{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Vedecke aplikace\n",
    "Projdeme zakladni balicky a jejich pouziti pri zpracovani dat v Pythonu.\n",
    "http://scipy.org/"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## matplotlib\n",
    "Comprehensive 2D Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "vars = [1,2,5,8,52,150]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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TSi9FxGLgIOA5gIgYBowmu45BklQFTrOsdSl7kZBSehd4oe9YRLwLvJlSml8Y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\nskiQJElFWSRIkqSiLBIkSVJR/x+GoOBS2rxUIwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aef906947f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(vars)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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czgyWLl23dKdNS0a7Cxcmvw64MKSBGW3jmJ2AvYHDgP2B6ry6/nPgYZhZarZD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\nkSzw/2A+Rs87cTFYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aef90694860>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(vars)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "x = [1,2,5,8,52,150]\n",
    "y = [10,23,54,87,258,623]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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kW5N8NMmSPfS7KMl97Wt1fZJFM1HvE5XkbUkeT3JpX/vQjCfJkUk+lOSBtt7P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\nSZKqMXhIkqRqDB6SJKma/w8t64GkI+VU/wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aef907a4eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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DjTfCBRdAlSqwdKlfnXHaaWFHJiJSuVRIiMRh2za46y5o1syvxnjmGf9vu3Zh\nRyYiEg7NkRApg+++g2nT/GTKXbv8fIg774SaNcOOTEQkXCokRI5i4UI/gfKDD+BXv/JzIBo0CDsq\nEZHUoKENkcMoLoarr4ZOnaB2bVi+3K/OUBEhIvJPKiRESvn6axg4EM4+G95/H2bMgCVL/OoMERE5\nmIY2RAL79sF//ReMHOk/Hj3aFxQ1aoQdmYhI6lIhIRnPOZg7FwYNgpUr4dZbfRFRr17YkYmIpD4N\nbUhG++gjuOwyuOoq+PGPoagInnhCRYSISFmpkJCMtG2bX4nRvDmsWQNz5sCiRf65iIiUnYY2JKOU\nlPhNpIYMgR07/BBGbi5Urx52ZCIi6Uk9EpIxCguhfXvo3t2fyFlcDEOHqogQESkPFRISeV99Bb17\nQ6tWvhfif/4H8vK0H4SISDJoaEMia/9+ePxxuPtuP6QxaRL06QPH6v96EZGkUY+ERNJbb/keiD59\n4Lrr/LLO229XESEikmwqJCRSNmyAbt38XIhjj/Unc06bBqecEnZkIiLRpEJCImHfPvjd76BpU5g3\nz+8FkZ8PbdqEHZmISLSpo1fS3qJFftiiuNgPZYwaBXXqhB2ViEhmUI+EpK1PP4Ubb4RLL4WTTvLL\nO6dMUREhIlKZVEhI2vn2W7+RVLNm8Pbb8Oc/w5tvaldKEZEwaGhD0oZz8PLLfifKdev8vyNGQK1a\nYUcmIpK51CMhaeGTT+DKK6FLF2jUCN57Dx56SEWEiEjYVEhIStu5E4YNg7PP9id1zp4N8+fDGWeE\nHZmIiICGNiRFOQczZsCdd/otrocN8wdtHXdc2JGJiEgs9UhIynn/fX+oVk4OtG4NK1bAyJEqIkRE\nUpEKCUkZ//gHDBgALVr4HSrnz4cXXoDTTgs7MhERORwNbUjoSkrgySf9kd67d8PYsb6gqFYt7MhE\nRORo1CMhofrb36BtW7j1VujUye9OOXiwiggRkXShQkJCsXkz/OY3/iyMb7+FN96AZ5+F+vXDjkxE\nROIRVyFhZsPMbLmZbTezTWY228yaHKLdKDNbb2bfmNlCM2tU6n51M5tqZlvMbIeZzTIznc+YAb77\nDqZOhSZNYNYsmDwZCgqgY8ewIxMRkUTE2yPRAZgCtAEuAaoCr5rZ9/PpzWwI0A/oBbQGdgELzCy2\ns3oScCVwA9ARqA88n2AOkiaWLIHsbOjf35+RsXIl9Ovnj/sWEZH0FNdbuHPuitjnZtYd+BLIBpYG\nlwcAo506w237AAATEklEQVRzrwRtugGbgGuBmWaWBfQEujrn3gja9ABWmFlr59zyxNORVLR+vZ/3\n8Nxzfjlnfj6cd17YUYmISDKUd47EiYADvgYws4ZAPWDxgQbOue1APtA2uNQKX8DEtikGPotpIxGw\ndy+MHw9Nm8LChfCnP8GyZSoiRESiJOFOZTMz/BDFUufcR8HlevjCYlOp5puCewB1gb1BgXG4NpLm\nFizwSzhXrYK+feH+++HEE8OOSkREkq08PRJ/AM4EuiYpFomANWvguuugc2eoVw+KiuD3v1cRISIS\nVQn1SJjZI8AVQAfn3IaYWxsBw/c6xPZK1AWKYtpUM7OsUr0SdYN7h5Wbm0vt2rUPupaTk0NOTk4i\naUgS7d4N48bBgw/CSSfB9Olw001gFnZkIiKZIy8vj7y8vIOubdu2rUK/pjnn4vsEX0R0AS5wzq0+\nxP31wHjn3MTgeRa+qOjmnPtL8HwzfrLl7KBNU2AFcP6hJluaWUugoKCggJYtW8YVr1Qs52DOHMjN\nhS++gEGD4O674YQTwo5MREQACgsLyc7OBsh2zhUm+/Xj6pEwsz8AOcA1wC4zqxvc2uac+zb4eBIw\nwsxWAWuB0cDnwBzwky/NbBowwcy2AjuAycBbWrGRXoqL4fbb4dVX4fLL/byIJj/YVURERKIs3qGN\n3vjJlK+Xut4DeBrAOTfOzI4HHsOv6lgCXO6c2xvTPhfYD8wCqgPzgb7xBi/h2LEDRo+GSZOgQQN4\n6SW46ioNY4iIZKJ495Eo0+RM59xIYOQR7u8B+gcPSRPOQV4e3HmnP6nznnv8/hA1aoQdmYiIhEVn\nbUiZvPsuXHAB3Hwz/PznsGKFLyRURIiIZDYVEnJEW7f6baxbtvQHbS1c6M/I+Nd/DTsyERFJBTrl\nQA6ppMTvRDlsGOzZ45d29u+v471FRORg6pGQH1i+HM4/3x/zffnlfnXGoEEqIkRE5IdUSMj3vvwS\nbr0V2rSBfftg6VJ4+mk49dSwIxMRkVSloQ3hu+/gD3+Ae++FY47xH/fqBVWqhB2ZiIikOvVIZLjX\nX4cWLWDgQOjaFVauhN/+VkWEiIiUjQqJDPX5575wuPBCqFUL/vY3ePRROPnksCMTEZF0okIiw+zZ\n4w/WatrU90Y89ZSfC+G3YRcREYmP5khkkHnzYMAAWL3an5Fx331Q6jBVERGRuKhHIgOsXg1dusAV\nV8BPfuJ3qZwwQUWEiIiUnwqJCPvmG78S48wzoagIZs6ERYvgrLPCjkxERKJCQxsR5By88ALccQds\n3OgP1ho2DGrWDDsyERGJGhUSEbNihZ//sGiRP9p78WJo1CjsqEREJKo0tBER27f7473POQfWrIFX\nXoGXX1YRISIiFUs9EmnOOXj2WbjrLl9M3H+/H9LQ8d4iIlIZ1CORxoqKoEMH6NYNOnaEjz+G4cNV\nRIiISOVRIZGGvv4a+vSBVq1g61Y/D2LGDL+0U0REpDJpaCON7N8Pf/wj3H23P53zd7+Dvn2hatWw\nIxMRkUylHok0sWwZtG4NvXvD1Vf7w7UGDlQRISIi4VIhkeI2boTu3eHnP/fP334b/vu/oW7dUMMS\nEREBVEikrH37YOJEf7jWK6/AY4/B8uXQtm3YkYmIiPyT5kikoNdeg/79/SqM3r1h9Gj4l38JOyoR\nEZEfUo9ECvnsM7jpJrj4YqhTBwoKYOpUFREiIpK6VEikgG+/hf/8T2jWDJYsgWee8f+ee27YkYmI\niByZhjZC9sorfvXFp5/6f++5B7Kywo5KRESkbNQjEZJVq/yhWldfDQ0bwnvvwfjxKiJERCS9qJCo\nZLt2+Q2lzjoL3n8fnn8eXn3VD2uIiIikGw1tVBLnYNYsf6DW5s0wdCgMGQLHHx92ZCIiIolTj0Ql\n+PBDvxLjppsgOxs++sif0qkiQkRE0p0KiQq0bRvk5kLz5vD55zB3Lrz4Ipx+etiRiYiIJIeGNipA\nSYlfwnnXXX5OxH/+p1+RUb162JGJiIgkl3okkqygANq18+djXHSR351yyBAVESIiEk0qJJJkyxa4\n7TY47zzYuRNefx3y8qBBg7AjExERqThxFxJm1sHMXjKzL8ysxMyuOUSbUWa23sy+MbOFZtao1P3q\nZjbVzLaY2Q4zm2Vmp5QnkbDs3w9/+AM0aQIzZsDvfw9FRXDBBWFHJiIiUvES6ZGoCfwv0AdwpW+a\n2RCgH9ALaA3sAhaYWbWYZpOAK4EbgI5AfeD5BGIJ1VtvQatW0LcvXH89rFzpD9s6VjNPREQkQ8T9\nK885Nx+YD2BmdogmA4DRzrlXgjbdgE3AtcBMM8sCegJdnXNvBG16ACvMrLVzbnlCmVSiDRv8RMpn\nn/VDGfn50Lp12FGJiIhUvqTOkTCzhkA9YPGBa8657UA+0Da41ApfwMS2KQY+i2mTkvbuhYcf9sMY\n8+fDH/8I77yjIkJERDJXsjvh6+GHOzaVur4puAdQF9gbFBiHa5NyFi6E22/3wxd9+sCoUf6obxER\nkUyWVqP5ubm51K5d+6BrOTk55OTkVNjX/PRTv631Cy9Ahw4wfbrfYEpERCTV5OXlkZeXd9C1bdu2\nVejXTHYhsREwfK9DbK9EXaAopk01M8sq1StRN7h3WBMnTqRly5ZJDPfwdu/2p3GOHQv/8i/w3HPQ\ntSscclaIiIhICjjUH9eFhYVkZ2dX2NdM6hwJ59wafDFw8YFrweTKNsDbwaUC4LtSbZoCPwWWJTOe\nRDgHL73kT+d84AE/nPHxx5CToyJCRESktLh7JMysJtAI3/MAcLqZNQe+ds6twy/tHGFmq4C1wGjg\nc2AO+MmXZjYNmGBmW4EdwGTgrbBXbKxcCQMG+ImUl10G8+ZB06ZhRiQiIpLaEhnaaAX8D35SpQN+\nF1x/CujpnBtnZscDjwEnAkuAy51ze2NeIxfYD8wCquOXk/ZNKIMk2LnT9z5MmAA//rE/WOuaa9QD\nISIicjSJ7CPxBkcZEnHOjQRGHuH+HqB/8AjVJ5/AhRfCV1/BiBEweDAcd1zYUYmIiKSHtFq1kWzO\n+aWc1avDihVw2mlhRyQiIpJeMrqQeOEFWLQIXnlFRYSIiEgiMvb0z127IDcXrroKrrwy7GhERETS\nU8YWEmPGwJdf+tM6RUREJDEZWUh88ok/M2PIEDj99LCjERERSV8ZV0g45/eKqF8fhg4NOxoREZH0\nlnGTLV96yW