{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# Средњошколци у Републици Србији"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"У Стратегији развоја образовања у Србији до 2020. године, неки од постављених циљева тичу се повећања ученика у гимназијама, повећања доступности образовања у различитим географским регијама и исто је мотивисано следећим циатаом из Стратегије: \"Када се погледа просечна зарада по општинама и окрузима у јуну 2010. године, види се да велики део општина које имају скромну понуду средњих школа спада у категорију оних где су мање просечне зараде (РЗС, 2010).\". У наставку ћемо испитати да ли доступност различитих средњошколских профила зависи од општине и округа и проверити да ли и даље важи запажање из стретегије које повезује приходе у општини и доступност образовања.\n",
"\n",
"Као и у претходој радној свесци о средњошколском образовању користимо дотеране податке (радна свеска у којој су подаци сређени такође се налази у локалном фолдеру) преузете са платформе отворених података Министарства за просвету, науку и технолошки развој: http://opendata.mpn.gov.rs/. Додатно, анализу у наставку допунићемо и подацима Републичког завода за статистику о просечним примањима по општинама које је могуће преузети овде: https://data.stat.gov.rs/\n",
"У овој радној свесци поновићемо цртање хистограма, а податке о зарадама и доступном образовању анализираћемо тачкастим дијаграмима."
]
},
{
"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 манипулацију низовима\n",
"import matplotlib.pyplot as plt # библиотека функција за визуелизацију податка\n",
"import seaborn as sns # библиотека функција за визуелизацију податка"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Као и претходно, истраживање података ћемо почети њиховим учитавањем уз помоћ [*pandas*](https://pandas.pydata.org/) библиотеке:"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"data": {
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"text/plain": [
" Okrug Broj učenika Broj devojčica Broj odeljenja \\\n",
"0 Borski upravni okrug 3281 1542 140 \n",
"1 Braničevski upravni okrug 4953 2452 196 \n",
"\n",
" Broj nastavnika - bez zamena Ukupna norma nastavnikaa - bez zamena \\\n",
"0 432 290.9780 \n",
"1 525 393.9538 \n",
"\n",
" Broj skola Područje rada Obrazovni profil Ukupno gimnazijalaca \n",
"0 10 9 52 770 \n",
"1 10 9 52 1137 "
]
},
"execution_count": 2,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ss_okruzi = pd.read_csv('data/srednjoskolci data/MPNTR_podaci_po_okruzima.csv',encoding='UTF-16')\n",
"ss_okruzi.head(2)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
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Ukupna norma nastavnikaa - bez zamena
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"text/plain": [
" Okrug Opština Broj učenika Broj devojčica \\\n",
"0 Borski upravni okrug Bor 1544 751 \n",
"1 Borski upravni okrug Kladovo 465 210 \n",
"\n",
" Broj odeljenja Broj nastavnika - bez zamena \\\n",
"0 64 184 \n",
"1 19 51 \n",
"\n",
" Ukupna norma nastavnikaa - bez zamena Broj skola Područje rada \\\n",
"0 130.8821 4 9 \n",
"1 38.8610 1 5 \n",
"\n",
" Obrazovni profil Ukupno gimnazijalaca Ukupno u trogodisnjim ss \n",
"0 30 303 163 \n",
"1 15 93 70 "
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ss_opstine = pd.read_csv('data/srednjoskolci data/MPNTR_podaci_po_opstinama.csv',encoding='UTF-16')\n",
"ss_opstine.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Кратко подсећање на хистограме којима смо се бавили у претходној радној свесци - погледајмо како изгледа број ученика у различитим општинама:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.hist(ss_opstine['Broj učenika'],bins=range(0,18000,1000),color='darkgrey')\n",
"plt.xlabel('Broj učenika srednje škole u opštini')\n",
"plt.ylabel('Broj opština sa datim brojem učenika srednje škole')\n",
"medijalnavrednost=np.median(ss_opstine['Broj učenika'])\n",
