prednasky/03/03_prieskumna-analyza.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Prieskumná analýza dát\n",
    "\n",
    "### Róbert Móro, Jakub Ševcech\n",
    "\n",
    "IAU, 10.10.2018"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Spätnú väzbu nám môžete nechať tu: https://tinyurl.com/iau2019-w03"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pýtať sa môžete na http://slido.com#iau2019-w03"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Spätná väzba z minulých prednášok"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Čo je to Docker? https://www.docker.com/resources/what-container"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Online kurzy na štatistiku\n",
    "\n",
    "- https://www.coursera.org/learn/statistical-inference\n",
    "- https://www.coursera.org/specializations/statistics-with-python\n",
    "- https://www.coursera.org/specializations/statistics\n",
    "- stay tuned na 5. prednášku"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "V akom softvéri z bežného života sa používa, čo sa učíme na IAU?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Dnes sa budeme venovať tomu, ako..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- načítať dáta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- analyzovať jednotlivé atribúty"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- analyzovať vzťahy medzi atribútmi"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "- vizualizovať dáta (*vhodné typy vizualizácií, vlastnosti dobrých vizualizácií, ako neklamať vizualizáciou*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**...ako spoznať dáta**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Predtým, ako začneme analyzovať dáta, by sme si mali ujasniť..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Aké otázky máme analýzou zodpovedať"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Akú úlohu máme riešiť"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Na tomto predmete sa budeme zaoberať len dvomi úlohami"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Klasifikácia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Regresia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### V oboch prípadoch sa snažíme nájsť funkciu $f$ atribútov $X$, ktorá bude predikovať hodnotu závislej premennej $Y$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "V prípade regresie $Y \\in R$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "V prípade klasifikácie $Y \\in \\{C_1, C_2,\\dots, C_N\\}$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Obe úlohy sú príkladom **učenia s učiteľom**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Vieme, akú úlohu ideme riešiť"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Máme vyjasnené počiatočné otázky, ktoré by sme chceli zodpovedať"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"img/I-got-all-the-data-I-have-the-best-data.jpg\" alt=\"I got all the data, I have the best data!\" style=\"width: 1000px; margin-left: auto; margin-right: auto;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Skúsme ich načítať..."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [],
   "source": [
    "%matplotlib inline\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import scipy.stats as stats"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true,
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>1990-01-01\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1990-01-02\\t-999.000\\t0\\t-999.000\\t0\\t-999.000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1990-01-03\\t51.000\\t1\\t50.000\\t1\\t47.000\\t1\\t4...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1990-01-04\\t-999.000\\t0\\t-999.000\\t0\\t-999.000...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1990-01-05\\t51.000\\t1\\t51.000\\t1\\t48.000\\t1\\t5...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1990-01-06\\t-999.000\\t0\\t-999.000\\t0\\t-999.000...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  1990-01-01\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\\t-999.000\\t0\n",
       "0  1990-01-02\\t-999.000\\t0\\t-999.000\\t0\\t-999.000...                                                                                                                                                                                                                                                                                \n",
       "1  1990-01-03\\t51.000\\t1\\t50.000\\t1\\t47.000\\t1\\t4...                                                                                                                                                                                                                                                                                \n",
       "2  1990-01-04\\t-999.000\\t0\\t-999.000\\t0\\t-999.000...                                                                                                                                                                                                                                                                                \n",
       "3  1990-01-05\\t51.000\\t1\\t51.000\\t1\\t48.000\\t1\\t5...                                                                                                                                                                                                                                                                                \n",
       "4  1990-01-06\\t-999.000\\t0\\t-999.000\\t0\\t-999.000...                                                                                                                                                                                                                                                                                "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv(\"data/BETR8010000800100hour.1-1-1990.31-12-2012\")\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Vidíme, že pri načítaní nastalo viacero problémov - všetky dáta skončili v jednom stĺpci a navyše prvý riadok bol automaticky interpretovaný ako hlavička."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Skúsme sa teda pozrieť na dáta v nejakom editore (postačí aj konzola) predtým, ako ich načítame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1990-01-01\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\r\n",
      "1990-01-02\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t48.000\t1\t-999.000\t0\t-999.000\t0\t48.000\t1\t50.000\t1\t55.000\t1\t59.000\t1\t58.000\t1\t59.000\t1\t58.000\t1\t57.000\t1\t58.000\t1\t54.000\t1\t49.000\t1\t48.000\t1\r\n",
      "1990-01-03\t51.000\t1\t50.000\t1\t47.000\t1\t48.000\t1\t51.000\t1\t52.000\t1\t58.000\t1\t57.000\t1\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t69.000\t1\t74.000\t1\t-999.000\t0\t-999.000\t0\t103.000\t1\t84.000\t1\t75.000\t1\t-999.000\t0\t-999.000\t0\t-999.000\t0\r\n",
      "1990-01-04\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t67.000\t1\t57.000\t1\t57.000\t1\t-999.000\t0\t71.000\t1\t74.000\t1\t70.000\t1\t70.000\t1\t69.000\t1\t65.000\t1\t64.000\t1\t60.000\t1\t59.000\t1\r\n",
      "1990-01-05\t51.000\t1\t51.000\t1\t48.000\t1\t50.000\t1\t51.000\t1\t58.000\t1\t65.000\t1\t66.000\t1\t69.000\t1\t74.000\t1\t80.000\t1\t83.000\t1\t84.000\t1\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\r\n",
      "1990-01-06\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\r\n",
      "1990-01-07\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\r\n",
      "1990-01-08\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t69.000\t1\t63.000\t1\t64.000\t1\t60.000\t1\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\r\n",
      "1990-01-09\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t61.000\t1\t-999.000\t0\t-999.000\t0\t63.000\t1\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\r\n",
      "1990-01-10\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t-999.000\t0\t52.000\t1\t-999.000\t0\t-999.000\t0\t54.000\t1\t58.000\t1\t64.000\t1\t60.000\t1\t63.000\t1\t63.000\t1\t65.000\t1\t69.000\t1\t73.000\t1\t60.000\t1\t63.000\t1\t64.000\t1\r\n"
     ]
    }
   ],
   "source": [
    "%%bash\n",
    "head data/BETR8010000800100hour.1-1-1990.31-12-2012"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Teraz už vieme načítať dáta lepšie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th>1</th>\n",
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       "      <th>9</th>\n",
       "      <th>10</th>\n",
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       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "      <th>42</th>\n",
       "      <th>43</th>\n",
       "      <th>44</th>\n",
       "      <th>45</th>\n",
       "      <th>46</th>\n",
       "      <th>47</th>\n",
       "      <th>48</th>\n",
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       "      <th>0</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1990-01-01</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
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       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-02</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>57.0</td>\n",
       "      <td>1</td>\n",
       "      <td>58.0</td>\n",
       "      <td>1</td>\n",
       "      <td>54.0</td>\n",
       "      <td>1</td>\n",
       "      <td>49.0</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-03</th>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>47.0</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>84.0</td>\n",
       "      <td>1</td>\n",
       "      <td>75.0</td>\n",
       "      <td>1</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-04</th>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>...</td>\n",
       "      <td>69.0</td>\n",
       "      <td>1</td>\n",
       "      <td>65.0</td>\n",
       "      <td>1</td>\n",
       "      <td>64.0</td>\n",
       "      <td>1</td>\n",
       "      <td>60.0</td>\n",
       "      <td>1</td>\n",
       "      <td>59.0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1990-01-05</th>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "      <td>48.0</td>\n",
       "      <td>1</td>\n",
       "      <td>50.0</td>\n",
       "      <td>1</td>\n",
       "      <td>51.0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 48 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "              1   2     3   4     5   6     7   8     9   10 ...    39  40  \\\n",
