numpy.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# CS228 Python Tutorial"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Volodymyr Kuleshov](http://web.stanford.edu/~kuleshov/) ve [Isaac Caswell](https://symsys.stanford.edu/viewing/symsysaffiliate/21335) tarafından the Justin Johnson (http://cs231n.github.io/python-numpy-tutorial/)'ın `CS231n` Python tutorialından uyarlanmıştır. [Sinan Onur ALTINUÇ](https://github.com/sinanonur) tarafından Türkçe'ye çevirilmiştir. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Numpy"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy Python için bilimsel hesaplamanın temel kütüphanesidir.YÜksek performanslı ve çok boyutlu dizi nesnesi ve bunlarla çalışabileceğimiz araçlar sağlar.Eğer matlaba aşinaysanız bu [tutorial](http://wiki.scipy.org/NumPy_for_Matlab_Users) başlangıç için faydalı olabilir."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpykullanmak için önce `numpy` paketini import ederiz.:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Diziler (Arrays)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bir numpy dizisi hepsi aynı türden olan, negatif olmayan tamsayılar tarafından indislenen bir değerler örgüsüdür(grid). Boyut sayısı dizinin derecesi(rank) dir ve şekli(shape) her boyutun büyüklüğünü integer şeklinde veren bir tuple değeridir.."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "numpy arrayleri pythondaki iç içe geçmiş(nested) listelerle oluşturulabilir ve elemanlara köşeli parantez kullanarak erişilebilir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<type 'numpy.ndarray'> (3,) 1 2 3\n",
      "[5 2 3]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([1, 2, 3])  # 1. dereceden bir dizi oluştur\n",
    "print type(a), a.shape, a[0], a[1], a[2]\n",
    "a[0] = 5                 # dizinin elemanını değiştir.\n",
    "print a                  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]\n",
      " [4 5 6]]\n"
     ]
    }
   ],
   "source": [
    "b = np.array([[1,2,3],[4,5,6]])   # 2. dereceden bir dizi oluştur\n",
    "print b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(2, 3)\n",
      "1 2 4\n"
     ]
    }
   ],
   "source": [
    "print b.shape                   \n",
    "print b[0, 0], b[0, 1], b[1, 0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy aynı zamanda arraylerşle çalışmak için bir dizi fonksiyon sağlar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "a = np.zeros((2,2))  # Sıfırlardan oluşan birdizi oluşturur"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  1.]]\n"
     ]
    }
   ],
   "source": [
    "b = np.ones((1,2))   # Birlerden oluşan bir dizi oluşturur\n",
    "print b"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 7.  7.]\n",
      " [ 7.  7.]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/home/sinanonur/anaconda2/lib/python2.7/site-packages/numpy/core/numeric.py:301: FutureWarning: in the future, full((2, 2), 7) will return an array of dtype('int64')\n",
      "  format(shape, fill_value, array(fill_value).dtype), FutureWarning)\n"
     ]
    }
   ],
   "source": [
    "c = np.full((2,2), 7) # içinde sabir sayılar olan bir dizi oluşturur\n",
    "print c "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.  0.]\n",
      " [ 0.  1.]]\n"
     ]
    }
   ],
   "source": [
    "d = np.eye(2)        # 2x2 identity matrisi\n",
    "print d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.72614105  0.85178797]\n",
      " [ 0.2490879   0.68129004]]\n"
     ]
    }
   ],
   "source": [
    "e = np.random.random((2,2)) # Rastgele değerlerle dolu 2x2 array oluştur.\n",
    "print e"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dizi indizleme (Array indexing)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy dizileri indislemek için birden fazla yol sunar."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kesme(Slicing): Python listelerine benzer şekilde numpy dizileri de kesilebilir.Diziler çok boyutlu olabileceği için dizinin her boyutu için bir değer belirmelisiniz:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 3]\n",
      " [6 7]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# (3, 4) lük 2. dereceden aşşağıdaki diziyi oluşturalım\n",
    "# [[ 1  2  3  4]\n",
    "#  [ 5  6  7  8]\n",
    "#  [ 9 10 11 12]]\n",
    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
    "\n",
    "# Kesme işlemi kullanarak  ilk 2 satır\n",
    "# 1 ve 3üncü sütunlardan oluşan (2, 2)lik bir dizi oluşturalım:\n",
    "# [[2 3]\n",
    "#  [6 7]]\n",
    "b = a[:2, 1:3]\n",
    "print b"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bir dizinin kesimi orjinal verinin bir parçasının gösterir yani bu değerleri değiştirmek orijinal veriyi değiştirir."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2\n",
