Data Science Degree - 815 - Aula 2 - Pandas e Gráficos de Barra & Boxplot.ipynb

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    "# Data assessment, Bar & Boxplot Graphs using Pandas\n",
    "\n",
    "**Degree**: Data Science | **Class**: 815 | **Institution**: Let's Code | **Module**: V - Programming Techniques II \n",
    "\n",
    "#### 👨‍🏫 Professor\n",
    "[Gabriela Uhrigshardt](https://github.com/gabrielagu)\n",
    "\n",
    "#### Student:\n",
    "👩‍🎓: [Ana Gabriela de Castro Almeida](https://github.com/almeidacastrogabriela)"
   ]
  },
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    "### Aula 2: Exercícios\n",
    "\n",
    "Para estes exercícios, iremos utilizar duas bases:\n",
    "\n",
    "1) base fictícia de funcionários criada por cientistas de dados da IBM.\n",
    "\n",
    "**Variáveis:**\n",
    "- **Age:** Idade\n",
    "- **Attrition:** Se saiu da empresa (Sim ou não)\n",
    "- **BusinessTravel:** Frequência de viagens a trabalho\n",
    "- **DailyRate:** Custo do funcionário por dia\n",
    "- **Department:** Departamento\n",
    "- **DistanceFromHome:** Distância do trabalho até a casa do funcionário\n",
    "- **Education:** Nível de educação:\n",
    "    - 1: 'Below College'\n",
    "    - 2: 'College'\n",
    "    - 3: 'Bachelor'\n",
    "    - 4: 'Master'\n",
    "    - 5: 'Doctor'\n",
    "- **EducationField:** Área da educação\n",
    "- **EmployeeNumber:** ID do funcionário\n",
    "- **EnvironmentSatisfaction:** Satisfação com o ambiente de trabalho:\n",
    "    - 1: 'Low'\n",
    "    - 2: 'Medium'\n",
    "    - 3: 'High'\n",
    "    - 4: 'Very High'\n",
    "- **Gender:** Gênero\n",
    "- **HourlyRate:** Valor/hora\n",
    "- **JobInvolvement:** Nível de envolvimento com o trabalho (psicologica e emocionalmente):\n",
    "    - 1: 'Low'\n",
    "    - 2: 'Medium'\n",
    "    - 3: 'High'\n",
    "    - 4: 'Very High'\n",
    "- **JobLevel:** Nível do funcionário\n",
    "- **JobRole:** Cargo\n",
    "- **JobSatisfaction:** Nível de satisfação com o trabalho:\n",
    "    - 1: 'Low'\n",
    "    - 2: 'Medium'\n",
    "    - 3: 'High'\n",
    "    - 4: 'Very High'\n",
    "- **MaritalStatus:** Estado Civil\n",
    "- **NumCompaniesWorked:** Número de empresas que já trabalhou\n",
    "- **PercentSalaryHike:** Aumento salarial (%)\n",
    "- **PerformanceRating:** Nível de performance:\n",
    "    - 1: 'Low'\n",
    "    - 2: 'Good'\n",
    "    - 3: 'Excellent'\n",
    "    - 4: 'Outstanding'\n",
    "- **RelationshipSatisfaction:** Satisfação de relacionamento:\n",
    "    - 1: 'Low'\n",
    "    - 2: 'Medium'\n",
    "    - 3: 'High'\n",
    "    - 4: 'Very High'\n",
    "- **TotalWorkingYears:** Número de anos trabalhados\n",
    "- **TrainingTimesLastYear:** Número de treinamentos no ano anterior\n",
    "- **WorkLifeBalance:** Equilíbrio da vida profissional\n",
    "    - 1: 'Bad'\n",
    "    - 2: 'Good'\n",
    "    - 3: 'Better'\n",
    "    - 4: 'Best'\n",
    "- **YearsAtCompany:** Anos na empresa\n",
    "- **YearsInCurrentRole:** Anos na posição atual\n",
    "- **YearsSinceLastPromotion:** Anos desde a última promoção\n",
    "- **YearsWithCurrManager:** Anos com o mesmo gestor\n",
    "\n",
    "2) Base de dados de casos e óbitos de covid no estado de SP.\n",
    "\n",
    "- **nome_munic:** Nome do município\n",
    "- **codigo_ibge:** Código do município no IBGE (7 dígitos)\n",
    "- **casos_novos:** Casos novos registrados na data\n",
    "- **obitos_novos:** Óbitos novos registrados na data\n",
    "- **nome_drs:** Nome do Dpto. Regional de Saúde\n",
    "- **cod_drs:** Código do Dpto. Regional de Saúde\n",
    "- **pop:** População Estimada (fonte: SEADE)\n",
    "- **pop60:** População acima de 60 anos (fonte: SEADE)\n",
    "- **ano_mes:** Ano e mês dos dados"
   ]
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   "source": [
    "# 🇬🇧 **Note**:\n",
    "\n",
    "As the file has questions and information in Brazilian Portguese, code was developed and comments were added to make it clear for non-portuguese speakers.\n",
    "\n",
    "The core of this exercise consisted in working with two different databases that had the variables listed above."
