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Atividade S11 - Mari Oliveira #8

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Atividade S11 - Mari Oliveira #8

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da4f403
alterações na pasta exercícios
Mariana-Ol Oct 18, 2023
939b956
Merge branch 'main' of https://github.com/Mariana-Ol/on26-python-s11-…
Mariana-Ol Oct 18, 2023
3bd8500
Entrega da atividade de casa
Mariana-Ol Oct 18, 2023
d8327d5
Alteração no arquivo ipynb da atividade para casa
Mariana-Ol Oct 18, 2023
c38c9f7
Incluí a função "resetar índice" no ipynb da atividade de casa
Mariana-Ol Oct 18, 2023
a108b7f
Exclui os outputs do ipynb da atividade para casa
Mariana-Ol Oct 18, 2023
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229 changes: 229 additions & 0 deletions exercicios/para-casa/Atividade_casa.ipynb
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Um pouco sobre a base de dados \n",
"\n",
"O arquivo mostra dados coletados entre 2022 e 2023 pela Eurostat, uma organização europeia de estatística, que dizem respeito ao fluxo de imigrantes nos países europeus. São apresentadas informações como a quantidade de imigrantes com nacionalidade e sem nacionalidade residindo em cada país europeu e dados sobre o perfil dessa população, como o percentual de homens e mulheres e de pessoas provenientes de países-membros ou não da União Europeia."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Importação das bibliotecas + leitura e visão geral da base de dados"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd\n",
"import numpy as np\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df = pd.read_csv(\"EU Immigrants.csv\")\n",
"\n",
"df.head()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.shape"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df.columns"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"df_slice_sem_nulos = df[2:31] #separando a parte da tabela em que as linhas estão preenchidas para verificar se há dados duplicados nelas\n",
"df_slice_sem_nulos\n",
"\n",
"df_slice_sem_nulos.duplicated() #verificando se há dados duplicados na parte da tabela que não apresenta linhas vazias (e não há dados duplicados)"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#método incluído para contabilizar a quantidade de posições nulas na tabela \n",
"df.isnull().sum()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Função para tratamento dos dados\n",
"Esta função foi criada para:\n",
"\n",
"* Remover valores nulos (no caso deste DF em particular, as últimas 34 linhas)\n",
