{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Средњошколци у Републици Србији"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У овом темату позабавићемо се подацима о средњошколском образовању у Републици Србији који су нам доступни кроз отворене податке Министарства за просвету, науку и технолошки развој: http://opendata.mpn.gov.rs/index.php?ucenici_studenti=srednje. Прерађени фајлови у .csv формату налази се у фолдеру *data* (као и радна свеска коришћена за његову припрему), а у редовима који следе, бавићемо се експлоративном обрадом ових података са следећим циљевима: \n",
    "- да истражимо врсте средњошколских образовних профила и њихову географску заступљеност\n",
    "- који образовни профили су попупларни међу средњошколцима у Србији и да ли постоји разлика међу половима\n",
    "- да визуелизујемо податаке различитим типовима стубичастих дијаграма"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Кренућемо са учитавањем неопходних библиотека за обраду ових података - иако се потребна библиотека може учитати наредним командама у било ком делу радне свеске, зарад прегледности се то обично ради на самом почетку, а у наставку ће бити наглашено када је која од библиотека први пут искоришћена."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd # библиотека функција за обраду табеларних података\n",
    "import numpy as np # библиотека функција за баратање низовима и матрицама\n",
    "import matplotlib.pyplot as plt # библиотека функција за визуелизацију податка"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "За учитавање и обраду података изузетно практична и најпопуларнија библиотека функција је [*pandas*](https://pandas.pydata.org/), њу ћемо одмах искористити због изузетне практичности учитавања табеларних података. Постоји пар верзија функција за учитавање фајлова, али у анализи табеларних података начешће користимо [**read_csv**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_csv.html) за читање .csv фајлова (односно фајлова у којима су зарезом раздвојене вредности)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "srednjeskole = pd.read_csv('data/srednjoskolci data/MPNTRopendata_sskole_pripremljeni.csv') # подаци о средњим школама на територији Србије\n",
    "srednjoskolci = pd.read_csv('data/srednjoskolci data/MPNTRopendata_ss_pripremljeni.csv') # подаци о образовним профилима и њиховој популарности за сваку средњу школу"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Да бисмо осмотрили шта се налази у променљивој *srednjeskole* искористићемо опцију [**head(n)**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html) којом излиставамо првих n редова учитаних података:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID ustanove</th>\n",
       "      <th>Okrug</th>\n",
       "      <th>Opština</th>\n",
       "      <th>Naziv ustanove</th>\n",
       "      <th>Broj odeljenja</th>\n",
       "      <th>Broj kombinovanih odeljenja</th>\n",
       "      <th>Broj specijalnih odeljenja</th>\n",
       "      <th>Broj učenika</th>\n",
       "      <th>Broj devojčica</th>\n",
       "      <th>Broj nastavnika - bez zamena</th>\n",
       "      <th>Ukupna norma nastavnikaa - bez zamena</th>\n",
       "      <th>Broj zaposlenih - bez zamena</th>\n",
       "      <th>Ukupna norma zaposlenih- bez zamena</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1322</td>\n",
       "      <td>Severnobački upravni okrug</td>\n",
       "      <td>Bačka Topola</td>\n",
       "      <td>Poljoprivredna škola</td>\n",
       "      <td>26</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>524</td>\n",
       "      <td>222</td>\n",
       "      <td>74</td>\n",
       "      <td>65.9749</td>\n",
       "      <td>109</td>\n",
       "      <td>103.4649</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1323</td>\n",
       "      <td>Severnobački upravni okrug</td>\n",
       "      <td>Bačka Topola</td>\n",
       "      <td>Srednja tehnička škola Šinković Jožef</td>\n",
       "      <td>15</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>318</td>\n",
       "      <td>61</td>\n",
       "      <td>46</td>\n",
       "      <td>32.8130</td>\n",
       "      <td>60</td>\n",
       "      <td>46.0330</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID ustanove                       Okrug       Opština  \\\n",
       "0         1322  Severnobački upravni okrug  Bačka Topola   \n",
       "1         1323  Severnobački upravni okrug  Bačka Topola   \n",
       "\n",
       "                          Naziv ustanove  Broj odeljenja  \\\n",
       "0                   Poljoprivredna škola              26   \n",
       "1  Srednja tehnička škola Šinković Jožef              15   \n",
       "\n",
       "   Broj kombinovanih odeljenja  Broj specijalnih odeljenja  Broj učenika  \\\n",
       "0                            5                           0           524   \n",
       "1                            3                           0           318   \n",
       "\n",
       "   Broj devojčica  Broj nastavnika - bez zamena  \\\n",
       "0             222                            74   \n",
       "1              61                            46   \n",
       "\n",
       "   Ukupna norma nastavnikaa - bez zamena  Broj zaposlenih - bez zamena  \\\n",
       "0                                65.9749                           109   \n",
       "1                                32.8130                            60   \n",
       "\n",
       "   Ukupna norma zaposlenih- bez zamena  \n",
       "0                             103.4649  \n",
       "1                              46.0330  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srednjeskole.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Слично ћемо погледати прва 2 реда у табели *srednjoskolci*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID ustanove</th>\n",
       "      <th>Okrug</th>\n",
       "      <th>Opština</th>\n",
       "      <th>Naziv ustanove</th>\n",
       "      <th>Područje rada</th>\n",
       "      <th>Obrazovni profil</th>\n",
       "      <th>Trajanje obrazovanja</th>\n",
       "      <th>Jezik nastave</th>\n",
       "      <th>Ukupno odeljenja</th>\n",
       "      <th>Ukupno učenika</th>\n",
       "      <th>Prosečan broj učenika u odeljenju</th>\n",
       "      <th>Ukupno devojcica</th>\n",
       "      <th>Ukupno decaka</th>\n",
       "      <th>Procenat devojcica</th>\n",
       "      <th>Procenat decaka</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1322</td>\n",
       "      <td>Severnobački upravni okrug</td>\n",