      "77\n"
     ]
    }
   ],
   "source": [
    "print a[0, 1]  \n",
    "b[0, 0] = 77    # b[0, 0],  a[0, 1] ile aynı veriyi taşır\n",
    "print a[0, 1] "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kesme ile indislemeyi beraber kullanabilirsiniz. Fakat böyle yapmak elinizdeki diziden daha düşük dereceli bir dizi söndürür. Bu MATLAB'ın dizileri ele alışından daha farklıdır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3  4]\n",
      " [ 5  6  7  8]\n",
      " [ 9 10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "# (3, 4) lük 2. dereceden aşşağıdaki diziyi yaratalım\n",
    "a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])\n",
    "print a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Dizinin ortasındaki veriye erişmenin iki yolu vardır. \n",
    "indislemeyi ve kesmeyi karıştırarak düşük derecen,\n",
    "sadece kesme işlemi kullanarak aynı dereceden diziler elde ederiz.\n",
    "\n",
    "Orjinal dizi:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 6 7 8] (4,)\n",
      "[[5 6 7 8]] (1, 4)\n",
      "[[5 6 7 8]] (1, 4)\n"
     ]
    }
   ],
   "source": [
    "row_r1 = a[1, :]    # a nın ikinci satırırının 1. dereceden görünümü  \n",
    "row_r2 = a[1:2, :]  # a nın ikinci satırırının 2. dereceden görünümü \n",
    "row_r3 = a[[1], :]  # a nın ikinci satırırının 2. dereceden görünümü \n",
    "print row_r1, row_r1.shape \n",
    "print row_r2, row_r2.shape\n",
    "print row_r3, row_r3.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 2  6 10] (3,)\n",
      "\n",
      "[[ 2]\n",
      " [ 6]\n",
      " [10]] (3, 1)\n"
     ]
    }
   ],
   "source": [
    "# Aynı ayrımı dizinin sütunlarına erişirken de yapabiliriz.\n",
    "col_r1 = a[:, 1]\n",
    "col_r2 = a[:, 1:2]\n",
    "print col_r1, col_r1.shape\n",
    "print\n",
    "print col_r2, col_r2.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tamsayı dizisi indisleme: Eğer kesme yoluyla numpy dizilerine erişirseniz sonuç her zaman orjinal dizinin bir alt dizisi olur. Buna karşın tamsayı dizi indisleme size bir diziden yeni diziler oluşturma imkanı verir. Bir örnek:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 4 5]\n",
      "[1 4 5]\n"
     ]
    }
   ],
   "source": [
    "a = np.array([[1,2], [3, 4], [5, 6]])\n",
    "\n",
    "# Tamsayı dizi indislemeye bir örnek\n",
    "# Tönen değerin şekli (3,) olacaktır \n",
    "print a[[0, 1, 2], [0, 1, 0]]\n",
    "\n",
    "# Yukarıdaki tamsayı dizi indisleme şuna eşdeğerdir.\n",
    "print np.array([a[0, 0], a[1, 1], a[2, 0]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2 2]\n",
      "[2 2]\n"
     ]
    }
   ],
   "source": [
    "# Tamsayı dizi indisleme yaparken kaynak dizideki\n",
    "# elemanlarına aynısını kullanabilirsiniz\n",
    "print a[[0, 0], [1, 1]]\n",
    "\n",
    "# Örneğin eşdeği şu şekildedir.\n",
    "print np.array([a[0, 1], a[0, 1]])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Tamsayı dizi indisleme ie yapılabilecek yararlı bir numara her satırdan bir elemanı seçme ya da değiştirme olabilir:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1  2  3]\n",
      " [ 4  5  6]\n",
      " [ 7  8  9]\n",
      " [10 11 12]]\n"
     ]
    }
   ],
   "source": [
    "# İçinden eleman seçeceğimiz bir dizi oluştur\n",
    "a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
    "print a"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[ 1  6  7 11]\n"
     ]
    }
   ],
   "source": [
    "# indis dizisi oluştur.\n",
    "b = np.array([0, 2, 0, 1])\n",
    "\n",
    "# b'deki indisleri kullanarak a'nın her satırından bir eleman seç\n",
    "print a[np.arange(4), b]  # \"[ 1  6  7 11]\" yazdırır"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[11  2  3]\n",
      " [ 4  5 16]\n",
      " [17  8  9]\n",
      " [10 21 12]]\n"
     ]
    }
   ],
   "source": [
    "# B'deki indisleri kullanarak her satırdan bir eleman değiştir.\n",
    "a[np.arange(4), b] += 10\n",
    "print a"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Boolean dizi indisleme(Boolean array indexing): Boolean dizi indisleme keyfi eleman seçme şansı sağlar. Genellikle diziden belli koşulu sağlayan sayıları almak için kullanılır. İşte bir örnek:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[False False]\n",
      " [ True  True]\n",
      " [ True  True]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "a = np.array([[1,2], [3, 4], [5, 6]])\n",
    "\n",
    "bool_idx = (a > 2)  # 2'den büyük elemanları bul\n",
    "                    # bu aynı büyüklükte boolean bir arraay döndürür\n",
    "                    # bu arrayde bool_idx'in her slotu\n",
    "                    # o elemanın > 2 olup olmaığına bakar.\n",
    "\n",
    "print bool_idx"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[3 4 5 6]\n",