"* Reordenar valores (arbitrariamente)\n",
"* Excluir 1 coluna (arbitrariamente)\n",
"* Resetar o índice\n",
"* Exportar o DF modificado para csv\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"def processamento(df):\n",
" df.drop(columns=['IMMIGRANTS WITH NATIONALITY(THOUSANDS)'], inplace=True) #remove 1 coluna\n",
" df.dropna(subset=['EU COUNTRIES'], inplace=True) #remove as 34 linhas finais, que estão vazias\n",
" df.sort_values(['PERCENTAGE OF IMMIGRANTS BY SEX(MALE)'], axis = 0, ascending=True, inplace=True) #coloca os percentuais dessa coluna em ordem crescente\n",
" df.reset_index(drop=True, inplace=True)\n",
" df.to_csv(\"exercicio_imigracao_ue.csv\")\n",
" \n",
" \n",
" "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"copia_df_inicial = df.copy() #as alterações do tratamento de dados serão feitas nessa cópia\n",
"copia_df_inicial"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"processamento(copia_df_inicial)\n",
"copia_df_inicial"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Insights\n",
"### Algumas curiosidades aferidas...\n",
"* A média do número de imigrantes nos países europeus no período contemplado pela pesquisa é de 127,5 (em milhares).\n",
"* O país europeu com a maior população imigrante feminina é o Chipre, com 53,5% de mulheres neste grupo.\n",
"* Já a Croácia é o país com a maior população imigrante masculina, contabilizando 72,7% de homens neste grupo.\n",
"* Quase metade da população de imigrantes de Lieshtenstein provém de países que não fazem parte da União Europeia (47,3%). Este também é o país com o maior percentual de imigrantes (68,6%) em relação à população total, ou seja, há mais imigrantes do que locais vivendo em Lieshtenstein. \n",
"* A Romênia, em contrapartida, apresenta o menor percentual de imigrantes nascidos em países que não fazem parte da União Europeia (0,7%) e o menor percentual de imigrantes em relação à população total (1,7%). "
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"copia_df_inicial[['PERCENTAGE OF IMMIGRANTS BY SEX(FEMALE)', 'PERCENTAGE OF IMMIGRANTS BY SEX(MALE)']].max()\n"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"valor_min = copia_df_inicial['%AGE OF BORN IN NON-EU COUNTRY'].min()\n",
"valor_min \n",
"resultado = copia_df_inicial[copia_df_inicial['%AGE OF BORN IN NON-EU COUNTRY'] == valor_min]\n",
"resultado"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"#valor_max = copia_df_inicial['%AGE OF BORN IN NON-EU COUNTRY'].max()\n",
"#valor_max\n",
