31102017-Ders (1).ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "Dictionaries\n",
    "\n",
    "\n",
    "json"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Taylan\n",
      "Taylan Guzin tahta\n",
      "Taylan Cemgil\n",
      "Guzin Erdem\n",
      "tahta odun\n"
     ]
    }
   ],
   "source": [
    "person={'name':'Taylan','surname':'Cemgil','age': 48}\n",
    "person2={'name':'Guzin','surname':'Erdem','age': 28}\n",
    "person3={'name':'tahta','surname':'odun','age': 100}\n",
    "#person'lar dictinary, people ise bu dictionarylerden oluşan liste\n",
    "\n",
    "print(person['name'])\n",
    "\n",
    "print(person['name'], person2['name'], person3['name'])\n",
    "\n",
    "people=[person,person2,person3]\n",
    "\n",
    "for x in people:\n",
    "    print(x['name'],x['surname'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#txt='JSON (Javascript object notation) is a lightweight data -interchange format'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{' ': 18,\n",
       " '(': 1,\n",
       " ')': 1,\n",
       " '-': 1,\n",
       " '.': 2,\n",
       " 'I': 1,\n",
       " 'J': 2,\n",
       " 'N': 2,\n",
       " 'O': 2,\n",
       " 'S': 1,\n",
       " 'a': 12,\n",
       " 'b': 1,\n",
       " 'c': 3,\n",
       " 'd': 3,\n",
       " 'e': 7,\n",
       " 'f': 2,\n",
       " 'g': 3,\n",
       " 'h': 4,\n",
       " 'i': 8,\n",
       " 'j': 1,\n",
       " 'l': 1,\n",
       " 'm': 2,\n",
       " 'n': 5,\n",
       " 'o': 5,\n",
       " 'p': 1,\n",
       " 'r': 6,\n",
       " 's': 5,\n",
       " 't': 12,\n",
       " 'u': 1,\n",
       " 'v': 1,\n",
       " 'w': 2,\n",
       " 'y': 1}"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#text içindeki her karakteri saydırmak:\n",
    "\n",
    "txt='JSON (Javascript Object Notation) is a lightweight data -interchange format. It is easy for humans to read and write.It is easy for machines to parse and generate. It is based on a subset of the JavaScript Programming Language, Standard ECMA-262 3rd Edition - December 1999.'\n",
    "counts={}\n",
    "for i in range (len(txt)):\n",
    "    key=txt[i]\n",
    "    if key in counts.keys():\n",
    "        counts[key] += 1\n",
    "    else:\n",
    "        counts[key] = 1\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'a': 12, 'b': 15, 'c': 8, 'd': 0}"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "txt2='babababcccbcbcbcbcbcaabababababaaba'\n",
    "counts={'a':0,'b':0,'c':0}\n",
    "for i in range(len(txt2)):\n",
    "    counts[txt2[i]] += 1\n",
    "    \n",
    "counts\n",
    "\n",
    "counts['d'] = 0\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "ab\n"
     ]
    }
   ],
   "source": [
    "print('a'+'b')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "txt2='babababcccbcbcbcbcbcaabababababaaba'\n",
    "counts={'a':0,'b':0,'b':0,'c':0}\n",
    "for i in range(len(txt2)):\n",
    "    counts[txt2[i]] += 1\n",
    "    \n",
    "counts\n",
    "\n",
    "counts['d'] = 0\n",
    "counts"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'aa': 2, 'ab': 9, 'ba': 9, 'bc': 6, 'ca': 1, 'cb': 5, 'cc': 2}"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "counts = {}\n",
    "for i in range (len(txt2)-1):\n",
    "    key=txt2[i]+txt2[i+1]\n",
    "    if key in counts.keys():\n",
    "        counts[key] += 1\n",
    "    else:\n",
    "        counts[key] = 1\n",
    "counts "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#gereksiz karakterleri çıkart:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JSON\n",
      "Javascript\n",
      "Object\n",
      "Notation\n",
      "is\n",
      "a\n",
      "lightweight\n",
      "data\n",
      "interchange\n",
      "format\n",
      "It\n",
      "is\n",
      "easy\n",
      "for\n",
      "humans\n",
      "to\n",
      "read\n",
      "and\n"
     ]
    }
   ],
   "source": [
    "temp= txt\n",
    "removelist= ['(',')','.',',','-']\n",
    "while True:\n",
    "    words =temp.partition(' ')\n",
    "    if words[2] == '':\n",
    "        break\n",
    "    w = words[0]\n",
    "    \n",
    "    for l in removelist:\n",
    "        w = w.replace(l,'')\n",
    "    if len(w)>0:\n",
    "        print(w)\n",
    "    temp = words[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'abcdfg'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = 'abc dfg'\n",
    "c=x.partition(' ')\n",
    "c\n",
    "\n",
    "x.replace('a','')\n",
    "x.replace(' ','')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'JJSSOONN  ((JJaavvaassccrriipptt  OObbjjeecctt  NNoottaattiioonn))  iiss  aa  lliigghhttwweeiigghhtt  ddaattaa  --iinntteerrcchhaannggee  ffoorrmmaatt..  IItt  iiss  eeaassyy  ffoorr  hhuummaannss  ttoo  rreeaadd  aanndd  wwrriittee..'"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "txt3='JSON (Javascript Object Notation) is a lightweight data -interchange format. It is easy for humans to read and write.'\n",
    "\n",
    "\n",
    "dup1=str()\n",
    "\n",
    "for i in txt3:\n",
    "    dup1 += i+i\n",
    "dup1\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "JSON (Javascript Object Notation) is a lightweight data -interchange format. It is easy for humans to read and write.\n",
      "JJSSOONN  ((JJaavvaassccrriipptt  OObbjjeecctt  NNoottaattiioonn))  iiss  aa  lliigghhttwweeiigghhtt  ddaattaa  --iinntteerrcchhaannggee  ffoorrmmaatt..  IItt  iiss  eeaassyy  ffoorr  hhuummaannss  ttoo  rreeaadd  aanndd  wwrriittee..\n",
