{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Основни типовивизуализације података"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У овој радној свесци ћемо увести (или обновити) неке основне типове визуализације података (линијске, стубичасте и секторске дијаграме) на примеру припремљених података о студентима и дипломцима преузетих са сајта Републичког завода за статистику. Уз обнављање већ познатих опција које ови дијаграми нуде, искористићемо и увести и неке нове (ознаке на линијама, тип линије, наслагање стубића и измештање стубића) у складу са подацима који су нам на располагању. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Иако се учитавање библиотеке може одиграти на било ком месту у радној свесци, \"бонтон\" је да се све библиотеке увезу на почетку, чиме читалац одмах на почетку има представу које све додатне функције се користе у анализи у наставку. Користићемо библиотеке [*pandas*](https://pandas.pydata.org/) (у овој свесци само за учитавање и преглед табеларних података) и [*matplotlib*](https://matplotlib.org/) (за цртање дијаграма) које ћемо учитати и преименовати у њихове типичне скраћенице."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Наша највећа мотивација за коришћење *pandas* библиотеке је у једноставности учитавања, манипулације и чувања табеларних података. Стога ћемо и у наставку почети са учитавањем [**read_csv**](https://pandas.pydata.org/pandas-docs/stable/generated/pandas.read_csv.html) и прегледањем [**head**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.head.html) фајла *republika_srbija_visoko_2009_2017.csv*:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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>Skolska godina</th>\n",
       "      <th>Godina upisa/diplome</th>\n",
       "      <th>Broj visih</th>\n",
       "      <th>Upisani studenti vise</th>\n",
       "      <th>Budzetski studenti vise</th>\n",
       "      <th>Diplomirani studenti vise</th>\n",
       "      <th>Broj fakulteta</th>\n",
       "      <th>Upisani studenti fakulteti</th>\n",
       "      <th>Budzetski studenti fakulteti</th>\n",
       "      <th>Diplomirani studenti fakulteti</th>\n",
       "      <th>Ukupno studenata</th>\n",
       "      <th>Ukupno studentkinja</th>\n",
       "      <th>Ukupno diplomiranih</th>\n",
       "      <th>Ukupno diplomiranih zene</th>\n",
       "      <th>Diploma I stepen visa</th>\n",
       "      <th>Diploma II stepen visa</th>\n",
       "      <th>Diploma I stepen fakultet</th>\n",
       "      <th>Diploma II stepen fakultet</th>\n",
       "      <th>Diploma III stepen fakultet</th>\n",
       "      <th>Diploma stari program</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2009-2010</td>\n",
       "      <td>2009</td>\n",
       "      <td>59</td>\n",
       "      <td>43707</td>\n",
       "      <td>15081</td>\n",
       "      <td>11674</td>\n",
       "      <td>130</td>\n",
       "      <td>183065</td>\n",
       "      <td>83528</td>\n",
       "      <td>31871</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2010-2011</td>\n",
       "      <td>2010</td>\n",
       "      <td>59</td>\n",
       "      <td>47169</td>\n",
       "      <td>16091</td>\n",
       "      <td>10057</td>\n",
       "      <td>130</td>\n",
       "      <td>181362</td>\n",
       "      <td>81699</td>\n",
       "      <td>36105</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-2012</td>\n",
       "      <td>2011</td>\n",
       "      <td>58</td>\n",
       "      <td>47322</td>\n",
       "      <td>16740</td>\n",
       "      <td>10751</td>\n",
       "      <td>130</td>\n",
       "      <td>184339</td>\n",
       "      <td>85362</td>\n",
       "      <td>36772</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2012-2013</td>\n",
       "      <td>2012</td>\n",
       "      <td>57</td>\n",
       "      <td>46649</td>\n",
       "      <td>17213</td>\n",
       "      <td>11648</td>\n",
       "      <td>125</td>\n",
       "      <td>192296</td>\n",
       "      <td>89271</td>\n",
       "      <td>36149</td>\n",
       "      <td>238945.0</td>\n",
       "      <td>133427.0</td>\n",
       "      <td>47797.0</td>\n",
       "      <td>27930.0</td>\n",
       "      <td>10184.0</td>\n",
       "      <td>1464.0</td>\n",
       "      <td>6318.0</td>\n",
       "      <td>29081.0</td>\n",
       "      <td>750.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2013-2014</td>\n",
       "      <td>2013</td>\n",
       "      <td>56</td>\n",
       "      <td>48052</td>\n",
       "      <td>17117</td>\n",
       "      <td>12768</td>\n",
       "      <td>124</td>\n",
       "      <td>194796</td>\n",
       "      <td>87316</td>\n",
       "      <td>37960</td>\n",
       "      <td>242848.0</td>\n",
       "      <td>134448.0</td>\n",
       "      <td>50728.0</td>\n",
       "      <td>29340.0</td>\n",
       "      <td>11060.0</td>\n",
       "      <td>1708.0</td>\n",
       "      <td>7163.0</td>\n",
       "      <td>30056.0</td>\n",
       "      <td>741.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  Skolska godina  Godina upisa/diplome  Broj visih  Upisani studenti vise  \\\n",
       "0      2009-2010                  2009          59                  43707   \n",
       "1      2010-2011                  2010          59                  47169   \n",
       "2      2011-2012                  2011          58                  47322   \n",
       "3      2012-2013                  2012          57                  46649   \n",
       "4      2013-2014                  2013          56                  48052   \n",
       "\n",
       "   Budzetski studenti vise  Diplomirani studenti vise  Broj fakulteta  \\\n",
       "0                    15081                      11674             130   \n",
       "1                    16091                      10057             130   \n",
