CD_Simulado2.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "# SIMULADO 2\n",
    "\n",
    "## 2023 - 2o. semestre \n",
    "\n",
    "___\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## NOME: Coloque seu nome AQUI!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1295,
   "metadata": {},
   "outputs": [],
   "source": [
    "# %matplotlib inline\n",
    "import math\n",
    "from math import sqrt\n",
    "\n",
    "import os\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "from numpy import arange\n",
    "import pandas as pd\n",
    "\n",
    "from IPython.display import display  # Para ter melhor print.\n",
    "\n",
    "# Pode trazer mais bibliotecas, caso julgue interessante!!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"indice\"></div>\n",
    "\n",
    "## Índice\n",
    "\n",
    "1. [Pergunta 1](#p1)\n",
    "\n",
    "1. [Pergunta 2](#p2)\n",
    "\n",
    "1. [Pergunta 3](#p3)\n",
    "\n",
    "1. [Pergunta 4](#p4)\n",
    "\n",
    "1. [Pergunta 5](#p5)\n",
    "\n",
    "1. [Pergunta 6](#p5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"abrindo-arquivo\"></div>\n",
    "\n",
    "___\n",
    "\n",
    "**Abrindo um arquivo de dados e visualizando seu conteúdo:**\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1296,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Esperamos trabalhar no diretório: \n",
      "c:\\Users\\PH\\Desktop\\Repositorios\\cdados\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(f'Esperamos trabalhar no diretório: \\n{os.getcwd()}\\n')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1297,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parece que o arquivo df_simulado2.xlsx está na mesma pasta do notebook, yay!\n"
     ]
    }
   ],
   "source": [
    "filename = 'df_simulado2.xlsx'\n",
    "\n",
    "if filename in os.listdir():\n",
    "    print(f'Parece que o arquivo {filename} está na mesma pasta do notebook, yay!')\n",
    "    \n",
    "else:\n",
    "    print(f'Não encontrei o arquivo {filename}. Lembre-se que precisa estar na mesma pasta do arquivo notebook')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Abrindo a base de dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1298,
   "metadata": {
    "scrolled": false
   },
   "outputs": [],
   "source": [
    "df = pd.read_excel(filename)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avaliando as 3 primeiras linhas da base de dados:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1299,
   "metadata": {
    "scrolled": true
   },
   "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>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary</th>\n",
       "      <th>salary_currency</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "      <th>monthly_expensies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>70000</td>\n",
       "      <td>EUR</td>\n",
       "      <td>79833</td>\n",
       "      <td>DE</td>\n",
       "      <td>0</td>\n",
       "      <td>DE</td>\n",
       "      <td>L</td>\n",
       "      <td>1382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Machine Learning Scientist</td>\n",
       "      <td>260000</td>\n",
       "      <td>USD</td>\n",
       "      <td>260000</td>\n",
       "      <td>JP</td>\n",
       "      <td>0</td>\n",
       "      <td>JP</td>\n",
       "      <td>S</td>\n",
       "      <td>1405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Big Data Engineer</td>\n",
       "      <td>85000</td>\n",
       "      <td>GBP</td>\n",
       "      <td>109024</td>\n",
       "      <td>GB</td>\n",
       "      <td>50</td>\n",
       "      <td>GB</td>\n",
       "      <td>M</td>\n",
       "      <td>1441</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   work_year experience_level employment_type                   job_title  \\\n",
       "0       2020               MI              FT              Data Scientist   \n",
       "1       2020               SE              FT  Machine Learning Scientist   \n",
       "2       2020               SE              FT           Big Data Engineer   \n",
       "\n",
       "   salary salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
       "0   70000             EUR          79833                 DE             0   \n",
       "1  260000             USD         260000                 JP             0   \n",
       "2   85000             GBP         109024                 GB            50   \n",
       "\n",
       "  company_location company_size  monthly_expensies  \n",
       "0               DE            L               1382  \n",
       "1               JP            S               1405  \n",
       "2               GB            M               1441  "
      ]
     },
     "execution_count": 1299,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Avaliando o tipo de cada variável disponível no *dataframe* `df`:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Voltar ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"p1\"></div>\n",
    "\n",
    "## PERGUNTA 1: \n",
    "Leia Enunciado no Blackboard. Preencha as respostas no Blackboard! Envie o Notebook antes de finalizar o proctorio!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1300,