      "JJSSOONN  ((JJaavvaassccrriipptt  OObbjjeecctt  NNoottaattiioonn))  iiss  aa  lliigghhttwweeiigghhtt  ddaattaa  --iinntteerrcchhaannggee  ffoorrmmaatt..  IItt  iiss  eeaassyy  ffoorr  hhuummaannss  ttoo  rreeaadd  aanndd  wwrriittee..JJJSSSOOONNN   (((JJJaaavvvaaassscccrrriiipppttt   OOObbbjjjeeecccttt   NNNoootttaaatttiiiooonnn)))   iiisss   aaa   llliiiggghhhtttwwweeeiiiggghhhttt   dddaaatttaaa   ---iiinnnttteeerrrccchhhaaannngggeee   fffooorrrmmmaaattt...   IIIttt   iiisss   eeeaaasssyyy   fffooorrr   hhhuuummmaaannnsss   tttooo   rrreeeaaaddd   aaannnddd   wwwrrriiittteee...\n"
     ]
    }
   ],
   "source": [
    "print(txt3)\n",
    "\n",
    "txt4=''\n",
    "\n",
    "for i in range(len(txt3)):\n",
    "    txt4=txt4+txt3[i]*2\n",
    "    \n",
    "print(txt4)\n",
    "\n",
    "\n",
    "for i in range(len(txt3)):\n",
    "    txt4=txt4+txt3[i]*3\n",
    "    \n",
    "print(txt4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "a = (3,'abc',3.4)\n",
    "a[2] = 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "ename": "ValueError",
     "evalue": "Shape of passed values is (2, 5), indices imply (2, 4)",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mcreate_block_manager_from_arrays\u001b[1;34m(arrays, names, axes)\u001b[0m\n\u001b[0;32m   4309\u001b[0m         \u001b[0mblocks\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mform_blocks\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4310\u001b[1;33m         \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mBlockManager\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mblocks\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4311\u001b[0m         \u001b[0mmgr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_consolidate_inplace\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, blocks, axes, do_integrity_check, fastpath)\u001b[0m\n\u001b[0;32m   2794\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mdo_integrity_check\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2795\u001b[1;33m             \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_verify_integrity\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2796\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36m_verify_integrity\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m   3005\u001b[0m             \u001b[1;32mif\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_verify_integrity\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mmgr_shape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3006\u001b[1;33m                 \u001b[0mconstruction_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mtot_items\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mblock\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   3007\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mitems\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m!=\u001b[0m \u001b[0mtot_items\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mconstruction_error\u001b[1;34m(tot_items, block_shape, axes, e)\u001b[0m\n\u001b[0;32m   4279\u001b[0m     raise ValueError(\"Shape of passed values is {0}, indices imply {1}\".format(\n\u001b[1;32m-> 4280\u001b[1;33m         passed, implied))\n\u001b[0m\u001b[0;32m   4281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Shape of passed values is (2, 5), indices imply (2, 4)",
      "\nDuring handling of the above exception, another exception occurred:\n",
      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-17-7aee193485c1>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[0;32m     24\u001b[0m \u001b[0mdf3\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'Name'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Surname'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0msurnames\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     25\u001b[0m \u001b[0mdf3\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 26\u001b[1;33m \u001b[0mdf4\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;33m{\u001b[0m\u001b[1;34m'Name'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mnames\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;34m'Surname'\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0msurnames\u001b[0m\u001b[1;33m}\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnames\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     27\u001b[0m \u001b[0md4\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, data, index, columns, dtype, copy)\u001b[0m\n\u001b[0;32m    273\u001b[0m                                  dtype=dtype, copy=copy)\n\u001b[0;32m    274\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdict\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 275\u001b[1;33m             \u001b[0mmgr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_init_dict\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    276\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mMaskedArray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    277\u001b[0m             \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mma\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmrecords\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mmrecords\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_init_dict\u001b[1;34m(self, data, index, columns, dtype)\u001b[0m\n\u001b[0;32m    409\u001b[0m             \u001b[0marrays\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mdata\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mk\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mk\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mkeys\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    