      "[3 4 5 6]\n"
     ]
    }
   ],
   "source": [
    "# Birinci dereceden bir dizi yaratmak için ilgili True degerlerinden olusan\n",
    "# bool_idx ile boolean dizi indisleme kullanırız\n",
    "print a[bool_idx]\n",
    "\n",
    "# Yukaridakinin tamamini daha sade bir ifadeyle yapabiliriz.\n",
    "print a[a > 2]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Kısa tutmak için numpy dizi indislemeyle ilgili bir çok detay kapsam dışı bırakılmıştır. Daha fazla öğrenmek için numpy dökümantasyonuna başvurabilirsiniz."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Veri Türleri (Datatypes)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy dizileri aynı türden elemanların birleşimidir. Numpy dizi oluşturmak için çok miktarda nümerik veri türü sağlar. Siz bir dizi oluşturduğunuzda otomatik olarak türünü algılamaya çalışır. Dizileri oluşturan fonksiyonlar genellikle veri tipini özellikle belirtmek için opsyonel bir argüman sağlarlar. Örneğin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "int64 float64 int64\n"
     ]
    }
   ],
   "source": [
    "x = np.array([1, 2])  # Numpy'ın veri türünü seçmesine izin verelim\n",
    "y = np.array([1.0, 2.0])  # Numpy'ın veri türünü seçmesine izin verelim\n",
    "z = np.array([1, 2], dtype=np.int64)  # Bir veri tipini zorlayalım\n",
    "\n",
    "print x.dtype, y.dtype, z.dtype"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy veri türleriyle ilgili dökümantasyonu [buradan](http://docs.scipy.org/doc/numpy/reference/arrays.dtypes.html) okuyabilirsiniz. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dizi matematiği(Array math)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Basit matematiksel gonksiyonlar dizilerde eleman bazlı işlerler ve hem operator overload hem de fonksiyonlar olarak numpy modülünde yer alırlar:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  6.   8.]\n",
      " [ 10.  12.]]\n",
      "[[  6.   8.]\n",
      " [ 10.  12.]]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1,2],[3,4]], dtype=np.float64)\n",
    "y = np.array([[5,6],[7,8]], dtype=np.float64)\n",
    "\n",
    "# Eleman bazlı toplama, ikisi de dizi üretir.\n",
    "print x + y\n",
    "print np.add(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[-4. -4.]\n",
      " [-4. -4.]]\n",
      "[[-4. -4.]\n",
      " [-4. -4.]]\n"
     ]
    }
   ],
   "source": [
    "# Eleman bazlı toplama, ikisi de dizi üretir.\n",
    "print x - y\n",
    "print np.subtract(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[  5.  12.]\n",
      " [ 21.  32.]]\n",
      "[[  5.  12.]\n",
      " [ 21.  32.]]\n"
     ]
    }
   ],
   "source": [
    "# Eleman bazlı çarpma, ikisi de dizi üretir.\n",
    "print x * y\n",
    "print np.multiply(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 0.2         0.33333333]\n",
      " [ 0.42857143  0.5       ]]\n",
      "[[ 0.2         0.33333333]\n",
      " [ 0.42857143  0.5       ]]\n"
     ]
    }
   ],
   "source": [
    "# Eleman bazlı bölme, ikisi de dizi üretir.\n",
    "# [[ 0.2         0.33333333]\n",
    "#  [ 0.42857143  0.5       ]]\n",
    "print x / y\n",
    "print np.divide(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 1.          1.41421356]\n",
      " [ 1.73205081  2.        ]]\n"
     ]
    }
   ],
   "source": [
    "# Eleman bazlı kare kök, ikisi de dizi üretir.\n",
    "# [[ 1.          1.41421356]\n",
    "#  [ 1.73205081  2.        ]]\n",
    "print np.sqrt(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "MATLAB'ın aksine `*` eleman bazlı çarpma işlemi yapar matris çarpımı yapmaz. Bunun yerine vektörlerin iç çarpım (inner product) hesaplamak, bir vektörü matrisle çarpmak, matrisleri birbiriyle çarpmak için dot fonksiyonunu kullanırız. dot hem bir numpy modülü fonksiyonu hem de dizi nesnelerinin bir methodu olarak kullanılabilir:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "219\n",
      "219\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1,2],[3,4]])\n",
    "y = np.array([[5,6],[7,8]])\n",
    "\n",
    "v = np.array([9,10])\n",
    "w = np.array([11, 12])\n",
    "\n",
    "# Vektörlerin iç çarpımı. İkisi de 219 üretir.\n",
    "print v.dot(w)\n",
    "print np.dot(v, w)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[29 67]\n",
      "[29 67]\n"
     ]
    }
   ],
   "source": [
    "# Matris / vektör çarpımı; hepsi birinci dereceden [29 67] dizisi üretir.\n",
    "print x.dot(v)\n",
    "print np.dot(x, v)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[19 22]\n",
      " [43 50]]\n",
      "[[19 22]\n",
      " [43 50]]\n"
     ]
    }
   ],
   "source": [
    "# Matris / matris çarpımı; hepsi ikinci dereceden dizi üretir.\n",
    "# [[19 22]\n",
    "#  [43 50]]\n",
    "print x.dot(y)\n",