"prosecnavrednost=np.mean(ss_opstine['Broj učenika'])\n",
"plt.axvline(x=medijalnavrednost,color='darkred',linestyle='dashed')\n",
"plt.axvline(x=prosecnavrednost,color='black')\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1116.0"
]
},
"execution_count": 6,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"medijalnavrednost"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"1910.8145161290322"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"prosecnavrednost"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"57"
]
},
"execution_count": 8,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"len(ss_opstine[ss_opstine['Broj učenika']<1000])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Расподела општина по броју ученика је још један пример у коме су медијална и просечна вредност различите и другачије од најчешће вредности у узорку - средња по броју ученика је општина са 1116 ученика (медијална вредност), аритметична средина броја ученика по општини, односно просечна вредност је 1911 ученика, док далеко највише општина има од 0 до 1000 ученика - скоро 60! \n",
"\n",
"\n",
"Често мешање ове три величине долази када се о подацима размишља као да су распоређени по нормалној, односно Гаусовој распоредли, где су све ове три вредности једнаке - види пример:"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"data": {
"image/png": "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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"gausrandom=np.random.randn(10000) \n",
"plt.hist(gausrandom,bins=50)\n",
"plt.axvline(x=np.mean(gausrandom),color='red')\n",
"plt.axvline(x=np.median(gausrandom),color='black')\n",
"plt.xlabel('Nasumični brojevi')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"[**random.randn(N)**](https://numpy.org/devdocs/reference/random/generated/numpy.random.randn.html) је функција numpy библиотеке којом генеришемо N насумичних бројева по нормалној расподели са просечном вредношћу 0 и стандардном девијацијом 1. На слици изнад, представили смо хистограмом N = 10000 таквих бројева и видимо да су средња и просечна вредност идентичне и једнаке 0, као и да је то уједно и најчешћа вредност у овим подацима. Иако се нормална расподела често може приметити и у анализи реалних података, битно је имати на уму да су и асиметричне расподеле честе (попут горе приказане расподеле општина по броју ученика, таква је и расподела зарада и многе друге) и да су у том случају медијална и просечна вредност најчешће различите."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Вратимо се сада општинама и истраживању доступности разноврсних образовних профила ученицима. Једна једноставна провера коју можемо урадити је тачкасти дијаграм у коме свака тачка представља један округ (или општину) док су х и у координата број ученика и број образовних профила у датој просторној јединици, такве графике приказујемо у наставку:"
]
},
{
"cell_type": "code",
"execution_count": 10,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.figure(figsize=(12,4))\n",
"plt.subplot(1,2,1)\n",
"plt.scatter(ss_okruzi['Broj učenika'],ss_okruzi['Obrazovni profil'],label='Različiti obrazovni profili')\n",
"plt.xlabel('Broj učenika u okrugu')\n",
"plt.ylabel('Broj različitih obrazovnih profila u okrugu')\n",
"\n",
"plt.subplot(1,2,2)\n",
"plt.scatter(ss_opstine['Broj učenika'],ss_opstine['Obrazovni profil'])\n",
"plt.xlabel('Broj učenika u opštini')\n",
"plt.ylabel('Broj različitih obrazovnih profila u opštini')\n",
"\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Као што смо вероватно и очекивали, што је веће место у питању (лево округ, десно општина), то је број различитих смерова доступних у средњим школама већи. \n",