       "2                    16740                      10751             130   \n",
       "3                    17213                      11648             125   \n",
       "4                    17117                      12768             124   \n",
       "\n",
       "   Upisani studenti fakulteti  Budzetski studenti fakulteti  \\\n",
       "0                      183065                         83528   \n",
       "1                      181362                         81699   \n",
       "2                      184339                         85362   \n",
       "3                      192296                         89271   \n",
       "4                      194796                         87316   \n",
       "\n",
       "   Diplomirani studenti fakulteti  Ukupno studenata  Ukupno studentkinja  \\\n",
       "0                           31871               NaN                  NaN   \n",
       "1                           36105               NaN                  NaN   \n",
       "2                           36772               NaN                  NaN   \n",
       "3                           36149          238945.0             133427.0   \n",
       "4                           37960          242848.0             134448.0   \n",
       "\n",
       "   Ukupno diplomiranih  Ukupno diplomiranih zene  Diploma I stepen visa  \\\n",
       "0                  NaN                       NaN                    NaN   \n",
       "1                  NaN                       NaN                    NaN   \n",
       "2                  NaN                       NaN                    NaN   \n",
       "3              47797.0                   27930.0                10184.0   \n",
       "4              50728.0                   29340.0                11060.0   \n",
       "\n",
       "   Diploma II stepen visa  Diploma I stepen fakultet  \\\n",
       "0                     NaN                        NaN   \n",
       "1                     NaN                        NaN   \n",
       "2                     NaN                        NaN   \n",
       "3                  1464.0                     6318.0   \n",
       "4                  1708.0                     7163.0   \n",
       "\n",
       "   Diploma II stepen fakultet  Diploma III stepen fakultet  \\\n",
       "0                         NaN                          NaN   \n",
       "1                         NaN                          NaN   \n",
       "2                         NaN                          NaN   \n",
       "3                     29081.0                        750.0   \n",
       "4                     30056.0                        741.0   \n",
       "\n",
       "   Diploma stari program  \n",
       "0                    NaN  \n",
       "1                    NaN  \n",
       "2                    NaN  \n",
       "3                    NaN  \n",
       "4                    NaN  "
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('data/studenti data/republika_srbija_visoko_2009_2017.csv')\n",
    "data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ова табела настала је комбиновањем више извештаја које издаје Републички завод за статистику (Општине и региони у Републици Србији), а у којима се налазе информације о броју уписаних студената у току одговарајуће школске године на нивоу државе, али и појединачних региона и општина. Како се формат тих табела пар пута мењао у протеклих 10 година, у учитаној табели су само сумирани подаци на нивоу целе Србије, а у некој наредној радној свесци ћемо додатно анализирати и једну конкретну годину."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Како табела садржи велики број колона па их не сагледавамо лепо прегледом првих пар редова, све колоне можемо излистати функцијом [**columns**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.columns.html):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Skolska godina', 'Godina upisa/diplome', 'Broj visih',\n",
       "       'Upisani studenti vise', 'Budzetski studenti vise',\n",
       "       'Diplomirani studenti vise', 'Broj fakulteta',\n",
       "       'Upisani studenti fakulteti', 'Budzetski studenti fakulteti',\n",
       "       'Diplomirani studenti fakulteti', 'Ukupno studenata',\n",
       "       'Ukupno studentkinja', 'Ukupno diplomiranih',\n",
       "       'Ukupno diplomiranih zene', 'Diploma I stepen visa',\n",
       "       'Diploma II stepen visa', 'Diploma I stepen fakultet',\n",
       "       'Diploma II stepen fakultet', 'Diploma III stepen fakultet',\n",
       "       'Diploma stari program'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Међутим поред назива колона, уз помоћ функција [**info**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.info.html) можемо сазнати и тип података који се крије у свакој од колона:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 9 entries, 0 to 8\n",
      "Data columns (total 20 columns):\n",
      "Skolska godina                    9 non-null object\n",
      "Godina upisa/diplome              9 non-null int64\n",
      "Broj visih                        9 non-null int64\n",
      "Upisani studenti vise             9 non-null int64\n",
      "Budzetski studenti vise           9 non-null int64\n",
      "Diplomirani studenti vise         9 non-null int64\n",
      "Broj fakulteta                    9 non-null int64\n",
      "Upisani studenti fakulteti        9 non-null int64\n",
      "Budzetski studenti fakulteti      9 non-null int64\n",
      "Diplomirani studenti fakulteti    9 non-null int64\n",
      "Ukupno studenata                  6 non-null float64\n",
      "Ukupno studentkinja               6 non-null float64\n",
      "Ukupno diplomiranih               6 non-null float64\n",
      "Ukupno diplomiranih zene          6 non-null float64\n",
      "Diploma I stepen visa             6 non-null float64\n",
      "Diploma II stepen visa            6 non-null float64\n",
