diff --git a/exercicios/para-casa/claire.ipynb b/exercicios/para-casa/claire.ipynb
new file mode 100644
index 0000000..cf4b78b
--- /dev/null
+++ b/exercicios/para-casa/claire.ipynb
@@ -0,0 +1,1030 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from scipy.stats import ttest_ind\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# leitura do nosso arquivo csv\n",
+    "df = pd.read_csv(\"ds_salaries.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>Unnamed: 0</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",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>0</td>\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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>1</td>\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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2</td>\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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>3</td>\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",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>4</td>\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",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   Unnamed: 0  work_year experience_level employment_type  \\\n",
+       "0           0       2020               MI              FT   \n",
+       "1           1       2020               SE              FT   \n",
+       "2           2       2020               SE              FT   \n",
+       "3           3       2020               MI              FT   \n",
+       "4           4       2020               SE              FT   \n",
+       "\n",
+       "                    job_title  salary salary_currency  salary_in_usd  \\\n",
+       "0              Data Scientist   70000             EUR          79833   \n",
+       "1  Machine Learning Scientist  260000             USD         260000   \n",
+       "2           Big Data Engineer   85000             GBP         109024   \n",
+       "3        Product Data Analyst   20000             USD          20000   \n",
+       "4   Machine Learning Engineer  150000             USD         150000   \n",
+       "\n",
+       "  employee_residence  remote_ratio company_location company_size  \n",
+       "0                 DE             0               DE            L  \n",
+       "1                 JP             0               JP            S  \n",
+       "2                 GB            50               GB            M  \n",
+       "3                 HN             0               HN            S  \n",
+       "4                 US            50               US            L  "
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(607, 12)"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# quantidads de linhas e colunas\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# backup\n",
+    "df_backup = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Unnamed: 0            0\n",
+      "work_year             0\n",
+      "experience_level      0\n",
+      "employment_type       0\n",
+      "job_title             0\n",
+      "salary                0\n",
+      "salary_currency       0\n",
+      "salary_in_usd         0\n",
+      "employee_residence    0\n",
+      "remote_ratio          0\n",
+      "company_location      0\n",
+      "company_size          0\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#contar dados nulos em cada coluna\n",
+    "numos_por_colunas = df.isnull().sum()\n",
+    "print(numos_por_colunas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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>Unnamed: 0</th>\n",
+       "      <th>work_year</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>salary_in_usd</th>\n",
+       "      <th>remote_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>607.000000</td>\n",
+       "      <td>607.000000</td>\n",
+       "      <td>6.070000e+02</td>\n",
+       "      <td>607.000000</td>\n",
+       "      <td>607.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>2021.405272</td>\n",
+       "      <td>3.240001e+05</td>\n",
+       "      <td>112297.869852</td>\n",
+       "      <td>70.92257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>175.370085</td>\n",
+       "      <td>0.692133</td>\n",
+       "      <td>1.544357e+06</td>\n",
+       "      <td>70957.259411</td>\n",
+       "      <td>40.70913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>4.000000e+03</td>\n",
+       "      <td>2859.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>151.500000</td>\n",
+       "      <td>2021.000000</td>\n",
+       "      <td>7.000000e+04</td>\n",
+       "      <td>62726.000000</td>\n",
+       "      <td>50.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>2022.000000</td>\n",
+       "      <td>1.150000e+05</td>\n",
+       "      <td>101570.000000</td>\n",
+       "      <td>100.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>454.500000</td>\n",
+       "      <td>2022.000000</td>\n",
+       "      <td>1.650000e+05</td>\n",
+       "      <td>150000.000000</td>\n",
+       "      <td>100.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>606.000000</td>\n",
+       "      <td>2022.000000</td>\n",
+       "      <td>3.040000e+07</td>\n",
+       "      <td>600000.000000</td>\n",
+       "      <td>100.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0    work_year        salary  salary_in_usd  remote_ratio\n",
+       "count  607.000000   607.000000  6.070000e+02     607.000000     607.00000\n",
+       "mean   303.000000  2021.405272  3.240001e+05  112297.869852      70.92257\n",
+       "std    175.370085     0.692133  1.544357e+06   70957.259411      40.70913\n",
+       "min      0.000000  2020.000000  4.000000e+03    2859.000000       0.00000\n",
+       "25%    151.500000  2021.000000  7.000000e+04   62726.000000      50.00000\n",
+       "50%    303.000000  2022.000000  1.150000e+05  101570.000000     100.00000\n",
+       "75%    454.500000  2022.000000  1.650000e+05  150000.000000     100.00000\n",
+       "max    606.000000  2022.000000  3.040000e+07  600000.000000     100.00000"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# descrição dos dados\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#converter a colunas salary para float e formartar casas decimais\n",
+    "df['salary'] = df['salary'].astype(float).round(2)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 607 entries, 0 to 606\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column              Non-Null Count  Dtype  \n",
+      "---  ------              --------------  -----  \n",
+      " 0   Unnamed: 0          607 non-null    int64  \n",
+      " 1   work_year           607 non-null    int64  \n",
+      " 2   experience_level    607 non-null    object \n",
+      " 3   employment_type     607 non-null    object \n",
+      " 4   job_title           607 non-null    object \n",
+      " 5   salary              607 non-null    float64\n",
+      " 6   salary_currency     607 non-null    object \n",
+      " 7   salary_in_usd       607 non-null    int64  \n",
+      " 8   employee_residence  607 non-null    object \n",
+      " 9   remote_ratio        607 non-null    int64  \n",
+      " 10  company_location    607 non-null    object \n",
+      " 11  company_size        607 non-null    object \n",
+      "dtypes: float64(1), int64(4), object(7)\n",
+      "memory usage: 57.0+ KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "#verificr informações\n",
+    "info_df =df.info()\n",
+    "print(info_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['Unnamed: 0', 'work_year', 'experience_level', 'employment_type',\n",
+       "       'job_title', 'salary', 'salary_currency', 'salary_in_usd',\n",
+       "       'employee_residence', 'remote_ratio', 'company_location',\n",
+       "       'company_size'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>Unnamed: 0</th>\n",
+       "      <th>work_year</th>\n",
+       "      <th>salary</th>\n",
+       "      <th>salary_in_usd</th>\n",
+       "      <th>remote_ratio</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>607.000000</td>\n",
+       "      <td>607.000000</td>\n",
+       "      <td>6.070000e+02</td>\n",
+       "      <td>607.000000</td>\n",
+       "      <td>607.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>2021.405272</td>\n",
+       "      <td>3.240001e+05</td>\n",
+       "      <td>112297.869852</td>\n",
+       "      <td>70.92257</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>175.370085</td>\n",
+       "      <td>0.692133</td>\n",
+       "      <td>1.544357e+06</td>\n",
+       "      <td>70957.259411</td>\n",
+       "      <td>40.70913</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2020.000000</td>\n",
+       "      <td>4.000000e+03</td>\n",
+       "      <td>2859.000000</td>\n",
+       "      <td>0.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>151.500000</td>\n",
+       "      <td>2021.000000</td>\n",
+       "      <td>7.000000e+04</td>\n",
+       "      <td>62726.000000</td>\n",
+       "      <td>50.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>303.000000</td>\n",
+       "      <td>2022.000000</td>\n",
+       "      <td>1.150000e+05</td>\n",
+       "      <td>101570.000000</td>\n",
+       "      <td>100.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>454.500000</td>\n",
+       "      <td>2022.000000</td>\n",
+       "      <td>1.650000e+05</td>\n",
+       "      <td>150000.000000</td>\n",
