diff --git "a/1-analise-explorat\303\263ria-basica/01-lendo-dados-e-medidas-centralidade/aula.ipynb" "b/1-analise-explorat\303\263ria-basica/01-lendo-dados-e-medidas-centralidade/aula.ipynb"
index 46d1fb8..8eb6da5 100644
--- "a/1-analise-explorat\303\263ria-basica/01-lendo-dados-e-medidas-centralidade/aula.ipynb"
+++ "b/1-analise-explorat\303\263ria-basica/01-lendo-dados-e-medidas-centralidade/aula.ipynb"
@@ -22,7 +22,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 1,
    "metadata": {},
    "outputs": [
     {
@@ -34,7 +34,7 @@
        "<IPython.core.display.HTML object>"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 1,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -99,7 +99,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 3,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -121,7 +121,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -158,7 +158,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -167,7 +167,7 @@
        "list"
       ]
      },
-     "execution_count": 5,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -178,7 +178,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -187,7 +187,7 @@
        "str"
       ]
      },
-     "execution_count": 6,
+     "execution_count": 5,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -226,7 +226,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -264,7 +264,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 41,
    "metadata": {
     "scrolled": true
    },
@@ -273,7 +273,7 @@
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Média da coluna Idade: 41.67\n"
+      "Média da coluna Idade: 37.63\n"
      ]
     }
    ],
@@ -284,7 +284,7 @@
     "    lista_exemplo = []\n",
     "    somatorio = 0\n",
     "    for i, line in enumerate(data):\n",
-    "        if i < 10 and i>0:\n",
+    "        if i>0:\n",
     "            somatorio +=  int(line[2])\n",
     "            lista_exemplo.append(line[2])\n",
     "            \n",
@@ -303,7 +303,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 8,
    "metadata": {},
    "outputs": [
     {
@@ -319,10 +319,10 @@
      "evalue": "could not convert string to float: ",
      "output_type": "error",
      "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-10-4e87affea75f>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m     14\u001b[0m             \u001b[0msomatorio_idade\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m2\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     15\u001b[0m             \u001b[0msomatorio_estudo\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m4\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 16\u001b[1;33m             \u001b[0msomatorio_salario\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m6\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m     17\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m     18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n",
-      "\u001b[1;31mValueError\u001b[0m: could not convert string to float: "
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-8-4e87affea75f>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m     14\u001b[0m             \u001b[0msomatorio_idade\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     15\u001b[0m             \u001b[0msomatorio_estudo\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m4\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 16\u001b[0;31m             \u001b[0msomatorio_salario\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mrow\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     17\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     18\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: "
      ]
     }
    ],
@@ -357,7 +357,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 9,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -367,29 +367,77 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 10,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "SyntaxError",
+     "evalue": "unexpected EOF while parsing (<ipython-input-10-42147fe43dc9>, line 7)",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;36m  File \u001b[0;32m\"<ipython-input-10-42147fe43dc9>\"\u001b[0;36m, line \u001b[0;32m7\u001b[0m\n\u001b[0;31m    #return saida\u001b[0m\n\u001b[0m                 ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m unexpected EOF while parsing\n"
+     ]
+    }
+   ],
    "source": [
     "def substitui_nulo_por_zero(entrada):\n",
+    "\n",
     "    #\n",
     "    #\n",
-    "    return saida"
+    "    #\n",
+    "\n",
+    "    #return saida"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 11,
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "ename": "NameError",
+     "evalue": "name 'substitui_nulo_por_zero' is not defined",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-11-cbef955e0243>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m     \u001b[0mdata\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcsv\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mreader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata_csv\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0msubstitui_nulo_por_zero\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mdata\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mlista_exemplo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m[\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mNameError\u001b[0m: name 'substitui_nulo_por_zero' is not defined"
+     ]
+    }
+   ],
+   "source": [
+    "somatorio_idade = 0\n",
+    "somatorio_estudo = 0\n",
+    "somatorio_salario = 0\n",
+    "\n",
+    "\n",
+    "with open(r'data/data.csv', 'r') as data_csv:\n",
+    "    data = csv.reader(data_csv)\n",
+    "    \n",
+    "    substitui_nulo_por_zero(data)\n",
+    "    \n",
+    "    lista_exemplo = []\n",
+    "    somatorio = 0\n",
+    "    \n",
+    "    for i, row in enumerate(data):\n",
+    "        \n",
+    "        if i>0:\n",
+    "            print(row)\n",
+    "            somatorio_idade += int(row[2])\n",
+    "            somatorio_estudo += float(row[4])\n",
+    "            somatorio_salario += float(row[6])\n",
+    "            \n",
+    "        \n",
+    "media_idade = round(somatorio_idade/i,2)\n",