      "Diploma I stepen fakultet         6 non-null float64\n",
      "Diploma II stepen fakultet        6 non-null float64\n",
      "Diploma III stepen fakultet       6 non-null float64\n",
      "Diploma stari program             4 non-null float64\n",
      "dtypes: float64(10), int64(9), object(1)\n",
      "memory usage: 1.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Корисно је што поред типа података (*int64* или *float64*) сазнајемо и колико има уноса у којој колони, види нпр. \"Ukupno diplomiranih zene: 6 non-null float64\" где сазнајемо да само за 6 од 9 година имамо информације о броју жена које су дипломирале (то је једна од поменутих измена која се дешавала у публикацију РСЗ-а, тек у неком каснијем моменту кренули су да прикупљају и бележе одвојене податке за жене, што је направило измене у формату у коме се чувају подаци)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ова табелица као што видимо, не садржи тако пуно података и одабрана је намерно да буде тако мала и сагледива, али ћемо и даље приметити разне начине да ове податке визуелно представимо и тако сагледамо информације које нам нису видљиве у табеларном облику.\n",
    "\n",
    "За почетак ћемо представити укупан број студената на факултетима линијским дијаграмом. У наставку је најосновнији облик фунцкије [**plot**](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.plot.html) са само једним аргументом. Ако је функцији прослеђена само 1 листа, вредности из листе коришћене су за координате на у оси, а на х оси су бројеви редом од 0:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Upisani studenti fakulteti'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Да прикажемо нацртани график, користимо и функцију [**show**](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.show.html), пробајте шта се дешава без ње."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Међутим, ми у табели имамо и године којима одговарају ови бројеви студената, па можемо и то проследити функцији **plot** тако да координате на х-оси постану смисленије:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Godina upisa/diplome'], data['Upisani studenti fakulteti'])\n",
    "#plt.plot(data['Godina upisa/diplome'], data['Upisani studenti fakulteti'], 'o')\n",
    "#plt.plot(data['Godina upisa/diplome'], data['Upisani studenti fakulteti'], 'o-')\n",
    "#plt.plot(data['Godina upisa/diplome'], data['Upisani studenti fakulteti'], 'o--', color = 'tomato')\n",
    "plt.xlabel('Godina upisa')\n",
    "plt.ylabel('Ukupan broj upisnih studenata')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У претходном блоку кода додали смо имена х и у осе (користећи функције [**xlabel**](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.xlabel.html) и [**ylabel**](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.ylabel.html)), али смо додали и пар линија које су коментарисане (више различитих опција линијских дијаграма за исте податке), истражите разлике!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У табели имамо и број студената у вишим школама, али и број студената који су дипломирали и слично. Ако желимо да представимо више различитих линија на дијаграмима, то можемо видети на следећи начин.\n",
    "У наставку смо и одступили од стандардних боја, бирајући неке од именованих боја које можете наћи на [овој листи](https://matplotlib.org/3.1.0/_images/sphx_glr_named_colors_003.png).\n",
    "Додали смо такође имена овим линијама *labels* и уз помоћ њих можемо лако додати легенду графику користећи функцију [**legend**](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.legend.html)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Godina upisa/diplome'], data['Upisani studenti fakulteti'], 'o--', color = 'salmon',label = 'Ukupan broj studenata na fakultetima')\n",
    "plt.plot(data['Godina upisa/diplome'], data['Budzetski studenti fakulteti'], 'o--', color = 'darkcyan',label = 'Budžetski studenti na fakultetima')\n",
    "plt.xlabel('Godina upisa')\n",
    "plt.ylabel('Broj studenata na fakultetima')\n",
    "plt.ylim([0,230000])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Међутим, када се вредности у два различита низа оволико разликују, није лако више увидети шта се дешава на појединачним линијама. Обратите пажњу, на претходном дијаграму где смо посматрали само укупан број студената на факултетима, видели смо и неке успоне и падове у броју студената током година, док је овде та линија практично равна. Ово је последица \"зумираности\" дијаграма зато што је раније посматрани опсег на у оси био између 180 000 и 220 000 студената, док на последњем дијаграму гледамо цео распон у осе од 0 до 220 000 студената (ово смо контролисани користећи функцију [**ylim**](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.ylim.html)). \n",
    "Слично можемо погледати у каквом су односу бројеви студената на факултетима и вишим школама:"
   ]
  },
  {
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Godina upisa/diplome'], data['Upisani studenti fakulteti'], 'o--', color = 'salmon',label = 'Ukupan broj studenata na fakultetima')\n",
    "plt.plot(data['Godina upisa/diplome'], data['Upisani studenti vise'], 'o--', color = 'darkcyan',label = 'Ukupan broj studenata na visim skolama')\n",
    "plt.xlabel('Godina upisa')\n",
    "plt.ylabel('Broj studenata')\n",
    "plt.ylim([0,225000])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У овом случају је још теже уочити да ли постоје неке везе међу овим бројевима зато што је укупан број студената на вишим школама далеко мањи. Међутим, ако смо заинтересовани да ли дати бројеви студената варирају и колико, уместо да пратимо апсолутни број студената из године у годину, можемо истражити како се број студената мења у односу на прву посматрану годину:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(data['Godina upisa/diplome'],data['Upisani studenti fakulteti']/data['Upisani studenti fakulteti'][0], 'o--', color = 'salmon')\n",