    "print np.dot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy diziler üzerinde işlem yapmak için bir çok faydalı fonksiyon sağlar. En faydalılarından bir tanesi `sum`'dır:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n",
      "[4 6]\n",
      "[3 7]\n"
     ]
    }
   ],
   "source": [
    "x = np.array([[1,2],[3,4]])\n",
    "\n",
    "print np.sum(x)  # bütün elemanların toplamını hesaplar; \"10\" yazdırır\n",
    "print np.sum(x, axis=0)  # Her sütunun toplamını hesaplar; \"[4 6]\" yazdırır\n",
    "print np.sum(x, axis=1)  # Her satırın toplamını hesaplar;  \"[3 7]\" yazdırır"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy tarafından sağlanan matematikel fonksyonların listesini ilgili [dökümantasyondan](http://docs.scipy.org/doc/numpy/reference/routines.math.html) bulabilirsiniz.\n",
    "\n",
    "Matematiksel fonksiyonları kullanmanın dışında, sıkça dizilerdeki veriyi tekrar şekillendirme ve manipüle etme ihtiyacı duyarız. Bunun en basit örneği bir matrisi transpoze etmektir. Bir matrisi transpoze etmek için dizi nesnesinin T niteliğini(attribute) kullanabiliriz:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2]\n",
      " [3 4]]\n",
      "[[1 3]\n",
      " [2 4]]\n"
     ]
    }
   ],
   "source": [
    "print x\n",
    "print x.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 2 3]]\n",
      "[[1]\n",
      " [2]\n",
      " [3]]\n"
     ]
    }
   ],
   "source": [
    "v = np.array([[1,2,3]])\n",
    "print v \n",
    "print v.T"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Broadcasting"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Broadcasting, numpy'a aritmetik işlemler yaparken, farlı şekillerde dizilerle çalışma imkanı veren oldukça kuvvetli bir özelliktir.  Genellikle bir büyük bir de küçük dizimiz olur ve küçük diziyi büyük dizi üzerinde bir çok kez kullanarak bir işlem yapmak isteriz.\n",
    "\n",
    "Mesela matrisin her satırına sabit bit vektörü eklemek istediğimizi düşünelim. Bunu şu şekilde yapabiliriz:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  2  4]\n",
      " [ 5  5  7]\n",
      " [ 8  8 10]\n",
      " [11 11 13]]\n"
     ]
    }
   ],
   "source": [
    "# x matrisinin her satırına v vektörünü ekleyelim\n",
    "# sonucu y matrisinde saklayalım\n",
    "x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
    "v = np.array([1, 0, 1])\n",
    "y = np.empty_like(x)   # x ile aynı şekle sahip bir matris yaratalım\n",
    "\n",
    "# v vektörünü matrisin her satırına bir döngü ile ekleyelim.\n",
    "for i in range(4):\n",
    "    y[i, :] = x[i, :] + v\n",
    "\n",
    "print y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Bu yöntem işe yarar fakat `x` matrisi çok büyükse, pythonda bir döngüyü işletmek yavaş olabilir. `x` matrisinin her satırına v vektörü eklemek, alt alta dizilmiş v vektörlerinden oluşan bir `vv` matrisi oluşturarak bunu `x` ile eleman bazlı toplamaya sokmakla eşdeğerdir. Bu y aklaşımı  şu şekilde  uygulayabiliriz:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[1 0 1]\n",
      " [1 0 1]\n",
      " [1 0 1]\n",
      " [1 0 1]]\n"
     ]
    }
   ],
   "source": [
    "vv = np.tile(v, (4, 1))  # v'nin 4 kopyasını üst üste koy\n",
    "print vv                 # Şunu yazdırır\"  [[1 0 1]\n",
    "                         #                 [1 0 1]\n",
    "                         #                 [1 0 1]\n",
    "                         #                 [1 0 1]]\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  2  4]\n",
      " [ 5  5  7]\n",
      " [ 8  8 10]\n",
      " [11 11 13]]\n"
     ]
    }
   ],
   "source": [
    "y = x + vv  # x ve vv yı eleman bazlı olarak topla\n",
    "print y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Numpy broadcasting bize v'nin çoklu kopyasını oluşturmadan bunu yapmamıza imkan verir.Bu versiyonu broadcasting kullanarak yapalım:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  2  4]\n",
      " [ 5  5  7]\n",
      " [ 8  8 10]\n",
      " [11 11 13]]\n"
     ]
    }
   ],
   "source": [
    "import numpy as np\n",
    "\n",
    "# v vektörünü  x'ın her satırına ekleyeceğiz\n",
    "# sonucu da y  matrisinde saklayalım.\n",
    "x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]])\n",
    "v = np.array([1, 0, 1])\n",
    "y = x + v  # v'yı x'in her satırına broadcasting kullanarak ekleyelim\n",
    "print y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`x`in şeklinin `(4,3)` `v`nin şeklinin `(3,)` olmasına rağmen `y = x + v` satırı broadcastingden dolayı `v`'nin şekli `(4,3)`müş ve her satıra `v` kopyalanmış ve eleman tabanlı toplama yapılmış gibi çalışmaktadır.  \n",
    "\n",
    "İki diziyi Broadcast ile kullanmak aşşağıdaki kurallarla olur:\n",
    "\n",
    "\n",