+       "      <td>100.00000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>606.000000</td>\n",
+       "      <td>2022.000000</td>\n",
+       "      <td>3.040000e+07</td>\n",
+       "      <td>600000.000000</td>\n",
+       "      <td>100.00000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       Unnamed: 0    work_year        salary  salary_in_usd  remote_ratio\n",
+       "count  607.000000   607.000000  6.070000e+02     607.000000     607.00000\n",
+       "mean   303.000000  2021.405272  3.240001e+05  112297.869852      70.92257\n",
+       "std    175.370085     0.692133  1.544357e+06   70957.259411      40.70913\n",
+       "min      0.000000  2020.000000  4.000000e+03    2859.000000       0.00000\n",
+       "25%    151.500000  2021.000000  7.000000e+04   62726.000000      50.00000\n",
+       "50%    303.000000  2022.000000  1.150000e+05  101570.000000     100.00000\n",
+       "75%    454.500000  2022.000000  1.650000e+05  150000.000000     100.00000\n",
+       "max    606.000000  2022.000000  3.040000e+07  600000.000000     100.00000"
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# descrição dos dados\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [Unnamed: 0, work_year, experience_level, employment_type, job_title, salary, salary_currency, salary_in_usd, employee_residence, remote_ratio, company_location, company_size]\n",
+      "Index: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "def visualizar_duplicados(df):\n",
+    "    #idenfica as linhas duplicadas\n",
+    "    duplicados = df[df.duplicated(keep=False)]\n",
+    "\n",
+    "    return duplicados\n",
+    "# vizualiza as ,inhas duplicadas\n",
+    "duplicados = visualizar_duplicados(df)\n",
+    "print(duplicados)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Analises"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0             Germany\n",
+      "1               Japan\n",
+      "2      United Kingdom\n",
+      "3            Honduras\n",
+      "4       United States\n",
+      "            ...      \n",
+      "602     United States\n",
+      "603     United States\n",
+      "604     United States\n",
+      "605     United States\n",
+      "606     United States\n",
+      "Name: company_location, Length: 607, dtype: object\n",
+      "['Germany' 'Japan' 'United Kingdom' 'Honduras' 'United States' 'Hungary'\n",
+      " 'New Zealand' 'France' 'India' 'Pakistan' 'China' 'Greece'\n",
+      " 'United Arab Emirates' 'Netherlands' 'Mexico' 'Canada' 'Austria'\n",
+      " 'Nigeria' 'Spain' 'Portugal' 'Denmark' 'Italy' 'Croatia' 'Luxembourg'\n",
+      " 'Poland' 'Singapore' 'Romania' 'Iraq' 'Brazil' 'Belgium' 'Ukraine'\n",
+      " 'Israel' 'Russia' 'Malta' 'Chile' 'Iran' 'Colombia' 'Moldova' 'Kenya'\n",
+      " 'Slovenia' 'Switzerland' 'Vietnam' 'American Samoa' 'Turkey'\n",
+      " 'Czech Republic' 'Algeria' 'Estonia' 'Malaysia' 'Australia' 'Ireland']\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Dicionário de mapeamento de códigos de países para nomes completos\n",
+    "country_mapping = {\n",
+    "    'DE': 'Germany', 'JP': 'Japan', 'GB': 'United Kingdom', 'HN': 'Honduras', 'US': 'United States',\n",
+    "    'HU': 'Hungary', 'NZ': 'New Zealand', 'FR': 'France', 'IN': 'India', 'PK': 'Pakistan',\n",
+    "    'CN': 'China', 'GR': 'Greece', 'AE': 'United Arab Emirates', 'NL': 'Netherlands',\n",
+    "    'MX': 'Mexico', 'CA': 'Canada', 'AT': 'Austria', 'NG': 'Nigeria', 'ES': 'Spain',\n",
+    "    'PT': 'Portugal', 'DK': 'Denmark', 'IT': 'Italy', 'HR': 'Croatia', 'LU': 'Luxembourg',\n",
+    "    'PL': 'Poland', 'SG': 'Singapore', 'RO': 'Romania', 'IQ': 'Iraq', 'BR': 'Brazil',\n",
+    "    'BE': 'Belgium', 'UA': 'Ukraine', 'IL': 'Israel', 'RU': 'Russia', 'MT': 'Malta',\n",
+    "    'CL': 'Chile', 'IR': 'Iran', 'CO': 'Colombia', 'MD': 'Moldova', 'KE': 'Kenya',\n",
+    "    'SI': 'Slovenia', 'CH': 'Switzerland', 'VN': 'Vietnam', 'AS': 'American Samoa',\n",
+    "    'TR': 'Turkey', 'CZ': 'Czech Republic', 'DZ': 'Algeria', 'EE': 'Estonia',\n",
+    "    'MY': 'Malaysia', 'AU': 'Australia', 'IE': 'Ireland'\n",
+    "}\n",
+    "\n",
+    "\n",
+    "# Renomeando os códigos dos países\n",
+    "df['company_location'] = df['company_location'].replace(country_mapping)\n",
+    "\n",
+    "# Verificando o resultado\n",
+    "print(df['company_location'])\n",
+    "\n",
+    "print(df['company_location'].unique())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['Germany' 'Japan' 'United Kingdom' 'Honduras' 'United States' 'Hungary'\n",
+      " 'New Zealand' 'France' 'India' 'Pakistan' 'China' 'Greece'\n",
+      " 'United Arab Emirates' 'Netherlands' 'Mexico' 'Canada' 'Austria'\n",
+      " 'Nigeria' 'Spain' 'Portugal' 'Denmark' 'Italy' 'Croatia' 'Luxembourg'\n",
+      " 'Poland' 'Singapore' 'Romania' 'Iraq' 'Brazil' 'Belgium' 'Ukraine'\n",
+      " 'Israel' 'Russia' 'Malta' 'Chile' 'Iran' 'Colombia' 'Moldova' 'Kenya'\n",
+      " 'Slovenia' 'Switzerland' 'Vietnam' 'American Samoa' 'Turkey'\n",
+      " 'Czech Republic' 'Algeria' 'Estonia' 'Malaysia' 'Australia' 'Ireland']\n"
+     ]
+    }
+   ],
+   "source": [
+    "distinct_locations = df['company_location'].unique()\n",
+    "print(distinct_locations)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Maior Salário: 600000\n",
+      "Cargo: Principal Data Engineer\n",
+      "Tamanho da Empresa: L\n",
+      "Localização da Empresa: United States\n",
+      "\n",
+      "Menor Salário: 2859\n",
+      "Cargo: Data Scientist\n",
+      "Tamanho da Empresa: S\n",
+      "Localização da Empresa: Mexico\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Qual o menor e maior salário de acordo com o cargo e tamanho da Empresa?\n",
+    "#    'S': 'Small','M': 'Medium','L': 'Large'\n",
+    "# \n",
+    "    \n",
+    "    \n",
+    "max_salary_row = df.loc[df['salary_in_usd'].idxmax()]\n",
+    "min_salary_row = df.loc[df['salary_in_usd'].idxmin()]\n",
+    "\n",
+    "max_salary = max_salary_row['salary_in_usd']\n",
+    "min_salary = min_salary_row['salary_in_usd']\n",
+    "\n",
+    "max_salary_job_title = max_salary_row['job_title']\n",
+    "min_salary_job_title = min_salary_row['job_title']\n",
+    "\n",
+    "max_salary_company_size = max_salary_row['company_size']\n",
+    "min_salary_company_size = min_salary_row['company_size']\n",
+    "\n",
+    "max_salary_company_location = max_salary_row['company_location']\n",
+    "min_salary_company_location = min_salary_row['company_location']\n",
+    "\n",
+    "print(\"Maior Salário:\",max_salary)\n",
+    "print(\"Cargo:\", max_salary_job_title)\n",
+    "print(\"Tamanho da Empresa:\", max_salary_company_size)\n",
+    "print(\"Localização da Empresa:\", max_salary_company_location)\n",
+    "\n",
+    "print(\"\\nMenor Salário:\", min_salary)\n",
+    "print(\"Cargo:\", min_salary_job_title)\n",
+    "print(\"Tamanho da Empresa:\", min_salary_company_size)\n",
+    "print(\"Localização da Empresa:\", min_salary_company_location)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "experience_level\n",
+      "EN     88\n",
+      "EX     26\n",
+      "MI    213\n",
+      "SE    280\n",
+      "Name: job_title, dtype: int64\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1200x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Crie visualizações de gráficos com pandas, sendo no mínimo 4 gráficos.\n",
+    "\n",
+    "# EN: Entry Level (Nível de Entrada) / MI: Mid Level (Nível Intermediário)/SE: Senior Level (Nível Sênior)/EX: Executive Level (Nível Executivo)\n",
+    "\n",
+    "\n",
+    "# Contagem de Cargos por Nível de Experiência\n",
+    "contagem_cargos = df.groupby('experience_level')['job_title'].count()\n",
+    "print(contagem_cargos)\n",
+    "\n",
+    "plt.figure(figsize=(12, 5)) #  é usado para definir o tamanho da figura do gráfico que você está prestes a criar.\n",
+    "sns.barplot(x=contagem_cargos.index, y=contagem_cargos.values)\n",
+    "plt.title('Contagem de Cargos por Nível de Experiência')\n",
+    "plt.xlabel('Nível de Experiência')\n",
+    "plt.ylabel('Quantidade de Cargos')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Distribuição de Tamanho das Empresas\n",
+    "# '%1.1f%%  é usado para formatar os rótulos de porcentagem exibidos em cada setor do gráfico.\n",
+    "df['company_size'].value_counts().plot.pie(autopct='%1.1f%%', figsize=(8, 8))\n",
+    "plt.title('Distribuição de Tamanho das Empresas')\n",
+    "plt.ylabel('')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x800 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Distribuição por nível de Experiência\n",
+    "# EN: Entry Level (Nível de Entrada) / MI: Mid Level (Nível Intermediário)/SE: Senior Level (Nível Sênior)/EX: Executive Level (Nível Executivo)\n",
+    "plt.figure(figsize=(8, 8))\n",
+    "sizes = df['experience_level'].value_counts()\n",
+    "plt.pie(sizes, labels=sizes.index, autopct='%1.1f%%', startangle=140)\n",
+    "plt.title('Distribuição por Nível de Experiência')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 35,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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+      "text/plain": [
+       "<Figure size 1200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "\n",
+    "\n",
+    "# Calcular a média de salário por tamanho da empresa\n",
+    "media_salario = df.groupby('company_size')['salary_in_usd'].mean()\n",
+    "\n",
+    "# Criar o gráfico de barras com cores personalizadas\n",