   ]
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    "#Importando as bibliotecas necessárias\n",
    "\n",
    "#Importing necessary libraries\n",
    "\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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   "source": [
    "# Leitura da base de dados que será analisada e transformação em dataframe\n",
    "\n",
    "# Reading the file that contains the data to be assessed and transforming it into a dataframe\n",
    "\n",
    "df2 = pd.read_csv(\"dados_covid_sp_20.csv\")"
   ]
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       "        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>nome_munic</th>\n",
       "      <th>codigo_ibge</th>\n",
       "      <th>nome_drs</th>\n",
       "      <th>cod_drs</th>\n",
       "      <th>pop</th>\n",
       "      <th>pop_60</th>\n",
       "      <th>ano_mes</th>\n",
       "      <th>casos</th>\n",
       "      <th>casos_novos</th>\n",
       "      <th>obitos</th>\n",
       "      <th>obitos_novos</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Adamantina</td>\n",
       "      <td>3500105</td>\n",
       "      <td>Marília</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33894.0</td>\n",
       "      <td>7398.0</td>\n",
       "      <td>2020-02</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Adamantina</td>\n",
       "      <td>3500105</td>\n",
       "      <td>Marília</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33894.0</td>\n",
       "      <td>7398.0</td>\n",
       "      <td>2020-03</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Adamantina</td>\n",
       "      <td>3500105</td>\n",
       "      <td>Marília</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33894.0</td>\n",
       "      <td>7398.0</td>\n",
       "      <td>2020-04</td>\n",
       "      <td>28</td>\n",
       "      <td>5</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Adamantina</td>\n",
       "      <td>3500105</td>\n",
       "      <td>Marília</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33894.0</td>\n",
       "      <td>7398.0</td>\n",
       "      <td>2020-05</td>\n",
       "      <td>717</td>\n",
       "      <td>34</td>\n",
       "      <td>27</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Adamantina</td>\n",
       "      <td>3500105</td>\n",
       "      <td>Marília</td>\n",
       "      <td>5.0</td>\n",
       "      <td>33894.0</td>\n",
       "      <td>7398.0</td>\n",
       "      <td>2020-06</td>\n",
       "      <td>163</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
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      "text/plain": [
       "   nome_munic  codigo_ibge nome_drs  cod_drs      pop  pop_60  ano_mes  casos  \\\n",
       "0  Adamantina      3500105  Marília      5.0  33894.0  7398.0  2020-02      0   \n",
       "1  Adamantina      3500105  Marília      5.0  33894.0  7398.0  2020-03      0   \n",
       "2  Adamantina      3500105  Marília      5.0  33894.0  7398.0  2020-04     28   \n",
       "3  Adamantina      3500105  Marília      5.0  33894.0  7398.0  2020-05    717   \n",
       "4  Adamantina      3500105  Marília      5.0  33894.0  7398.0  2020-06    163   \n",
       "\n",
       "   casos_novos  obitos  obitos_novos  \n",
       "0            0       0             0  \n",
       "1            0       0             0  \n",
       "2            5       0             0  \n",
       "3           34      27             2  \n",
       "4            3      10             1  "
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   "source": [
    "# Verificando o nome das colunas e demais visualização do dataframe\n",
    "\n",
    "# Checking dataframe's columns and other visual attributes\n",
    "\n",
    "df2.head()"
   ]
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    "## Exercício 1:\n",
    "Utilizando a base de dados de covid:\n",
    "\n",
    "**a)** Faça um gráfico de barras mostrando o total de óbitos (considerando todos os meses) para todos os DRS, excluindo a Grande São Paulo. Coloque título no gráfico e nos eixos, defina um tamanho de imagem bom e intercale as barras com as cores 'red' e 'green'."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "db992733",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Filtrando o dataframe para pegar os DRS que são diferentes de Grande São Paulo\n",
    "\n",
    "# Filtering the dataframe to collect all DRS that are not named Great São Paulo\n",
    "\n",
    "df2_without_gsp = df2[df2[\"nome_drs\"] != \"Grande São Paulo\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "72435629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Total Covid-19 Deaths by Regional Health Dept'}, xlabel='Regional Health Dept.', ylabel='Deaths'>"
      ]
     },
     "execution_count": 7,
     "metadata": {},
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    {
     "data": {
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\n",
      "text/plain": [
       "<Figure size 1080x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Agrupando os Departamentos Regionais e Novos Óbitos para verificar o total de óbitos em todos os meses de 2020\n",
    "\n",
    "df2_without_gsp.groupby([\"nome_drs\"])[\"obitos_novos\"].sum().plot(kind=\"bar\", \n",
    "                                                                 title=\"Total Covid-19 Deaths by Regional Health Dept\", \n",
    "                                                                 figsize=(15,10), xlabel=\"Regional Health Dept.\", \n",
    "                                                                 ylabel=\"Deaths\", color=[\"red\", \"green\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3c0ff011",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "id": "5889d8f5",
