python-exercise.ipynb

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    "\n",
    "It is the kernel that I have tried and compiled from the courses of [DATAI Team](https://www.udemy.com/user/datai-team/) (Language of the courses is Turkish: [Python: Sıfırdan Uzmanlığa Programlama (1)](https://www.udemy.com/python-sfrdan-uzmanlga-programlama-1/)), which is [Grandmaster on Kaggle](https://www.kaggle.com/kanncaa1) and has more than 15 courses on Udemy.\n",
    "\n",
    "\n",
    "# **Content**\n",
    "\n",
    " \n",
    "1. [Python Basics](#1.)\n",
    "   * [variable](#2.)\n",
    "   * [user defined functions](#3.)\n",
    "   * [default ve flexible functions](#4.)\n",
    "   * [lambda function](#5.)\n",
    "   * [nested function](#6.)\n",
    "   * [anonymous function](#7.)\n",
    "   * [list](#8.)\n",
    "   * [tuple](#9.)\n",
    "   * [dictionary](#10.)\n",
    "   * [conditionals](#11.)\n",
    "   * [loops](#12.)\n",
    "1. [Object Oriented Programming](#13.)\n",
    "    * [class](#14.)\n",
    "1. [Numpy](#15.)\n",
    "    * [basic operations](#16.)\n",
    "    * [indexing and slicing](#17.)\n",
    "    * [shape manipulation](#18.)\n",
    "    * [convert and copy](#19.)\n",
    "1. [Pandas](#20.)\n",
    "    * [indexing and slicing](#21.)\n",
    "    * [filtering](#22.)\n",
    "    * [list comprehension](#23.)\n",
    "    * [drop and concatenating](#24.)\n",
    "    * [transforming data](#25.)\n",
    "    * [iteration example](#26.)\n",
    "    * [zip example](#27.)\n",
    "    * [example of list comprehension](#28.)\n",
    "1. [Visualization with Matplotlib](#29.)\n",
    "    * [line Plot example](#30.)\n",
    "    * [scatter plot](#31.)\n",
    "    * [histogram](#32.)\n",
    "    * [bar plot](#33.)\n",
    "    * [subplots](#34.)\n",
    "    \n",
    "\n"
   ]
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    "\n",
    "<a class=\"anchor\" id=\"1.\"></a> \n",
    "# 1.Python Basics"
   ]
  },
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   "source": [
    "<a class=\"anchor\" id=\"2.\"></a> \n",
    "## variable "
   ]
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     "text": [
      "<class 'str'>\n"
     ]
    }
   ],
   "source": [
    "var1 = 10 # integer = int\n",
    "ay = \"temmuz\"\n",
    "var3 = 10.3 # double (float)\n",
    "\n",
    "s = \"bugun gunlerden pazartesi\"\n",
    "variable_type = type(s)   # str = string\n",
    "print(variable_type)"
   ]
  },
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    "\n",
    "<a class=\"anchor\" id=\"3.\"></a> \n",
    "## user defined functions "
   ]
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     "text": [
      "14.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def benim_ilk_func(a,b):   \n",
    "    \"\"\"\n",
    "    bu benim ilk denemem\n",
    "    parametre: \n",
    "    return: \n",
    "    \"\"\"\n",
    "    output = (((a+b)*50)/100.0)*a/b\n",
    "    return output\n",
    "\n",
    "print(benim_ilk_func(20,50))"
   ]
  },
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   "source": [
    "\n",
    "    \n",
    "<a class=\"anchor\" id=\"4.\"></a> \n",
    "## default ve flexible functions"
   ]
  },
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     "text": [
      "31.400000000000002\n",
      "(10, 20, 30, 40)\n",
      "100\n"
     ]
    }
   ],
   "source": [
    "# %% \n",
    "# default f: cemberin cevre uzunlugu = 2*pi*r\n",
    "def cember_cevresi_hesapla(r,pi=3.14):\n",
    "    \"\"\"\n",
    "    cember cevresi hesapla\n",
    "    input(parametre): r,pi\n",
    "    output = cemberin cevresi\n",
    "    \"\"\"\n",
    "    output = 2*pi*r\n",
    "    return output\n",
    "\n",
    "# flexible\n",
    "def hesapla(boy,kilo,*args):\n",
    "    print(args)\n",
    "    output = (boy+kilo)*args[0]\n",
    "    return output\n",
    "\n",
    "print(cember_cevresi_hesapla(5))\n",
    "print(hesapla(5,5,10,20,30,40))"
   ]
  },
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   "source": [
    "\n",
    "<a class=\"anchor\" id=\"5.\"></a> \n",
    "## lambda function"
   ]
  },
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     "text": [
      "9\n",
      "9\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def hesapla(x):\n",
    "    return x*x\n",
    "print(hesapla(3))\n",
    "\n",
    "sonuc2 = lambda x: x*x\n",
    "print(sonuc2(3))"
   ]
  },
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   "source": [
    "<a class=\"anchor\" id=\"6.\"></a> \n",
    "## nested function"
   ]
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     "text": [
      "25\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def square():\n",
    "    \"\"\" return square of value \"\"\"\n",
    "    def add():\n",
    "        \"\"\" add two local variable \"\"\"\n",
    "        x = 2\n",
    "        y = 3\n",
    "        z = x + y\n",
    "        return z\n",
    "    return add()**2\n",
    "print(square())"
   ]
  },
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   "source": [
    "\n",
    "<a class=\"anchor\" id=\"7.\"></a> \n",
    "## anonymous function"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1, 4, 9]\n"
     ]
    }
   ],
   "source": [
    "number_list = [1,2,3]\n",
    "y = map(lambda x:x**2,number_list)\n",
    "print(list(y))"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "\n",