+    "plt.figure(figsize=(12, 6))\n",
+    "colors = ['skyblue', 'lightgreen', 'salmon']  # Definir cores personalizadas\n",
+    "media_salario.plot(kind='bar', color=colors)\n",
+    "\n",
+    "# Adicionar títulos e rótulos\n",
+    "plt.title('Média de Salário por Tamanho da Empresa')\n",
+    "plt.xlabel('Tamanho da Empresa')\n",
+    "plt.ylabel('Média de Salário')\n",
+    "\n",
+    "# Exibir o gráfico\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Faça  consultas em sql\n",
+    "\n",
+    "import sqlite3 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "baby_df = df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "  company_size  Contagem\n",
+      "0            L       198\n",
+      "1            M       326\n",
+      "2            S        83\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function Connection.close()>"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# conexao\n",
+    "\n",
+    "conn = sqlite3.connect(':memory:')\n",
+    "\n",
+    "# escrever o df em um SQL\n",
+    "baby_df.to_sql('baby_df', conn, index=False, if_exists='replace')\n",
+    "\n",
+    "# executar a consulta\n",
+    "\n",
+    "query_sql = \"\"\"\n",
+    "SELECT  company_size, COUNT('job_title') AS Contagem\n",
+    "FROM baby_df\n",
+    "GROUP BY company_size;\n",
+    "\"\"\"\n",
+    "contagem_por_jobtitle = pd.read_sql_query(query_sql, conn)\n",
+    "print(contagem_por_jobtitle)\n",
+    "\n",
+    "#fechar\n",
+    "conn.close"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Utilize a biblioteca Matplotlib ou Seaborn para construir novos gráficos\n",
+    "# Teste Hipóteses\n",
+    "\n",
+    "# Teste de maior salário conforme localidade da Empresa\n",
+    "\n",
+    "\n",
+    "# Hipótese Nula H0: Os maiores salário não relacionam com  o localidade da Empresa no United States.\n",
+    "# Hipótese Alternativa H1: maiores salários relacionam com a localidade no United States\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Teste T de Idade\n",
+      " Estatística T: -2.592664200794162\n",
+      " Valor P: 0.009753769047118726\n"
+     ]
+    }
+   ],
+   "source": [
+    "# amostras\n",
+    "\n",
+    "\n",
+    "# Filtrar salários para empresas localizadas nos EUA\n",
+    "salario_usa = df[df['company_location'] == 'United States']['salary']\n",
+    "\n",
+    "# Filtrar salários para empresas localizadas fora dos EUA\n",
+    "salario_nao_usa = df[df['company_location'] != 'United States']['salary']\n",
+    "\n",
+    "# teste t\n",
+    "estatistica_t, valor_p = ttest_ind(salario_usa, salario_nao_usa)\n",
+    "\n",
+    "print(\"Teste T de Idade\")\n",
+    "print(f\" Estatística T: {estatistica_t}\")\n",
+    "print(f\" Valor P: {valor_p}\")\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rejeitamos a hipótese nula\n"
+     ]
+    }
+   ],
+   "source": [
+    "#interpretação\n",
+    "if valor_p < 0.05:\n",
+    "    print(\"Rejeitamos a hipótese nula\")\n",
+    "else:\n",
+    "    print(\"Nâo rejeitamos a hipótese nula\")"
+   ]
+  }
+ ],
+ "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-casa/ds_salaries.csv b/exercicios/para-casa/ds_salaries.csv
new file mode 100644
index 0000000..4f56347
--- /dev/null
+++ b/exercicios/para-casa/ds_salaries.csv
@@ -0,0 +1,608 @@
+,work_year,experience_level,employment_type,job_title,salary,salary_currency,salary_in_usd,employee_residence,remote_ratio,company_location,company_size
+0,2020,MI,FT,Data Scientist,70000,EUR,79833,DE,0,DE,L
+1,2020,SE,FT,Machine Learning Scientist,260000,USD,260000,JP,0,JP,S
+2,2020,SE,FT,Big Data Engineer,85000,GBP,109024,GB,50,GB,M
+3,2020,MI,FT,Product Data Analyst,20000,USD,20000,HN,0,HN,S
+4,2020,SE,FT,Machine Learning Engineer,150000,USD,150000,US,50,US,L
+5,2020,EN,FT,Data Analyst,72000,USD,72000,US,100,US,L
+6,2020,SE,FT,Lead Data Scientist,190000,USD,190000,US,100,US,S
+7,2020,MI,FT,Data Scientist,11000000,HUF,35735,HU,50,HU,L
+8,2020,MI,FT,Business Data Analyst,135000,USD,135000,US,100,US,L
+9,2020,SE,FT,Lead Data Engineer,125000,USD,125000,NZ,50,NZ,S
+10,2020,EN,FT,Data Scientist,45000,EUR,51321,FR,0,FR,S
+11,2020,MI,FT,Data Scientist,3000000,INR,40481,IN,0,IN,L
+12,2020,EN,FT,Data Scientist,35000,EUR,39916,FR,0,FR,M
+13,2020,MI,FT,Lead Data Analyst,87000,USD,87000,US,100,US,L
+14,2020,MI,FT,Data Analyst,85000,USD,85000,US,100,US,L
+15,2020,MI,FT,Data Analyst,8000,USD,8000,PK,50,PK,L
+16,2020,EN,FT,Data Engineer,4450000,JPY,41689,JP,100,JP,S
+17,2020,SE,FT,Big Data Engineer,100000,EUR,114047,PL,100,GB,S
+18,2020,EN,FT,Data Science Consultant,423000,INR,5707,IN,50,IN,M
+19,2020,MI,FT,Lead Data Engineer,56000,USD,56000,PT,100,US,M
+20,2020,MI,FT,Machine Learning Engineer,299000,CNY,43331,CN,0,CN,M
+21,2020,MI,FT,Product Data Analyst,450000,INR,6072,IN,100,IN,L
+22,2020,SE,FT,Data Engineer,42000,EUR,47899,GR,50,GR,L
+23,2020,MI,FT,BI Data Analyst,98000,USD,98000,US,0,US,M
+24,2020,MI,FT,Lead Data Scientist,115000,USD,115000,AE,0,AE,L
+25,2020,EX,FT,Director of Data Science,325000,USD,325000,US,100,US,L
+26,2020,EN,FT,Research Scientist,42000,USD,42000,NL,50,NL,L
+27,2020,SE,FT,Data Engineer,720000,MXN,33511,MX,0,MX,S
+28,2020,EN,CT,Business Data Analyst,100000,USD,100000,US,100,US,L
+29,2020,SE,FT,Machine Learning Manager,157000,CAD,117104,CA,50,CA,L
+30,2020,MI,FT,Data Engineering Manager,51999,EUR,59303,DE,100,DE,S
+31,2020,EN,FT,Big Data Engineer,70000,USD,70000,US,100,US,L
+32,2020,SE,FT,Data Scientist,60000,EUR,68428,GR,100,US,L
+33,2020,MI,FT,Research Scientist,450000,USD,450000,US,0,US,M
+34,2020,MI,FT,Data Analyst,41000,EUR,46759,FR,50,FR,L
+35,2020,MI,FT,Data Engineer,65000,EUR,74130,AT,50,AT,L
+36,2020,MI,FT,Data Science Consultant,103000,USD,103000,US,100,US,L
+37,2020,EN,FT,Machine Learning Engineer,250000,USD,250000,US,50,US,L
+38,2020,EN,FT,Data Analyst,10000,USD,10000,NG,100,NG,S
+39,2020,EN,FT,Machine Learning Engineer,138000,USD,138000,US,100,US,S
+40,2020,MI,FT,Data Scientist,45760,USD,45760,PH,100,US,S
+41,2020,EX,FT,Data Engineering Manager,70000,EUR,79833,ES,50,ES,L
+42,2020,MI,FT,Machine Learning Infrastructure Engineer,44000,EUR,50180,PT,0,PT,M
+43,2020,MI,FT,Data Engineer,106000,USD,106000,US,100,US,L
+44,2020,MI,FT,Data Engineer,88000,GBP,112872,GB,50,GB,L
+45,2020,EN,PT,ML Engineer,14000,EUR,15966,DE,100,DE,S
+46,2020,MI,FT,Data Scientist,60000,GBP,76958,GB,100,GB,S
+47,2020,SE,FT,Data Engineer,188000,USD,188000,US,100,US,L
+48,2020,MI,FT,Data Scientist,105000,USD,105000,US,100,US,L
+49,2020,MI,FT,Data Engineer,61500,EUR,70139,FR,50,FR,L
+50,2020,EN,FT,Data Analyst,450000,INR,6072,IN,0,IN,S
+51,2020,EN,FT,Data Analyst,91000,USD,91000,US,100,US,L
+52,2020,EN,FT,AI Scientist,300000,DKK,45896,DK,50,DK,S
+53,2020,EN,FT,Data Engineer,48000,EUR,54742,PK,100,DE,L
+54,2020,SE,FL,Computer Vision Engineer,60000,USD,60000,RU,100,US,S
+55,2020,SE,FT,Principal Data Scientist,130000,EUR,148261,DE,100,DE,M
+56,2020,MI,FT,Data Scientist,34000,EUR,38776,ES,100,ES,M
+57,2020,MI,FT,Data Scientist,118000,USD,118000,US,100,US,M
+58,2020,SE,FT,Data Scientist,120000,USD,120000,US,50,US,L
+59,2020,MI,FT,Data Scientist,138350,USD,138350,US,100,US,M
+60,2020,MI,FT,Data Engineer,110000,USD,110000,US,100,US,L
+61,2020,MI,FT,Data Engineer,130800,USD,130800,ES,100,US,M
+62,2020,EN,PT,Data Scientist,19000,EUR,21669,IT,50,IT,S
+63,2020,SE,FT,Data Scientist,412000,USD,412000,US,100,US,L
+64,2020,SE,FT,Machine Learning Engineer,40000,EUR,45618,HR,100,HR,S
+65,2020,EN,FT,Data Scientist,55000,EUR,62726,DE,50,DE,S
+66,2020,EN,FT,Data Scientist,43200,EUR,49268,DE,0,DE,S
+67,2020,SE,FT,Data Science Manager,190200,USD,190200,US,100,US,M
+68,2020,EN,FT,Data Scientist,105000,USD,105000,US,100,US,S
+69,2020,SE,FT,Data Scientist,80000,EUR,91237,AT,0,AT,S
+70,2020,MI,FT,Data Scientist,55000,EUR,62726,FR,50,LU,S
+71,2020,MI,FT,Data Scientist,37000,EUR,42197,FR,50,FR,S
+72,2021,EN,FT,Research Scientist,60000,GBP,82528,GB,50,GB,L
+73,2021,EX,FT,BI Data Analyst,150000,USD,150000,IN,100,US,L
+74,2021,EX,FT,Head of Data,235000,USD,235000,US,100,US,L
+75,2021,SE,FT,Data Scientist,45000,EUR,53192,FR,50,FR,L
+76,2021,MI,FT,BI Data Analyst,100000,USD,100000,US,100,US,M
+77,2021,MI,PT,3D Computer Vision Researcher,400000,INR,5409,IN,50,IN,M
+78,2021,MI,CT,ML Engineer,270000,USD,270000,US,100,US,L
+79,2021,EN,FT,Data Analyst,80000,USD,80000,US,100,US,M
+80,2021,SE,FT,Data Analytics Engineer,67000,EUR,79197,DE,100,DE,L
+81,2021,MI,FT,Data Engineer,140000,USD,140000,US,100,US,L
+82,2021,MI,FT,Applied Data Scientist,68000,CAD,54238,GB,50,CA,L
+83,2021,MI,FT,Machine Learning Engineer,40000,EUR,47282,ES,100,ES,S
+84,2021,EX,FT,Director of Data Science,130000,EUR,153667,IT,100,PL,L
+85,2021,MI,FT,Data Engineer,110000,PLN,28476,PL,100,PL,L
+86,2021,EN,FT,Data Analyst,50000,EUR,59102,FR,50,FR,M
+87,2021,MI,FT,Data Analytics Engineer,110000,USD,110000,US,100,US,L
+88,2021,SE,FT,Lead Data Analyst,170000,USD,170000,US,100,US,L
+89,2021,SE,FT,Data Analyst,80000,USD,80000,BG,100,US,S
+90,2021,SE,FT,Marketing Data Analyst,75000,EUR,88654,GR,100,DK,L
+91,2021,EN,FT,Data Science Consultant,65000,EUR,76833,DE,100,DE,S
+92,2021,MI,FT,Lead Data Analyst,1450000,INR,19609,IN,100,IN,L
+93,2021,SE,FT,Lead Data Engineer,276000,USD,276000,US,0,US,L
+94,2021,EN,FT,Data Scientist,2200000,INR,29751,IN,50,IN,L
+95,2021,MI,FT,Cloud Data Engineer,120000,SGD,89294,SG,50,SG,L
+96,2021,EN,PT,AI Scientist,12000,USD,12000,BR,100,US,S
+97,2021,MI,FT,Financial Data Analyst,450000,USD,450000,US,100,US,L
+98,2021,EN,FT,Computer Vision Software Engineer,70000,USD,70000,US,100,US,M
