-
--Desenvolvido com :purple_heart: -
- - diff --git a/Readme.md b/Readme.md new file mode 100644 index 0000000..3aa4e73 --- /dev/null +++ b/Readme.md @@ -0,0 +1,84 @@ + +
+Nos últimos anos, especialmente durante e após a pandemia, o acesso à internet cresceu significativamente, assim como as oportunidades de emprego na área de tecnologia. Diante desse cenário, realizamos uma análise para evidenciar dados alarmantes sobre o impacto das vagas de emprego online no mundo, considerando que uma parte considerável da população ainda não possui acesso a computadores e internet em casa.
+ +Os dados utilizados para alimentar nossa aplicação (atualmente em execução apenas localmente) foram extraídos de fontes como o IBGE e Kaggle, com base em informações coletadas nos anos de 2022 e 2023.
+ +Esperamos que essa análise contribua para uma melhor compreensão do acesso à internet e suas oportunidades, sobretudo para empresas que buscam pessoas na área da tecnologia, ajudando a classe minoritária, obtendo um olhar mais humanizado sobre seu desempenho.
+ +Para realizar essa análise, utilizamos as seguintes ferramentas:
+ +
Pandas: Biblioteca Python amplamente utilizada para análise de dados, que nos permite ler e manipular arquivos XLSX (Excel), além de transformar e limpar os dados conforme necessário.
Google Colab: Plataforma para criação de notebooks, facilitando a análise colaborativa dos dados.
Tableau: Utilizado para criar dashboards e gráficos apresentados na análise.O Pandas é uma biblioteca do Python muito utilizada para análise de dados. Com ele podemos ler nossos dados, que é um arquivo XLSX (Excel), e começar a manipular, transformar e limpar, caso necessário.
+
+ Em nosso arquivo temos as seguintes colunas: brasil e Grande Região, Existência de microcomputador ou tablet no domicílio, Ano x Situação do domicílio, etc
+
+
+Estrutura dos Dados
+
+Em nosso arquivo, temos as seguintes colunas:
+
| [
+| :---: | :---: |
+
diff --git a/gapminder_internet.csv b/gapminder_internet.csv
new file mode 100644
index 0000000..6eb703d
--- /dev/null
+++ b/gapminder_internet.csv
@@ -0,0 +1,214 @@
+country,incomeperperson,internetuserate,urbanrate
+Afghanistan,,3.654121623,24.04
+Albania,1914.996551,44.98994696,46.72
+Algeria,2231.993335,12.50007331,65.22
+Andorra,21943.3399,81,88.92
+Angola,1381.004268,9.999953883,56.7
+Antigua and Barbuda,11894.46407,80.64545455,30.46
+Argentina,10749.41924,36.00033495,92
+Armenia,1326.741757,44.00102458,63.86
+Aruba,,41.80088889,46.78
+Australia,25249.98606,75.8956538,88.74
+Austria,26692.98411,72.73157554,67.16
+Azerbaijan,2344.896916,46.67970157,51.92
+Bahamas,19630.54055,42.98458017,83.7
+Bahrain,12505.21254,54.99280903,88.52
+Bangladesh,558.0628766,3.70000326,27.14
+Barbados,9243.587053,70.02859927,39.84
+Belarus,2737.670379,32.05214391,73.46
+Belgium,24496.04826,73.73393447,97.36
+Belize,3545.652174,12.64573333,51.7
