{
 "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": {},
   "outputs": [
    {
     "data": {
      "image/png": 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i6EyJYGbZZrbEzF4POkuimNl6M/vEzJaaWUncjpOqQzfR2xV+Rr3bFQI3t/Tt\nCpOJmY0E9gPPuPtZQedJBDPrAfRw98Vm1g5YBFyX5r9nA/Ldfb+Z5QDzgAfd/aOAo8WVmf0AKAba\nu/s1QedJBDNbDxS7e1y/O5DKZ/RHblfo7tXAl7crTFvu/h6wK+gcieTuW919cXR5H7CaujuYpS2v\nsz/6MCf6JzXPyE6QmRUCVwPTgs6SjlK56Bu6XWFaF0CmM7Mi4HxgQbBJ4i86jLEUqADecvd0f8+/\nAX4MRIIOkmAOvG1mi6K3V42LVC56ySBm1hZ4Gfieu+8NOk+8uXutu59H3d3ZhplZ2g7Vmdk1QIW7\nLwo6SwAuif6evwHcHx2ebXGpXPS6XWGGiI5Tvww85+6vBJ0nkdx9N/AucFXQWeLoYuDa6Hj1DOBr\nZvZssJESw903R39WAK9SNyTd4lK56HW7wgwQ/WDyCWC1u/9n0HkSwcwKzKxjdLk1dRccfBpsqvhx\n94fdvdDdi6j77/gdd7814FhxZ2b50QsMMLN84AogLlfUpWzRu3sY+PJ2hauBmel+u0IzewGYD5xm\nZuVmNj7oTAlwMXAbdWd5S6N/vhl0qDjrAbxrZsupO6F5y90z5pLDDNIdmGdmy4CFwJ/d/Y14HChl\nL68UEZETk7Jn9CIicmJU9CIiaU5FLyKS5lT0IiJpTkUvIpLmVPQiImlORS8ikuZU9CIiae7/A4Ps\ni/mk8JmYAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c73cf9400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(vars)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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8D0Xt8sIuJ6kUECIi+zF51ioArj/98JArST4FhIjIPmzdWcujc9cx5vhe9OrUJuxykk4B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\n/wfMu/143IdLtQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c73cf93c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(vars,\"*-.\")\n",
    "plt.title(\"Název grafu\")\n",
    "plt.xlabel(\"x osa\")\n",
    "plt.ylabel(\"y osa\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RT8bb3zYxOrxi511NC/c9fklYV8sZMJ7D2EQzu8e7OLZ1qcPmHqf1uaXOkr0ddOM41Z77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\ngFAhi4gEhApZRCQg/j9zeV/KAAWQwwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c53743ba8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(vars)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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40H5GQh+1ksvL/q6eXq53EYtllkCOQx1TaAWQTg+nT9ub7RfrXNOi2GP5VP0A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\nH85XhLacSw1VAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c76395e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.pie(vars, shadow=True, startangle=180)\n",
    "plt.title(\"Kolacovy graf\")\n",
    "plt.legend([\"a\",\"b\",\"c\",\"d\",\"e\",\"f\",\"g\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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A55m71LssGPrX89YN57juF5d3A4eq6hPjnmcUkkwkuah7/hbmPgzwzfFONbyq2l5Va6tq\nkrn/hr5cVTeOeaxeklzQ/fKfJBcAHwSWzSfZDP2AqjoJ/OTWDYeA3cv91g1J7gO+ClyW5GiSm8c9\nU09XAjcxd5b4dPfnw+Meqqc1wMNJvs7cycbeqmriI4kNWQ08muQZ4Angwap6aMwzLZgfr5SkxnlG\nL0mNM/SS1DhDL0mNM/SS1DhDL0mNM/SS1DhDL0mNM/SS1Lj/A+vywf0/Lk/iAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c763a6cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(vars)),vars)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "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": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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fjX1PVR0EDgJMTEx8/6kzA/DqlJI0u2FN3fwpsDnJpiQ/AOwGjg/pviRJdzCUZ/RV9W6S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\nQweGUhI8odkAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c76592588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EpOBnmNlYYD0wGsDds81sBpADlAE3u3t5DWU/Lr98LZfpb6+lQ4vG/OHqIVx2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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c762f70f0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c76711e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(x,y,\"rv\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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DN0fEAmANsC0z5wHbimWKdcuBi4ElwJ0RMan0yJKkylUWEJl5IDMfL+b/FdgDzAKWAhuK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\n9f8BPrO1FJF72S4AAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c76730e80>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "x = [1,2,5,8,52,150]\n",
    "x2 = [10,20,50,80,320,550]\n",
    "y = [10,23,54,87,258,623]\n",
    "plt.scatter(x,y)\n",
    "plt.scatter(x2,y,marker=\"*\")\n",