+99,2021,MI,FT,Computer Vision Software Engineer,81000,EUR,95746,DE,100,US,S
+100,2021,MI,FT,Data Analyst,75000,USD,75000,US,0,US,L
+101,2021,SE,FT,Data Engineer,150000,USD,150000,US,100,US,L
+102,2021,MI,FT,BI Data Analyst,11000000,HUF,36259,HU,50,US,L
+103,2021,MI,FT,Data Analyst,62000,USD,62000,US,0,US,L
+104,2021,MI,FT,Data Scientist,73000,USD,73000,US,0,US,L
+105,2021,MI,FT,Data Analyst,37456,GBP,51519,GB,50,GB,L
+106,2021,MI,FT,Research Scientist,235000,CAD,187442,CA,100,CA,L
+107,2021,SE,FT,Data Engineer,115000,USD,115000,US,100,US,S
+108,2021,SE,FT,Data Engineer,150000,USD,150000,US,100,US,M
+109,2021,EN,FT,Data Engineer,2250000,INR,30428,IN,100,IN,L
+110,2021,SE,FT,Machine Learning Engineer,80000,EUR,94564,DE,50,DE,L
+111,2021,SE,FT,Director of Data Engineering,82500,GBP,113476,GB,100,GB,M
+112,2021,SE,FT,Lead Data Engineer,75000,GBP,103160,GB,100,GB,S
+113,2021,EN,PT,AI Scientist,12000,USD,12000,PK,100,US,M
+114,2021,MI,FT,Data Engineer,38400,EUR,45391,NL,100,NL,L
+115,2021,EN,FT,Machine Learning Scientist,225000,USD,225000,US,100,US,L
+116,2021,MI,FT,Data Scientist,50000,USD,50000,NG,100,NG,L
+117,2021,MI,FT,Data Science Engineer,34000,EUR,40189,GR,100,GR,M
+118,2021,EN,FT,Data Analyst,90000,USD,90000,US,100,US,S
+119,2021,MI,FT,Data Engineer,200000,USD,200000,US,100,US,L
+120,2021,MI,FT,Big Data Engineer,60000,USD,60000,ES,50,RO,M
+121,2021,SE,FT,Principal Data Engineer,200000,USD,200000,US,100,US,M
+122,2021,EN,FT,Data Analyst,50000,USD,50000,US,100,US,M
+123,2021,EN,FT,Applied Data Scientist,80000,GBP,110037,GB,0,GB,L
+124,2021,EN,PT,Data Analyst,8760,EUR,10354,ES,50,ES,M
+125,2021,MI,FT,Principal Data Scientist,151000,USD,151000,US,100,US,L
+126,2021,SE,FT,Machine Learning Scientist,120000,USD,120000,US,50,US,S
+127,2021,MI,FT,Data Scientist,700000,INR,9466,IN,0,IN,S
+128,2021,EN,FT,Machine Learning Engineer,20000,USD,20000,IN,100,IN,S
+129,2021,SE,FT,Lead Data Scientist,3000000,INR,40570,IN,50,IN,L
+130,2021,EN,FT,Machine Learning Developer,100000,USD,100000,IQ,50,IQ,S
+131,2021,EN,FT,Data Scientist,42000,EUR,49646,FR,50,FR,M
+132,2021,MI,FT,Applied Machine Learning Scientist,38400,USD,38400,VN,100,US,M
+133,2021,SE,FT,Computer Vision Engineer,24000,USD,24000,BR,100,BR,M
+134,2021,EN,FT,Data Scientist,100000,USD,100000,US,0,US,S
+135,2021,MI,FT,Data Analyst,90000,USD,90000,US,100,US,M
+136,2021,MI,FT,ML Engineer,7000000,JPY,63711,JP,50,JP,S
+137,2021,MI,FT,ML Engineer,8500000,JPY,77364,JP,50,JP,S
+138,2021,SE,FT,Principal Data Scientist,220000,USD,220000,US,0,US,L
+139,2021,EN,FT,Data Scientist,80000,USD,80000,US,100,US,M
+140,2021,MI,FT,Data Analyst,135000,USD,135000,US,100,US,L
+141,2021,SE,FT,Data Science Manager,240000,USD,240000,US,0,US,L
+142,2021,SE,FT,Data Engineering Manager,150000,USD,150000,US,0,US,L
+143,2021,MI,FT,Data Scientist,82500,USD,82500,US,100,US,S
+144,2021,MI,FT,Data Engineer,100000,USD,100000,US,100,US,L
+145,2021,SE,FT,Machine Learning Engineer,70000,EUR,82744,BE,50,BE,M
+146,2021,MI,FT,Research Scientist,53000,EUR,62649,FR,50,FR,M
+147,2021,MI,FT,Data Engineer,90000,USD,90000,US,100,US,L
+148,2021,SE,FT,Data Engineering Manager,153000,USD,153000,US,100,US,L
+149,2021,SE,FT,Cloud Data Engineer,160000,USD,160000,BR,100,US,S
+150,2021,SE,FT,Director of Data Science,168000,USD,168000,JP,0,JP,S
+151,2021,MI,FT,Data Scientist,150000,USD,150000,US,100,US,M
+152,2021,MI,FT,Data Scientist,95000,CAD,75774,CA,100,CA,L
+153,2021,EN,FT,Data Scientist,13400,USD,13400,UA,100,UA,L
+154,2021,SE,FT,Data Science Manager,144000,USD,144000,US,100,US,L
+155,2021,SE,FT,Data Science Engineer,159500,CAD,127221,CA,50,CA,L
+156,2021,MI,FT,Data Scientist,160000,SGD,119059,SG,100,IL,M
+157,2021,MI,FT,Applied Machine Learning Scientist,423000,USD,423000,US,50,US,L
+158,2021,SE,FT,Data Analytics Manager,120000,USD,120000,US,100,US,M
+159,2021,EN,FT,Machine Learning Engineer,125000,USD,125000,US,100,US,S
+160,2021,EX,FT,Head of Data,230000,USD,230000,RU,50,RU,L
+161,2021,EX,FT,Head of Data Science,85000,USD,85000,RU,0,RU,M
+162,2021,MI,FT,Data Engineer,24000,EUR,28369,MT,50,MT,L
+163,2021,EN,FT,Data Science Consultant,54000,EUR,63831,DE,50,DE,L
+164,2021,EX,FT,Director of Data Science,110000,EUR,130026,DE,50,DE,M
+165,2021,SE,FT,Data Specialist,165000,USD,165000,US,100,US,L
+166,2021,EN,FT,Data Engineer,80000,USD,80000,US,100,US,L
+167,2021,EX,FT,Director of Data Science,250000,USD,250000,US,0,US,L
+168,2021,EN,FT,BI Data Analyst,55000,USD,55000,US,50,US,S
+169,2021,MI,FT,Data Architect,150000,USD,150000,US,100,US,L
+170,2021,MI,FT,Data Architect,170000,USD,170000,US,100,US,L
+171,2021,MI,FT,Data Engineer,60000,GBP,82528,GB,100,GB,L
+172,2021,EN,FT,Data Analyst,60000,USD,60000,US,100,US,S
+173,2021,SE,FT,Principal Data Scientist,235000,USD,235000,US,100,US,L
+174,2021,SE,FT,Research Scientist,51400,EUR,60757,PT,50,PT,L
+175,2021,SE,FT,Data Engineering Manager,174000,USD,174000,US,100,US,L
+176,2021,MI,FT,Data Scientist,58000,MXN,2859,MX,0,MX,S
+177,2021,MI,FT,Data Scientist,30400000,CLP,40038,CL,100,CL,L
+178,2021,EN,FT,Machine Learning Engineer,81000,USD,81000,US,50,US,S
+179,2021,MI,FT,Data Scientist,420000,INR,5679,IN,100,US,S
+180,2021,MI,FT,Big Data Engineer,1672000,INR,22611,IN,0,IN,L
+181,2021,MI,FT,Data Scientist,76760,EUR,90734,DE,50,DE,L
+182,2021,MI,FT,Data Engineer,22000,EUR,26005,RO,0,US,L
+183,2021,SE,FT,Finance Data Analyst,45000,GBP,61896,GB,50,GB,L
+184,2021,MI,FL,Machine Learning Scientist,12000,USD,12000,PK,50,PK,M
+185,2021,MI,FT,Data Engineer,4000,USD,4000,IR,100,IR,M
+186,2021,SE,FT,Data Analytics Engineer,50000,USD,50000,VN,100,GB,M
+187,2021,EX,FT,Data Science Consultant,59000,EUR,69741,FR,100,ES,S
+188,2021,SE,FT,Data Engineer,65000,EUR,76833,RO,50,GB,S
+189,2021,MI,FT,Machine Learning Engineer,74000,USD,74000,JP,50,JP,S
+190,2021,SE,FT,Data Science Manager,152000,USD,152000,US,100,FR,L
+191,2021,EN,FT,Machine Learning Engineer,21844,USD,21844,CO,50,CO,M
+192,2021,MI,FT,Big Data Engineer,18000,USD,18000,MD,0,MD,S
+193,2021,SE,FT,Data Science Manager,174000,USD,174000,US,100,US,L
+194,2021,SE,FT,Research Scientist,120500,CAD,96113,CA,50,CA,L
+195,2021,MI,FT,Data Scientist,147000,USD,147000,US,50,US,L
+196,2021,EN,FT,BI Data Analyst,9272,USD,9272,KE,100,KE,S
+197,2021,SE,FT,Machine Learning Engineer,1799997,INR,24342,IN,100,IN,L
+198,2021,SE,FT,Data Science Manager,4000000,INR,54094,IN,50,US,L
+199,2021,EN,FT,Data Science Consultant,90000,USD,90000,US,100,US,S
+200,2021,MI,FT,Data Scientist,52000,EUR,61467,DE,50,AT,M
+201,2021,SE,FT,Machine Learning Infrastructure Engineer,195000,USD,195000,US,100,US,M
+202,2021,MI,FT,Data Scientist,32000,EUR,37825,ES,100,ES,L
+203,2021,SE,FT,Research Scientist,50000,USD,50000,FR,100,US,S
+204,2021,MI,FT,Data Scientist,160000,USD,160000,US,100,US,L
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+462,2022,MI,PT,Data Engineer,50000,EUR,54957,DE,50,DE,L
+463,2022,EN,FT,Data Scientist,1400000,INR,18442,IN,100,IN,M
+464,2022,SE,FT,Principal Data Scientist,148000,EUR,162674,DE,100,DE,M
+465,2022,EN,FT,Data Engineer,120000,USD,120000,US,100,US,M
+466,2022,SE,FT,Research Scientist,144000,USD,144000,US,50,US,L
+467,2022,SE,FT,Data Scientist,104890,USD,104890,US,100,US,M
+468,2022,SE,FT,Data Engineer,100000,USD,100000,US,100,US,M
+469,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
+470,2022,MI,FT,Data Analyst,135000,USD,135000,US,100,US,M
+471,2022,MI,FT,Data Analyst,50000,USD,50000,US,100,US,M
+472,2022,SE,FT,Data Scientist,220000,USD,220000,US,100,US,M
+473,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
+474,2022,MI,FT,Data Scientist,140000,GBP,183228,GB,0,GB,M
+475,2022,MI,FT,Data Scientist,70000,GBP,91614,GB,0,GB,M
+476,2022,SE,FT,Data Scientist,185100,USD,185100,US,100,US,M
+477,2022,SE,FT,Machine Learning Engineer,220000,USD,220000,US,100,US,M
+478,2022,MI,FT,Data Scientist,200000,USD,200000,US,100,US,M
+479,2022,MI,FT,Data Scientist,120000,USD,120000,US,100,US,M
+480,2022,SE,FT,Machine Learning Engineer,120000,USD,120000,AE,100,AE,S
+481,2022,SE,FT,Machine Learning Engineer,65000,USD,65000,AE,100,AE,S
+482,2022,EX,FT,Data Engineer,324000,USD,324000,US,100,US,M
+483,2022,EX,FT,Data Engineer,216000,USD,216000,US,100,US,M
+484,2022,SE,FT,Data Engineer,210000,USD,210000,US,100,US,M
+485,2022,SE,FT,Machine Learning Engineer,120000,USD,120000,US,100,US,M
+486,2022,SE,FT,Data Scientist,230000,USD,230000,US,100,US,M
+487,2022,EN,PT,Data Scientist,100000,USD,100000,DZ,50,DZ,M
+488,2022,MI,FL,Data Scientist,100000,USD,100000,CA,100,US,M
+489,2022,EN,CT,Applied Machine Learning Scientist,29000,EUR,31875,TN,100,CZ,M
+490,2022,SE,FT,Head of Data,200000,USD,200000,MY,100,US,M
+491,2022,MI,FT,Principal Data Analyst,75000,USD,75000,CA,100,CA,S
+492,2022,MI,FT,Data Scientist,150000,PLN,35590,PL,100,PL,L
+493,2022,SE,FT,Machine Learning Developer,100000,CAD,78791,CA,100,CA,M
+494,2022,SE,FT,Data Scientist,100000,USD,100000,BR,100,US,M
+495,2022,MI,FT,Machine Learning Scientist,153000,USD,153000,US,50,US,M
+496,2022,EN,FT,Data Engineer,52800,EUR,58035,PK,100,DE,M
+497,2022,SE,FT,Data Scientist,165000,USD,165000,US,100,US,M
+498,2022,SE,FT,Research Scientist,85000,EUR,93427,FR,50,FR,L
+499,2022,EN,FT,Data Scientist,66500,CAD,52396,CA,100,CA,L
+500,2022,SE,FT,Machine Learning Engineer,57000,EUR,62651,NL,100,NL,L
+501,2022,MI,FT,Head of Data,30000,EUR,32974,EE,100,EE,S
+502,2022,EN,FT,Data Scientist,40000,USD,40000,JP,100,MY,L
+503,2022,MI,FT,Machine Learning Engineer,121000,AUD,87425,AU,100,AU,L
+504,2022,SE,FT,Data Engineer,115000,USD,115000,US,100,US,M
