diff --git a/exercicios/para-casa/Obesity_Classification.csv b/exercicios/para-casa/Obesity_Classification.csv
new file mode 100644
index 0000000..b29018a
--- /dev/null
+++ b/exercicios/para-casa/Obesity_Classification.csv
@@ -0,0 +1,109 @@
+ID,Age,Gender,Height,Weight,BMI,Label
+1,25,Male,175,80,25.3,Normal Weight
+2,30,Female,160,60,22.5,Normal Weight
+3,35,Male,180,90,27.3,Overweight
+4,40,Female,150,50,20,Underweight
+5,45,Male,190,100,31.2,Obese
+6,50,Female,140,40,16.7,Underweight
+7,55,Male,200,110,34.2,Obese
+8,60,Female,130,30,13.3,Underweight
+9,65,Male,210,120,37.2,Obese
+10,70,Female,120,20,10,Underweight
+11,18,Male,175,70,23.4,Normal Weight
+12,23,Female,160,50,20,Underweight
+13,28,Male,180,80,25.3,Normal Weight
+14,33,Female,150,60,22.5,Normal Weight
+15,38,Male,190,90,27.3,Overweight
+16,43,Female,140,50,20,Underweight
+17,48,Male,200,100,31.2,Obese
+19,53,Female,130,40,16.7,Underweight
+20,58,Male,210,110,34.2,Obese
+21,63,Female,120,30,13.3,Underweight
+22,68,Male,175,80,25.3,Normal Weight
+23,73,Female,160,60,22.5,Normal Weight
+24,78,Male,180,90,27.3,Overweight
+25,83,Female,150,50,20,Underweight
+26,88,Male,190,100,31.2,Obese
+27,93,Female,140,40,16.7,Underweight
+28,98,Male,200,110,34.2,Obese
+29,103,Female,130,30,13.3,Underweight
+30,108,Male,210,120,37.2,Obese
+31,19,Male,175,75,24.2,Normal Weight
+32,24,Female,160,55,21.2,Normal Weight
+33,29,Male,180,85,26.1,Overweight
+35,39,Male,190,95,27,Overweight
+36,44,Female,140,75,25,Overweight
+37,49,Male,200,105,28.9,Obese
+38,54,Female,130,85,27.5,Overweight
+39,59,Male,210,115,30.8,Obese
+40,64,Female,120,95,29.1,Overweight
+41,17,Male,175,65,22.7,Normal Weight
+42,22,Female,160,45,18.7,Underweight
+43,27,Male,180,75,24.2,Normal Weight
+44,32,Female,150,55,21.2,Normal Weight
+45,37,Male,190,85,26.1,Overweight
+46,42,Female,140,65,22.7,Normal Weight
+47,47,Male,200,95,27,Overweight
+48,52,Female,130,75,25,Overweight
+49,57,Male,210,105,28.9,Obese
+50,62,Female,120,85,27.5,Overweight
+51,67,Male,175,65,22.7,Normal Weight
+52,72,Female,160,45,18.7,Underweight
+53,77,Male,180,75,24.2,Normal Weight
+54,82,Female,150,55,21.2,Normal Weight
+55,87,Male,190,85,26.1,Overweight
+56,92,Female,140,65,22.7,Normal Weight
+57,97,Male,200,95,27,Overweight
+58,102,Female,130,75,25,Overweight
+59,107,Male,210,105,28.9,Obese
+60,112,Female,120,85,27.5,Overweight
+61,16,Male,175,55,21.2,Normal Weight
+62,21,Female,160,35,16.7,Underweight
+63,26,Male,180,65,22.7,Normal Weight
+64,31,Female,150,45,18.7,Underweight
+65,36,Male,190,75,24.2,Normal Weight
+66,41,Female,140,55,21.2,Normal Weight
+67,46,Male,200,85,26.1,Overweight
+68,51,Female,130,65,22.7,Normal Weight
+69,56,Male,210,95,27,Overweight
+70,61,Female,120,75,25,Overweight
+71,15,Male,175,45,18.7,Underweight
+72,20,Female,160,30,13.3,Underweight
+73,25,Male,180,55,21.2,Normal Weight
+74,30,Female,150,40,16.7,Underweight
+75,35,Male,190,65,22.7,Normal Weight
+76,40,Female,140,50,20,Underweight
+77,45,Male,200,75,24.2,Normal Weight
+78,50,Female,130,60,22.5,Normal Weight
+79,55,Male,210,85,26.1,Overweight
+80,60,Female,120,70,23.4,Normal Weight
+81,14,Male,175,35,16.7,Underweight
+82,19,Female,160,25,10,Underweight
+83,24,Male,180,45,18.7,Underweight
+84,29,Female,150,30,13.3,Underweight
+85,34,Male,190,55,21.2,Normal Weight
+86,13,Male,175,25,10,Underweight
+87,18,Female,160,20,8.3,Underweight
+88,23,Male,180,30,13.3,Underweight
+89,28,Female,150,25,10,Underweight
+90,33,Male,190,40,16.7,Underweight
+91,38,Female,140,35,16.7,Underweight
+92,43,Male,200,50,20,Underweight
+93,48,Female,130,40,16.7,Underweight
+94,53,Male,210,55,21.2,Normal Weight
+95,58,Female,120,35,16.7,Underweight
+96,12,Male,175,15,5.6,Underweight
+97,17,Female,160,15,5.6,Underweight
+98,22,Male,180,20,8.3,Underweight
+99,27,Female,150,20,8.3,Underweight
+100,32,Male,190,25,10,Underweight
+101,37,Female,140,25,10,Underweight
+102,42,Male,200,30,13.3,Underweight
+103,47,Female,130,30,13.3,Underweight
+104,52,Male,210,35,16.7,Underweight
+105,57,Female,120,25,10,Underweight
+106,11,Male,175,10,3.9,Underweight
+107,16,Female,160,10,3.9,Underweight
+108,21,Male,180,15,5.6,Underweight
+109,26,Female,150,15,5.6,Underweight
+110,31,Male,190,20,8.3,Underweight
\ No newline at end of file
diff --git a/exercicios/para-casa/banco_obesidade.db b/exercicios/para-casa/banco_obesidade.db
new file mode 100644
index 0000000..6335cd3
Binary files /dev/null and b/exercicios/para-casa/banco_obesidade.db differ
diff --git a/exercicios/para-casa/banco_obesidade.ipynb b/exercicios/para-casa/banco_obesidade.ipynb
new file mode 100644
index 0000000..7afc457
--- /dev/null
+++ b/exercicios/para-casa/banco_obesidade.ipynb
@@ -0,0 +1,254 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import csv\n",
+    "import sqlite3"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "file = open(\"Obesity_Classification.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "conteudo = csv.reader(file)\n",
+    "\n",
+    "connection = sqlite3.connect(\"banco_obesidade.db\")\n",
+    "\n",
+    "cursor = connection.cursor()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sqlite3.Cursor at 0x1df249ebf40>"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cursor.execute('''CREATE TABLE IF NOT EXISTS tabela_obesidade(\n",
+    "    ID INT,\n",
+    "    Age INT , \n",
+    "    Gender VARCHAR,\n",
+    "    Height INT,\n",
+    "    Weight INT ,\n",
+    "    BMI FLOAT,\n",
+    "    Label VARCHAR\n",
+    ")''')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<sqlite3.Cursor at 0x1df249ebf40>"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "inserir_conteudo = \"INSERT INTO tabela_obesidade(ID, Age, Gender, Height, Weight, BMI, Label)VALUES(?, ?, ?, ?, ?, ?, ?)\"\n",
+    "\n",
+    "cursor.executemany(inserir_conteudo, conteudo)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "selecionar_tudo = \"SELECT * FROM tabela_obesidade\"\n",
+    "entradas = cursor.execute(selecionar_tudo).fetchall()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "('ID', 'Age', 'Gender', 'Height', 'Weight', 'BMI', 'Label')\n",