       "      <td>Bačka Topola</td>\n",
       "      <td>Poljoprivredna škola</td>\n",
       "      <td>Poljoprivreda, proizvodnja i prerada hrane</td>\n",
       "      <td>Poljoprivredni tehničar</td>\n",
       "      <td>4</td>\n",
       "      <td>Mađarski jezik</td>\n",
       "      <td>4</td>\n",
       "      <td>74</td>\n",
       "      <td>18.50</td>\n",
       "      <td>25</td>\n",
       "      <td>49</td>\n",
       "      <td>33.78</td>\n",
       "      <td>66.22</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1322</td>\n",
       "      <td>Severnobački upravni okrug</td>\n",
       "      <td>Bačka Topola</td>\n",
       "      <td>Poljoprivredna škola</td>\n",
       "      <td>Poljoprivreda, proizvodnja i prerada hrane</td>\n",
       "      <td>Veterinarski tehničar</td>\n",
       "      <td>4</td>\n",
       "      <td>Mađarski jezik</td>\n",
       "      <td>4</td>\n",
       "      <td>83</td>\n",
       "      <td>20.75</td>\n",
       "      <td>43</td>\n",
       "      <td>40</td>\n",
       "      <td>51.81</td>\n",
       "      <td>48.19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   ID ustanove                       Okrug       Opština  \\\n",
       "0         1322  Severnobački upravni okrug  Bačka Topola   \n",
       "1         1322  Severnobački upravni okrug  Bačka Topola   \n",
       "\n",
       "         Naziv ustanove                               Područje rada  \\\n",
       "0  Poljoprivredna škola  Poljoprivreda, proizvodnja i prerada hrane   \n",
       "1  Poljoprivredna škola  Poljoprivreda, proizvodnja i prerada hrane   \n",
       "\n",
       "          Obrazovni profil  Trajanje obrazovanja   Jezik nastave  \\\n",
       "0  Poljoprivredni tehničar                     4  Mađarski jezik   \n",
       "1    Veterinarski tehničar                     4  Mađarski jezik   \n",
       "\n",
       "   Ukupno odeljenja  Ukupno učenika  Prosečan broj učenika u odeljenju  \\\n",
       "0                 4              74                              18.50   \n",
       "1                 4              83                              20.75   \n",
       "\n",
       "   Ukupno devojcica  Ukupno decaka  Procenat devojcica  Procenat decaka  \n",
       "0                25             49               33.78            66.22  \n",
       "1                43             40               51.81            48.19  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srednjoskolci.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видимо да оба сета података имају колоне \"ID ustanove\", \"Okrug\", \"Opština\", \"Naziv ustanove\" који ће нам бити на располагању за укрштање информација (довољана нам је и једна колона, нпр. јединствена бројчана колона \"ID ustanove\" али за читљивост и означавање визуализација у наставку сачуваћемо и имена школа и њихове локације по окрузима и општинама). Подаци о средњошколцима су детаљнији зато што за сваку школу имамо одвојене информације о свим образовним профилима у оквиру школе, за које имамо податке о броју ученика по разредима. Како у том сету имамо више од 30 колона, опцијом head их нисмо све осмотрили. Други начин да видимо шта све имамо на располагању је [**info**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.info.html) којим излиставамо све колоне и сазнајемо који тип података је пајтон препознао при учитавању."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 3177 entries, 0 to 3176\n",
      "Data columns (total 15 columns):\n",
      " #   Column                             Non-Null Count  Dtype  \n",
      "---  ------                             --------------  -----  \n",
      " 0   ID ustanove                        3177 non-null   int64  \n",
      " 1   Okrug                              3177 non-null   object \n",
      " 2   Opština                            3177 non-null   object \n",
      " 3   Naziv ustanove                     3177 non-null   object \n",
      " 4   Područje rada                      3177 non-null   object \n",
      " 5   Obrazovni profil                   3177 non-null   object \n",
      " 6   Trajanje obrazovanja               3177 non-null   int64  \n",
      " 7   Jezik nastave                      3177 non-null   object \n",
      " 8   Ukupno odeljenja                   3177 non-null   int64  \n",
      " 9   Ukupno učenika                     3177 non-null   int64  \n",
      " 10  Prosečan broj učenika u odeljenju  3177 non-null   float64\n",
      " 11  Ukupno devojcica                   3177 non-null   int64  \n",
      " 12  Ukupno decaka                      3177 non-null   int64  \n",
      " 13  Procenat devojcica                 3177 non-null   float64\n",
      " 14  Procenat decaka                    3177 non-null   float64\n",
      "dtypes: float64(3), int64(6), object(6)\n",
      "memory usage: 372.4+ KB\n"
     ]
    }
   ],
   "source": [
    "srednjoskolci.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 446 entries, 0 to 445\n",
      "Data columns (total 13 columns):\n",
      " #   Column                                 Non-Null Count  Dtype  \n",
      "---  ------                                 --------------  -----  \n",
      " 0   ID ustanove                            446 non-null    int64  \n",
      " 1   Okrug                                  446 non-null    object \n",
      " 2   Opština                                446 non-null    object \n",
      " 3   Naziv ustanove                         446 non-null    object \n",
      " 4   Broj odeljenja                         446 non-null    int64  \n",
      " 5   Broj kombinovanih odeljenja            446 non-null    int64  \n",
      " 6   Broj specijalnih odeljenja             446 non-null    int64  \n",
      " 7   Broj učenika                           446 non-null    int64  \n",
      " 8   Broj devojčica                         446 non-null    int64  \n",
      " 9   Broj nastavnika - bez zamena           446 non-null    int64  \n",
      " 10  Ukupna norma nastavnikaa - bez zamena  446 non-null    float64\n",
      " 11  Broj zaposlenih - bez zamena           446 non-null    int64  \n",
      " 12  Ukupna norma zaposlenih- bez zamena    446 non-null    float64\n",
      "dtypes: float64(2), int64(8), object(3)\n",
      "memory usage: 45.4+ KB\n"
     ]
    }
   ],
   "source": [