    "<a class=\"anchor\" id=\"8.\"></a> \n",
    "## list"
   ]
  },
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     "text": [
      "<class 'list'>\n",
      "<class 'list'>\n",
      "2\n",
      "6\n",
      "[1, 2, 3]\n",
      "[1, 2, 3, 4, 5, 6, 7]\n",
      "[1, 2, 3, 4, 5, 6]\n",
      "[6, 5, 4, 3, 2, 1]\n",
      "[1, 2, 3, 4, 5, 6, 7]\n"
     ]
    }
   ],
   "source": [
    "liste = [1,2,3,4,5,6]\n",
    "print(type(liste))\n",
    "\n",
    "liste_str = [\"ptesi\",\"sali\",\"cars\"]\n",
    "print(type(liste_str))\n",
    "\n",
    "print(liste[1])\n",
    "print(liste[-1])\n",
    "print(liste[0:3])\n",
    "\n",
    "liste.append(7)\n",
    "print(liste)\n",
    "liste.remove(7)\n",
    "print(liste)\n",
    "liste.reverse()\n",
    "print(liste)\n",
    "\n",
    "liste2 = [1,5,4,3,6,7,2]\n",
    "liste2.sort()\n",
    "print(liste2)\n",
    "\n",
    "string_int_liste = [1,2,3,\"aa\",\"bb\"]"
   ]
  },
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   "source": [
    "\n",
    "<a class=\"anchor\" id=\"9.\"></a> \n",
    "## tuple"
   ]
  },
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    {
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     "output_type": "stream",
     "text": [
      "1\n",
      "2\n"
     ]
    }
   ],
   "source": [
    "\n",
    "t = (1,2,3,3,4,5,6)\n",
    "\n",
    "print(t.count(5))\n",
    "print(t.index(3))"
   ]
  },
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   "source": [
    "\n",
    "<a class=\"anchor\" id=\"10.\"></a> \n",
    "## dictionary"
   ]
  },
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     "text": [
      "{'ali': 32, 'veli': 45, 'ayse': 13}\n",
      "dict_keys(['spain', 'usa'])\n",
      "dict_values(['madrid', 'vegas'])\n",
      "{'spain': 'barcelona', 'usa': 'vegas'}\n",
      "{'spain': 'barcelona', 'usa': 'vegas', 'france': 'paris'}\n",
      "{'usa': 'vegas', 'france': 'paris'}\n",
      "True\n",
      "{}\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def deneme():\n",
    "    dictionary = {\"ali\":32,\"veli\":45,\"ayse\":13}\n",
    "    # ali ,veli ,ayse = keys\n",
    "# 32,45,13 = values\n",
    "    return dictionary\n",
    "\n",
    "dic = deneme()\n",
    "print(dic)\n",
    "\n",
    "dictionary = {'spain' : 'madrid','usa' : 'vegas'}\n",
    "print(dictionary.keys())\n",
    "print(dictionary.values())\n",
    "dictionary['spain'] = \"barcelona\"    # update existing entry\n",
    "print(dictionary)\n",
    "dictionary['france'] = \"paris\"       # Add new entry\n",
    "print(dictionary)\n",
    "del dictionary['spain']              # remove entry with key 'spain'\n",
    "print(dictionary)\n",
    "print('france' in dictionary)        # check include or not\n",
    "dictionary.clear()                   # remove all entries in dict\n",
    "print(dictionary)"
   ]
  },
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   "source": [
    "\n",
    "<a class=\"anchor\" id=\"11.\"></a> \n",
    "## conditionals"
   ]
  },
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "var1 kucuktur var2\n",
      "evet 3 degeri listenin icinde\n",
      "evet\n"
     ]
    }
   ],
   "source": [
    "# if else statement\n",
    "\n",
    "var1 = 10\n",
    "var2 = 20\n",
    "\n",
    "if(var1 > var2):\n",
    "    print(\"var1 buyuktur var2\")\n",
    "elif(var1 == var2):\n",
    "    print(\"var and var2 esitler\")\n",
    "else:\n",
    "    print(\"var1 kucuktur var2\")\n",
    "\n",
    "\n",
    "liste = [1,2,3,4,5]\n",
    "\n",
    "value = 3\n",
    "if value in liste:\n",
    "    print(\"evet {} degeri listenin icinde\".format(value))\n",
    "else:\n",
    "    print(\"hayir\")\n",
    "\n",
    "dictionary = {\"ali\":32,\"veli\":45,\"ayse\":13}\n",
    "keys = dictionary.keys()\n",
    "\n",
    "if \"veli\" in keys:\n",
    "    print(\"evet\")\n",
    "else:\n",
    "    print(\"hayir\")"
   ]
  },
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   "source": [
    "\n",
    "<a class=\"anchor\" id=\"12.\"></a> \n",
    "## loops"
   ]
  },
  {
   "cell_type": "code",
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   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "2\n",
      "a\n",
      "n\n",
      "k\n",
      " \n",
      "i\n",
      "s\n",
      "t\n",
      "ank\n",
      "ist\n",
      "101\n",
      "1\n",
      "5\n",
      "10\n",
      "16\n",
      "24\n",
      "27\n",
      "30\n",
      "34\n",
      "101\n",
      "0\n",
      "1\n",
      "2\n",
      "3\n"
     ]
    }
   ],
   "source": [
    "# for loop\n",
    "for each in range(1,3):\n",
    "    print(each)\n",
    "    \n",
    "for each in \"ank ist\":\n",
    "    print(each)\n",
    "    \n",
    "for each in \"ank ist\".split(): \n",
    "    print(each)\n",
    "    \n",
    "liste = [1,4,5,6,8,3,3,4,67]\n",
    " \n",
    "print(sum(liste))  \n",
    "\n",
    "count = 0\n",
    "for each in liste:\n",
    "    count = count + each\n",
    "    print(count)\n",
    "\n",
    "# while loop\n",
    "i = 0\n",
    "while(i <4):\n",
    "    print(i)\n",
    "    i = i + 1"
   ]
  },
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   "source": [
    "\n",
    "<a class=\"anchor\" id=\"13.\"></a> \n",
    "# 2.Object Oriented Programming"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "<a class=\"anchor\" id=\"14.\"></a> \n",
    "## class"
   ]
  },
  {