+505,2022,EN,FT,Data Scientist,120000,AUD,86703,AU,50,AU,M
+506,2022,MI,FT,Applied Machine Learning Scientist,75000,USD,75000,BO,100,US,L
+507,2022,MI,FT,Research Scientist,59000,EUR,64849,AT,0,AT,L
+508,2022,EN,FT,Research Scientist,120000,USD,120000,US,100,US,L
+509,2022,MI,FT,Applied Data Scientist,157000,USD,157000,US,100,US,L
+510,2022,EN,FT,Computer Vision Software Engineer,150000,USD,150000,AU,100,AU,S
+511,2022,MI,FT,Business Data Analyst,90000,CAD,70912,CA,50,CA,L
+512,2022,EN,FT,Data Engineer,65000,USD,65000,US,100,US,S
+513,2022,SE,FT,Machine Learning Engineer,65000,EUR,71444,IE,100,IE,S
+514,2022,EN,FT,Data Analytics Engineer,20000,USD,20000,PK,0,PK,M
+515,2022,MI,FT,Data Scientist,48000,USD,48000,RU,100,US,S
+516,2022,SE,FT,Data Science Manager,152500,USD,152500,US,100,US,M
+517,2022,MI,FT,Data Engineer,62000,EUR,68147,FR,100,FR,M
+518,2022,MI,FT,Data Scientist,115000,CHF,122346,CH,0,CH,L
+519,2022,SE,FT,Applied Data Scientist,380000,USD,380000,US,100,US,L
+520,2022,MI,FT,Data Scientist,88000,CAD,69336,CA,100,CA,M
+521,2022,EN,FT,Computer Vision Engineer,10000,USD,10000,PT,100,LU,M
+522,2022,MI,FT,Data Analyst,20000,USD,20000,GR,100,GR,S
+523,2022,SE,FT,Data Analytics Lead,405000,USD,405000,US,100,US,L
+524,2022,MI,FT,Data Scientist,135000,USD,135000,US,100,US,L
+525,2022,SE,FT,Applied Data Scientist,177000,USD,177000,US,100,US,L
+526,2022,MI,FT,Data Scientist,78000,USD,78000,US,100,US,M
+527,2022,SE,FT,Data Analyst,135000,USD,135000,US,100,US,M
+528,2022,SE,FT,Data Analyst,100000,USD,100000,US,100,US,M
+529,2022,SE,FT,Data Analyst,90320,USD,90320,US,100,US,M
+530,2022,MI,FT,Data Analyst,85000,USD,85000,CA,0,CA,M
+531,2022,MI,FT,Data Analyst,75000,USD,75000,CA,0,CA,M
+532,2022,SE,FT,Machine Learning Engineer,214000,USD,214000,US,100,US,M
+533,2022,SE,FT,Machine Learning Engineer,192600,USD,192600,US,100,US,M
+534,2022,SE,FT,Data Architect,266400,USD,266400,US,100,US,M
+535,2022,SE,FT,Data Architect,213120,USD,213120,US,100,US,M
+536,2022,SE,FT,Data Analyst,112900,USD,112900,US,100,US,M
+537,2022,SE,FT,Data Engineer,155000,USD,155000,US,100,US,M
+538,2022,MI,FT,Data Scientist,141300,USD,141300,US,0,US,M
+539,2022,MI,FT,Data Scientist,102100,USD,102100,US,0,US,M
+540,2022,SE,FT,Data Analyst,115934,USD,115934,US,100,US,M
+541,2022,SE,FT,Data Analyst,81666,USD,81666,US,100,US,M
+542,2022,MI,FT,Data Engineer,206699,USD,206699,US,0,US,M
+543,2022,MI,FT,Data Engineer,99100,USD,99100,US,0,US,M
+544,2022,SE,FT,Data Engineer,130000,USD,130000,US,100,US,M
+545,2022,SE,FT,Data Engineer,115000,USD,115000,US,100,US,M
+546,2022,SE,FT,Data Engineer,110500,USD,110500,US,100,US,M
+547,2022,SE,FT,Data Engineer,130000,USD,130000,US,100,US,M
+548,2022,SE,FT,Data Analyst,99050,USD,99050,US,100,US,M
+549,2022,SE,FT,Data Engineer,160000,USD,160000,US,100,US,M
+550,2022,SE,FT,Data Scientist,205300,USD,205300,US,0,US,L
+551,2022,SE,FT,Data Scientist,140400,USD,140400,US,0,US,L
+552,2022,SE,FT,Data Scientist,176000,USD,176000,US,100,US,M
+553,2022,SE,FT,Data Scientist,144000,USD,144000,US,100,US,M
+554,2022,SE,FT,Data Engineer,200100,USD,200100,US,100,US,M
+555,2022,SE,FT,Data Engineer,160000,USD,160000,US,100,US,M
+556,2022,SE,FT,Data Engineer,145000,USD,145000,US,100,US,M
+557,2022,SE,FT,Data Engineer,70500,USD,70500,US,0,US,M
+558,2022,SE,FT,Data Scientist,205300,USD,205300,US,0,US,M
+559,2022,SE,FT,Data Scientist,140400,USD,140400,US,0,US,M
+560,2022,SE,FT,Analytics Engineer,205300,USD,205300,US,0,US,M
+561,2022,SE,FT,Analytics Engineer,184700,USD,184700,US,0,US,M
+562,2022,SE,FT,Data Engineer,175100,USD,175100,US,100,US,M
+563,2022,SE,FT,Data Engineer,140250,USD,140250,US,100,US,M
+564,2022,SE,FT,Data Analyst,116150,USD,116150,US,100,US,M
+565,2022,SE,FT,Data Engineer,54000,USD,54000,US,0,US,M
+566,2022,SE,FT,Data Analyst,170000,USD,170000,US,100,US,M
+567,2022,MI,FT,Data Analyst,50000,GBP,65438,GB,0,GB,M
+568,2022,SE,FT,Data Analyst,80000,USD,80000,US,100,US,M
+569,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
+570,2022,SE,FT,Data Scientist,210000,USD,210000,US,100,US,M
+571,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
+572,2022,SE,FT,Data Analyst,100000,USD,100000,US,100,US,M
+573,2022,SE,FT,Data Analyst,69000,USD,69000,US,100,US,M
+574,2022,SE,FT,Data Scientist,210000,USD,210000,US,100,US,M
+575,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
+576,2022,SE,FT,Data Scientist,210000,USD,210000,US,100,US,M
+577,2022,SE,FT,Data Analyst,150075,USD,150075,US,100,US,M
+578,2022,SE,FT,Data Engineer,100000,USD,100000,US,100,US,M
+579,2022,SE,FT,Data Engineer,25000,USD,25000,US,100,US,M
+580,2022,SE,FT,Data Analyst,126500,USD,126500,US,100,US,M
+581,2022,SE,FT,Data Analyst,106260,USD,106260,US,100,US,M
+582,2022,SE,FT,Data Engineer,220110,USD,220110,US,100,US,M
+583,2022,SE,FT,Data Engineer,160080,USD,160080,US,100,US,M
+584,2022,SE,FT,Data Analyst,105000,USD,105000,US,100,US,M
+585,2022,SE,FT,Data Analyst,110925,USD,110925,US,100,US,M
+586,2022,MI,FT,Data Analyst,35000,GBP,45807,GB,0,GB,M
+587,2022,SE,FT,Data Scientist,140000,USD,140000,US,100,US,M
+588,2022,SE,FT,Data Analyst,99000,USD,99000,US,0,US,M
+589,2022,SE,FT,Data Analyst,60000,USD,60000,US,100,US,M
+590,2022,SE,FT,Data Architect,192564,USD,192564,US,100,US,M
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+592,2022,SE,FT,Data Scientist,230000,USD,230000,US,100,US,M
+593,2022,SE,FT,Data Scientist,150000,USD,150000,US,100,US,M
+594,2022,SE,FT,Data Analytics Manager,150260,USD,150260,US,100,US,M
+595,2022,SE,FT,Data Analytics Manager,109280,USD,109280,US,100,US,M
+596,2022,SE,FT,Data Scientist,210000,USD,210000,US,100,US,M
+597,2022,SE,FT,Data Analyst,170000,USD,170000,US,100,US,M
+598,2022,MI,FT,Data Scientist,160000,USD,160000,US,100,US,M
+599,2022,MI,FT,Data Scientist,130000,USD,130000,US,100,US,M
+600,2022,EN,FT,Data Analyst,67000,USD,67000,CA,0,CA,M
+601,2022,EN,FT,Data Analyst,52000,USD,52000,CA,0,CA,M
+602,2022,SE,FT,Data Engineer,154000,USD,154000,US,100,US,M
+603,2022,SE,FT,Data Engineer,126000,USD,126000,US,100,US,M
+604,2022,SE,FT,Data Analyst,129000,USD,129000,US,0,US,M
+605,2022,SE,FT,Data Analyst,150000,USD,150000,US,100,US,M
+606,2022,MI,FT,AI Scientist,200000,USD,200000,IN,100,US,L
diff --git a/exercicios/para-sala/Titanica_tratado.csv b/exercicios/para-sala/Titanica_tratado.csv
new file mode 100644
index 0000000..52235b6
--- /dev/null
+++ b/exercicios/para-sala/Titanica_tratado.csv
@@ -0,0 +1,892 @@
+IdPassegeiro,Sobreviveu,Classe,Nome,Genero,Idade,Bilhete,Tarifa,Cabine,Embarque
+1,0,3,"Braund, Mr. Owen Harris",male,22.0,A/5 21171,7.25,,S
+2,1,1,"Cumings, Mrs. John Bradley (Florence Briggs Thayer)",female,38.0,PC 17599,71.2833,C85,C
+3,1,3,"Heikkinen, Miss. Laina",female,26.0,STON/O2. 3101282,7.925,,S
+4,1,1,"Futrelle, Mrs. Jacques Heath (Lily May Peel)",female,35.0,113803,53.1,C123,S
+5,0,3,"Allen, Mr. William Henry",male,35.0,373450,8.05,,S
+6,0,3,"Moran, Mr. James",male,,330877,8.4583,,Q
+7,0,1,"McCarthy, Mr. Timothy J",male,54.0,17463,51.8625,E46,S
+8,0,3,"Palsson, Master. Gosta Leonard",male,2.0,349909,21.075,,S
+9,1,3,"Johnson, Mrs. Oscar W (Elisabeth Vilhelmina Berg)",female,27.0,347742,11.1333,,S
+10,1,2,"Nasser, Mrs. Nicholas (Adele Achem)",female,14.0,237736,30.0708,,C
+11,1,3,"Sandstrom, Miss. Marguerite Rut",female,4.0,PP 9549,16.7,G6,S
+12,1,1,"Bonnell, Miss. Elizabeth",female,58.0,113783,26.55,C103,S
+13,0,3,"Saundercock, Mr. William Henry",male,20.0,A/5. 2151,8.05,,S
+14,0,3,"Andersson, Mr. Anders Johan",male,39.0,347082,31.275,,S
+15,0,3,"Vestrom, Miss. Hulda Amanda Adolfina",female,14.0,350406,7.8542,,S
+16,1,2,"Hewlett, Mrs. (Mary D Kingcome) ",female,55.0,248706,16.0,,S
+17,0,3,"Rice, Master. Eugene",male,2.0,382652,29.125,,Q
+18,1,2,"Williams, Mr. Charles Eugene",male,,244373,13.0,,S
+19,0,3,"Vander Planke, Mrs. Julius (Emelia Maria Vandemoortele)",female,31.0,345763,18.0,,S
+20,1,3,"Masselmani, Mrs. Fatima",female,,2649,7.225,,C
+21,0,2,"Fynney, Mr. Joseph J",male,35.0,239865,26.0,,S
+22,1,2,"Beesley, Mr. Lawrence",male,34.0,248698,13.0,D56,S
+23,1,3,"McGowan, Miss. Anna ""Annie""",female,15.0,330923,8.0292,,Q
+24,1,1,"Sloper, Mr. William Thompson",male,28.0,113788,35.5,A6,S
+25,0,3,"Palsson, Miss. Torborg Danira",female,8.0,349909,21.075,,S
+26,1,3,"Asplund, Mrs. Carl Oscar (Selma Augusta Emilia Johansson)",female,38.0,347077,31.3875,,S
+27,0,3,"Emir, Mr. Farred Chehab",male,,2631,7.225,,C
+28,0,1,"Fortune, Mr. Charles Alexander",male,19.0,19950,263.0,C23 C25 C27,S
+29,1,3,"O'Dwyer, Miss. Ellen ""Nellie""",female,,330959,7.8792,,Q
+30,0,3,"Todoroff, Mr. Lalio",male,,349216,7.8958,,S
+31,0,1,"Uruchurtu, Don. Manuel E",male,40.0,PC 17601,27.7208,,C
+32,1,1,"Spencer, Mrs. William Augustus (Marie Eugenie)",female,,PC 17569,146.5208,B78,C
+33,1,3,"Glynn, Miss. Mary Agatha",female,,335677,7.75,,Q
+34,0,2,"Wheadon, Mr. Edward H",male,66.0,C.A. 24579,10.5,,S
+35,0,1,"Meyer, Mr. Edgar Joseph",male,28.0,PC 17604,82.1708,,C
+36,0,1,"Holverson, Mr. Alexander Oskar",male,42.0,113789,52.0,,S
+37,1,3,"Mamee, Mr. Hanna",male,,2677,7.2292,,C
+38,0,3,"Cann, Mr. Ernest Charles",male,21.0,A./5. 2152,8.05,,S
+39,0,3,"Vander Planke, Miss. Augusta Maria",female,18.0,345764,18.0,,S
+40,1,3,"Nicola-Yarred, Miss. Jamila",female,14.0,2651,11.2417,,C
+41,0,3,"Ahlin, Mrs. Johan (Johanna Persdotter Larsson)",female,40.0,7546,9.475,,S
+42,0,2,"Turpin, Mrs. William John Robert (Dorothy Ann Wonnacott)",female,27.0,11668,21.0,,S
+43,0,3,"Kraeff, Mr. Theodor",male,,349253,7.8958,,C
+44,1,2,"Laroche, Miss. Simonne Marie Anne Andree",female,3.0,SC/Paris 2123,41.5792,,C