    "srednjeskole.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Број ученика у средњим школама"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На располагању имамо доста података, тачније 3177 различитих образовних смерова У 446 средњих школа (што читамо из броја уноса у свакој од табела) и да бисмо сазнали више о овим подацима кренућемо са пар једноставних визуализација, нпр. анализирајући број ученика у школама. Један начин је да представимо сваку школу стубићем висине која одговара броју ученика у школи на стубичастом дијаграму (уз помоћ библиотеке [*matplotlib*](https://matplotlib.org/) коју скраћено позивамо са *plt*, функцијом [**bar**](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.bar.html)): "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Пре цртања дијаграма, сортираћемо табелу по колони Број ученика користећи фунцију [**sort_values**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.sort_values.html) са аргументом *by* коме прослеђујемо име колоне по којој сортирамо. \n",
    "\n",
    "*Испитајте како исти дијаграм у наставку изгледа када је табела сортирана по некој другој колони.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "srednjeskole = srednjeskole.sort_values(by='Broj učenika')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Обратите пажњу да **sort_values** креира нову табелу (енг. dataframe), тј. ако не доделите исход сортирања поново табели srednjeskole као што је урађено претходно, оригинална табела се неће променити. (Обратите пажњу да је ово је другачије од **list.sort** у пајтону.)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(range(len(srednjeskole)),srednjeskole['Broj učenika'],color='darkgrey')\n",
    "plt.xlabel('Škole poređane od najmanje do najveće po broju učenika')\n",
    "plt.ylabel('Broj učenika u srednjoj školi')\n",
    "plt.grid()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функцији *bar* је поред висина стубића (које смо проследили кроз колону Број ученика као други аргумент), потребно је проследити и позиције на х оси. У овом случају, проследили смо редне бројеве у дужини колоне уз помоћ функције **range** и тиме на х оси добили бројеве од 0 до дужине колоне умањене за 1 (што смо добили уз помоћ *len(srednjeskole)*). Поред имена х и у осе ([**xlabel**](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.xlabel.html) и [**ylabel**](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.ylabel.html)), дијаграму смо додали и хоризонталне и вертикалне линије зарад боље читљивости (функцијом [**grid**](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.grid.html)). \n",
    "\n",
    "На дијаграму можемо видети да око 50 школа има преко 1000 ученика, као и да средња по величини средња школа има око 500 ученика, али да то не бисмо оцењивали визуелно могу нам помоћи функције у оквиру [*numpy*](https://numpy.org/) библиотеке. Медијална вредност је вредност у низу за коју важи да има једнак број вредности које су од ње мање и веће, и осим што је визуелно можемо потражити на претходном графику, очитавањем средишњег елемента низа који је сортиран или користећи функцију [**np.median**](https://numpy.org/doc/stable/reference/generated/numpy.median.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "497.0"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "medijalnavrednost = np.median(srednjeskole['Broj učenika'])\n",
    "medijalnavrednost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Наша визуелна процена је била доста добра зато што је стубичасти дијаграм сортираних вредности изузетно погодан за процену ове вредности. Битно је не мешати управо израчунату медијалну вредност са просечном вредношћу или типичном/најчешћом вредношћу, да се све ове три вредности разликују видећемо у наставку. Просечну вредност рачунамо користећи аритметичку средину - можемо сабрати све вредности низа и поделити дужином низа или користити функцију [**np.mean**](https://numpy.org/doc/stable/reference/generated/numpy.mean.html#:~:text=mean-,numpy.,otherwise%20over%20the%20specified%20axis.):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "559.7668161434977"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sum(srednjeskole['Broj učenika'])/len(srednjeskole['Broj učenika'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "559.7668161434977"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "prosecnavrednost = np.mean(srednjeskole['Broj učenika'])\n",
    "prosecnavrednost"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Дијаграм који нам може помоћи да видимо узроке ових разлика, као и која величина школе је најпопуларнија у подацима зове се хистограм такође се налази у оквиру *matplotlib* библиотеке. Позивање ове фукције обавља фреквенцијску анализу за нас, тј. уместо да ручно поделимо величине школа у групе по величинама (нпр. од 1 до 100 ученика, од 101 до 200 ученика,...) и пребројимо колико школа има у свакој групи, функција [**hist**](https://matplotlib.org/3.2.2/api/_as_gen/matplotlib.pyplot.hist.html) ради то за нас. Уколико осим података не унесемо више аргумената при позивању функције, подаци ће бити подељени у 10 једнаких група (нпр. ако најмања школа има 1 ученика а највећа 1000, групе ће бити школе са 1 до 100, 101 до 200 ученика итд.), а опцијом *bins* можемо директно дефинисати или тачан број група које хоћемо на дијаграму (нпр. bins=40) или тачне интервале величине група (нпр. bins=[0,100,200]). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(srednjeskole['Broj učenika'],color='darkgrey')\n",
    "plt.xlabel('Broj učenika u srednjoj školi')\n",
    "plt.ylabel('Broj srednjih škola sa datim brojem učenika')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На овом дијаграму нацртали смо најосновнију верзију хистограма, са 10 стубића без контроле ширине или броја стубића. Свакако, ово нам даје информацију да највише школа има око 500 ђака, али да бисмо то испитали мало детаљније, у наставку ћемо контролисати ширину стубића (аргументом *bins*) и додати вертикалне линије (функцијом [**axvline**](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.axvline.html)) да означимо просечну и медијалну вредност."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(srednjeskole['Broj učenika'],bins=range(0,2000,50),color='darkgrey')\n",
    "plt.xlabel('Broj učenika u srednjoj školi')\n",
    "plt.ylabel('Broj srednjih škola sa datim brojem učenika')\n",
    "plt.axvline(x=medijalnavrednost, color='r',linestyle='dashed')\n",
