simgeekiz/boxofficeproje2.ipynb

## boxofficeproje2.ipynb
{
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  "signature": "sha256:d3421269fe3e98a6a41fc6eff0322b94092ab1c469b23ff9a42e473b6a3578ef"
 },
 "nbformat": 3,
 "nbformat_minor": 0,
 "worksheets": [
  {
   "cells": [
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import numpy as np\n",
      "import matplotlib.pyplot as plt\n",
      "from pandas import DataFrame, read_csv, concat\n",
      "from numpy import random\n",
      "import numpy.numarray as na\n",
      "import pandas.util.testing as tm\n",
      "from pylab import *\n",
      "from __future__ import division\n",
      "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"
       ]
      },
      {
       "output_type": "stream",
       "stream": "stderr",
       "text": [
        "WARNING: pylab import has clobbered these variables: ['linalg', 'draw_if_interactive', 'random', 'power', 'info', 'fft']\n",
        "`%matplotlib` prevents importing * from pylab and numpy\n"
       ]
      }
     ],
     "prompt_number": 1
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data=pd.read_csv('items4.csv')"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 2
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "# Baz\u0131lar\u0131 ilk ba\u015fta float oldu\u011fu i\u00e7in replace methodunu kullanam\u0131yorduk stringe cevirdik ve replace methodunu kullanabildik.\n",
      "data.ilkhaftasonuseyirci=data.ilkhaftasonuseyirci.astype(str)      \n",
      "data.toplamhasilat=data.toplamhasilat.astype(str)\n",
      "data.toplamseyirci=data.toplamseyirci.astype(str)\n",
      "data.ilkhaftasonuhasilat=data.ilkhaftasonuhasilat.astype(str)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 3
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "a=0                                   # Orjinal data str ve format\u0131 71.353.657,88 do\u011fru bir \u015fekilde order olmuyordu\n",
      "for i in range ((len(data))):\n",
      "    try:\n",
      "        data.toplamhasilat[i]=data.toplamhasilat[i].replace('.','')\n",
      "        data.toplamhasilat[i]=data.toplamhasilat[i].replace(',','.')\n",
      "               \n",
      "        data.ilkhaftasonuhasilat[i]=data.ilkhaftasonuhasilat[i].replace('.','')\n",
      "        data.ilkhaftasonuhasilat[i]=data.ilkhaftasonuhasilat[i].replace(',','.')\n",
      "        \n",
      "        data.ilkhaftasonuseyirci[i]=data.ilkhaftasonuseyirci[i].replace('.','')\n",
      "        data.toplamseyirci[i]=data.toplamseyirci[i].replace('.','')\n",
      "    except:\n",
      "        a=a+1\n",
      "print a    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0\n"
       ]
      }
     ],
     "prompt_number": 4
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data.toplamhasilat=data.toplamhasilat.astype(float)\n",
      "data.ilkhaftasonuseyirci=data.ilkhaftasonuseyirci.astype(float)  # sort d\u00fczg\u00fcn \u00e7al\u0131\u015fmas\u0131 i\u00e7in float a \u00e7eviriyoruz\n",
      "data.ilkhaftasonuhasilat=data.ilkhaftasonuhasilat.astype(float)\n",
      "data.toplamseyirci=data.toplamseyirci.astype(float)\n",
      "data.imdbpuan=data.imdbpuan.astype(float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 5
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['filmin_turu']=data['filmin_turu'].fillna('Girilmemis')\n",
      "data['toplamhasilat']=data['toplamhasilat'].fillna(0)\n",
      "data['toplamseyirci']=data['toplamseyirci'].fillna(0)\n",
      "data['ilkhaftasonuhasilat']=data['toplamseyirci'].fillna(0)\n",
      "data['ilkhaftasonuseyirci']=data['toplamseyirci'].fillna(0)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 6
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "veri=data\n",
      "data[:4]"
     ],
     "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>kopya</th>\n",
        "      <th>filmin_turu</th>\n",
        "      <th>dagitim</th>\n",
        "      <th>toplamhasilat</th>\n",
        "      <th>vizyon_tarihi</th>\n",
        "      <th>ilkhaftasonuhasilat</th>\n",
        "      <th>ilkhaftasonuseyirci</th>\n",
        "      <th>yili</th>\n",
        "      <th>baslik</th>\n",
        "      <th>ulke</th>\n",
        "      <th>sirket</th>\n",
        "      <th>toplamseyirci</th>\n",
        "      <th>imdbpuan</th>\n",
        "      <th>sure</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> 250</td>\n",
        "      <td> Komedi, Gen\u00e7lik</td>\n",
        "      <td>    \u00d6zen Film</td>\n",
        "      <td> 15377292.0</td>\n",
        "      <td>   14 Ocak 2005</td>\n",
        "      <td> 2587824</td>\n",
        "      <td> 2587824</td>\n",
        "      <td> 2005</td>\n",
        "      <td> Hababam S\u0131n\u0131f\u0131 Askerde</td>\n",