    "plt.xlabel(\"X\")\n",
    "plt.xlim(-10,650)\n",
    "plt.ylabel(\"Y\")\n",
    "plt.ylim(-10,650)\n",
    "plt.legend((\"X1\",\"X2\"))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c76834e10>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "plt.title(\"x1 vs y\")\n",
    "plt.scatter(x,y)\n",
    "plt.subplot(122)\n",
    "plt.title(\"x2 vs y\")\n",
    "plt.scatter(x2,y)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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EqsWVfVpyeocEHpm8lDUa8S2kqBhEpFqYGY9f1p2YqEj++OECCouKvY4klaRi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\nSUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c76655cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import math\n",
    "import numpy as np\n",
    "t = np.arange(0., 5., 0.2)\n",
    "plt.plot(t,np.sin(t))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## pandas\n",
    "Data structures & analysis "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "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": 16,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import pandas as p"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "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": 18,
   "metadata": {},
   "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 = p.read_csv(\"tresne.csv\", sep=';')\n",
    "print(tresne)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "prumer    float64\n",
       "vyska       int64\n",
       "objem     float64\n",
       "dtype: object"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresne.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "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>prumer</th>\n",
       "      <th>vyska</th>\n",
       "      <th>objem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>31.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>13.248387</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>30.170968</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>3.138139</td>\n",
       "      <td>6.371813</td>\n",
       "      <td>16.437846</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>8.300000</td>\n",
       "      <td>63.000000</td>\n",
       "      <td>10.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>11.050000</td>\n",
       "      <td>72.000000</td>\n",
       "      <td>19.400000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>12.900000</td>\n",
       "      <td>76.000000</td>\n",
       "      <td>24.200000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>15.250000</td>\n",
       "      <td>80.000000</td>\n",
       "      <td>37.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>20.600000</td>\n",
       "      <td>87.000000</td>\n",
       "      <td>77.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          prumer      vyska      objem\n",
       "count  31.000000  31.000000  31.000000\n",
       "mean   13.248387  76.000000  30.170968\n",
       "std     3.138139   6.371813  16.437846\n",
       "min     8.300000  63.000000  10.200000\n",
       "25%    11.050000  72.000000  19.400000\n",
       "50%    12.900000  76.000000  24.200000\n",
       "75%    15.250000  80.000000  37.300000\n",
       "max    20.600000  87.000000  77.000000"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresne.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "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": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresne[\"objem\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
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       "    .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>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": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresne[[\"objem\",\"vyska\"]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "        text-align: right;\n",
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       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
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       "</style>\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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>10.3</td>\n",