    "plt.axvline(x=prosecnavrednost, color='k')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Са овог графика видимо да највише школа има између 400 и 450 ученика (и 200 до 250 одмах затим), док су и медијална и просечна вредност веће (обележене црвеном и црном бојом, редом), у овој и наредним радним свескама ћемо на више места видети да ове три вредности често нису исте и битно је раздвојити их. Интересантно је приметити и да постоје изузетно велике школе са скоро 2000 ученика, као и оне мале, са до 50 ученика, те податке можемо појединачно погледати селектовањем одређених делова табеле."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Команда попут ове srednjeskole['Broj učenika']<50 враћа нам низ вредности Тачно/Нетачно у зависности од тога да ли је дати елемент колоне мањи или не од броја 50:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "413     True\n",
       "429     True\n",
       "411     True\n",
       "412     True\n",
       "430     True\n",
       "       ...  \n",
       "442    False\n",
       "83     False\n",
       "81     False\n",
       "77     False\n",
       "133    False\n",
       "Name: Broj učenika, Length: 446, dtype: bool"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srednjeskole['Broj učenika']<50"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Када овакав низ тачних и нетачних вредности проследимо као аргумент табели srednjeskole добићемо оне редове табеле за коју је тврдња у заградама тачна (у овом случају, оне редове табеле који одговарају школама са мање од 50 ученика):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID ustanove</th>\n",
       "      <th>Okrug</th>\n",
       "      <th>Opština</th>\n",
       "      <th>Naziv ustanove</th>\n",
       "      <th>Broj odeljenja</th>\n",
       "      <th>Broj kombinovanih odeljenja</th>\n",
       "      <th>Broj specijalnih odeljenja</th>\n",
       "      <th>Broj učenika</th>\n",
       "      <th>Broj devojčica</th>\n",
       "      <th>Broj nastavnika - bez zamena</th>\n",
       "      <th>Ukupna norma nastavnikaa - bez zamena</th>\n",
       "      <th>Broj zaposlenih - bez zamena</th>\n",
       "      <th>Ukupna norma zaposlenih- bez zamena</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1778</td>\n",
       "      <td>Pećki upravni okrug</td>\n",
       "      <td>Peć</td>\n",
       "      <td>Ekonomska škola</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>4.2490</td>\n",
       "      <td>12</td>\n",
       "      <td>4.2490</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>1804</td>\n",
       "      <td>Kosovsko-mitrovački upravni okrug</td>\n",
       "      <td>Srbica</td>\n",
       "      <td>Tehnička škola</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>6.5181</td>\n",
       "      <td>25</td>\n",
       "      <td>16.6203</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>411</th>\n",
       "      <td>1776</td>\n",
       "      <td>Prizrenski upravni okrug</td>\n",
       "      <td>Orahovac</td>\n",
       "      <td>Gimnazija Orahovac</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>12</td>\n",
       "      <td>5</td>\n",
       "      <td>13</td>\n",
       "      <td>4.7944</td>\n",
       "      <td>20</td>\n",
       "      <td>10.7444</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>412</th>\n",
       "      <td>1777</td>\n",
       "      <td>Pećki upravni okrug</td>\n",
       "      <td>Peć</td>\n",
       "      <td>Gimnazija</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>17</td>\n",
       "      <td>9</td>\n",
       "      <td>31</td>\n",
       "      <td>14.7830</td>\n",
       "      <td>42</td>\n",
       "      <td>24.7830</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>430</th>\n",
       "      <td>1805</td>\n",
       "      <td>Kosovsko-mitrovački upravni okrug</td>\n",
       "      <td>Vučitrn</td>\n",
       "      <td>Gimnazija Vučitrn</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>29</td>\n",
       "      <td>21</td>\n",
       "      <td>25</td>\n",
       "      <td>8.2320</td>\n",
       "      <td>30</td>\n",
       "      <td>13.7320</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>394</th>\n",
       "      <td>1754</td>\n",
       "      <td>Kosovski upravni okrug</td>\n",
       "      <td>Priština - grad</td>\n",
       "      <td>Mašinska škola - Priština</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>31</td>\n",
       "      <td>3</td>\n",
       "      <td>26</td>\n",
       "      <td>10.8681</td>\n",
       "      <td>39</td>\n",
       "      <td>21.3681</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID ustanove                              Okrug          Opština  \\\n",
       "413         1778                Pećki upravni okrug              Peć   \n",
       "429         1804  Kosovsko-mitrovački upravni okrug           Srbica   \n",
       "411         1776           Prizrenski upravni okrug         Orahovac   \n",
       "412         1777                Pećki upravni okrug              Peć   \n",
       "430         1805  Kosovsko-mitrovački upravni okrug          Vučitrn   \n",
       "394         1754             Kosovski upravni okrug  Priština - grad   \n",
       "\n",
       "                Naziv ustanove  Broj odeljenja  Broj kombinovanih odeljenja  \\\n",
       "413            Ekonomska škola               3                            0   \n",
       "429             Tehnička škola               3                            0   \n",
       "411         Gimnazija Orahovac               2                            0   \n",
       "412                  Gimnazija               7                            0   \n",
       "430          Gimnazija Vučitrn               4                            0   \n",
       "394  Mašinska škola - Priština               5                            0   \n",
       "\n",
       "     Broj specijalnih odeljenja  Broj učenika  Broj devojčica  \\\n",
       "413                           0             5               1   \n",
       "429                           0            10               6   \n",
       "411                           0            12               5   \n",
       "412                           0            17               9   \n",
       "430                           0            29              21   \n",
       "394                           0            31               3   \n",
       "\n",
       "     Broj nastavnika - bez zamena  Ukupna norma nastavnikaa - bez zamena  \\\n",
       "413                            12                                 4.2490   \n",
       "429                            14                                 6.5181   \n",
       "411                            13                                 4.7944   \n",
       "412                            31                                14.7830   \n",
       "430                            25                                 8.2320   \n",
