simgeekiz/ders4.ipynb

## ders4.ipynb
{
 "metadata": {
  "name": "",
  "signature": "sha256:5c82238d764d22523797a69420e7f98d8402d720bed85b0600beb4380a7fd687"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import pandas as pd\n",
      "%pylab inline\n",
      "figsize(15, 5)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "Populating the interactive namespace from numpy and matplotlib\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "heading",
     "level": 2,
     "metadata": {},
     "source": [
      "Dataframe'e weekday(haftan\u0131n g\u00fcnleri) kolonunu ekleyelim."
     ]
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "datam\u0131z\u0131 y\u00fckleyelim."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bikes= pd.read_csv('dosya/bikes.csv', sep=';', encoding='latin1', parse_dates=['Date'], dayfirst=True, index_col='Date')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bikes['Berri 1'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 3,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f1a330e5190>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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BCj/vLS6/PcUC57wxDMMwOWV4WE24hPW8WQfYfsPazO1YwyZnzFDPeYvK8xY0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R2TpYBjz733lgumkht6XNpDbWkXtWdudOkSvj5v5RQ2mWxPJ3Tynnuk9sChh2Yfw4t4ewIYB2wP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\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1a500ecad0>"
       ]
      }
     ],
     "prompt_number": 3
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Sadece berri bikepathinin oldu\u011fu bir dataframe yaratal\u0131m."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "berri_bikes = bikes[['Berri 1']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "berri_bikes[: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>Berri 1</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-01-01</th>\n",
        "      <td>  35</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02</th>\n",
        "      <td>  83</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03</th>\n",
        "      <td> 135</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-04</th>\n",
        "      <td> 144</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-05</th>\n",
        "      <td> 197</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 5,
       "text": [
        "            Berri 1\n",
        "Date               \n",
        "2012-01-01       35\n",
        "2012-01-02       83\n",
        "2012-01-03      135\n",
        "2012-01-04      144\n",
        "2012-01-05      197\n",
        "\n",
        "[5 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 5
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Sonra weekday kolonu ekleyebiliriz.weekdaye index ile ula\u015fabiliriz.  \n",
      "Basit\u00e7e y\u0131l\u0131n b\u00fct\u00fcn g\u00fcnleri diyebiliriz.  "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "berri_bikes.index"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 6,
       "text": [
        "<class 'pandas.tseries.index.DatetimeIndex'>\n",
        "[2012-01-01, ..., 2012-11-05]\n",
        "Length: 310, Freq: None, Timezone: None"
       ]
      }
     ],
     "prompt_number": 6
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "      \n",
      "baz\u0131 g\u00fcnlerin eksik oldu\u011funu g\u00f6r\u00fcyoruz. Uzunlu\u011fu 310 sadece 310 g\u00fcn\u00fc g\u00f6rebiliriz.  \n",
      "\n",
      "pandas\u0131n time series fonksiyonelli\u011fi var.  e\u011fer biz her bir sat\u0131r i\u00e7in ay\u0131n g\u00fcnlerine ula\u015fmak istersek:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "berri_bikes.index.day"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17,\n",
        "       18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  1,  2,  3,\n",
        "        4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20,\n",
        "       21, 22, 23, 24, 25, 26, 27, 28, 29,  1,  2,  3,  4,  5,  6,  7,  8,\n",
        "        9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
        "       26, 27, 28, 29, 30, 31,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
        "       12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n",
        "       29, 30,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,\n",
        "       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  1,\n",
        "        2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18,\n",
        "       19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30,  1,  2,  3,  4,  5,\n",
        "        6,  7,  8,  9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n",
        "       23, 24, 25, 26, 27, 28, 29, 30, 31,  1,  2,  3,  4,  5,  6,  7,  8,\n",
        "        9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25,\n",
        "       26, 27, 28, 29, 30, 31,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11,\n",
        "       12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28,\n",
        "       29, 30,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14, 15,\n",
        "       16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31,  1,\n",
        "        2,  3,  4,  5], dtype=int32)"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "ayn\u0131 \u015fekilde hangi g\u00fcn oldu\u011funa da weekday ile ula\u015fabiliriz."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "berri_bikes.index.weekday"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 8,
       "text": [
        "array([6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
        "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
        "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
        "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
        "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
        "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
        "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5,\n",
        "       6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0,\n",
        "       1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2,\n",
        "       3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4,\n",
        "       5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6,\n",
        "       0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1,\n",
        "       2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0, 1, 2, 3,\n",
        "       4, 5, 6, 0, 1, 2, 3, 4, 5, 6, 0], dtype=int32)"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "Bize ay\u0131n g\u00fcnlerini g\u00f6steriyor. 0 pazartesidir.  \n",
      "haftan\u0131n g\u00fcnlerine nas\u0131l ula\u015faca\u011f\u0131m\u0131z\u0131 bulduk bunu dataframimize ekleyebiliriz."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "berri_bikes['weekday'] = berri_bikes.index.weekday\n",
      "berri_bikes[: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>Berri 1</th>\n",
        "      <th>weekday</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Date</th>\n",
        "      <th></th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>2012-01-01</th>\n",
        "      <td>  35</td>\n",
        "      <td> 6</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-02</th>\n",
        "      <td>  83</td>\n",
        "      <td> 0</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-03</th>\n",
        "      <td> 135</td>\n",
        "      <td> 1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-04</th>\n",
        "      <td> 144</td>\n",
        "      <td> 2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2012-01-05</th>\n",
        "      <td> 197</td>\n",
        "      <td> 3</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>5 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "            Berri 1  weekday\n",
        "Date                        \n",
        "2012-01-01       35        6\n",
        "2012-01-02       83        0\n",
        "2012-01-03      135        1\n",
        "2012-01-04      144        2\n",
        "2012-01-05      197        3\n",
        "\n",
        "[5 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "berri_bikes.groupby('weekday').aggregate(sum)   \n",
      "kodu sat\u0131rlar\u0131 haftan\u0131n g\u00fcnlerine g\u00f6re grupla ve sonra ayn\u0131 g\u00fcnlerin de\u011ferlerini topla."