+      "(1, 25, 'Male', 175, 80, 25.3, 'Normal Weight')\n",
+      "(2, 30, 'Female', 160, 60, 22.5, 'Normal Weight')\n",
+      "(3, 35, 'Male', 180, 90, 27.3, 'Overweight')\n",
+      "(4, 40, 'Female', 150, 50, 20.0, 'Underweight')\n",
+      "(5, 45, 'Male', 190, 100, 31.2, 'Obese')\n",
+      "(6, 50, 'Female', 140, 40, 16.7, 'Underweight')\n",
+      "(7, 55, 'Male', 200, 110, 34.2, 'Obese')\n",
+      "(8, 60, 'Female', 130, 30, 13.3, 'Underweight')\n",
+      "(9, 65, 'Male', 210, 120, 37.2, 'Obese')\n",
+      "(10, 70, 'Female', 120, 20, 10.0, 'Underweight')\n",
+      "(11, 18, 'Male', 175, 70, 23.4, 'Normal Weight')\n",
+      "(12, 23, 'Female', 160, 50, 20.0, 'Underweight')\n",
+      "(13, 28, 'Male', 180, 80, 25.3, 'Normal Weight')\n",
+      "(14, 33, 'Female', 150, 60, 22.5, 'Normal Weight')\n",
+      "(15, 38, 'Male', 190, 90, 27.3, 'Overweight')\n",
+      "(16, 43, 'Female', 140, 50, 20.0, 'Underweight')\n",
+      "(17, 48, 'Male', 200, 100, 31.2, 'Obese')\n",
+      "(19, 53, 'Female', 130, 40, 16.7, 'Underweight')\n",
+      "(20, 58, 'Male', 210, 110, 34.2, 'Obese')\n",
+      "(21, 63, 'Female', 120, 30, 13.3, 'Underweight')\n",
+      "(22, 68, 'Male', 175, 80, 25.3, 'Normal Weight')\n",
+      "(23, 73, 'Female', 160, 60, 22.5, 'Normal Weight')\n",
+      "(24, 78, 'Male', 180, 90, 27.3, 'Overweight')\n",
+      "(25, 83, 'Female', 150, 50, 20.0, 'Underweight')\n",
+      "(26, 88, 'Male', 190, 100, 31.2, 'Obese')\n",
+      "(27, 93, 'Female', 140, 40, 16.7, 'Underweight')\n",
+      "(28, 98, 'Male', 200, 110, 34.2, 'Obese')\n",
+      "(29, 103, 'Female', 130, 30, 13.3, 'Underweight')\n",
+      "(30, 108, 'Male', 210, 120, 37.2, 'Obese')\n",
+      "(31, 19, 'Male', 175, 75, 24.2, 'Normal Weight')\n",
+      "(32, 24, 'Female', 160, 55, 21.2, 'Normal Weight')\n",
+      "(33, 29, 'Male', 180, 85, 26.1, 'Overweight')\n",
+      "(35, 39, 'Male', 190, 95, 27.0, 'Overweight')\n",
+      "(36, 44, 'Female', 140, 75, 25.0, 'Overweight')\n",
+      "(37, 49, 'Male', 200, 105, 28.9, 'Obese')\n",
+      "(38, 54, 'Female', 130, 85, 27.5, 'Overweight')\n",
+      "(39, 59, 'Male', 210, 115, 30.8, 'Obese')\n",
+      "(40, 64, 'Female', 120, 95, 29.1, 'Overweight')\n",
+      "(41, 17, 'Male', 175, 65, 22.7, 'Normal Weight')\n",
+      "(42, 22, 'Female', 160, 45, 18.7, 'Underweight')\n",
+      "(43, 27, 'Male', 180, 75, 24.2, 'Normal Weight')\n",
+      "(44, 32, 'Female', 150, 55, 21.2, 'Normal Weight')\n",
+      "(45, 37, 'Male', 190, 85, 26.1, 'Overweight')\n",
+      "(46, 42, 'Female', 140, 65, 22.7, 'Normal Weight')\n",
+      "(47, 47, 'Male', 200, 95, 27.0, 'Overweight')\n",
+      "(48, 52, 'Female', 130, 75, 25.0, 'Overweight')\n",
+      "(49, 57, 'Male', 210, 105, 28.9, 'Obese')\n",
+      "(50, 62, 'Female', 120, 85, 27.5, 'Overweight')\n",
+      "(51, 67, 'Male', 175, 65, 22.7, 'Normal Weight')\n",
+      "(52, 72, 'Female', 160, 45, 18.7, 'Underweight')\n",
+      "(53, 77, 'Male', 180, 75, 24.2, 'Normal Weight')\n",
+      "(54, 82, 'Female', 150, 55, 21.2, 'Normal Weight')\n",
+      "(55, 87, 'Male', 190, 85, 26.1, 'Overweight')\n",
+      "(56, 92, 'Female', 140, 65, 22.7, 'Normal Weight')\n",
+      "(57, 97, 'Male', 200, 95, 27.0, 'Overweight')\n",
+      "(58, 102, 'Female', 130, 75, 25.0, 'Overweight')\n",
+      "(59, 107, 'Male', 210, 105, 28.9, 'Obese')\n",
+      "(60, 112, 'Female', 120, 85, 27.5, 'Overweight')\n",
+      "(61, 16, 'Male', 175, 55, 21.2, 'Normal Weight')\n",
+      "(62, 21, 'Female', 160, 35, 16.7, 'Underweight')\n",
+      "(63, 26, 'Male', 180, 65, 22.7, 'Normal Weight')\n",
+      "(64, 31, 'Female', 150, 45, 18.7, 'Underweight')\n",
+      "(65, 36, 'Male', 190, 75, 24.2, 'Normal Weight')\n",
+      "(66, 41, 'Female', 140, 55, 21.2, 'Normal Weight')\n",
+      "(67, 46, 'Male', 200, 85, 26.1, 'Overweight')\n",
+      "(68, 51, 'Female', 130, 65, 22.7, 'Normal Weight')\n",
+      "(69, 56, 'Male', 210, 95, 27.0, 'Overweight')\n",
+      "(70, 61, 'Female', 120, 75, 25.0, 'Overweight')\n",
+      "(71, 15, 'Male', 175, 45, 18.7, 'Underweight')\n",
+      "(72, 20, 'Female', 160, 30, 13.3, 'Underweight')\n",
+      "(73, 25, 'Male', 180, 55, 21.2, 'Normal Weight')\n",
+      "(74, 30, 'Female', 150, 40, 16.7, 'Underweight')\n",
+      "(75, 35, 'Male', 190, 65, 22.7, 'Normal Weight')\n",
+      "(76, 40, 'Female', 140, 50, 20.0, 'Underweight')\n",
+      "(77, 45, 'Male', 200, 75, 24.2, 'Normal Weight')\n",
+      "(78, 50, 'Female', 130, 60, 22.5, 'Normal Weight')\n",
+      "(79, 55, 'Male', 210, 85, 26.1, 'Overweight')\n",
+      "(80, 60, 'Female', 120, 70, 23.4, 'Normal Weight')\n",
+      "(81, 14, 'Male', 175, 35, 16.7, 'Underweight')\n",
+      "(82, 19, 'Female', 160, 25, 10.0, 'Underweight')\n",
+      "(83, 24, 'Male', 180, 45, 18.7, 'Underweight')\n",
+      "(84, 29, 'Female', 150, 30, 13.3, 'Underweight')\n",
+      "(85, 34, 'Male', 190, 55, 21.2, 'Normal Weight')\n",
+      "(86, 13, 'Male', 175, 25, 10.0, 'Underweight')\n",
+      "(87, 18, 'Female', 160, 20, 8.3, 'Underweight')\n",
+      "(88, 23, 'Male', 180, 30, 13.3, 'Underweight')\n",
+      "(89, 28, 'Female', 150, 25, 10.0, 'Underweight')\n",
+      "(90, 33, 'Male', 190, 40, 16.7, 'Underweight')\n",
+      "(91, 38, 'Female', 140, 35, 16.7, 'Underweight')\n",
+      "(92, 43, 'Male', 200, 50, 20.0, 'Underweight')\n",
+      "(93, 48, 'Female', 130, 40, 16.7, 'Underweight')\n",
+      "(94, 53, 'Male', 210, 55, 21.2, 'Normal Weight')\n",
+      "(95, 58, 'Female', 120, 35, 16.7, 'Underweight')\n",
+      "(96, 12, 'Male', 175, 15, 5.6, 'Underweight')\n",
+      "(97, 17, 'Female', 160, 15, 5.6, 'Underweight')\n",
+      "(98, 22, 'Male', 180, 20, 8.3, 'Underweight')\n",
+      "(99, 27, 'Female', 150, 20, 8.3, 'Underweight')\n",
+      "(100, 32, 'Male', 190, 25, 10.0, 'Underweight')\n",
+      "(101, 37, 'Female', 140, 25, 10.0, 'Underweight')\n",
+      "(102, 42, 'Male', 200, 30, 13.3, 'Underweight')\n",
+      "(103, 47, 'Female', 130, 30, 13.3, 'Underweight')\n",
+      "(104, 52, 'Male', 210, 35, 16.7, 'Underweight')\n",
+      "(105, 57, 'Female', 120, 25, 10.0, 'Underweight')\n",