"resultado = copia_df_inicial[copia_df_inicial['%AGE OF BORN IN NON-EU COUNTRY'] == valor_max]\n",
"resultado"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"valor_max = copia_df_inicial['%AGE OF FOREIGN BORN IMMIGRANTS BY TOTAL POPULATION'].max()\n",
"copia_df_inicial[copia_df_inicial['%AGE OF FOREIGN BORN IMMIGRANTS BY TOTAL POPULATION'] == valor_max]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"valor_min = copia_df_inicial['%AGE OF FOREIGN BORN IMMIGRANTS BY TOTAL POPULATION'].min()\n",
"copia_df_inicial[copia_df_inicial['%AGE OF FOREIGN BORN IMMIGRANTS BY TOTAL POPULATION'] == valor_min]"
]
},
{
"cell_type": "code",
"execution_count": null,
"metadata": {},
"outputs": [],
"source": [
"media_total_de_imigrantes = copia_df_inicial['TOTAL IMMIGRANTS(IN THOUSANDS)'].mean()\n",
"media_total_de_imigrantes"
]
}
],
"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": "3.11.1"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
66 changes: 66 additions & 0 deletions exercicios/para-casa/EU Immigrants.csv
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EU COUNTRIES,TOTAL IMMIGRANTS(IN THOUSANDS),IMMIGRANTS WITH NATIONALITY(THOUSANDS),IMMIage),NON_NATIONAL IMMIGRANTS FROM OTHER EU MEMBER STATES(THOUSANDS),NON_NATIONAL IMMIGRANTS FROM OTHER EU MEMBER STATES(%age),NON_NATIONAL IMMIGRANTS FROM NON-EU STATES(THOUSANDS),NON_NATIONAL IMMIGRANTS FROM NON-EU STATES(%age),STATELESS IMMIGRANTS(THOUSANDS),IMMIGRANTS WITH NATIONALITY(%age),NON-NATIONAL IMMIGRANTS (THOUSANDS),NON-NATIONAL IMMIGRANTS (%,NUMBER OF IMMIGRANTS(PER THOUSAND INHABITANTS),DISTRIBUTION OF IMMIGRANTS IN %AGE (NATIONALS),DISTRIBUTION OF IMMIGRANTS IN %AGE (NON-NATIONALS),DISTRIBUTION OF IMMIGRANTS IN %AGE(UNKNOWN),PERCENTAGE OF IMMIGRANTS BY SEX(MALE),PERCENTAGE OF IMMIGRANTS BY SEX(FEMALE),%AGE OF NON-NATIONAL IMMIGRANTS IN TOTAL POPULATION(CITIZENS OF EU MEMBER STATES),%AGE OF NON-NATIONAL IMMIGRANTS IN TOTAL POPULATION(CITIZENS OF NON-EU COUNTRIES),%AGE OF FOREIGN BORN IMMIGRANTS BY TOTAL POPULATION,%AGE OF BORN IN ANOTHER EU-MEMBER STATES,%AGE OF BORN IN NON-EU COUNTRY
Austria,118.5,9.6,8.1,108.8,91.8,64.4,54.4,44.1,37.2,0.3,0.2,13.2,8.1,91.8,0.1,57.5,42.5,9.2,8.3,20.4 ,9.3 ,11.1
Belgium,139.7,17.4,12.4,121.7,87.1,63.1,45.2,58.5,41.9,0.0,0.0,12.1,12.4,87.1,0.5,54.9,45.1,8.2,4.6,18.2 ,7.9 ,10.3
Bulgaria,39.5,22.1,56.0,17.3,43.9,5.5,13.8,11.8,30.0,0.0,0.1,5.7,56.0,43.9,0.1,55.1,44.9,0.2,1.6,3.2 ,1.0 ,2.2
Croatia,35.9,10.6,29.6,25.3,70.4,4.8,13.2,20.5,57.2,0.0,0.0,9.1,29.6,70.4,0.0,72.7,27.3,0.3,0.6,12.2 ,1.7 ,10.6