+Benin,377.0396995,3.129961803,41.2
+Bermuda,62682.14701,84.65451409,100
+Bhutan,1324.194906,13.59887603,34.48
+Bolivia,1232.794137,20.00171014,65.58
+Bosnia and Herzegovina,2183.344867,52.00206064,47.44
+Botswana,4189.436587,5.999835575,59.58
+Brazil,4699.411262,40.650098,85.58
+Brunei,17092.46,49.98997494,74.82
+Bulgaria,2549.558474,45.98658991,71.1
+Burkina Faso,276.200413,1.4000607,19.56
+Burundi,115.3059959,2.100212706,10.4
+Cambodia,557.9475126,1.259933609,21.56
+Cameroon,713.6393027,3.999977346,56.76
+Canada,25575.35262,81.33839269,80.4
+Cape Verde,1959.844472,29.99993952,59.62
+Cayman Islands,,66,100
+Central African Rep.,239.5187494,2.300026653,38.58
+Chad,275.8842865,1.700031496,26.68
+Chile,6334.105194,45,88.44
+China,2425.471293,34.37778952,43.1
+Colombia,3233.42378,36.49987464,74.5
+Comoros,336.3687495,5.098265306,28.08
+"Congo, Dem. Rep.",103.7758572,0.720008677,33.96
+"Congo, Rep.",1253.292015,4.999875093,61.34
+Cook Islands,,,
+Costa Rica,5188.900935,36.49911472,63.26
+Cote d'Ivoire,591.0679443,2.599973655,48.78
+Croatia,6338.494668,60.11970702,57.28
+Cuba,4495.046262,15.89997034,75.66
+Cyprus,15313.85935,53.02474483,69.9
+Czech Rep.,7381.312751,68.63813347,73.5
+Denmark,30532.27704,88.77025387,86.68
+Djibouti,895.3183396,6.49792351,87.3
+Dominica,6147.77961,47.28043603,73.92
+Dominican Rep.,4049.169629,39.53127426,69.02
+Ecuador,1728.020976,28.99947674,65.58
+Egypt,1975.551906,26.74002538,42.72
+El Salvador,2557.433638,15.89998203,60.7
+Equatorial Guinea,8654.536845,6.003437143,39.38
+Eritrea,131.796207,5.399666997,20.72
+Estonia,6238.537506,74.1630403,69.46
+Ethiopia,220.8912479,0.74999585,17
+Faeroe Islands,,75.2,41.42
+Fiji,2230.676374,14.83073588,52.36
+Finland,27110.73159,86.89884451,63.3
+France,22878.46657,77.49861935,77.36
+French Polynesia,,48.95732841,51.64
+Gabon,4180.765821,7.232224246,85.04
+Gambia,354.5997263,9.196775477,56.42
+Georgia,1258.762596,26.29725148,52.74
+Germany,25306.18719,82.52689791,73.64
+Ghana,358.9795398,9.549930701,50.02
+Gibraltar,,65,
+Greece,13577.87989,44.57007444,61
+Greenland,20751.89342,63.84915272,83.52
+Grenada,5330.401612,33.61668288,30.84
+Guadeloupe,,,
+Guam,,,93.16
+Guatemala,1860.753895,10.49994819,48.58
+Guinea,411.5014473,0.999958926,34.44
+Guinea-Bissau,161.3171371,2.450362244,29.84
+Guyana,1200.652075,29.87992146,28.38
+Haiti,371.4241975,8.370206884,46.84
+Honduras,1392.411829,11.09076463,47.88
+"Hong Kong, China",35536.07247,71.84912394,100
+Hungary,5634.003948,65.16325092,67.5
+Iceland,33945.31442,95.63811321,92.26
+India,786.7000981,7.499995878,29.54
+Indonesia,1143.831514,9.900038672,51.46
+Iran,2161.54651,13.00011072,68.46
+Iraq,736.2680538,2.471948347,66.6
+Ireland,27595.09135,69.77039441,61.34
+Israel,22275.75166,65.38778594,91.66
+Italy,18982.26929,53.7402166,68.08
+Jamaica,3665.348369,26.47722324,53.3
+Japan,39309.47886,77.63853515,66.48
+Jordan,2534.00038,38.88120159,78.42