+      "(106, 11, 'Male', 175, 10, 3.9, 'Underweight')\n",
+      "(107, 16, 'Female', 160, 10, 3.9, 'Underweight')\n",
+      "(108, 21, 'Male', 180, 15, 5.6, 'Underweight')\n",
+      "(109, 26, 'Female', 150, 15, 5.6, 'Underweight')\n",
+      "(110, 31, 'Male', 190, 20, 8.3, 'Underweight')\n"
+     ]
+    }
+   ],
+   "source": [
+    "for entrada in entradas:\n",
+    "  print(entrada)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "connection.commit()\n",
+    "connection.close()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-casa/projeto3_julia.ipynb b/exercicios/para-casa/projeto3_julia.ipynb
new file mode 100644
index 0000000..fa8fb50
--- /dev/null
+++ b/exercicios/para-casa/projeto3_julia.ipynb
@@ -0,0 +1,1156 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 48,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv(\"Obesity_Classification.csv\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(type(df))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID          int64\n",
+       "Age         int64\n",
+       "Gender     object\n",
+       "Height      int64\n",
+       "Weight      int64\n",
+       "BMI       float64\n",
+       "Label      object\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 50,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 108 entries, 0 to 107\n",
+      "Data columns (total 7 columns):\n",
+      " #   Column  Non-Null Count  Dtype  \n",
+      "---  ------  --------------  -----  \n",
+      " 0   ID      108 non-null    int64  \n",
+      " 1   Age     108 non-null    int64  \n",
+      " 2   Gender  108 non-null    object \n",
+      " 3   Height  108 non-null    int64  \n",
+      " 4   Weight  108 non-null    int64  \n",
+      " 5   BMI     108 non-null    float64\n",
+      " 6   Label   108 non-null    object \n",
+      "dtypes: float64(1), int64(4), object(2)\n",
+      "memory usage: 6.0+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(108, 7)"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['Weight'].notnull()].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 54,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(108, 7)"
+      ]
+     },
+     "execution_count": 54,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 55,
+   "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>ID</th>\n",
+       "      <th>Age</th>\n",
+       "      <th>Gender</th>\n",
+       "      <th>Height</th>\n",
+       "      <th>Weight</th>\n",
+       "      <th>BMI</th>\n",
+       "      <th>Label</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>175</td>\n",
+       "      <td>80</td>\n",
+       "      <td>25.3</td>\n",
+       "      <td>Normal Weight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>160</td>\n",
+       "      <td>60</td>\n",
+       "      <td>22.5</td>\n",
+       "      <td>Normal Weight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>35</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>180</td>\n",
+       "      <td>90</td>\n",
+       "      <td>27.3</td>\n",
+       "      <td>Overweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>150</td>\n",
+       "      <td>50</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>45</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>190</td>\n",
+       "      <td>100</td>\n",
+       "      <td>31.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>50</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>140</td>\n",
+       "      <td>40</td>\n",
+       "      <td>16.7</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>55</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>200</td>\n",
+       "      <td>110</td>\n",
+       "      <td>34.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>8</td>\n",
+       "      <td>60</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>130</td>\n",
+       "      <td>30</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>65</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>210</td>\n",
+       "      <td>120</td>\n",
+       "      <td>37.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>10</td>\n",
+       "      <td>70</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>120</td>\n",
+       "      <td>20</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID  Age  Gender  Height  Weight   BMI          Label\n",
+       "0   1   25    Male     175      80  25.3  Normal Weight\n",
+       "1   2   30  Female     160      60  22.5  Normal Weight\n",
+       "2   3   35    Male     180      90  27.3     Overweight\n",
+       "3   4   40  Female     150      50  20.0    Underweight\n",
+       "4   5   45    Male     190     100  31.2          Obese\n",
+       "5   6   50  Female     140      40  16.7    Underweight\n",
+       "6   7   55    Male     200     110  34.2          Obese\n",
+       "7   8   60  Female     130      30  13.3    Underweight\n",
+       "8   9   65    Male     210     120  37.2          Obese\n",
+       "9  10   70  Female     120      20  10.0    Underweight"
+      ]
+     },
+     "execution_count": 55,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['Id', 'Idade', 'Genero', 'Altura', 'Peso', 'IMC', 'Classificação']"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "traducoes = {\n",
+    "    'ID': 'Id',\n",
+    "    'Age': 'Idade',\n",
+    "    'Gender': 'Genero',\n",
+    "    'Height': 'Altura',\n",
+    "    'Weight': 'Peso',\n",
+    "    'BMI': 'IMC',\n",
+    "    'Label': 'Classificação'\n",
+    "}\n",
+    "\n",
+    "novas_colunas = []\n",
+    "\n",
+    "for chave, valor in traducoes.items():\n",
+    "    novas_colunas.append(valor)\n",
+    "\n",
+    "novas_colunas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "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>Id</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>Genero</th>\n",
+       "      <th>Altura</th>\n",
+       "      <th>Peso</th>\n",
+       "      <th>IMC</th>\n",
+       "      <th>Classificação</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>25</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>175</td>\n",