80NXu2lnmKiIiUV0b1SOzeDQMHQufO0KVL2NGIiIikv4zqkXjoIVi/Hl59VbtWioiI\nJEPG9EisXg0PPgiDBkHjxmFHIyIiEg0ZU0gMHAinnAJ33x12JCIiItGREUMbf/0rvPwy/OUvULNm\n2NGIiIhER+R7JL791i/3vPhiuOGGsKMRERGJlsj3SDz8MHz6qe+R0ARLERGR5Ip0j8Tq1X4r7Nxc\naNYs7GhERESiJ7KFhHPQu7efYHnvvWFHIyIiEk2RHdp49llYuBDmzoUTTgg7GhERkWiKZI/E5s1+\nOCMnBy6/POxoREREoiuShcSgQVBSApMmhR2JiIhItEVuaOPVV+GZZ+BPf/LzI0RERKTiRKpHYtcu\nP8Hywguhe/ewoxEREYm+SPVI3H+/P5RrwQLtGSEiIlIZIlNIFBXBhAkwerQO5RIREakskRja+O47\n+M1v4Mwz4c47w45GREQkc0SiR2LyZCgshGXLoGrVsKMRERHJHGnfI7FmDdxzD/TvD23ahB2NiIhI\nZknrQsI56NMHTjoJHngg7GhEREQyT1oPbeTlwfz5/mTPWrXCjkZERCTzpG2PxJ49MGQI3HADXHVV\n2NGIiIhkprQtJKZN83tGaEhDREQkPGlZSHz7LYwZ4w/lOuOMsKMRERHJXGlZSDzxBGzYAPfeG3Yk\nIiIimS3tCondu2HsWLjlFmjSJOxoki8vLy/sECpFpuQJmZOr8owW5SllFWohYWZ9zWyNme02s3fM\n7Lyjfc7jj8OXX/q9I6IoU/6nzpQ8IXNyVZ7RojylrEIrJMzs34HfAfcBLYB3gQVmdvKRPw/69YNG\njSohSBERETmiMPeRyAUec849DWBmvYErgZ7AuMN90u23V05wIiIicnSh9EiYWVUgG1h84JpzzgGL\ngLZhxCQiIiLxC6tH4mSgCrCp1PVNQNNDtK8BsGLFigoOK3zbtm2jsLAw7DAqXKbkCZmTq/KMFuUZ\nHTG/O2tUxOub7wioXGZ2KvAF0NY5lx9z/SGgo3Ouban2/w/4c+VGKSIiEik3O+eeS/aLhtUjsQXY\nD9Qtdb0usPEQ7RcANwNrgW8rNDIREZFoqQGchv9dmnSh9EgAmNk7QL5zbkDw3IDPgMnOufGhBCUi\nIiJxCXPVxgTgSTMrAJbjV3EcDzwZYkwiIiISh9AKCefczGDPiFH4IY3/BS5zzm0OKyYRERGJT2hD\nGyIiIpL+0u6sDREREUkdKiREREQkYWlRSCRyuFeqMrNhZrbczLab2SYzm21mPzjH1MxGmdl6M/vG\nzBaaWVqfLmJmQ82sxMwmlLoeiTzNrL6ZPWNmW4Jc3jWzlqXapHWuZnaMmY02s9VBDqvMbMQh2qVV\nnmbWwcxeMrMvgv9HrzlEmyPmZGbVzWxq8P3fYWazzOyUysvi6I6Up5kda2YPmdl7ZrYzaPNUsOdP\n7GukdZ6HaPto0Ob2UtcjkaeZNTOzOWb2j+D7mm9mDWLuJyXPlC8kEj3cK4V1AKYAbYBLgKrAq2Z2\n3IEGZjYE6Af0AloDu/A5V6v8cMsvKPx64b93sdcjkaeZnQi8BewBLgOaAYOArTFtopDrUOA2oA9w\nBnAXcJeZ9TvQIE3zrImf7N0H+MGksTLmNAl/VtANQEegPvB8xYYdtyPleTxwLnA//n32Ovwuw3NK\ntUv3PL9nZtfh34e/OMTttM/TzP4NWAJ8hM/hZ8BoDt6LKTl5OudS+gG8A/w+5rkBnwN3hR1bkvI7\nGSgB2sdcWw/kxjzPAnYDN4UdbwL5nQAUAxcB/wNMiFqewIPAG0dpk/a5Ai8DT5S6Ngt4Oip5Bj+L\n18TzvQue7wGui2nTNHit1mHnVNY8D9GmFX7jwAZRyxP4MX7fombAGuD2Ut/ftM8TyAOeOsLnJC3P\nlO6RsMw43OtEfDX5NYCZNQTqcXDO24F80jPnqcDLzrnXYi9GLM+rgb+b2cxguKrQzH594GaEcn0b\nuNjMGgOYWXOgHTA3eB6VPL9Xxpxa4ZfSx7Ypxv+iSsu8Awfem/4RPM8mAnmamQFPA+Occ4c6wCnt\n8wxyvBL4xMzmB+9L75hZl5hmScszpQsJjny4V73KDye5gm/2JGCpc+6j4HI9/A9v2udsZl3x3aXD\nDnE7MnkCpwO/xfe8dAL+C5hsZr8K7kcl1weBGcDHZrYXKAAmOeemB/ejkmessuRUF9gbFBiHa5NW\nzKw6/vv9nHNuZ3C5HtHIcyg+j0cOcz8KeZ6C7w0egi/0LwVmAy+YWYegTdLyDHNnS4E/AGfi/6qL\nlGBCzyTgEufcvrDjqWDHAMudc/cEz981s7OB3sAz4YWVdP8O/D+gK37c9Vzg92a23jkXpTwzmpkd\nC/wFX0D1CTmcpDKzbOB2/DyQKDvQSfCic25y8PF7ZvZz/PvSkor4Yqkq3sO90oaZPQJcAfzCObch\n5tZG/DyQdM85G/gRUGhm+8xsH3ABMCD4a3YT0cgTYANQuot0BfDT4OOofE/HAQ865/7inPvQOfdn\nYCL/7HGKSp6xypLTRqCamWUdoU1aiCkifgJ0iumNgGjk2R7/vrQu5n3pX4EJZrY6aBOFPLcA33H0\n96Wk5JnShUTwl2wBcPGBa8FwwMX48dq0FBQRXYALnXOfxd5zzq3BfxNjc87Czy5Op5wX4WcJnws0\nDx5/B54FmjvnVhONPMGv2Gha6lpT4FOI1Pf0eHxhH6uE4H0kQnl+r4w5FeDftGPbNMW/YS+rtGDL\nKaaIOB242Dm3tVSTKOT5NHAO/3xPao6fTDsOv+IKIpBn8Lvzb/zwfakJwfsSycwz7NmmZZiNehPw\nDdANv+TsMeAr4Edhx5ZgPn/ALwvsgK/8DjxqxLS5K8jxavwv4xeBT4BqYcdfztxLr9qIRJ74yXZ7\n8H+Z/xu++38H0DVKuQL/jZ+IdQX+r7jrgC+BMemcJ34ZXXN80VsCDAye/6SsOQU/12uAX+B7494C\nloSdW1nzxA9zz8H/kvlZqfemqlHJ8zDtD1q1EZU8gWvxSz1/Hbwv9QP2Am2TnWfo/zHK+B+sD7AW\nv+