Cyprus,24.0,4.0,16.6,20.0,83.4,8.0,33.2,12.1,50.2,0.0,0.0,26.7,16.6,83.4,0.0,46.5,53.5,10.4,8.4,22.7 ,10.6 ,12.2
Czechia,69.4,2.9,4.2,66.4,95.8,14.5,21.0,51.9,74.8,0.0,0.0,6.6,4.2,95.8,0.0,61.4,38.6,1.6,3.5,4.3 ,1.3 ,3.0
Denmark,63.5,16.2,25.5,47.3,74.4,29.6,46.6,17.6,27.7,0.1,0.1,10.8,25.5,74.4,0.0,54.2,45.8,4.0,5.4,12.7 ,4.2 ,8.5
Estonia,19.5,7.1,36.1,12.3,63.1,4.1,20.9,8.2,42.2,0.0,0.0,14.7,36.1,63.1,0.8,60.3,39.7,1.6,13.6,15.1 ,1.9 ,13.1
Finland,36.4,8.3,22.9,28.0,76.9,8.1,22.2,19.9,54.6,0.0,0.1,6.6,22.9,76.9,0.3,55.5,44.5,1.9,3.4,7.7 ,2.3 ,5.4
France,336.4,106.6,31.7,229.8,68.3,58.6,17.4,171.1,50.9,0.0,0.0,5.0,31.7,68.3,0.0,49.6,50.4,2.2,5.6,12.7 ,2.9 ,9.9
Germany,874.4,148.6,17.0,722.2,82.6,291.4,33.3,430.3,49.2,0.5,0.1,10.5,17.0,82.6,0.4,57.1,42.9,5.4,7.6,18.4 ,7.5 ,10.9
Greece,57.1,28.4,49.7,28.7,50.3,3.3,5.8,25.4,44.5,0.0,0.0,5.4,49.7,50.3,0.0,58.0,42.0,1.1,6.0,11.5 ,2.4 ,9.1
Hungary,80.5,31.4,39.0,49.1,61.0,13.0,16.1,36.1,44.8,0.0,0.0,8.3,39.0,61.0,0.0,58.3,41.7,0.8,1.3,6.3 ,3.5 ,2.9
Iceland,9.0,2.0,22.1,7.0,77.9,5.3,58.5,1.7,19.4,0.0,0.0,24.1,22.1,77.9,0.0,58.0,42.0,11.6,3.0,19.1 ,12.6 ,6.4
Ireland,80.7,32.8,40.6,47.0,58.2,17.2,21.3,29.7,36.8,0.0,0.0,16.0,40.6,58.2,1.2,51.6,48.4,7.0,6.2,17.9 ,6.7 ,11.2
Italy,318.4,74.8,23.5,243.6,76.5,44.4,14.0,199.1,62.5,0.0,0.0,5.4,23.5,76.5,0.0,52.8,47.2,2.4,6.2,10.4 ,2.6 ,7.8
Latvia ,12.7,6.2,48.9,6.4,50.7,0.7,5.5,5.7,45.1,0.0,0.1,6.7,48.9,50.7,0.3,66.2,33.8,0.4,12.7,11.9 ,1.2 ,10.7
Liechtenstein,0.7,0.2,25.0,0.5,75.0,0.2,36.5,0.3,38.6,0.0,0.0,17.1,25.0,75.0,0.0,51.6,48.4,18.0,16.4,68.6 ,21.3 ,47.3
Lithuania,44.9,23.7,52.9,21.1,47.1,0.9,2.1,20.1,44.8,0.1,0.2,16.0,52.9,47.1,0.0,67.7,32.3,0.1,1.1,6.0 ,0.6 ,5.4
Luxembourg,25.3,1.6,6.2,23.7,93.6,15.7,62.0,8.0,31.5,0.0,0.1,39.6,6.2,93.6,0.2,54.0,46.0,38.1,9.0,49.4 ,33.8 ,15.6
Malta ,18.1,2.8,15.3,15.4,84.7,7.0,38.7,8.3,46.0,0.0,0.0,35.0,15.3,84.7,0.0,61.4,38.6,8.6,12.0,23.6 ,7.9 ,15.7
Netherlands,214.1,40.2,18.8,173.3,80.9,89.8,42.0,82.7,38.6,0.7,0.3,12.2,18.8,80.9,0.3,51.8,48.2,3.7,3.3,14.5 ,4.0 ,10.5
Norway,53.9,7.3,13.6,46.6,86.4,26.6,49.3,19.9,36.9,0.1,0.2,10.0,13.6,86.4,0.0,54.4,45.6,6.7,4.1,16.7 ,6.7 ,10.1
Poland,241.1,43.4,18.0,197.7,82.0,80.4,33.3,117.3,48.6,0.0,0.0,6.4,18.0,82.0,0.0,61.8,38.2,0.1,1.1,2.5 ,0.6 ,1.9
Portugal,50.7,38.2,75.3,12.5,24.7,3.2,6.3,9.4,18.5,0.0,0.0,4.9,75.3,24.7,0.0,50.9,49.1,1.6,5.1,11.6 ,2.8 ,8.8
Romania,194.6,149.5,76.8,45.0,23.1,9.9,5.1,35.2,18.1,0.0,0.0,10.2,76.8,23.1,0.0,58.5,41.5,0.1,0.1,1.7 ,1.0 ,0.7
Slovakia,5.7,3.7,65.1,2.0,34.9,1.5,26.6,0.5,8.3,0.0,0.0,1.1,65.1,34.9,0.0,52.3,47.7,0.7,0.4,3.9 ,2.9 ,1.0