+       "      <td>80</td>\n",
+       "      <td>25.3</td>\n",
+       "      <td>Normal Weight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>30</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>160</td>\n",
+       "      <td>60</td>\n",
+       "      <td>22.5</td>\n",
+       "      <td>Normal Weight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>35</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>180</td>\n",
+       "      <td>90</td>\n",
+       "      <td>27.3</td>\n",
+       "      <td>Overweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>150</td>\n",
+       "      <td>50</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>45</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>190</td>\n",
+       "      <td>100</td>\n",
+       "      <td>31.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>103</th>\n",
+       "      <td>106</td>\n",
+       "      <td>11</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>175</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>104</th>\n",
+       "      <td>107</td>\n",
+       "      <td>16</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>160</td>\n",
+       "      <td>10</td>\n",
+       "      <td>3.9</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>105</th>\n",
+       "      <td>108</td>\n",
+       "      <td>21</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>180</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>106</th>\n",
+       "      <td>109</td>\n",
+       "      <td>26</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>150</td>\n",
+       "      <td>15</td>\n",
+       "      <td>5.6</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>110</td>\n",
+       "      <td>31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>190</td>\n",
+       "      <td>20</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>108 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Id  Idade  Genero  Altura  Peso   IMC  Classificação\n",
+       "0      1     25    Male     175    80  25.3  Normal Weight\n",
+       "1      2     30  Female     160    60  22.5  Normal Weight\n",
+       "2      3     35    Male     180    90  27.3     Overweight\n",
+       "3      4     40  Female     150    50  20.0    Underweight\n",
+       "4      5     45    Male     190   100  31.2          Obese\n",
+       "..   ...    ...     ...     ...   ...   ...            ...\n",
+       "103  106     11    Male     175    10   3.9    Underweight\n",
+       "104  107     16  Female     160    10   3.9    Underweight\n",
+       "105  108     21    Male     180    15   5.6    Underweight\n",
+       "106  109     26  Female     150    15   5.6    Underweight\n",
+       "107  110     31    Male     190    20   8.3    Underweight\n",
+       "\n",
+       "[108 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.columns = novas_colunas\n",
+    "df"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(108, 7)"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['Genero'] == 'Female']\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "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>Id</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>Genero</th>\n",
+       "      <th>Altura</th>\n",
+       "      <th>Peso</th>\n",
+       "      <th>IMC</th>\n",
+       "      <th>Classificação</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>35</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>180</td>\n",
+       "      <td>90</td>\n",
+       "      <td>27.3</td>\n",
+       "      <td>Overweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>40</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>150</td>\n",
+       "      <td>50</td>\n",
+       "      <td>20.0</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>45</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>190</td>\n",
+       "      <td>100</td>\n",
+       "      <td>31.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>6</td>\n",
+       "      <td>50</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>140</td>\n",
+       "      <td>40</td>\n",
+       "      <td>16.7</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>55</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>200</td>\n",
+       "      <td>110</td>\n",
+       "      <td>34.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>99</th>\n",
+       "      <td>102</td>\n",
+       "      <td>42</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>200</td>\n",
+       "      <td>30</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>100</th>\n",
+       "      <td>103</td>\n",
+       "      <td>47</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>130</td>\n",
+       "      <td>30</td>\n",
+       "      <td>13.3</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>101</th>\n",
+       "      <td>104</td>\n",
+       "      <td>52</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>210</td>\n",
+       "      <td>35</td>\n",
+       "      <td>16.7</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>102</th>\n",
+       "      <td>105</td>\n",
+       "      <td>57</td>\n",
+       "      <td>Female</td>\n",
+       "      <td>120</td>\n",
+       "      <td>25</td>\n",
+       "      <td>10.0</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>107</th>\n",
+       "      <td>110</td>\n",
+       "      <td>31</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>190</td>\n",
+       "      <td>20</td>\n",
+       "      <td>8.3</td>\n",
+       "      <td>Underweight</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>74 rows × 7 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "      Id  Idade  Genero  Altura  Peso   IMC Classificação\n",
+       "2      3     35    Male     180    90  27.3    Overweight\n",
+       "3      4     40  Female     150    50  20.0   Underweight\n",
+       "4      5     45    Male     190   100  31.2         Obese\n",
+       "5      6     50  Female     140    40  16.7   Underweight\n",