       "      <td>65</td>\n",
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       "    <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",
       "  </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"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresne[[\"objem\",\"vyska\"]][:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe thead tr:only-child th {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "</style>\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",
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       "  </thead>\n",
       "  <tbody>\n",
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       "    <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": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresne.iloc[:10,0:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "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>prumer</th>\n",
       "      <th>objem</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>8.6</td>\n",
       "      <td>10.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>10.5</td>\n",
       "      <td>16.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>10.8</td>\n",
       "      <td>19.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>11.0</td>\n",
       "      <td>18.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>11.2</td>\n",
       "      <td>19.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>11.4</td>\n",
       "      <td>21.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>11.7</td>\n",
       "      <td>21.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>12.9</td>\n",
       "      <td>22.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>13.3</td>\n",
       "      <td>27.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>13.8</td>\n",
       "      <td>24.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>21</th>\n",
       "      <td>14.2</td>\n",
       "      <td>31.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>23</th>\n",
       "      <td>16.0</td>\n",
       "      <td>38.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25</th>\n",
       "      <td>17.3</td>\n",
       "      <td>55.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>27</th>\n",
       "      <td>17.9</td>\n",
       "      <td>58.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>29</th>\n",
       "      <td>18.0</td>\n",
       "      <td>51.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    prumer  objem\n",
       "1      8.6   10.3\n",
       "3     10.5   16.4\n",
       "5     10.8   19.7\n",
       "7     11.0   18.2\n",
       "9     11.2   19.9\n",
       "11    11.4   21.0\n",
       "13    11.7   21.3\n",
       "15    12.9   22.2\n",
       "17    13.3   27.4\n",
       "19    13.8   24.9\n",
       "21    14.2   31.7\n",
       "23    16.0   38.3\n",
       "25    17.3   55.4\n",
       "27    17.9   58.3\n",
       "29    18.0   51.0"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tresne.iloc[1::2,[0,2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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cRkfh8GEYGiqu1h9/HFat6nVlklROpQ51gKmpIsxfeKFY37PHJhhJWkzpQ33//hc3wYyM\nwPr1NsFI0kJKH+rzm2BWrCiWo6M2wUjSQhq+o7QMDh6ED34QHn4YzjvP8eqStJjSX6kDbN9eNLvs\n2lUst2/vdUWSVE6lD3XHqktS80of6vVj1e0olaTFLSnUI+IjEbEnInZHxLaIWNGpwubYUSpJzWs7\n1CPiVcBfAxOZeS4wBFzZqcLmO3gQNm6EnTuLpR2lkrSwpY5+OREYjohfASPAT5de0kvN7xi96aZu\nnEGSqqHtK/XMfAq4EfgxMAP8T2Z+q1OFSZJat5Tml1OBy4DXAL8NnBIRH1jguMmImI6I6dnZ2fYr\nlSQ1tJSO0rcD+zJzNjN/BWwH/rj+oMycysyJzJwYGxtbwukkSY0sJdR/DFwYESMREcAlwKOdKUuS\n1I6ltKnfD3wVeBB4uPa9pjpUlySpDUsa/ZKZNwA3dKgWSdISRWYu38kiZoEftfFHVwJPd7ic5dCP\ndfdjzWDdy6kfa4b+rHuu5t/JzKY6JZc11NsVEdOZOdHrOlrVj3X3Y81g3cupH2uG/qy7nZpLP/eL\nJKl5hrokVUi/hHq/jqrpx7r7sWaw7uXUjzVDf9bdcs190aYuSWpOv1ypS5KaULpQj4itEXEoInbP\n23ZaRNwdEU/Ulqf2ssZ6EXFGRNwbEY/U5pf/cG172eteERHfi4iHanV/pra91HUDRMRQRHw/Iu6q\nrfdDzU9GxMMRsSsipmvbSl13RLwyIr4aEY9FxKMR8Ud9UPNZtd/x3NcvIuK6Pqj7Je+naKfm0oU6\ncCtwad22jwP3ZOaZwD219TI5Anw0M88GLgQ+FBFnU/66nwcuzszzgQuASyPiQspfN8CHefG0FP1Q\nM8DbMvOCecPUyl73F4F/z8zfB86n