        "      <td> T\u00fcrkiye</td>\n",
        "      <td> Fida Film, Arzu Film</td>\n",
        "      <td> 2587824</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 110 dakika</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>  25</td>\n",
        "      <td>            Dram</td>\n",
        "      <td>    \u00d6zen Film</td>\n",
        "      <td>   466216.5</td>\n",
        "      <td> 02 Aral\u0131k 2005</td>\n",
        "      <td>   51608</td>\n",
        "      <td>   51608</td>\n",
        "      <td> 2005</td>\n",
        "      <td>          Yerinde Olsam</td>\n",
        "      <td>     ABD</td>\n",
        "      <td>     20th Century Fox</td>\n",
        "      <td>   51608</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 130 dakika</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td> NaN</td>\n",
        "      <td>            Dram</td>\n",
        "      <td> Warner Bros.</td>\n",
        "      <td>   401633.0</td>\n",
        "      <td>  25 Kas\u0131m 2005</td>\n",
        "      <td>   49166</td>\n",
        "      <td>   49166</td>\n",
        "      <td> 2005</td>\n",
        "      <td>           Kutsal Y\u00fcrek</td>\n",
        "      <td>  \u0130talya</td>\n",
        "      <td>                  AFS</td>\n",
        "      <td>   49166</td>\n",
        "      <td>NaN</td>\n",
        "      <td> 120 dakika</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>3</th>\n",
        "      <td> NaN</td>\n",
        "      <td> Gerilim, Komedi</td>\n",
        "      <td> Warner Bros.</td>\n",
        "      <td>   268483.0</td>\n",
        "      <td>  11 \u015eubat 2005</td>\n",
        "      <td>    4786</td>\n",
        "      <td>    4786</td>\n",
        "      <td> 2004</td>\n",
        "      <td>                  Bebek</td>\n",
        "      <td> Romanya</td>\n",
        "      <td>             Film Pop</td>\n",
        "      <td>    4786</td>\n",
        "      <td>NaN</td>\n",
        "      <td>  87 dakika</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>4 rows \u00d7 14 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 7,
       "text": [
        "  kopya      filmin_turu       dagitim  toplamhasilat   vizyon_tarihi  \\\n",
        "0   250  Komedi, Gen\u00e7lik     \u00d6zen Film     15377292.0    14 Ocak 2005   \n",
        "1    25             Dram     \u00d6zen Film       466216.5  02 Aral\u0131k 2005   \n",
        "2   NaN             Dram  Warner Bros.       401633.0   25 Kas\u0131m 2005   \n",
        "3   NaN  Gerilim, Komedi  Warner Bros.       268483.0   11 \u015eubat 2005   \n",
        "\n",
        "   ilkhaftasonuhasilat  ilkhaftasonuseyirci  yili                  baslik  \\\n",
        "0              2587824              2587824  2005  Hababam S\u0131n\u0131f\u0131 Askerde   \n",
        "1                51608                51608  2005           Yerinde Olsam   \n",
        "2                49166                49166  2005            Kutsal Y\u00fcrek   \n",
        "3                 4786                 4786  2004                   Bebek   \n",
        "\n",
        "      ulke                sirket  toplamseyirci  imdbpuan        sure  \n",
        "0  T\u00fcrkiye  Fida Film, Arzu Film        2587824       NaN  110 dakika  \n",
        "1      ABD      20th Century Fox          51608       NaN  130 dakika  \n",
        "2   \u0130talya                   AFS          49166       NaN  120 dakika  \n",
        "3  Romanya              Film Pop           4786       NaN   87 dakika  \n",
        "\n",
        "[4 rows x 14 columns]"
       ]
      }
     ],
     "prompt_number": 7
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data['turler'] = \"\"\n",
      "a=0                                   # \n",
      "for i in range ((len(data))-1):\n",
      "    try:\n",
      "        # d.turler[i] = d.tur[i].split(\",\").strip()   \n",
      "        data.turler[i] = data.filmin_turu[i].split(\",\")\n",
      "        data.turler[i] = map(lambda str: str.strip(), data.turler[i])\n",
      "    except:\n",
      "        a=a+1\n",
      "print a "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "output_type": "stream",
       "stream": "stdout",
       "text": [
        "0\n"
       ]
      }
     ],
     "prompt_number": 8
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "data[['filmin_turu','turler']][:3]"
     ],
     "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>filmin_turu</th>\n",
        "      <th>turler</th>\n",
        "    </tr>\n",
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
        "      <th>0</th>\n",
        "      <td> Komedi, Gen\u00e7lik</td>\n",
        "      <td> [Komedi, Gen\u00e7lik]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>1</th>\n",