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    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "giveNameSurname:  ali veli\n",
      "maas:  100\n",
      "yeni maas:  280.0\n",
      "600\n",
      "ayse yelda\n"
     ]
    }
   ],
   "source": [
    "class Calisan:\n",
    "    zam_orani = 1.8\n",
    "    counter = 0\n",
    "    def __init__(self,isim,soyisim,maas): # constructor\n",
    "        self.isim = isim\n",
    "        self.soyisim = soyisim\n",
    "        self.maas = maas\n",
    "        self.email = isim+soyisim+\"@asd.com\"\n",
    "        Calisan.counter = Calisan.counter + 1\n",
    "    \n",
    "    def giveNameSurname(self):\n",
    "        return self.isim +\" \" +self.soyisim\n",
    "        \n",
    "    def zam_yap(self):\n",
    "        self.maas = self.maas + self.maas*self.zam_orani\n",
    "# class variable\n",
    "calisan1 = Calisan(\"ali\", \"veli\",100) \n",
    "print(\"giveNameSurname: \",calisan1.giveNameSurname())\n",
    "print(\"maas: \",calisan1.maas)\n",
    "calisan1.zam_yap()\n",
    "print(\"yeni maas: \",calisan1.maas)\n",
    "\n",
    "#  class example\n",
    "calisan2 = Calisan(\"ayse\", \"hatice\",200) \n",
    "calisan3 = Calisan(\"ayse\", \"yelda\",600) \n",
    "liste  = [calisan1,calisan2,calisan3]\n",
    "\n",
    "maxi_maas = -1\n",
    "index = -1\n",
    "for each in liste:\n",
    "    if(each.maas>maxi_maas):\n",
    "        maxi_maas = each.maas\n",
    "        index = each\n",
    "        \n",
    "print(maxi_maas)\n",
    "print(index.giveNameSurname())"
   ]
  },
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   "source": [
    "\n",
    "<a class=\"anchor\" id=\"15.\"></a> \n",
    "# 3.Numpy"
   ]
  },
  {
   "cell_type": "code",
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    {
     "name": "stdout",
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     "text": [
      "(15,)\n",
      "shape:  (15, 1)\n",
      "dimension:  2\n",
      "data type:  int64\n",
      "size:  15\n",
      "type:  <class 'numpy.ndarray'>\n",
      "[[1 2 3 4]\n",
      " [5 6 7 8]\n",
      " [9 8 7 5]]\n",
      "[[5. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]\n",
      " [0. 0. 0. 0.]]\n",
      "[[1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]\n",
      " [1. 1. 1. 1.]]\n",
      "[[6.90775820e-310 4.68763801e-310 0.00000000e+000]\n",
      " [0.00000000e+000 0.00000000e+000 0.00000000e+000]]\n",
      "[10 15 20 25 30 35 40 45]\n",
      "[10. 20. 30. 40. 50.]\n"
     ]
    }
   ],
   "source": [
    "# importing\n",
    "import numpy as np\n",
    "# numpy basics\n",
    "array = np.array([1,2,3,4,5,6,7,8,9,10,11,12,13,14,15])  # 1*15 vector\n",
    "print(array.shape)\n",
    "a = array.reshape(-1,1)\n",
    "print(\"shape: \",a.shape)\n",
    "print(\"dimension: \", a.ndim)\n",
    "print(\"data type: \",a.dtype.name)\n",
    "print(\"size: \",a.size)\n",
    "print(\"type: \",type(a))\n",
    "\n",
    "array1 = np.array([[1,2,3,4],[5,6,7,8],[9,8,7,5]])\n",
    "print(array1)\n",
    "zeros = np.zeros((3,4))\n",
    "zeros[0,0] = 5\n",
    "print(zeros)\n",
    "\n",
    "print(np.ones((3,4)))\n",
    "print(np.empty((2,3)))\n",
    "\n",
    "a = np.arange(10,50,5) # 10 dan 50 ye 5er 5 er artır\n",
    "print(a)\n",
    "a = np.linspace(10,50,5) #10 50 ye 5 tane yerleştir\n",
    "print(a)"
   ]
  },
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   "source": [
    "\n",
    "      \n",
    "<a class=\"anchor\" id=\"16.\"></a> \n",
    "## basic operations"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "_uuid": "092d482355b4507b48f04a51f76c7c6ddc2c889b",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[5 7 9]\n",
      "[-3 -3 -3]\n",
      "[1 4 9]\n",
      "[[ 1  4  9]\n",
      " [16 25 36]]\n",
      "[[14 32]\n",
      " [32 77]]\n",
      "1.6048975803280638\n",
      "0.6462414920820414\n",
      "0.17529048584698315\n",
      "[0.35945829 1.24543929]\n",
      "[0.77448829 0.83040929]\n",
      "[[0.41867707 0.77407868]\n",
      " [0.42914776 0.80389147]]\n",
      "[[0.03072675 0.359038  ]\n",
      " [0.03391778 0.41762807]]\n",
      "[[0.35058097 1.1983956 ]\n",
      " [0.36833561 1.29248298]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "a = np.array([1,2,3])\n",
    "b = np.array([4,5,6])\n",
    "\n",
    "print(a+b)\n",
    "print(a-b)\n",
    "print(a**2)\n",
    "\n",
    "a = np.array([[1,2,3],[4,5,6]])\n",
    "b = np.array([[1,2,3],[4,5,6]])\n",
    "# element wise prodcut\n",
    "print(a*b)\n",
    "# matrix prodcut\n",
    "print(a.dot(b.T))\n",
    "\n",
    "a = np.random.random((2,2)) # 2 2 lık 0-1 arasında sayı uretiyor\n",
    "print(a.sum())\n",
    "print(a.max())\n",
    "print(a.min())\n",
    "print(a.sum(axis=0)) # sutunları topla\n",
    "print(a.sum(axis=1)) # satırları topla\n",
    "print(np.sqrt(a))\n",
    "print(np.square(a)) # a**2\n",
    "print(np.add(a,a))"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "<a class=\"anchor\" id=\"17.\"></a> \n",
    "## indexing and slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "_uuid": "6d25f2b499560986d82e12ac1fe4431e497bcade",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1\n",
      "[1 2 3 4]\n",
      "[7 6 5 4 3 2 1]\n",
      "7\n",
      "[2 7]\n",
      "[7 8 9]\n",
      "[ 6  7  8  9 10]\n",
      "[ 5 10]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "array = np.array([1,2,3,4,5,6,7])   #  vector dimension = 1\n",