Slovenia,23.6,4.0,16.8,19.7,83.2,2.7,11.4,17.0,71.8,0.0,0.0,11.2,16.8,83.2,0.0,61.2,38.8,1.0,7.2,14.0 ,3.0 ,11.0
Spain,528.9,72.3,13.7,456.6,86.3,110.2,20.8,346.0,65.4,0.4,0.1,11.2,13.7,86.3,0.0,51.6,48.4,3.7,7.7,15.5 ,3.5 ,12.0
Sweden,90.6,16.0,17.7,74.4,82.1,22.9,25.3,51.1,56.4,0.4,0.4,8.7,17.7,82.1,0.3,51.3,48.7,2.9,5.3,20.0 ,5.1 ,14.9
Switzerland,144.9,22.1,15.3,122.7,84.7,83.7,57.7,39.1,27.0,0.0,0.0,16.6,15.3,84.7,0.0,52.9,47.1,16.4,9.2,29.7 ,16.4 ,13.3
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32 changes: 32 additions & 0 deletions exercicios/para-casa/exercicio_imigracao_ue.csv
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,EU COUNTRIES,TOTAL IMMIGRANTS(IN THOUSANDS),IMMIage),NON_NATIONAL IMMIGRANTS FROM OTHER EU MEMBER STATES(THOUSANDS),NON_NATIONAL IMMIGRANTS FROM OTHER EU MEMBER STATES(%age),NON_NATIONAL IMMIGRANTS FROM NON-EU STATES(THOUSANDS),NON_NATIONAL IMMIGRANTS FROM NON-EU STATES(%age),STATELESS IMMIGRANTS(THOUSANDS),IMMIGRANTS WITH NATIONALITY(%age),NON-NATIONAL IMMIGRANTS (THOUSANDS),NON-NATIONAL IMMIGRANTS (%,NUMBER OF IMMIGRANTS(PER THOUSAND INHABITANTS),DISTRIBUTION OF IMMIGRANTS IN %AGE (NATIONALS),DISTRIBUTION OF IMMIGRANTS IN %AGE (NON-NATIONALS),DISTRIBUTION OF IMMIGRANTS IN %AGE(UNKNOWN),PERCENTAGE OF IMMIGRANTS BY SEX(MALE),PERCENTAGE OF IMMIGRANTS BY SEX(FEMALE),%AGE OF NON-NATIONAL IMMIGRANTS IN TOTAL POPULATION(CITIZENS OF EU MEMBER STATES),%AGE OF NON-NATIONAL IMMIGRANTS IN TOTAL POPULATION(CITIZENS OF NON-EU COUNTRIES),%AGE OF FOREIGN BORN IMMIGRANTS BY TOTAL POPULATION,%AGE OF BORN IN ANOTHER EU-MEMBER STATES,%AGE OF BORN IN NON-EU COUNTRY
0,Cyprus,24.0,16.6,20.0,83.4,8.0,33.2,12.1,50.2,0.0,0.0,26.7,16.6,83.4,0.0,46.5,53.5,10.4,8.4,22.7,10.6,12.2
1,France,336.4,31.7,229.8,68.3,58.6,17.4,171.1,50.9,0.0,0.0,5.0,31.7,68.3,0.0,49.6,50.4,2.2,5.6,12.7,2.9,9.9
2,Portugal,50.7,75.3,12.5,24.7,3.2,6.3,9.4,18.5,0.0,0.0,4.9,75.3,24.7,0.0,50.9,49.1,1.6,5.1,11.6,2.8,8.8
3,Sweden,90.6,17.7,74.4,82.1,22.9,25.3,51.1,56.4,0.4,0.4,8.7,17.7,82.1,0.3,51.3,48.7,2.9,5.3,20.0,5.1,14.9
4,Spain,528.9,13.7,456.6,86.3,110.2,20.8,346.0,65.4,0.4,0.1,11.2,13.7,86.3,0.0,51.6,48.4,3.7,7.7,15.5,3.5,12.0
5,Liechtenstein,0.7,25.0,0.5,75.0,0.2,36.5,0.3,38.6,0.0,0.0,17.1,25.0,75.0,0.0,51.6,48.4,18.0,16.4,68.6,21.3,47.3
6,Ireland,80.7,40.6,47.0,58.2,17.2,21.3,29.7,36.8,0.0,0.0,16.0,40.6,58.2,1.2,51.6,48.4,7.0,6.2,17.9,6.7,11.2
7,Netherlands,214.1,18.8,173.3,80.9,89.8,42.0,82.7,38.6,0.7,0.3,12.2,18.8,80.9,0.3,51.8,48.2,3.7,3.3,14.5,4.0,10.5