410\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 411\u001b[1;33m         \u001b[1;32mreturn\u001b[0m \u001b[0m_arrays_to_mgr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    412\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    413\u001b[0m     \u001b[1;32mdef\u001b[0m \u001b[0m_init_ndarray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mvalues\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mindex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcolumns\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcopy\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_arrays_to_mgr\u001b[1;34m(arrays, arr_names, index, columns, dtype)\u001b[0m\n\u001b[0;32m   5504\u001b[0m     \u001b[0maxes\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0m_ensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0m_ensure_index\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5505\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 5506\u001b[1;33m     \u001b[1;32mreturn\u001b[0m \u001b[0mcreate_block_manager_from_arrays\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marr_names\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   5507\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   5508\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mcreate_block_manager_from_arrays\u001b[1;34m(arrays, names, axes)\u001b[0m\n\u001b[0;32m   4312\u001b[0m         \u001b[1;32mreturn\u001b[0m \u001b[0mmgr\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4313\u001b[0m     \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 4314\u001b[1;33m         \u001b[0mconstruction_error\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0marrays\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0marrays\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxes\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   4315\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4316\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\internals.py\u001b[0m in \u001b[0;36mconstruction_error\u001b[1;34m(tot_items, block_shape, axes, e)\u001b[0m\n\u001b[0;32m   4278\u001b[0m         \u001b[1;32mraise\u001b[0m \u001b[0mValueError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Empty data passed with indices specified.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4279\u001b[0m     raise ValueError(\"Shape of passed values is {0}, indices imply {1}\".format(\n\u001b[1;32m-> 4280\u001b[1;33m         passed, implied))\n\u001b[0m\u001b[0;32m   4281\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   4282\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mValueError\u001b[0m: Shape of passed values is (2, 5), indices imply (2, 4)"
     ]
    }
   ],
   "source": [
    "#pandas lib genelde finansal veriler için kullanılıyor!!\n",
    "\n",
    "import pandas as pd\n",
    "\n",
    "ids=[100,200,300,301,308]\n",
    "names=['Ali','Veli','Ayse','Fatma','Gamze']\n",
    "surnames=['Yilmaz','Gorali','Tasci','Bakkaloglu','Yilmaz']\n",
    "ages=[27,32,19,28,32]\n",
    "gender=['M','M','F','F','F']\n",
    "city=['Istanbul','Istanbul','Ankara','Istanbul','Izmir']\n",
    "\n",
    "#tuple'lardan oluşan liste\n",
    "numberplate=[('Adana','01'),('Eskisehir','26'),('Istanbul','34'),('Ankara','06'),('Izmir','35'),('Denizli','20')]\n",
    "a=pd.Series(names)\n",
    "\n",
    "\n",
    "df=pd.DataFrame(data={'Name': names, 'Surname': surnames, 'Age': ages, 'Gender': gender, 'City': city},index=ids)\n",
    "\n",
    "#df1=df[[3,0,2,1]]\n",
    "\n",
    "#df1= df\n",
    "#df=pd.DataFrame(numberplate,columns=['City','Plate'])\n",
    "\n",
    "df3=pd.DataFrame(data={'Name': names, 'Surname': surnames})\n",
    "df3\n",
    "df4=pd.DataFrame(data={'Name': names, 'Surname': surnames}, index=range(1,len(names)+1))\n",
    "d4"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Plate</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adana</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Eskisehir</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Istanbul</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ankara</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Izmir</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Denizli</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City Plate\n",
       "0      Adana    01\n",
       "1  Eskisehir    26\n",
       "2   Istanbul    34\n",
       "3     Ankara    06\n",
       "4      Izmir    35\n",
       "5    Denizli    20"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1= df\n",
    "#numberplate'deki taple'lardan birine City label'ı diğerine de Plate(Plaka) label'ı atayarak alacak:\n",
    "df=pd.DataFrame(numberplate,columns=['City','Plate'])\n",
    "df2=df\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Lat</th>\n",
       "      <th>Lon</th>\n",
       "      <th>Place</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>40.001</td>\n",
       "      <td>29.37</td>\n",
       "      <td>Kadikoy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>41.002</td>\n",
       "      <td>29.38</td>\n",
       "      <td>Etiler</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>40.376</td>\n",
       "      <td>28.97</td>\n",
       "      <td>Yesilkoy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      Lat    Lon     Place\n",
       "0  40.001  29.37   Kadikoy\n",
       "1  41.002  29.38    Etiler\n",