+Kazakhstan,2481.718918,33.38212816,57.94
+Kenya,468.6960436,25.8997967,21.6
+Kiribati,760.262365,8.95914,43.84
+"Korea, Dem. Rep.",,,62.68
+"Korea, Rep.",16372.49978,82.515928,81.46
+Kuwait,,38.26023355,98.36
+Kyrgyzstan,372.728414,19.58231645,36.28
+Laos,554.8798401,6.999880342,30.88
+Latvia,5011.219456,71.51472354,68.12
+Lebanon,6746.612632,31.00437828,86.96
+Lesotho,495.7342469,3.860565398,25.46
+Liberia,155.0332312,7.000213821,60.14
+Libya,7885.468037,14.00024673,77.54
+Liechtenstein,81647.10003,80,14.32
+Lithuania,5332.238591,62.81190001,66.96
+Luxembourg,52301.58718,90.07952663,82.44
+"Macao, China",33923.31387,56.76408235,100
+"Macedonia, FYR",2221.185664,51.91418432,66.9
+Madagascar,242.6775342,1.699985145,29.52
+Malawi,184.1417966,2.259975885,18.8
+Malaysia,5184.709328,56.3000337,70.36
+Maldives,4038.857818,28.28970095,37.86
+Mali,269.8928811,2.699966448,32.18
+Malta,11066.78414,63.07799279,94.26
+Marshall Islands,2437.282445,,71.08
+Martinique,,,
+Mauritania,609.1312059,2.999803179,41
+Mauritius,5182.143721,28.7318835,42.48
+Mexico,6105.280743,31.05001287,77.2
+"Micronesia, Fed. Sts.",2146.358593,20.01153153,22.54
+Moldova,595.8745345,40.1222347,41.76
+Monaco,105147.4377,,100
+Mongolia,772.9333448,12.90000468,57.18
+Montenegro,2222.335052,51.95803797,60.18
+Morocco,1844.351028,49.00063184,56.02
+Mozambique,389.7636343,4.170136385,36.84
+Myanmar,,,32.58
+Namibia,2667.24671,6.500822821,36.84
+Nauru,,,
+Nepal,268.2594495,7.930096349,17.24
+Netherlands,26551.84424,90.70355509,81.82
+Netherlands Antilles,,,92.68
+New Caledonia,,,64.78
+New Zealand,14778.16393,83.00258425,86.56
+Nicaragua,948.355952,9.998554154,56.74
+Niger,180.083376,0.829997485,16.54
+Nigeria,544.5994767,28.43003266,48.36
+Niue,,,
+Norway,39972.35277,93.27750793,77.48
+Oman,11191.81101,61.98741286,71.62
+Pakistan,668.547943,16.78003702,36.16
+Palau,6243.571318,,80.46
+Panama,5900.616944,42.74781206,73.2
+Papua New Guinea,744.2394132,1.280049647,12.54
+Paraguay,1621.177078,19.80168119,60.3
+Peru,3180.430612,34.30060399,71.4
+Philippines,1383.401869,24.99994585,64.92
+Poland,6575.745044,62.47123013,61.32
+Portugal,11744.83417,51.2804784,59.46
+Puerto Rico,15822.11214,42.69233477,98.32
+Qatar,33931.83208,81.59039727,95.64
+Reunion,,,
+Romania,2636.7878,40.02009488,54.24
+Russia,2923.144355,43.36649772,72.84
+Rwanda,338.2663912,13.00000612,18.34
+Saint Kitts and Nevis,9175.796015,76.58753846,32.32
+Saint Lucia,5248.582321,40.06137931,27.84
+Saint Vincent and the Grenadines,4885.046701,,47.04
+Samoa,1784.071284,6.965038043,23
+San Marino,31993.20069,,94.22
+Sao Tome and Principe,,18.79511364,60.56
+Saudi Arabia,9425.32587,41.00012846,82.42
+Senegal,561.7085848,15.99964999,42.38
+Serbia,1194.711433,43.05506706,52.04
+Serbia and Montenegro,,,
+Seychelles,8614.120219,40.77285057,54.34
+Sierra Leone,268.3317903,,37.76
+Singapore,32535.83251,71.13170731,100
