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simgeekiz/ders3.ipynb

Created September 14, 2014 11:11
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ders3.ipynb
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"worksheets": [
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"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd\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": "code",
"collapsed": false,
"input": [
"df = pd.read_csv('dosya/usa.csv')"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 2
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df[: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> Jesse Owens</td>\n",
" <td> Male</td>\n",
" <td> 22</td>\n",
" <td> Athletics</td>\n",
" <td> 4</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1</th>\n",
" <td> 2</td>\n",
" <td> Jack Medica</td>\n",
" <td> Male</td>\n",
" <td> 21</td>\n",
" <td> Swimming</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td>NaN</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2</th>\n",
" <td> 3</td>\n",
" <td> Helen Stephens</td>\n",
" <td> Female</td>\n",
" <td> 18</td>\n",
" <td> Athletics</td>\n",
" <td> 2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3</th>\n",
" <td> 4</td>\n",
" <td> Ralph Metcalfe</td>\n",
" <td> Male</td>\n",
" <td> 26</td>\n",
" <td> Athletics</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4</th>\n",
" <td> 5</td>\n",
" <td> Marshall Wayne</td>\n",
" <td> Male</td>\n",
" <td> 24</td>\n",
" <td> Diving</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>5 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 Jesse Owens Male 22 Athletics 4 NaN NaN 4\n",
"1 2 Jack Medica Male 21 Swimming 1 2 NaN 3\n",
"2 3 Helen Stephens Female 18 Athletics 2 NaN NaN 2\n",
"3 4 Ralph Metcalfe Male 26 Athletics 1 1 NaN 2\n",
"4 5 Marshall Wayne Male 24 Diving 1 1 NaN 2\n",
"\n",
"[5 rows x 9 columns]"
]
}
],
"prompt_number": 3
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Sadece alt\u0131n madalya alanlar\u0131 se\u00e7mek istiyoruz."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"alt\u0131n madalya alanlar\u0131 bulmak i\u00e7in \"Gold\" kolonunun 1 veya 1'den b\u00fcy\u00fck olan sat\u0131rlar\u0131 getiriyor."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gold_medals = df[df['Gold'] >= 1]\n",
"gold_medals[: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>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> Jesse Owens</td>\n",
" <td> Male</td>\n",
" <td> 22</td>\n",
" <td> Athletics</td>\n",
" <td> 4</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 4</td>\n",
" </tr>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> 2</td>\n",
" <td> Jack Medica</td>\n",
" <td> Male</td>\n",
" <td> 21</td>\n",
" <td> Swimming</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td>NaN</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>2 </th>\n",
" <td> 3</td>\n",
" <td> Helen Stephens</td>\n",
" <td> Female</td>\n",
" <td> 18</td>\n",
" <td> Athletics</td>\n",
" <td> 2</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 4</td>\n",
" <td> Ralph Metcalfe</td>\n",
" <td> Male</td>\n",
" <td> 26</td>\n",
" <td> Athletics</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 5</td>\n",
" <td> Marshall Wayne</td>\n",
" <td> Male</td>\n",
" <td> 24</td>\n",
" <td> Diving</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>5 </th>\n",
" <td> 6</td>\n",
" <td> Dorothy Poynton-Hill</td>\n",
" <td> Female</td>\n",
" <td> 21</td>\n",
" <td> Diving</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 8</td>\n",
" <td> Gordy Adam</td>\n",
" <td> Male</td>\n",
" <td> 21</td>\n",
" <td> Rowing</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 9</td>\n",
" <td> Sam Balter</td>\n",
" <td> Male</td>\n",
" <td> 26</td>\n",