8,Slovakia,5.7,65.1,2.0,34.9,1.5,26.6,0.5,8.3,0.0,0.0,1.1,65.1,34.9,0.0,52.3,47.7,0.7,0.4,3.9,2.9,1.0
9,Italy,318.4,23.5,243.6,76.5,44.4,14.0,199.1,62.5,0.0,0.0,5.4,23.5,76.5,0.0,52.8,47.2,2.4,6.2,10.4,2.6,7.8
10,Switzerland,144.9,15.3,122.7,84.7,83.7,57.7,39.1,27.0,0.0,0.0,16.6,15.3,84.7,0.0,52.9,47.1,16.4,9.2,29.7,16.4,13.3
11,Luxembourg,25.3,6.2,23.7,93.6,15.7,62.0,8.0,31.5,0.0,0.1,39.6,6.2,93.6,0.2,54.0,46.0,38.1,9.0,49.4,33.8,15.6
12,Denmark,63.5,25.5,47.3,74.4,29.6,46.6,17.6,27.7,0.1,0.1,10.8,25.5,74.4,0.0,54.2,45.8,4.0,5.4,12.7,4.2,8.5
13,Norway,53.9,13.6,46.6,86.4,26.6,49.3,19.9,36.9,0.1,0.2,10.0,13.6,86.4,0.0,54.4,45.6,6.7,4.1,16.7,6.7,10.1
14,Belgium,139.7,12.4,121.7,87.1,63.1,45.2,58.5,41.9,0.0,0.0,12.1,12.4,87.1,0.5,54.9,45.1,8.2,4.6,18.2,7.9,10.3
15,Bulgaria,39.5,56.0,17.3,43.9,5.5,13.8,11.8,30.0,0.0,0.1,5.7,56.0,43.9,0.1,55.1,44.9,0.2,1.6,3.2,1.0,2.2
16,Finland,36.4,22.9,28.0,76.9,8.1,22.2,19.9,54.6,0.0,0.1,6.6,22.9,76.9,0.3,55.5,44.5,1.9,3.4,7.7,2.3,5.4
17,Germany,874.4,17.0,722.2,82.6,291.4,33.3,430.3,49.2,0.5,0.1,10.5,17.0,82.6,0.4,57.1,42.9,5.4,7.6,18.4,7.5,10.9
18,Austria,118.5,8.1,108.8,91.8,64.4,54.4,44.1,37.2,0.3,0.2,13.2,8.1,91.8,0.1,57.5,42.5,9.2,8.3,20.4,9.3,11.1
19,Greece,57.1,49.7,28.7,50.3,3.3,5.8,25.4,44.5,0.0,0.0,5.4,49.7,50.3,0.0,58.0,42.0,1.1,6.0,11.5,2.4,9.1
20,Iceland,9.0,22.1,7.0,77.9,5.3,58.5,1.7,19.4,0.0,0.0,24.1,22.1,77.9,0.0,58.0,42.0,11.6,3.0,19.1,12.6,6.4
21,Hungary,80.5,39.0,49.1,61.0,13.0,16.1,36.1,44.8,0.0,0.0,8.3,39.0,61.0,0.0,58.3,41.7,0.8,1.3,6.3,3.5,2.9
22,Romania,194.6,76.8,45.0,23.1,9.9,5.1,35.2,18.1,0.0,0.0,10.2,76.8,23.1,0.0,58.5,41.5,0.1,0.1,1.7,1.0,0.7
23,Estonia,19.5,36.1,12.3,63.1,4.1,20.9,8.2,42.2,0.0,0.0,14.7,36.1,63.1,0.8,60.3,39.7,1.6,13.6,15.1,1.9,13.1
24,Slovenia,23.6,16.8,19.7,83.2,2.7,11.4,17.0,71.8,0.0,0.0,11.2,16.8,83.2,0.0,61.2,38.8,1.0,7.2,14.0,3.0,11.0
25,Czechia,69.4,4.2,66.4,95.8,14.5,21.0,51.9,74.8,0.0,0.0,6.6,4.2,95.8,0.0,61.4,38.6,1.6,3.5,4.3,1.3,3.0
26,Malta ,18.1,15.3,15.4,84.7,7.0,38.7,8.3,46.0,0.0,0.0,35.0,15.3,84.7,0.0,61.4,38.6,8.6,12.0,23.6,7.9,15.7
27,Poland,241.1,18.0,197.7,82.0,80.4,33.3,117.3,48.6,0.0,0.0,6.4,18.0,82.0,0.0,61.8,38.2,0.1,1.1,2.5,0.6,1.9
28,Latvia ,12.7,48.9,6.4,50.7,0.7,5.5,5.7,45.1,0.0,0.1,6.7,48.9,50.7,0.3,66.2,33.8,0.4,12.7,11.9,1.2,10.7
29,Lithuania,44.9,52.9,21.1,47.1,0.9,2.1,20.1,44.8,0.1,0.2,16.0,52.9,47.1,0.0,67.7,32.3,0.1,1.1,6.0,0.6,5.4
30,Croatia,35.9,29.6,25.3,70.4,4.8,13.2,20.5,57.2,0.0,0.0,9.1,29.6,70.4,0.0,72.7,27.3,0.3,0.6,12.2,1.7,10.6
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