   "metadata": {},
   "source": [
    "**b)**  Faça um gráfico de barras horizontal mostrando o total de óbitos no mês de abril para 3 DRS da sua escolha. Coloque título no gráfico e nos eixos, defina um tamanho de imagem bom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "42b00983",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Criação de uma cópia do dataframe original para fazer o pivo.\n",
    "\n",
    "#Creating a copy of the original dataframe in order to pivot it.\n",
    "\n",
    "df2_copy = df2.copy()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f56a777a",
   "metadata": {},
   "source": [
    "🇧🇷\n",
    "**Observação**: Fazendo um pivo com os dados dos DRS selecionados e medindo o total de óbitos para essas regiões.\n",
    "Como o dataframe é pequeno, podemos ver que o índice para o mês de \"Abril\" é 2, já o mês de \"Junho\" é a última posição do dataframe.\n",
    "\n",
    "A solicitação pede uma análise somente do mês de Abril\n",
    "\n",
    "-----------------------------\n",
    "🇬🇧\n",
    "**Note**: Creating a pivot with data of the selected DRS, measuring total deaths for those cities.\n",
    "\n",
    "The question requested an analysis of April."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "25c5868f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Selecionando três cidades do dataframe para visualizar o total de óbitos do mês de Abril de 2020\n",
    "\n",
    "# Choosing 3 cities of the dataframe to check the total death during April 2020\n",
    "\n",
    "chosen_cities = df2_copy.pivot_table(index=\"ano_mes\", columns=\"nome_drs\", values=\"obitos_novos\", aggfunc=\"sum\") \n",
    "\n",
    "chosen_cities = chosen_cities[[\"Baixada Santista\", \"Sorocaba\", \"Bauru\"]]\n",
    "\n",
    "chosen_cities = chosen_cities.reset_index()\n",
    "\n",
    "chosen_cities = chosen_cities[chosen_cities[\"ano_mes\"] == \"2020-04\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "4740686f",
   "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>nome_drs</th>\n",
       "      <th>ano_mes</th>\n",
       "      <th>Baixada Santista</th>\n",
       "      <th>Sorocaba</th>\n",
       "      <th>Bauru</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020-04</td>\n",
       "      <td>86</td>\n",
       "      <td>31</td>\n",
       "      <td>26</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "nome_drs  ano_mes  Baixada Santista  Sorocaba  Bauru\n",
       "2         2020-04                86        31     26"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "chosen_cities.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "10d0f677",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Total Covid-19 Deaths during April 2020'}, ylabel='Regional Health Dept.'>"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 720x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criação do gráfico de barras horizontais para observar as informações coletadas no pivo para as cidades\n",
    "\n",
    "# Creating a horizontal bar graph to observe the information collected through the pivot for the selected cities\n",
    "\n",
    "chosen_cities.plot(kind=\"barh\", figsize=(10,10), color={\"#800080\", \"#04D8B2\", \"#FFC0CB\"},\n",
    "                   title=\"Total Covid-19 Deaths during April 2020\", ylabel=\"Number of Deaths\",\n",
    "                   xlabel=\"Regional Health Dept.\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5d4d84ed",
   "metadata": {},
   "source": [
    "---------------------\n",
    "---------------------"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "90d2e4f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Leitura da base de dados que será analisada e transformação em dataframe\n",
    "\n",
    "# Reading the file that contains the data to be assessed and transforming it into a dataframe\n",
    "\n",
    "df1 = pd.read_csv(\"IBM_Employees.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "cdcbe7ac",
   "metadata": {},
   "source": [
    "## Exercício 2\n",
    "\n",
    "**a)** Utilizando a base de dados da IBM, crie um histograma para as idades dos funcionários, separando por gênero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "71c2fb1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criando um histograma para idades separadas por gênero\n",
    "\n",
    "# Creating a histogram with the ages of employees separated by gender\n",
    "\n",
    "\n",
    "ax1 = df1[df1[\"Gender\"]==\"Male\"].plot(kind=\"hist\", figsize=(20,10), y=\"Age\", color=\"teal\", alpha=0.5, label=\"Male\", \n",
    "                                      title=(\"Employees Age seen by Gender\"))\n",
    "ax2 = df1[df1[\"Gender\"]==\"Female\"].plot(kind=\"hist\", y=\"Age\", color=\"#FFC0CB\", label=\"Female\", ax=ax1)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c9a5551",
   "metadata": {},
   "source": [
    "**b)** Faça agora um boxplot das idades, por gênero."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "19ebc888",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<AxesSubplot:title={'center':'Age'}, xlabel='Gender'>"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x720 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Criando um boxplot para idades separadas por gênero\n",
    "\n",
    "# Creating a boxplot with the ages of employees separated by gender\n",
    "\n",
    "df1.boxplot(column=[\"Age\"], by=\"Gender\", grid=False, color={\"boxes\":\"thistle\", \"medians\" : \"blue\"}, \n",
    "            figsize=(20,10), showfliers=False)"
   ]
  }
 ],
 "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": "3.9.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}