   "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>count</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data Scientist</th>\n",
       "      <td>143</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Engineer</th>\n",
       "      <td>132</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Analyst</th>\n",
       "      <td>97</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machine Learning Engineer</th>\n",
       "      <td>41</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research Scientist</th>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           count\n",
       "job_title                       \n",
       "Data Scientist               143\n",
       "Data Engineer                132\n",
       "Data Analyst                  97\n",
       "Machine Learning Engineer     41\n",
       "Research Scientist            16"
      ]
     },
     "execution_count": 1300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coloque seu código aqui!\n",
    "df.loc[:, ['job_title']].value_counts().to_frame().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Voltar ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1301,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>proportion</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>job_title</th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Data Scientist</th>\n",
       "      <td>0.235585</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Engineer</th>\n",
       "      <td>0.217463</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Data Analyst</th>\n",
       "      <td>0.159802</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Machine Learning Engineer</th>\n",
       "      <td>0.067545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Research Scientist</th>\n",
       "      <td>0.026359</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                           proportion\n",
       "job_title                            \n",
       "Data Scientist               0.235585\n",
       "Data Engineer                0.217463\n",
       "Data Analyst                 0.159802\n",
       "Machine Learning Engineer    0.067545\n",
       "Research Scientist           0.026359"
      ]
     },
     "execution_count": 1301,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, ['job_title']].value_counts(normalize=True).to_frame().head(5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"p2\"></div>\n",
    "\n",
    "## PERGUNTA 2:\n",
    "Leia Enunciado no Blackboard. Preencha as respostas no Blackboard! Envie o Notebook antes de finalizar o proctorio!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1302,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Coloque seu código aqui!\n",
    "df2021_fulltime = df.loc[(df.work_year == 2021) & (df.employment_type == \"FT\"), :]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Voltar ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1303,
   "metadata": {},
   "outputs": [],
   "source": [
    "paises = list(df2021_fulltime.loc[:, ['company_location']].value_counts().head(5).keys())\n",
    "for i, j in enumerate(paises):\n",
    "    paises[i] = j[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1304,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "126644.5021 5679 68604.7854 117789.0 104095.4\n"
     ]
    }
   ],
   "source": [
    "salario = df.loc[(df.company_location.isin(paises)) & (df.employment_type == \"FT\"), :].salary_in_usd\n",
    "print(round(salario.mean(), 4), round(salario.min(), 4), round(salario.std(), 4), round(salario.median(), 4), round(salario.quantile(q=0.4), 4))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "___\n",
    "<div id=\"p3\"></div>\n",
    "\n",
    "## PERGUNTA 3:\n",
    "Leia Enunciado no Blackboard. Preencha as respostas no Blackboard! Envie o Notebook antes de finalizar o proctorio!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1305,
   "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",
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       "    }\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>remote_ratio</th>\n",
       "      <th>0</th>\n",
       "      <th>50</th>\n",
       "      <th>100</th>\n",
       "      <th>All</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>company_size</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>L</th>\n",
       "      <td>0.052718</td>\n",
       "      <td>0.098847</td>\n",
       "      <td>0.174629</td>\n",
       "      <td>0.326194</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>M</th>\n",
       "      <td>0.130148</td>\n",
       "      <td>0.034596</td>\n",
       "      <td>0.372323</td>\n",
       "      <td>0.537068</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>S</th>\n",
       "      <td>0.026359</td>\n",
       "      <td>0.029654</td>\n",
       "      <td>0.080725</td>\n",
       "      <td>0.136738</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>All</th>\n",
       "      <td>0.209226</td>\n",
       "      <td>0.163097</td>\n",
       "      <td>0.627677</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "remote_ratio         0        50       100       All\n",