    "print(array[0])\n",
    "print(array[0:4])\n",
    "\n",
    "reverse_array = array[::-1]\n",
    "print(reverse_array)\n",
    "\n",
    "array1 = np.array([[1,2,3,4,5],[6,7,8,9,10]])\n",
    "print(array1[1,1]) # 1 satır 1 sutun 7 \n",
    "print(array1[:,1]) # tum satır 1 sutun 2,7 \n",
    "print(array1[1,1:4]) # 1 satır 1-4 sutun 7-8-9\n",
    "print(array1[-1,:]) #son satırın tum sutunları\n",
    "print(array1[:,-1]) # tum satırların son sutunu 5,10"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "<a class=\"anchor\" id=\"18.\"></a> \n",
    "## shape manipulation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "_uuid": "1f32a201c7f1ccb512fae53289b9b198e37ef105",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1 2 3 4 5 6 7 8 9]\n",
      "[[1 2 3]\n",
      " [4 5 6]\n",
      " [7 8 9]]\n",
      "[[ 1  2]\n",
      " [ 3  4]\n",
      " [-1 -2]\n",
      " [-3 -4]]\n",
      "[[ 1  2 -1 -2]\n",
      " [ 3  4 -3 -4]]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "array = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
    "\n",
    "# flatten\n",
    "array1 = array.ravel() # duz hale getırıldı\n",
    "print(array1)\n",
    "array2 = array1.reshape(3,3) # matrıse cevır\n",
    "print(array2)\n",
    "\n",
    "# %% stacking arrays\n",
    "array1 = np.array([[1,2],[3,4]])\n",
    "array2 = np.array([[-1,-2],[-3,-4]])\n",
    "\n",
    "# veritical\n",
    "#array([[1, 2],\n",
    "#       [3, 4]])\n",
    "#array([[-1, -2],\n",
    "#       [-3, -4]])\n",
    "array3 = np.vstack((array1,array2))\n",
    "print(array3)\n",
    "# horizontal\n",
    "#array([[1, 2],[-1, -2],\n",
    "#       [3, 4]],[-3, -4]]\n",
    "array4 = np.hstack((array1,array2))\n",
    "print(array4)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "      \n",
    "<a class=\"anchor\" id=\"19.\"></a> \n",
    "## convert and copy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "_uuid": "e5c51e86355b8155f8455a2cac4a2ae3fb67a07b",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "5\n",
      "5\n",
      "5\n",
      "1\n"
     ]
    }
   ],
   "source": [
    "\n",
    "liste = [1,2,3,4]   # list\n",
    "array = np.array(liste) #np.array\n",
    "liste2 = list(array) # list\n",
    "\n",
    "a = np.array([1,2,3])\n",
    "b = a\n",
    "b[0] = 5\n",
    "print(b[0])\n",
    "print(a[0]) # a da degıstı\n",
    "\n",
    "d =  np.array([1,2,3])\n",
    "e = d.copy()\n",
    "d[0] = 5\n",
    "print(d[0])\n",
    "print(e[0]) # e da degısmedi"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "_uuid": "27ea0c3c2dc2bdde3d24c1a25c9740d07b1eacb5"
   },
   "source": [
    "\n",
    "<a class=\"anchor\" id=\"20.\"></a> \n",
    "# 4.Pandas"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "_uuid": "cde13ac6290d7a4349b7006b78afe23f434740db",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    NAME  AGE  MAAS\n",
      "0    ali   15   100\n",
      "1   veli   16   150\n",
      "2  kenan   17   240\n",
      "3  hilal   33   350\n",
      "4   ayse   45   110\n",
      "    NAME  AGE  MAAS\n",
      "4   ayse   45   110\n",
      "5  evren   66   220\n",
      "6  isim1   70   300\n",
      "7  isim2   70   300\n",
      "8  isim3   70   300\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "dictionary = {\"NAME\":[\"ali\",\"veli\",\"kenan\",\"hilal\",\"ayse\",\"evren\",\"isim1\",\"isim2\",\"isim3\"],\n",
    "              \"AGE\":[15,16,17,33,45,66,70,70,70],\n",
    "              \"MAAS\": [100,150,240,350,110,220,300,300,300]} \n",
    "\n",
    "dataFrame1 = pd.DataFrame(dictionary)\n",
    "\n",
    "print(dataFrame1.head()) # ilk 5 kısım, içerindeki görmek için\n",
    "print(dataFrame1.tail()) # sondaki 5 tane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "_uuid": "808748687e9dbd5df59942ce93f847053ff6e5d5",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['NAME', 'AGE', 'MAAS'], dtype='object')\n",
      "------\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9 entries, 0 to 8\n",
      "Data columns (total 3 columns):\n",
      "NAME    9 non-null object\n",
      "AGE     9 non-null int64\n",
      "MAAS    9 non-null int64\n",
      "dtypes: int64(2), object(1)\n",
      "memory usage: 296.0+ bytes\n",
      "None\n",
      "------\n",
      "NAME    object\n",
      "AGE      int64\n",
      "MAAS     int64\n",
      "dtype: object\n",
      "------\n",
      "             AGE        MAAS\n",
      "count   9.000000    9.000000\n",
      "mean   44.666667  230.000000\n",
      "std    24.959968   91.515026\n",
      "min    15.000000  100.000000\n",
      "25%    17.000000  150.000000\n",
      "50%    45.000000  240.000000\n",
      "75%    70.000000  300.000000\n",
      "max    70.000000  350.000000\n"
     ]
    }
   ],
   "source": [
    "# pandas basic method\n",
    "print(dataFrame1.columns)\n",
    "print(\"------\")\n",
    "print(dataFrame1.info())\n",
    "print(\"------\")\n",
    "print(dataFrame1.dtypes)\n",
    "print(\"------\")\n",
    "print(dataFrame1.describe())  # numeric feature = columns (age,maas)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "<a class=\"anchor\" id=\"21.\"></a> \n",
    "## indexing and slicing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "_uuid": "f1f3602dd3c895e42b132fa98ce115c41c931010",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    15\n",
