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@simgeekiz
Created September 12, 2014 23:02
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python_pandas-ders1
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{
"metadata": {
"name": "",
"signature": "sha256:e29e1177b41fef96196b89025d876532218ff0098a42ea8b98108e5075278b1b"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": [
"import pandas as pd #pandas k\u00fct\u00fcphanesini import ediyoruz.\n",
"%pylab inline"
],
"language": "python",
"metadata": {},
"outputs": [
{
"output_type": "stream",
"stream": "stdout",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"prompt_number": 2
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Reading data from a csv file"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Csv dosyas\u0131n\u0131 okutuyoruz. Bunu read_csv fonksiyonunu kullanarak yap\u0131yoruz. \n",
"Varsay\u0131lan olarak, alanlar\u0131 virg\u00fclle ayr\u0131lm\u0131\u015f oldu\u011funu varsayar."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataframe = pd.read_csv('dosya/deneme.csv') "
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 3
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataframe[:3] #dataframemizin ilk 3 sat\u0131r\u0131n\u0131 g\u00f6rebilmek i\u00e7in [:3] yaz\u0131yoruz.\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",
" </tbody>\n",
"</table>\n",
"<p>3 rows \u00d7 9 columns</p>\n",
"</div>"
],
"metadata": {},
"output_type": "pyout",
"prompt_number": 4,
"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",
"\n",
"[3 rows x 9 columns]"
]
}
],
"prompt_number": 4
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"#dataframe2 = pd.read_csv('dosya/deneme.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')\n"
],
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 5
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"kolon ayrac\u0131n\u0131 ; olarak de\u011fi\u015ftirebiliriz (default olarak ,) \n",
"Kodlamas\u0131n\u0131 'latin1' olarak de\u011fi\u015ftirebiliriz (default olarak 'utf8') \n",
"'Date' kolonunu belirleyebiliriz. \n",
"Tarihlerde ilk k\u0131s\u0131m\u0131n g\u00fcn yerine ay oldu\u011funu s\u00f6yleyebiliriz \n",
"'Date' kolonunu index olarak belirleyebiliriz."
]
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Selecting a column"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"csvden veri okudu\u011fumuzda sat\u0131r ve s\u00fctunlardan olu\u015fan dataframe denilen bir obje elde ederiz.Dataframin kolonunda bulunan verileri s\u00f6zl\u00fcktekine benzer \u015fekilde getirebiliriz."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataframe['Age'][:2]"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 11,
"text": [
"0 27\n",
"1 28\n",
"Name: Age, dtype: int64"
]
}
],
"prompt_number": 11
},
{
"cell_type": "heading",
"level": 1,
"metadata": {},
"source": [
"Plotting a column"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataframe['Age'].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 12,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7f5350c00750>"
]
},
{
"metadata": {},
"output_type": "display_data",
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Zjj2YcpuScc8euYGPGdN//Ze+BE88Ea0jL8WY5ElJdIIGEbk9oeIkrYwbJ5Ut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\ng8FgsPP/Aed4SH05RWqEAAAAAElFTkSuQmCC\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5350cd9310>"
]
}
],
"prompt_number": 12
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Kolayca t\u00fcm s\u00fctunlar\u0131 da \u00e7izdirebiliriz."
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"dataframe.plot(figsize=(10, 7))"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 16,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7f535083d590>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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hhBDqpdPBxx/D3Lkwbx6MHQvF7HAlhNOxVZ8oV+AosPnufW/gV+AU8AvgaXqI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\nF9wAAAAASUVORK5CYII=\n",
"text": [
"<matplotlib.figure.Figure at 0x7f5350c9ed90>"
]
}
],
"prompt_number": 16
},
{
"cell_type": "heading",
"level": 2,
"metadata": {},
"source": [
"Hepsini bir yerde g\u00f6stermek i\u00e7in\n"
]
},
{
"cell_type": "code",
"collapsed": false,
"input": [
"df = pd.read_csv('dosya/deneme.csv', sep=',', encoding='UTF-8')\n",
"df['Age'].plot()"
],
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "pyout",
"prompt_number": 18,
"text": [
"<matplotlib.axes.AxesSubplot at 0x7f53503d1850>"
]
},
{
"metadata": {},
"output_type": "display_data",
"png": 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Zjj2YcpuScc8euYGPGdN//Ze+BE88Ea0jL8WY5ElJdIIGEbk9oeIkrYwbJ5Ut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\n2JmzbjOglM9GYGOYjUoCSdcgnRKefX3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"text": [
"<matplotlib.figure.Figure at 0x7f536d506450>"
]
}
],
"prompt_number": 18
}
],
"metadata": {}
}
]
}
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