+       "6      7     55    Male     200   110  34.2         Obese\n",
+       "..   ...    ...     ...     ...   ...   ...           ...\n",
+       "99   102     42    Male     200    30  13.3   Underweight\n",
+       "100  103     47  Female     130    30  13.3   Underweight\n",
+       "101  104     52    Male     210    35  16.7   Underweight\n",
+       "102  105     57  Female     120    25  10.0   Underweight\n",
+       "107  110     31    Male     190    20   8.3   Underweight\n",
+       "\n",
+       "[74 rows x 7 columns]"
+      ]
+     },
+     "execution_count": 63,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[df['Idade'] > 30]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "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>Id</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>Genero</th>\n",
+       "      <th>Altura</th>\n",
+       "      <th>Peso</th>\n",
+       "      <th>IMC</th>\n",
+       "      <th>Classificação</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>45</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>190</td>\n",
+       "      <td>100</td>\n",
+       "      <td>31.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>7</td>\n",
+       "      <td>55</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>200</td>\n",
+       "      <td>110</td>\n",
+       "      <td>34.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>9</td>\n",
+       "      <td>65</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>210</td>\n",
+       "      <td>120</td>\n",
+       "      <td>37.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>16</th>\n",
+       "      <td>17</td>\n",
+       "      <td>48</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>200</td>\n",
+       "      <td>100</td>\n",
+       "      <td>31.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>20</td>\n",
+       "      <td>58</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>210</td>\n",
+       "      <td>110</td>\n",
+       "      <td>34.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>24</th>\n",
+       "      <td>26</td>\n",
+       "      <td>88</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>190</td>\n",
+       "      <td>100</td>\n",
+       "      <td>31.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>26</th>\n",
+       "      <td>28</td>\n",
+       "      <td>98</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>200</td>\n",
+       "      <td>110</td>\n",
+       "      <td>34.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>28</th>\n",
+       "      <td>30</td>\n",
+       "      <td>108</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>210</td>\n",
+       "      <td>120</td>\n",
+       "      <td>37.2</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>34</th>\n",
+       "      <td>37</td>\n",
+       "      <td>49</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>200</td>\n",
+       "      <td>105</td>\n",
+       "      <td>28.9</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>36</th>\n",
+       "      <td>39</td>\n",
+       "      <td>59</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>210</td>\n",
+       "      <td>115</td>\n",
+       "      <td>30.8</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>46</th>\n",
+       "      <td>49</td>\n",
+       "      <td>57</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>210</td>\n",
+       "      <td>105</td>\n",
+       "      <td>28.9</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>56</th>\n",
+       "      <td>59</td>\n",
+       "      <td>107</td>\n",
+       "      <td>Male</td>\n",
+       "      <td>210</td>\n",
+       "      <td>105</td>\n",
+       "      <td>28.9</td>\n",
+       "      <td>Obese</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Id  Idade Genero  Altura  Peso   IMC Classificação\n",
+       "4    5     45   Male     190   100  31.2         Obese\n",
+       "6    7     55   Male     200   110  34.2         Obese\n",
+       "8    9     65   Male     210   120  37.2         Obese\n",
+       "16  17     48   Male     200   100  31.2         Obese\n",
+       "18  20     58   Male     210   110  34.2         Obese\n",
+       "24  26     88   Male     190   100  31.2         Obese\n",
+       "26  28     98   Male     200   110  34.2         Obese\n",
+       "28  30    108   Male     210   120  37.2         Obese\n",
+       "34  37     49   Male     200   105  28.9         Obese\n",
+       "36  39     59   Male     210   115  30.8         Obese\n",
+       "46  49     57   Male     210   105  28.9         Obese\n",
+       "56  59    107   Male     210   105  28.9         Obese"
+      ]
+     },
+     "execution_count": 66,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df[(df['Classificação'] == 'Obese') & (df['Idade'] >30)]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<Axes: ylabel='Frequency'>"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['IMC'].plot.hist(bins=20, edgecolor='red', color=\"pink\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 67,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(108, 7)"
+      ]
+     },
+     "execution_count": 67,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "iVBORw0KGgoAAAANSUhEUgAAAh8AAAHVCAYAAABRxpmeAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguMCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy81sbWrAAAACXBIWXMAAA9hAAAPYQGoP6dpAAAhUklEQVR4nO3df5TWdZ3//8eMwKDAgKAOoID4KyDDBBPn+KNCktUWM9m2PLoa0e6WZAqau2ylZVsYp6OuJ9TcUNc9GZtFblpqeyjJH1CIm5IZKbhC4oy/Dj+NgXWuzx9+ne9OqDkyvK4ZvN3OeR+5Xtd73tcTcODO+3pf11VTqVQqAQAopLbaAwAAby/iAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAiupR7QH+VGtra9atW5d+/fqlpqam2uMAAG9CpVLJpk2bMnTo0NTWvvG5jS4XH+vWrcuwYcOqPQYA8BasXbs2BxxwwBvu0+Xio1+/fkleGb6+vr7K0wAAb8bGjRszbNiwtr/H30iXi49Xn2qpr68XHwDQzbyZSyZccAoAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBRXe5Tbd/W1qxJnn++2lNQyj77JMOHV3sKgOLER1exZk0yenTy0kvVnoRS9toreewxAQK87YiPruL5518Jj89flowYWe1p2NWeejL56iWv/L6LD+BtRnx0NSNGJoeNqvYUALDLuOAUAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQVI9qDwDwtrBmTfL889WeglL22ScZPrzaU3RZ4gNgV1uzJhk9OnnppWpPQil77ZU89pgAeR3iA2BXe/75V8Lj85clI0ZWexp2taeeTL56ySu/7+LjNYkPgFJGjEwOG1XtKaDqXHAKABQlPgCAosQHAFCU+AAAihIfAEBRHYqPL33pS6mpqWm3jRr1/1+5vXXr1syYMSODBg1K3759M3Xq1DQ3N3f60ABA99XhMx/vfOc788wzz7Rt9913X9t9M2fOzO23355bb701ixcvzrp163L66ad36sAAQPfW4ff56NGjRwYPHrzD+oYNGzJ//vzccsstmThxYpLkxhtvzOjRo7N06dIcc8wxOz8tANDtdfjMx+OPP56hQ4fmoIMOyplnnpk1a9YkSZYvX57t27dn0qRJbfuOGjUqw4cPz5IlS173eC0tLdm4cWO7DQDYfXUoPiZMmJCbbropd911V6699to8+eSTOf7447Np06Y0NTWlV69eGTBgQLuvaWhoSFNT0+sec86cOenfv3/bNmzYsLf0EwEAuocOPe1y8sknt/147NixmTBhQkaMGJHvfe972XPPPd/SALNnz86sWbPabm/cuFGAAMBubKdeajtgwIAcdthheeKJJzJ48OBs27Yt69evb7dPc3Pza14j8qq6urrU19e32wCA3ddOxcfmzZuzatWqDBkyJOPHj0/Pnj2zaNGitvtXrlyZNWvWpLGxcacHBQB2Dx162uWiiy7KlClTMmLEiKxbty6XXnpp9thjj5xxxhnp379/pk+fnlmzZmXgwIGpr6/Peeedl8bGRq90AQDadCg+/vCHP+SMM87ICy+8kH333TfHHXdcli5dmn333TdJcuWVV6a2tjZTp05NS0tLJk+enGuuuWaXDA4AdE8dio8FCxa84f29e/fOvHnzMm/evJ0aCgDYfflsFwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFDUTsXH5ZdfnpqamlxwwQVta1u3bs2MGTMyaNCg9O3bN1OnTk1zc/POzgkA7CbecnwsW7Ys3/rWtzJ27Nh26zNnzsztt9+eW2+9NYsXL866dety+umn7/SgAMDu4S3Fx+bNm3PmmWfmX//1X7P33nu3rW/YsCHz58/PFVdckYkTJ2b8+PG58cYb88ADD2Tp0qWdNjQA0H29pfiYMWNGPvjBD2bSpEnt1pcvX57t27e3Wx81alSGDx+eJUuWvOaxWlpasnHjxnYbALD76tHRL1iwYEEeeuihLFu2bIf7mpqa0qtXrwwYMKDdekNDQ5qaml7zeHPmzMmXv/zljo4BAHRTHTrzsXbt2px//vn5zne+k969e3fKALNnz86GDRvatrVr13bKcQGArqlD8bF8+fI8++yzGTduXHr06JEePXpk8eLFufrqq9OjR480NDRk27ZtWb9+fbuva25uzuDBg1/zmHV1damvr2+3AQC7rw497XLiiSdmxYoV7damTZuWUaNG5R/+4R8ybNiw9OzZM4sWLcrUqVOTJCtXrsyaNWvS2NjYeVMDAN1Wh+KjX79+Ofzww9ut9enTJ4MGDWpbnz59embNmpWBAwemvr4+5513XhobG3PMMcd03tQAQLfV4QtO/5wrr7wytbW1mTp1alpaWjJ58uRcc801nf0wAEA3tdPxcc8997S73bt378ybNy/z5s3b2UMDALshn+0CABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAiupQfFx77bUZO3Zs6uvrU19fn8bGxtx5551t92/dujUzZszIoEGD0rdv30ydOjXNzc2dPjQA0H11KD4OOOCAXH755Vm+fHkefPDBTJw4MR/60Ify6KOPJklmzpyZ22+/PbfeemsWL16cdevW5fTTT98lgwMA3VOPjuw8ZcqUdre/+tWv5tprr83SpUtzwAEHZP78+bnlllsyceLEJMmNN96Y0aNHZ+nSpTnmmGM6b2oAoNt6y9d8vPzyy1mwYEG2bNmSxsbGLF++PNu3b8+kSZPa9hk1alSGDx+eJUuWvO5xWlpasnHjxnYbALD76nB8rFixIn379k1dXV0+9alP5Yc//GHGjBmTpqam9OrVKwMGDGi3f0NDQ5qaml73eHPmzEn//v3btmHDhnX4JwEAdB8djo93vOMd+fWvf51f/vKX+fSnP51zzjknv/3tb9/yALNnz86GDRvatrVr177lYwEAXV+HrvlIkl69euWQQw5JkowfPz7Lli3Lv/zLv+SjH/1otm3blvXr17c7+9Hc3JzBgwe/7vHq6upSV1fX8ckBgG5pp9/no7W1NS0tLRk/fnx69uyZRYsWtd23cuXKrFmzJo2NjTv7MADAbqJDZz5mz56dk08+OcOHD8+mTZtyyy235J577sndd9+d/v37Z/r06Zk1a1YGDhyY+vr6nHfeeWlsbPRKFwCgTYfi49lnn83ZZ5+dZ555Jv3798/YsWNz99135wMf+ECS5Morr0xtbW2mTp2alpaWTJ48Oddcc80uGRwA6J46FB/z589/w/t79+6defPmZd68eTs1FACw+/LZLgBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARXUoPubMmZP3vOc96devX/bbb7+cdtppWblyZbt9tm7dmhkzZmTQoEHp27dvpk6dmubm5k4dGgDovjoUH4sXL86MGTOydOnS/Nd//Ve2b9+ek046KVu2bGnbZ+bMmbn99ttz6623ZvHixVm3bl1OP/30Th8cAOieenRk57vuuqvd7Ztuuin77bdfli9fnhNOOCEbNmzI/Pnzc8stt2TixIlJkhtvvDGjR4/O0qVLc8wxx3Te5ABAt7RT13xs2LAhSTJw4MAkyfLly7N9+/ZMmjSpbZ9Ro0Zl+PDhWbJkyc48FACwm+jQmY//q7W1NRdccEGOPfbYHH744UmSpqam9OrVKwMGDGi3b0NDQ5qaml7zOC0tLWlpaWm7vXHjxrc6EgDQDbzlMx8zZszIb37zmyxYsGCnBpgzZ0769+/ftg0bNmynjgcAdG1vKT4+85nP5I477sjPf/7zHHDAAW3rgwcPzrZt27J+/fp2+zc3N2fw4MGveazZs2dnw4YNbdvatWvfykgAQDfRofioVCr5zGc+kx/+8If52c9+lpEjR7a7f/z48enZs2cWLVrUtrZy5cqsWbMmjY2Nr3nMurq61NfXt9sAgN1Xh675mDFjRm655Zb853/+Z/r169d2HUf//v2z5557pn///pk+fXpmzZqVgQMHpr6+Puedd14aGxu90gUASNLB+Lj22muTJO973/vard944435+Mc/niS58sorU1tbm6lTp6alpSWTJ0/ONddc0ynDAgDdX4fio1Kp/Nl9evfunXnz5mXevHlveSgAYPfls10AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUR2Oj1/84heZMmVKhg4dmpqamtx2223t7q9UKrnkkksyZMiQ7Lnnnpk0aVIef/zxzpoXAOjmOhwfW7ZsyRFHHJF58+a95v1z587N1Vdfneuuuy6//OUv06dPn0yePDlbt27d6WEBgO6vR0e/4OSTT87JJ5/8mvdVKpVcddVV+cIXvpAPfehDSZKbb745DQ0Nue222/Kxj31s56YFALq9Tr3m48knn0xTU1MmTZrUtta/f/9MmDAhS5Ys6cyHAgC6qQ6f+XgjTU1NSZKGhoZ26w0NDW33/amWlpa0tLS03d64cWNnjgQAdDFVf7XLnDlz0r9//7Zt2LBh1R4JANiFOjU+Bg8enCRpbm5ut97c3Nx235+aPXt2NmzY0LatXbu2M0cCALqYTo2PkSNHZvDgwVm0aFHb2saNG/PLX/4yjY2Nr/k1dXV1qa+vb7cBALuvDl/zsXnz5jzxxBNtt5988sn8+te/zsCBAzN8+PBccMEF+ed//ucceuihGTlyZL74xS9m6NChOe200zpzbgCgm+pwfDz44IN5//vf33Z71qxZSZJzzjknN910Uy6++OJs2bIlf/d3f5f169fnuOOOy1133ZXevXt33tQAQLfV4fh43/vel0ql8rr319TU5LLLLstll122U4MBALunqr/aBQB4exEfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+AICixAcAUJT4AACKEh8AQFHiAwAoSnwAAEWJDwCgKPEBABQlPgCAosQHAFCU+AAAihIfAEBR4gMAKEp8AABF7bL4mDdvXg488MD07t07EyZMyK9+9atd9VAAQDeyS+LjP/7jPzJr1qxceumleeihh3LEEUdk8uTJefbZZ3fFwwEA3cguiY8rrrgif/u3f5tp06ZlzJgxue6667LXXnvlhhtu2BUPBwB0Iz06+4Dbtm3L8uXLM3v27La12traTJo0KUuWLNlh/5aWlrS0tLTd3rBhQ5Jk48aNnT1a17Z58yv//f1jyR//WN1Z2PXW/s8r/928OXm7/b/+duT7++3lbfr9/erf25VK5c/vXOlkTz/9dCVJ5YEHHmi3/rnPfa5y9NFH77D/pZdeWklis9lsNpttN9jWrl37Z1uh0898dNTs2bMza9asttutra158cUXM2jQoNTU1FRxMkrYuHFjhg0blrVr16a+vr7a4wCdyPf320ulUsmmTZsydOjQP7tvp8fHPvvskz322CPNzc3t1pubmzN48OAd9q+rq0tdXV27tQEDBnT2WHRx9fX1/nCC3ZTv77eP/v37v6n9Ov2C0169emX8+PFZtGhR21pra2sWLVqUxsbGzn44AKCb2SVPu8yaNSvnnHNOjjrqqBx99NG56qqrsmXLlkybNm1XPBwA0I3skvj46Ec/mueeey6XXHJJmpqa8u53vzt33XVXGhoadsXD0Y3V1dXl0ksv3eGpN6D78/3N66mpVN7Ma2IAADqHz3YBAIoSHwBAUeIDAChKfAAARYkPAKAo8QEAFCU+qKonnngid999d/74/33Sp1d+A+z+xAdV8cILL2TSpEk57LDDcsopp+SZZ55JkkyfPj0XXnhhlacDdtb69evz7W9/O7Nnz86LL76YJHnooYfy9NNPV3kyugLxQVXMnDkzPXr0yJo1a7LXXnu1rX/0ox/NXXfdVcXJgJ31yCOP5LDDDsvXv/71fOMb38j69euTJAsXLszs2bOrOxxdgvigKn7605/m61//eg444IB264ceemieeuqpKk0FdIZZs2bl4x//eB5//PH07t27bf2UU07JL37xiypORlchPqiKLVu2tDvj8aoXX3zR50BAN7ds2bL8/d///Q7r+++/f5qamqowEV2N+KAqjj/++Nx8881tt2tqatLa2pq5c+fm/e9/fxUnA3ZWXV1dNm7cuMP673//++y7775VmIiuxgfLURW/+c1vcuKJJ2bcuHH52c9+llNPPTWPPvpoXnzxxdx///05+OCDqz0i8BZ98pOfzAsvvJDvfe97GThwYB555JHsscceOe2003LCCSfkqquuqvaIVJn4oGo2bNiQb37zm3n44YezefPmjBs3LjNmzMiQIUOqPRqwEzZs2JC/+qu/yoMPPphNmzZl6NChaWpqSmNjY37yk5+kT58+1R6RKhMfAOwS9913Xx555JG2f1xMmjSp2iPRRYgPinnkkUfe9L5jx47dhZMAUE3ig2Jqa2tTU1PzZ9/FtKamJi+//HKhqYDOcPXVV7/pfT/72c/uwknoDsQHxXTk/TtGjBixCycBOtvIkSPf1H41NTVZvXr1Lp6Grk58AABF9aj2ALy9/fa3v82aNWuybdu2duunnnpqlSYCYFcTH1TF6tWr8+EPfzgrVqxodx1ITU1NkrjmA7q5P/zhD/nRj370mv+4uOKKK6o0FV2F+KAqzj///IwcOTKLFi3KyJEj86tf/SovvPBCLrzwwnzjG9+o9njATli0aFFOPfXUHHTQQfnd736Xww8/PP/zP/+TSqWScePGVXs8ugBvr05VLFmyJJdddln22Wef1NbWpra2Nscdd1zmzJnjSnjo5mbPnp2LLrooK1asSO/evfODH/wga9euzXvf+9585CMfqfZ4dAHig6p4+eWX069fvyTJPvvsk3Xr1iV55VUuK1eurOZowE567LHHcvbZZydJevTokT/+8Y/p27dvLrvssnz961+v8nR0BZ52oSoOP/zwPPzwwxk5cmQmTJiQuXPnplevXrn++utz0EEHVXs8YCf06dOn7TqPIUOGZNWqVXnnO9+ZJHn++eerORpdhPigKr7whS9ky5YtSZIvf/nLmTJlSo4//vgMGjQoCxYsqPJ0wM445phjct9992X06NE55ZRTcuGFF2bFihVZuHBhjjnmmGqPRxfgfT7oMl588cXsvffeba94Abqn1atXZ/PmzRk7dmy2bNmSCy+8MA888EAOPfTQXHHFFd5EEPFBWZ/4xCfe1H433HDDLp4EgGoRHxRVW1ubESNG5Mgjj3zDz3j54Q9/WHAqYFfZvHlzWltb263V19dXaRq6Ctd8UNSnP/3pfPe7382TTz6ZadOm5ayzzsrAgQOrPRbQiZ588sl85jOfyT333JOtW7e2rVcqFR8cSRJnPqiClpaWLFy4MDfccEMeeOCBfPCDH8z06dNz0kknud4DdgPHHntsKpVKzj///DQ0NOzwff3e9763SpPRVYgPquqpp57KTTfdlJtvvjn/+7//m0cffTR9+/at9ljATujbt2+WL1+ed7zjHdUehS7Km4xRVbW1tW2f7eJULOwe3vOe92Tt2rXVHoMuzJkPivu/T7vcd999+cu//MtMmzYtf/EXf5HaWj0M3d2qVavyqU99KmeddVYOP/zw9OzZs939Y8eOrdJkdBUuOKWoc889NwsWLMiwYcPyiU98It/97nezzz77VHssoBM999xzWbVqVaZNm9a29uoZTheckjjzQWG1tbUZPnx4jjzyyDe8uHThwoUFpwI605gxYzJ69OhcfPHFr3nBqTcZw5kPijr77LO9ogV2c0899VR+9KMf5ZBDDqn2KHRR4oOibrrppmqPAOxiEydOzMMPPyw+eF3iA4BONWXKlMycOTMrVqzIu971rh0uOD311FOrNBldhWs+AOhUb/SqNReckogPAKAwb6oAwC7zfz/bBV4lPgDoVC+//HK+8pWvZP/990/fvn2zevXqJMkXv/jFzJ8/v8rT0RWIDwA61Ve/+tXcdNNNmTt3bnr16tW2fvjhh+fb3/52FSejqxAfAHSqm2++Oddff33OPPPM7LHHHm3rRxxxRH73u99VcTK6CvEBQKd6+umnX/M9PlpbW7N9+/YqTERXIz4A6FRjxozJvffeu8P697///Rx55JFVmIiuxpuMAdCpLrnkkpxzzjl5+umn09ramoULF2blypW5+eabc8cdd1R7PLoA7/MBQKdYvXp1Ro4cmZqamtx777257LLL8vDDD2fz5s0ZN25cLrnkkpx00knVHpMuwJkPADrFoYcemmeeeSb77bdfjj/++AwcODArVqxIQ0NDtUeji3HNBwCd4k9PpN95553ZsmVLlaahKxMfAOwSntXn9YgPADpFTU1NampqdliDP+WaDwA6RaVSycc//vHU1dUleeVzXT71qU+lT58+7fZbuHBhNcajCxEfAHSKc845p93ts846q0qT0NV5qS0AUJRrPgCAosQHAFCU+AAAihIfAEBR4gN4TU1NTTn//PNzyCGHpHfv3mloaMixxx6ba6+9Ni+99FK1xwO6MS+1BXawevXqHHvssRkwYEC+9rWv5V3velfq6uqyYsWKXH/99dl///1z6qmnVmW2SqWSl19+OT16+OMLuitnPoAdnHvuuenRo0cefPDB/PVf/3VGjx6dgw46KB/60Ify4x//OFOmTEmSrF+/Pp/85Cez7777pr6+PhMnTszDDz/cdpwvfelLefe7351///d/z4EHHpj+/fvnYx/7WDZt2tS2T2tra+bMmZORI0dmzz33zBFHHJHvf//7bfffc889qampyZ133pnx48enrq4u9913X1paWvLZz342++23X3r37p3jjjsuy5YtK/eLBLxl4gNo54UXXshPf/rTzJgxY4d3pnzVq2+Z/ZGPfCTPPvts7rzzzixfvjzjxo3LiSeemBdffLFt31WrVuW2227LHXfckTvuuCOLFy/O5Zdf3nb/nDlzcvPNN+e6667Lo48+mpkzZ+ass87K4sWL2z3mP/7jP+byyy/PY489lrFjx+biiy/OD37wg/zbv/1bHnrooRxyyCGZPHlyu8cGuqgKwP+xdOnSSpLKwoUL260PGjSo0qdPn0qfPn0qF198ceXee++t1NfXV7Zu3dpuv4MPPrjyrW99q1KpVCqXXnppZa+99qps3Lix7f7Pfe5zlQkTJlQqlUpl69atlb322qvywAMPtDvG9OnTK2eccUalUqlUfv7zn1eSVG677ba2+zdv3lzp2bNn5Tvf+U7b2rZt2ypDhw6tzJ07txN+FYBdyZOmwJvyq1/9Kq2trTnzzDPT0tKShx9+OJs3b86gQYPa7ffHP/4xq1atart94IEHpl+/fm23hwwZkmeffTZJ8sQTT+Sll17KBz7wgXbH2LZtW4488sh2a0cddVTbj1etWpXt27fn2GOPbVvr2bNnjj766Dz22GM7/5MFdinxAbRzyCGHpKamJitXrmy3ftBBByVJ9txzzyTJ5s2bM2TIkNxzzz07HGPAgAFtP+7Zs2e7+2pqatLa2tp2jCT58Y9/nP3337/dfq9+ONmrXu8pIKD7ER9AO4MGDcoHPvCBfPOb38x55533un/pjxs3Lk1NTenRo0cOPPDAt/RYY8aMSV1dXdasWZP3vve9b/rrDj744PTq1Sv3339/RowYkSTZvn17li1blgsuuOAtzQKUIz6AHVxzzTU59thjc9RRR+VLX/pSxo4dm9ra2ixbtiy/+93vMn78+EyaNCmNjY057bTTMnfu3Bx22GFZt25dfvzjH+fDH/5wu6dJXk+/fv1y0UUXZebMmWltbc1xxx2XDRs25P777099ff0On5L6qj59+uTTn/50Pve5z2XgwIEZPnx45s6dm5deeinTp0/v7F8OoJOJD2AHBx98cP77v/87X/va1zJ79uz84Q9/SF1dXcaMGZOLLroo5557bmpqavKTn/wkn//85zNt2rQ899xzGTx4cE444YQ0NDS86cf6yle+kn333Tdz5szJ6tWrM2DAgIwbNy7/9E//9IZfd/nll6e1tTV/8zd/k02bNuWoo47K3Xffnb333ntnf/rALlZTqVQq1R4CAHj78D4fAEBR4gMAKEp8AABFiQ8AoCjxAQAUJT4AgKLEBwBQlPgAAIoSHwBAUeIDAChKfAAARYkPAKCo/wfbZcFX3W6UngAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "df['Genero'].value_counts().plot(kind='bar', edgecolor='red', color=\"pink\");\n",
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<pandas.core.groupby.generic.DataFrameGroupBy object at 0x00000263DED11D50>"
+      ]
+     },
+     "execution_count": 68,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dado_agrupado = df.groupby('IMC')\n",
+    "dado_agrupado"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 73,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Classificação\n",
+       "Underweight      47\n",
+       "Normal Weight    29\n",
+       "Overweight       20\n",
+       "Obese            12\n",
+       "Name: count, dtype: int64"
+      ]
+     },
+     "execution_count": 73,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "contagem_classificacao = df['Classificação'].value_counts()\n",
+    "contagem_classificacao"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Visualização das classificações')"
+      ]
+     },
+     "execution_count": 86,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "visu_classif= df['Classificação'].value_counts()\n",
+    "visu_classif.plot(kind='bar', edgecolor='purple', color=\"pink\");\n",
+    "\n",
+    "plt.xlabel('')\n",
+    "plt.ylabel('Quantidade')\n",
+    "plt.title('Visualização das classificações')\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 92,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class_mulheres = df[df['Genero'] == 'Female']['Classificação'].value_counts()\n",
+    "class_mulheres.plot(kind='bar', edgecolor='purple', color=\"pink\");\n",
+    "\n",
+    "plt.xlabel('Mulheres')\n",
+    "plt.ylabel('Classificação')\n",
+    "plt.title('Classificação Feminina')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 105,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Classificação das crianças')"
+      ]
+     },
+     "execution_count": 105,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class_criancas = df[df['Idade'] < 17]['Classificação'].value_counts()\n",
+    "class_criancas.plot(kind='bar', edgecolor='green', color=\"pink\")\n",
+    "\n",
+    "plt.xlabel('Classificação')\n",
+    "plt.ylabel('Idade')\n",
+    "plt.title('Classificação das crianças')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 110,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Text(0.5, 1.0, 'Classificação dos idosos')"
+      ]
+     },
+     "execution_count": 110,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "class_criancas = df[df['Idade'] >60]['Classificação'].value_counts()\n",
+    "class_criancas.plot(kind='bar', edgecolor='green', color=\"pink\")\n",
+    "\n",
+    "plt.xlabel('Classificação')\n",
+    "plt.ylabel('Idade')\n",