" <td> Basketball</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 10</td>\n",
" <td> Ralph Bishop</td>\n",
" <td> Male</td>\n",
" <td> 20</td>\n",
" <td> Basketball</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>10</th>\n",
" <td> 11</td>\n",
" <td> Harriet Bland</td>\n",
" <td> Female</td>\n",
" <td> 21</td>\n",
" <td> Athletics</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows \u00d7 9 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"text": [
" Rk Athlete Gender Age Sport Gold Silver Bronze \\\n",
"0 1 Jesse Owens Male 22 Athletics 4 NaN NaN \n",
"1 2 Jack Medica Male 21 Swimming 1 2 NaN \n",
"2 3 Helen Stephens Female 18 Athletics 2 NaN NaN \n",
"3 4 Ralph Metcalfe Male 26 Athletics 1 1 NaN \n",
"4 5 Marshall Wayne Male 24 Diving 1 1 NaN \n",
"5 6 Dorothy Poynton-Hill Female 21 Diving 1 NaN 1 \n",
"7 8 Gordy Adam Male 21 Rowing 1 NaN NaN \n",
"8 9 Sam Balter Male 26 Basketball 1 NaN NaN \n",
"9 10 Ralph Bishop Male 20 Basketball 1 NaN NaN \n",
"10 11 Harriet Bland Female 21 Athletics 1 NaN NaN \n",
"\n",
" Total \n",
"0 4 \n",
"1 3 \n",
"2 2 \n",
"3 2 \n",
"4 2 \n",
"5 2 \n",
"7 1 \n",
"8 1 \n",
"9 1 \n",
"10 1 \n",
"\n",
"[10 rows x 9 columns]"
]
}
],
"prompt_number": 4
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"sadece gold de\u011feri 1 ve 1den b\u00fcy\u00fck olanlar\u0131 getirdi\u011fini g\u00f6rebiliriz.peki bu nas\u0131l \u00e7al\u0131\u015f\u0131yor."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df['Gold'] >= 1"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 5,
"text": [
"0 True\n",
"1 True\n",
"2 True\n",
"3 True\n",
"4 True\n",
"5 True\n",
"6 False\n",
"7 True\n",
"8 True\n",
"9 True\n",
"10 True\n",
"11 True\n",
"12 True\n",
"13 True\n",
"14 True\n",
"...\n",
"344 False\n",
"345 False\n",
"346 False\n",
"347 False\n",
"348 False\n",
"349 False\n",
"350 False\n",
"351 False\n",
"352 False\n",
"353 False\n",
"354 False\n",
"355 False\n",
"356 False\n",
"357 False\n",
"358 False\n",
"Name: Gold, Length: 359, dtype: bool"
]
}
],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"True falselardan olu\u015fan b\u00fcy\u00fck bir array. Dataframimizi bu arrayle indexledi\u011fimizde sat\u0131rlar\u0131 elde ediyoruz\n",
"\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"& operator\u00fcn\u00fc kullanarak birden \u00e7ok ko\u015fulda atayabiliriz. mesela hem alt\u0131n madalya alanlar ve erkek olanlar\u0131 listeleyelim. "
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"is_gold = df['Gold'] == 1\n",
"is_male = df['Gender'] == \"Male\"\n",
"df[is_gold & is_male][: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>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>1 </th>\n",
" <td> 2</td>\n",
" <td> Jack Medica</td>\n",
" <td> Male</td>\n",
" <td> 21</td>\n",
" <td> Swimming</td>\n",
" <td> 1</td>\n",
" <td> 2</td>\n",
" <td>NaN</td>\n",
" <td> 3</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> 4</td>\n",
" <td> Ralph Metcalfe</td>\n",
" <td> Male</td>\n",
" <td> 26</td>\n",
" <td> Athletics</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> 5</td>\n",
" <td> Marshall Wayne</td>\n",
" <td> Male</td>\n",
" <td> 24</td>\n",
" <td> Diving</td>\n",
" <td> 1</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td> 2</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> 8</td>\n",
" <td> Gordy Adam</td>\n",
" <td> Male</td>\n",
" <td> 21</td>\n",
" <td> Rowing</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> 9</td>\n",
" <td> Sam Balter</td>\n",
" <td> Male</td>\n",
" <td> 26</td>\n",
" <td> Basketball</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> 10</td>\n",
" <td> Ralph Bishop</td>\n",
" <td> Male</td>\n",
" <td> 20</td>\n",
" <td> Basketball</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> 12</td>\n",
" <td> Ken Carpenter</td>\n",