      "1    16\n",
      "2    17\n",
      "3    33\n",
      "4    45\n",
      "5    66\n",
      "6    70\n",
      "7    70\n",
      "8    70\n",
      "Name: AGE, dtype: int64\n",
      "0    15\n",
      "1    16\n",
      "2    17\n",
      "3    33\n",
      "4    45\n",
      "5    66\n",
      "6    70\n",
      "7    70\n",
      "8    70\n",
      "Name: AGE, dtype: int64\n",
      "---1---\n",
      "0    15\n",
      "1    16\n",
      "2    17\n",
      "Name: AGE, dtype: int64\n",
      "---2---\n",
      "0    15\n",
      "1    16\n",
      "Name: AGE, dtype: int64\n",
      "---3---\n",
      "Empty DataFrame\n",
      "Columns: []\n",
      "Index: [0, 1]\n",
      "---4---\n",
      "   AGE  NAME\n",
      "0   15   ali\n",
      "1   16  veli\n",
      "---5---\n",
      "    NAME  AGE  MAAS  yeni_feature\n",
      "2  kenan   17   240            -3\n",
      "1   veli   16   150            -2\n",
      "0    ali   15   100            -1\n",
      "---6---\n",
      "    NAME\n",
      "0    ali\n",
      "1   veli\n",
      "2  kenan\n",
      "---7---\n",
      "0      ali\n",
      "1     veli\n",
      "2    kenan\n",
      "Name: NAME, dtype: object\n",
      "---8---\n",
      "0    100\n",
      "1    150\n",
      "2    240\n",
      "Name: MAAS, dtype: int64\n"
     ]
    }
   ],
   "source": [
    "\n",
    "print(dataFrame1[\"AGE\"])\n",
    "print(dataFrame1.AGE)\n",
    "\n",
    "dictionary = {\"NAME\":[\"ali\",\"veli\",\"kenan\",],\n",
    "              \"AGE\":[15,16,17,],\n",
    "              \"MAAS\": [100,150,240]} \n",
    "\n",
    "dataFrame1 = pd.DataFrame(dictionary)\n",
    "dataFrame1[\"yeni_feature\"] = [-1,-2,-3]\n",
    "print(\"---1---\")\n",
    "print(dataFrame1.loc[:, \"AGE\"])\n",
    "print(\"---2---\")\n",
    "print(dataFrame1.loc[:1, \"AGE\"])\n",
    "print(\"---3---\")\n",
    "print(dataFrame1.loc[:1, \"AGE\":\"NAME\"])\n",
    "print(\"---4---\")\n",
    "print(dataFrame1.loc[:1, [\"AGE\",\"NAME\"]])\n",
    "print(\"---5---\")\n",
    "print(dataFrame1.loc[::-1,:]) #ters yazdı\n",
    "print(\"---6---\")\n",
    "print(dataFrame1.loc[:,:\"NAME\"])\n",
    "print(\"---7---\")\n",
    "print(dataFrame1.loc[:,\"NAME\"])\n",
    "print(\"---8---\")\n",
    "print(dataFrame1.iloc[:,2]) #i integer location name yerıne sutun ındexı verdık"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "      \n",
    "<a class=\"anchor\" id=\"22.\"></a> \n",
    "## filtering"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "_uuid": "349b2efb9a38b68f05f0a3667dd99cc3eb04d7b4",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0    False\n",
      "1    False\n",
      "2     True\n",
      "3     True\n",
      "4    False\n",
      "5     True\n",
      "Name: MAAS, dtype: bool\n",
      "    NAME  AGE  MAAS\n",
      "2  kenan   17   240\n",
      "3  hilal   33   350\n",
      "5  evren   66   220\n",
      "    NAME  AGE  MAAS\n",
      "5  evren   66   220\n"
     ]
    }
   ],
   "source": [
    "\n",
    "dictionary = {\"NAME\":[\"ali\",\"veli\",\"kenan\",\"hilal\",\"ayse\",\"evren\"],\n",
    "              \"AGE\":[15,16,17,33,45,66],\n",
    "              \"MAAS\": [100,150,240,350,110,220]} \n",
    "dataFrame1 = pd.DataFrame(dictionary)\n",
    "filtre1 = dataFrame1.MAAS > 200\n",
    "print(filtre1)\n",
    "filtrelenmis_data = dataFrame1[filtre1]\n",
    "print(filtrelenmis_data)\n",
    "filtre2 = dataFrame1.AGE <20\n",
    "dataFrame1[filtre1 & filtre2]\n",
    "print(dataFrame1[dataFrame1.AGE > 60])"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "<a class=\"anchor\" id=\"23.\"></a> \n",
    "## list comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "_uuid": "547721af11c24798a31f3b851879a1d035bea167",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    NAME  AGE  MAAS maas_seviyesi\n",
      "0    ali   15   100         dusuk\n",
      "1   veli   16   150         dusuk\n",
      "2  kenan   17   240        yuksek\n",
      "3  hilal   33   350        yuksek\n",
      "4   ayse   45   110         dusuk\n",
      "5  evren   66   220        yuksek\n",
      "Index(['NAME', 'AGE', 'MAAS', 'maas_seviyesi'], dtype='object')\n",
      "Index(['name', 'age', 'maas', 'maas_seviyesi'], dtype='object')\n",
      "    name  age  maas maas_seviyesi\n",
      "0    ali   15   100         dusuk\n",
      "1   veli   16   150         dusuk\n",
      "2  kenan   17   240        yuksek\n",
      "3  hilal   33   350        yuksek\n",
      "4   ayse   45   110         dusuk\n",
      "5  evren   66   220        yuksek\n"
     ]
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "dataFrame1 = pd.DataFrame(dictionary)\n",
    "ortalama_maas = dataFrame1.MAAS.mean()\n",
    "# ortalama_maas_np = np.mean(dataFrame1.MAAS)\n",
    "dataFrame1[\"maas_seviyesi\"] = [\"dusuk\" if ortalama_maas > each else \"yuksek\" for each in dataFrame1.MAAS]\n",
    "print(dataFrame1)\n",
    "print(dataFrame1.columns)\n",
    "dataFrame1.columns = [ each.lower() for each in dataFrame1.columns] \n",
    "print(dataFrame1.columns)\n",
    "dataFrame1.columns = [each.split()[0]+\"_\"+each.split()[1] if(len(each.split())>1) else each for each in dataFrame1.columns]\n",
    "print(dataFrame1) # bosluklu sutun adı varsa _ ekledı ama bızım verıler zaten bosluksuz"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "<a class=\"anchor\" id=\"24.\"></a> \n",