       "0                                                            ...             \n",
       "1990-01-01   NaN   0   NaN   0   NaN   0   NaN   0   NaN   0 ...   NaN   0   \n",
       "1990-01-02   NaN   0   NaN   0   NaN   0   NaN   0   NaN   0 ...  57.0   1   \n",
       "1990-01-03  51.0   1  50.0   1  47.0   1  48.0   1  51.0   1 ...  84.0   1   \n",
       "1990-01-04   NaN   0   NaN   0   NaN   0   NaN   0   NaN   0 ...  69.0   1   \n",
       "1990-01-05  51.0   1  51.0   1  48.0   1  50.0   1  51.0   1 ...   NaN   0   \n",
       "\n",
       "              41  42    43  44    45  46    47  48  \n",
       "0                                                   \n",
       "1990-01-01   NaN   0   NaN   0   NaN   0   NaN   0  \n",
       "1990-01-02  58.0   1  54.0   1  49.0   1  48.0   1  \n",
       "1990-01-03  75.0   1   NaN   0   NaN   0   NaN   0  \n",
       "1990-01-04  65.0   1  64.0   1  60.0   1  59.0   1  \n",
       "1990-01-05   NaN   0   NaN   0   NaN   0   NaN   0  \n",
       "\n",
       "[5 rows x 48 columns]"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filename = \"data/BETR8010000800100hour.1-1-1990.31-12-2012\"\n",
    "\n",
    "data = pd.read_csv(filename, sep='\\t', header=None,\n",
    "                   na_values=[-999, -9999], index_col=0)\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Lessons learned\n",
    "\n",
    "Už pred načítaním dát by sme sa mali sami seba spýtať:\n",
    "\n",
    "* Odkiaľ sú dané dáta? Kto ich vytvoril?\n",
    "* V akom formáte sú reprezentované?\n",
    "* Koľko ich je?\n",
    "* Je niekde dostupná interpretácia jednotlivých stĺpcov?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Môžeme sa pustiť do prieskumnej analýzy! Chceme..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Opísať dáta spolu s ich charakteristikami = **Deskriptívna štatistika**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Formulovať a overiť hypotézy o dátach = **Vizualizácia dát** + inferenčná štatistika"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Identifikovať vzťahy medzi atribútmi = **Závislosti** (napr. korelácie)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Identifikovať problémy v dátach = Čo budeme musieť riešiť v rámci predspracovania"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Možné problémy v dátach\n",
    "\n",
    "* nevhodná štruktúra dát (dáta nie sú v tabuľkovej podobe alebo jedna entita je opísaná viacerými riadkami tabuľky)\n",
    "* duplicitné záznamy, resp. nejednoznačné mapovanie medzi záznamami\n",
    "* nejednotné formáty dát\n",
    "* chýbajúce hodnoty\n",
    "* vychýlené (odľahlé) hodnoty (angl. *outliers*)\n",
    "* a ďalšie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Iris dataset\n",
    "\n",
    "iris = kosatec\n",
    "\n",
    "Tri druhy: setosa, virginica, versicolor\n",
    "\n",
    "<img src=\"img/iris_setosa.jpg\" alt=\"Iris setosa\" style=\"width: 200px; float: left; margin-right: 20px;\"/>\n",
    "<img src=\"img/iris_virginica.jpg\" alt=\"Iris virginica\" style=\"width: 200px; float: left; margin-right: 20px;\"/>\n",
    "<img src=\"img/iris_versicolor.jpg\" alt=\"Iris versicolor\" style=\"width: 200px; float: left; margin-right: 20px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 150 entries, 0 to 149\n",
      "Data columns (total 5 columns):\n",
      "sepal_length    150 non-null float64\n",
      "sepal_width     150 non-null float64\n",
      "petal_length    150 non-null float64\n",
      "petal_width     150 non-null float64\n",
      "species         150 non-null object\n",
      "dtypes: float64(4), object(1)\n",
      "memory usage: 5.9+ KB\n"
     ]
    }
   ],
   "source": [
    "iris = sns.load_dataset(\"iris\")\n",
    "iris.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.shape[0] - iris.dropna().shape[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: [sepal_length, sepal_width, petal_length, petal_width, species]\n",
       "Index: []"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris[iris.isnull().any(axis=1)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['setosa', 'versicolor', 'virginica'], dtype=object)"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.species.unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Typy atribútov"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "* Spojité (numerické)\n",
    "* Diskrétne (kategorické) - nominálne vs. ordinálne"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**Pozor na kategorické atribúty, ktoré sú reprezentované číselne, t. j. čísla len kódujú kategóriu**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Analýza atribútov po jednom (univariate)\n",
    "\n",
    "* **spojité** - deskriptívna štatistika (priemer, medián, ...), rozdelenia\n",
    "* **kategorické** - počet unikátnych hodnôt, frekvencia ich výskytov"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "### Párová analýza (bivariate)\n",
    "\n",
    "* **spojitý-spojitý** - závislosť, korelácia\n",
    "* **spojitý-kategorický** - rozdiely v hodnote spojitého atribútu v závislosti od kategórie\n",
    "* **kategorický-kategorický** - tabuľka, pomer početnosti hodnôt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "      <td>150.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>5.843333</td>\n",
       "      <td>3.057333</td>\n",
       "      <td>3.758000</td>\n",
       "      <td>1.199333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>0.828066</td>\n",
       "      <td>0.435866</td>\n",
       "      <td>1.765298</td>\n",
       "      <td>0.762238</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>4.300000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.100000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>5.100000</td>\n",
       "      <td>2.800000</td>\n",
       "      <td>1.600000</td>\n",
       "      <td>0.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>5.800000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>4.350000</td>\n",
       "      <td>1.300000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>6.400000</td>\n",
       "      <td>3.300000</td>\n",
       "      <td>5.100000</td>\n",
       "      <td>1.800000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>7.900000</td>\n",
       "      <td>4.400000</td>\n",
       "      <td>6.900000</td>\n",
       "      <td>2.500000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       sepal_length  sepal_width  petal_length  petal_width\n",
       "count    150.000000   150.000000    150.000000   150.000000\n",
       "mean       5.843333     3.057333      3.758000     1.199333\n",
       "std        0.828066     0.435866      1.765298     0.762238\n",
       "min        4.300000     2.000000      1.000000     0.100000\n",
       "25%        5.100000     2.800000      1.600000     0.300000\n",
       "50%        5.800000     3.000000      4.350000     1.300000\n",
       "75%        6.400000     3.300000      5.100000     1.800000\n",
       "max        7.900000     4.400000      6.900000     2.500000"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>150</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>unique</th>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>top</th>\n",
       "      <td>virginica</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>freq</th>\n",
       "      <td>50</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          species\n",
       "count         150\n",
       "unique          3\n",
       "top     virginica\n",
       "freq           50"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.describe(exclude=np.number)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Analýza po jednom: Spojité atribúty\n",
    "\n",
    "Chceme zobraziť, aký je tvar rozdelenia dát, či sa zoskupujú okolo nejakého **centra**, a aká je **rozptýlenosť** hodnôt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Zobrazenie centrálnosti\n",
    "\n",
    "* **priemer** (mean)\n",
    "* **medián** (median, stredná hodnota): hodnota, ktorá rozdeľuje vyššie a nižšie hodnoty\n",
    "* **modus** (mode, modálna hodnota, najpravdepodobnejšia hodnota): najčastejsia hodnota (hodnota s najväčšou pravdepodobnosťou výskytu)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2860.0"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = np.array([1000, 1000, 1200, 1100, 10000])\n",
    "x.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1100.0"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.median(x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "ModeResult(mode=array([1000]), count=array([2]))"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stats.mode(x)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Rozptýlenosť (1/2)\n",
    "\n",
    "* **variancia** (variance): priemerná kvadratická odchýlka od priemeru \n",
    "$$ E[(X-E[X])^2] $$\n",
    "\n",
    "* **smerodajná odchýlka** (standard deviation): druhá odmocnina variancie, je v jednotkách meranej premennej\n",
    "$$ s = \\sqrt{\\frac{1}{N-1}\\sum_{i=1}^N{(x_i-\\overline{x})^2}} $$ "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Rozptýlenosť (2/2)\n",
    "\n",
    "* **rozsah** (range): max - min\n",
    "* **horný/dolný kvartil** (quartile): hodnota, od ktorej je 25%, resp. 75% hodnôt menších \n",
    "* **percentil** (percentile): hodnota, od ktorej je XX% hodnôt menších\n",
    "* **medzikvartilové rozpätie** (inter quartile range): rozdiel medzi 25% a 75% kvartilom, menej náchylné na outlierov ako rozsah"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Na vizualizáciu spojitých premenných používame dva základe typy grafov\n",
    "\n",
    "* Krabicový graf (boxplot)\n",
    "* Histogram (graf hustoty)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Boxplot\n",
    "\n",
    "<img src=\"img/outlier_box_plot.gif\" alt=\"Boxplot explained\" style=\"width: 500px; margin-right: auto; margin-left: auto;\"/>\n",
    "   \n",