       "company_size                                        \n",
       "L             0.052718  0.098847  0.174629  0.326194\n",
       "M             0.130148  0.034596  0.372323  0.537068\n",
       "S             0.026359  0.029654  0.080725  0.136738\n",
       "All           0.209226  0.163097  0.627677  1.000000"
      ]
     },
     "execution_count": 1305,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coloque seu código aqui!\n",
    "tabela = pd.crosstab(df.company_size, df.remote_ratio, normalize='all', margins=True)\n",
    "tabela"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1306,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.5354"
      ]
     },
     "execution_count": 1306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "P100dL = tabela[100]['L']/tabela['All']['L']\n",
    "round(P100dL, 4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1307,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0346"
      ]
     },
     "execution_count": 1307,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(tabela[50]['M'], 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Voltar ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"p4\"></div>\n",
    "\n",
    "## PERGUNTA 4:\n",
    "Leia Enunciado no Blackboard. Preencha as respostas no Blackboard! Envie o Notebook antes de finalizar o proctorio!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1308,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary</th>\n",
       "      <th>salary_currency</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "      <th>monthly_expensies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>150000</td>\n",
       "      <td>USD</td>\n",
       "      <td>150000</td>\n",
       "      <td>US</td>\n",
       "      <td>50</td>\n",
       "      <td>US</td>\n",
       "      <td>L</td>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2020</td>\n",
       "      <td>EN</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>72000</td>\n",
       "      <td>USD</td>\n",
       "      <td>72000</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>L</td>\n",
       "      <td>1473</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Lead Data Scientist</td>\n",
       "      <td>190000</td>\n",
       "      <td>USD</td>\n",
       "      <td>190000</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>S</td>\n",
       "      <td>1573</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Business Data Analyst</td>\n",
       "      <td>135000</td>\n",
       "      <td>USD</td>\n",
       "      <td>135000</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>L</td>\n",
       "      <td>1148</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Lead Data Analyst</td>\n",
       "      <td>87000</td>\n",
       "      <td>USD</td>\n",
       "      <td>87000</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>L</td>\n",
       "      <td>1493</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>599</th>\n",
       "      <td>2022</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>130000</td>\n",
       "      <td>USD</td>\n",
       "      <td>130000</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>M</td>\n",
       "      <td>1331</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>602</th>\n",
       "      <td>2022</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>154000</td>\n",
       "      <td>USD</td>\n",
       "      <td>154000</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>M</td>\n",
       "      <td>1223</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>603</th>\n",
       "      <td>2022</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Engineer</td>\n",
       "      <td>126000</td>\n",
       "      <td>USD</td>\n",
       "      <td>126000</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>M</td>\n",
       "      <td>1201</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>604</th>\n",
       "      <td>2022</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>129000</td>\n",
       "      <td>USD</td>\n",
       "      <td>129000</td>\n",
       "      <td>US</td>\n",
       "      <td>0</td>\n",
       "      <td>US</td>\n",
       "      <td>M</td>\n",
       "      <td>1121</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>605</th>\n",
       "      <td>2022</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>150000</td>\n",
       "      <td>USD</td>\n",
       "      <td>150000</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>M</td>\n",
       "      <td>1310</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>332 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     work_year experience_level employment_type                  job_title  \\\n",
       "4         2020               SE              FT  Machine Learning Engineer   \n",
       "5         2020               EN              FT               Data Analyst   \n",
       "6         2020               SE              FT        Lead Data Scientist   \n",
       "8         2020               MI              FT      Business Data Analyst   \n",