RqGdAq7JjKkUsJ/q+60o9updqNxFfK3+DPkG8UduxJyP01YgqJKOWJ/+X6XpDHh0DPQ7RJ61yD\nN64JwRvPruCX6f3AsemcJ37I7VA/l38qa05Adfz+MFvwReRfgFPCzq2seeILw9L3DjzvGJU8D9N+\nNT8sJCKRJ9AdWBn8vBYCV1VEnjq0S0RERBKW0nMkREREJLWpkBAREZGEqZAQERGRhKmQEBERkYSp\nkBAREZGEqZAQERGRhKmQEBERkYSpkBAREZGEqZAQERGRhKmQEBERkYSpkBAREZGE/X++vdKow7hF\nGAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aef93743160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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D0ee7aPr40F72D0Ofh9GsBl9AE/R/HbgO+Mskr2xr5qzPfn6ypeDDwC/Q/FU3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\nVfv/P4zuWE2UEbMAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aef93959668>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,\"rv\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas\n",
    "Data structures & analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "prumer;vyska;objem\n",
      "8.3;70;10.3\n",
      "8.6;65;10.3\n",
      "8.8;63;10.2\n",
      "10.5;72;16.4\n",
      "10.7;81;18.8\n",
      "10.8;83;19.7\n",
      "11;66;15.6\n",
      "11;75;18.2\n",
      "11.1;80;22.6\n",
      "11.2;75;19.9\n",
      "11.3;79;24.2\n",
      "11.4;76;21\n",
      "11.4;76;21.4\n",
      "11.7;69;21.3\n",
      "12;75;19.1\n",
      "12.9;74;22.2\n",
      "12.9;85;33.8\n",
      "13.3;86;27.4\n",
      "13.7;71;25.7\n",
      "13.8;64;24.9\n",
      "14;78;34.5\n",
      "14.2;80;31.7\n",
      "14.5;74;36.3\n",
      "16;72;38.3\n",
      "16.3;77;42.6\n",
      "17.3;81;55.4\n",
      "17.5;82;55.7\n",
      "17.9;80;58.3\n",
      "18;80;51.5\n",
      "18;80;51\n",
      "20.6;87;77\n",
      "\n"
     ]
    }
   ],
   "source": [
    "with open(\"tresne.csv\") as tresne_soubor:\n",
    "    tresne_raw = tresne_soubor.read()\n",
    "print(tresne_raw)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import pandas as p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    prumer  vyska  objem\n",
      "0      8.3     70   10.3\n",
      "1      8.6     65   10.3\n",
      "2      8.8     63   10.2\n",
      "3     10.5     72   16.4\n",
      "4     10.7     81   18.8\n",
      "5     10.8     83   19.7\n",
      "6     11.0     66   15.6\n",
      "7     11.0     75   18.2\n",
      "8     11.1     80   22.6\n",
      "9     11.2     75   19.9\n",
      "10    11.3     79   24.2\n",
      "11    11.4     76   21.0\n",
      "12    11.4     76   21.4\n",
      "13    11.7     69   21.3\n",
      "14    12.0     75   19.1\n",
      "15    12.9     74   22.2\n",
      "16    12.9     85   33.8\n",
      "17    13.3     86   27.4\n",
      "18    13.7     71   25.7\n",
      "19    13.8     64   24.9\n",
      "20    14.0     78   34.5\n",
      "21    14.2     80   31.7\n",
      "22    14.5     74   36.3\n",
      "23    16.0     72   38.3\n",
      "24    16.3     77   42.6\n",
      "25    17.3     81   55.4\n",
      "26    17.5     82   55.7\n",
      "27    17.9     80   58.3\n",
      "28    18.0     80   51.5\n",
      "29    18.0     80   51.0\n",
      "30    20.6     87   77.0\n"
     ]
    }
   ],
   "source": [