+Slovak Republic,8445.526689,79.88977734,56.56
+Slovenia,12729.4544,69.33997072,48.6
+Solomon Islands,1144.102193,5.001375465,17.96
+Somalia,,,36.52
+South Africa,3745.649852,12.33489326,60.74
+Spain,15461.75837,65.80855367,77.12
+Sri Lanka,1295.742686,11.99997066,15.1
+Sudan,523.9501515,,43.44
+Suriname,2668.020519,31.5680976,74.92
+Swaziland,1810.230533,9.007735909,24.94
+Sweden,32292.48298,90.01619002,84.54
+Switzerland,37662.75125,82.16665988,73.48
+Syria,1525.780116,20.66315568,54.22
+Taiwan,,,
+Tajikistan,279.1804526,11.54939051,26.46
+Tanzania,456.3857117,11.00005544,25.52
+Thailand,2712.517199,21.20007177,33.32
+Timor-Leste,369.5729537,0.210066326,27.3
+Togo,285.2244493,5.379819715,42
+Tonga,2025.282665,12.00669231,24.78
+Trinidad and Tobago,10480.8172,48.51681767,13.22
+Tunisia,3164.927693,36.56255296,66.5
+Turkey,5348.597192,39.82017789,68.68
+Turkmenistan,2062.125152,2.199997818,48.62
+Tuvalu,1714.94289,25,
+Uganda,377.4211133,12.50025543,12.98
+Ukraine,1036.830725,44.58535469,67.98
+United Arab Emirates,21087.39412,77.99678115,77.88
+United Kingdom,28033.48928,84.73170475,89.94
+United States,37491.17952,74.247572,81.7
+Uruguay,9106.327234,47.86746863,92.3
+Uzbekistan,952.8272608,19.44502055,36.82
+Vanuatu,1543.956457,7.988366667,24.76
+Venezuela,5528.363114,35.85043696,93.32
+Vietnam,722.8075588,27.85182156,27.84
+West Bank and Gaza,,36.42277179,71.9
+"Yemen, Rep.",610.3573673,12.34975046,30.64
+Zambia,432.226337,10.12498646,35.42
+Zimbabwe,320.7718899,11.50041532,37.34
diff --git a/image.png b/image.png
new file mode 100644
index 0000000..d5c0c59
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diff --git a/material/analise-exploratoria/analise.ipynb b/material/analise-exploratoria/analise.ipynb
deleted file mode 100644
index 1cce302..0000000
--- a/material/analise-exploratoria/analise.ipynb
+++ /dev/null
@@ -1,22 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": [
- "#Utilizar as bibliotecas de Python aprendidas em aula (pandas, matplotlib, seaborn, etc);\n",
- "#Trazer um notebook estruturado e organizado com o uso de Markdown. O uso de textos no notebook é altamente incentivado);\n",
- "#Mínimo de 3 visualizações que ajudem a sumarizar os resultados da sua análise."
- ]
- }
- ],
- "metadata": {
- "language_info": {
- "name": "python"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 2
-}
diff --git a/material/datasets/arquivo.csv b/material/datasets/arquivo.csv
deleted file mode 100644
index e69de29..0000000
diff --git a/material/nome-projeto.md b/material/nome-projeto.md
deleted file mode 100644
index 55c478b..0000000
--- a/material/nome-projeto.md
+++ /dev/null
@@ -1,12 +0,0 @@
-## Contexto
-Esse projeto consiste na análise de xxxxxx. O objetivo desse projeto é xxxxxxxxx.
-Para desenvolver esse projeto, desenvolvemos uma análise exploratória de dados xxxxxxx e utilizamos o Tableau para gerar a visualização das nossas análises.