       "394                            26                                10.8681   \n",
       "\n",
       "     Broj zaposlenih - bez zamena  Ukupna norma zaposlenih- bez zamena  \n",
       "413                            12                               4.2490  \n",
       "429                            25                              16.6203  \n",
       "411                            20                              10.7444  \n",
       "412                            42                              24.7830  \n",
       "430                            30                              13.7320  \n",
       "394                            39                              21.3681  "
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srednjeskole[srednjeskole['Broj učenika']<50]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "***Задатак:*** Издвојите на сличан начин школе са више од 1700 ученика:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "# место за ваш код"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Као што сте можда и претпоставили школе са великим бројем ученика налазе се у великим градовима и имају велики број одељења и на такве \"скривене\" узроке је битно обраћати пажњу при обради података. На пример, у потрази за потенцијалним зависностима које постоје међу подацима сачуваним у различитим колонама, често ћемо цртати и тачкасте дијаграме [**scatter**](https://matplotlib.org/api/_as_gen/matplotlib.pyplot.scatter.html), као што је и овај:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(srednjeskole['Broj odeljenja'],srednjeskole['Broj učenika'],color='darkred')\n",
    "plt.xlabel('Broj odeljenja u školi')\n",
    "plt.ylabel('Broj učenika u školi')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функцијом **scatter** на дијаграму тачкицама представљамо средње школе из табеле srednjeskole. За сваку школу знамо укупан број одељења као и ученика и те информације користимо као х и у координате за представљање школа на овом дијаграму."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Међутим, ретко ће нам се дешавати да испитиване две величине овако изразито зависе једна од друге као што можемо видети на овом графику (где видимо да школе са великим бројем ученика имају и велики број одељења). Разлог у овоме је што ми заправо и не очекујемо да ове две величине представљене на x и y оси буду независне - тежећи да одрже број ученика у одељењима на разумном нивоу тако да настава може да се одржи, популарне и велике школе имају више одељења. Ако смо у потрази за неким мање очигледним зависностима, можемо проверити да ли постоји зависност између броја ученика по одељењу и броја одељења."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Прилично згодна карактеристика *pandas* библиотеке је што овакве операције, на пример рачунања просечног броја ученика у једном одељењу, можемо брзо и једноставно урадити за целу колону. Креираћемо нову колону 'Prosečan broj učenika u odeljenju' у коју ћемо сместити 0 за почетак:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "srednjeskole['Prosečan broj učenika u odeljenju'] = 0"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Колони можемо доделити и листу вредности, тако да на пример, можемо редом проћи кроз редове колона 'Broj učenika' и 'Broj odeljenja' и израчунати однос ова два броја, тј. просечан број ученика по одељењу:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "radna_lista = []\n",
    "for i in range(len(srednjeskole['Broj učenika'])):\n",
    "    radna_lista.append(srednjeskole['Broj učenika'].iloc[i]/srednjeskole['Broj odeljenja'].iloc[i])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Вредности прикупљене у овој радној листи, сада просто можемо проследити колони у оквиру табеле и провером првих пар редова можемо проверити да ли се бројеви слажу:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>ID ustanove</th>\n",
       "      <th>Okrug</th>\n",
       "      <th>Opština</th>\n",
       "      <th>Naziv ustanove</th>\n",
       "      <th>Broj odeljenja</th>\n",
       "      <th>Broj kombinovanih odeljenja</th>\n",
       "      <th>Broj specijalnih odeljenja</th>\n",
       "      <th>Broj učenika</th>\n",
       "      <th>Broj devojčica</th>\n",
       "      <th>Broj nastavnika - bez zamena</th>\n",
       "      <th>Ukupna norma nastavnikaa - bez zamena</th>\n",
       "      <th>Broj zaposlenih - bez zamena</th>\n",
       "      <th>Ukupna norma zaposlenih- bez zamena</th>\n",
       "      <th>Prosečan broj učenika u odeljenju</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>413</th>\n",
       "      <td>1778</td>\n",
       "      <td>Pećki upravni okrug</td>\n",
       "      <td>Peć</td>\n",
       "      <td>Ekonomska škola</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>12</td>\n",
       "      <td>4.2490</td>\n",
       "      <td>12</td>\n",
       "      <td>4.2490</td>\n",
       "      <td>1.666667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>429</th>\n",
       "      <td>1804</td>\n",
       "      <td>Kosovsko-mitrovački upravni okrug</td>\n",
       "      <td>Srbica</td>\n",
       "      <td>Tehnička škola</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>10</td>\n",
       "      <td>6</td>\n",
       "      <td>14</td>\n",
       "      <td>6.5181</td>\n",
       "      <td>25</td>\n",
       "      <td>16.6203</td>\n",
       "      <td>3.333333</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     ID ustanove                              Okrug Opština   Naziv ustanove  \\\n",
       "413         1778                Pećki upravni okrug     Peć  Ekonomska škola   \n",
       "429         1804  Kosovsko-mitrovački upravni okrug  Srbica   Tehnička škola   \n",
       "\n",
       "     Broj odeljenja  Broj kombinovanih odeljenja  Broj specijalnih odeljenja  \\\n",
       "413               3                            0                           0   \n",
       "429               3                            0                           0   \n",
       "\n",
       "     Broj učenika  Broj devojčica  Broj nastavnika - bez zamena  \\\n",
       "413             5               1                            12   \n",
       "429            10               6                            14   \n",
       "\n",
       "     Ukupna norma nastavnikaa - bez zamena  Broj zaposlenih - bez zamena  \\\n",
       "413                                 4.2490                            12   \n",
       "429                                 6.5181                            25   \n",