    "## drop and concatenating"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "_uuid": "9a36d4d2063ed86983a48b9933763a05c76d7f09",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    name  age  maas maas_seviyesi\n",
      "0    ali   15   100         dusuk\n",
      "1   veli   16   150         dusuk\n",
      "2  kenan   17   240        yuksek\n",
      "3  hilal   33   350        yuksek\n",
      "4   ayse   45   110         dusuk\n",
      "    name  age  maas maas_seviyesi\n",
      "1   veli   16   150         dusuk\n",
      "2  kenan   17   240        yuksek\n",
      "3  hilal   33   350        yuksek\n",
      "4   ayse   45   110         dusuk\n",
      "5  evren   66   220        yuksek\n",
      "    name  age  maas maas_seviyesi\n",
      "0    ali   15   100         dusuk\n",
      "1   veli   16   150         dusuk\n",
      "2  kenan   17   240        yuksek\n",
      "3  hilal   33   350        yuksek\n",
      "4   ayse   45   110         dusuk\n",
      "1   veli   16   150         dusuk\n",
      "2  kenan   17   240        yuksek\n",
      "3  hilal   33   350        yuksek\n",
      "4   ayse   45   110         dusuk\n",
      "5  evren   66   220        yuksek\n",
      "   maas  age\n",
      "0   100   15\n",
      "1   150   16\n",
      "2   240   17\n",
      "3   350   33\n",
      "4   110   45\n",
      "5   220   66\n"
     ]
    }
   ],
   "source": [
    "\n",
    "dataFrame1[\"yeni_feature\"] = [-1,-2,-3,-4,-5,-6]\n",
    "dataFrame1.drop([\"yeni_feature\"],axis=1,inplace = True)\n",
    "# dataFrame1 = dataFrame1.drop([\"yeni_feature\"],axis=1)\n",
    "data1 = dataFrame1.head()\n",
    "print(data1)\n",
    "data2 = dataFrame1.tail()\n",
    "print(data2)\n",
    "# vertical\n",
    "data_concat = pd.concat([data1,data2],axis=0) #dusey bırlestırdı yanı ust uste\n",
    "print(data_concat)\n",
    "# horizontal\n",
    "maas = dataFrame1.maas\n",
    "age = dataFrame1.age\n",
    "data_h_concat = pd.concat([maas,age],axis=1) #yatay bırlestırdı yanı yanyana\n",
    "print(data_h_concat)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "<a class=\"anchor\" id=\"25.\"></a> \n",
    "## transforming data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "_uuid": "8dfdfc99525c62b3219467cd6199063368a379a9",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    name  age  maas maas_seviyesi  apply_metodu  list_comp\n",
      "0    ali   15   100         dusuk            30         30\n",
      "1   veli   16   150         dusuk            32         32\n",
      "2  kenan   17   240        yuksek            34         34\n",
      "3  hilal   33   350        yuksek            66         66\n",
      "4   ayse   45   110         dusuk            90         90\n",
      "5  evren   66   220        yuksek           132        132\n"
     ]
    }
   ],
   "source": [
    "\n",
    "def multiply(age):\n",
    "    return age*2\n",
    "dataFrame1[\"apply_metodu\"] = dataFrame1.age.apply(multiply)\n",
    "dataFrame1[\"list_comp\"] = [ each*2 for each in dataFrame1.age]\n",
    "print(dataFrame1)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "      \n",
    "<a class=\"anchor\" id=\"26.\"></a> \n",
    "## iteration example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "_uuid": "793a4edc49e0f8da8ab4b87808dc0bc74e413ed6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "r\n",
      "o n a l d o\n"
     ]
    }
   ],
   "source": [
    "\n",
    "name = \"ronaldo\"\n",
    "it = iter(name)\n",
    "print(next(it))    # print next iteration\n",
    "print(*it)         # print remaining iteration"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "<a class=\"anchor\" id=\"27.\"></a> \n",
    "## zip example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "_uuid": "c67a856e50020bcf76c46641158269b20abefe7d"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<zip object at 0x7f28e50d5188>\n",
      "[(1, 5), (2, 6), (3, 7), (4, 8)]\n",
      "-----\n",
      "(1, 2, 3, 4)\n",
      "(5, 6, 7, 8)\n",
      "<class 'tuple'>\n"
     ]
    }
   ],
   "source": [
    "\n",
    "list1 = [1,2,3,4]\n",
    "list2 = [5,6,7,8]\n",
    "z = zip(list1,list2)\n",
    "print(z)\n",
    "z_list = list(z)\n",
    "print(z_list)\n",
    "print(\"-----\")\n",
    "un_zip = zip(*z_list)\n",
    "un_list1,un_list2 = list(un_zip) # unzip returns tuble\n",
    "print(un_list1)\n",
    "print(un_list2)\n",
    "print(type(un_list2))"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "     \n",
    "<a class=\"anchor\" id=\"28.\"></a> \n",
    "## example of list comprehension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "_uuid": "b043f9a779b251c5ebdabece0e3e2c50b6cdef7e"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[2, 3, 4]\n",
      "[0, 100, 20]\n"
     ]
    }
   ],
   "source": [
    "\n",
    "num1 = [1,2,3]\n",
    "num2 = [i + 1 for i in num1 ]\n",
    "print(num2)\n",
    "# Conditionals on iterable\n",
    "num1 = [5,10,15]\n",
    "num2 = [i**2 if i == 10 else i-5 if i < 7 else i+5 for i in num1]\n",
    "print(num2)"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {
    "_uuid": "12cc13c9c94d044af4a43b73ef6e88d9c8b8e997"
   },
   "source": [
    "\n",
    "<a class=\"anchor\" id=\"29.\"></a> \n",
    "# 5.Visualization with Matplotlib"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "_uuid": "b1e8f0ec920434a3085efc3dd1964cd2842f85ad"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Index(['Id', 'SepalLengthCm', 'SepalWidthCm', 'PetalLengthCm', 'PetalWidthCm',\n",