"Битно је приметити да није у питању линеаран тренд (тачке нису на правој линији - што је посебно видљиво на левом графику) - за мале округе мале промене у величини ученичке популације воде већим променама у броју доступних смерова. Међутим оно што чуди на десном графику су све ове општине које као да су \"спале\" са главног тренда међу подацима - шта је разлог за чињеницу да различите општине са сличним бројем средњошколаца имају разлику од 30 до 60 доступних образовних профила (нпр. види општине са око 5000 ученика)? То можемо истражити идентификовањем једне од тачака чије х,у координате можемо лако уочити:"
]
},
{
"cell_type": "code",
"execution_count": 11,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Okrug
\n",
"
Opština
\n",
"
Broj učenika
\n",
"
Broj devojčica
\n",
"
Broj odeljenja
\n",
"
Broj nastavnika - bez zamena
\n",
"
Ukupna norma nastavnikaa - bez zamena
\n",
"
Broj skola
\n",
"
Područje rada
\n",
"
Obrazovni profil
\n",
"
Ukupno gimnazijalaca
\n",
"
Ukupno u trogodisnjim ss
\n",
"
\n",
" \n",
" \n",
"
\n",
"
18
\n",
"
Grad Beograd
\n",
"
Beograd - Vračar
\n",
"
4205
\n",
"
1902
\n",
"
146
\n",
"
338
\n",
"
301.7369
\n",
"
4
\n",
"
5
\n",
"
12
\n",
"
2232
\n",
"
511
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Okrug Opština Broj učenika Broj devojčica \\\n",
"18 Grad Beograd Beograd - Vračar 4205 1902 \n",
"\n",
" Broj odeljenja Broj nastavnika - bez zamena \\\n",
"18 146 338 \n",
"\n",
" Ukupna norma nastavnikaa - bez zamena Broj skola Područje rada \\\n",
"18 301.7369 4 5 \n",
"\n",
" Obrazovni profil Ukupno gimnazijalaca Ukupno u trogodisnjim ss \n",
"18 12 2232 511 "
]
},
"execution_count": 11,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ss_opstine[(ss_opstine['Broj učenika']>4000)&(ss_opstine['Obrazovni profil']<20)]"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"У питању је Београд! Како ученици у оквиру Београда лако могу путовати између општина, Београдске општине не прате исти тренд, то ћемо проверити тиме што ћемо све Београдске општине нацртати посебно на графику:"
]
},
{
"cell_type": "code",
"execution_count": 12,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.scatter(ss_opstine[ss_opstine['Okrug']!='Grad Beograd']['Broj učenika'],ss_opstine[ss_opstine['Okrug']!='Grad Beograd']['Obrazovni profil'],label='Opštine u Srbiji')\n",
"plt.scatter(ss_opstine[ss_opstine['Okrug']=='Grad Beograd']['Broj učenika'],ss_opstine[ss_opstine['Okrug']=='Grad Beograd']['Obrazovni profil'],label='Beogradske opštine')\n",
"plt.legend()\n",
"plt.xlabel('Broj učenika u opštini')\n",
"plt.ylabel('Broj različitih obrazovnih profila u opštini')\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"*Додатни задатак за знатижељне може бити идентификација општине са највише средњошколаца (тачка у горњем десном углу).*"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Осим очигледне претпоставке да већа потражња (више средњошколаца) изискује већу понуду средњих школа и профила, поставља се питање да ли и неке друге карактеристике општина утичу на ученицима доступне профле."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"У претходној радној свесци видели да су гимназије најпопуларније међу средњошколцима. Хајде да срачунамо проценат средњошколаца који се тренутно школује у гимназијама:"
]
},
{
"cell_type": "code",
"execution_count": 13,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"27.448183303016364"
]
},
"execution_count": 13,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"100*np.sum(ss_opstine['Ukupno gimnazijalaca'])/np.sum(ss_opstine['Broj učenika'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Поређења ради, подаци цитирани у Стратегији образовања до 2020. године, говоре да у Србији 2010. 23.35% ученика уписало гимназију, а поменута стратегије је као један од циљева дефинисала раст броја гимназијалаца."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Хајде да погледамо како се у различитим општинама односе удео ученика у гимназијама и трогодишњим средњим школама. За ово поређење, искористићемо тачкасти дијаграм на коме ће свака тачка одговарати једној општини. Ова два типа образовања су изабрана за поређење због тога што су она често супротстављена у односу на високошколско образовање. Трогодишње средње школе обично не воде високошколском образовању, док су гимназије виђене као школе које је нужно наставити у високошколским институцијама.\n",