    "(zdroj obrázku: https://taps-graph-review.wikispaces.com/Box+and+Whisker+Plots)  "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143e6d19908>"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris[iris.columns.difference(['species'])].plot.box()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Histogram"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143e6be8ba8>"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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7qmrL0LWM0fqqOov5OwS/vutmnVZHAWcB76qqM4EfAlN96/uue+t84MZxbdMgmBJdX/pNwPVV9Ymh6xmXrpn+T8ALBy5ludYD53f96h8BnpfkQ8OWtDJV9UD3vgu4mfk7Bk+rHcCOBS3OjzMfDNPsRcCdVfXguDZoEEyB7uTqNcC2qrpq6HpWKslMkhO66ccAzwfuHbaq5amqN1fVE6tqlvnm+j9W1e8NXNayJTmuG5BA14XyAmBqR95V1X8A30tyWrfoHGAqB1kscCFj7BaCga4snoQkNwDPAdYk2QG8taquGbaqZVsPvAq4q+tXB7i8u0J7Gq0FrutGPhwBfKyqpn7Y5SpxEnDz/O8eHAV8uKo+M2xJK/bHwPVdl8q3gFcPXM+yJTkWOBd43Vi3u1qHj0qSRmPXkCQ1ziCQpMYZBJLUOINAkhpnEEhS4wwCSWqcQSBJjTMIJKlx/wfRvtq1qOWSOAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "iris.petal_length.plot.hist(bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Graf hustoty pravdepodobnostného rozdelenia"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143e6c2b588>"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.distplot(iris.petal_length, bins=30)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Koeficienty asymetrie a špicatosti"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Koeficient asymetrie (skewness)\n",
    "\n",
    "Koeficient asymetrie je metrika toho, ako je rozdelenie asymetrické. Úplne symetrické rozdelenie má hodnotu koeficientu rovnú 0. V podstate to porovnáva relatívnu veľkosť dvoch chvostov rozdelenia.\n",
    "Rozdelenie naklonené doľava bude mať hodnotu koeficientu väčšiu ako 0, naklonené doprava bude mať menej ako 0.\n",
    "\n",
    "<img src=\"img/skewness.png\" alt=\"Skewness explained\" style=\"width: 1000px;\"/>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\robom\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Normalne rozdelenie: Skewness -0.00668')"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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P2sqx097O+DPyGDeoK7+ZU8LhmlACIzQmcSzpm4ymCj8LfYEe7OVLwXebvL37rxhCxcEaXpi/penBGdMM4rk5y5i0NccZyTIdwmNZf4jv4m0DT+U8D7g48C2eeesgt7w/gQ5yxJ7MaZKK1fRNRvtt6Bp6U84NwQ8Sts1vZL3KAdoxPXxJwrZpTKJY0jcZa+Mn1SzWodyaNZscSVyPm+GBzRTJBv4SvoQku/fRGEv6JnNNW7yNbEJcH/x3wrd9c9a7bNaeLHAKE75tY5rCkr7JSEdrw/xt2Q6uCCwhT6oSvv0rA4voyEFeCk9K+LaNaQpL+iYjzVq9iwNHark5AT126tJaavl88N+845xPxcHT7wZqTKJZ0jcZadqibQzKy2VcYG3DhU/TzcH3qCWLV4rLmm0fxjSWJX2TcT7+pJrirfu4aXQ/pK6x3RLkjMBORss6/rJ4mw20YpKGJX2TcaYt2kZOMMDnz+vT7Pv6Uta7bKs8zIebKpp9X8bEw5K+ySihsMMbq3ZyWWF3uuTmNPv+rggsoX3rLP654qRRQo3xhSV9k1EWba6k4mAN15zTs0X211pqubywB2+v2U1NyGmRfRpzKnElfRGZLCIbRKRERB6sY/ndIrJaRFaIyDwRKfTmDxCRI978FSLyu0QfgDGN8caqXbTNCXLRmd1abJ9Xj+hJ9dEQ80rKW2yfxtSnwaQvIkHgaWAKUAjcFEnqUaap6nBVHQn8FPhF1LJNqjrSe92dqMCNaaxQ2OGtj3Zx6dDutM4Otth+Lzgjjw6ts3hj5a4W26cx9Ymnpj8aKFHVUlWtAaYDU6MLqGr03S25gHVVMElnQele9h2u5aoRLdO0E5GTFeCKYT2YvfYTG2DF+C6epN8b2B41XebNO4GI3CMim3Br+vdFLRooIstF5AMRmVjXDkTkLhEpFpHi8nL7CWyax8xVu2jXKosLh+S3+L6vGtGT6mMh5m60XjzGX/Ek/bp6Mp9Uk1fVp1V1MPAA8F1v9i6gn6qOAr4BTBORDnWs+6yqFqlqUX5+y/9BmvRXG3Z4a81uLits2aadiAvOyKNT22xmrrJePMZf8ST9MqBv1HQf4FTf3OnAdQCqekxV93rvlwKbgCGnF6oxp+/Dkgr2H67lquEt27QTkR0MMNmaeEwSiCfpLwEKRGSgiOQANwIzoguISEHU5FXARm9+vnchGBEZBBQApYkI3JjGmLlqF+1bZTFxSJ5vMVw1oieHasLM2WBNmMY/DSZ9VQ0B9wJvA+uAl1V1jYg8KiLXesXuFZE1IrICtxnnNm/+Z4BVIrISeBW4W1UrE34UxpxC2FFmr/uESUO70Sqr5Zt2IsYN6kqnttm8s3a3bzEYE9dwiao6C5gVM++hqPdfr2e914DXmhKgMU21fNs+9h+u5dLC7r7GkRUMcNGQfOZsKCfsKMFAMz74x5h62B25Ju29t34PWQFhYoH/nQQuGdqdykM1rCzb73coJkNZ0jdp7731ezh/QBc6tsn2OxQuLMgnGBDeW7fH71BMhrKkb9Ja2b7DrN9dzaShLffYhVPp2Dabov6deXe9JX3jj7ja9I1JCY90PGnW+6FLgTu4ePaV8G5yPAZh0tBu/GjWenbuP0KvTm38DsdkGKvpm7T2rnMuA2Q3gyQ5Ej7AJWe5F5Tfs9q+8YElfZO2Dmsr5juFXBJY1qwjZDXW4Pxc+ndta0nf+MKSvklbHzrDqCGHSYHlfodyAhHhkrO68WFJBUdq7O5c07Is6Zu09Z5zLu04zPmB9X6HcpJJZ3XnWMhhvg2jaFqYJX2TllTh/fA5TAysJkeSrzY9emAXcnOC1sRjWpwlfZOWNmpvdtOVCwMr/Q6lTjlZAcYNzrNHLZsWZ0nfpKW5znAAJgQ/8jmS+k0syGNb5WG27j3kdygmg1jSN2lprjOCQbKTPpK8NemJBe4TP622b1qSJX2Tdo5pFoucs5gYWO13KKc0MC+X3p3aMM+SvmlBdkeuSTtLnSEcoTUTkiXp13GnMLhD0k2s/Qoz14wh9PClZIkTtc6BlonNZByr6Zu0M88ZThYhxgbW+R1KgyYEVlNNLqt0kN+hmAxhSd+knbnOcEZJCe3liN+hNOiCwBoE5/iFZ2OaW1xJX0Qmi8gGESkRkQfrWH63iKwWkRUiMk9ECqOWfdtbb4OIXJHI4I2JVant+UgHMDGYJE07DegsBxkum5kXtqRvWkaDSd8b4/ZpYApQCNwUndQ901R1uKqOBH4K/MJbtxB3TN1hwGTgN5Exc41pDh86w1ACTAys8juUuE0MrGaZFlCt9sRN0/ziqemPBkpUtVRVa4DpwNToAqpaFTWZC6j3fiowXVWPqepmoMTbnjHNYp4znA4cYoSU+h1K3CYEVhMmyEJnqN+hmAwQT9LvDWyPmi7z5p1ARO4RkU24Nf37GrOuMYmgCnPDw7kg8BFB0YZXSBLnBjbSlqPMdUb4HYrJAPEk/boeSnvSX5SqPq2qg4EHgO82Zl0RuUtEikWkuLy8PI6QjDnZVu3OTvIYH1jjdyiN0kpCjA6sZ74T22pqTOLFk/TLgL5R032AnacoPx24rjHrquqzqlqkqkX5+f4PXm1S03xnGEDKJX2AcYG1lGgf9mgnv0MxaS6epL8EKBCRgSKSg3thdkZ0AREpiJq8CtjovZ8B3CgirURkIFAALG562MacbIFTSHcqk2qUrHhFTlQLrLZvmlmDd+SqakhE7gXeBoLAc6q6RkQeBYpVdQZwr4hcCtQC+4DbvHXXiMjLwFogBNyjqsn3nFuT8lSVBc4wJgZWJdUoWfEqlC104BALnEKmBuf7HY5JY3E9hkFVZwGzYuY9FPX+66dY9zHgsdMN0Jh4bNxzkAo6Mi6w1u9QTktQlDGBdcebqIxpLnZHrkkLCzbtBWBcCrbnR4wPrGGbdme7k+d3KCaNWdI3aWH+pgr6yh76BlL3iZWftutbbd80H0v6JuU5jrKwtDKla/kAQ6SMrhxgoV3MNc3Ikr5JeWt3VXHgSC3jU7Q9P0IExgbWMt8pRDV1bi4zqcWSvkl56dCeHzEusJbddGVzhQ2haJqHJX2T8haU7mVQfi7dZb/foTTZ8Xb90r0+R2LSlSV9k9JCYYfFmysZP7ir36EkxEDZTQ/2Mr/Ekr5pHpb0TUr7aGcVB4+FGDsoPZK+266/jkWb91q7vmkWlvRNSlvoNYOMGZgeSR/ci7kVB2vYVH7Q71BMGrKkb1LawtK9nNGtHfntW/kdSsJExvZdWFrpcyQmHVnSNykrFHYo3rKPMQO7+B1KQvWXT+jeodXxXzHGJJIlfZOy1qRZe36ECIwd1JWFpZXWrm8SzpK+SVnH2/MHpVdNH9ykX3HwGKXWX98kmCV9k7IWba5kcH4u3dq39juUhIv8erEmHpNolvRNSgqFHZZsrmRMmjXtRAzo2pZu7VvZxVyTcJb0TUpau6uK6jRsz48QEcYO6sqiUuuvbxLLkr5JSZFmj7Fp1nMn2thBXdlTfcyew2MSKq6kLyKTRWSDiJSIyIN1LP+GiKwVkVUi8q6I9I9aFhaRFd5rRuy6xpyORaWVDMrLpVuH9GvPj4hcoF602Zp4TOI0mPRFJAg8DUwBCoGbRCT2gd/LgSJVHQG8Cvw0atkRVR3pva5NUNwmg4UdZXEat+dHDMrLJb+99dc3iRVPTX80UKKqpapaA0wHpkYXUNX3VfWwN7kQ6JPYMI351Nqdkfb89G3aAbddf8zALiy0dn2TQPEk/d7A9qjpMm9efe4E3oyabi0ixSKyUESuq2sFEbnLK1NcXl4eR0gmky3anH7P26nPmEFd+aTqGFv3Hm64sDFxiCfpSx3z6qx2iMgtQBHweNTsfqpaBNwMPCEig0/amOqzqlqkqkX5+flxhGQy2cLSSvp3bUuPjunbnh8RuVAdOdEZ01TxJP0yoG/UdB9gZ2whEbkU+A5wraoei8xX1Z3ev6XAHGBUE+I1Gc5xlCVbKhmbAbV8gDO6taNrbg6LrL++SZB4kv4SoEBEBopIDnAjcEIvHBEZBTyDm/D3RM3vLCKtvPd5wAVAag9kany1fnc1B47UpuWjF+oiIowZ1MV68JiEaTDpq2oIuBd4G1gHvKyqa0TkURGJ9MZ5HGgHvBLTNXMoUCwiK4H3gR+rqiV9c9qOt+enec+daGMGdmXH/iNsr7R2fdN0WfEUUtVZwKyYeQ9Fvb+0nvXmA8ObEqAx0RaW7qVP5zb07tTG71BaTORXzcLSvfTt0tbnaEyqsztyTcpwIv3zM6Q9P2JIt/Z0apttTTwmISzpm5Sxcc9B9h3OnPb8iEBAGD2gi/XgMQlhSd+kjEjSy5SeO9HGDOrK9soj7Nx/xO9QTIqLq03fmBb3SMeTZi2quY+eFND3Vz3rvnskjY2J6q//2VF2w7s5fVbTNylBFRY5QxkTWIdkWMIHGNqzA+1bZ1l/fdNklvRNStikvaigI2MC6/wOxRdBr13fHr5mmsqad0xKWOgMBWBspiT9Opq3xoau5N3QLex+eAA9ZF9M+QMtFJhJdVbTNylhkTOU7lQyQHb7HYpvIie8Rd4J0JjTYUnfJD1Vt6Y/NkPb8yMKZQvtOXz8V48xp8OSvkl6pdqTcjozNpDZT/AIijI6sN5q+qZJLOmbpJdx7fmnMDawllLtxSfaye9QTIqypG+S3kKnMOPb8yMiJz5r4jGny5K+SWqZ3j8/1qft+rHDVBsTH0v6Jqlt1h7sobM17XiCopxv7fqmCSzpm6QWqdFm+kXcaGMD6yjVXuyxdn1zGizpm6S20BlKN/Yx0Nrzj4ucAK1d35wOS/omaUXa88cG1lp7fpRC2Wr99c1piyvpi8hkEdkgIiUi8mAdy78hImtFZJWIvCsi/aOW3SYiG73XbYkM3qS3LdqDT+hi7fkxssTh/MB6u5hrTkuDSV9EgsDTwBSgELhJRGK/bcuBIlUdAbwK/NRbtwvwMDAGGA08LCKdExe+SWcLvKSWqQ9ZO5VIu7711zeNFU9NfzRQoqqlqloDTAemRhdQ1fdVNTJq80Ig8sDvK4DZqlqpqvuA2cDkxIRu0t18Zxg92Msg2eV3KElnfGANAAucYT5HYlJNPEm/N7A9arrMm1efO4E3G7OuiNwlIsUiUlxeXh5HSCbdqbo1/fHWnl+nobKVjhxkvjXxmEaKJ+nX9SendRYUuQUoAh5vzLqq+qyqFqlqUX5+fhwhmXT3sfZhLx0Z59VozYmCoowNrGW+1fRNI8WT9MuAvlHTfYCdsYVE5FLgO8C1qnqsMesaEyuSzMYFrX9+fcYF1lKm3djuWEXJxC+epL8EKBCRgSKSA9wIzIguICKjgGdwE/6eqEVvA5eLSGfvAu7l3jxjTmm+M4x+8gl9pMLvUJLWp+361sRj4tdg0lfVEHAvbrJeB7ysqmtE5FERudYr9jjQDnhFRFaIyAxv3UrgB7gnjiXAo948Y+oVdpRFztDjSc3UrUB2kMcBa+IxjRLXcImqOguYFTPvoaj3l55i3eeA5043QJN51u6soopca89vgAiMC6xhvjMMVUXsireJg92Ra5LO/E1uk844e95Og8YH1rCHzpRWHPI7FJMiLOmbpDN/014KpIxuYoN9NyTSBDZ/016fIzGpwpK+SSo1IYclWyqtPT9O/WQPvSlnwSa74G3iY0nfJJVVZfs5XBO2pp04ibhP3VywaS+OU+ftM8acwJK+SSrzSiqOJzITnwuCa9h3uJa1u6r8DsWkAEv6JqnM21jBiN4d6SR2YTJeEwIfAe4J05iGWNI3SaP6aC3Lt+9nQkGe36GklG6ynzO7t2feRkv6pmGW9E3SWFhaSdhRJpxhjxVorAkFeSzeUsnR2rDfoZgkZ0nfJI15G8tpkx3k3P72jPjGmlCQR03IYfFmu+HdnJolfZM05pZUMGZQF1plBf0OJeWMGdiFnGDA2vVNgyzpm6Swc/8RSssPMeEMa88/HW1zsji3fyfmWru+aYAlfZMUIhch7SLu6ZtYkM+6XVWUVx9ruLDJWJb0TVKYW1JBfvtWnNm9vd+hpKzIr6T5dneuOQVL+sZ3jqN8WFLBhDPy7EmRTXB27450bJNtTTzmlCzpG9+t3VVF5aEaa89vomBAuOCMrszbWIGOFahVAAAVBElEQVSqPZLB1M2SvvHdXGvPT5iJBfnsrjrKxj0H/Q7FJKm4kr6ITBaRDSJSIiIP1rH8MyKyTERCInJ9zLKwN5rW8RG1jIn2/oY9FPbsQPcOrf0OJeVdOMS9sW3Ohj0NlDSZqsGkLyJB4GlgClAI3CQisYNybgNuB6bVsYkjqjrSe11bx3KTwaqO1rJ06z4uOtPuwk2EXp3acGb39szZUO53KCZJxVPTHw2UqGqpqtYA04Gp0QVUdYuqrgKcZojRpLF5GysIO8rFZ3XzO5S0cdFZ+SzZUsnBYyG/QzFJKJ4xcnsD26Omy4AxjdhHaxEpBkLAj1X1H7EFROQu4C6Afv36NWLTJmU80rHO2XNq/4MOjGbU84NBrM6QCBcN6cYzH5TyYUkFVwzr4Xc4JsnEU9Ovqw9dY7oG9FPVIuBm4AkRGXzSxlSfVdUiVS3Kz7ef+ZlCFeaERzIxsIosS/gJUzSgM+1aZVm7vqlTPEm/DOgbNd0H2BnvDlR1p/dvKTAHGNWI+EwaW6v92UNnLg6u8DuUtJIdDDDhjDzmbCi3rpvmJPEk/SVAgYgMFJEc4EYgrl44ItJZRFp57/OACwAbEskAMMcZCcCFgZU+R5J+Lj4rn10HjrLhk2q/QzFJpsGkr6oh4F7gbWAd8LKqrhGRR0XkWgAROV9EyoAbgGdEJDKq9VCgWERWAu/jtulb0jcAzAmfw3ApJV9smL9Eu3CIe2HcevGYWPFcyEVVZwGzYuY9FPV+CW6zT+x684HhTYzRpKEDmstSHcK9wZOu65vTEXOhvAcwVH7E+2+t5e73f1jPOgeaPy6TdOyOXOOLfzvDcQhwYdCadprLxYGVFOuZVGkbv0MxScSSvvHFu+Fz6UIVI6XE71DS1sXBFYQJ8oFzjt+hmCRiSd+0uFoN8p4ziknBZQTFepc0l3PlY7pygNnh8/wOxSQRS/qmxS12zqKKXC4LLPU7lLQWFGVScBnvO6OoURuC0rgs6ZsW945TRGuOMTGw2u9Q0t7lgaVU05ZFzlC/QzFJwpK+aVGqMDt8HhMDq2kjNX6Hk/YmBFbThqO84xT5HYpJEpb0TYtaowPYSR6XB4r9DiUjtJZaPhNYxezwedjNuQYs6ZsW9k74PAI4TAou9zuUjHFZcCm76cpqHeh3KCYJWNI3Leodp4gi2UAXsccDtJRJgeUEcJgdtiYeY0nftKDtTj7rtT+XB63XTkvqLAc5X9bzjmNdN40lfdOCIknnMmvPb3GXB4vZoP3Y6thgNZnOkr5pMTPDYzlLttI/YM95b2lXBN0T7UynMeMfmXRkSd+0iB3alWU6hGuCC/wOJSP1kQpGyUbeCI/zOxTjM0v6pkXMDI8F4JrAQp8jyVxXBxewVgewyenpdyjGR5b0TYt4PTyOc2QT/axpxzdXBRchOLzhjPU7FOMjS/qm2W2pOMRqHcTV1rTjqx6yj/NlA6+Hx9mNWhnMkr5pdm+scodUvipoTTt+uya4gBLtwwbt23Bhk5biSvoiMllENohIiYg8WMfyz4jIMhEJicj1MctuE5GN3uu2RAVuUscbq3ZRJBvoJZV+h5LxpgQXE8DhjbA18WSqBpO+iASBp4EpQCFwk4gUxhTbBtwOTItZtwvwMDAGGA08LCKdmx62SRUbP6lm/e5qa9pJEnlSxfjAGt5wxqHWxpOR4qnpjwZKVLVUVWuA6cDU6AKqukVVVwFOzLpXALNVtVJV9wGzgckJiNukiNdX7UIErgwu9jsU47k6sIAt2oPVO2yM3EwUT9LvDWyPmi7z5sUjrnVF5C4RKRaR4vLy8jg3bZKd4yh/W1bG+MFd6Sb7/Q7HeKYEl5BDLX9btsPvUIwP4kn6Use8eH8XxrWuqj6rqkWqWpSfnx/npk2yW7h5L2X7jnDDeXbRMJl0lENcFijmHyt2cCwU9jsc08LiSfplQPRfbR9gZ5zbb8q6JsW9WlxG+9ZZTD67h9+hmBhfCH7A/sO1vLvO7pvINPEk/SVAgYgMFJEc4EZgRpzbfxu4XEQ6exdwL/fmmTRXdbSWWR/t4ppzetE628ZnTTYTAqvp2bE1Lxdvb7iwSSsNJn1VDQH34ibrdcDLqrpGRB4VkWsBROR8ESkDbgCeEZE13rqVwA9wTxxLgEe9eSbNzVy1i6O1Djec18fvUEwdgqJ87tze/PvjcnYfOOp3OKYFxdVPX1VnqeoQVR2sqo958x5S1Rne+yWq2kdVc1W1q6oOi1r3OVU9w3s93zyHYZLNK8XbKejWjpF9O/kdiqnHDef1xVH42/Iyv0MxLcjuyDUJV7LnIMu27eeGoj6I1HUt3ySDAXm5jB7QhVeLy6zPfgaxpG8S7pWl2wkGhOtGxduz1/jl+qI+lFYconjrPr9DMS3Ekr5JqKO1YV4pLmPSWd3o1r613+GYBlw1vCftW2Xx4sKtfodiWoglfZNQr6/cSeWhGm4fP8DvUEwccltlcX1RH2at3sWeKrugmwks6ZuEUVVemL+FId3bMW5wV7/DMXG6bdwAQo7y0qJtfodiWoAlfZMwxVv3sWZnFbePH2gXcFPIgLxcLj6zGy8t2mZ36GYAS/omYV74cAsd22Rz3ahefodiGun28QOoOHiMWat3+R2KaWZZfgdgUtAjHU+atUu78NaxJ/lKcBZtf3SDD0GZpphYkMfg/FxemL+Vz46yG+rSmdX0TUK8GLoURbglONvvUMxpEBFuGz+Aldv3s2ybdd9MZ1bTN01WrW34c/gyLgsU0zdQ4Xc4Jl4xv9g+p635Gb/mmd89wTM5T9Szjj2DP9VZTd802Z/Cl1FFLvdm/dPvUEwTtJOjfDn4Fm87o1nn2OOw05UlfdMkh7QVfwhdxcWB5QwPbPY7HNNEd2S9RTsO81ToOr9DMc3Ekr5pkhfDl7GP9vzfrL/7HYpJgI5yiNuC7zDLGcNGxx6jkY4s6ZvTdkRz+H3oKiYGVnFuoMTvcEyC3Jn1Jm2osdp+mrKkb07btPAlVNCR+6yWn1a6SDW3BmfzujOOUsdGPUs3lvTNaTmorflt6FrGBtZwfmCD3+GYBPtK1kxyqOUXIbvnIt3ElfRFZLKIbBCREhF5sI7lrUTkr97yRSIywJs/QESOiMgK7/W7xIZv/PKb0FQq6MSDWdP9DsU0g3yp4q7gTN5wxrHUKfA7HJNADSZ9EQkCTwNTgELgJhEpjCl2J7BPVc8Afgn8JGrZJlUd6b3uTlDcxkfbnTz+EJ7C5wJzGRnY5Hc4ppl8Net1ulPJo7W34qg9SyldxFPTHw2UqGqpqtYA04GpMWWmAv/rvX8VmCT2xK209ePQTQRQvpX9V79DMc0oV47xrey/slLP4J/OeL/DMQkST9LvDWyPmi7z5tVZxhtI/QAQebbuQBFZLiIfiMjEunYgIneJSLGIFJeXlzfqAEzLWrKlkpnOOO7Oep2eYmPcp7vPBeYxXEr5Se2NHNZWfodjEiCepF9XjT12QM36yuwC+qnqKOAbwDQR6XBSQdVnVbVIVYvy8/PjCMn4IRR2ePT1tfRgL3cFZ/odjmkBAVG+l/1ndtOV34Wu8TsckwDxJP0yIPqe7D7AzvrKiEgW0BGoVNVjqroXQFWXApuAIU0N2vjj93M3s3rHAb6T/RJt5Zjf4ZgWMjqwgesC8/hN+FrW7qzyOxzTRPEk/SVAgYgMFJEc4EZgRkyZGcBt3vvrgfdUVUUk37sQjIgMAgqA0sSEblrSxk+q+eXsj5k8rAdXBxb6HY5pYQ9n/4lOHOT+V1ZSG3b8Dsc0QYNJ32ujvxd4G1gHvKyqa0TkURG51iv2R6CriJTgNuNEunV+BlglIitxL/DerarWEJxiQmGH+19ZSW6rID+47mzsEn3m6SwH+VH2H1m7q4qn37e7r1NZXI9WVtVZwKyYeQ9FvT8KnHQXh6q+BrzWxBiNz56dW8rKsgP8+qZR5Le3i3mZ6vLgUq4b1oun3ivhssLuDOt18mA6JvnZHbnmlJZv28cTszcy5eweXD2ip9/hGJ89cu0wOufm8PXpKzh4LOR3OOY0WNI39SqvPsbXXlxG946t+J/PDbfBzg2d2ubw5I0j2VxxiG+9shLV2I58JtlZ0jd1qg073PPSMvYfqeGZW4ro1DbH75BMkhg/OI9vTzmLNz/azW8/sDuyU40lfVOnx2auY/GWSn7y+REU9jrp1gqT4e6cMJBrzunF429v4IOP7YbKVGJJ35zkmQ828cL8LdxxwUCmjrSBNMzJRISffH44Z3Zvzz0vLWPF9v1+h2TiZEnfnODPC7bwP2+u5+oRPfnOVUP9DscksbY5Wbzw5dF0zs3mtucWs26X3biVCuLqsmkyw6tLy/jeP9dw6dDu/PKLIwkG7MKtifHIid00ewDTnHy+UPMQtz75Bn/NeZTBgV1R5Q+0bHymQZJsV9+Lioq0uLjY7zAyh/dH/L+hy/l+6P/jgsBH/D7757SWWp8DM6lkk9OTL9Y8BCjP5TzOiMBmd4El/RYjIktVtaihcta8k+HCKny/9lYeDt3OpMAyns3+hSV802iDA7uYnvMDWksNX6h5iHfC5/kdkqmHJf0MVnW0lq/W/hfPh6dwZ3AWv8v+JW2kxu+wTIo6I7CTv+c8xJmyna/W/he/D11p/fiTkCX9DLWodC9TnpjL+84ovp/1At/LfpGg2B+oaZp8qWJ6zg+ZHFjCY6FbuO35JXxSddTvsEwUS/oZ5mhtmP95cx03/n4h2UHh1ZxHuC3rHb/DMmmkjdTwm+wn+UHWcyzevJcrnvg3M1ftslp/krCknyEcR/nH8h1c8rM5PPNBKTee34+Z901klI1xa5qBCNya9S9m3jeRfl3acs+0Zdz0+4V8tMMu7PrNumymOcdR3lu/h1+/t5GVZQc4u3cHfv6FkYwb3LXhlY1posH57Xjta+P5y+JtPPGvjVzz1Dw+O7I3d180mCHd2/sdXkayLpvpxuuCWa1t+Ef4Ap4PT6ZUe9GLCr6Z/QqfDcwjYG33pqVEddmsOlrL0++X8MKHWzgWcphYkMcdEwbymYJ8uyckAeLtsmk1/TRyuCbEB+HzmREez3vOKI6RwwjZxK+yf82UwGKyJex3iCbTRN3M1QH4NvDVYHumcQn/u/Fyvryxgnz2cVVwEdcEFzBSSgh+3x7p0Jyspp/CDteE+GhHFYs372VeSQXLtu6nJuyQxwGuCi5kavBDRkmJjXRlktIxzeJfznnMCI/jfWckNeTQiWouGD6EC87IY1S/ThR0a0dW0C49xiOhNX0RmQw8CQSBP6jqj2OWtwL+BJwH7AW+qKpbvGXfBu4EwsB9qvp2I44j46kq+w/XsvPAEbbuPcymPQfZVH6Qdbuq2binGsc7Zxf27MCXLxjAhQvuYHRgPVli45ia5NZKQlwVXMRVwUVUaRved0Yx1xnOvK35zFztPsqhTXaQs3t3oKB7ewbnt2NQfi79urSlV8c2tMkJ+nwEqanBmr43sPnHwGVAGe5A6Tep6tqoMv8HGKGqd4vIjcBnVfWLIlII/AUYDfQC/gUMUdV62xlSqaavqqiCo4rj/Rt2lLAq4bAScpSQ4xAKKzVhh9qww7Fah6O1YY6FHA7XhDlcE+JQTZjqo7VUHQlx4Egt+w7VsPfQMSq2rmW3duYIrU/Yb2/KKQjsYIRs4pxAKSMDJXSVap8+BWMSSxU2aw9W6mBWOoP5yBnIRu3NAdqdUK4z1XSTfXQdNIq8dq3o1Dabjm2y6dA6m3ats2ibE6RtThZtsoO0zg7QKitIq+wA2cEA2UEhOxggGBCyAkLQewXk038DQkoNHJTImv5ooERVS70NTwemAmujykwFHvHevwo8Je6nNRWYrqrHgM3ewOmjgQXxHki8Kg/VMOEn7zVY7lTnOEWPL9cTFxxfprjJ3mmGVrEcaunAYTpLNV3lAIVSxUWBFfSWCnpLBX2lnEGyi7ZyLPE7NyZJiMAg2c0gdvPZ4IeA+3dbSXs2aS92aB47NY8d2pUK7cje0vmspCMHNJcqcnES3BNdBAT3BCDHp72ZkTInlK37RBHP+WNEn45Mv2tcU0M+pXiSfm9ge9R0GTCmvjKqGhKRA0BXb/7CmHVPekC7iNwF3OVNHhSRDXFFn1zygAq/g2hhdsyZIUmOuQrY0VI78+WY1wF//eppr94/nkLxJP26zk+x9dz6ysSzLqr6LPBsHLEkLREpjuenVTqxY84MdszpJZ7fQWVA36jpPsDO+sqISBbQEaiMc11jjDEtJJ6kvwQoEJGBIpID3AjMiCkzA7jNe3898J66V4hnADeKSCsRGQgUAIsTE7oxxpjGarB5x2ujvxd4G7fL5nOqukZEHgWKVXUG8Efgz96F2krcEwNeuZdxL/qGgHtO1XMnxaV089RpsmPODHbMaSTpbs4yxhjTfOxWN2OMySCW9I0xJoNY0m8GInK/iKiI5PkdS3MTkcdFZL2IrBKRv4tIJ79jag4iMllENohIiYg86Hc8zU1E+orI+yKyTkTWiMjX/Y6ppYhIUESWi8gbfsfSHCzpJ5iI9MV9ZMU2v2NpIbOBs1V1BO7jOr7tczwJ5z2K5GlgClAI3OQ9YiSdhYBvqupQYCxwTwYcc8TXce+TSkuW9BPvl8B/U8dNaOlIVd9R1ZA3uRD3Xox0c/xRJKpaA0QeRZK2VHWXqi7z3lfjJsGT7qZPNyLSB7gK+IPfsTQXS/oJJCLXAjtUdaXfsfjkDuBNv4NoBnU9iiTtE2CEiAwARgGL/I2kRTyBW2lL28fU2iAqjSQi/wJ61LHoO8D/Ay5v2Yia36mOWVX/6ZX5Dm6TwEstGVsLietxIulIRNoBrwH/qapVfsfTnETkamCPqi4VkYv8jqe5WNJvJFW9tK75IjIcGAis9B7H2gdYJiKjVXV3C4aYcPUdc4SI3AZcDUzS9LzxIyMfJyIi2bgJ/yVV/Zvf8bSAC4BrReRKoDXQQUReVNVbfI4roezmrGYiIluAIlVNgqcTNh9vgJ1fABeqarnf8TQH73lSHwOTcB/zuAS4WVXX+BpYM/Iejf6/QKWq/qff8bQ0r6Z/v6pe7XcsiWZt+qapngLaA7NFZIWI/M7vgBLNu1AdeRTJOuDldE74nguAW4FLvP/XFV4N2KQ4q+kbY0wGsZq+McZkEEv6xhiTQSzpG2NMBrGkb4wxGcSSvjHGZBBL+sYYk0Es6RtjTAb5/wGdzK6vDdtsIAAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_size = 10000\n",
    "\n",
    "norm = stats.norm(0, 1)\n",
    "x = np.linspace(-5, 5, 100)\n",
    "sample = norm.rvs(sample_size)\n",
    "\n",
    "plt.plot(x, norm.pdf(x))\n",
    "plt.hist(sample, normed=True, bins=20)\n",
    "plt.title(\"Normalne rozdelenie: \"\"Skewness %.5f\" % (stats.skew(sample), ))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\robom\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Chi-kvadrat(5) rozdelenie: Skewness 1.19267')"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_size = 1000\n",
    "\n",
    "chi2 = stats.chi2(5)\n",
    "x = np.linspace(0, 30, 100)\n",
    "sample = chi2.rvs(sample_size)\n",
    "\n",
    "plt.plot(x, chi2.pdf(x))\n",
    "plt.hist(sample, normed=True, bins=20)\n",
    "plt.title(\"Chi-kvadrat(5) rozdelenie: \"\"Skewness %.5f\" % (stats.skew(sample)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\robom\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'30 - Chi-kvadrat(5) rozdelenie: Skewness -1.35403')"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_size = 1000\n",
    "\n",
    "chi2 = stats.chi2(5)\n",
    "x = np.linspace(0, 30, 100)\n",
    "sample = 30 - chi2.rvs(sample_size)\n",
    "\n",
    "plt.plot(x, chi2.pdf(30 - x))\n",
    "plt.hist(sample, normed=True, bins=20)\n",
    "plt.title(\"30 - Chi-kvadrat(5) rozdelenie: \"\"Skewness %.5f\" % (stats.skew(sample), ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Koeficient špicatosti (kurtosis)\n",
    "\n",
    "Koeficient špicatosti meria množstvo dát sustredené v chvostoch. Vyjadruje teda množstvo, resp. náchylnosť danej distribúcie produkovať odľahlé (od stredu distribúcie vzdialené) hodnoty."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Veľmi často sa porovnáva k hodnote koeficientu normálneho rozdelenia, ktorá je 3. \n",
    "Ak je to viac ako 3, viac dát je sustredených na okrajoch. Ak menej ako 3, tak je menej dát v okrajoch."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Často sa používa aj *excess kurtosis*, čo je rozdiel oproti normálnemu rozdeleniu, čize kurtosis - 3."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Koeficient špicatosti (kurtosis)\n",
    "\n",
    "![kurtosis](img/kurtosis.png)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\robom\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Normalne rozdelenie: Kurtosis 0.00000')"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_size = 100000\n",
    "\n",
    "norm = stats.norm(0, 1)\n",
    "x = np.linspace(-5, 5, 100)\n",
    "sample = norm.rvs(sample_size)\n",
    "\n",
    "plt.plot(x, norm.pdf(x))\n",
    "plt.hist(sample, normed=True, bins=20)\n",
    "plt.title(\"Normalne rozdelenie: \"\"Kurtosis %.5f\" % (stats.kurtosis(sample), ))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "V základnom nastavení vracia funkcia excess kurtosis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\robom\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Normalne rozdelenie: Kurtosis 2.98399')"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_size = 100000\n",
    "\n",
    "norm = stats.norm(0,1)\n",
    "x = np.linspace(-7, 7, 100)\n",
    "sample = norm.rvs(sample_size)\n",
    "\n",
    "plt.plot(x, norm.pdf(x))\n",
    "plt.hist(sample, normed=True, bins=20)\n",
    "plt.title(\"Normalne rozdelenie: \"\"Kurtosis %.5f\" % (stats.kurtosis(sample, fisher=False), ))\n",
    "# musime prestavit parameter fisher na False"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\robom\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Logisticke rozdelenie: Kurtosis 4.14697')"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_size = 1000\n",
    "\n",
    "logistic = stats.logistic()\n",
    "x = np.linspace(-7, 7, 100)\n",
    "sample = logistic.rvs(sample_size)\n",
    "\n",
    "plt.plot(x, logistic.pdf(x))\n",
    "plt.hist(sample, normed=True, bins=20)\n",
    "\n",
    "plt.title(\"Logisticke rozdelenie: \"\"Kurtosis %.5f\" % (stats.kurtosis(sample, fisher=False)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "slideshow": {
     "slide_type": "skip"
    }
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\robom\\Anaconda3\\lib\\site-packages\\matplotlib\\axes\\_axes.py:6462: UserWarning: The 'normed' kwarg is deprecated, and has been replaced by the 'density' kwarg.\n",
      "  warnings.warn(\"The 'normed' kwarg is deprecated, and has been \"\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0.5,1,'Uniformne rozdelenie: Kurtosis 1.79400')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sample_size = 1000\n",
    "\n",
    "uniform = stats.uniform()\n",
    "x = np.linspace(-7, 7, 100)\n",
    "sample = uniform.rvs(sample_size)\n",
    "\n",
    "plt.plot(x, uniform.pdf(x))\n",
    "plt.hist(sample, normed=True, bins=20)\n",
    "\n",
    "plt.title(\"Uniformne rozdelenie: \"\"Kurtosis %.5f\" % (stats.kurtosis(sample, fisher=False)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Analýza po jednom: Kategorické atribúty\n",
    "\n",
    "Najčastejším spôsobom zobrazenia je frekvenčná tabuľka zobrazujúca buď počty pozorovaní pre jednotlivé unikátne hodnoty atribútu alebo pomer voči celkovému počtu pozorovaní. \n",
    "\n",
    "Na grafickú vizualizáciu sa používa **stĺpcový graf (bar plot)**."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Unnamed: 0</th>\n",
       "      <th>carat</th>\n",
       "      <th>cut</th>\n",
       "      <th>color</th>\n",
       "      <th>clarity</th>\n",
       "      <th>depth</th>\n",
       "      <th>table</th>\n",
       "      <th>price</th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "      <th>z</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Ideal</td>\n",