" <td> Male</td>\n",
" <td> 23</td>\n",
" <td> Athletics</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> 13</td>\n",
" <td> Chuck Day</td>\n",
" <td> Male</td>\n",
" <td> 21</td>\n",
" <td> Rowing</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> 14</td>\n",
" <td> Dick Degener</td>\n",
" <td> Male</td>\n",
" <td> 24</td>\n",
" <td> Diving</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> 15</td>\n",
" <td> Foy Draper</td>\n",
" <td> Male</td>\n",
" <td> 22</td>\n",
" <td> Athletics</td>\n",
" <td> 1</td>\n",
" <td>NaN</td>\n",
" <td>NaN</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows \u00d7 9 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 6,
"text": [
" Rk Athlete Gender Age Sport Gold Silver Bronze Total\n",
"1 2 Jack Medica Male 21 Swimming 1 2 NaN 3\n",
"3 4 Ralph Metcalfe Male 26 Athletics 1 1 NaN 2\n",
"4 5 Marshall Wayne Male 24 Diving 1 1 NaN 2\n",
"7 8 Gordy Adam Male 21 Rowing 1 NaN NaN 1\n",
"8 9 Sam Balter Male 26 Basketball 1 NaN NaN 1\n",
"9 10 Ralph Bishop Male 20 Basketball 1 NaN NaN 1\n",
"11 12 Ken Carpenter Male 23 Athletics 1 NaN NaN 1\n",
"12 13 Chuck Day Male 21 Rowing 1 NaN NaN 1\n",
"13 14 Dick Degener Male 24 Diving 1 NaN NaN 1\n",
"14 15 Foy Draper Male 22 Athletics 1 NaN NaN 1\n",
"\n",
"[10 rows x 9 columns]"
]
}
],
"prompt_number": 6
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"is_gold & is_male"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 7,
"text": [
"0 False\n",
"1 True\n",
"2 False\n",
"3 True\n",
"4 True\n",
"5 False\n",
"6 False\n",
"7 True\n",
"8 True\n",
"9 True\n",
"10 False\n",
"11 True\n",
"12 True\n",
"13 True\n",
"14 True\n",
"...\n",
"344 False\n",
"345 False\n",
"346 False\n",
"347 False\n",
"348 False\n",
"349 False\n",
"350 False\n",
"351 False\n",
"352 False\n",
"353 False\n",
"354 False\n",
"355 False\n",
"356 False\n",
"357 False\n",
"358 False\n",
"Length: 359, dtype: bool"
]
}
],
"prompt_number": 7
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"tabloyu istedi\u011fimiz kolonlarla listelemek i\u00e7in:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df[is_gold & is_male][['Athlete', 'Gender', 'Sport', 'Gold']][:10]\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>Athlete</th>\n",
" <th>Gender</th>\n",
" <th>Sport</th>\n",
" <th>Gold</th>\n",
" </tr>\n",
" </thead>\n",
" <tbody>\n",
" <tr>\n",
" <th>1 </th>\n",
" <td> Jack Medica</td>\n",
" <td> Male</td>\n",
" <td> Swimming</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>3 </th>\n",
" <td> Ralph Metcalfe</td>\n",
" <td> Male</td>\n",
" <td> Athletics</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>4 </th>\n",
" <td> Marshall Wayne</td>\n",
" <td> Male</td>\n",
" <td> Diving</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>7 </th>\n",
" <td> Gordy Adam</td>\n",
" <td> Male</td>\n",
" <td> Rowing</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>8 </th>\n",
" <td> Sam Balter</td>\n",
" <td> Male</td>\n",
" <td> Basketball</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>9 </th>\n",
" <td> Ralph Bishop</td>\n",
" <td> Male</td>\n",
" <td> Basketball</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>11</th>\n",
" <td> Ken Carpenter</td>\n",
" <td> Male</td>\n",
" <td> Athletics</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>12</th>\n",
" <td> Chuck Day</td>\n",
" <td> Male</td>\n",
" <td> Rowing</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>13</th>\n",
" <td> Dick Degener</td>\n",
" <td> Male</td>\n",
" <td> Diving</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" <tr>\n",
" <th>14</th>\n",
" <td> Foy Draper</td>\n",
" <td> Male</td>\n",
" <td> Athletics</td>\n",
" <td> 1</td>\n",
" </tr>\n",
" </tbody>\n",
"</table>\n",
"<p>10 rows \u00d7 4 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 8,
"text": [