-
-### Objetivos gerais e específicos do projeto
-
-### Bases escolhidas
-
-- Base 1 (fonte)
-- Base 2 (fonte)
-
-## Ferramentas utilizadas
\ No newline at end of file
diff --git a/s17_Projeto_Final_Tamy_Vero.ipynb b/s17_Projeto_Final_Tamy_Vero.ipynb
new file mode 100644
index 0000000..4364176
--- /dev/null
+++ b/s17_Projeto_Final_Tamy_Vero.ipynb
@@ -0,0 +1,3089 @@
+{
+ "nbformat": 4,
+ "nbformat_minor": 0,
+ "metadata": {
+ "colab": {
+ "provenance": [],
+ "collapsed_sections": [
+ "NG6IEh8AwcX0",
+ "bdETNLverP0_"
+ ],
+ "include_colab_link": true
+ },
+ "kernelspec": {
+ "name": "python3",
+ "display_name": "Python 3"
+ },
+ "language_info": {
+ "name": "python"
+ }
+ },
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {
+ "id": "view-in-github",
+ "colab_type": "text"
+ },
+ "source": [
+ "![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "# **PROJETO FINAL**\n",
+ "\n",
+ "# **TEMA: \"Como acesso populacional a internet se relaciona as oportunidades disponiveis no mercado de tecnologia\"**\n",
+ "\n",
+ "\n",
+ "\n",
+ "## BASES DE DADOS\n",
+ "\n",
+ "1. https://www.kaggle.com/datasets/sansuthi/gapminder-internet (acesso a internet no mundo em 2022) ✅\n",
+ "\n",
+ "2. https://sidra.ibge.gov.br/tabela/7302 (base de dados do IBGE sobre Domicílios e Moradores, por situação do domicílio e existência de televisão no domicílio 2022 e 2023 - foi baixado o csv, no drive: https://docs.google.com/spreadsheets/d/1n9T3-S5f5--G37962E7JQ-4doz7nvwV4/edit?usp=sharing&ouid=104113402736912319813&rtpof=true&sd=true) OBS: LINK ONDE FOI BUSCADO O CSV https://www.ibge.gov.br/estatisticas/sociais/trabalho/17270-pnad-continua.html?edicao=38243&t=resultados\n",
+ "\n",
+ "\n",
+ "3. https://www.kaggle.com/datasets/datahackers/state-of-data-brazil-2023/code (essa aqui usar para analisar o mercado de trabalho e o perfil das pessoas que conseguiram vagas em tech em 2023, verificar de qual estado e classe social elas são, se tem mais homens ou mulheres e a faixa de idade) ✅\n"
+ ],
+ "metadata": {
+ "id": "EFraJgVQX9Zq"
+ }
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "id": "w4h9zFI7X7kJ"
+ },
+ "outputs": [],
+ "source": [
+ "import pandas as pd\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "\n",
+ "Para entender melhor os dados que temos, a distribuição deles e seus tipos, vamos verificar suas colunas e tamanho. Depois vamos limpar o que achamos necessário, como retirar linhas duplicadas ou deletar colunas que não nos ajudariam durante nossa análise."
+ ],
+ "metadata": {
+ "id": "wGKD3A3iJ81s"
+ }
+ },
+ {
+ "cell_type": "markdown",
+ "source": [
+ "## IBGE"
+ ],
+ "metadata": {
+ "id": "NG6IEh8AwcX0"
+ }
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# A base de dados do IBGE \"tabela7302\" estava em xlsx, para utilizar ele, subimos o arquivo no colab e convertemos em csv. Esta tabela é sobre a quantidade de notebooks e tablets no Brasil por domicilio.\n",
+ "\n",
+ "# Carregue o arquivo XLSX\n",
+ "df_dispositivo_per_capto = pd.read_excel('tabela7302_nv.xlsx')\n",
+ "\n",
+ "# Salve como CSV\n",
+ "df_dispositivo_per_capto.to_csv('tabela7302_nv.csv', index=False)\n"
+ ],
+ "metadata": {
+ "id": "i1hd4b8jwruR"
+ },
+ "execution_count": null,
+ "outputs": []
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Base de Dados do IBGE - Dispositivo por Região do Brasil\n",
+ "df_dispositivo_per_capto = pd.read_csv('tabela7302_nv.csv')\n",
+ "\n",
+ "# Visualização do numero de linhas e colunas do dataframe\n",
+ "num_linhas = df_dispositivo_per_capto.shape[0]\n",
+ "num_colunas = df_dispositivo_per_capto.shape[1]\n",
+ "colunas = df_dispositivo_per_capto.columns.values\n",
+ "\n",