      "       'Species'],\n",
      "      dtype='object')\n"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "data = pd.read_csv(\"../input/Iris.csv\")\n",
    "print(data.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "_uuid": "0876fef0405c953e7013f8120ede8fef5ea6dd53",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "['Iris-setosa' 'Iris-versicolor' 'Iris-virginica']\n"
     ]
    }
   ],
   "source": [
    "print(data.Species.unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "_uuid": "d6bd22f365ff765d75c5dba3f1d2609bfbe09523",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 6 columns):\n",
      "Id               150 non-null int64\n",
      "SepalLengthCm    150 non-null float64\n",
      "SepalWidthCm     150 non-null float64\n",
      "PetalLengthCm    150 non-null float64\n",
      "PetalWidthCm     150 non-null float64\n",
      "Species          150 non-null object\n",
      "dtypes: float64(4), int64(1), object(1)\n",
      "memory usage: 7.1+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "_uuid": "75859b43e3c96aa3908db8aff61d20c3091b23f4",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Id</th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.054000</td>\n",
       "      <td>3.758667</td>\n",
       "      <td>1.198667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>43.445368</td>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.433594</td>\n",
       "      <td>1.764420</td>\n",
       "      <td>0.763161</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>38.250000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>75.500000</td>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>112.750000</td>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               Id  SepalLengthCm      ...       PetalLengthCm  PetalWidthCm\n",
       "count  150.000000     150.000000      ...          150.000000    150.000000\n",
       "mean    75.500000       5.843333      ...            3.758667      1.198667\n",
       "std     43.445368       0.828066      ...            1.764420      0.763161\n",
       "min      1.000000       4.300000      ...            1.000000      0.100000\n",
       "25%     38.250000       5.100000      ...            1.600000      0.300000\n",
       "50%     75.500000       5.800000      ...            4.350000      1.300000\n",
       "75%    112.750000       6.400000      ...            5.100000      1.800000\n",
       "max    150.000000       7.900000      ...            6.900000      2.500000\n",
       "\n",
       "[8 rows x 5 columns]"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "_uuid": "5acceff465c1b5c2b27a5c40f5b2356f20cfbe94",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <th>PetalWidthCm</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>SepalLengthCm</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.109369</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817954</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>SepalWidthCm</th>\n",
       "      <td>-0.109369</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>-0.356544</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalLengthCm</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.420516</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962757</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>PetalWidthCm</th>\n",
       "      <td>0.817954</td>\n",
       "      <td>-0.356544</td>\n",
       "      <td>0.962757</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               SepalLengthCm      ...       PetalWidthCm\n",
       "SepalLengthCm       1.000000      ...           0.817954\n",
       "SepalWidthCm       -0.109369      ...          -0.356544\n",
       "PetalLengthCm       0.871754      ...           0.962757\n",
       "PetalWidthCm        0.817954      ...           1.000000\n",
       "\n",
       "[4 rows x 4 columns]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1 = data.drop([\"Id\"],axis=1)\n",
    "df1.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "_uuid": "808ae3fb573c385295bcd15d9a533a45ecb31043",
    "collapsed": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "             Id  SepalLengthCm      ...       PetalLengthCm  PetalWidthCm\n",
      "count  50.00000       50.00000      ...           50.000000      50.00000\n",
      "mean   25.50000        5.00600      ...            1.464000       0.24400\n",
      "std    14.57738        0.35249      ...            0.173511       0.10721\n",
      "min     1.00000        4.30000      ...            1.000000       0.10000\n",
      "25%    13.25000        4.80000      ...            1.400000       0.20000\n",
      "50%    25.50000        5.00000      ...            1.500000       0.20000\n",
      "75%    37.75000        5.20000      ...            1.575000       0.30000\n",
      "max    50.00000        5.80000      ...            1.900000       0.60000\n",
      "\n",
      "[8 rows x 5 columns]\n",
      "              Id  SepalLengthCm      ...       PetalLengthCm  PetalWidthCm\n",
      "count   50.00000      50.000000      ...           50.000000     50.000000\n",
      "mean    75.50000       5.936000      ...            4.260000      1.326000\n",
      "std     14.57738       0.516171      ...            0.469911      0.197753\n",