    "with open(\"tresne.csv\") as tresne_soubor:\n",
    "    tresne = p.read_csv(tresne_soubor, sep=';')\n",
    "print(tresne)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "    prumer  vyska  objem\n",
      "0      8.3     70   10.3\n",
      "1      8.6     65   10.3\n",
      "2      8.8     63   10.2\n",
      "3     10.5     72   16.4\n",
      "4     10.7     81   18.8\n",
      "5     10.8     83   19.7\n",
      "6     11.0     66   15.6\n",
      "7     11.0     75   18.2\n",
      "8     11.1     80   22.6\n",
      "9     11.2     75   19.9\n",
      "10    11.3     79   24.2\n",
      "11    11.4     76   21.0\n",
      "12    11.4     76   21.4\n",
      "13    11.7     69   21.3\n",
      "14    12.0     75   19.1\n",
      "15    12.9     74   22.2\n",
      "16    12.9     85   33.8\n",
      "17    13.3     86   27.4\n",
      "18    13.7     71   25.7\n",
      "19    13.8     64   24.9\n",
      "20    14.0     78   34.5\n",
      "21    14.2     80   31.7\n",
      "22    14.5     74   36.3\n",
      "23    16.0     72   38.3\n",
      "24    16.3     77   42.6\n",
      "25    17.3     81   55.4\n",
      "26    17.5     82   55.7\n",
      "27    17.9     80   58.3\n",
      "28    18.0     80   51.5\n",
      "29    18.0     80   51.0\n",
      "30    20.6     87   77.0\n"
     ]
    }
   ],
   "source": [
    "tresne = read_csv(\"tresne.csv\", sep=';')\n",
    "print(tresne)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0     10.3\n",
       "1     10.3\n",
       "2     10.2\n",
       "3     16.4\n",
       "4     18.8\n",
       "5     19.7\n",
       "6     15.6\n",
       "7     18.2\n",
       "8     22.6\n",
       "9     19.9\n",
       "10    24.2\n",
       "11    21.0\n",
       "12    21.4\n",
       "13    21.3\n",
       "14    19.1\n",
       "15    22.2\n",
       "16    33.8\n",
       "17    27.4\n",
       "18    25.7\n",
       "19    24.9\n",
       "20    34.5\n",
       "21    31.7\n",
       "22    36.3\n",
       "23    38.3\n",
       "24    42.6\n",
       "25    55.4\n",
       "26    55.7\n",
       "27    58.3\n",
       "28    51.5\n",
       "29    51.0\n",
       "30    77.0\n",
       "Name: objem, dtype: float64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresne[\"objem\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>objem</th>\n",
       "      <th>vyska</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>10.3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.3</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>10.2</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16.4</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>18.8</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19.7</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>15.6</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>18.2</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>22.6</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>19.9</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>24.2</td>\n",
       "      <td>79</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>21.0</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>21.4</td>\n",