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weekday_counts = berri_bikes.groupby('weekday').aggregate(sum)\n",
      "weekday_counts"
     ],
     "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>Berri 1</th>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>weekday</th>\n",
        "      <th></th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 134298</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td> 135305</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> 152972</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> 160131</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>4</th>\n",
        "      <td> 141771</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>5</th>\n",
        "      <td> 101578</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>6</th>\n",
        "      <td>  99310</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>7 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 10,
       "text": [
        "         Berri 1\n",
        "weekday         \n",
        "0         134298\n",
        "1         135305\n",
        "2         152972\n",
        "3         160131\n",
        "4         141771\n",
        "5         101578\n",
        "6          99310\n",
        "\n",
        "[7 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 10
    },
    {
     "cell_type": "markdown",
     "metadata": {},
     "source": [
      "0 \u0131n pazartesi oldu\u011funu herkes anlamayabilri bu y\u00fczden d\u00fczeltme yap\u0131p bunu grifi\u011fe d\u00f6kelim:"
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weekday_counts.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
      "weekday_counts"
     ],
     "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>Berri 1</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>Monday</th>\n",
        "      <td> 134298</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Tuesday</th>\n",
        "      <td> 135305</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Wednesday</th>\n",
        "      <td> 152972</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Thursday</th>\n",
        "      <td> 160131</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Friday</th>\n",
        "      <td> 141771</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Saturday</th>\n",
        "      <td> 101578</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>Sunday</th>\n",
        "      <td>  99310</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>7 rows \u00d7 1 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 11,
       "text": [
        "           Berri 1\n",
        "Monday      134298\n",
        "Tuesday     135305\n",
        "Wednesday   152972\n",
        "Thursday    160131\n",
        "Friday      141771\n",
        "Saturday    101578\n",
        "Sunday       99310\n",
        "\n",
        "[7 rows x 1 columns]"
       ]
      }
     ],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "weekday_counts.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 12,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f1a32e4e890>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PgLWc4DNayy7qnTN7c2c0hSRJkiRpRnhmb4qa9S5I0/iuVj2sZV2sZz2sZV2s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\nSZIkSZIkSZIkSZIkSZIk1en/ASr2RXQoEh3fAAAAAElFTkSuQmCC\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f1a3306c4d0>"
       ]
      }
     ],
     "prompt_number": 12
    },
    {
     "cell_type": "heading",
     "level": 1,
     "metadata": {},
     "source": [
      "4.3 Sonu\u00e7 olarak "
     ]
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "bikes_ = pd.read_csv('dosya/bikes.csv',sep=';',encoding='latin1',parse_dates=['Date'],dayfirst=True,index_col='Date')\n",
      "# weekday s\u00fctununu ekleyelim\n",
      "berri_bikes_ = bikes_[['Berri 1']]\n",
      "berri_bikes_['weekday'] = berri_bikes_.index.weekday\n",
      "\n",
      "# ka\u00e7 bisiklet\u00e7i oldu\u011funu hesaplay\u0131p \u00e7izdirelim!\n",
      "weekday_counts_ = berri_bikes_.groupby('weekday').aggregate(sum)\n",
      "weekday_counts_.index = ['Monday', 'Tuesday', 'Wednesday', 'Thursday', 'Friday', 'Saturday', 'Sunday']\n",
      "weekday_counts_.plot(kind='bar')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 13,
       "text": [
        "<matplotlib.axes.AxesSubplot at 0x7f1a32e868d0>"
       ]
      },
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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PgLWc4DNayy7qnTN7c2c0hSRJkiRpRnhmb4qa9S5I0/iuVj2sZV2sZz2sZV2s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\nN/HSJ9ZN6i1zGb3Y9gnEXTmvSLdbSoW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       "text": [
        "<matplotlib.figure.Figure at 0x7f1a32deda50>"
       ]
      }
     ],
     "prompt_number": 13
    }
   ],
   "metadata": {}
  }
 ]
}