       "      <td>E</td>\n",
       "      <td>SI2</td>\n",
       "      <td>61.5</td>\n",
       "      <td>55.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.95</td>\n",
       "      <td>3.98</td>\n",
       "      <td>2.43</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>0.21</td>\n",
       "      <td>Premium</td>\n",
       "      <td>E</td>\n",
       "      <td>SI1</td>\n",
       "      <td>59.8</td>\n",
       "      <td>61.0</td>\n",
       "      <td>326</td>\n",
       "      <td>3.89</td>\n",
       "      <td>3.84</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>0.23</td>\n",
       "      <td>Good</td>\n",
       "      <td>E</td>\n",
       "      <td>VS1</td>\n",
       "      <td>56.9</td>\n",
       "      <td>65.0</td>\n",
       "      <td>327</td>\n",
       "      <td>4.05</td>\n",
       "      <td>4.07</td>\n",
       "      <td>2.31</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>0.29</td>\n",
       "      <td>Premium</td>\n",
       "      <td>I</td>\n",
       "      <td>VS2</td>\n",
       "      <td>62.4</td>\n",
       "      <td>58.0</td>\n",
       "      <td>334</td>\n",
       "      <td>4.20</td>\n",
       "      <td>4.23</td>\n",
       "      <td>2.63</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0.31</td>\n",
       "      <td>Good</td>\n",
       "      <td>J</td>\n",
       "      <td>SI2</td>\n",
       "      <td>63.3</td>\n",
       "      <td>58.0</td>\n",
       "      <td>335</td>\n",
       "      <td>4.34</td>\n",
       "      <td>4.35</td>\n",
       "      <td>2.75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Unnamed: 0  carat      cut color clarity  depth  table  price     x     y  \\\n",
       "0           1   0.23    Ideal     E     SI2   61.5   55.0    326  3.95  3.98   \n",
       "1           2   0.21  Premium     E     SI1   59.8   61.0    326  3.89  3.84   \n",
       "2           3   0.23     Good     E     VS1   56.9   65.0    327  4.05  4.07   \n",
       "3           4   0.29  Premium     I     VS2   62.4   58.0    334  4.20  4.23   \n",
       "4           5   0.31     Good     J     SI2   63.3   58.0    335  4.34  4.35   \n",
       "\n",
       "      z  \n",
       "0  2.43  \n",
       "1  2.31  \n",
       "2  2.31  \n",
       "3  2.63  \n",
       "4  2.75  "
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds = pd.read_csv('data/diamonds.csv')\n",
    "diamonds.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "G    11292\n",
       "E     9797\n",
       "F     9542\n",
       "H     8304\n",
       "D     6775\n",
       "I     5422\n",
       "J     2808\n",
       "Name: color, dtype: int64"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "diamonds.color.value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143e740a048>"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diamonds.color.value_counts().plot(kind='bar')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Kedy je vhodné použiť stĺpcový, a kedy koláčový graf? Aké sú ich výhody a nevýhody?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143e7631b70>"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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S7tFXregxmWbeV5h/0RWlxRt3WK17ReeJFecIPxS6eZroHERVFgC3iQ4xHkYq7n8SHcAITpjN824pLii/rqTwnYNms+621DzE8zedQHa+6BxEdXeLDjAehihuT5PHCeBy0TkMgzGpzWpd9GlXUeFtRflrT5qkk6IjReu/wjdbRGcgCeEuW7FqoegQsTJEcQP4FJRvj0giMWb5ICVlaW1pieNr+bmreyTWLTrShYS46eAqef5s0TlIwuh21G2U4r5OdABDYyz1rVTHpYsnuOT7c7PXBBiGREcaye8i1QfopqSh1IkOECvNPjnJGIsA2H7Oh67jnPvH+j6eJo8VQCeU7R2JBkicH7u7u2ff8tPdi0yAJva55hyhuYH/PtOJTF2vNiBjNt3fWLdLdIix0vKIe5BzPuuclz/G96kFlbamyIwV/TzTuWRuWemhlzPSNmjhIZ4DvHATlbYhXSY6QCy0XNzx8inRAcjIQoxNaszJXrBgosv3l1RHq8gsD4VvpgezjKlWdIBY6GWq5ADn/PpY3sfT5NkEgJ6C04GsSOSDRzo6rfOGAtMTed0gN/srA00TaX7bkE4ByPM31mmzCD+BlvdjGOSczxrPG3iaPHYAF8UpD1HZaZPp4ruLClASCr/3RMfJ3KpgqDwR1309suQgwMoScS2iOTkAZgLQ1dF9yT5VMgu0DFB3jlrM828qLiy7qbjw7SNm01E1r8U5Ag+HPz9DzWsQzdPdPLcRipvoEWMmn826+BpXce7dhflruiTplBqXaePFm7vgzFHjvYlu6O4f7lGLmzFmYox9IxFhVEDTJHrHmG2jPaWmZkKJ9d/zctb0M9YXz7f/SfiWpD8UgoxqkugAYzVqcXPOIwA+m4As5183LQ5vQ5sFJQvG0v+allqzcKJr6Mc5WWuCQHC8bxng5gN/ly+h78pImegAYxXtVMl6xtjTjLEljLHZH75UTRYfxaIDkPjijOW+mpFeM6+stGNlZsZ6GZBjfa9fR2oOxTMb0a3SshWrNPEgWLSiWg7IGHtrhA9zzrmm10B6mjw9oIdvkppNlvet6DrddWNv/7yxfB3nCMwOrOw/jYxstbIRXSnzN9YdFB0iWlEtB+Sc6+6u6/BSQCrtJBeQpCkP5Obg0eysHQ+ePBWuHRiMaupjLy/ZdBoZ1WrnI7oxCYBuijuqqRLGmJMx9hhjbNPw61HGmFPtcONUKDoASZw+SZrx9YK8WZeVlmxutVm9o33+T8JfjMc9FJI8dHWDMto57ucA9AL4/PCrB8DzaoWKEzqh24A6zaZLbi8qqPq0q+jdNovZP9LnDHFL21vyxTMTHI1om66mzKIt7nLO+f2c8/3DrwcATFYzWBzQ2lyjYowdtFgWXldS5LqlqGDdcZPp+Ln/uTly2RFR0YhmWUUHGItoi3uQMbb4w58wxqoBDKoTKW50tfcAUQFj5h0ptiVXlhY77y3IW9MtSWc4x9Bj4RtptE3Op6snrKPdq+Q+AE3D89oMQBeAO9QKFScR0QGIRjBmX++w11zvLN53165JH9SkDGQXp2SaLMyU7E8Okyh1g/eKzjAW0a4q2QJgJmMsY/jnPaqmio+w6ABEMM759IN819Wb+cmZB3ihLYSp7837YsfkwT1hHvjdvNwUV5srtbKzIKXMmmbJLJWYySU6MhHmTdEBxuKCxc0Y+7dP+DgAgHP+mAqZ4oWK24DsAd67dAffefkWOTzhJKZKHB9tEcvB5AF7QZWFFWaHpdR3OgbXzO0YOvTRf7eb0k4UO6b4SxxTBrNtxdlWKWUKY4weiTeGkOgAYzHaiFvP66CpuA3CdZL7l22WDy7YzTMyBjCDAQtG+rwzznIfGJsGAOaUSxYx5tgUGvjzNAAOABiM9BW09W4paOtVdvhkYOEcW4nPlVrRUWifZEq3ZJdKzDQhUb8uklDJU9zDq0f0SpMH0pLxM0V4aM5evmNZK++ZephPtMgoQxT7TbQXVZ/82PvY3HMg2beH+n5bCiDz/M/n4ObOwJGqzsCRKkB5eNgmOTqLHeUHShwVAzkpJRk2yV7BGKM14fp3RnSAsYhqjpsx5gLwFIBqKKs13gbwdc65lpdVaTkbGSNnHz95xRa+u2a7bC44g2kMuHis73EqZ/o/lLPJUuZh6V/cG+x9NQDwUdf+B+SB3AN923MP9H10jrWcbS3a40qtPFFon8QyrDklEkxljNFpOjrjFx1gLKLdq+R/AbwC4KXhD90K4Euc8ytVzDZunibPAAC76BwkBpzzqUfgu3qz3HFxG8+1BzGNKSuaYhI22XrXLn40BYyNuOxLjnQdCva8yAF5YuyhFVYp5UyhffI+V2pFf67NlZZiSp2igyeNja7S1bhkr+gQ0Yq2uLecf4zYSB/TGk+TxwdgqugcJDq2IO9fvJPvvGKLHCg7gQoTj9+2Be2FCzf6qm694EZUXO7rCHQ/1w2EK+J13Q/fOtOav7/EUdFe7ChHhiW30MTM5YwxWo6oDRxAiqtxybi3Ck6UaNdxdzLGbgXw6vDPb4FyyKbW+UHFrWmFXfzwss3ygUVenprZjxkMGNMuf9E6VrRw1HseTErLtznvsQV6nt0OHvDE8fLsTLCj/Eywo3znmfUAAAuz9hTYJ+1zpVb25qWUOuymtHLGmK4eu04i7XoqbSD64r4LwNMAHofyr9M7AO5UK1Qc6Wa3L6OQZB6+uI3vXLaZn552iJdaIygHUKr2dXvSJ5ZF83lMSnHanPeUB7qf2wTeP0etPCEezDgysHv2kYHdH30sw5LrL3FUHCl2lPNMa16+iVmmMMZ0tU+0TvlFBxiraIv7hwBu55yfBoDhkcEjUApdy/aJDkCA9AHeVbuVey/bJktFXZjGlFO1E2bAnn+IS+aol/ExZnHYnF+ZGex56R0udy1SM9u5ekKdZT3dnWXe7ncBACZm6S+wT9znclSezrdPcDhM6ZMYk/ISlcdA/KIDjFW0xX3Rh6UNAJzzLsbYmO/qC/Ce6ABGVd7O9169WT46Zy/PcQQwnSkrkoRoL1p0EMCY1l8zZrJYM25fEOxtXssj7UtVinZBER5KbR/YN7N94Oz4I82cdbgkteJwsX1KKMuWn2dm1gr2CTdcSdRG3QZYa6ItbokxlnXeiDvarxXpfSgL6+kPtsqsIT640Md3XNkqD5UfQ7mJowJAvG/yxaQjb3ZMO78xxiRbxs1Lg31/XC2H9l0a51gx6QufLt3dvbF0d/dGAIDETEP5KRO8Lkfl6QL7RJvD7CyTmER70Y/N+li+iDHWF6ezccd+7ShXldwG4FsAfgNljvvzAB7knL90wS/UAE+TZwOA+aJzJKO8M7z9qla5bfEunpLdixlMg0svZSaFVy99cgDD++zEKjTw5ppIYMtSjGNJYqI4zM72EseUgyWOKcEsa2G2RbJVMsZsonNpVBiA09W4ZGCsXyiyuKPdZOpFxtgmALVQ/uDewDnfpWqy+FkPKu64YJzLngN859WbeZfHz4ttYVRA4wcyn86q2gXGLhrv+1gctTWMpa4PD62fD41/tzkQ7i7e27O5eG/PZgCABCmYm+La6UqdeqogZaKFNtT6mNZYSlu0qP8ADhe1Xsr6XOsBjLhZFhld6iDvvnQ731W7VZZLOlElAfFcJqe6o0XVp0f/rOiY7fOrIaVuDA/87SIAKfF6X7XJkK0dQ4emdwydPdR+eEOtAyWOikC2rSjLKqVUMMY09x1TAvyf6ACx0PTIIU7WAZAR/aERhld2nLcta5UPz9vNs9KGMJ0BC0VnitXprKm58Xw/s23GPMYcW0P9v58EYFzTLyJdeEOtyeZ0S1aJxEzjfopUB/4mOkAsoprj1jtPk+dNALo7qT5RzGEemL+bb7/qA3mg4igmmWX111UnQsicenpd9UNOqPCEohxu9wV7X8sBkLTL887ZUKs/J6Uk0ybZpyTZhlq9AHJcjUti2hlQ83PcSeA1UHF/THYPP3HlB/KeJTu5La8b0xmg2sMmohwvmOsDY6p8tyCZi6usGbf7gz0vBgCelPPF52+oxcAiWbbCPS5H0myo9edYS1s0oxT36wCegXF+vf+Icz79EN919WbeOXM/z7eFUMWAUXfD07NjhfNVPb5OMuWU2TLuPhboeb4NiJSreS0t4OCmrsCxyq7Ascptp9cAAKxSyuki++S2En1uqPVcrF/IGDMDCMQxy9iub4SpEgDwNHn+AmCZ6ByJNMJpMEn7bf1I3qx58hiYqUjt63B5oCvQ/dwxIDh99M9OejzTmr/f5ag8VuSYzJ2W3CKJmSdrcEOtwwDKXI1L5Fi+mDE2E8DPOeeq7K0zGiONQF+DAYrbdZL7l7XKBxf4eHrGADyfdBpMsutNc7WBmRIyCmaSI9uWeY810P1cK/jA7ERcU8M+2lBrx5m3AYy4odYUxliW4JwvjKO07wXwNQD/Gt9IY8hgoBF3BoCjAJLp5spHp8Fc1cp7qo7wiZbI6CfBGMHuis+vOVpSU5PIa3IeCQZ7Xmjlcrch/7EcC8EbanEA5a7GJQcSdL24M0xxA4CnyfM4BP4rGS8jnAaj22Vpanl74YObgrbMhN9w5VyOBHtffYdHTixJ9LX1bHhDrb0uR2V3vn2CXeUNtd50NS65XKX3TggjTZUAwBMA/gU6/HVPPcx9y1rl47P38bzh02AMNV89FjIzB4JWp5D5ZsYkky3jS0uCvb9dLYf9l4rIoEfDG2rNOndDrXRz1uHi1IpDJY4pkUxrfm4cN9R6Ng7vIZShRtwA4GnyvALlIAhNswV5f/UuvuPKD+RgvE+DSXYdeRe37pj+FeFzzaH+v62JBHckdLommZmYeTAvpXSfy1HZNY4NtdoBTHY1LhG2IiQedDfyjINHoNHiPuc0GEdmPzyM9liJSXvRol7RGQDAknpVDaS0dZGhDYsA0IEI4xThYfvxwQOe44Nnp6Y/tqGWrTDHwmwVo2yo9ZDeSxsw4IgbADxNnr9D2TBLKEnm4VltfOeyVn5m+kHuGj4NhozT6iWP75FN1krROT4UHtqyITz45sUAaIc+lSkbapXudaVWniqwl5nTzJkTJSaVDP/ndig3JUc9xk7rjDjiBoAGCCru4dNgfJduk1Es4DSYZBewOju0VNoAYE6ZtYBJjtZQf0slkmxVk9YoG2odnN4xdPbUwuENtfxFjvKfzX/yTt2XNmDQETcAeJo8vwJwUyKudd5pMNMYfdusGv+EZW/vn3ztYtE5RiKHDu8K9v26EAAdCpx4bQDc9c0tunzE/XxGHXEDwH8A+AxU2J7TGuKDC3x8x1XKaTCTtXQaTLI7XjBXa0/ofUSylE6zpt/aFux9OQBw1Z/oJB/znWQpbcDAxb399u0HPU2eRwF8Jx7vl3eGt1/1gbxv8U5uHz4NZm483pdEjwN8wFGgqWmS80nm/HJrxp1Hgj0vHADkSaLzGMRGAL8SHSKeDFvcw34C5aT6MY9+zjsNpsgWRiU0fhpMsuvOmLwbTKoSnWM0kinTZXN+9WSg+zkfENJ8Xp0LAbinvrklqeaEDTvH/SFPk+c2AE3RfO4Ip8HkqByPjMGuqi+vPl644FLROaLFeaAn0P3sfvChWaKzJLEH6ptbGkSHiDfDFzdw4Z0DJ57gbVdvlo/M280zh0+DMfp3KZq1btFDW0LWNF2VIOfhoUD389vAe4XsMpfktgGYk0xz2x+iElLcDWAHgExzmAfm7eE7rmqV+yqV02DKAVpfrXURydofsqROE51jrBgzp9icd88O9ry0nsunqkXnSSJhAHckY2kDNOL+yM0PTL/pvjfkfx8+DSZVdB4yNscKF2z0Vn1Zt6NWzjkP9f1mrRw+TI/Ix8eD9c0t3xUdQi2aXTqVaK/dv/PX+d3YR6WtT+2FC3X9YAVjjFnTb6qRLFWrRWdJAjsB/EB0CDVRcX/cfQB0u0evkfVklCXFAcfWtE9darJdshZATJv8E/QBuKW+uSUoOoiaqLjP4fZ5ewDcCGBQdBYSvQF77hEumZNmTbTFUbPUbK/ZAGUpG4keB3B7fXPLdtFB1EbFfR63z9sKZW030YljhYuS7rskc8oliyyOa7YCGBCdRUd+VN/c8lvRIRKBinsEbp/3NQCNonOQ6JzInx2PzfU1x2Rzz7Gk3dAepU92AAAKMElEQVQGoFt0Fh34A4D7RYdIFCruT/YdAKtEhyAXxsEiQym5btE51GKylHms6V/sAFiH6CwathPAl5Pt6cgLoeL+BG6fVwbwRSh/KIhGdWVN9YIxp+gcapLMhRXWjNuHAOmQ6CwadBrAZ+ubWzRxeEaiUHFfwPDNyqtAK000q72oulN0hkSQTNkTbM67bYB5r+gsGjIA4Lr65pY20UESjYp7FG6ftx3AFVBOzyAa05XtzhWdIVGYlF5gc96TD2ZL+lUTURgCcG19c8ta0UFEoOKOgtvn3Q9l5H1KdBZyVshs746YUpJ2fnskTEpx2pz3lIOlbhKdRaAAlJH230UHEYWKO0pun3cngKsBGGouTctO5M/1gjHDnSbEmMVhc35lJpOy3hGdRYAQgBvrm1v+KjqISFTcY+D2eTdB2UXwjOgsBDhWuMCwD6gwZrJYM+5YwExFRpoqCAO4ub65pUV0ENGouMfI7fO+C6AGwAnRWYyuL82VNE9LxoIxJtkyblkqWaasEZ0lASIAbjXKAzajoeKOgdvn3QZgMYCDo30uUUdfavEBLplconNogTXt2hqTbeYaKI98J6NBAJ+rb25pFh1EK6i4Y+T2efcBqAbgFZ3FiNqLqmlN8zksjstrzCnV66FMJySTLgBX1De3/EF0EC2h4h4Ht897FMBSAEa8SSTUybyZDtEZtMZsn7/Y7LiyFcpSuWTgB1Bd39xCf7/OQ8U9Tm6ftxPAZQCeF53FKGRmCgasmbo77SYRzDbPPEvqZ30AekRnGad3Acyvb27xiQ6iRVTcceD2eYNun/cuAP8G5SYKUVFnzoxdYIwOvPgEJmv5LGv6F44C0OtTpa8AuKy+uWXM+7MwxvpUyKM5VNxx5PZ5HwfwKdByQVUdK1pEv7+jkMwlbmvGbb0AOyI6yxhEAHynvrnlS/XNLQHRYbSMijvO3D7v3wDMh3L4MFHB6cyKItEZ9EAy5U6yZdxtAkx62MvjCIBL