       "      <td>76</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>21.3</td>\n",
       "      <td>69</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>19.1</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>22.2</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>33.8</td>\n",
       "      <td>85</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>27.4</td>\n",
       "      <td>86</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>25.7</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>24.9</td>\n",
       "      <td>64</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>20</th>\n",
       "      <td>34.5</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>31.7</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>22</th>\n",
       "      <td>36.3</td>\n",
       "      <td>74</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>38.3</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>24</th>\n",
       "      <td>42.6</td>\n",
       "      <td>77</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>55.4</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>26</th>\n",
       "      <td>55.7</td>\n",
       "      <td>82</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>58.3</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>28</th>\n",
       "      <td>51.5</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>51.0</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>30</th>\n",
       "      <td>77.0</td>\n",
       "      <td>87</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    objem  vyska\n",
       "0    10.3     70\n",
       "1    10.3     65\n",
       "2    10.2     63\n",
       "3    16.4     72\n",
       "4    18.8     81\n",
       "5    19.7     83\n",
       "6    15.6     66\n",
       "7    18.2     75\n",
       "8    22.6     80\n",
       "9    19.9     75\n",
       "10   24.2     79\n",
       "11   21.0     76\n",
       "12   21.4     76\n",
       "13   21.3     69\n",
       "14   19.1     75\n",
       "15   22.2     74\n",
       "16   33.8     85\n",
       "17   27.4     86\n",
       "18   25.7     71\n",
       "19   24.9     64\n",
       "20   34.5     78\n",
       "21   31.7     80\n",
       "22   36.3     74\n",
       "23   38.3     72\n",
       "24   42.6     77\n",
       "25   55.4     81\n",
       "26   55.7     82\n",
       "27   58.3     80\n",
       "28   51.5     80\n",
       "29   51.0     80\n",
       "30   77.0     87"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresne[[\"objem\",\"vyska\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>prumer</th>\n",
       "      <th>vyska</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>8.3</td>\n",
       "      <td>70</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.6</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>8.8</td>\n",
       "      <td>63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.5</td>\n",
       "      <td>72</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>10.7</td>\n",