       "13        2020               MI              FT          Lead Data Analyst   \n",
       "..         ...              ...             ...                        ...   \n",
       "599       2022               MI              FT             Data Scientist   \n",
       "602       2022               SE              FT              Data Engineer   \n",
       "603       2022               SE              FT              Data Engineer   \n",
       "604       2022               SE              FT               Data Analyst   \n",
       "605       2022               SE              FT               Data Analyst   \n",
       "\n",
       "     salary salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
       "4    150000             USD         150000                 US            50   \n",
       "5     72000             USD          72000                 US           100   \n",
       "6    190000             USD         190000                 US           100   \n",
       "8    135000             USD         135000                 US           100   \n",
       "13    87000             USD          87000                 US           100   \n",
       "..      ...             ...            ...                ...           ...   \n",
       "599  130000             USD         130000                 US           100   \n",
       "602  154000             USD         154000                 US           100   \n",
       "603  126000             USD         126000                 US           100   \n",
       "604  129000             USD         129000                 US             0   \n",
       "605  150000             USD         150000                 US           100   \n",
       "\n",
       "    company_location company_size  monthly_expensies  \n",
       "4                 US            L               1182  \n",
       "5                 US            L               1473  \n",
       "6                 US            S               1573  \n",
       "8                 US            L               1148  \n",
       "13                US            L               1493  \n",
       "..               ...          ...                ...  \n",
       "599               US            M               1331  \n",
       "602               US            M               1223  \n",
       "603               US            M               1201  \n",
       "604               US            M               1121  \n",
       "605               US            M               1310  \n",
       "\n",
       "[332 rows x 12 columns]"
      ]
     },
     "execution_count": 1308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coloque seu código aqui!\n",
    "US = df.loc[df.employee_residence == 'US', :]\n",
    "US"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1309,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.031"
      ]
     },
     "execution_count": 1309,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "round(US.monthly_expensies.corr(US.salary_in_usd), 4)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Voltar ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"p5\"></div>\n",
    "\n",
    "## PERGUNTA 5:\n",
    "Leia Enunciado no Blackboard. Preencha as respostas no Blackboard! Envie o Notebook antes de finalizar o proctorio!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1310,
   "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>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary</th>\n",
       "      <th>salary_currency</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "      <th>monthly_expensies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>70000</td>\n",
       "      <td>EUR</td>\n",
       "      <td>79833</td>\n",
       "      <td>DE</td>\n",
       "      <td>0</td>\n",
       "      <td>DE</td>\n",
       "      <td>L</td>\n",
       "      <td>1382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Machine Learning Scientist</td>\n",
       "      <td>260000</td>\n",
       "      <td>USD</td>\n",
       "      <td>260000</td>\n",
       "      <td>JP</td>\n",
       "      <td>0</td>\n",
       "      <td>JP</td>\n",
       "      <td>S</td>\n",
       "      <td>1405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Big Data Engineer</td>\n",
       "      <td>85000</td>\n",
       "      <td>GBP</td>\n",
       "      <td>109024</td>\n",
       "      <td>GB</td>\n",
       "      <td>50</td>\n",
       "      <td>GB</td>\n",
       "      <td>M</td>\n",
       "      <td>1441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Product Data Analyst</td>\n",
       "      <td>20000</td>\n",
       "      <td>USD</td>\n",
       "      <td>20000</td>\n",
       "      <td>HN</td>\n",
       "      <td>0</td>\n",
       "      <td>HN</td>\n",
       "      <td>S</td>\n",
       "      <td>987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>11000000</td>\n",
       "      <td>HUF</td>\n",
       "      <td>35735</td>\n",
       "      <td>HU</td>\n",
       "      <td>50</td>\n",
       "      <td>HU</td>\n",
       "      <td>L</td>\n",
       "      <td>1498</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>531</th>\n",
