011fan</td>\n",
" <td> 31</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8</th>\n",
" <td> Serap Akta\u015f</td>\n",
" <td> 28</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9</th>\n",
" <td> Abdul Aziz Alpak</td>\n",
" <td> 25</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows \u00d7 2 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
" Athlete Age\n",
"0 Halil Mutlu 27\n",
"1 H\u00fcseyin \u00d6zkan 28\n",
"2 Hamza Yerlikaya 24\n",
"3 Adem Bereket 27\n",
"4 Hamide B\u0131k\u00e7\u0131n 22\n",
"5 Ali Enver Adakan 23\n",
"6 \u00d6zdemir Akbal 23\n",
"7 \u0130lknur Akdo\u011fan 31\n",
"8 Serap Akta\u015f 28\n",
"9 Abdul Aziz Alpak 25\n",
"\n",
"[10 rows x 2 columns]"
]
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"En \u00e7ok hangi ya\u015f grubu kat\u0131lm\u0131\u015ft\u0131r?"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
".value_counts() methodu. verilerin her birinin ka\u00e7ar tane oldu\u011funu hesapl\u0131yor."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataframe['Age'].value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"22 9\n",
"27 7\n",
"24 7\n",
"26 6\n",
"28 5\n",
"23 5\n",
"20 3\n",
"30 2\n",
"29 2\n",
"25 2\n",
"21 2\n",
"19 2\n",
"16 2\n",
"33 1\n",
"31 1\n",
"17 1\n",
"dtype: int64"
]
}
],
"prompt_number": 9
},
{
"cell_type": "heading",
"level": 5,
"metadata": {},
"source": [
"en \u00e7ok olan ilk 5 de\u011feri bulmak i\u00e7in"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"age_counts = dataframe['Age'].value_counts()\n",
"age_counts[:5]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"22 9\n",
"27 7\n",
"24 7\n",
"26 6\n",
"28 5\n",
"dtype: int64"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"age_counts"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"22 9\n",
"27 7\n",
"24 7\n",
"26 6\n",
"28 5\n",
"23 5\n",
"20 3\n",
"30 2\n",
"29 2\n",
"25 2\n",
"21 2\n",
"19 2\n",
"16 2\n",
"33 1\n",
"31 1\n",
"17 1\n",
"dtype: int64"
]
}
],
"prompt_number": 11
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"age_counts[:5].plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7fdf4f594a90>"
]
},
{
"output_type": "stream",
"stream": "stderr",
"text": [
"/usr/lib/pymodules/python2.7/matplotlib/font_manager.py:1236: UserWarning: findfont: Font family ['monospace'] not found. Falling back to Bitstream Vera Sans\n",
" (prop.get_family(), self.defaultFamily[fontext]))\n",
"/usr/lib/pymodules/python2.7/matplotlib/font_manager.py:1246: UserWarning: findfont: Could not match :family=Bitstream Vera Sans:style=normal:variant=normal:weight=normal:stretch=normal:size=10.0. Returning /usr/share/matplotlib/mpl-data/fonts/ttf/cmb10.ttf\n",
" UserWarning)\n"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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"text": [
"<matplotlib.figure.Figure at 0x7fdf4f5944d0>"
]
}
],
"prompt_number": 12
}
],
"metadata": {}
}
]
}
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