    "#TODO: Çevir\n",
    "1. If the arrays do not have the same rank, prepend the shape of the lower rank array with 1s until both shapes have the same length.\n",
    "2. The two arrays are said to be compatible in a dimension if they have the same size in the dimension, or if one of the arrays has size 1 in that dimension.\n",
    "3. The arrays can be broadcast together if they are compatible in all dimensions.\n",
    "4. After broadcasting, each array behaves as if it had shape equal to the elementwise maximum of shapes of the two input arrays.\n",
    "5. In any dimension where one array had size 1 and the other array had size greater than 1, the first array behaves as if it were copied along that dimension\n",
    "\n",
    "If this explanation does not make sense, try reading the explanation from the [documentation](http://docs.scipy.org/doc/numpy/user/basics.broadcasting.html) or this [explanation](http://wiki.scipy.org/EricsBroadcastingDoc).\n",
    "\n",
    "Functions that support broadcasting are known as universal functions. You can find the list of all universal functions in the [documentation](http://docs.scipy.org/doc/numpy/reference/ufuncs.html#available-ufuncs).\n",
    "\n",
    "Here are some applications of broadcasting:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 243,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 4  5]\n",
      " [ 8 10]\n",
      " [12 15]]\n"
     ]
    }
   ],
   "source": [
    "# Vektörlerin dış çarpımını(outer product) hesapla\n",
    "v = np.array([1,2,3])  # v'nin şekli (3,)\n",
    "w = np.array([4,5])    # w'nun şekli (2,)\n",
    "# To compute an outer product, we first reshape v to be a column\n",
    "# vector of shape (3, 1); we can then broadcast it against w to yield\n",
    "# an output of shape (3, 2), which is the outer product of v and w:\n",
    "\n",
    "print np.reshape(v, (3, 1)) * w"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[2 4 6]\n",
      " [5 7 9]]\n"
     ]
    }
   ],
   "source": [
    "# Add a vector to each row of a matrix\n",
    "x = np.array([[1,2,3], [4,5,6]])\n",
    "# x has shape (2, 3) and v has shape (3,) so they broadcast to (2, 3),\n",
    "# giving the following matrix:\n",
    "\n",
    "print x + v"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5  6  7]\n",
      " [ 9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "# Add a vector to each column of a matrix\n",
    "# x has shape (2, 3) and w has shape (2,).\n",
    "# If we transpose x then it has shape (3, 2) and can be broadcast\n",
    "# against w to yield a result of shape (3, 2); transposing this result\n",
    "# yields the final result of shape (2, 3) which is the matrix x with\n",
    "# the vector w added to each column. Gives the following matrix:\n",
    "\n",
    "print (x.T + w).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 5  6  7]\n",
      " [ 9 10 11]]\n"
     ]
    }
   ],
   "source": [
    "# Another solution is to reshape w to be a row vector of shape (2, 1);\n",
    "# we can then broadcast it directly against x to produce the same\n",
    "# output.\n",
    "print x + np.reshape(w, (2, 1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 247,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[[ 2  4  6]\n",
      " [ 8 10 12]]\n"
     ]
    }
   ],
   "source": [
    "# Multiply a matrix by a constant:\n",
    "# x has shape (2, 3). Numpy treats scalars as arrays of shape ();\n",
    "# these can be broadcast together to shape (2, 3), producing the\n",
    "# following array:\n",
    "print x * 2"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Broadcasting typically makes your code more concise and faster, so you should strive to use it where possible."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This brief overview has touched on many of the important things that you need to know about numpy, but is far from complete. Check out the [numpy reference](http://docs.scipy.org/doc/numpy/reference/) to find out much more about numpy."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Matplotlib"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Matplotlib bir grafik çizme (ploting) kütüphanesidir. Bu kısımda MATLAB benzeri bir çizim kütüphanesi sağlayan `matplotlib.pyplot` modülüne, özet bir giriş yapılacaktır."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Şu özel iPython komutunu çalıştırarak, grafikleri inline olarak görebiliyor olacağız."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Çizdirme (Plotting)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " `matplotlib`'in en önemli fonksiyonu 2D veriyi çizmeyi sağlayan plot fonksiyonudur.Örneğin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7fca24098110>]"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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caeRQZb1+0S20JCuJ0nOXOFBRb3UoqhebiypJjgojO1WH2LbC7JExDA0P/qTK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\nAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca4cd46a10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sinüs eğrisindeki x ve y koordinatlarını hesaplayalım\n",