    "plt.plot(data['Godina upisa/diplome'],data['Upisani studenti vise']/data['Upisani studenti vise'][0], 'o--', color = 'darkcyan')\n",
    "plt.axhline(y=1,color='grey')\n",
    "plt.xlabel('Godina upisa')\n",
    "plt.ylabel('Odnos broja studenata u datoj i 2009. godini')\n",
    "plt.legend(['Fakulteti','Vise skole'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Упозорење у вези са повезаним линијским дијаграмима: Покушајте да прво сортирате табелу по колони коју желите да нацртате па тек онда да нацртате одговарајући дијаграм."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "# место за ваш код"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "# data = data.sort_values(by='Godina upisa/diplome')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Како је колона која садржи информације о укупном броју студената (и на факултетима и на вишим школама) непотпуна, у наставку ћемо је допунити:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "data['Ukupno studenata'] = data['Upisani studenti fakulteti'] + data['Upisani studenti vise']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Иако на х оси ових наших дијаграма имамо време, које је често довољан разлог да се одлучимо за линијски дијаграм, чињеница да број студената као измерена вредност има смисла само у дискретним временским тренуцима, позива да ипак можда ове податке представимо стубичастим дијаграмом (функција [**bar**](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.bar.html)). Основни формат ове функције:"
   ]
  },
  {
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['Godina upisa/diplome'], data['Upisani studenti fakulteti'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "За разлику од функције **plot**, **bar** функцији је потребно проследити и х координате, тј. није могуће проследити само висине барова. Као и у функцији **plot** могуће је доделити одређену боју стубићима, као и означити одређен сет података за касније коришћење у легенди:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['Godina upisa/diplome'], data['Upisani studenti fakulteti'], color = 'salmon',label = 'Fakulteti')\n",
    "plt.bar(data['Godina upisa/diplome'], data['Upisani studenti vise'], color = 'darkcyan',label = 'Vise skole')\n",
    "plt.xlabel('Godina upisa')\n",
    "plt.ylabel('Ukupan broj upisnih studenata')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.9))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ако нам је циљ да сагледамо укупан број студената који се школује у датом тренутку (без обзира на институцију у којој се школује), претходни дијаграм захтева од нас да у глави сабирамо висине стубића две различите боје, и када су међу њима разлике овако мале, са овог дијаграма није једноставно рећи да ли је укупно више студената било у 2013 или 2014 години. Постоје бар два начин да превазиђемо овај проблем у визуализацијама.\n",
    "Први је да уместо веће колоне (у овом случају броја студената на факултетима) цртамо стубиће укупног броја студената (и факултети и више школе), а затим преко нацртамо стубиће који одговарају колони са мањим вредностима:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['Godina upisa/diplome'], data['Ukupno studenata'], color = 'salmon',label = 'Fakulteti')\n",
    "plt.bar(data['Godina upisa/diplome'], data['Upisani studenti vise'], color = 'darkcyan',label = 'Vise skole')\n",
    "plt.xlabel('Godina upisa')\n",
    "plt.ylabel('Ukupan broj upisnih studenata')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 0.9))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На овај начин су делови розе дијаграма прекривени и само они видљиви делови одговарају броју студената на факултетима. Овако са у осе директно очитавамо укупан број студената и број студената на вишим школама, док је број студената на факултетима разлика ова два броја (приметимо, добили смо олакшицу у представљању једног податка, али сада други податак захтева рачун у глави).\n",
    "Други начин да се разреши иста ситуација, је коришћење опције *bottom* у оквиру *bar* функције. Ова опција дозвољава да се стубићи настављају један на други тиме што се висина једног слоја стубића користи као почетна тачка за висине нових стубића. Ово је посебно погодно кад имате више од 2 категорије стубића које бисте надовезивали."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['Godina upisa/diplome'], data['Upisani studenti fakulteti'], color = 'salmon',label = 'Fakulteti')\n",
    "plt.bar(data['Godina upisa/diplome'], data['Upisani studenti vise'], bottom = data['Upisani studenti fakulteti'], color = 'darkcyan',label = 'Vise skole')\n",
    "plt.xlabel('Godina upisa')\n",
    "plt.ylabel('Ukupan broj upisnih studenata')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "На последња два дијаграма смо обрнули редослед стубића да бисмо оставили могућност да се и број студената на факултету директно очитава са у осе. Поправите претходне редове кода да добијете идентичан график претходном!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Коначно, код оваквих, надовезујућих графика, једна од идеја је и да се на једноставан начин осмотри како се однос међу категоријама мења током времена, па онда уместо да гледамо апсолутне вредности на у оси можемо посматрати проценте. Погледајмо исте податке и на тај начин:"
   ]
  },
  {
   "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": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['Godina upisa/diplome'], 100*data['Upisani studenti fakulteti']/data['Ukupno studenata'], color = 'salmon',label = 'Fakulteti')\n",
    "plt.bar(data['Godina upisa/diplome'], 100*data['Upisani studenti vise']/data['Ukupno studenata'], bottom = 100*data['Upisani studenti fakulteti']/data['Ukupno studenata'], color = 'darkcyan',label = 'Vise skole')\n",
    "plt.xlabel('Godina upisa')\n",
    "plt.ylabel('Procenat upisnih studenata')\n",
    "plt.grid(True,axis='y')\n",