+45,1,3,"Devaney, Miss. Margaret Delia",female,19.0,330958,7.8792,,Q
+46,0,3,"Rogers, Mr. William John",male,,S.C./A.4. 23567,8.05,,S
+47,0,3,"Lennon, Mr. Denis",male,,370371,15.5,,Q
+48,1,3,"O'Driscoll, Miss. Bridget",female,,14311,7.75,,Q
+49,0,3,"Samaan, Mr. Youssef",male,,2662,21.6792,,C
+50,0,3,"Arnold-Franchi, Mrs. Josef (Josefine Franchi)",female,18.0,349237,17.8,,S
+51,0,3,"Panula, Master. Juha Niilo",male,7.0,3101295,39.6875,,S
+52,0,3,"Nosworthy, Mr. Richard Cater",male,21.0,A/4. 39886,7.8,,S
+53,1,1,"Harper, Mrs. Henry Sleeper (Myna Haxtun)",female,49.0,PC 17572,76.7292,D33,C
+54,1,2,"Faunthorpe, Mrs. Lizzie (Elizabeth Anne Wilkinson)",female,29.0,2926,26.0,,S
+55,0,1,"Ostby, Mr. Engelhart Cornelius",male,65.0,113509,61.9792,B30,C
+56,1,1,"Woolner, Mr. Hugh",male,,19947,35.5,C52,S
+57,1,2,"Rugg, Miss. Emily",female,21.0,C.A. 31026,10.5,,S
+58,0,3,"Novel, Mr. Mansouer",male,28.5,2697,7.2292,,C
+59,1,2,"West, Miss. Constance Mirium",female,5.0,C.A. 34651,27.75,,S
+60,0,3,"Goodwin, Master. William Frederick",male,11.0,CA 2144,46.9,,S
+61,0,3,"Sirayanian, Mr. Orsen",male,22.0,2669,7.2292,,C
+62,1,1,"Icard, Miss. Amelie",female,38.0,113572,80.0,B28,
+63,0,1,"Harris, Mr. Henry Birkhardt",male,45.0,36973,83.475,C83,S
+64,0,3,"Skoog, Master. Harald",male,4.0,347088,27.9,,S
+65,0,1,"Stewart, Mr. Albert A",male,,PC 17605,27.7208,,C
+66,1,3,"Moubarek, Master. Gerios",male,,2661,15.2458,,C
+67,1,2,"Nye, Mrs. (Elizabeth Ramell)",female,29.0,C.A. 29395,10.5,F33,S
+68,0,3,"Crease, Mr. Ernest James",male,19.0,S.P. 3464,8.1583,,S
+69,1,3,"Andersson, Miss. Erna Alexandra",female,17.0,3101281,7.925,,S
+70,0,3,"Kink, Mr. Vincenz",male,26.0,315151,8.6625,,S
+71,0,2,"Jenkin, Mr. Stephen Curnow",male,32.0,C.A. 33111,10.5,,S
+72,0,3,"Goodwin, Miss. Lillian Amy",female,16.0,CA 2144,46.9,,S
+73,0,2,"Hood, Mr. Ambrose Jr",male,21.0,S.O.C. 14879,73.5,,S
+74,0,3,"Chronopoulos, Mr. Apostolos",male,26.0,2680,14.4542,,C
+75,1,3,"Bing, Mr. Lee",male,32.0,1601,56.4958,,S
+76,0,3,"Moen, Mr. Sigurd Hansen",male,25.0,348123,7.65,F G73,S
+77,0,3,"Staneff, Mr. Ivan",male,,349208,7.8958,,S
+78,0,3,"Moutal, Mr. Rahamin Haim",male,,374746,8.05,,S
+79,1,2,"Caldwell, Master. Alden Gates",male,0.83,248738,29.0,,S
+80,1,3,"Dowdell, Miss. Elizabeth",female,30.0,364516,12.475,,S
+81,0,3,"Waelens, Mr. Achille",male,22.0,345767,9.0,,S
+82,1,3,"Sheerlinck, Mr. Jan Baptist",male,29.0,345779,9.5,,S
+83,1,3,"McDermott, Miss. Brigdet Delia",female,,330932,7.7875,,Q
+84,0,1,"Carrau, Mr. Francisco M",male,28.0,113059,47.1,,S
+85,1,2,"Ilett, Miss. Bertha",female,17.0,SO/C 14885,10.5,,S
+86,1,3,"Backstrom, Mrs. Karl Alfred (Maria Mathilda Gustafsson)",female,33.0,3101278,15.85,,S
+87,0,3,"Ford, Mr. William Neal",male,16.0,W./C. 6608,34.375,,S
+88,0,3,"Slocovski, Mr. Selman Francis",male,,SOTON/OQ 392086,8.05,,S
+89,1,1,"Fortune, Miss. Mabel Helen",female,23.0,19950,263.0,C23 C25 C27,S
+90,0,3,"Celotti, Mr. Francesco",male,24.0,343275,8.05,,S
+91,0,3,"Christmann, Mr. Emil",male,29.0,343276,8.05,,S
+92,0,3,"Andreasson, Mr. Paul Edvin",male,20.0,347466,7.8542,,S
+93,0,1,"Chaffee, Mr. Herbert Fuller",male,46.0,W.E.P. 5734,61.175,E31,S
+94,0,3,"Dean, Mr. Bertram Frank",male,26.0,C.A. 2315,20.575,,S
+95,0,3,"Coxon, Mr. Daniel",male,59.0,364500,7.25,,S
+96,0,3,"Shorney, Mr. Charles Joseph",male,,374910,8.05,,S
+97,0,1,"Goldschmidt, Mr. George B",male,71.0,PC 17754,34.6542,A5,C
+98,1,1,"Greenfield, Mr. William Bertram",male,23.0,PC 17759,63.3583,D10 D12,C
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+803,1,1,"Carter, Master. William Thornton II",male,11.0,113760,120.0,B96 B98,S
+804,1,3,"Thomas, Master. Assad Alexander",male,0.42,2625,8.5167,,C
+805,1,3,"Hedman, Mr. Oskar Arvid",male,27.0,347089,6.975,,S
+806,0,3,"Johansson, Mr. Karl Johan",male,31.0,347063,7.775,,S
+807,0,1,"Andrews, Mr. Thomas Jr",male,39.0,112050,0.0,A36,S
+808,0,3,"Pettersson, Miss. Ellen Natalia",female,18.0,347087,7.775,,S
+809,0,2,"Meyer, Mr. August",male,39.0,248723,13.0,,S
+810,1,1,"Chambers, Mrs. Norman Campbell (Bertha Griggs)",female,33.0,113806,53.1,E8,S
+811,0,3,"Alexander, Mr. William",male,26.0,3474,7.8875,,S
+812,0,3,"Lester, Mr. James",male,39.0,A/4 48871,24.15,,S
+813,0,2,"Slemen, Mr. Richard James",male,35.0,28206,10.5,,S
+814,0,3,"Andersson, Miss. Ebba Iris Alfrida",female,6.0,347082,31.275,,S
+815,0,3,"Tomlin, Mr. Ernest Portage",male,30.5,364499,8.05,,S
+816,0,1,"Fry, Mr. Richard",male,,112058,0.0,B102,S
+817,0,3,"Heininen, Miss. Wendla Maria",female,23.0,STON/O2. 3101290,7.925,,S
+818,0,2,"Mallet, Mr. Albert",male,31.0,S.C./PARIS 2079,37.0042,,C
+819,0,3,"Holm, Mr. John Fredrik Alexander",male,43.0,C 7075,6.45,,S
+820,0,3,"Skoog, Master. Karl Thorsten",male,10.0,347088,27.9,,S
+821,1,1,"Hays, Mrs. Charles Melville (Clara Jennings Gregg)",female,52.0,12749,93.5,B69,S
+822,1,3,"Lulic, Mr. Nikola",male,27.0,315098,8.6625,,S
+823,0,1,"Reuchlin, Jonkheer. John George",male,38.0,19972,0.0,,S
+824,1,3,"Moor, Mrs. (Beila)",female,27.0,392096,12.475,E121,S
+825,0,3,"Panula, Master. Urho Abraham",male,2.0,3101295,39.6875,,S
+826,0,3,"Flynn, Mr. John",male,,368323,6.95,,Q
+827,0,3,"Lam, Mr. Len",male,,1601,56.4958,,S
+828,1,2,"Mallet, Master. Andre",male,1.0,S.C./PARIS 2079,37.0042,,C
+829,1,3,"McCormack, Mr. Thomas Joseph",male,,367228,7.75,,Q
+830,1,1,"Stone, Mrs. George Nelson (Martha Evelyn)",female,62.0,113572,80.0,B28,
+831,1,3,"Yasbeck, Mrs. Antoni (Selini Alexander)",female,15.0,2659,14.4542,,C
+832,1,2,"Richards, Master. George Sibley",male,0.83,29106,18.75,,S
+833,0,3,"Saad, Mr. Amin",male,,2671,7.2292,,C
+834,0,3,"Augustsson, Mr. Albert",male,23.0,347468,7.8542,,S
+835,0,3,"Allum, Mr. Owen George",male,18.0,2223,8.3,,S
+836,1,1,"Compton, Miss. Sara Rebecca",female,39.0,PC 17756,83.1583,E49,C
+837,0,3,"Pasic, Mr. Jakob",male,21.0,315097,8.6625,,S
+838,0,3,"Sirota, Mr. Maurice",male,,392092,8.05,,S
+839,1,3,"Chip, Mr. Chang",male,32.0,1601,56.4958,,S
+840,1,1,"Marechal, Mr. Pierre",male,,11774,29.7,C47,C
+841,0,3,"Alhomaki, Mr. Ilmari Rudolf",male,20.0,SOTON/O2 3101287,7.925,,S
+842,0,2,"Mudd, Mr. Thomas Charles",male,16.0,S.O./P.P. 3,10.5,,S
+843,1,1,"Serepeca, Miss. Augusta",female,30.0,113798,31.0,,C
+844,0,3,"Lemberopolous, Mr. Peter L",male,34.5,2683,6.4375,,C
+845,0,3,"Culumovic, Mr. Jeso",male,17.0,315090,8.6625,,S
+846,0,3,"Abbing, Mr. Anthony",male,42.0,C.A. 5547,7.55,,S
+847,0,3,"Sage, Mr. Douglas Bullen",male,,CA. 2343,69.55,,S
+848,0,3,"Markoff, Mr. Marin",male,35.0,349213,7.8958,,C
+849,0,2,"Harper, Rev. John",male,28.0,248727,33.0,,S
+850,1,1,"Goldenberg, Mrs. Samuel L (Edwiga Grabowska)",female,,17453,89.1042,C92,C
+851,0,3,"Andersson, Master. Sigvard Harald Elias",male,4.0,347082,31.275,,S
+852,0,3,"Svensson, Mr. Johan",male,74.0,347060,7.775,,S
+853,0,3,"Boulos, Miss. Nourelain",female,9.0,2678,15.2458,,C
+854,1,1,"Lines, Miss. Mary Conover",female,16.0,PC 17592,39.4,D28,S
+855,0,2,"Carter, Mrs. Ernest Courtenay (Lilian Hughes)",female,44.0,244252,26.0,,S
+856,1,3,"Aks, Mrs. Sam (Leah Rosen)",female,18.0,392091,9.35,,S
+857,1,1,"Wick, Mrs. George Dennick (Mary Hitchcock)",female,45.0,36928,164.8667,,S
+858,1,1,"Daly, Mr. Peter Denis ",male,51.0,113055,26.55,E17,S
+859,1,3,"Baclini, Mrs. Solomon (Latifa Qurban)",female,24.0,2666,19.2583,,C
+860,0,3,"Razi, Mr. Raihed",male,,2629,7.2292,,C
+861,0,3,"Hansen, Mr. Claus Peter",male,41.0,350026,14.1083,,S
+862,0,2,"Giles, Mr. Frederick Edward",male,21.0,28134,11.5,,S
+863,1,1,"Swift, Mrs. Frederick Joel (Margaret Welles Barron)",female,48.0,17466,25.9292,D17,S
+864,0,3,"Sage, Miss. Dorothy Edith ""Dolly""",female,,CA. 2343,69.55,,S
+865,0,2,"Gill, Mr. John William",male,24.0,233866,13.0,,S
+866,1,2,"Bystrom, Mrs. (Karolina)",female,42.0,236852,13.0,,S
+867,1,2,"Duran y More, Miss. Asuncion",female,27.0,SC/PARIS 2149,13.8583,,C
+868,0,1,"Roebling, Mr. Washington Augustus II",male,31.0,PC 17590,50.4958,A24,S
+869,0,3,"van Melkebeke, Mr. Philemon",male,,345777,9.5,,S
+870,1,3,"Johnson, Master. Harold Theodor",male,4.0,347742,11.1333,,S
+871,0,3,"Balkic, Mr. Cerin",male,26.0,349248,7.8958,,S
+872,1,1,"Beckwith, Mrs. Richard Leonard (Sallie Monypeny)",female,47.0,11751,52.5542,D35,S
+873,0,1,"Carlsson, Mr. Frans Olof",male,33.0,695,5.0,B51 B53 B55,S
+874,0,3,"Vander Cruyssen, Mr. Victor",male,47.0,345765,9.0,,S
+875,1,2,"Abelson, Mrs. Samuel (Hannah Wizosky)",female,28.0,P/PP 3381,24.0,,C
+876,1,3,"Najib, Miss. Adele Kiamie ""Jane""",female,15.0,2667,7.225,,C
+877,0,3,"Gustafsson, Mr. Alfred Ossian",male,20.0,7534,9.8458,,S
+878,0,3,"Petroff, Mr. Nedelio",male,19.0,349212,7.8958,,S
+879,0,3,"Laleff, Mr. Kristo",male,,349217,7.8958,,S
+880,1,1,"Potter, Mrs. Thomas Jr (Lily Alexenia Wilson)",female,56.0,11767,83.1583,C50,C
+881,1,2,"Shelley, Mrs. William (Imanita Parrish Hall)",female,25.0,230433,26.0,,S
+882,0,3,"Markun, Mr. Johann",male,33.0,349257,7.8958,,S
+883,0,3,"Dahlberg, Miss. Gerda Ulrika",female,22.0,7552,10.5167,,S
+884,0,2,"Banfield, Mr. Frederick James",male,28.0,C.A./SOTON 34068,10.5,,S
+885,0,3,"Sutehall, Mr. Henry Jr",male,25.0,SOTON/OQ 392076,7.05,,S
+886,0,3,"Rice, Mrs. William (Margaret Norton)",female,39.0,382652,29.125,,Q
+887,0,2,"Montvila, Rev. Juozas",male,27.0,211536,13.0,,S
+888,1,1,"Graham, Miss. Margaret Edith",female,19.0,112053,30.0,B42,S
+889,0,3,"Johnston, Miss. Catherine Helen ""Carrie""",female,,W./C. 6607,23.45,,S
+890,1,1,"Behr, Mr. Karl Howell",male,26.0,111369,30.0,C148,C
+891,0,3,"Dooley, Mr. Patrick",male,32.0,370376,7.75,,Q
diff --git a/exercicios/para-sala/exercicio.ipynb b/exercicios/para-sala/exercicio.ipynb