       "\n",
       "     Ukupna norma zaposlenih- bez zamena  Prosečan broj učenika u odeljenju  \n",
       "413                               4.2490                           1.666667  \n",
       "429                              16.6203                           3.333333  "
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srednjeskole['Prosečan broj učenika u odeljenju'] = radna_lista\n",
    "srednjeskole.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Иако увек можемо да прођемо кроз колоне и извршимо израчунавања кроз петљу као што је приказано раније, доста бржи и једноставнији начин је да проследимо новој колони количник две колоне:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [],
   "source": [
    "srednjeskole['Prosečan broj učenika u odeljenju'] = srednjeskole['Broj učenika']/srednjeskole['Broj odeljenja']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Када имате две пајтон листе, ово неће радити (пробајте!), док у оквиру пандас библиотеке, количник две колоне значи да се израчунава редом количник свака два елемента и то се извршава брже него претходно испробана петља. Ово баратање колонама није резервисано само за дељење, већ се све аритметичке и логичке операције могу користити на нивоу колона, што ћемо у наставку често користити."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У наставку је нова верзија тачкастог дијаграма, са просечним бројем ученика по одељењу за све анализиране школе:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.scatter(srednjeskole['Broj odeljenja'],srednjeskole['Prosečan broj učenika u odeljenju'],color='darkred')\n",
    "plt.xlabel('Broj odeljenja u školi')\n",
    "plt.ylabel('Prosečan broj učenika u odeljenju')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На овом графику, повезаност између просечног броја ученика у одељењу и броју одељења у школи није више тако јасна, но и овај дијаграм носи неке информације: \n",
    "- да постоје школе у којима има 5-6 одељења са по 5-6 ђака у одељењу (доњи леви део дијаграма)\n",
    "- да школе које имају између 10 и 30 одељења имају изузетно велики распон величина одељења, неке имају по десетак ђака у разреду, а неке и до тридесетак\n",
    "- да постоје изузетно велике школе које имају и јако пуно одељења и у њима просечно одељење има 25-30 ђака"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Како смо приметили да постоји варијабилитет у просечној величини одељења по школи, и тај податак можемо представити хистограмом као и раније."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(srednjeskole['Prosečan broj učenika u odeljenju'],bins=int(max(srednjeskole['Prosečan broj učenika u odeljenju'])),color='darkgrey')\n",
    "plt.xlabel('Prosečan broj učenika u odeljenju')\n",
    "plt.ylabel('Broj odeljenja sa odgovarajućim brojem učenika')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можемо истражити и однос броја ученика и наставника и представити и то једним хистограмом:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(srednjeskole['Broj učenika']/srednjeskole['Ukupna norma nastavnikaa - bez zamena'],20,color='darkgrey')\n",
    "plt.xlabel('Broj učenika po zaposlenom nastavniku u srednjoj školi')\n",
    "plt.ylabel('Broj škola sa datim odnosom brojeva učenika i nastavnika')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Разноврсност средњошколског образовања"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У претходним редовима бавили смо се само бројем ученика у школама и одељењима, без залажења у детаље који образовни профили су у питању, то можемо истражити додатно у наставку користећи опцију груписања података по одређеним критеријума. За почетак истражићемо која су све подручја рада средњих школа. То можемо испитати функцијом **unique** која нам за дату колону издваја различите вредности у њој:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Poljoprivreda, proizvodnja i prerada hrane', 'Elektrotehnika',\n",
       "       'Geodezija i građevinarstvo', 'Ostala delatnost ličnih usluga',\n",
       "       'Mašinstvo i obrada metala', 'Gimnazija',\n",
       "       'Trgovina, ugostiteljstvo i turizam',\n",
       "       'Ekonomija, pravo i administracija',\n",
       "       'Zdravstvo i socijalna zaštita', 'Šumarstvo i obrada drveta',\n",
       "       'Hemija, nemetali i grafičarstvo', 'Tekstilstvo i kožarstvo',\n",
       "       'Saobraćaj', 'Geologija, rudarstvo i metalurgija',\n",
       "       'Kultura, umetnost i javno informisanje', 'Hidrometeorologija'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srednjoskolci['Područje rada'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видимо да су школе организоване у више различитих подручја рада, од гимназија, преко 'здравства и социјалне заштите' до 'геологије, рударства и металургије', а сада можемо проверити и колико има ученика који се школује у сваком од ових подручја. То радимо груписањем по подручју рада ([**groupby**](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.DataFrame.groupby.html)) и сабрањем (**sum**) укупног броја ученика у оквиру сваке од група:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Područje rada\n",
       "Hidrometeorologija                              210\n",
       "Geologija, rudarstvo i metalurgija             1081\n",
       "Kultura, umetnost i javno informisanje         1646\n",
       "Šumarstvo i obrada drveta                      2595\n",
       "Ostala delatnost ličnih usluga                 3240\n",
       "Tekstilstvo i kožarstvo                        3241\n",
       "Geodezija i građevinarstvo                     6365\n",
       "Hemija, nemetali i grafičarstvo                8977\n",
       "Saobraćaj                                     12939\n",
       "Poljoprivreda, proizvodnja i prerada hrane    14364\n",
       "Trgovina, ugostiteljstvo i turizam            19922\n",
       "Mašinstvo i obrada metala                     22965\n",
       "Zdravstvo i socijalna zaštita                 23666\n",
       "Elektrotehnika                                28269\n",
       "Ekonomija, pravo i administracija             32583\n",
       "Gimnazija                                     66250\n",
       "Name: Ukupno učenika, dtype: int64"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ss_podrucja = srednjoskolci.groupby(srednjoskolci['Područje rada'])\n",