    "plt.legend(loc='center left', bbox_to_anchor=(1, 1))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "И на овај начин видимо да из године у годину више студената студира на факултетима, углавном преко 80%, да бисмо ово лакше уочили, на последњи график додали смо и хоризонталне линије на сваких 20%, функцијом [**grid**](https://matplotlib.org/3.1.1/api/_as_gen/matplotlib.pyplot.grid.html)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Слично визуелно истраживање можемо извести и поредећи буџетских и самофинансирајуће студената на факултетима и(ли) вишим школама, пробајте сами једну од претходних визуализација и упредите ове две колоне:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mesto za vas kod"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [],
   "source": [
    "# mesto za vas kod"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У наставку ћемо испробати још једну изузетно корисну функцију [**subplot**](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.subplot.html) којојм олакшавамо преглед више графика одједном тиме што креирамо поддијаграме:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1080x360 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(15,5))\n",
    "plt.subplot(1,2,1)\n",
    "plt.plot(data['Godina upisa/diplome'], 100*data['Budzetski studenti fakulteti']/data['Upisani studenti fakulteti'],'o--',color='grey')\n",
    "plt.xlabel('Godina upisa')\n",
    "plt.ylabel('Procenat budzetskih studenata na fakultetima')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(data['Godina upisa/diplome'], data['Budzetski studenti fakulteti'],'o--',color='grey')\n",
    "plt.plot(data['Godina upisa/diplome'], data['Upisani studenti fakulteti']-data['Budzetski studenti fakulteti'],'o--',color='salmon')\n",
    "plt.xlabel('Godina upisa')\n",
    "plt.ylabel('Ukupan broj studenata na fakultetima')\n",
    "plt.legend(['Studenti na budzetu','Samofinansirajuci studenti'])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ова два графика, на два начина приказују податке из исте две колоне и на тај  начин нам можемо да схватимо више информација које подаци из тих колона садрже. На пример, график са леве стране приказује како се мењао проценат буџетских студената на факултетима у посматраном временском периоду. Видимо да је у току последње 2 године проценат буџетских студената опао за 3-4% у односу на почетне године за које имамо податке. Како разматрамо проценте, разлог за овај уочени пад може бити смањење државног буџета, па самим тим и укупан број студената на буџету. А може бити и да је укупан број буџетских студената исти, али да се број самофинансирајућих студената повећао. Како би разоткрили узрок, на десној страни цртамо укупан број буџетских и самофинансирајућих студената. Примећујемо да је број буџетских студената доста сличан током година, чак у последњих пар година има више студената на буџету у односу на почетне године. Са друге стране, број студената који се самофинансирају је порастао за више од 20 000 што објашњава пад процента буџетских студената који смо опазили на левом графику."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У наставку ћемо једним примером демонстрирати различите начине да прикажемо упоредне вредности уз помоћ стубичастих дијаграма. Наиме, у табели постоје и подаци о укупном броју студената и студенткиња, па њих можемо из године у годину приказати на 2 дијаграма:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3))\n",
    "plt.subplot(1,2,1)\n",
    "plt.bar(data['Godina upisa/diplome'], data['Ukupno studentkinja'], label='Studentkinje',color='#D6ED17FF')\n",
    "plt.title('Ukupan broj upisanih studentkinja')\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.bar(data['Godina upisa/diplome'], data['Ukupno studenata']-data['Ukupno studentkinja'], label='Studenti',color='#606060')\n",
    "plt.title('Ukupan broj upisanih studenata')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Функција *bar* уколико се другачије не нагласи подешава опсег у осе тако да увек креће од 0, док је максимум такав да и највиши стубић стаје са мало празног простора изнад. Када гледамо само један дијаграм, то нам је углавном у реду, али ако као сада, хоћемо да упоредимо два типа информација, уколико не обратимо пажњу на чињеницу да су распони у оса различити може нам се учинити да не постоји родна разлика."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x216 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,3))\n",
    "plt.subplot(1,2,1)\n",
    "plt.bar(data['Godina upisa/diplome'], data['Ukupno studentkinja'], label='Studentkinje',color='#D6ED17FF')\n",
    "plt.title('Ukupan broj upisanih studentkinja')\n",
    "plt.ylim([0,150000])\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.bar(data['Godina upisa/diplome'], data['Ukupno studenata']-data['Ukupno studentkinja'], label='Studenti',color='#606060')\n",
    "plt.title('Ukupan broj upisanih studenata')\n",
    "plt.ylim([0,150000])\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Сада већ видимо да постоји родна разлика, међутим ово није оптималан начин представљања података ако је циљ да се истакне разлика која постоји из године у годину. Ови појединачни дијаграми су ок ако је циљ да уочавамо потенцијалне трендове по групама, мада у том случају је боље исте податке сагледати као линијске дијаграме (проверите!)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [],
   "source": [
    "# место за ваш код"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Коначно, погледајмо ове исте податке када се налазе на једном заједничком дијаграму, тако да су позиције стубића смакнуте (не преклапају се) да би поређење било олакшано:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sirina = 0.3\n",
    "plt.bar(data['Godina upisa/diplome'] - sirina/2, data['Ukupno studentkinja'], sirina, label='Studentkinje',color='#D6ED17FF')\n",