       "      <td>2022</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>75000</td>\n",
       "      <td>USD</td>\n",
       "      <td>75000</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1439</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>567</th>\n",
       "      <td>2022</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>50000</td>\n",
       "      <td>GBP</td>\n",
       "      <td>65438</td>\n",
       "      <td>GB</td>\n",
       "      <td>0</td>\n",
       "      <td>GB</td>\n",
       "      <td>M</td>\n",
       "      <td>1204</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>586</th>\n",
       "      <td>2022</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>35000</td>\n",
       "      <td>GBP</td>\n",
       "      <td>45807</td>\n",
       "      <td>GB</td>\n",
       "      <td>0</td>\n",
       "      <td>GB</td>\n",
       "      <td>M</td>\n",
       "      <td>1172</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>600</th>\n",
       "      <td>2022</td>\n",
       "      <td>EN</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>67000</td>\n",
       "      <td>USD</td>\n",
       "      <td>67000</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1102</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>601</th>\n",
       "      <td>2022</td>\n",
       "      <td>EN</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Analyst</td>\n",
       "      <td>52000</td>\n",
       "      <td>USD</td>\n",
       "      <td>52000</td>\n",
       "      <td>CA</td>\n",
       "      <td>0</td>\n",
       "      <td>CA</td>\n",
       "      <td>M</td>\n",
       "      <td>1356</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>252 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "     work_year experience_level employment_type                   job_title  \\\n",
       "0         2020               MI              FT              Data Scientist   \n",
       "1         2020               SE              FT  Machine Learning Scientist   \n",
       "2         2020               SE              FT           Big Data Engineer   \n",
       "3         2020               MI              FT        Product Data Analyst   \n",
       "7         2020               MI              FT              Data Scientist   \n",
       "..         ...              ...             ...                         ...   \n",
       "531       2022               MI              FT                Data Analyst   \n",
       "567       2022               MI              FT                Data Analyst   \n",
       "586       2022               MI              FT                Data Analyst   \n",
       "600       2022               EN              FT                Data Analyst   \n",
       "601       2022               EN              FT                Data Analyst   \n",
       "\n",
       "       salary salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
       "0       70000             EUR          79833                 DE             0   \n",
       "1      260000             USD         260000                 JP             0   \n",
       "2       85000             GBP         109024                 GB            50   \n",
       "3       20000             USD          20000                 HN             0   \n",
       "7    11000000             HUF          35735                 HU            50   \n",
       "..        ...             ...            ...                ...           ...   \n",
       "531     75000             USD          75000                 CA             0   \n",
       "567     50000             GBP          65438                 GB             0   \n",
       "586     35000             GBP          45807                 GB             0   \n",
       "600     67000             USD          67000                 CA             0   \n",
       "601     52000             USD          52000                 CA             0   \n",
       "\n",
       "    company_location company_size  monthly_expensies  \n",
       "0                 DE            L               1382  \n",
       "1                 JP            S               1405  \n",
       "2                 GB            M               1441  \n",
       "3                 HN            S                987  \n",
       "7                 HU            L               1498  \n",
       "..               ...          ...                ...  \n",
       "531               CA            M               1439  \n",
       "567               GB            M               1204  \n",
       "586               GB            M               1172  \n",
       "600               CA            M               1102  \n",
       "601               CA            M               1356  \n",
       "\n",
       "[252 rows x 12 columns]"
      ]
     },
     "execution_count": 1310,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coloque seu código aqui!\n",
    "nUS = df.loc[df.company_location != 'US', :]\n",
    "nUS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1311,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\PH\\AppData\\Local\\Temp\\ipykernel_7332\\3815165277.py:1: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  nUS['salary_in_10kusd'] = nUS.salary_in_usd/10000\n"