new file mode 100644
index 0000000..8a4046a
--- /dev/null
+++ b/exercicios/para-sala/exercicio.ipynb
@@ -0,0 +1,1089 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# leitura do nosso arquivo csv\n",
+    "df = pd.read_csv(\"titanic.csv\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Name</th>\n",
+       "      <th>Sex</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Ticket</th>\n",
+       "      <th>Fare</th>\n",
+       "      <th>Cabin</th>\n",
+       "      <th>Embarked</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Futrelle, Mrs. Jacques Heath (Lily May Peel)</td>\n",
+       "      <td>female</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>113803</td>\n",
+       "      <td>53.1000</td>\n",
+       "      <td>C123</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Allen, Mr. William Henry</td>\n",
+       "      <td>male</td>\n",
+       "      <td>35.0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>0</td>\n",
+       "      <td>373450</td>\n",
+       "      <td>8.0500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   PassengerId  Survived  Pclass  \\\n",
+       "0            1         0       3   \n",
+       "1            2         1       1   \n",
+       "2            3         1       3   \n",
+       "3            4         1       1   \n",
+       "4            5         0       3   \n",
+       "\n",
+       "                                                Name     Sex   Age  SibSp  \\\n",
+       "0                            Braund, Mr. Owen Harris    male  22.0      1   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female  38.0      1   \n",
+       "2                             Heikkinen, Miss. Laina  female  26.0      0   \n",
+       "3       Futrelle, Mrs. Jacques Heath (Lily May Peel)  female  35.0      1   \n",
+       "4                           Allen, Mr. William Henry    male  35.0      0   \n",
+       "\n",
+       "   Parch            Ticket     Fare Cabin Embarked  \n",
+       "0      0         A/5 21171   7.2500   NaN        S  \n",
+       "1      0          PC 17599  71.2833   C85        C  \n",
+       "2      0  STON/O2. 3101282   7.9250   NaN        S  \n",
+       "3      0            113803  53.1000  C123        S  \n",
+       "4      0            373450   8.0500   NaN        S  "
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(5)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(891, 12)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# quantidads de linhas e colunas\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# backup\n",
+    "df_backup = df.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "PassengerId      0\n",
+      "Survived         0\n",
+      "Pclass           0\n",
+      "Name             0\n",
+      "Sex              0\n",
+      "Age            177\n",
+      "SibSp            0\n",
+      "Parch            0\n",
+      "Ticket           0\n",
+      "Fare             0\n",
+      "Cabin          687\n",
+      "Embarked         2\n",
+      "dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#contar dados nulos em cada coluna\n",
+    "numos_por_colunas = df.isnull().sum()\n",
+    "print(numos_por_colunas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "0      1\n",
+      "1      0\n",
+      "2      1\n",
+      "3      0\n",
+      "4      1\n",
+      "      ..\n",
+      "886    1\n",
+      "887    0\n",
+      "888    2\n",
+      "889    0\n",
+      "890    1\n",
+      "Length: 891, dtype: int64\n"
+     ]
+    }
+   ],
+   "source": [
+    "#contar dados nulos por linhas\n",
+    "nulos_por_linhas = df.isnull().sum(axis=1)\n",
+    "print(nulos_por_linhas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "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>PassengerId</th>\n",
+       "      <th>Survived</th>\n",
+       "      <th>Pclass</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>SibSp</th>\n",
+       "      <th>Parch</th>\n",
+       "      <th>Fare</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>714.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>891.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.383838</td>\n",
+       "      <td>2.308642</td>\n",
+       "      <td>29.699118</td>\n",
+       "      <td>0.523008</td>\n",
+       "      <td>0.381594</td>\n",
+       "      <td>32.204208</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>257.353842</td>\n",
+       "      <td>0.486592</td>\n",
+       "      <td>0.836071</td>\n",
+       "      <td>14.526497</td>\n",
+       "      <td>1.102743</td>\n",
+       "      <td>0.806057</td>\n",
+       "      <td>49.693429</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.420000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>223.500000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>2.000000</td>\n",
+       "      <td>20.125000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>7.910400</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>446.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>28.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>14.454200</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>668.500000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>38.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>0.000000</td>\n",
+       "      <td>31.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>891.000000</td>\n",
+       "      <td>1.000000</td>\n",
+       "      <td>3.000000</td>\n",
+       "      <td>80.000000</td>\n",
+       "      <td>8.000000</td>\n",
+       "      <td>6.000000</td>\n",
+       "      <td>512.329200</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "       PassengerId    Survived      Pclass         Age       SibSp  \\\n",
+       "count   891.000000  891.000000  891.000000  714.000000  891.000000   \n",
+       "mean    446.000000    0.383838    2.308642   29.699118    0.523008   \n",
+       "std     257.353842    0.486592    0.836071   14.526497    1.102743   \n",
+       "min       1.000000    0.000000    1.000000    0.420000    0.000000   \n",
+       "25%     223.500000    0.000000    2.000000   20.125000    0.000000   \n",
+       "50%     446.000000    0.000000    3.000000   28.000000    0.000000   \n",
+       "75%     668.500000    1.000000    3.000000   38.000000    1.000000   \n",
+       "max     891.000000    1.000000    3.000000   80.000000    8.000000   \n",
+       "\n",
+       "            Parch        Fare  \n",
+       "count  891.000000  891.000000  \n",
+       "mean     0.381594   32.204208  \n",
+       "std      0.806057   49.693429  \n",
+       "min      0.000000    0.000000  \n",
+       "25%      0.000000    7.910400  \n",
+       "50%      0.000000   14.454200  \n",
+       "75%      0.000000   31.000000  \n",
+       "max      6.000000  512.329200  "
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# descrição dos dados\n",
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 891 entries, 0 to 890\n",
+      "Data columns (total 12 columns):\n",
+      " #   Column       Non-Null Count  Dtype  \n",
+      "---  ------       --------------  -----  \n",
+      " 0   PassengerId  891 non-null    int64  \n",
+      " 1   Survived     891 non-null    int64  \n",
+      " 2   Pclass       891 non-null    int64  \n",
+      " 3   Name         891 non-null    object \n",
+      " 4   Sex          891 non-null    object \n",
+      " 5   Age          714 non-null    float64\n",
+      " 6   SibSp        891 non-null    int64  \n",
+      " 7   Parch        891 non-null    int64  \n",
+      " 8   Ticket       891 non-null    object \n",
+      " 9   Fare         891 non-null    float64\n",
+      " 10  Cabin        204 non-null    object \n",
+      " 11  Embarked     889 non-null    object \n",
+      "dtypes: float64(2), int64(5), object(5)\n",
+      "memory usage: 83.7+ KB\n",
+      "None\n"
+     ]
+    }
+   ],
+   "source": [
+    "#verificr informações\n",
+    "info_df =df.info()\n",
+    "print(info_df)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#remover linhas duplicadas\n",
+    "df= df.drop_duplicates()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#apagar linhas duplicadas da colunas\n",
+    "df_teste = df.drop_duplicates([\"PassengerId\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(891, 12)"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Empty DataFrame\n",
+      "Columns: [PassengerId, Survived, Pclass, Name, Sex, Age, SibSp, Parch, Ticket, Fare, Cabin, Embarked]\n",
+      "Index: []\n"
+     ]
+    }
+   ],
+   "source": [
+    "def visualizar_duplicados(df):\n",
+    "    #idenfica as linhas duplicadas\n",
+    "    duplicados = df[df.duplicated(keep=False)]\n",
+    "\n",
+    "    return duplicados\n",
+    "# vizualiza as linhas duplicadas\n",
+    "duplicados = visualizar_duplicados(df)\n",
+    "print(duplicados)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Explicação:\n",
+    "df.duplicated(keep=false): Identifica todas as linhas duplicadas no Dataframe. O prâmetro keep=False marca todas as linhas duplicadas\n",
+    "\n",
+    "df[df.duplicated(keep=False)]: Filtra o DataFrame para exibir apenas as linhas que são duplicadas."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#apagar colunas do df\n",
+    "df =df.drop(columns=[\"SibSp\",\"Parch\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#apagar as informações que estão NAN\n",