      "min     51.00000       4.900000      ...            3.000000      1.000000\n",
      "25%     63.25000       5.600000      ...            4.000000      1.200000\n",
      "50%     75.50000       5.900000      ...            4.350000      1.300000\n",
      "75%     87.75000       6.300000      ...            4.600000      1.500000\n",
      "max    100.00000       7.000000      ...            5.100000      1.800000\n",
      "\n",
      "[8 rows x 5 columns]\n"
     ]
    }
   ],
   "source": [
    "setosa = data[data.Species == \"Iris-setosa\"]\n",
    "versicolor = data[data.Species == \"Iris-versicolor\"]\n",
    "print(setosa.describe())\n",
    "print(versicolor.describe())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "_uuid": "432d8e2c3bb84bd9d7dcc084bd18347e816445b7"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "\n",
    "setosa = data[data.Species == \"Iris-setosa\"]\n",
    "versicolor = data[data.Species == \"Iris-versicolor\"]\n",
    "virginica = data[data.Species == \"Iris-virginica\"]\n",
    "\n",
    "plt.plot(setosa.Id,setosa.PetalLengthCm,color=\"red\",label= \"setosa\")\n",
    "plt.plot(versicolor.Id,versicolor.PetalLengthCm,color=\"green\",label= \"versicolor\")\n",
    "plt.plot(virginica.Id,virginica.PetalLengthCm,color=\"blue\",label= \"virginica\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"Id\")\n",
    "plt.ylabel(\"PetalLengthCm\")\n",
    "plt.show()\n",
    "\n",
    "# clf() = cleans it up again you can start a fresh\n",
    "#plt.clf()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "     \n",
    "<a class=\"anchor\" id=\"30.\"></a> \n",
    "## line Plot example"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "_uuid": "0ffe93b66174e50a54b722f9500f53041a355601"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "# color = color, label = label, linewidth = width of line, alpha = opacity, grid = grid, linestyle = sytle of line\n",
    "data.SepalLengthCm.plot(kind = 'line', color = 'g',label = 'SepalLengthCm',linewidth=1,alpha = 0.5,grid = True,linestyle = ':')\n",
    "data.PetalLengthCm.plot(color = 'r',label = 'PetalLengthCm',linewidth=1, alpha = 0.5,grid = True,linestyle = '-.')\n",
    "plt.legend(loc='upper right')     # legend = puts label into plot\n",
    "plt.xlabel('x axis')              # label = name of label\n",
    "plt.ylabel('y axis')\n",
    "plt.title('Line Plot')            # title = title of plot\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "<a class=\"anchor\" id=\"31.\"></a> \n",
    "## scatter plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "_uuid": "96399300317ddd073d22011e230f3aade5b5f23f"
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "setosa = data[data.Species == \"Iris-setosa\"]\n",
    "versicolor = data[data.Species == \"Iris-versicolor\"]\n",
    "virginica = data[data.Species == \"Iris-virginica\"]\n",
    "\n",
    "plt.scatter(setosa.PetalLengthCm,setosa.PetalWidthCm,color=\"red\",label=\"setosa\")\n",
    "plt.scatter(versicolor.PetalLengthCm,versicolor.PetalWidthCm,color=\"green\",label=\"versicolor\")\n",
    "plt.scatter(virginica.PetalLengthCm,virginica.PetalWidthCm,color=\"blue\",label=\"virginica\")\n",
    "plt.legend()\n",
    "plt.xlabel(\"PetalLengthCm\")\n",
    "plt.ylabel(\"PetalWidthCm\")\n",
    "plt.title(\"scatter plot\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "      \n",
    "<a class=\"anchor\" id=\"32.\"></a> \n",
    "## histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "_uuid": "3c06cf8beb0415b422b1d398b0e1400cba83c749"
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plt.hist(setosa.PetalLengthCm,bins= 10)\n",
    "plt.xlabel(\"PetalLengthCm values\")\n",
    "plt.ylabel(\"frekans\")\n",
    "plt.title(\"hist\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "\n",
    "<a class=\"anchor\" id=\"33.\"></a> \n",
    "## bar plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "_uuid": "031b747e37242b1e328833051b5b7c356f96baf8",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "import numpy as np\n",
    "\n",
    "x = np.array([1,2,3,4,5,6,7])\n",
    "a = [\"turkey\",\"usa\",\"a\",\"b\",\"v\",\"d\",\"s\"]\n",
    "y = x*2+5\n",
    "\n",
    "plt.bar(a,y)\n",
    "plt.title(\"bar plot\")\n",
    "plt.xlabel(\"x\")\n",
    "plt.ylabel(\"y\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "execution_count": null,
   "metadata": {},
   "source": [
    "    \n",
    "<a class=\"anchor\" id=\"34.\"></a> \n",
    "## subplots"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "_uuid": "6a7dd2ac850611be29f9b5d96d8529344c23d086",
    "collapsed": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "df1 = data.drop([\"Id\"],axis=1)\n",
    "df1.plot(grid=True,alpha= 0.9,subplots = True)\n",
    "plt.show()\n",
    "\n",
    "setosa = data[data.Species == \"Iris-setosa\"]\n",
    "versicolor = data[data.Species == \"Iris-versicolor\"]\n",
    "virginica = data[data.Species == \"Iris-virginica\"]\n",
    "\n",
    "plt.subplot(2,1,1)\n",
    "plt.plot(setosa.Id,setosa.PetalLengthCm,color=\"red\",label= \"setosa\")\n",
    "plt.ylabel(\"setosa -PetalLengthCm\")\n",
    "plt.subplot(2,1,2)\n",
    "plt.plot(versicolor.Id,versicolor.PetalLengthCm,color=\"green\",label= \"versicolor\")\n",
    "plt.ylabel(\"versicolor -PetalLengthCm\")\n",
    "plt.show()\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}