       "2  40.376  28.97  Yesilkoy"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data=[('Kadikoy',40.001, 29.37),('Etiler', 41.002,29.38),('Yesilkoy',40.376,28.97)]\n",
    "\n",
    "mahalle=pd.DataFrame(data, columns=['Place','Lat','Lon'])\n",
    "mahalle[['Lat','Lon']]\n",
    "mahalle[['Lat','Lon','Place']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>City</th>\n",
       "      <th>Plate_x</th>\n",
       "      <th>Plate_y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adana</td>\n",
       "      <td>01</td>\n",
       "      <td>01</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Eskisehir</td>\n",
       "      <td>26</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Istanbul</td>\n",
       "      <td>34</td>\n",
       "      <td>34</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Ankara</td>\n",
       "      <td>06</td>\n",
       "      <td>06</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Izmir</td>\n",
       "      <td>35</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Denizli</td>\n",
       "      <td>20</td>\n",
       "      <td>20</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        City Plate_x Plate_y\n",
       "0      Adana      01      01\n",
       "1  Eskisehir      26      26\n",
       "2   Istanbul      34      34\n",
       "3     Ankara      06      06\n",
       "4      Izmir      35      35\n",
       "5    Denizli      20      20"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#raw merge gibi karşısına ne geliyorsa onları joinliyor(ortak olanları joinliyor inner join dediğimiz için)\n",
    "#(outer join var tüm kombinasyonları hesaba katarak merge ediyor)\n",
    "pd.merge(df1,df2,on='City', how='inner')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "#jupitere  kütüphanesine datayı at;(pandas lib) pd.read_cvs(\"bilgisayardaki lokasyon(R'daki gibi)\", index_col=0, parse_dates=True)\n",
    "#pd.read yazdıktan sonra tab'e tıklayarak seçenekleri görebilirsin!!!nasıl kullanıldığına bakmak için de shift+tab"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "\"['Name' 'Plate'] not in index\"",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32m<ipython-input-22-c5570bc94680>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m()\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mpd\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmerge\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdf1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mdf2\u001b[0m\u001b[1;33m,\u001b[0m\u001b[0mon\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'City'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhow\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'inner'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Name'\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;34m'Plate'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1956\u001b[0m         \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mSeries\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIndex\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlist\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1957\u001b[0m             \u001b[1;31m# either boolean or fancy integer index\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1958\u001b[1;33m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_array\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1959\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1960\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_frame\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m_getitem_array\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   2000\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2001\u001b[0m         \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2002\u001b[1;33m             \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_convert_to_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   2003\u001b[0m             \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtake\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   2004\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32m~\\Anaconda3\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_convert_to_indexer\u001b[1;34m(self, obj, axis, is_setter)\u001b[0m\n\u001b[0;32m   1229\u001b[0m                 \u001b[0mmask\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcheck\u001b[0m \u001b[1;33m==\u001b[0m \u001b[1;33m-\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1230\u001b[0m                 \u001b[1;32mif\u001b[0m \u001b[0mmask\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0many\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1231\u001b[1;33m                     \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'%s not in index'\u001b[0m \u001b[1;33m%\u001b[0m \u001b[0mobjarr\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mmask\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m   1232\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   1233\u001b[0m                 \u001b[1;32mreturn\u001b[0m \u001b[0m_values_from_object\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: \"['Name' 'Plate'] not in index\""
     ]
    }
   ],
   "source": [
    "pd.merge(df1,df2,on='City', how='inner')[['Name','Plate']]\n",
    "pd.merge(df1,df2,on='City', how='inner').sort_values['Plate']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "execution_count": null,
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   "cell_type": "code",
   "execution_count": null,
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