+J2XuubMfLz2O74AeBPwLPB1Slz3cd5P0XrNmVm6L2ANsHve\n+uPAeO3zOPB4r2tsUP83gHf0U90U8+E/CPxh2esGVtf+gl8M3NUvf0eAJ4GVddtKWzfwCmAftb63\nfqh5gZ/hncB/lr1u4FXAT4DTKJ70v6tWe8s1l/FKfSGnZ+ZM7fMB4PReFnM8EbEGeANwP31Qd60Z\nYxdwCLg7izl9yl733wEfA47O21b2mgES+HZEPBARk7VtZa77NcAs8I+1pq5/iIhTKHfN9a4EttU+\nl7buXPz9FC3X3C+h/mtZ/JdVyiE7EfFy4GvAdZn5i/n7ylp3Zr6QxW3qauDNEXFu3f5S1R0R7wEO\nZeYDix1TtprneWvtd/0uiia6P52/s4R1nwi8EdiSmW8A/o+62/8S1vxrEXEy8F7gjvp9Zau7mfdT\nNFtzv4T6wYgYB6gtD/W4npeIiJMoAv3LmTn3Ar7S1z0nM/8buJeiP6PMdb8FeG9EPAn8K3BxRPwL\n5a4Z+PXVGJl5iKKN982Uu+79wP7a3RsUs7K+kXLXPN+7gAcz82Btvcx1L/Z+ipZr7pdQ/yawofZ5\nA0WbdWnU5pO/BXg0M78wb1fZ6x6LiFfWPg9T9AM8RonrzsxPZObqzFxDcWv9ncz8ACWuGSAiTomI\n35j7TNFeupsS152ZB4CfRMRZtU2XAI9Q4prrXMWxphcod92LvZ+i9Zp73UGwQIfBNoo2pV9RXClc\nDfwmRcfYE8C3gdN6XWddzW+luC36AbCr9vXuPqj79cD3a3XvBj5d217quufVfxHHOkpLXTPwu8BD\nta89wKf6pO4LgOna35E7gVPLXnOt7lOAnwGvmLet1HUDn6G4qNoN/DPwsnZq9olSSaqQfml+kSQ1\nwVCXpAox1CWpQgx1SaoQQ12SKsRQl6QKMdQlqUIMdUmqkP8HJUGugXHMmeUAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c76655240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(tresne[\"objem\"],tresne[\"prumer\"],\"b*\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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iuBe/CORfIUrTXBP6ZmB1Pr0auGUuG6n+JZShwQEWHjlwyLR/IcW6qCOxPVsM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\nZolwQjczS4QTuplZIv4/R54Z11l1uTEAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c762e4400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplot(121)\n",
    "plt.title(\"prumer vs objem\")\n",
    "plt.scatter(tresne[\"prumer\"],tresne[\"objem\"])\n",
    "plt.subplot(122)\n",
    "plt.title(\"vyska vs objem\")\n",
    "plt.scatter(tresne[\"vyska\"],tresne[\"objem\"])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## scipy\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "import scipy.stats as st"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "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": 30,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn import linear_model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=1, normalize=False)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "regr = linear_model.LinearRegression()\n",
    "regr.fit(tresne[[\"prumer\"]], tresne[\"objem\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 5.06585642]\n"
     ]
    }
   ],
   "source": [
    "print('Coefficients: \\n', regr.coef_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Intercept: \n",
      " -36.9434591246\n"
     ]
    }
   ],
   "source": [
    "print('Intercept: \\n', regr.intercept_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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TToIPPoCxY/sSCmBaqDtERGIl7EOXzKirq+OMM6pYvnw48ByffbZt3PHvfhdu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\ngg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c86826048>"
      ]
     },
     "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": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Coefficients: \n",
      " [ 5.06585642]\n",
      "Intercept: \n",
      " -36.9434591246\n"
     ]
    },
    {
     "data": {
      "image/png": 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6kw89FH78EV56CR55xJOCc5G4iaGdpAGSXpI0LnGLexJJDSVNlTQ6etxM0hhJ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\n55xzG/h/TtQXQg8xZ6YAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x2b3c86ae4cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "regr2 = linear_model.LinearRegression()\n",
    "regr2.fit(tresne[[\"prumer\",\"vyska\"]], tresne[\"objem\"])\n",
    "pred2 = regr2.predict(tresne[[\"prumer\",\"vyska\"]])\n",
    "print('Coefficients: \\n', regr.coef_)\n",
    "print('Intercept: \\n', regr.intercept_)\n",
    "plt.scatter(tresne[\"objem\"],pred2,color=\"black\")\n",
    "plt.plot(range(0,80),color=\"red\")\n",
    "plt.xlim(10,80)\n",
    "plt.ylim(10,80)\n",
    "plt.title(\"Predikce objemu tresne\")\n",
    "plt.xlabel(\"Reálný objem třešně\")\n",
    "plt.ylabel(\"Predikovaný objem třešně\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.6.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}