65tbfiw6iB5QcavA7fPuATAPwErRWZJNwJJ+UpaslaJz6AUzZRTZnF/NAiy7RGe5gD8BmFnf3PK26CB6Qae8q8xb5b4ewLMAskRnSQYHS698p638ukWic+gN58G+QPeze8AHZ4vOco4ggP+sb275abzekDHWxzlPi9f7aRWNuFXm9nl/B2AmgHWisySD4wXzaKQRA8asaTbnV6czyblBdJZh+wAsimdpGwkVdwK4fd7DUJYMfhfKzmYkBhzg/amFFaJz6BVjZps14865zJQvehDxEoDZ9c0tmwXn0C2aKkkwb5V7KoCfQdnvhIxBd3rZ7s2X/MdU0TmSQbD3t6vlsP/SBF92P4D76ptb/qbWBWiqhKjC7fPuhjL6/gqUY5dIlNqLq4+LzpAsrOk3XGqyTl+doMuFATwEYIaapQ0ARihtgEbcQnmr3AUAngBws+gserBuUWNryJqupZtruhcaXL8uMvTeIgBq7Wv+HoB76ptbtqn0/oZEI26B3D7vCbfPewuUh3b8guNoWkSyDoQsadNF50g2Fnv1ErO99n0oKzziqQfAv0C5AUmlHWc04tYIb5XbAeAHAP4V6o1+dOt4wdxNu9x3zBGdI1lFgntaQ/0tlQDGO9UgA3gZwLfqm1uOjj8ZGQkVt8YM37z8IYAbATDBcTSjdebX15zJqqQbuiqKhA7vDPX9ughAdoxv8QaUwqYRtsqouDXKW+WeDeBBKOdcGt5bS5/YzyXLZNE5kp0c7mgL9r7sAPhYthV4D8A365tbjHASjyZQcWuct8q9FMBPABj2acHBlOz2dxf8sFh0DqOQI2eOBHteCANy2SifuhvAt+k4scSjm5Ma5/Z517p93moAdQC08tRbQh0rXLRfdAYjkUyZLpvzq6mA5ZP2wj4C4B4A06m0xaARt854q9yXQ3kC81LBURLm3Xn3vzvoyF8oOofRcB7oCXQ/ewB8aObwh3wA/gvAL+ubW0ICoxkeFbdOeavci6AU+DWis6iJg8lv1TzVA8YyRWcxIs7DQ8Gepj9wubsZwO/rm1uoMDSAijvBzn8klzF2B4A5nPN/ieX9hm9ifg3ATQCS7hzGrszKnVtmfZ3WbydeBMDrAB5fvrLWkFN0WmYWHYCMj9vnbQVwh7fK/TUAtwC4G8Bcsanip72oWq/Hb+lVN4CfA3hq+craQ6LDkJFRcScJt8/bA2Xzqp95q9wXQdkL5UuIfU2uJnRlT6N9zNXHAayBsmvfr5avrDXEuY16RlMlCcYYiwDYfs6HsgH8MdapkgvxVrltAG6AMgqvhc4e6AmZ7N3rFj+cCsZogKGO3VDK+pfLV9YeFB2GRI+KO8HiPccdLW+VexKAuwDcCaBEzWvFy9GixRt2T71lgegcSeYUgNcAvLh8Ze1G0WFIbGgkYxBun/cAgO95q9wNAJZBGYVfA8AuMteFHCtaQEvO4iMIoAXAiwDeWL6yln5fdY6K22DcPm8Eyp4Sb3ir3HYoe4N/avg1SWS28/WmTSgTnUHH+gD8HcAqAK8vX1nbJTgPiSMqbgNz+7yDGC5xAPBWud04W+KLAVhFZet3FPq5ZCoTdX2d2gXgz8OvdctX1sZ7q1aiETTHTUbkrXKnA7gCSolfgwTPi+8t/9zaw6W1SxN5TR3qhzKq/jOAP9MNRuOg4iZR8Va5Z+LsaHwhVN4zfP2CH24MpGTPU/MaOsShjKr/CuW7JBpVGxQVNxkzb5XbCWA2gEuGX3MAlCNOyw1lJoVWL30yAMYMcX7gBRwDsBHKtqkbAby/fGVtj9hIRAuouElcjFDmlwCYghjK/GSOZ8t2z72z4ptQ8/oAbMbZkn5v+craI2IjEa2im5MkLtw+bzeAt4ZfAD4q84vx8TKvwChl3l60qFu9pMJxAEeh7LTnA7AFSlHvXL6yVhYZjOgHjbhJQnmr3BkA3AAmQ1l++OFrMoBSAOY1ix/1RswpbnEp4+I4gP3nvHZDKerdy1fW9osMRvSPiptohrfKbQJQ+mbNUy4wqRTKSpZzX0UAnADSAVgEROwF0Hne69Q5Pz4KpaQPLF9ZOyggHzEIKm6iS8/c+2YKlAKP5sUAhKFsVRqJ8sf/UNK0goNoBRU3IYToDJ05SQghOkOrSghJsHO29rVAmZ5pAvAE55xWlZCoUHETkniDnPNZAMAYywfwCpSbrvcLTUV0g+a4CUmwEfZknwzgfQC5nP5CkijQHDchgnHO90P5u5gvOgvRBypuQrRBV8fKEbGouAkRbHiqJAKgQ3QWog9U3IQIxBjLA7ASwNM0v02iRTcnCUmwEZYDvgTgMVoOSKJFxU0IITpDUyWEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIzVNyEEKIz/x8OKJqo5fvwGQAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "diamonds.color.value_counts().plot(kind='pie')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "notes"
    }
   },
   "source": [
    "Pri počte hodnôt väčšom ako 3-4 je lepšie použiť stĺpcový graf."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Párová analýza"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Spojitý - spojitý: Scatter plot\n",
    "\n",
    "Najčastejší spôsob ako vizualizovať vzťah dvoch spojitých atribútov.\n",
    "\n",
    "Zobrazuje rozmiestnenie v priestore hodnôt. Umožňuje zistiť, či sú v dátach nejaké prirodzené zhluky."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PathCollection at 0x143e76ac908>"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(iris.sepal_length, iris.sepal_width)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143e76c99b0>"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.scatterplot(iris.sepal_length, iris.sepal_width, hue=iris.species)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.PairGrid at 0x143e767af28>"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 804.75x720 with 20 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.pairplot(iris, hue=\"species\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Korelácia\n",
    "\n",
    "Hodnota v rozsahu [-1, 1], ktorá hovorí o tom, aký silný lineárny vzťah je medzi atribútmi.\n",
    "\n",
    "* -1 perfektná negatívna korelácia\n",
    "* 0 žiadna korelácia\n",
    "* 1 perfektná kladná korelácia"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Pearsonov korelačný koeficient:\n",
    "$$ corr(X, Y) = \\frac{cov(X,Y)}{\\sigma_X\\sigma_Y} = \\frac{E[(X-E[X])(Y-E[Y])]}{\\sigma_X\\sigma_Y}$$\n",
    "\n",
    "$$ r_{xy} = \\frac{\\sum_{i=1}^{n}{(x_i-\\overline{x})(y_i-\\overline{y})}}{(n-1)s_xs_y} $$"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "![scatter plot correlation](img/scatter-continuous-continuous.png)\n",
    "\n",
    "Zdroj obrázku: https://www.analyticsvidhya.com/blog/2016/01/guide-data-exploration/#one"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Pearsonov korelačný koeficient meria **lineárnu závislosť** medzi dvomi premennými."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Medzi dvomi premennými však môže byť aj iný typ závislosti."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Alternatívy k Pearsonovmu korelačnému koeficientu, ktoré nevyžadujú linearitu, len monotónnosť, sú:\n",
    "* Spearmanov koeficient\n",
    "* Kendalovo $\\tau$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Pearson correlation: 0.963\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(x=\"petal_length\", y=\"petal_width\", data=iris)\n",
    "print(\"Pearson correlation: %.3f\" % iris.petal_length.corr(iris.petal_width))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>sepal_length</th>\n",
       "      <th>sepal_width</th>\n",
       "      <th>petal_length</th>\n",
       "      <th>petal_width</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>sepal_length</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.117570</td>\n",
       "      <td>0.871754</td>\n",
       "      <td>0.817941</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sepal_width</th>\n",
       "      <td>-0.117570</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>-0.366126</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_length</th>\n",
       "      <td>0.871754</td>\n",
       "      <td>-0.428440</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.962865</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>petal_width</th>\n",
       "      <td>0.817941</td>\n",
       "      <td>-0.366126</td>\n",
       "      <td>0.962865</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "              sepal_length  sepal_width  petal_length  petal_width\n",
       "sepal_length      1.000000    -0.117570      0.871754     0.817941\n",
       "sepal_width      -0.117570     1.000000     -0.428440    -0.366126\n",
       "petal_length      0.871754    -0.428440      1.000000     0.962865\n",
       "petal_width       0.817941    -0.366126      0.962865     1.000000"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "iris.corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143e8d1bcc0>"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x576 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10,8))\n",
    "sns.heatmap(iris.corr(), ax=ax, annot=True, fmt=\".3f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Korelácia $\\neq$ kauzalita"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Ak dva javy spolu korelujú, môže to byť náhoda. (*Príklady vtipných korelácií nájdete tu:* http://tylervigen.com/spurious-correlations)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Alebo môže existovať nejaký iný jav, ktorý je pôvodcom oboch. (*Napr. účasť študentov na prednáškach môže korelovať s ich finálnym hodnotením na predmete, ale možno len na prednášky chodí viac snaživejších študentov, ktorí by lepšie hodnotenie mali tak či tak.*)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Dokázať kauzalitu je netriviálne - kontrolovaný (randomizovaný) experiment"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"img/xkcd_correlation.png\" alt=\"Correlation is not causation\" style=\"width: 800px; margin-right: auto; margin-left: auto;\"/>\n",
    "\n",
    "(Zdroj: https://xkcd.com/552/)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Vždy použite aj scatterplot na kontrolu miery korelácie"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [],
   "source": [
    "df = sns.load_dataset(\"anscombe\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th>mean</th>\n",
       "      <th>mean</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
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       "  <tbody>\n",
       "    <tr>\n",
       "      <th>I</th>\n",
       "      <td>9.0</td>\n",
       "      <td>7.500909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>II</th>\n",
       "      <td>9.0</td>\n",
       "      <td>7.500909</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>III</th>\n",
       "      <td>9.0</td>\n",
       "      <td>7.500000</td>\n",
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       "      <th>IV</th>\n",
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       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           x         y\n",
       "        mean      mean\n",
       "dataset               \n",
       "I        9.0  7.500909\n",
       "II       9.0  7.500909\n",
       "III      9.0  7.500000\n",
       "IV       9.0  7.500909"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('dataset').agg(['mean'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
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       "      <th></th>\n",
       "      <th>x</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dataset</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">I</th>\n",
       "      <th>x</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.816421</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.816421</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">II</th>\n",
       "      <th>x</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.816237</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.816237</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">III</th>\n",
       "      <th>x</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.816287</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.816287</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">IV</th>\n",
       "      <th>x</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.816521</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>y</th>\n",
       "      <td>0.816521</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                  x         y\n",
       "dataset                      \n",
       "I       x  1.000000  0.816421\n",