" Athlete Gender Sport Gold\n",
"1 Jack Medica Male Swimming 1\n",
"3 Ralph Metcalfe Male Athletics 1\n",
"4 Marshall Wayne Male Diving 1\n",
"7 Gordy Adam Male Rowing 1\n",
"8 Sam Balter Male Basketball 1\n",
"9 Ralph Bishop Male Basketball 1\n",
"11 Ken Carpenter Male Athletics 1\n",
"12 Chuck Day Male Rowing 1\n",
"13 Dick Degener Male Diving 1\n",
"14 Foy Draper Male Athletics 1\n",
"\n",
"[10 rows x 4 columns]"
]
}
],
"prompt_number": 8
},
{
"cell_type": "heading",
"level": 3,
"metadata": {},
"source": [
"3.2 Numpy arrayleri hakk\u0131nda hat\u0131rlatma"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.Series([1,2,3])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 9,
"text": [
"0 1\n",
"1 2\n",
"2 3\n",
"dtype: int64"
]
}
],
"prompt_number": 9
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"pandas serileri i\u00e7yap\u0131 olarak numpy arrayleridir. E\u011fer serinin sonuna \".values\" eklersek o arrayin i\u00e7 numpy arrayine ula\u015f\u0131r\u0131z."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"\n",
"np.array([1,2,3])"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 10,
"text": [
"array([1, 2, 3])"
]
}
],
"prompt_number": 10
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"pd.Series([1,2,3]).values"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"array([1, 2, 3])"
]
}
],
"prompt_number": 11
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"bu y\u00fczden binary-array-se\u00e7imi numpy arrayleri i\u00e7in \u00e7al\u0131\u015f\u0131yor.\n",
"//So this binary-array-selection business is actually something that works with any numpy array:"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr = np.array([1,2,3])"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 12
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr != 2"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 13,
"text": [
"array([ True, False, True], dtype=bool)"
]
}
],
"prompt_number": 13
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"arr[arr != 2]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 14,
"text": [
"array([1, 3])"
]
}
],
"prompt_number": 14
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"3.3 Hangi cinsiyet daha \u00e7ok alt\u0131n kazanm\u0131\u015f?"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"is_gold = df['Gold'] >= 1\n",
"gold_ones = df[is_gold]\n",
"gold_ones['Gender'].value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 15,
"text": [
"Male 41\n",
"Female 6\n",
"dtype: int64"
]
}
],
"prompt_number": 15
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Erkek 41 alt\u0131n madalya kad\u0131nlarda 6 alt\u0131n madalya kazanan var.\n",
"Bunu y\u00fczde olarak oranlamak istersek \n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gold_ones_counts = gold_ones['Gender'].value_counts()\n",
"gold__counts = df['Gender'].value_counts()"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 16
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"gold_ones_counts/ gold__counts.astype(float)"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 17,
"text": [
"Male 0.130990\n",
"Female 0.130435\n",
"dtype: float64"
]
}
],
"prompt_number": 17
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"(gold_ones_counts/ gold__counts.astype(float)).plot(kind='bar')"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7f3cce31b550>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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4d83vSut/d60WYgdQdkLsAKpI4UoVWzEjF8j78rRU3zanlWm4As6p0VqqFWNhr5H/CzFn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\n5/2HJEmSJEmSJEmSJEmSJJ0j/w/Ba8MFkuddWgAAAABJRU5ErkJggg==\n",
"text": [
"<matplotlib.figure.Figure at 0x7f3cce2fe390>"
]
}
],
"prompt_number": 18
}
],
"metadata": {}
}
]
}
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