+    "df_teste = df.dropna(subset=[\"Cabin\"])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(204, 10)"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_teste.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# reset index\n",
+    "# df = df.reset_index(drop=True)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['PassengerId', 'Survived', 'Pclass', 'Name', 'Sex', 'Age', 'Ticket',\n",
+       "       'Fare', 'Cabin', 'Embarked'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Renomeando as coluans do DataFRame para português\n",
+    "df.rename(columns={\n",
+    "    'PassengerId':'IdPassegeiro',\n",
+    "    'Survived':'Sobreviveu', \n",
+    "    'Pclass':'Classe',\n",
+    "    'Name': 'Nome',\n",
+    "    'Sex':'Genero',\n",
+    "    'Age':'Idade', \n",
+    "    'Ticket':'Bilhete',\n",
+    "    'Fare':'Tarifa',\n",
+    "    'Cabin': 'Cabine',\n",
+    "    'Embarked' : 'Embarque'\n",
+    "}, inplace=True)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['IdPassegeiro', 'Sobreviveu', 'Classe', 'Nome', 'Genero', 'Idade',\n",
+       "       'Bilhete', 'Tarifa', 'Cabine', 'Embarque'],\n",
+       "      dtype='object')"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# salvar no csv\n",
+    "df.to_csv('Titanica_tratado.csv', index=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "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>IdPassegeiro</th>\n",
+       "      <th>Sobreviveu</th>\n",
+       "      <th>Classe</th>\n",
+       "      <th>Nome</th>\n",
+       "      <th>Genero</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>Bilhete</th>\n",
+       "      <th>Tarifa</th>\n",
+       "      <th>Cabine</th>\n",
+       "      <th>Embarque</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>0</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Braund, Mr. Owen Harris</td>\n",
+       "      <td>male</td>\n",
+       "      <td>22.0</td>\n",
+       "      <td>A/5 21171</td>\n",
+       "      <td>7.2500</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>1</td>\n",
+       "      <td>1</td>\n",
+       "      <td>Cumings, Mrs. John Bradley (Florence Briggs Th...</td>\n",
+       "      <td>female</td>\n",
+       "      <td>38.0</td>\n",
+       "      <td>PC 17599</td>\n",
+       "      <td>71.2833</td>\n",
+       "      <td>C85</td>\n",
+       "      <td>C</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>1</td>\n",
+       "      <td>3</td>\n",
+       "      <td>Heikkinen, Miss. Laina</td>\n",
+       "      <td>female</td>\n",
+       "      <td>26.0</td>\n",
+       "      <td>STON/O2. 3101282</td>\n",
+       "      <td>7.9250</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>S</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   IdPassegeiro  Sobreviveu  Classe  \\\n",
+       "0             1           0       3   \n",
+       "1             2           1       1   \n",
+       "2             3           1       3   \n",
+       "\n",
+       "                                                Nome  Genero  Idade  \\\n",
+       "0                            Braund, Mr. Owen Harris    male   22.0   \n",
+       "1  Cumings, Mrs. John Bradley (Florence Briggs Th...  female   38.0   \n",
+       "2                             Heikkinen, Miss. Laina  female   26.0   \n",
+       "\n",
+       "            Bilhete   Tarifa Cabine Embarque  \n",
+       "0         A/5 21171   7.2500    NaN        S  \n",
+       "1          PC 17599  71.2833    C85        C  \n",
+       "2  STON/O2. 3101282   7.9250    NaN        S  "
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(3)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Analises"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Quantos passageiros estavam em cada classe do Titanic?\n",
+    "\n",
+    "#contagem de númeors de passageiros\n",
+    "contagem_passageiros = df[\"Classe\"].value_counts()\n",
+    "\n",
+    "#criaação do gráfico\n",
+    "contagem_passageiros.plot(kind=\"bar\", edgecolor=\"Blue\", color=\"pink\")\n",
+    "\n",
+    "#configurações\n",
+    "plt.xlabel(\"Classe do Passageiro\")\n",
+    "plt.ylabel(\"Quantidade\")\n",
+    "plt.title(\"Número de Passageiros por Classe\")\n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Qual a taxa de sobrevivência por gênero?\n",
+    "\n",
+    "# agrupamento genero por sobreviventes\n",
+    "taxa_sob_genero = df.groupby(\"Genero\")[\"Sobreviveu\"].mean()\n",
+    "\n",
+    "# cores para barras\n",
+    "\n",
+    "cores = [\"lightpink\",\"lightblue\"]\n",
+    "\n",
+    "# plotagem\n",
+    "barras = taxa_sob_genero.plot.bar(edgecolor = \"Blue\", color= cores)\n",
+    "\n",
+    "# os rotulos\n",
+    "plt.xlabel = \"Genero\"\n",
+    "plt.ylabel = \"Taxa de Sobreviventes\"\n",
+    "plt.title = \"Taxa de Sobrevivente por Genero\"\n",
+    "\n",
+    "\n",
+    "# adicionar Legendas\n",
+    "plt.legend(['Taxa de Sobrevivencia'])\n",
+    "\n",
+    "#adicionar rotulos nos graficos\n",
+    "for i, v in enumerate(taxa_sob_genero):\n",
+    "    barras.text(i, v + 0.01, f'{v:.2f}', color=\"black\", ha = \"center\") \n",
+    "\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Teste Hipóteses"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Teste de Idade e Sobrevivência\n",
+    "\n",
+    "Hipótese Nula H0: Os sobreviventes não relacionam com  a Idade dos passageiros.\n",
+    "Hipótese Alternativa H1: Sobreviventes tem relação com  a Idade dos passageiros."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from scipy.stats import ttest_ind\n",
+    "import seaborn as sns\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Teste T de Idade\n",
+      " Estatística T: -2.06668694625381\n",
+      " Valor P: 0.03912465401348249\n"
+     ]
+    },
+    {
+     "ename": "TypeError",
+     "evalue": "'str' object is not callable",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mTypeError\u001b[0m                                 Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[40], line 20\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[38;5;66;03m# rótulos\u001b[39;00m\n\u001b[0;32m     19\u001b[0m plt\u001b[38;5;241m.\u001b[39mlegend()\n\u001b[1;32m---> 20\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtitle\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mDistribuição de Idade dos sobreviventes\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[0;32m     21\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mIdade\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m     22\u001b[0m plt\u001b[38;5;241m.\u001b[39mylabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mContagem\u001b[39m\u001b[38;5;124m'\u001b[39m)\n",
+      "\u001b[1;31mTypeError\u001b[0m: 'str' object is not callable"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# amostras\n",
+    "idade_sobreviventes = df[df[\"Sobreviveu\"] == 1][\"Idade\"].dropna()\n",
+    "idade_nao_sobreviveu = df[df[\"Sobreviveu\"] == 0][\"Idade\"].dropna()\n",
+    "\n",
+    "# teste t\n",
+    "estatistica_t, valor_p = ttest_ind(idade_sobreviventes, idade_nao_sobreviveu)\n",
+    "\n",
+    "print(\"Teste T de Idade\")\n",
+    "print(f\" Estatística T: {estatistica_t}\")\n",
+    "print(f\" Valor P: {valor_p}\")\n",
+    "\n",
+    "\n",
+    "# gráfico\n",
+    "\n",
+    "sns.histplot(idade_sobreviventes, color = 'blue', label =\"Sobreviventes\",kde=True , bins = 20)\n",
+    "sns.histplot(idade_nao_sobreviveu, color = 'red', label = 'Não sobreviveu',  kde=True , bins = 20)\n",
+    "\n",
+    "# rótulos\n",
+    "plt.legend()\n",
+    "plt.title('Distribuição de Idade dos sobreviventes')\n",
+    "plt.xlabel('Idade')\n",
+    "plt.ylabel('Contagem')\n",
+    "plt.show()\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 41,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Rejeitamos a hipótese nula\n"
+     ]
+    }
+   ],
+   "source": [
+    "#interpretação\n",
+    "if valor_p < 0.05:\n",
+    "    print(\"Rejeitamos a hipótese nula\")\n",
+    "else:\n",
+    "    print(\"Nâo rejeitamos a hipótese nula\")\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Amostra e SQL"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#amostra\n",
+    "baby_df = df.sample(100)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import sqlite3 "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Genero  Contagem\n",
+      "0  female        29\n",
+      "1    male        71\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<function Connection.close()>"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# conexao\n",
+    "\n",
+    "conn = sqlite3.connect(':memory:')\n",
+    "\n",
+    "# escrever o df em um SQL\n",
+    "baby_df.to_sql('baby_df', conn, index=False, if_exists='replace')\n",
+    "\n",
+    "# executar a consulta\n",
+    "\n",
+    "query_sql = \"\"\"\n",
+    "SELECT Genero, COUNT('IdPassageiro') AS Contagem\n",
+    "FROM baby_df\n",
+    "GROUP BY Genero;\n",
+    "\"\"\"\n",
+    "contagem_por_gen = pd.read_sql_query(query_sql, conn)\n",
+    "print(contagem_por_gen)\n",
+    "\n",
+    "#fechar\n",
+    "conn.close"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "   Genero  Contagem\n",
+      "0    male        71\n",
+      "1  female        29\n"
+     ]
+    }
+   ],
+   "source": [
+    "# pandas\n",
+    "contagem_por_genero = baby_df['Genero'].value_counts().reset_index()\n",
+    "contagem_por_genero.columns = ['Genero','Contagem']\n",
+    "print(contagem_por_genero)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.12.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}