    "plt.bar(data['Godina upisa/diplome'] + sirina/2, data['Ukupno studenata']-data['Ukupno studentkinja'], sirina, label='Studenti',color='#606060')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Да бисмо ово постигли, функцији **bar** проследили смо још један опциони аргумент а који се тиче ширине стубића (приметите да су стубићи сада ужи, овај аргумент можете додати и на претходним дијаграмима!). Али само увођење ширине не би померило стубиће лево и десно, то смо урадили мењајући х координате позиција стубића, где смо податке о студенткињама померили у лево у односу на године, а студенте у десно (одузимањем и додавањем половине ширине стубића). Испробајте пар различитих верзија за ширину, али и позиције стубића и додајте имена оса и легенду."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Коначно, додајте овом дијаграму десни дијаграм на коме ће на исти начин бити представљени и упоређени бројеви студената и студенткиња који су дипломирали:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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3TOv1afvAch20mVkbqUis8RcJTgJmR0QVcAZwr6Td3nMj4vaIqI6I6srKyr2Qqpl1VS05EzcbGNcothgYFhHHAi8D1wCka0ImAsekfb4vqaKZ60e+DdwcEYOBjcDUFJ8KbIyIjwE3p3ZmZp1BA9C/YL2K3YdLpwIPAETEk0AvoG+7ZGdm3UKzRVxEPAFsaBRbFBHb0+oysjcwyK4JmRcR70XEK0A92bUjRa8fkSTgM8D8tP8c4OyCvuak5fnAaam9mVlHWwEMTiMJ+5F9eF3QqM3vgdMAJA0hK+I8XmpmZVOOa+L+FngkLTd1nUhT8cOBPxQUhIXXlezcJ23flNqbmXWo9J50OfAoUEs2C3WlpOskTUjNvgpcKul5YC5wcfjebWZWRm26Y4OkbwDbgft3hIo0C4oXi7GH9nvqq1ge04BpAAMGDNhDxmZm5RERC8kmLBTGvlmwvAo4ub3zMrPuo+QzcZKmAGcCFxR8umzqOpGm4m8BfST1aBTfpa+0/RAaDevu4AuDzczMrLspqYiTNA64CpgQEe8WbFoATEwzSwcBg4HlNHH9SCr+lgDnpP2nAA8X9DUlLZ8D/MJDEWZmZmaZZodTJc0FTgX6pu87mkE2G7UnsDjNNVgWEX+Xrgl5AFhFNsw6PSL+lPrZcf1IBTArIlamp7gKmCfpeuBZ4K4Uv4tsSn492Rm4iWU4XjMzM7MuodkiLiImFQnfVSS2o/0NwA1F4rtdP5Liq8lmrzaObwXObS4/MzMzs+7Id2wwMzMzy6E2zU41s453ySWXtKr93XffvZcyMTOz9uQzcWZmZmY55CLOzMzMLIdcxJmZmZnlkIs4MzMzsxzyxAazJrR2wgB40oCZmbUfF3Fmttd5Bq2ZWfl5ONXMzMwsh1zEmZmZmeWQizgzMzOzHPI1cV2Qrz8yMzPr+nwmzszMzCyHXMSZmZmZ5ZCHU61D+DvYzMzM2sZFXCv5ejMzMzPrDDycamZmZpZDLuLMzMzMcshFnJmZmVkOuYgzMzMzy6FmizhJsyStk/TrgthhkhZLqks/D01xSbpFUr2kFyQNL9hnSmpfJ2lKQfwESS+mfW6RpD09h5mZmZm17EzcbGBco9jVwGMRMRh4LK0DjAcGp8c04DbICjJgBnAScCIwo6Aouy213bHfuGaew8zMzKzba7aIi4gngA2NwmcBc9LyHODsgvg9kVkG9JHUDxgLLI6IDRGxEVgMjEvbDo6IJyMigHsa9VXsOczMzMy6vVKviTsiItYCpJ8fSvEjgVcL2jWk2J7iDUXie3qO3UiaJqlGUs369etLPCQzMzOz/Cj3xAYViUUJ8VaJiNsjojoiqisrK1u7u5mZmVnulFrEvZmGQkk/16V4A9C/oF0V8Hoz8aoi8T09h5mZmVm3V2oRtwDYMcN0CvBwQXxymqU6EtiUhkIfBcZIOjRNaBgDPJq2vSNpZJqVOrlRX8Wew8zMzKzba/beqZLmAqcCfSU1kM0yvRF4QNJU4PfAuan5QuAMoB54F7gEICI2SPoWsCK1uy4idkyWuIxsBuz+wCPpwR6ew8zMzKzba7aIi4hJTWw6rUjbAKY30c8sYFaReA0wrEj87WLPYWZmZma+Y4OZmZlZLrmIMzMrgaRxkl5Kd5sp+mXkks6TtErSSkk/bO8czaxra3Y41czMdiWpArgVOJ1slv0KSQsiYlVBm8HANcDJEbFRUpPfdWlmVgqfiTMza70TgfqIWB0R24B5ZHeZKXQpcGu6Sw0R4a9JMrOychFnZtZ6Td2FptBRwFGSlkpaJqnxPagB33HGzErnIs7MrPVacreZHsBgsq9omgTcKanPbjv5jjNmViIXcWZmrdfUXWgat3k4It6PiFeAl8iKOjOzsnARZ2bWeiuAwZIGSdoPmEh2l5lCPwFGA0jqSza8urpdszSzLs1FnJlZK0XEduByslsK1gIPRMRKSddJmpCaPQq8LWkVsAT43+lLzM3MysJfMWJmVoKIWEh2q8HC2DcLlgO4Mj3MzMrOZ+LMzMzMcshFnJmZmVkOuYgzMzMzyyEXcWZmZmY55CLOzMzMLIdcxJmZmZnlkIs4MzMzsxxyEWdmZmaWQy7izMzMzHKoTUWcpL+XtFLSryXNldQr3UvwKUl1kn6U7iuIpJ5pvT5tH1jQzzUp/pKksQXxcSlWL+nqtuRqZmZm1pWUXMRJOhL4MlAdEcOACrKbQH8buDkiBgMbgalpl6nAxoj4GHBzaoekoWm/Y4BxwPclVUiqAG4FxgNDgUmprZmZmVm319bh1B7A/pJ6AAcAa4HPAPPT9jnA2Wn5rLRO2n6aJKX4vIh4LyJeAeqBE9OjPiJWR8Q2YF5qa2ZmZtbtlVzERcRrwP8Ffk9WvG0Cngb+EBHbU7MG4Mi0fCTwatp3e2p/eGG80T5NxXcjaZqkGkk169evL/WQzMzMzHKjLcOph5KdGRsEfAQ4kGzos7HYsUsT21ob3z0YcXtEVEdEdWVlZXOpm5mZmeVeW4ZTPwu8EhHrI+J94D+ATwF90vAqQBXwelpuAPoDpO2HABsK4432aSpuZmZm1u21pYj7PTBS0gHp2rbTgFXAEuCc1GYK8HBaXpDWSdt/ERGR4hPT7NVBwGBgObACGJxmu+5HNvlhQRvyNTMzM+syejTfpLiIeErSfOAZYDvwLHA78F/APEnXp9hdaZe7gHsl1ZOdgZuY+lkp6QGyAnA7MD0i/gQg6XLgUbKZr7MiYmWp+ZqZmZl1JSUXcQARMQOY0Si8mmxmaeO2W4Fzm+jnBuCGIvGFwMK25GhmZmbWFfmODWZmZmY55CLOzMzMLIdcxJmZmZnlkIs4MzMzsxxyEWdmZmaWQy7izMzMzHLIRZyZmZlZDrmIMzMzM8shF3FmZmZmOeQizszMzCyHXMSZmZmZ5ZCLODMzM7McchFnZmZmlkMu4szMzMxyyEWcmZmZWQ65iDMzMzPLIRdxZmZmZjnkIs7MrASSxkl6SVK9pKv30O4cSSGpuj3zM7Ouz0WcmVkrSaoAbgXGA0OBSZKGFml3EPBl4Kn2zdDMuoM2FXGS+kiaL+k3kmoljZJ0mKTFkurSz0NTW0m6JX1qfUHS8IJ+pqT2dZKmFMRPkPRi2ucWSWpLvmZmZXIiUB8RqyNiGzAPOKtIu28B3wG2tmdyZtY9tPVM3EzgZxFxNPBJoBa4GngsIgYDj6V1yD6xDk6PacBtAJIOA2YAJ5G9Mc7YUfilNtMK9hvXxnzNzMrhSODVgvWGFNtJ0vFA/4j46Z46kjRNUo2kmvXr15c/UzPrskou4iQdDJwC3AUQEdsi4g9kn0bnpGZzgLPT8lnAPZFZBvSR1A8YCyyOiA0RsRFYDIxL2w6OiCcjIoB7CvoyM+tIxUYFYudGaR/gZuCrzXUUEbdHRHVEVFdWVpYxRTPr6tpyJu6jwHrgbknPSrpT0oHAERGxFiD9/FBq39Qn1z3FG4rEd+NPsmbWzhqA/gXrVcDrBesHAcOAxyWtAUYCCzy5wczKqS1FXA9gOHBbRBwP/A9/HjotpqlPrq2N7x70J1kza18rgMGSBknaD5gILNixMSI2RUTfiBgYEQOBZcCEiKjpmHTNrCtqSxHXADRExI5ZV/PJiro301Ao6ee6gvbFPrnuKV5VJG5m1qEiYjtwOfAo2bXAD0TESknXSZrQsdmZWXdRchEXEW8Ar0r6eAqdBqwi+zS6Y4bpFODhtLwAmJxmqY4ENqXh1keBMZIOTRMaxgCPpm3vSBqZZqVOLujLzKxDRcTCiDgqIv4yIm5IsW9GxIIibU/1WTgzK7cebdz/S8D9aThhNXAJWWH4gKSpwO+Bc1PbhcAZQD3wbmpLRGyQ9C2y4QmA6yJiQ1q+DJgN7A88kh5mZmZm3V6biriIeA4odqHuaUXaBjC9iX5mAbOKxGvILg42MzMzswK+Y4OZmZlZDrmIMzMzM8shF3FmZmZmOeQizszMzCyHXMSZmZmZ5ZCLODMzM7McchFnZmZmlkMu4szMzMxyyEWcmZmZWQ65iDMzMzPLIRdxZmZmZjnkIs7MzMwsh1zEmZmZmeWQizgzMzOzHHIRZ2ZmZpZDLuLMzMzMcshFnJmZmVkOuYgzMzMzyyEXcWZmZmY51OYiTlKFpGcl/TStD5L0lKQ6ST+StF+K90zr9Wn7wII+rknxlySNLYiPS7F6SVe3NVczMzOzrqIcZ+KuAGoL1r8N3BwRg4GNwNQUnwpsjIiPATendkgaCkwEjgHGAd9PhWEFcCswHhgKTEptzczMzLq9NhVxkqqAvwbuTOsCPgPMT03mAGen5bPSOmn7aan9WcC8iHgvIl4B6oET06M+IlZHxDZgXmprZmZm1u219Uzc94CvAx+k9cOBP0TE9rTeAByZlo8EXgVI2zel9jvjjfZpKr4bSdMk1UiqWb9+fRsPyczMzKzzK7mIk3QmsC4ini4MF2kazWxrbXz3YMTtEVEdEdWVlZV7yNrMzMysa+jRhn1PBiZIOgPoBRxMdmauj6Qe6WxbFfB6at8A9AcaJPUADgE2FMR3KNynqbiZmZlZt1bymbiIuCYiqiJiINnEhF9ExAXAEuCc1GwK8HBaXpDWSdt/ERGR4hPT7NVBwGBgObACGJxmu+6XnmNBqfmamZmZdSVtORPXlKuAeZKuB54F7krxu4B7JdWTnYGbCBARKyU9AKwCtgPTI+JPAJIuBx4FKoBZEbFyL+RrZmZmljtlKeIi4nHg8bS8mmxmaeM2W4Fzm9j/BuCGIvGFwMJy5GhmZmbWlfiODWZmZmY55CLOzMzMLIdcxJmZmZnlkIs4MzMzsxxyEWdmVgJJ4yS9JKle0tVFtl8paZWkFyQ9JukvOiJPM+u6XMSZmbWSpArgVmA8MBSYJGloo2bPAtURcSzZ/aK/075ZmllX5yLOzKz1TgTqI2J1RGwD5gFnFTaIiCUR8W5aXUZ21xkzs7JxEWdm1npHAq8WrDekWFOmAo8U2yBpmqQaSTXr168vY4pm1tW5iDMzaz0ViUXRhtKFQDVwU7HtEXF7RFRHRHVlZWUZUzSzrm5v3HbLzKyrawD6F6xXAa83biTps8A3gE9HxHvtlJuZdRM+E2dm1norgMGSBknaj+xe0AsKG0g6HvgBMCEi1nVAjmbWxbmIMzNrpYjYDlwOPArUAg9ExEpJ10makJrdBPQGfizpOUkLmujOzKwkHk41MytBRCwEFjaKfbNg+bPtnpSZdSs+E2dmZmaWQy7izMzMzHLIRZyZmZlZDrmIMzMzM8shF3FmZmZmOeQizszMzCyHSi7iJPWXtERSraSVkq5I8cMkLZZUl34emuKSdIukekkvSBpe0NeU1L5O0pSC+AmSXkz73CKp2K1uzMzMzLqdtpyJ2w58NSKGACOB6ZKGAlcDj0XEYOCxtA4wHhicHtOA2yAr+oAZwEnAicCMHYVfajOtYL9xbcjXzMzMrMsouYiLiLUR8UxafofsW8uPBM4C5qRmc4Cz0/JZwD2RWQb0kdQPGAssjogNEbERWAyMS9sOjognIyKAewr6MjMzM+vWynJNnKSBwPHAU8AREbEWskIP+FBqdiTwasFuDSm2p3hDkbiZmZlZt9fmIk5Sb+BB4CsR8cc9NS0SixLixXKYJqlGUs369eubS9nMzMws99pUxEnal6yAuz8i/iOF30xDoaSf61K8AehfsHsV8Hoz8aoi8d1ExO0RUR0R1ZWVlW05JDMzM7NcaMvsVAF3AbUR8d2CTQuAHTNMpwAPF8Qnp1mqI4FNabj1UWCMpEPThIYxwKNp2zuSRqbnmlzQl5mZmVm31qMN+54MXAS8KOm5FPsH4EbgAUlTgd8D56ZtC4EzgHrgXeASgIjYIOlbwIrU7rqI2JCWLwNmA/sDj6SHmZmZWbdXchEXEb+k+HVrAKcVaR/A9Cb6mgXMKhKvAYaVmqOZmZlZV+U7NpiZmZnlkIs4MzMzsxxyEWdmZmaWQy7izMzMzHLIRZyZmZlZDrmIMzMzM8shF3FmZmZmOeQizszMzCyHXMSZmZmZ5ZCLODMzM7McchFnZmZmlkMu4szMzMxyyEWcmZmZWQ65iDMzMzPLIRdxZmZmZjnkIs7MzMwsh1zEmZmZmeWQizgzMzOzHHI9yN8eAAAH5klEQVQRZ2ZmZpZDLuLMzMzMcqjTF3GSxkl6SVK9pKs7Oh8zM2j+vUlST0k/StufkjSw/bM0s66sUxdxkiqAW4HxwFBgkqShHZuVmXV3LXxvmgpsjIiPATcD327fLM2sq+vURRxwIlAfEasjYhswDzirg3MyM2vJe9NZwJy0PB84TZLaMUcz6+IUER2dQ5MknQOMi4jPp/WLgJMi4vJG7aYB09Lqx4GX9mJafYG39mL/5eAcyyMPOUI+8tzbOf5FRFTuxf530ZL3Jkm/Tm0a0vpvU5u3GvVV+P41DPh1OxxCe8jD32VLdZVj6SrHAV3rWD4eEQeVsmOPcmdSZsU+te5WdUbE7cDtez8dkFQTEdXt8Vylco7lkYccIR955iHHVmrJe1Or37+60u/Jx9L5dJXjgK53LKXu29mHUxuA/gXrVcDrHZSLmdkOLXlv2tlGUg/gEGBDu2RnZt1CZy/iVgCDJQ2StB8wEVjQwTmZmbXkvWkBMCUtnwP8Ijrz9Stmljudejg1IrZLuhx4FKgAZkXEyg5Oq12GbdvIOZZHHnKEfOSZhxxbrKn3JknXATURsQC4C7hXUj3ZGbiJLei6K/2efCydT1c5DvCxAJ18YoOZmZmZFdfZh1PNzMzMrAgXcWZmZmY51O2LOEn9JS2RVCtppaQrUvwwSYsl1aWfh6b40ZKelPSepK81108ny7GXpOWSnk/9/J/OlmNBfxWSnpX0086Yo6Q1kl6U9Fxbpoe3Q559JM2X9JvU36jOlKOkj6ff4Y7HHyV9pRw5dmbqQrfsasGxXClplaQXJD0m6S86Is+WaO5YCtqdIykkdcqvuGjJcUg6L70uKyX9sL1zbKkW/H0NSO9Fz6a/sTM6Is/mSJolaZ2y748stl2SbknH+YKk4S3qOCK69QPoBwxPywcBL5PdRuc7wNUpfjXw7bT8IWAEcAPwteb66WQ5CuidlvcFngJGdqYcC/q7Evgh8NPO9lqnbWuAvp35bzJtmwN8Pi3vB/TpbDkW9FkBvEH2xb0d9p6wtx/pOH8LfDS9Js83fq8Avgj8e1qeCPyoo/Nuw7GMBg5Iy5fl+VhSu4OAJ4BlQHVH513iazIYeBY4NK1/qKPzbsOx3A5clpaHAms6Ou8mjuUUYDjw6ya2nwE8Qvb/9EjgqZb02+3PxEXE2oh4Ji2/A9QCR7LrLXPmAGenNusiYgXwfgv76Uw5RkRsTqv7pkdZZraUK0cASVXAXwN3liO3vZHj3lSuPCUdTPbGcVdqty0i/tCZcmzkNOC3EfG7cuTYiXWlW3Y1eywRsSQi3k2ry8i+U68zaultHr9F9mFla3sm1wotOY5LgVsjYiNk/z7bOceWasmxBHBwWj6ETvpdshHxBHv+nsizgHvS/9PLgD6S+jXXb7cv4gqlIYvjyc5QHRERayH7D4vsTEIp/XSqHNMw5XPAOmBxRHS6HIHvAV8HPih3bjuUIccAFkl6Wtltkzpjnh8F1gN3p6GGOyUd2MlyLDQRmFvu/DqhI4FXC9Yb2P0D3842EbEd2AQc3i7ZtU5LjqXQVLKzDZ1Rs8ci6Xigf0SU7TKPvaAlr8lRwFGSlkpaJmlcu2XXOi05ln8CLpTUACwEvtQ+qZVda/8tAS7idpLUG3gQ+EpE/LGj+9lbfUfEnyLiOLJPwydKGtaZcpR0JrAuIp4uZ16NnqMcr9HJETEcGA9Ml3RK2RJMypBnD7LT97dFxPHA/5ANcZZNGf/d7AdMAH5crtw6sbLdsqsTaHGeki4EqoGb9mpGpdvjsUjaB7gZ+Gq7ZVSalrwmPciGVE8FJgF3Suqzl/MqRUuOZRIwOyKqyIYk702vVd6U9G8+jwdadpL2JfuP6P6I+I8UfnPHqcz0s9nTzU3006ly3CENqz0OlO0TWJlyPBmYIGkN2anzz0i6r5PlSES8nn6uAx4iO+1fNmXKswFoKDjbOp+sqOtMOe4wHngmIt4sV36dWFe6ZVeLbo0o6bPAN4AJEfFeO+XWWs0dy0HAMODx9P40EljQCSc3tPTv6+GIeD8iXgFeIivqOpuWHMtU4AGAiHgS6AX0bZfsyquk24x2+yIuXWdyF1AbEd8t2FR4y5wpwMMl9tOZcqzc8WlL0v7AZ4HfdKYcI+KaiKiKiIFkw2u/iIgLO1OOkg6UdNCOZWAMUHTGUUfmGRFvAK9K+ngKnQas6kw5FphE9xhKha51y65mjyUNQf6ArIDrrNdeQTPHEhGbIqJvRAxM70/LyI6prLPTy6Alf18/IZtwgqS+ZMOrq9s1y5ZpybH8nuy9DUlDyIq49e2aZXksACanWaojgU07Lk3Zo+gEszY68gH8FdkpyxeA59LjDLLrTx4D6tLPw1L7D5NVzH8E/pCWD26qn06W47FkM5JeICs6vtnZfo+N+jyV8s5OLdfv8aNks6SeB1YC3+iMf5Np23FATerrJ6TZaJ0sxwOAt4FD2vvff0c90u/qZbKZd99IsevIigLI/iP6MVAPLAc+2tE5t+FYfg68WfB3sqCjcy71WBq1fZxOODu1ha+JgO+Sfah7EZjY0Tm34ViGAkvT+/FzwJiOzrmJ45gLrCWb3NVAdgbx74C/K3hNbk3H+WJL/7Z82y0zMzOzHOr2w6lmZmZmeeQizszMzCyHXMSZmZmZ5ZCLODMzM7McchFnZmZmlkMu4szMzMxyyEWcmZmZWQ79f2c5Mw56oGwgAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 720x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,4))\n",
    "sirina = 0.3\n",
    "\n",
    "plt.subplot(1,2,1)\n",
    "plt.bar(data['Godina upisa/diplome'] - sirina/2, data['Ukupno studentkinja'], sirina, label='Studentkinje',color='#D6ED17FF')\n",
    "plt.bar(data['Godina upisa/diplome'] + sirina/2, data['Ukupno studenata']-data['Ukupno studentkinja'], sirina, label='Studenti',color='#606060')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Претходне информације можемо сумирати у један дијаграм поредећи процентуалну заступљеност студенткиња међу уписаним и дипломираним студентима:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": "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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['Godina upisa/diplome']-sirina/2,100*data['Ukupno studentkinja']/data['Ukupno studenata'],sirina,color='#D7C49EFF',label='Upisane studentkinje')\n",
    "plt.bar(data['Godina upisa/diplome']+sirina/2,100*data['Ukupno diplomiranih zene']/data['Ukupno diplomiranih'],sirina,color='#343148FF',label='Diplomirale studentkinje')\n",
    "plt.axhline(y=50,color='grey')\n",
    "plt.ylim([0,100])\n",
    "plt.legend()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Са употребом **секторских дијаграма** (pie charts) треба бити пажљив (иако су доста доминантни у јавној комуникацији) пре свега зато што људи нису претерано добри у процени и поређењу углова/лукова. Но ако поредите само пар величина или вам је циљ да само демонстрирате да је једна од групација доминантна, питице су изузетне због нашег свакодневног искуства са питама-пицама-тортама :)\n",
    "\n",
    "Стога ћемо у наставку приказати један део ових података који је погодан за секторски дијаграм, као и кад је секторски дијаграм непогодан и потребно је вратити се стубићима или линијама."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Фокусираћемо се на број дипломираних по различитим степенима факултетског образовања који су дати колонама Диплома I,II,III степен."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 360x360 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5,5))\n",