        "      <td>            Dram</td>\n",
        "      <td>            [Dram]</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>2</th>\n",
        "      <td>            Dram</td>\n",
        "      <td>            [Dram]</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "<p>3 rows \u00d7 2 columns</p>\n",
        "</div>"
       ],
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 9,
       "text": [
        "       filmin_turu             turler\n",
        "0  Komedi, Gen\u00e7lik  [Komedi, Gen\u00e7lik]\n",
        "1             Dram             [Dram]\n",
        "2             Dram             [Dram]\n",
        "\n",
        "[3 rows x 2 columns]"
       ]
      }
     ],
     "prompt_number": 9
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mydata=data[['baslik','toplamhasilat','turler']]"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 10
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filmturleritoplam=[]\n",
      "for lists in mydata['turler']:\n",
      "    for item in lists:\n",
      "            filmturleritoplam.append(item)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 11
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "filmturleri=[]\n",
      "import collections\n",
      "for x, y in collections.Counter(filmturleritoplam).items():\n",
      "     if y >= 1:\n",
      "        filmturleri.append(x) "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 12
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def hesapla(tur):\n",
      "    mydataa=mydata[mydata['turler'].apply(lambda l: tur in l)]\n",
      "    for items in mydataa['turler']:\n",
      "        nethasilat=mydataa['toplamhasilat']/len(items)\n",
      "    return sum(nethasilat)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 13
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "'%.2f'%float(hesapla(\"Komedi\"))"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 14,
       "text": [
        "'532869222.05'"
       ]
      }
     ],
     "prompt_number": 14
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "hasilattur=[]\n",
      "for hft in filmturleri:\n",
      "    hasilattur.append('%.2f'%float(hesapla(hft)))\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 15
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\"\"\"mydataa=mydata[mydata['turler'].apply(lambda l: \"Komedi\" in l)]\n",
      "mydataa\"\"\"\n",
      "datalar={'filmturleri': filmturleri, 'hasilattur': hasilattur}\n",
      "mydata2 = DataFrame(datalar)\n",
      "mydata2=mydata2.rename(columns = {0:'filmturleri'})\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 16
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mydata2[mydata2['filmturleri']==\"Komedi\"]['hasilattur']"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 17,
       "text": [
        "11    532869222.05\n",
        "Name: hasilattur, dtype: object"
       ]
      }
     ],
     "prompt_number": 17
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "mydata2.hasilattur=mydata2.hasilattur.astype(float)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 18
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "#mydata2['hasilattur'].plot()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 19
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "from __future__ import unicode_literals\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 20
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "label=['Frogs', 'Hogs', 'D\u00fcgs', 'L\u015f\u00fc\u011fa\u00f6i\u015faf\u00f6ogs','Frogs', 'Hogs', 'Dogs', 'Logs','Frogs', 'Hogs', 'Dogs', 'Logs','Frogs', 'Hogs', 'Dogs', 'Logs','Frogs', 'Hogs', 'Dogs', 'Logs','Frogs', 'Hogs', 'Dogs', 'Logs','Frogs', 'Hogs', 'Dogs']\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 21
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "list(filmturleri)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "pyout",
       "prompt_number": 22,
       "text": [
        "['Tarihi',\n",
        " 'Aile',\n",
        " 'Aksiyon',\n",
        " 'Gerilim',\n",
        " 'Su\\xc3\\xa7',\n",
        " 'Macera',\n",
        " 'Romantik-Komedi',\n",
        " '3 Boyutlu',\n",
        " 'Girilmemis',\n",
        " 'Spor',\n",
        " 'Gen\\xc3\\xa7lik',\n",
        " 'Komedi',\n",
        " '\\xc3\\x87izgi Film',\n",
        " 'Western',\n",
        " 'Dram',\n",
        " 'Bilim-Kurgu',\n",
        " 'Belgesel',\n",
        " 'M\\xc3\\xbczikal',\n",
        " 'Biyografi',\n",
        " 'A\\xc5\\x9fk',\n",
        " 'D\\xc3\\xb6v\\xc3\\xbc\\xc5\\x9f',\n",
        " 'Korku',\n",
        " 'Politik',\n",
        " 'Sava\\xc5\\x9f',\n",
        " 'Terapi',\n",
        " 'Fantastik',\n",