     ]
    }
   ],
   "source": [
    "nUS['salary_in_10kusd'] = nUS.salary_in_usd/10000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1312,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(nUS.salary_in_10kusd, density=True, bins=arange(0, 27, 2), edgecolor=\"black\")\n",
    "plt.xticks(arange(0, 27, 2))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Voltar ao Índice](#indice)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<div id=\"p6\"></div>\n",
    "\n",
    "## PERGUNTA 6:\n",
    "Leia Enunciado no Blackboard. Preencha as respostas no Blackboard! Envie o Notebook antes de finalizar o proctorio!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1313,
   "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>work_year</th>\n",
       "      <th>experience_level</th>\n",
       "      <th>employment_type</th>\n",
       "      <th>job_title</th>\n",
       "      <th>salary</th>\n",
       "      <th>salary_currency</th>\n",
       "      <th>salary_in_usd</th>\n",
       "      <th>employee_residence</th>\n",
       "      <th>remote_ratio</th>\n",
       "      <th>company_location</th>\n",
       "      <th>company_size</th>\n",
       "      <th>monthly_expensies</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>70000</td>\n",
       "      <td>EUR</td>\n",
       "      <td>79833</td>\n",
       "      <td>DE</td>\n",
       "      <td>0</td>\n",
       "      <td>DE</td>\n",
       "      <td>L</td>\n",
       "      <td>1382</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Machine Learning Scientist</td>\n",
       "      <td>260000</td>\n",
       "      <td>USD</td>\n",
       "      <td>260000</td>\n",
       "      <td>JP</td>\n",
       "      <td>0</td>\n",
       "      <td>JP</td>\n",
       "      <td>S</td>\n",
       "      <td>1405</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Big Data Engineer</td>\n",
       "      <td>85000</td>\n",
       "      <td>GBP</td>\n",
       "      <td>109024</td>\n",
       "      <td>GB</td>\n",
       "      <td>50</td>\n",
       "      <td>GB</td>\n",
       "      <td>M</td>\n",
       "      <td>1441</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Product Data Analyst</td>\n",
       "      <td>20000</td>\n",
       "      <td>USD</td>\n",
       "      <td>20000</td>\n",
       "      <td>HN</td>\n",
       "      <td>0</td>\n",
       "      <td>HN</td>\n",
       "      <td>S</td>\n",
       "      <td>987</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Machine Learning Engineer</td>\n",
       "      <td>150000</td>\n",
       "      <td>USD</td>\n",
       "      <td>150000</td>\n",
       "      <td>US</td>\n",
       "      <td>50</td>\n",
       "      <td>US</td>\n",
       "      <td>L</td>\n",
       "      <td>1182</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>67</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Science Manager</td>\n",
       "      <td>190200</td>\n",
       "      <td>USD</td>\n",
       "      <td>190200</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>M</td>\n",
       "      <td>1474</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>68</th>\n",
       "      <td>2020</td>\n",
       "      <td>EN</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>105000</td>\n",
       "      <td>USD</td>\n",
       "      <td>105000</td>\n",
       "      <td>US</td>\n",
       "      <td>100</td>\n",
       "      <td>US</td>\n",
       "      <td>S</td>\n",
       "      <td>1301</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>69</th>\n",
       "      <td>2020</td>\n",
       "      <td>SE</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>80000</td>\n",
       "      <td>EUR</td>\n",
       "      <td>91237</td>\n",
       "      <td>AT</td>\n",
       "      <td>0</td>\n",
       "      <td>AT</td>\n",
       "      <td>S</td>\n",
       "      <td>1170</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>70</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>55000</td>\n",
       "      <td>EUR</td>\n",
       "      <td>62726</td>\n",
       "      <td>FR</td>\n",
       "      <td>50</td>\n",
       "      <td>LU</td>\n",
       "      <td>S</td>\n",
       "      <td>1267</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>71</th>\n",
       "      <td>2020</td>\n",
       "      <td>MI</td>\n",
       "      <td>FT</td>\n",
       "      <td>Data Scientist</td>\n",
       "      <td>37000</td>\n",
       "      <td>EUR</td>\n",
       "      <td>42197</td>\n",
       "      <td>FR</td>\n",
       "      <td>50</td>\n",
       "      <td>FR</td>\n",
       "      <td>S</td>\n",
       "      <td>1127</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>72 rows × 12 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "    work_year experience_level employment_type                   job_title  \\\n",
       "0        2020               MI              FT              Data Scientist   \n",
       "1        2020               SE              FT  Machine Learning Scientist   \n",