+ "print(f\"Número de linhas: {num_linhas} \\n\"\n",
+ " f\"Número de colunas: {num_colunas} \\n\"\n",
+ " f\"Colunas: {colunas} \\n\")"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/"
+ },
+ "id": "eSvsF3F4J3z2",
+ "outputId": "bc5ae328-eaf1-4aef-e937-ca99023b7ba6",
+ "collapsed": true
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "stream",
+ "name": "stdout",
+ "text": [
+ "Número de linhas: 37 \n",
+ "Número de colunas: 8 \n",
+ "Colunas: ['Unnamed: 0' 'Unnamed: 1' '2022' 'Unnamed: 3' 'Unnamed: 4' '2023'\n",
+ " 'Unnamed: 6' 'Unnamed: 7'] \n",
+ "\n"
+ ]
+ }
+ ]
+ },
+ {
+ "cell_type": "code",
+ "source": [
+ "# Visualização das dez primeiras linhas do dataframe IBGE\n",
+ "df_dispositivo_per_capto.head(10)\n"
+ ],
+ "metadata": {
+ "colab": {
+ "base_uri": "https://localhost:8080/",
+ "height": 415
+ },
+ "id": "RZLyLxVsKIi2",
+ "outputId": "3ea8c925-b9e6-4bec-d716-b454e021c4bc",
+ "collapsed": true
+ },
+ "execution_count": null,
+ "outputs": [
+ {
+ "output_type": "execute_result",
+ "data": {
+ "text/plain": [
+ " Unnamed: 0 Unnamed: 1 \\\n",
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| 1 | \n", + "Albania | \n", + "1914.996551 | \n", + "44.989947 | \n", + "46.72 | \n", + "
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| 3 | \n", + "Andorra | \n", + "21943.339900 | \n", + "81.000000 | \n", + "88.92 | \n", + "
| 4 | \n", + "Angola | \n", + "1381.004268 | \n", + "9.999954 | \n", + "56.70 | \n", + "
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| 6 | \n", + "Argentina | \n", + "10749.419240 | \n", + "36.000335 | \n", + "92.00 | \n", + "
| 7 | \n", + "Armenia | \n", + "1326.741757 | \n", + "44.001025 | \n", + "63.86 | \n", + "
| 8 | \n", + "Aruba | \n", + "NaN | \n", + "41.800889 | \n", + "46.78 | \n", + "
| 9 | \n", + "Australia | \n", + "25249.986060 | \n", + "75.895654 | \n", + "88.74 | \n", + "
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| 22 | \n", + "Sudeste | \n", + "Havia tablet | \n", + "4358 | \n", + "4270 | \n", + "88 | \n", + "4321 | \n", + "4255 | \n", + "67 | \n", + "
| 23 | \n", + "Sudeste | \n", + "Havia microcomputador e tablet | \n", + "3653 | \n", + "3601 | \n", + "52 | \n", + "3646 | \n", + "3611 | \n", + "35 | \n", + "
| 24 | \n", + "Sudeste | \n", + "Não havia microcomputador nem tablet | \n", + "16392 | \n", + "14814 | \n", + "1579 | \n", + "17246 | \n", + "15701 | \n", + "1544 | \n", + "
| 25 | \n", + "Sul | \n", + "Total | \n", + "11271 | \n", + "9836 | \n", + "1435 | \n", + "11584 | \n", + "10241 | \n", + "1343 | \n", + "
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| 1 | \n", + "Albania | \n", + "1914.996551 | \n", + "44.989947 | \n", + "46.72 | \n", + "
| 2 | \n", + "Algeria | \n", + "2231.993335 | \n", + "12.500073 | \n", + "65.22 | \n", + "
| 3 | \n", + "Andorra | \n", + "21943.339900 | \n", + "81.000000 | \n", + "88.92 | \n", + "
| 4 | \n", + "Angola | \n", + "1381.004268 | \n", + "9.999954 | \n", + "56.70 | \n", + "
| 5 | \n", + "Antigua and Barbuda | \n", + "11894.464070 | \n", + "80.645455 | \n", + "30.46 | \n", + "
| ... | \n", + "... | \n", + "... | \n", + "... | \n", + "... | \n", + "
| 207 | \n", + "Venezuela | \n", + "5528.363114 | \n", + "35.850437 | \n", + "93.32 | \n", + "
| 208 | \n", + "Vietnam | \n", + "722.807559 | \n", + "27.851822 | \n", + "27.84 | \n", + "
| 210 | \n", + "Yemen, Rep. | \n", + "610.357367 | \n", + "12.349750 | \n", + "30.64 | \n", + "
| 211 | \n", + "Zambia | \n", + "432.226337 | \n", + "10.124986 | \n", + "35.42 | \n", + "
| 212 | \n", + "Zimbabwe | \n", + "320.771890 | \n", + "11.500415 | \n", + "37.34 | \n", + "
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