    "x = np.arange(0, 3 * np.pi, 0.1)\n",
    "y = np.sin(x)\n",
    "\n",
    "# matplotlib kullanarak grafiği çizdirelim.\n",
    "plt.plot(x, y)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Biraz daha fazladan iş yaparak birden fazla grafiği bir seferde çizdirebiliriz. Bir başlık(title),  açıklama(legend), ve eksen(axis) ve etiket(label) ekleyelim:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x7fca23f7b450>"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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TelHQcvzdgIL8lW7X5W1umzSYuuYO3ikyxrqjbgZFRAKAJ4F5QDpwu4ikd3Po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\nI0eOZNy4cYSGhlKvXj3Gjx9PZmYmzZs3R0R4+eWXqVGjBlO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      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca275bd590>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "y_cos = np.cos(x)\n",
    "y_sin = np.sin(x)\n",
    "\n",
    "# Noktaları matplotlib ile çizdirelim\n",
    "plt.plot(x, y_sin)\n",
    "plt.plot(x, y_cos)\n",
    "plt.xlabel('x axis label')\n",
    "plt.ylabel('y axis label')\n",
    "plt.title('Sine and Cosine')\n",
    "plt.legend(['Sine', 'Cosine'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Subplots "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Aynı figürde birden fazla figürü subplot fonksiyonu kullanarak çizdirebiliriz. Örneğin:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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h2uRLrXWZ1rpGa71Sa/2oUspNKfWuUiq38c+7Sim3xp8NVEqtVEoVKaUKlVLr\nlFJOjd/br5S6qvHvzyulliqlPlZKnWz8HSSelqm1UmqZUipfKZWtlLqvCV+/v1LqC6XUbqVUulKq\nT1M9l7VSSj3Q+DtIU0otVkq5G53JmthMsSulTDSMwAYBnYExSqnOxqayqFrgIa11Z6A3cLeDvf7T\n3Q+kn/Z1H8AdWH6G+z9Fw3sWC3QHegJPN37vISCHhpPsWgJPAmfa8XQ9sATwB74G3gNo/I/gGyAV\nCAEGAFOVUmc9We8CTQNWaa2jaXg96We5v11RSoUA9wGJWuuugAkYbWwq62IzxU7DhzFTa52lta6m\n4QM21OBMFqO1PqK13tr495M0fJhDjE1leUqpUGAwMOe0mwOAAq117Rl+7GbgBa11ntY6H/gXML7x\nezVAKxrO6KvRWq/TZz6iYL3W+jutdR2wkIZSBegBBGmtX9BaV2uts4DZNEHZNG6d9AfmAjQ+X5G5\nn8cGOAMeSilnwBPINTiPVbGlYg8BDp32dQ4OWGwASqkIIA7YZGwSQ7wLPErDBV5OOQ4ENn7I/0pr\n/ryExYHG2wDeADKBH5RSWWeZ4jt62t/LAffG5wwHWjdO5xQppYpoGPm3PNcXdR4igXzgo8bpqDlK\nKa8meB6rpbU+DLwJHASOAMVa6x+MTWVdbKnYBaCU8gaWAVO11iVG57EkpdQQIE9rnfw/39oAVAHD\nzvCjuTSU7ylhjbehtT6ptX5Ia92WhqmWB5VSA84z2iEgW2vtf9ofH631tef5OOfCGYgH/qO1jgPK\nAEfb39SMhq31SBr+g/ZSSo0zNpV1saViPwy0Oe3r0MbbHIZSyoWGUl+ktf7S6DwGuAS4Xim1n4ap\nuCuVUp9orYuBZ4GZSqlhSilPpZSLUmqQUup1YDHwtFIqSCkV2HjfT6DhPwulVHvVcCWNYqCOP28N\nnIvNwEml1GNKKY/GnbtdlVI9zPKq/ywHyNFan9pa+4KGonckV9HwH2m+1roG+BLoa3Amq2JLxb4F\niFJKRSqlXGmYv/za4EwW01g8c4F0rfXbRucxgtb6Ca11qNY6gobf/89a63GN33sLeJCGnaL5NIyi\n7wG+Al4CkoDtwA5ga+NtAFHAT0ApDSP/97XWa88zVx0whIads9k0rDg4B/C70Nf6N891FDiklOrY\neNMAYJe5n8fKHQR6N/4Hrmh4DxxqB/LZ2NSZp0qpa2mYYzUB87TWLxscyWIaT7RZR0MxnRpRPqm1\n/s64VMbzuo5AAAAcLUlEQVRRSl1OwwXUhxidxdKUUrE0/MfhCmQBt2qtTxibyrKUUv8CRtFwtFgK\ncLvWusrYVNbDpopdCCHE2dnSVIwQQohzIMUuhBB2RopdCCHszJlO6GhSgYGBOiIiwoinFkIIm5Wc\nnFxwLtc8NUuxK6Xm0XC4V17j2g1/KyIigqSkJHM8tRBCOAyl1IGz38t8UzHzgYFmeiwhhBAXwSwj\ndq31b43rlzSp9CMl5J+swt/TBT8PF5p7ueLj7tLUTyuEzThRVk1+aRVlVbVUVNcBEOjjRpC3G34e\nLjg5KYMTCkuw2By7UmoyMBkgLCzsgh7jk40HWLTp4J9uC2vuSUyoH7Ft/LkiugXtgrwvOqsQtkBr\nzY7Dxfy06xjbDxeTfqSEYyVnPkfHzdmJ7qH+JEQ0o0dEM/q2C8TdxWTBxMJSzHaCUuOIfeW5zLEn\nJibqC5ljzy2qILeogqLyGoorajhaUsmOnGK25xSRW1wJQJfWvlzXvTU3xIXQ0lfW3hf2JzPvJIs2\nHWR12lFyiysxOSmiWnjTqZUvnVr50MrPA283ZzxdTdRrKCitoqC0ioOF5Ww9cIKduSXU1mt83Jy5\ntlsrbogPoWdEcxnN2wClVLLWOvGs97OlYv87uUUVfJ92lG9Sc9l2qAhXkxM3JoRwR/92RAQ61Kqm\nwg5prdmUXcjs37JYszsPV2cn+kcFMbBrMAOiW9DMy/WcH6uiuo4t+wtZsS2X79OOUF5dR4eW3tw3\nIIpru7aSgrdiDlfsp9tfUMac9VksTcqhtq6eYbEhPD4omhYyghc2KP1ICS98s4sNWcdp7uXKLX3C\nGd87nABvt4t+7PLqWr7fcZT//LqPzLxSolp48+DVHRjYNZiG9bWENbFosSulFgOXA4HAMeA5rfXc\nM92/qYv9lLySSuasz2b+H/txNTnxwNUdmNAnHGeTnJclrN+Jsmre/nEvizYdwNfDhakDohjdM6xJ\n5sXr6jXf7TjC9DUZZOSVclmHIF4a1pU2zT3N/lziwll8xH4+LFXsp+wvKOO5r3fy6958ooN9eHd0\nLNHBvhZ7fiHO19rdeTzyRSonymsY1yuMB67ugL/nuU+3XKi6es3HG/bz5uo91GnN/QM6MLl/W0wy\nPWMVpNj/h9aa1TuP8cyKNIoranh6cCfG9w6XzU1hVSqq63jlu3QWbjxAdLAP74yKpVMryw9CjhRX\n8NyKnfyw6xi92zZn2ug4ORjBCkixn0FBaRUPf57KL3vyuapTS94cGWORkZAQZ5OVX8rkhclk5pVy\ne79IHhnYETdn4w5H1FrzRXIOz67YiaeriXdGxdK/w1nPZhdN6FyL3eEmmwO93Zg3oQfPDOnMr3vz\nuOH9P8jKLzU6lnBwv+7NZ+jM3yksq+aTSb14ekhnQ0sdQCnFyMQ2fH3PJQR4uzLho83MXJuJXMPB\n+jlcsQM4OSkm9Yvk03/2priihhve/4M/MguMjiUckNaaOeuyuPWjzYT4e7Di7kvoFxVodKw/iWrp\nw4q7+3FdTGveWL2Hx5Ztp6bufC8LKyzJIYv9lB4RzVlx9yW09HXjlnmbWbrlkNGRhAOpr9f865td\nvPRtOtd0DmbZlL5WexSKh6uJaaNjue/K9ixNymHiR5sprqgxOpY4A4cudoA2zT1ZNqUvfdoF8Oiy\n7cxdn210JOEAaurqeejzVOb/sZ9J/SJ5/+Z4vNwMWUX7nCmlePCajrw5sjubswsZPWsjx0vlMqPW\nyOGLHcDH3YU5ExIZ1DWYF1fuYvqaDJlHFE2msqaOKZ8kszzlMA9f04GnB3eyqbM9RySEMmdCD7Ly\nSxk1ayN5JZVGRxL/Q4q9kZuziRlj4rgxPpS3f9zLq9/vlnIXZldZU8c/P05ize48XhzWlXuujLLJ\nQ24v6xDE/Ft7kltUwU0fbuBwUYXRkcRppNhP42xy4o0RMYzvHc6Hv2Xx9o97jY4k7Eh1bT13LdrK\nuowCXrux4d+ZLevTLoCFk3pxvLSa0bM2cLRYRu7WQor9fzg5Kf51fRdGJbZhxs+ZzFybaXQkYQdq\n6uq559Ot/Lw7j1du6MZNiW2MjmQWCeHNWHh7LwpLqxk3d5PMuVsJKfa/4OSkeGV4N4bGNhzeNU92\nqIqLUF+veWhpKj/sOsa/ru/C2F4Xdj0CaxXbxp+5E3twqLCcW+ZtpqRSjpYxmhT7GZicFG+N7M7A\nLsG8sHIXK7YdNjqSsEFaa178dhdfp+by2MBoJvSNMDpSk+jdNoAPxiew99hJJs3fQmVNndGRHJoU\n+99wNjkxbUwsvds25+HPU/ldTmIS52nWb1l89Pt+brskkjsva2t0nCZ1RccWvDsqjqQDJ5i6ZBt1\n9XLwgVGk2M/CzdnEh+MTaRvozZ0Lk0k/UmJ0JGEjlqfk8O/vdzMkphVPD+5kk0e/nK/BMa14ZnBn\nVu08ysvfphsdx2FJsZ8DPw8XPrq1B15uzkz8aDO5cmiXOIuNWcd55PPt9GkbwFs3dbep49Qv1m39\nIrntkkjm/Z4tJ/wZRIr9HLX292D+bT0oq6rj9gVJlFfXGh1JWKn9BWXc+Uky4QGefDA+wfDFvIzw\n1OBODOwSzEvf7uKHnUeNjuNwpNjPQ3SwL9PHxJJ+tISHlqZSL3OI4n8UV9QwacEWAOZO6IGfh4vB\niYxhclK8OzqWmFB/pn62jd1HZQrTkqTYz9OV0S15clAnvk87yrs/yQlM4v/UNh6rfrCwnA/GJTj8\nRdTdXUzMGp+At5szty9IkmPcLUiK/QLcfmkkIxNCmf5zJt+k5hodR1iJV7/fzbqMAl4a1pXebQOM\njmMVWvq6M+uWRPJOVjFl0Vaqa2W5X0uQYr8ASilevqEbieHNePSL7bKZKVix7TBz1mczoU84o3rY\n1wlIFyu2jT+v3xjD5uxCXly5y+g4DkGK/QK5Ojvx/s3x+Lg7c8fCZFmb2oHtyi3hsWXb6RnRnKeH\ndDY6jlUaFhfC5P5tWbjxAMuSc4yOY/ek2C9CC193/jMuntyiCh74bJvsTHVAReXV3PFJEv4ersy8\nOR4Xk3ykzuTRf3SkT9sAnly+g525xUbHsWvyr/AiJYQ359khnfl5dx7T1mQYHUdYUH29Zupn2zhW\nXMV/xsUT5ONmdCSr5mxyYsbYOJp5unLnJ8kUlVcbHcluSbGbwbje4QyPD2H6zxn8tjff6DjCQt7/\nJZNf9uTzzHWdiQtrZnQcmxDo7cb74+I5WlwpW7lNSIrdDJRSvDysGx1a+DD1s20cKZYzU+3dH5kF\nvP3jXobGtmacna3W2NTiw5rx7HVdWLsnnw9+22d0HLskxW4mHq4mZt4cT1VNHfd+miJXcbdjx0oq\nuW9JCm2DvHnlhm4OsQaMuY3rFcaQmFa8uXoPm7KOGx3H7kixm1H7Ft68MrwbSQdO8MbqPUbHEU2g\ntq6eexenUFZVx39s4ALU1kopxb+HdyM8wIt7F6dQICcvmZUUu5kNjQ1hXO8wZv2Wxc+7jxkdR5jZ\n9DUZbM4u5OUbuhLV0sfoODbNx92FmWPjKa6okfl2M5NibwJPD+5M51a+PLQ0Vebb7cjvmQXMWJvJ\nyIRQhseHGh3HLnRu7cu/ru/CuowC/vOrzLebixR7E3B3MfHe2Diqauu5f/E2amW+3ebln6zi/iXb\naBfkzb+GdjE6jl0Z1aMN13Vvzds/7iVpf6HRceyCFHsTaRvkzUvDurJ5fyHT5fh2m1Zfr3lw6TZO\nVtbw3tg4PF1lXt2clFK8ckNXQvw9uG9xihzfbgZS7E1oeHwoIxJCmbE2kz/2yWX1bNWsdVmsyyjg\nueu6EB3sa3Qcu+Tj7sJ7Y+PIL63i0S+2o7XMt18MKfYm9sLQLkQGevHAZ9soLJORiK1JOXiCN1fv\nYXC3Vozp2cboOHYtJtSfxwZG88OuYyzceMDoODZNir2Jebo6M310HCfKanj0i1QZidiQksoa7luS\nQktfd14ZLserW8Jtl0RyeccgXvo2XVZNvQhS7BbQNcSPxwdF81N6Hh9vkJGILdBa8/TyNHKLKpk+\nJtZhr4RkaU5OijdHdsfX3YX7FqdQUV1ndCSbJMVuIbdeEsGV0S14+bt00o/ISMTaLdt6mK9Tc7l/\nQBQJ4c2NjuNQAr3dePum7uw9VspL38r67RfCLMWulBqolNqjlMpUSj1ujse0N0op3hgRg5+HC/fK\nSMSqZReU8eyKNHpFNufuK9obHcch9e8QxB3927Jo00FWpcnFsM/XRRe7UsoEzAQGAZ2BMUopudrA\nXwhoHIlk5slIxFpV19Zz/5IUXExOvDMqFpOTzKsb5aFrOhIT6sfjX26XE/3OkzlG7D2BTK11lta6\nGlgCDDXD49qlS6NkJGLN3vpxD9tzinntxhha+3sYHcehuTo7MW10HNW19Uxdso06WXLgnJmj2EOA\nQ6d9ndN4258opSYrpZKUUkn5+Y69ZvlD13SkW4iMRKzN+owCPvw1i7G9whjYNdjoOAKIDPTiX9d3\nYVN2IR/IkgPnzGI7T7XWs7TWiVrrxKCgIEs9rVVydXZi+piGkcgDn8lIxBocL63igaXbaN/Cm2cG\ny0yiNRmREPrfJQe2HjxhdBybYI5iPwycfuZGaONt4m+cGolszJKRiNG01jzyxXaKK2qYMSYOD1eT\n0ZHEaZRSvDSsK8G+7ty/JIWSSrlw/NmYo9i3AFFKqUillCswGvjaDI9r92QkYh0W/LGfn3fn8cSg\naDq1kiUDrJGfhwvTx8SSW1TJ08vT5ES/s7joYtda1wL3AKuBdGCp1nrnxT6uI1BK8fINXWnl5859\ni2UkYoT0IyW88v1uroxuwcS+EUbHEX8jIbw5UwdE8XVqLsu2yqTA3zHLHLvW+jutdQetdTut9cvm\neExH4evuwrTRcRwplpGIpZVX13Lv4hT8PFx4Y0SMLBlgA+66oj29Ipvz7Io0svJLjY5jteTMUyuQ\nEN7svyORz5NzjI7jMF74Zhf78kt556ZYArzdjI4jzoHJSfHOqFhcTE7ctySF6lq51sFfkWK3Endd\n0Z7ebZvz3IqdZObJSKSpfZOay5Ith7jzsnb0iwo0Oo44D639PXh9RAxph0t4fdVuo+NYJSl2K2Fy\nUkwb3XBExj2fbqWyRpYcaCqHCst58ssdxIX58+DVHYyOIy7AP7oEc0ufcOasz5ZrC/8FKXYr0tLX\nnTdHxrD76En+/V260XHsUk1dPfcuTgEF00fH4WKSj4CtevLaTnRqvLbw0eJKo+NYFflXbWWujG7J\npH6RLNhwgFVpR4yOY3feWL2HbYeKeHV4DG2aexodR1yEP11beEmKnOh3Gil2K/TYwGi6h/rxyBfb\nOVRYbnQcu/Hz7mPM+i2Lcb3DGBzTyug4wgzaBXnzwtCubMouZJpcW/i/pNitkKuzE++NjQfgnk+3\nyp5/M8gtquDBpal0buXL07JkgF0ZkRDKjfGhzPg5g3UZjr0O1SlS7FaqTXNP3hjRndScYv79vcy3\nX4yaunruW5xCTW09M2+Ox91FlgywNy8O60L7IG+mLtnGsRKZb5dit2IDuwZz6yURfPT7fr7fIfPt\nF+r1VbtJOnCCV4Z3IzLQy+g4ogl4ujrz/s3xlFfXce/iFGrrHHsrV4rdyj0xqBPd2/jzyBfb5Uy7\nC7Aq7Qiz12Uzvnc4Q2P/v9WkhR2JaunDyzd0ZXN2IW/+sNfoOIaSYrdyrs5OvH9zPC4mxZRPtlJe\nXWt0JJuRlV/Kw59vp3sbf54e0snoOMIChseHMqZnGB/8us+hL2QjxW4DQvw9mD4mjr15J3niyx2y\nnsw5qKiu465FW3ExKd6/OR43Z5lXdxTPX9+Z7qF+PPx5qsNu5Uqx24hLo4J46OoOrNiWy4I/9hsd\nx6pprXls2Xb2HDvJu6PjCJFL3DkUN2cT749LwMWkuPOTZMqqHG8rV4rdhtx1eXuu6tSCl75NZ8O+\n40bHsVqzfsvi69RcHr6mI5d1cOyrdTmqEH8PZoyJJzOvlEeXbXe4rVwpdhvi5KR4e1Qs4QGe3P3p\nVjl56S/8ujef11bt5tpuwdx1eTuj4wgD9YsK5NGB0Xy7/Qgz12YaHceipNhtjK+7C7NvSaSmrp7J\nC5NlZ+pp9heUce+nW+nQ0oc3RnSX9dUFd/Rvy7DY1rz5w15+2Ok4O1Ol2G1Q2yBvZoyJY8/REh75\nfDv1skYGxRU1TFqwBScnxazxiXi5ORsdSVgBpRSv3hhDTKgfD3y2jT1HTxodySKk2G3U5R1b8Pig\naL7dcYS3ftxjdBxD1dTVc9eiZA4WlvPBuATCAmRxL/F/3F1MzBqfiKebM5MWbCH/ZJXRkZqcFLsN\n++elbRnTM4yZa/exdMsho+MYQmvNM1+l8Xvmcf49PIbebQOMjiSsULCfO3NuSaSgtIrbP06iotq+\nr3cgxW7DlFK8MLQLl0YF8uTyHazPKDA6ksV9+FsWS7Yc4p4r2jMiIdToOMKKdW/jz/TRcWzPKeI+\nO1/mV4rdxrmYGs5Mbd/CmymfJLMrt8ToSBbz5dYcXv1+N4NjWsmVkMQ5uaZLMM8N6cyPu47x4spd\ndnsYpBS7HfBxd2HexB54uztzy7zN7C8oMzpSk/t59zEe+WI7fdsF8PZN3XFykiNgxLmZeEkkt/eL\nZP4f+3n/l31Gx2kSUux2orW/Bwsn9aSuvp5xczfZ9aXCkg8UcteirXRq5cOH4xNkuQBx3p68thM3\nxIXwxuo9dnkmtxS7HWnfwocFt/XkRFk1t8zbxImyaqMjmV3a4WJum59EKz8P5t/aEx93F6MjCRvk\n5KR4Y0QMV3duyXNf72RZco7RkcxKit3OxIT6M3tCIvuPl3PznE0U2lG5px0u5uY5m/B2c+bj23oS\n6O1mdCRhw5xNTswYE8cl7QN4dNl2vt1uP9c8kGK3Q33bBTL7lkT25ZcydvZGjpfa/nG7O3OLGTe3\nodSXTO4tF6IWZnHqGPf4MH/uXbyV5Sn2MXKXYrdTl3UIYu6EHmQXlDFm9kabPiljR07DSN3TxcTi\nf0qpC/PycnNm/q096RUZwINLU+3inBApdjvWLyqQjyb24GBhOTd9uIEDx23vaJnf9uYzatYGvFyd\nWTK5j5xVKpqEl5sz8yb2oF/7QB5dtp35v2cbHemiSLHbub7tA1l0ey9OlFcz/P0/SD1UZHSkc/ZV\nymFum7+F8AAvlt/VV0pdNCkPVxOzb0nk6s4tef6bXby0cpfNrsMkxe4AEsKbs2xKXzxcTYyetZE1\n6ceMjvS3tNbMXJvJ1M+2kRjRjM/u6E0LX3ejYwkH4O5i4oNxCUzsG8Gc9dnctWirTS4/IMXuINoF\nefPlXX1p38Kb2z9OYtpPGVY5GimtqmXKJ1t5Y/Ueru/emvm39sRXDmkUFmRyUjx/fReeGdKZ1buO\nMmrWBpu79oEUuwNp4ePOZ3f0ZlhsCO/8tJfbFmyhqNx6Dofcl1/KsJm/82P6MZ4e3Ilpo2Nxd5GT\nj4QxJvWL5MNxCWTnlzF4+jqbWs9dit3BeLo68/ZN3XlpWFf+yDzO4Onr+T3T2MXD6us1C/7Yz5Dp\n6yksq2bhpJ7cfmlbuVCGMNw1XYJZeV8/wgI8mbwwmRdX7qKyxvqnZpQRi+AkJibqpKQkiz+v+LNt\nh4p44LNtDYdE9mzDE9d2svi0x6HCch5btp0/9h2nf4cgXruxG6385OLTwrpU1dbx8rfpfLzhAJGB\nXrxyQzf6tLP8EtFKqWStdeJZ7yfF7tgqa+p458e9zF6XRQsfdx4b1JHru4dgauJFtcqqapn1Wxaz\n12WhgKeHdGZ0jzYyShdWbX1GAU8u39FwCHFiKI/8I5ogH8udAS3FLs5L6qEinly+g525JUQH+/Dw\nNR0Z0KmF2Yu2qraOZcmHeeenveSfrGJwt1Y8PihaTjoSNqOiuo5pazKYvS4LF5NiQp8IJvdvS4AF\nlriwSLErpUYCzwOdgJ5a63Nqayl261Rfr/l2xxHe/nEv2QVlRAf7MLZXGENjQ/DzuLgpmiPFFXy6\n6SCLNx+koLSaxPBmPDm4E/FhzcyUXgjLyi4oY8aaDL7adhh3FxMjEkIZmdCGriG+Tbblaali7wTU\nAx8CD0ux24eaunqWbz3Mgg372ZlbgruLE1d3DubS9oH0bR9AaLOzj6611mTmlfLLnnzW7sljU3Yh\n9VozILoFt/SJ4NKoQJl2EXYhM6+UmWsz+XbHEapr64kO9mFITCt6tQ0gJtTPrMtKW3QqRin1C1Ls\ndmlHTjGfbj7Ij7uOUdC4mFiIvwfhAZ60aeZJK393TEpRr6Guvp7DRZVkF5SSVVBGUXkNAB1aenNV\np5aM6RkmUy7CbhWX1/DN9lw+T8757xners5OdGntS2t/D4J93Wnp68agrq0u+HNgdcWulJoMTAYI\nCwtLOHDgwEU/r7AcrTV7j5Xye2YBKYeKOFRYTs6Jiv+W/SnBvu5EBnoRGeRFl9a+XN6xBSH+cpSL\ncCyFZdUk7S9kc3YhabnFHCup4mhxJRU1dXwyqRf9ogIv6HHNVuxKqZ+A4L/41lNa6xWN9/kFGbE7\npJq6egBMSqEUMr0ixBlorSmtqsXV2emCp2fOtdidzyHMVReUQDgEF5Oc4ybEuVBKWeyKX/KpFEII\nO3NRxa6UukEplQP0Ab5VSq02TywhhBAXypATlJRS+cCF7j0NBIxd3MR48h7Ie+Dorx8c8z0I11oH\nne1OhhT7xVBKJZ3LzgN7Ju+BvAeO/vpB3oO/I3PsQghhZ6TYhRDCzthisc8yOoAVkPdA3gNHf/0g\n78EZ2dwcuxBCiL9niyN2IYQQf0OKXQgh7IxNFbtSaqBSao9SKlMp9bjReSxJKdVGKbVWKbVLKbVT\nKXW/0ZmMopQyKaVSlFIrjc5iBKWUv1LqC6XUbqVUulKqj9GZLE0p9UDj5yBNKbVYKeVudCZrYjPF\nrpQyATOBQUBnYIxSqrOxqSyqFnhIa90Z6A3c7WCv/3T3A+lGhzDQNGCV1joa6I6DvRdKqRDgPiBR\na90VMAGjjU1lXWym2IGeQKbWOktrXQ0sAYYanMlitNZHtNZbG/9+koYPc4ixqSxPKRUKDAbmGJ3F\nCEopP6A/MBdAa12ttS4yNpUhnAEPpZQz4AnkGpzHqthSsYcAh077OgcHLDYApVQEEAdsMjaJId4F\nHqXhyl2OKBLIBz5qnI6ao5TyMjqUJWmtDwNvAgeBI0Cx1voHY1NZF1sqdgEopbyBZcBUrXWJ0Xks\nSSk1BMjTWicbncVAzkA88B+tdRxQBjja/qZmNGytRwKtAS+l1DhjU1kXWyr2w0Cb074ObbzNYSil\nXGgo9UVa6y+NzmOAS4DrlVL7aZiKu1Ip9YmxkSwuB8jRWp/aWvuChqJ3JFcB2VrrfK11DfAl0Nfg\nTFbFlop9CxCllIpUSrnSsLPka4MzWYxquDTRXCBda/220XmMoLV+QmsdqrWOoOH3/7PW2qFGalrr\no8AhpVTHxpsGALsMjGSEg0BvpZRn4+diAA62A/lsznoFJWuhta5VSt0DrKZhL/g8rfVOg2NZ0iXA\neGCHUmpb421Paq2/MzCTMMa9wKLGAU4WcKvBeSxKa71JKfUFsJWGo8VSkOUF/kSWFBBCCDtjS1Mx\nQgghzoEUuxBC2BkpdiGEsDNS7EIIYWek2IUQws5IsQshhJ2RYhdCCDvz/wC/e+/7lR+3DgAAAABJ\nRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fca23f62a50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Sinüs ve Kosinüs eğrilerinin x ve y koordinarlarını hesaplayalım\n",
    "x = np.arange(0, 3 * np.pi, 0.1)\n",
    "y_sin = np.sin(x)\n",
    "y_cos = np.cos(x)\n",
    "\n",
    "# subplot gridini yüksekliği 2 genişliği 1 olacak şekilde ayarlayalım\n",
    "# ilk subplotu aktif olarak ayarlayalım\n",
    "plt.subplot(2, 1, 1)\n",
    "\n",
    "# ilk şekli çiz\n",
    "plt.plot(x, y_sin)\n",
    "plt.title('Sine')\n",
    "\n",
    "# İkinci subplotu aktifleştir ve ikinci şekli çiz\n",
    "plt.subplot(2, 1, 2)\n",
    "plt.plot(x, y_cos)\n",
    "plt.title('Cosine')\n",
    "\n",
    "# Figürü göster\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "`subplot` fonksiyonu hakkında detaylı bilgiyi ilgili [dökümantasyondan](http://matplotlib.org/api/pyplot_api.html#matplotlib.pyplot.subplot) bulabilirsiniz."
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python 2",
   "language": "python",
   "name": "python2"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 2
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython2",
   "version": "2.7.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}