       "2        2020               SE              FT           Big Data Engineer   \n",
       "3        2020               MI              FT        Product Data Analyst   \n",
       "4        2020               SE              FT   Machine Learning Engineer   \n",
       "..        ...              ...             ...                         ...   \n",
       "67       2020               SE              FT        Data Science Manager   \n",
       "68       2020               EN              FT              Data Scientist   \n",
       "69       2020               SE              FT              Data Scientist   \n",
       "70       2020               MI              FT              Data Scientist   \n",
       "71       2020               MI              FT              Data Scientist   \n",
       "\n",
       "    salary salary_currency  salary_in_usd employee_residence  remote_ratio  \\\n",
       "0    70000             EUR          79833                 DE             0   \n",
       "1   260000             USD         260000                 JP             0   \n",
       "2    85000             GBP         109024                 GB            50   \n",
       "3    20000             USD          20000                 HN             0   \n",
       "4   150000             USD         150000                 US            50   \n",
       "..     ...             ...            ...                ...           ...   \n",
       "67  190200             USD         190200                 US           100   \n",
       "68  105000             USD         105000                 US           100   \n",
       "69   80000             EUR          91237                 AT             0   \n",
       "70   55000             EUR          62726                 FR            50   \n",
       "71   37000             EUR          42197                 FR            50   \n",
       "\n",
       "   company_location company_size  monthly_expensies  \n",
       "0                DE            L               1382  \n",
       "1                JP            S               1405  \n",
       "2                GB            M               1441  \n",
       "3                HN            S                987  \n",
       "4                US            L               1182  \n",
       "..              ...          ...                ...  \n",
       "67               US            M               1474  \n",
       "68               US            S               1301  \n",
       "69               AT            S               1170  \n",
       "70               LU            S               1267  \n",
       "71               FR            S               1127  \n",
       "\n",
       "[72 rows x 12 columns]"
      ]
     },
     "execution_count": 1313,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Coloque seu código aqui!\n",
    "df2020 = df.loc[df.work_year == 2020, :]\n",
    "df2020"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1314,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    1.500000e+01\n",
       "mean     3.819467e+05\n",
       "std      7.527916e+05\n",
       "min      2.000000e+04\n",
       "25%      4.450000e+04\n",
       "50%      9.800000e+04\n",
       "75%      3.745000e+05\n",
       "max      3.000000e+06\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 1314,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2020.loc[df2020.remote_ratio == 0, :].salary.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1315,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df2020.loc[df2020.remote_ratio == 0, :].salary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1316,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    2.100000e+01\n",
       "mean     6.282619e+05\n",
       "std      2.378661e+06\n",
       "min      8.000000e+03\n",
       "25%      4.200000e+04\n",
       "50%      7.000000e+04\n",
       "75%      1.500000e+05\n",
       "max      1.100000e+07\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 1316,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2020.loc[df2020.remote_ratio == 50, :].salary.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1317,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df2020.loc[df2020.remote_ratio == 50, :].salary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1318,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    3.600000e+01\n",
       "mean     2.391419e+05\n",
       "std      7.283401e+05\n",
       "min      1.000000e+04\n",
       "25%      6.000000e+04\n",
       "50%      1.015000e+05\n",
       "75%      1.357500e+05\n",
       "max      4.450000e+06\n",
       "Name: salary, dtype: float64"
      ]
     },
     "execution_count": 1318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df2020.loc[df2020.remote_ratio == 100, :].salary.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1319,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.boxplot(df2020.loc[df2020.remote_ratio == 100, :].salary)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "[Voltar ao Índice](#indice)"
   ]
  }
 ],
 "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": "3.10.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}