    "ss_podrucja['Ukupno učenika'].sum().sort_values()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Да не бисмо ове податке само посматрали у оквиру табеле, можемо их представити и стубичастим дијаграмом, али, овај пут ћемо испробати хоризонталну верзију (**barh**) да бисмо лакше читали називе различитих подручја рада која ће бити исписана поред сваког стубића."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ss_podrucja['Ukupno učenika'].sum().sort_values().plot(kind='barh',color='darkred')\n",
    "plt.xlabel('Broj srednjoškolaca'); # ознака х осе"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видимо да је највише средњошколаца у гимназијама, а прва област потом обједињује економију, право и администрацију.\n",
    "Иако смо и овај график могли нацртати уз помоћ **plt.barh()** функције, зарад разноврсности различитих доступних опција, нацртали смо га користећи функцију **plоt** у оквиру *pandas* библиотеке, којој је потребно проследити и аргумент *kind* којим дефинишемо врсту графикона који желимо (подразумевана вредност је линијски дијаграм - испробајте без *kind* аргумента - који није најбољи избор за податке на располагању). Предност цртања стубичастог дијаграма на овај начин је и аутоматско означавање у осе и различитих стубића."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можемо истражити и популарност појединачних смерова у оквиру сваке од ових области. То радимо тако што прво одаберемо специфичну област, на пример најпопуларнију економију на следећи начин: \n",
    "\n",
    "```py\n",
    "srednjoskolci[srednjoskolci['Područje rada']=='Ekonomija, pravo i administracija']\n",
    "```\n",
    "\n",
    "што селектује само оне редове у нашој табели у којима је подручје рада баш ово изабрано. Након тога понављамо процедуру груписања, овај пут по образовним профилима и поново сумирамо све ученике на одговарајућем профилу:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "ekonomisti = srednjoskolci[srednjoskolci['Područje rada']=='Ekonomija, pravo i administracija']\n",
    "ekonomisti_po_profilima = ekonomisti.groupby('Obrazovni profil')\n",
    "ekonomisti_po_profilima['Ukupno učenika'].sum().sort_values().plot(kind='barh',color='tomato')\n",
    "plt.xlabel('Broj srednjoškolaca'); # ознака х осе"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Видимо да се у оквиру овог подручја рада највише ученика школује да постане Економски техничар, као и да постоје четири огледна профила чија је популарност далеко мања."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Слично, можемо истражити и трајање образовања:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Trajanje obrazovanja\n",
       "1       131\n",
       "2        45\n",
       "3     32860\n",
       "4    215277\n",
       "Name: Ukupno učenika, dtype: int64"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "srednjoskolci.groupby(srednjoskolci['Trajanje obrazovanja'])['Ukupno učenika'].sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Пол"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Како су нам на располагању и подаци о броју ученика на различитим смеровима раздвојени и по полу, можемо видети да ли постоји разлика у популарности средњих школа међу дечацима и девојчицама. \n",
    "Поновићемо груписање по подручјима рада, само што ћемо сада сумирати поред укупног броја ученика и укупне бројеве дечака и девојчица. Резулате груписања ћемо сачувати као додатну табелу *podrucjapopolu*."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "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>Ukupno učenika</th>\n",
       "      <th>Ukupno decaka</th>\n",
       "      <th>Ukupno devojcica</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Područje rada</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Ekonomija, pravo i administracija</th>\n",
       "      <td>32583</td>\n",
       "      <td>11106</td>\n",
       "      <td>21477</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elektrotehnika</th>\n",
       "      <td>28269</td>\n",
       "      <td>25776</td>\n",
       "      <td>2493</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Ukupno učenika  Ukupno decaka  \\\n",
       "Područje rada                                                      \n",
       "Ekonomija, pravo i administracija           32583          11106   \n",
       "Elektrotehnika                              28269          25776   \n",
       "\n",
       "                                   Ukupno devojcica  \n",
       "Područje rada                                        \n",
       "Ekonomija, pravo i administracija             21477  \n",
       "Elektrotehnika                                 2493  "
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "podrucjapopolu = pd.DataFrame(srednjoskolci.groupby(srednjoskolci['Područje rada'])[['Ukupno učenika','Ukupno decaka','Ukupno devojcica']].sum())\n",
    "podrucjapopolu.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Можемо срачунати проценат дечака и девојчица (како не бисмо чували бројеве на превеликом броју децимала, користићемо функцију **round** да заокружимо процентуалну вредност на само 2 цифре иза зареза: "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [],
   "source": [
    "podrucjapopolu['Procenat decaka'] = round(100*podrucjapopolu['Ukupno decaka']/podrucjapopolu['Ukupno učenika'],2)\n",
    "podrucjapopolu['Procenat devojcica'] = round(100*podrucjapopolu['Ukupno devojcica']/podrucjapopolu['Ukupno učenika'],2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Ukupno učenika</th>\n",
       "      <th>Ukupno decaka</th>\n",
       "      <th>Ukupno devojcica</th>\n",
       "      <th>Procenat decaka</th>\n",
       "      <th>Procenat devojcica</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Područje rada</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>Ekonomija, pravo i administracija</th>\n",
       "      <td>32583</td>\n",
       "      <td>11106</td>\n",
       "      <td>21477</td>\n",
       "      <td>34.09</td>\n",
       "      <td>65.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Elektrotehnika</th>\n",
       "      <td>28269</td>\n",
       "      <td>25776</td>\n",
       "      <td>2493</td>\n",
       "      <td>91.18</td>\n",
       "      <td>8.82</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                   Ukupno učenika  Ukupno decaka  \\\n",
       "Područje rada                                                      \n",
       "Ekonomija, pravo i administracija           32583          11106   \n",
       "Elektrotehnika                              28269          25776   \n",
       "\n",
       "                                   Ukupno devojcica  Procenat decaka  \\\n",
       "Područje rada                                                          \n",