    "plt.pie(data[data['Godina upisa/diplome']==2017][['Diploma I stepen fakultet','Diploma II stepen fakultet','Diploma III stepen fakultet']].iloc[0],labels=['Diploma I stepen fakultet','Diploma II stepen fakultet','Diploma III stepen fakultet'],colors=['#FC766A','#B0B8B4','#184A45'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Ако је циљ наше визуализације само да демонстрира да је највише дипломаца 2017 године са дипломом првог степена и то више од пола, док су дипломе другог и трећег степена ређе, ово је идеална визуализација. Међутим, ако очекујемо да се са овог дијаграма оцени у ком су односу дипломци првог и другог степена (дал' је ових првих дупло више од других или тако нешто), то је изузетно тешко на овом дијаграму. Слично, визуализација у наставку је један од лошијих избора, иако често присутна:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x216 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(16,3))\n",
    "for i in range(5):\n",
    "    plt.subplot(1,5,i+1)\n",
    "    plt.pie(data[data['Godina upisa/diplome']==2013+i][['Diploma I stepen fakultet','Diploma II stepen fakultet','Diploma III stepen fakultet']].iloc[0],colors=['#FC766A','#B0B8B4','#184A45'])\n",
    "    plt.title(str(2013+i)+'. godina')\n",
    "plt.legend(['I stepen','II stepen','III stepen'],loc='center left', bbox_to_anchor=(1, 0.5))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Опет, ако је циљ само проценити да се нешто десило између 2014 и 2015. године и тачни бројеви и односи међу бројевима нас не занимају, ова илустрација служи сврси. Међутим ако гледамо неке податке упоредно, из године у годину, обично је циљ да проверимо да ли постоје неки трендови а са поређење исечака није јендоставно, стога су у наставку два боља начина да се исти подаци представе стубићима и линијама:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 864x288 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(12,4))\n",
    "plt.subplot(1,2,1)\n",
    "plt.bar(data['Godina upisa/diplome']-sirina,100*data['Diploma I stepen fakultet']/data['Diplomirani studenti fakulteti'],sirina,color='#FC766A', label='I stepen')\n",
    "plt.bar(data['Godina upisa/diplome'],100*data['Diploma II stepen fakultet']/data['Diplomirani studenti fakulteti'],sirina,color='#B0B8B4', label='II stepen')\n",
    "plt.bar(data['Godina upisa/diplome']+sirina,100*data['Diploma III stepen fakultet']/data['Diplomirani studenti fakulteti'],sirina,color='#184A45', label='III stepen')\n",
    "plt.xlabel('Godina')\n",
    "plt.ylabel('Procenat studenata')\n",
    "plt.legend()\n",
    "\n",
    "plt.subplot(1,2,2)\n",
    "plt.plot(data['Godina upisa/diplome'],100*data['Diploma I stepen fakultet']/data['Diplomirani studenti fakulteti'],'o-',color='#FC766A')\n",
    "plt.plot(data['Godina upisa/diplome'],100*data['Diploma II stepen fakultet']/data['Diplomirani studenti fakulteti'],'o-',color='#B0B8B4')\n",
    "plt.plot(data['Godina upisa/diplome'],100*data['Diploma III stepen fakultet']/data['Diplomirani studenti fakulteti'],'o-',color='#184A45')\n",
    "plt.xlabel('Godina')\n",
    "\n",
    "plt.savefig('Diplomirani studenti po nivou studija.pdf',format='pdf')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Често ћемо желети да лепе дијаграме које правимо и сачувамо, ван радне свеске, за то нам је од користи функција [**savefig**](https://matplotlib.org/3.3.0/api/_as_gen/matplotlib.pyplot.savefig.html), видите локални фолдер у њему се сада налази фајл са направљеним дијаграмима."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Неочекивани скок у броју студената који су дипломирали на студијама I степена у 2015. години (испраћен истим наглим падом оних који су дипломирали са II степеном) је последица промене у методологији и дефиницијама I и II степена. Да видимо да ли се укупан број дипломаца мењао, можемо погледати број студената који су дипломирали на студијама првог и другог степена сумарно:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.bar(data['Godina upisa/diplome'],data['Diploma I stepen fakultet']+data['Diploma II stepen fakultet'],sirina,color='grey')\n",
    "#plt.grid(axis='y')\n",
    "#plt.ylim([32000,38000])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "У претходном смо опет пар линија коментарисали, проверите шта оне раде. Понекад, када су сви стубићи високи као на овом графику, и када су њихове међусобне разлике мале у односу на укупну висину, може бити корисно да промените опсег у осе. Али, будите опрезни са скраћивањем у осе, размишљајте о читаоцу ваше визуализације, који неће увек испратити да сте променили опсег што може довести до погрешних интерпретација, видите нпр. [овај пример](https://marcgawley.com/2014/12/07/misdirection/)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Циљ ове радне свеске био је да се упознамо (или подсетимо) са основним типовима визуализација података - линијским, стубичастим и секторским дијаграмима. Све ове визуализације направили смо користећи пајтон и библиотеку *matplotlib* тако да смо спремни да наставимо са обимнијим сетовима података.\n",
    "\n",
    "У наставку ћемо се често бавити и већим и неуреднијим сетовима података, где ће први део анализе бити чишћење и издвајању интересантних делова из табеле, тј. конструисање малог сета података који ће затим сличним начинима бити представљан на дијаграмима. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### За даље читање:\n",
    "\n",
    "[Coloring for Colorblindness](https://davidmathlogic.com/colorblind/#%23D81B60-%231E88E5-%23FFC107-%23004D40)\n",
    "\n",
    "[Why scientists need to be better at data visualization](https://www.knowablemagazine.org/article/mind/2019/science-data-visualization)"
   ]
  },
  {
   "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
}