       "        y  0.816421  1.000000\n",
       "II      x  1.000000  0.816237\n",
       "        y  0.816237  1.000000\n",
       "III     x  1.000000  0.816287\n",
       "        y  0.816287  1.000000\n",
       "IV      x  1.000000  0.816521\n",
       "        y  0.816521  1.000000"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('dataset').corr()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x143e8e47278>"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 576x576 with 4 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.lmplot(x=\"x\", y=\"y\", col=\"dataset\", hue=\"dataset\", data=df,\n",
    "           col_wrap=2, ci=None, palette=\"muted\", height=4,\n",
    "           scatter_kws={\"s\": 50, \"alpha\": 1})"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Čo dokáže jeden outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x143e8f79128>"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d3 = df.loc[df.dataset == 'III', ['x', 'y']]\n",
    "sns.lmplot(x='x', y='y', data=d3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "### Čo dokáže jeden outlier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<seaborn.axisgrid.FacetGrid at 0x143e8e1a978>"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "d3_no = d3.drop(24)\n",
    "sns.lmplot(x='x', y='y', data=d3_no)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Párová analýza: Spojitý - kategorický\n",
    "\n",
    "Tu sa najčastejšie používa rozdeľovanie pozorovaní podľa kategorickej hodnoty a zobrazovanie rozdelení podmnožín numerických hodnôt napríklad pomocou histogramov alebo boxplotov.\n",
    "\n",
    "Čize ide o viacnásobné použitie vizualizácií, ktoré sa použivajú na zobrazenie spojitých atribútov."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143e8f37710>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.boxplot('species', 'petal_length', data=iris)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Párová analýza: Kategorický - kategorický\n",
    "\n",
    "* Kontingenčná tabuľka\n",
    "* Teplotná mapa\n",
    "* Zložený stĺpcový graf\n",
    "* Chi-kvadrát testy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>PassengerId</th>\n",
       "      <th>Survived</th>\n",
       "      <th>Pclass</th>\n",
       "      <th>Name</th>\n",
       "      <th>Sex</th>\n",
       "      <th>Age</th>\n",
       "      <th>SibSp</th>\n",
       "      <th>Parch</th>\n",
       "      <th>Ticket</th>\n",
       "      <th>Fare</th>\n",
       "      <th>Cabin</th>\n",
       "      <th>Embarked</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Braund, Mr. Owen Harris</td>\n",
       "      <td>male</td>\n",
       "      <td>22.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>A/5 21171</td>\n",
       "      <td>7.2500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
       "      <td>female</td>\n",
       "      <td>38.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>PC 17599</td>\n",
       "      <td>71.2833</td>\n",
       "      <td>C85</td>\n",
       "      <td>C</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>Heikkinen, Miss. Laina</td>\n",
       "      <td>female</td>\n",
       "      <td>26.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>STON/O2. 3101282</td>\n",
       "      <td>7.9250</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
       "      <td>female</td>\n",
       "      <td>35.0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>113803</td>\n",
       "      <td>53.1000</td>\n",
       "      <td>C123</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>Allen, Mr. William Henry</td>\n",
       "      <td>male</td>\n",
       "      <td>35.0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>373450</td>\n",
       "      <td>8.0500</td>\n",
       "      <td>NaN</td>\n",
       "      <td>S</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   PassengerId  Survived  Pclass  \\\n",
       "0            1         0       3   \n",
       "1            2         1       1   \n",
       "2            3         1       3   \n",
       "3            4         1       1   \n",
       "4            5         0       3   \n",
       "\n",
       "                                                Name     Sex   Age  SibSp  \\\n",
       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
       "\n",
       "   Parch            Ticket     Fare Cabin Embarked  \n",
       "0      0         A/5 21171   7.2500   NaN        S  \n",
       "1      0          PC 17599  71.2833   C85        C  \n",
       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
       "3      0            113803  53.1000  C123        S  \n",
       "4      0            373450   8.0500   NaN        S  "
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "titanic = pd.read_csv('data/titanic/train.csv')\n",
    "titanic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    549\n",
       "1    342\n",
       "Name: Survived, dtype: int64"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Frekvencna tabulka\n",
    "titanic[\"Survived\"].value_counts()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>died</th>\n",
       "      <td>80</td>\n",
       "      <td>97</td>\n",
       "      <td>372</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>136</td>\n",
       "      <td>87</td>\n",
       "      <td>119</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass      1   2    3\n",
       "died       80  97  372\n",
       "survived  136  87  119"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "survived_class = pd.crosstab(index=titanic[\"Survived\"], \n",
    "                           columns=titanic[\"Pclass\"])\n",
    "survived_class.index= [\"died\",\"survived\"]\n",
    "survived_class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143e950c6a0>"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.heatmap(survived_class, annot=True, fmt=\"d\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th>Pclass</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>died</th>\n",
       "      <td>0.37037</td>\n",
       "      <td>0.527174</td>\n",
       "      <td>0.757637</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>survived</th>\n",
       "      <td>0.62963</td>\n",
       "      <td>0.472826</td>\n",
       "      <td>0.242363</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass          1         2         3\n",
       "died      0.37037  0.527174  0.757637\n",
       "survived  0.62963  0.472826  0.242363"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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I2IFAfrH1fKBlOa9BRKTSeaTiHnpw95HE5g8vvm1gsc9ObEhtwjPrJxp0XwW+NrN3ieVzLwBeTvQkIiKB2Z9f17Odu//ZzD4Ejo9vusrdp1Ves0RE9k4FDhmrFAm/mDI+d+4veOZuEdkvpEvQFRHZL6T2iyMUdEUkvXhhakddBV0RSS+pHXMVdEUkvaTNjTQRkf2CeroiIsFRT1dEJEjq6YqIBMcLk92CPVPQFZG0kqQ3qydMQVdE0ouCrohIcNTTFREJkIKuiEiAPFLaq81Sh4KuiKQV9XRFRALkUfV0RUQCo56uiEiA3NXTFREJjHq6IiIBimr0gohIcHQjTUQkQAq6IiIB8tSeTldBV0TSi3q6IiIB0pAxEZEARTR6QUQkOOrpiogESDldEZEAafSCiEiA1NMVEQlQJBpKdhP2KLVbJyJSTu6JL2Uxs15mNsfM5ppZ/1L2X2lmK81seny5tqw61dMVkbQSraDRC2YWBoYApwE5wGQzG+Hus3cq+oa735xoverpikhacbeElzJ0Aea6+zx3zweGA+fta/sUdEUkrVRgeqEZsLjYek58284uNLMZZvaWmbUoq9JKTy8MubFWZZ/iF2/sE5nJbkLaa9/0+GQ34RehMH/JPtdRnvSCmfUF+hbbNNTdh27fXcohO4fq94Fh7r7NzG4AXgZO3tM5ldMVkbRSntEL8QA7dDe7c4DiPdfmQO5Ox68utvpP4OGyzqn0goikFS/HUobJQFsza2VmWcBFwIjiBcysSbHVc4Hvy6pUPV0RSSsVNXrB3QvN7GZgFBAGXnD3WWY2GJji7iOAW83sXKAQWANcWVa9CroiklYqcsIbdx8JjNxp28Bin+8F7i1PnQq6IpJWUvxlwAq6IpJevNRBB6lDQVdE0kqh5tMVEQmOeroiIgFSTldEJEDq6YqIBEg9XRGRAEXU0xURCU6Kv61HQVdE0ktUPV0RkeCk+MuAFXRFJL3oRpqISICipvSCiEhgIsluQBkUdEUkrWj0gohIgDR6QUQkQBq9ICISIKUXREQCpCFjIiIBiqinKyISHPV0RUQCpKArIhKgFH9FmoKuiKQX9XRFRAKkx4BFRAKkcboiIgFSekFEJEAKuiIiAdLcCyIiAVJOV0QkQBq9ICISoGiKJxgUdEUkrehGmohIgFK7nwuhZDdARKQiRcuxlMXMepnZHDOba2b991DuN2bmZta5rDrV0xWRtFJoFdPXNbMwMAQ4DcgBJpvZCHefvVO5msCtwKRE6lVPV0TSipdjKUMXYK67z3P3fGA4cF4p5f4EPAJsTaR9CroiklbKk14ws75mNqXY0rdYVc2AxcXWc+LbipjZUUALd/8g0fYpvSAiaaU8Q8bcfSgwdDe7S3vMoqhyMwsBfwOuLEfz1NMVkfRSgemFHKBFsfXmQG6x9ZpAB2CsmS0AugEjyrqZpp6uiKSVChynOxloa2atgCXARcAl23e6+3qg/vZ1MxsL3OXuU/ZUqYKuiKSVSAWN1HX3QjO7GRgFhIEX3H2WmQ0Gprj7iL2pV0FXRNJKRT6R5u4jgZE7bRu4m7InJVKngq6IpBVP8WfSFHRFJK1o7oUkCLXqQNYpl0AoROG3n1M4aeQuZcKHHEtm99g45+iKxeS//xzWsAVZp1+OVakK0SgFX31A5IevY3UeeChZPX8L4TDRZQvJ//AF8Ngfb6hF+9j5wmF8y0a2DXs4uItNkvo9O3LYg1dg4RCLXxvDvKdKT281PrsrR//rdiacfh/rv51H0wu70/q/zynaX/OwA/ni1HvZPG8pR//zNqq1bIRHoqwYPZU5Dw4DILtZPTo+9d9k1KqGhUPMeXAYKz+dHsh1JtsZp5/EE08MJhwK8cKLw3jk0SEl9t/Wry9XX30xhYWFrFq5hmv73sGiRUuK9tesWYPvZozl3+99RL/bBlC1ajZvDBtK64MPIhKJ8J//jOa+P/y1RJ29e5/Fm8OH0rXbmXwzdUYg11mRNMtY0MzIOu0ytr3xGL5xDdlXDCQydzq+esdID6vTiMxuZ7H1f/8C27ZAtZqxHQX55P/neXztcqzGAWRf8Ufy5s+EbVvJOutatg1/BF+7nMwe5xM+ojuRGeOhSlWyTr+MbW8+gW9cs6OudBYyDn/oar7u82e25q6m+6i/sGLUN2z6cUmJYuHq2bS8thdrv/mpaFvu2xPIfXsCADUPbcExL9/FxlkLCVXNYt4zH7BmwmwsM0zXt+6nwcmdWDlmOm1u783S9yay6OXR1GjXjM6v9WfssbcEesnJEAqFePLvf6bXry8mJ2cpE78ayfsffMz33+/4PqdP/46u3c4kL28r1/e9nIf+OoBLLr2xaP8Dg+7m8/ETS9T7xN+eZey4L8nMzGT0qDfodUZPPhr1GQA1alTnlpuuZtKkqcFcZCVI7ZCbhuN0Q01a4+tW4OtXQjRC4fdfE257VIkyGR1PoGDqmFjABdiyEQBfuxxfuzz2edM6fMsGrFotqFoDIgVF+yILZpHRLjYUL+OwbkR+nBoLuMXqSmcHHN2GLfOXkbdwBV4QYem/v6RRr12HJrbr34d5Q94nurWg1HqaXNCd3He/BCCal8+aCbFH2r0gwvqZ88luWjdW0J2MmlUByKhVjW3L11bCVaWeLscexc8/L2D+/EUUFBTw5pvvce45Z5QoM3bcl+TlxZ4+nfT1NzRv1qRo39FHHUGjRg0YPfrzom15eVsZOy72nRcUFDB12kyaFTvmgUG/57HHn2Hr1oSeaE1JhXjCSzKkXdC1mnXwDWuK1n3jGqxGnZJl6jQmVLcRVS69jyqXDSDUqsMu9YSatIJwBr52BeRthFCYUOOWAITbH4vVigUEq9sYsqtR5eJ7yL7ij4QPP67yLi5FZDeuy9bc1UXreblrqNK4bokytTq0pGrTeqwYvfseU5PzfkXuuxN22Z5RqxqNTj+aVeO/A+CnR9+i2W960HPaEI597R5m3fdiBV1JamvarDGLc3b8QstZspSmTRvvtvxVV15c1GM1Mx59ZCD39H9wt+Vr167F2WedxpjPvgCgU6fDadGiCf8Z+UkFXUFyeDn+S4Y9phfMbCN76K27e63dHNcX6Avw1AW/4uqu7feljRWg5CVYKAR1GrFt2MNYzTpUufRetv5rAGzLixWoXpuss64jf+TzRcfmj3iWzJMvhnAG0QWzIBp/KYjFgvG24Y9ARhbZvxtANPfnol5xWir1HVTFvmMzDh18OTP6PbPbKmof3YZo3jY2/ZBTsupwiE7P3sqC5z8ib+EKAJpecBw5w8cx/9n/cEDntnR8+ibGn3g3eKr/kNw3Zrt+0b6ba77kkt50PqYjPU+5EIAbb7iCDz8aQ05Obqnlw+Ewr706hKeHvMD8+YswMx5/dBBXX3t7xV1AkuzXN9LcvSZAfDDwMuBVYv/kLiX2CNzujit6nnnLw1cF+i/DN64t6oUCWM26+KZ1JcpEN64lmvszRCP4+lX46mWE6jQmumw+ZGWT/ZvbKRj/DtHceTuOyf2Zba/HbjiEWh6O1W0UP98aonkboSAfCvKJ5swh1LAFkTQOuluXriG7ab2i9apN67Jt2Y6f/Bk1sql5SHO6vhMbzlilYW2OeeUuvrn8MdZ/G/tOm55/XFFqobgOj1/HlvlLWTD0w6JtzS/pyeSLHwJg3ZSfCGdnklWvJvmrNlTK9aWKJTlLadG8adF682ZNWLp0179Xp5x8PPf2v5WTT7mQ/Px8ALp1O4Ye3btyw/VXUKNGdbKyMtm8eXPRTbNnn3mEn+bO58mnngdiN9wOP/wQPh39FgCNGzfg3Xde5ILeV+13N9NSfchYoumFM9z9H+6+0d03uPszwIWV2bC9FV06H6vTEKtdH0JhMg7tQmTutBJlIj9NJXzgobGVqjWwuo2JrlsBoTBVLriFwlkTiMzZ6Um+7TfIwhlkdv01hdPGxuqaO41Q83ZgIcjIItSkNdHVSyv5KpNr/bSfqd66MVUPbIBlhmly/nEsH/VN0f7CjXl8clhfxh57C2OPvYV138wtEXAxo/E5Xcn9d8mg265/HzJrVmP2gFdKbM9bspp6x8dSQNXbNiVUJTPtAy7A5CnTadOmFS1btiAzM5M+fc7j/Q8+LlGmU6fD+ceQh7ig91WsXLkj5XP5FbfQuk0X2rTrxu/v+ROv/u9bRQF38AO/p3btmtxx5x+Lym/YsJHGTY+gTbtutGnXjUmTpu6XARcqdhLzypDo6IWImV1KbD5JBy4mVV+66VHyR79GlT53goUonDkeX5VLZo/ziS5bQGTudKLzv8NbdSD7mgfBnYKxb8DWzYQP+xWhFu2wqjXI6NADgG0jn8dXLCazy5mE23QEjMLpnxFd9H3sdKuXEpk/k+yrB4M7hTM+x1ct2UMD938eiTLr3hfpMvw+CIfIGfYZm+bk0Pb3/8X6b+exolgALk3dXx3K1qVritIHANlN6tLm9t5s+nEJPT6JBYcFL4wi57XP+GHQq3R4vC+trv81uDPj1mcr9fpSRSQSod9tAxj5n9cJh0K89PIbzJ79I4P+eBdTvvmWDz4YzcN/vZ8aNaozfNhzACxevIQLel+12zqbNWvCfff24/sffmLy16MA+Mc/XuSFF4cFck1BiKR42sl2lyMqUcisJfB3oDuxoDsBuM3dF5R1bNDphV+isU/kJbsJae/cteOT3YRfhML8JaXeMSiPSw66IOGY8/rCd/f5fOWVUE83HlxLmzFdRCSlpEVO18zamdmnZvZdfP1IMxtQuU0TESm/VM/pJnoj7Z/AvUABgLvPIDa3pIhISoniCS/JkOiNtGru/vVO4wYLK6E9IiL7JNXTC4kG3VVmdjDxEfBm9hsgvcdFich+KdVHLyQadG8i9rDDIWa2BJhP7AEJEZGUki6zjC1091PNrDoQcvf0n9VFRPZLqf4YcKI30uab2VBib7vcVIntERHZJ6k+4U2iQbc98AmxNMN8M3vazHpUXrNERPZOqo9eSCjounueu7/p7r2Bo4BawLhKbZmIyF5w94SXZEh4Pl0zO9HM/gFMBbKBPpXWKhGRvRTBE16SIaEbaWY2H5gOvAnc7e6bK7VVIiJ7KV1GL3R09/SfS09E9nvJShskqqw3R/ze3R8B/mxmu1yJu99aaS0TEdkL+3tP9/v4/07ZYykRkRSxXz8G7O7vxz/OcPdpeyorIpIKUv0x4ERHLzxhZj+Y2Z/M7PBKbZGIyD5Il3G6PYGTgJXAUDObqfl0RSQVpUXQBXD3Ze7+JHADseFjAyutVSIieynVH45IdJzuocBvgd8Aq4m9oPLOSmyXiMhe2d9HL2z3IjAMON3dcyuxPSIi+2S/Hr0AYGZh4Gd3/3sA7RER2ScRT+3JHcvM6bp7BKhnZlkBtEdEZJ9UZE7XzHqZ2Rwzm2tm/UvZf0N8YMF0M/vCzA4rq86EJzEHJpjZCKBo3gV3fyLB40VEAlFROd34r/whwGlADjDZzEa4++xixV5392fj5c8FngB67aneRINubnwJATXL2XYRkcBUYE63CzDX3ecBmNlw4DygKOjuNCdNdSj75AkFXXd/oFxNFRFJkmg5hoKZWV+gb7FNQ919aPxzM2BxsX05QNdS6rgJuAPIAk4u65yJDhn7jFIiuLuXeQIRkSCVp6cbD7BDd7PbSq1+1zqGAEPM7BJgAHDFns6ZaHrhrmKfs4ELgcIEjxURCUwFjl7IAVoUW29OLM26O8OBZ8qqNNH0wjc7bZpgZnpdj4iknPKkF8owGWhrZq2AJcBFwCXFC5hZW3f/Kb56FvATZUg0vVC32GoI6Aw0TuRYEZEgVdSNNHcvNLObgVFAGHjB3WeZ2WBgiruPAG42s1OBAmAtZaQWIPH0wjfEchkWr3wBcE25r0JEpJJVYE8Xdx8JjNxp28Bin/uVt85EJ7y5B+jk7q2AV4mN1d1S3pOJiFQ2L8d/yZBo0B3g7hvMrAexgcIvkUDCWEQkaBGPJLwkQ6JBd3vrzgKedff3iI1JExFJKWkxtSOwxMyeA04FHjazKpRjLl4RkaCk+tSOiQbOPsTu4PVy93VAXeDuSmuViMheSouerrtvAd4ptr4UWFpZjRIR2VsVOXqhMiSaXhAR2S/s95OYi4jsT1J9EnMFXREjWXFFAAAD0ElEQVRJK8nK1SZKQVdE0opyuiIiAVJPV0QkQKk+TldBV0TSinq6IiIB0ugFEZEA6UaaiEiAlF4QEQmQnkgTEQmQeroiIgFK9Zyupfr/KySDmfV196HJbkc603dc+fQdpyZNRF66vsluwC+AvuPKp+84BSnoiogESEFXRCRACrqlUx6s8uk7rnz6jlOQbqSJiARIPV0RkQAp6BZjZi+Y2Qoz+y7ZbUlXZtbCzD4zs+/NbJaZ9Ut2m9KNmWWb2ddm9m38O34g2W2SHZReKMbMTgA2Aa+4e4dktycdmVkToIm7TzWzmsA3wPnuPjvJTUsbZmZAdXffZGaZwBdAP3efmOSmCerpluDunwNrkt2OdObuS919avzzRuB7oFlyW5VePGZTfDUzvqh3lSIUdCVpzKwlcBQwKbktST9mFjaz6cAKYLS76ztOEQq6khRmVgN4G7jN3Tckuz3pxt0j7t4JaA50MTOly1KEgq4ELp5nfBt4zd3fSXZ70pm7rwPGAr2S3BSJU9CVQMVv8vwL+N7dn0h2e9KRmTUwswPin6sCpwI/JLdVsp2CbjFmNgz4CmhvZjlmdk2y25SGugOXASeb2fT48utkNyrNNAE+M7MZwGRiOd0PktwmidOQMRGRAKmnKyISIAVdEZEAKeiKiARIQVdEJEAKuiIiAVLQlX1iZpH4sK/vzOz/zKzaHsoOMrO7gmyfSKpR0JV9lefuneKzsuUDNyS7QSKpTEFXKtJ4oA2AmV1uZjPic7q+unNBM7vOzCbH97+9vYdsZv8V7zV/a2afx7cdHp8fdnq8zraBXpVIBdLDEbJPzGyTu9cwswxi8yl8BHwOvAN0d/dVZlbX3deY2SBgk7s/Zmb13H11vI4HgeXu/pSZzQR6ufsSMzvA3deZ2VPARHd/zcyygLC75yXlgkX2kXq6sq+qxqcQnAIsIjavwsnAW+6+CsDdS5ujuIOZjY8H2UuBw+PbJwAvmdl1QDi+7SvgPjO7BzhIAVf2ZxnJboDs9/LiUwgWiU9qU9ZPqJeIvTHiWzO7EjgJwN1vMLOuwFnAdDPr5O6vm9mk+LZRZnatu4+p4OsQCYR6ulIZPgX6mFk9ADOrW0qZmsDS+DSPl27faGYHu/skdx8IrAJamFlrYJ67PwmMAI6s9CsQqSTq6UqFc/dZZvZnYJyZRYBpwJU7Fbuf2BsjFgIziQVhgEfjN8qMWPD+FugP/M7MCoBlwOBKvwiRSqIbaSIiAVJ6QUQkQAq6IiIBUtAVEQmQgq6ISIAUdEVEAqSgKyISIAVdEZEAKeiKiATo/wHxEB3s4tbjegAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 432x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "survived_class_perc = pd.crosstab(index=titanic[\"Survived\"], \n",
    "                           columns=titanic[\"Pclass\"],\n",
    "                            normalize='columns') #'index', 'all'\n",
    "survived_class_perc.index= [\"died\",\"survived\"]\n",
    "\n",
    "sns.heatmap(survived_class_perc, annot=True, fmt=\".4f\")\n",
    "survived_class_perc"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th>Pclass</th>\n",
       "      <th colspan=\"2\" halign=\"left\">1</th>\n",
       "      <th colspan=\"2\" halign=\"left\">2</th>\n",
       "      <th colspan=\"2\" halign=\"left\">3</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Sex</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th>female</th>\n",
       "      <th>male</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Survived</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>3</td>\n",
       "      <td>77</td>\n",
       "      <td>6</td>\n",
       "      <td>91</td>\n",
       "      <td>72</td>\n",
       "      <td>300</td>\n",
       "      <td>549</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>91</td>\n",
       "      <td>45</td>\n",
       "      <td>70</td>\n",
       "      <td>17</td>\n",
       "      <td>72</td>\n",
       "      <td>47</td>\n",
       "      <td>342</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>94</td>\n",
       "      <td>122</td>\n",
       "      <td>76</td>\n",
       "      <td>108</td>\n",
       "      <td>144</td>\n",
       "      <td>347</td>\n",
       "      <td>891</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Pclass        1           2           3       All\n",
       "Sex      female male female male female male     \n",
       "Survived                                         \n",
       "0             3   77      6   91     72  300  549\n",
       "1            91   45     70   17     72   47  342\n",
       "All          94  122     76  108    144  347  891"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.crosstab(index=titanic[\"Survived\"], \n",
    "            columns=[titanic[\"Pclass\"], titanic[\"Sex\"]],\n",
    "            margins=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.axes._subplots.AxesSubplot at 0x143e95be7b8>"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pd.crosstab(index=titanic[\"Pclass\"], columns=titanic[\"Survived\"]).plot.bar(stacked=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Vizualizácie nám pomáhajú pochopiť dáta"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Ak sú spravené dobre..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pravidlá dobrých informačných vizualizácií podľa E. Tufteho (podľa Skiena, kap. 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Maximalizujte pomer medzi dátami a atramentom"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Minimalizujte faktor klamania"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Minimalizujte \"*chartjunk*\""
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Používajte vhodné škály a označenia osí"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Pravidlá dobrých informačných vizualizácií podľa E. Tufteho (podľa Skiena, kap. 6)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Používajte farby efektívne"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "\n",
    "Vhodne využite opakovanie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Príklady zavádzania vizualizáciami (Skiena, kap. 6.2.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Prezentovanie priemeru bez variancie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Prezentovanie interpolácie bez dát"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Skreslenie škály osí"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Príklady zavádzania vizualizáciami (Skiena, kap. 6.2.2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Neuvedenie čísel na osiach"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Skrytie alebo skreslenie nulového bodu na osi "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"img/axis_manipulation.png\" alt=\"Problem s osou y\" style=\"width: 600px; margin-right: auto; margin-left: auto;\"/>\n",
    "\n",
    "(Zdroj obrázku: https://cs.wikipedia.org/wiki/Sloupcov%C3%BD_graf)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"img/obamacareenrollment-fncchart.jpg\" alt=\"Obamacare na FOX News\" style=\"width: 1000px; margin-right: auto; margin-left: auto;\"/>\n",
    "\n",
    "(Zdroj obrázku: https://www.mediamatters.org/msnbc/msnbc-criticizes-fox-news-airing-dishonest-obamacare-enrollment-chart)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "<img src=\"img/kazimir_graf.jpg\" alt=\"Ako (ne)klesli ceny plynu\" style=\"width: 1000px; margin-right: auto; margin-left: auto;\"/>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "Príklady toho, ako sa dá zavádzať grafmi a štatistikami nájdete napr. v knihe \"*A Field Guide to Lies and Statistics*\" od D. Levitina.\n",
    "\n",
    "Príklady praktických úskalí vizualizácií nájdete tu: https://anaconda.org/jbednar/plotting_pitfalls/notebook"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "## Ďalšie užitočné zdroje\n",
    "\n",
    "Steven S. Skiena: The Data Science Design Manual. Springer International Publishing, kap. 6.\n",
    "\n",
    "Allen B. Downey: Think Stats: Exploratory Data Analysis in Python (2nd ed.). O'Reilly Media, 2014, kap. 1, 2, 7.\n",
    "\n",
    "Jake VanderPlas: Python Data Science Handbook: Essential Tools for Working with Data. O'Reilly Media, 2016, kap. 4.\n",
    "\n",
    "Edward R. Tufte: The Visual Display of Quantitative Information. Graphics Press, Cheshire, CT, 1983."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "slide"
    }
   },
   "source": [
    "# Čo si z dneška odniesť"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "**Spoznajte vaše dáta!** Dátový vedec musí byť zvedavý (a zdravo podozrievavý).\n",
    "\n",
    "*Robiť strojové učenie bez znalosti dát je ako utekať so zaviazanými očami*"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Pri prieskumnej analýze nám pomáhajú vizualizácie"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
     "slide_type": "fragment"
    }
   },
   "source": [
    "Ale musíme ich vedieť používať korektne"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "slideshow": {
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   "source": [
    "# Zadanie projektu..."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   "source": [
    "## Spätnú väzbu nám môžete nechať tu: https://tinyurl.com/iau2019-w03"
   ]
  }
 ],
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