       "Ekonomija, pravo i administracija             21477            34.09   \n",
       "Elektrotehnika                                 2493            91.18   \n",
       "\n",
       "                                   Procenat devojcica  \n",
       "Područje rada                                          \n",
       "Ekonomija, pravo i administracija               65.91  \n",
       "Elektrotehnika                                   8.82  "
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "podrucjapopolu.head(2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сортирајмо податке по популарности међу једним од полова и можемо их графички представити опет уз помоћ стубичастих дијаграма, овај пут користећи и опцију *stacked* тако да су све вредности за различита подручја рада представљене на истом стубићу само различитим бојама."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "metadata": {},
   "outputs": [],
   "source": [
    "podrucjapopolu = podrucjapopolu.sort_values(by='Procenat devojcica')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "podrucjapopolu[['Procenat decaka','Procenat devojcica']].plot(kind='barh',stacked=True,color=('#6fa98c','#643a78'))\n",
    "plt.xlim(0, 100)\n",
    "plt.xlabel('Procenat učenika')\n",
    "plt.legend(('Procenat dečaka','Procenat devojčica'), loc='center left', bbox_to_anchor=(1, 0.92))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Овде смо искористили пар додатних опција у односу на претходне графиконе (коментаришући појединачно сваку од линија можете истражити колико се добијени график разликује) - дефинисали смо специфичне боје користећи хексадекадне кодове (color=('#6fa98c','#643a78')), распон х осе (од 0 до 100%), као и позицију и садржај легенде."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Иако нам информације о заступљености полова у процентима говоре о односу дечака и девојчица у одређеним областима средњошколског образовања, изгубили смо из вида укупне бројеве заинтересованих по областима. Други начин да представимо ове податке је тако што упоредно цртамо стубиће за различите полове и приказујемо укупне бројеве ученика а не проценте:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "podrucjapopolu[['Ukupno decaka','Ukupno devojcica']].plot(kind='barh',color=('#6fa98c','#643a78'))\n",
    "plt.legend(('Ukupan broj dečaka','Ukupan broj devojčica'), loc='center left', bbox_to_anchor=(1, 0.92))\n",
    "plt.xlabel('Broj učenika')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Често је практично да два графика сагледамо упоредо, за шта је практична функција [**subplots**](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.subplot.html). Први аргументи ове функције су димензије \"табеле\" у коју ће графици бити постављени, оно што би нам тренутно било згодно је да имамо 2 графика постављена један до другог, дакле 1 ред, 2 колоне графика. Додатно, можемо дефинисати и величину слике. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Резултат **subplots** функције смештамо у **axs** што је низ оса, односно локација на које ћемо нацртати жељене подграфике. Цртање на овим подграфицима решавамо прослеђивањем ових локација функцији *plot*, као што је приказано у наставку:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 864x432 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axs = plt.subplots(1,2, figsize=(12, 6))\n",
    "\n",
    "podrucjapopolu[['Procenat decaka','Procenat devojcica']].plot(ax=axs[0], kind='barh',stacked=True,color=('#6fa98c','#643a78'))\n",
    "axs[0].set_xlim(0, 100)\n",
    "axs[0].set_xlabel('Procenat dečaka i devojčica')\n",
    "axs[0].legend('')\n",
    "\n",
    "podrucjapopolu[['Ukupno decaka','Ukupno devojcica']].plot(ax=axs[1],kind='barh',color=('#6fa98c','#643a78'))\n",
    "axs[1].set_yticks([]) # Нису нам потребни називи стубића на десном дијаграму обзиром да су исти као и на левом.\n",
    "axs[1].set_ylabel('') # Слично можемо извацити назив у осе.\n",
    "axs[1].set_xlabel('Ukupan broj dečaka i devojčica')\n",
    "axs[1].legend(['Dečaci','Devojčice'])\n",
    "\n",
    "plt.subplots_adjust(wspace=0.05)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Посматрајући ове графике заједно видимо да некада разлика од пар десетина процената може бити разлика од пар стотина ученика (нпр. култура, уметрност и јавно информисање), док некад разлика од пар процената може бити разлика од десет и више хиљада ученика (нпр. гимназије)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Да се убедимо да постоје средњошколци заинтересовани за Хидрометеорологију (обзиром да су невидљиви на десном графику), издвојићемо само ту област из табеле користећи `.loc`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Ukupno učenika        210.00\n",
       "Ukupno decaka          75.00\n",
       "Ukupno devojcica      135.00\n",
       "Procenat decaka        35.71\n",
       "Procenat devojcica     64.29\n",
       "Name: Hidrometeorologija, dtype: float64"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "podrucjapopolu.loc['Hidrometeorologija']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Задаци"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "1. Користећи хистограме истражити расподелу броја наставника запослених у средњим школама у Србији. Испробајте различите опције - од броја или граница интервала, до боја правоугаоника и њихових ивица. Додајте на овај график и вертикалне линије које означавају просечну и медијалну вредност."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2. Слично као што смо груписањем и сумирањем истражили број ученика по областима образовања и дужини трајања образовања, истражити разноврсност наставе на различитим језицима. \n",
    "\n",
    "    (а) Групишите податке по језику наставе и креирајте табелу у којој за сваки језик имате број одељења и укупан број ученика који се школују на том језику.\n",
    "\n",
    "    (б) Резултате представити хоризонталним стубичастим дијаграмима (где је дужина стубића број одељења или број ученика).\n",
    "\n",
    "    (в) За неки од заступљенијих језика по избору (нпр. мађарски), истражити популарност (укупан број ученика) различитих подручја рада. Да ли се ранг листа по популарности различитих образовних профила на различитим језицима разликују?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "3. Фокусирајући се на једну општину по избору, истражите величину школа, број различитих одељења као и број различитих образовних профила.\n",
    "(Различите општине можете истражити уз помоћ: ```srednjoskolci['Opština'].unique()```)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}