"\n",
"Користићемо функцију [**scatterplot**](https://seaborn.pydata.org/generated/seaborn.scatterplot.html) из библиотеке [*seaborn*](https://seaborn.pydata.org/):"
]
},
{
"cell_type": "code",
"execution_count": 14,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"
"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"sns.scatterplot(100*ss_opstine['Ukupno u trogodisnjim ss']/ss_opstine['Broj učenika'],100*ss_opstine['Ukupno gimnazijalaca']/ss_opstine['Broj učenika'],hue=ss_opstine['Okrug'],s=0.1*ss_opstine['Broj učenika'],alpha=0.8)\n",
"plt.xlabel('Procenat srednjoskolaca u opstini u trogodisnjim srednjim skolama')\n",
"plt.ylabel('Procenat srednjoskolaca u opstini u gimnazijama')\n",
"plt.legend([])\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"За разлику од претходних тачкастих дијаграма, сваки кружић поред х и у координате има и боју и променљив пречник. Боје одговарају различитим окрузима, док је величина круга везана за укупан број средњошколаца у посматраној општини. Видимо да је један број општина са великим бројем ученика који се школују на њиховој територији у близини државног просека што се тиче удела гимназијалаца (27.5%), али запажамо и општине различитих округа различитих величина које имају више и мање гимназијалаца. Примећујемо такође и један број општина (доле десно) које имају мање гимназијалаца а велики број ученика средњих стручних школа у трогодишњем трајању. \n",
"\n",
"У наставку ћемо испитати тврдњу из стратегије о повезаности примања у општини са доступним образовањем. За то смо преузели податке о просечним зарадама по општинама:"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": true
},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Region
\n",
"
Okrug
\n",
"
Oblast
\n",
"
Opština
\n",
"
Prosečne neto zarade [RSD]
\n",
"
Prosečne bruto zarade [RSD]
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
Beogradski region
\n",
"
Grad Beograd
\n",
"
Beogradska oblast
\n",
"
Beograd - Barajevo
\n",
"
43223
\n",
"
59653
\n",
"
\n",
"
\n",
"
1
\n",
"
Beogradski region
\n",
"
Grad Beograd
\n",
"
Beogradska oblast
\n",
"
Beograd - Voždovac
\n",
"
62898
\n",
"
87022
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Region Okrug Oblast Opština \\\n",
"0 Beogradski region Grad Beograd Beogradska oblast Beograd - Barajevo \n",
"1 Beogradski region Grad Beograd Beogradska oblast Beograd - Voždovac \n",
"\n",
" Prosečne neto zarade [RSD] Prosečne bruto zarade [RSD] \n",
"0 43223 59653 \n",
"1 62898 87022 "
]
},
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"zarade_opstine = pd.read_excel('data/srednjoskolci data/zarade_RZS.xls')\n",
"zarade_opstine.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Приметимо да библиотека pandas има згодну функцију и за учитавање фајлова .xls формата ([**read_excel**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.read_excel.html)).\n",
"zarade_opstine садржи податке преузете са сајта Републичког завода за статистику за годишњим просечним зарадама за 2018. годину по општинама пребивалишта запослених. Фајл је мало преуређен у односу на оригинални - општине и окрузи су преименовани да би били у истом формату као и подаци са којима хоћемо да их укрстимо."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Како и табела zarade_opstine и ss_opstine садрже пар (округ, општина), можемо их спојити користећи функцију [**merge**](https://pandas.pydata.org/pandas-docs/stable/reference/api/pandas.DataFrame.merge.html):"
]
},
{
"cell_type": "code",
"execution_count": 17,
"metadata": {},
"outputs": [],
"source": [