       "      <td>81</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.8</td>\n",
       "      <td>83</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>11.0</td>\n",
       "      <td>66</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.0</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>11.1</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.2</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   prumer  vyska\n",
       "0     8.3     70\n",
       "1     8.6     65\n",
       "2     8.8     63\n",
       "3    10.5     72\n",
       "4    10.7     81\n",
       "5    10.8     83\n",
       "6    11.0     66\n",
       "7    11.0     75\n",
       "8    11.1     80\n",
       "9    11.2     75"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresne.iloc[:10,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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FUpkGBgZYu/ZWBgcHa12KJOXCUCCVad26jQwP38y6dRtrXYok5cJQIJWgt3cNra1tLFx4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\nQ4EkScr8f0cY2kdbjAZTAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aef6d4378d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tresne[\"objem\"],tresne[\"prumer\"],\"b*\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scipy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "import scipy.stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(0.96711936825563061, 8.6443342117701769e-19)\n"
     ]
    }
   ],
   "source": [
    "print(st.pearsonr(tresne[\"objem\"],tresne[\"prumer\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scikit learn\n",
    "http://scikit-learn.org"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(tresne[[\"prumer\"]], tresne[\"objem\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 5.06585642]\n"
     ]
    }
   ],
   "source": [
    "print('Coefficients: \\n', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " -36.9434591246\n"
     ]
    }
   ],
   "source": [
    "print('Intercept: \\n', regr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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Pa2srra2tfa5t27Yt1NfIePMiM1sBrAfmARuA8e6+NqZ9JfC4uzck+HptXiQy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\nYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2aefa0a92e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(tresne[\"prumer\"], tresne[\"objem\"],  color='black')\n",
    "plt.plot(tresne[\"prumer\"], regr.predict(tresne[[\"prumer\"]]), color='blue',\n",
    "         linewidth=3)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 4.7081605   0.33925123]\n",
      "Intercept: \n",
      " -57.9876589184\n"
     ]
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(tresne[[\"prumer\",\"vyska\"]], tresne[\"objem\"])\n",
    "print('Coefficients: \\n', regr.coef_)\n",
    "print('Intercept: \\n', regr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "kernelspec": {
   "display_name": "Python [conda root]",
   "language": "python",
   "name": "conda-root-py"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.5.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}