+    "plt.title('Classificação dos idosos')\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-sala/Banco de dados.ipynb b/exercicios/para-sala/Banco de dados.ipynb
index e8f8484..8e8eaa2 100644
--- a/exercicios/para-sala/Banco de dados.ipynb	
+++ b/exercicios/para-sala/Banco de dados.ipynb	
@@ -21,20 +21,45 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "a7aa9973",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "FileNotFoundError",
+     "evalue": "[Errno 2] No such file or directory: 'youtubers_df.csv'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Angela\\Reprograma\\Estudos\\semana13\\on26-python-s13-projeto-guiado-II\\exercicios\\para-sala\\Banco de dados.ipynb Cell 3\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W2sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m file \u001b[39m=\u001b[39m \u001b[39mopen\u001b[39;49m(\u001b[39m\"\u001b[39;49m\u001b[39myoutubers_df.csv\u001b[39;49m\u001b[39m\"\u001b[39;49m, encoding\u001b[39m=\u001b[39;49m \u001b[39m'\u001b[39;49m\u001b[39mUTF-8\u001b[39;49m\u001b[39m'\u001b[39;49m) \n",
+      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.11_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python311\\site-packages\\IPython\\core\\interactiveshell.py:286\u001b[0m, in \u001b[0;36m_modified_open\u001b[1;34m(file, *args, **kwargs)\u001b[0m\n\u001b[0;32m    279\u001b[0m \u001b[39mif\u001b[39;00m file \u001b[39min\u001b[39;00m {\u001b[39m0\u001b[39m, \u001b[39m1\u001b[39m, \u001b[39m2\u001b[39m}:\n\u001b[0;32m    280\u001b[0m     \u001b[39mraise\u001b[39;00m \u001b[39mValueError\u001b[39;00m(\n\u001b[0;32m    281\u001b[0m         \u001b[39mf\u001b[39m\u001b[39m\"\u001b[39m\u001b[39mIPython won\u001b[39m\u001b[39m'\u001b[39m\u001b[39mt let you open fd=\u001b[39m\u001b[39m{\u001b[39;00mfile\u001b[39m}\u001b[39;00m\u001b[39m by default \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    282\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39mas it is likely to crash IPython. If you know what you are doing, \u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    283\u001b[0m         \u001b[39m\"\u001b[39m\u001b[39myou can use builtins\u001b[39m\u001b[39m'\u001b[39m\u001b[39m open.\u001b[39m\u001b[39m\"\u001b[39m\n\u001b[0;32m    284\u001b[0m     )\n\u001b[1;32m--> 286\u001b[0m \u001b[39mreturn\u001b[39;00m io_open(file, \u001b[39m*\u001b[39;49margs, \u001b[39m*\u001b[39;49m\u001b[39m*\u001b[39;49mkwargs)\n",
+      "\u001b[1;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: 'youtubers_df.csv'"
+     ]
+    }
+   ],
    "source": [
     "file = open(\"youtubers_df.csv\", encoding= 'UTF-8') "
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "id": "eaaee9c6",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'csv' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Angela\\Reprograma\\Estudos\\semana13\\on26-python-s13-projeto-guiado-II\\exercicios\\para-sala\\Banco de dados.ipynb Cell 4\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W3sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m conteudo \u001b[39m=\u001b[39m csv\u001b[39m.\u001b[39mreader(file)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W3sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m connection \u001b[39m=\u001b[39m sqlite3\u001b[39m.\u001b[39mconnect(\u001b[39m\"\u001b[39m\u001b[39mbanco_influencers.db\u001b[39m\u001b[39m\"\u001b[39m)\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W3sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m cursor \u001b[39m=\u001b[39m connection\u001b[39m.\u001b[39mcursor()\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'csv' is not defined"
+     ]
+    }
+   ],
    "source": [
     "conteudo = csv.reader(file)\n",
     "\n",
@@ -45,10 +70,22 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 2,
    "id": "25d07ce5",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'cursor' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[1;32mc:\\Users\\Angela\\Reprograma\\Estudos\\semana13\\on26-python-s13-projeto-guiado-II\\exercicios\\para-sala\\Banco de dados.ipynb Cell 5\u001b[0m line \u001b[0;36m1\n\u001b[1;32m----> <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W4sZmlsZQ%3D%3D?line=0'>1</a>\u001b[0m cursor\u001b[39m.\u001b[39mexecute(\u001b[39m'''\u001b[39m\u001b[39mCREATE TABLE IF NOT EXISTS rank (\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W4sZmlsZQ%3D%3D?line=1'>2</a>\u001b[0m \u001b[39m    Rank INT,\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W4sZmlsZQ%3D%3D?line=2'>3</a>\u001b[0m \u001b[39m    Username VARCHAR,\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W4sZmlsZQ%3D%3D?line=3'>4</a>\u001b[0m \u001b[39m    Categories VARCHAR, \u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W4sZmlsZQ%3D%3D?line=4'>5</a>\u001b[0m \u001b[39m    Suscribers FLOAT,\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W4sZmlsZQ%3D%3D?line=5'>6</a>\u001b[0m \u001b[39m    Country VARCHAR,\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W4sZmlsZQ%3D%3D?line=6'>7</a>\u001b[0m \u001b[39m    Visits FLOAR,\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W4sZmlsZQ%3D%3D?line=7'>8</a>\u001b[0m \u001b[39m    Likes FLOAT,\u001b[39m\n\u001b[0;32m      <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W4sZmlsZQ%3D%3D?line=8'>9</a>\u001b[0m \u001b[39m    Comments FLOAT,\u001b[39m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W4sZmlsZQ%3D%3D?line=9'>10</a>\u001b[0m \u001b[39m    Links VARCHAR\u001b[39m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W4sZmlsZQ%3D%3D?line=10'>11</a>\u001b[0m \u001b[39m    \u001b[39m\n\u001b[0;32m     <a href='vscode-notebook-cell:/c%3A/Users/Angela/Reprograma/Estudos/semana13/on26-python-s13-projeto-guiado-II/exercicios/para-sala/Banco%20de%20dados.ipynb#W4sZmlsZQ%3D%3D?line=11'>12</a>\u001b[0m \u001b[39m)\u001b[39m\u001b[39m'''\u001b[39m)\n",
+      "\u001b[1;31mNameError\u001b[0m: name 'cursor' is not defined"
+     ]
+    }
+   ],
    "source": [
     "\n",
     "cursor.execute('''CREATE TABLE IF NOT EXISTS rank (\n",
@@ -137,7 +174,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.9.7"
+   "version": "3.11.6"
   }
  },
  "nbformat": 4,