"ss_opstine_zarade = pd.merge(ss_opstine,zarade_opstine,on=['Okrug','Opština'])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"У позивању функције merge, минимални аргументи су две табеле које желимо спојити, у претходном случају zarade_opstine и ss_opstine, док је кроз аргумент on потребно проследити колону по чијим вредностима ће табеле бити спојене. Резултат коришћења ове функције је нова табела у којој ће се налазити све колоне које су постојале у спојене две табеле, тј. за сваки пар 'Okrug' и 'Opština', остатак реда нове табеле биће попуњен вредностима табеле ss_opstine['Okrug','Opština'] и zarade_opstine['Okrug','Opština'], што можемо видети провером прва два реда новонастале табеле:"
]
},
{
"cell_type": "code",
"execution_count": 18,
"metadata": {},
"outputs": [
{
"data": {
"text/html": [
"
\n",
"\n",
"
\n",
" \n",
"
\n",
"
\n",
"
Okrug
\n",
"
Opština
\n",
"
Broj učenika
\n",
"
Broj devojčica
\n",
"
Broj odeljenja
\n",
"
Broj nastavnika - bez zamena
\n",
"
Ukupna norma nastavnikaa - bez zamena
\n",
"
Broj skola
\n",
"
Područje rada
\n",
"
Obrazovni profil
\n",
"
Ukupno gimnazijalaca
\n",
"
Ukupno u trogodisnjim ss
\n",
"
Region
\n",
"
Oblast
\n",
"
Prosečne neto zarade [RSD]
\n",
"
Prosečne bruto zarade [RSD]
\n",
"
\n",
" \n",
" \n",
"
\n",
"
0
\n",
"
Borski upravni okrug
\n",
"
Bor
\n",
"
1544
\n",
"
751
\n",
"
64
\n",
"
184
\n",
"
130.8821
\n",
"
4
\n",
"
9
\n",
"
30
\n",
"
303
\n",
"
163
\n",
"
Region Južne i Istočne Srbije
\n",
"
Borska oblast
\n",
"
53023
\n",
"
73589
\n",
"
\n",
"
\n",
"
1
\n",
"
Borski upravni okrug
\n",
"
Kladovo
\n",
"
465
\n",
"
210
\n",
"
19
\n",
"
51
\n",
"
38.8610
\n",
"
1
\n",
"
5
\n",
"
15
\n",
"
93
\n",
"
70
\n",
"
Region Južne i Istočne Srbije
\n",
"
Borska oblast
\n",
"
45458
\n",
"
62961
\n",
"
\n",
" \n",
"
\n",
"
"
],
"text/plain": [
" Okrug Opština Broj učenika Broj devojčica \\\n",
"0 Borski upravni okrug Bor 1544 751 \n",
"1 Borski upravni okrug Kladovo 465 210 \n",
"\n",
" Broj odeljenja Broj nastavnika - bez zamena \\\n",
"0 64 184 \n",
"1 19 51 \n",
"\n",
" Ukupna norma nastavnikaa - bez zamena Broj skola Područje rada \\\n",
"0 130.8821 4 9 \n",
"1 38.8610 1 5 \n",
"\n",
" Obrazovni profil Ukupno gimnazijalaca Ukupno u trogodisnjim ss \\\n",
"0 30 303 163 \n",
"1 15 93 70 \n",
"\n",
" Region Oblast Prosečne neto zarade [RSD] \\\n",
"0 Region Južne i Istočne Srbije Borska oblast 53023 \n",
"1 Region Južne i Istočne Srbije Borska oblast 45458 \n",
"\n",
" Prosečne bruto zarade [RSD] \n",
"0 73589 \n",
"1 62961 "
]
},
"execution_count": 18,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"ss_opstine_zarade.head(2)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"ss_opstine_zarade табела сада за сваку општину садржи и информацију о просечним нето и бруто зарадама, које ћемо користити у наставку.\n",
"Са табелом зараде, додали смо и информације о регионима Србије које ће нам такође згодно доћи да бојимо тачкасте дијаграме пошто региона има далеко мање него округа."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"У табелу ss_opstine_zarade додаћемо и 2 колоне о проценту гимназијалаца и ученика трогодишњих средњих школа, да не бисмо исто рачунали при позивању функција за цртање графика као претходно:"
]
},
{
"cell_type": "code",
"execution_count": 19,
"metadata": {},
"outputs": [],
"source": [
"ss_opstine_zarade['Procenat u gimnaziji']=100*ss_opstine_zarade['Ukupno gimnazijalaca']/ss_opstine_zarade['Broj učenika']\n",
"\n",
"ss_opstine_zarade['Procenat u trogodisnjoj']=100*ss_opstine_zarade['Ukupno u trogodisnjim ss']/ss_opstine_zarade['Broj učenika']"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Тачкасти дијаграм образовних профила и просечних нето зарада за различите општине налази се у наставку. Величина сваког кружића одговара броју средњошколаца у датој општини:"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"data": {
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\n",
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