        " 'Erotik',\n",
        " 'Konser']"
       ]
      }
     ],
     "prompt_number": 22
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "figure(figsize=(18,8))\n",
      "label=['Frogs', 'Hogs', 'Dus', 'lsa','Frogs', 'Hogs', 'Dogs', 'Logs','Frogs', 'Hogs', 'Dogs', 'Logs','Frogs', 'Hogs', 'Dogs', 'Logs','Frogs', 'Hogs', 'Dogs', 'Logs','Frogs', 'Hogs', 'Dogs', 'Logs','Frogs', 'Hogs', 'Dogs']\n",
      "#label=filmturleri\n",
      "#plt.pie(mydata2['hasilattur'],labels=label)\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4d7d5ad210>"
       ]
      }
     ],
     "prompt_number": 23
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "def piechart(year):\n",
      "    c=data[data['yili']==year]\n",
      "    c=c.reset_index()\n",
      "    filmturleritoplam=[]\n",
      "    for lists in c['turler']:\n",
      "        for item in lists:\n",
      "            filmturleritoplam.append(item)\n",
      "            \n",
      "    filmturleri=[]\n",
      "    for x, y in collections.Counter(filmturleritoplam).items():\n",
      "        if y >= 1:\n",
      "            filmturleri.append(x)\n",
      "    hasilattur=[]\n",
      "    for hft in filmturleri:\n",
      "        hasilattur.append('%.2f'%float(hesapla(hft)))\n",
      "    \n",
      "    datalar={'filmturleri': filmturleri, 'hasilattur': hasilattur}\n",
      "    mydata3 = DataFrame(datalar)\n",
      "    mydata3=mydata3.rename(columns = {0:'filmturleri'})\n",
      "    mydata3.hasilattur=mydata3.hasilattur.astype(float)\n",
      "    figure(figsize=(18,8))\n",
      "    plt.pie(mydata3['hasilattur'])\n",
      "    "
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 24
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "piechart(2007)"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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eCsGGEDwB9EHrcok0Okr0RRoPA+ifDeMDsP4weP5RGLoWMj6CrPOB3FRHKDvk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\nRERERHLl/wNfuxXusBbjBAAAAABJRU5ErkJggg==\n",
       "text": [
        "<matplotlib.figure.Figure at 0x7f4d7d57ac90>"
       ]
      }
     ],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 25
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "\n",
      "def draw_pie(year,y,by):\n",
      "   \n",
      "        v=data[data['yili']==year]\n",
      "    \n",
      "        v=v.sort(     y    ,ascending=False).head(50)\n",
      "    \n",
      "        v=v.groupby(   by       ).aggregate('sum')\n",
      "    \n",
      "        veri= v[ y ]\n",
      "\n",
      "   \n",
      "    \n",
      "        plt.pie(veri, autopct='%1.1f%%', shadow=True, startangle=90)\n",
      "       # veri.plot(title=y,figsize=(15,15),kind='pie',         #labels=v.index, autopct='%.2f', fontsize=20)\n",
      "        plt.show()\n",
      "\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [],
     "prompt_number": 26
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "draw_pie(2014,'toplamhasilat','dagitim')\n"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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s2bJZnjmTLpxOJ0lJMG8eYvNmZJMmiLxGQ2rWhEmTVOjA7YYHHoClS1V8t3Zt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\npVWtWvWLfv36uQ4d0qXZJZWuXTvrIom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       "text": [
        "<matplotlib.figure.Figure at 0x7f4d7d59cd50>"
       ]
      }
     ],
     "prompt_number": 27
    },
    {
     "cell_type": "code",
     "collapsed": false,
     "input": [
      "import matplotlib.pyplot as plt\n",
      "\n",
      "\n",
      "# The slices will be ordered and plotted counter-clockwise.\n",
      "labels = 'Frogs', 'Hogs', 'Dogs', 'Logs'\n",
      "sizes = [15, 30, 45, 10]\n",
      "colors = ['yellowgreen', 'gold', 'lightskyblue', 'lightcoral']\n",
      "explode = (0, 0.1, 0, 0) # only \"explode\" the 2nd slice (i.e. 'Hogs')\n",
      "\n",
      "plt.pie(sizes, explode=explode, labels=labels, colors=colors,\n",
      "        autopct='%1.1f%%', shadow=True, startangle=90)\n",
      "# Set aspect ratio to be equal so that pie is drawn as a circle.\n",
      "plt.axis('equal')\n",
      "\n",
      "plt.show()"
     ],
     "language": "python",
     "metadata": {},
     "outputs": [
      {
       "metadata": {},
       "output_type": "display_data",
       "png": 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2eLqh7IwtIKYB0z8TLfDw5p10e/Q26vW6MDjHn3RauKoXugvSsMyYz81aDWEu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\nR6X20uL31I4jWKiZdCtRkuSRy4sc7e1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       "text": [
        "<matplotlib.figure.Figure at 0x7f4d7dbdf050>"
       ]
      }
     ],
     "prompt_number": 28
    }
   ],
   "metadata": {}
  }
 ]
}