+    "media_estudo = round(somatorio_estudo/i,2)\n",
+    "media_salario = round(somatorio_salario/i,2)\n",
+    "\n",
+    "\n",
+    "print(f'Média da coluna Idade: {media_idade}')\n",
+    "print(f'Média da coluna Anos de Estudo: {media_estudo}')\n",
+    "print(f'Média da coluna Salario: {media_salario}')"
+   ]
   },
   {
    "cell_type": "code",
@@ -439,14 +487,114 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "#### Função para calcular a Mediana com Dicionarios\n",
+    "#### Função para calcular a Mediana com Dicionarios"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Vamos criar um looping para procurar a pessoa mais velha da base"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A pessoa mais velha na base é o id 11002710101.0, que tem 60 anos.\n"
+     ]
+    }
+   ],
+   "source": [
+    "max_age = None\n",
+    "oldest_person = None\n",
+    "\n",
+    "input_file = csv.DictReader(open(r'data/data.csv', 'r'))\n",
+    "\n",
+    "for row in input_file:\n",
+    "    age = int(row[\"idade\"])\n",
+    "    if max_age == None or max_age < age:\n",
+    "        max_age = age\n",
+    "        oldest_person = row[\"id\"]\n",
+    "\n",
+    "if max_age != None:\n",
+    "    print(\"A pessoa mais velha na base é o id %s, que tem %d anos.\" % (oldest_person, max_age))\n",
+    "else:\n",
+    "    print(\"Não tem ninguém nesse arquivo\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Procurando a pessoa mais nova da base"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Calculando a mediana"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Moda\n",
+    "\n",
+    "\n",
+    "Em estatística, moda é uma das medidas de tendência central de um conjunto de dados, assim como a média e a mediana. Ela pode ser definida em moda amostral e populacional.\n",
+    "\n",
+    "Em relação à primeira delas, a moda amostral de um conjunto de dados trata do valor que ocorre com maior frequência ou o valor mais comum em um conjunto de dados. Moda é especialmente útil quando os valores ou as observações não são numéricos, casos em que a média e a mediana não podem ser definidas. \n",
+    "\n",
+    "Moda amostral não é necessariamente única como média ou mediana. Amostras que possuem uma moda são chamadas unimodais. Por exemplo, a amostra {1, 2, 3, 5, 5, 6, 7} tem moda 5. Amostras que possuem duas modas são chamadas bimodais. Por exemplo, a amostra {1, 2, 3, 5, 5, 6, 6} tem modas 5 e 6. Amostras que possuem várias modas são chamadas multimodais. Por exemplo, a amostra {1, 2 3, 5, 5, 6, 6, 7, 7} tem modas 5, 6 e 7. Amostras que não possuem moda são chamadas amodais. Por exemplo, a amostra {1, 3, 2, 5, 7, 6} não tem moda. \n",
+    "\n",
+    "Por exemplo, a moda de [0,1,1,2,2,2,2,3,3,4,4,4,5] é 2.\n",
+    "\n",
+    "A moda não é necessariamente única. Pode ser que existam dois valores diferentes que sejam os mais frequentes. Por exemplo, no caso de [10, 13, 13, 20, 20], tanto 13 como 20 são a moda.\n",
     "\n",
+    "<br>\n",
+    "<img src=\"img/moda2.png\">\n",
+    "<br>"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
     "Material complementar para dicionarios python: http://excript.com/python/funcoes-dicionarios.html"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 13,
    "metadata": {},
    "outputs": [
     {
@@ -488,7 +636,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 190,
+   "execution_count": 14,
    "metadata": {},
    "outputs": [
     {
@@ -505,7 +653,7 @@
        "             ('regiao', 'centro-oeste')])"
       ]
      },
-     "execution_count": 190,
+     "execution_count": 14,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -516,7 +664,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 191,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [
     {
@@ -525,7 +673,7 @@
        "'37'"
       ]
      },
-     "execution_count": 191,
+     "execution_count": 15,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -536,7 +684,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 192,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -545,7 +693,7 @@
        "'centro-oeste'"
       ]
      },
-     "execution_count": 192,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -563,7 +711,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 23,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -580,7 +728,7 @@
        "             ('regiao', 'centro-oeste')])"
       ]
      },
-     "execution_count": 23,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -591,7 +739,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 67,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -608,7 +756,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 69,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -617,7 +765,7 @@
        "True"
       ]
      },
-     "execution_count": 69,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -628,7 +776,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 70,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [
     {
@@ -637,7 +785,7 @@
        "{'idade': '53', 'sexo': 'homem', 'anos_estudo': '5.0', 'salario': '7128.0'}"
       ]
      },
-     "execution_count": 70,
+     "execution_count": 20,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -648,7 +796,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 71,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -657,7 +805,7 @@
        "'5.0'"
       ]
      },
-     "execution_count": 71,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -668,7 +816,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 72,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -677,7 +825,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 73,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -686,7 +834,7 @@
        "{'idade': '53', 'sexo': 'homem', 'anos_estudo': '6', 'salario': '7128.0'}"
       ]
      },
-     "execution_count": 73,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -697,7 +845,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 74,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -706,7 +854,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 75,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
@@ -714,10 +862,10 @@
      "evalue": "'11027211101.0'",
      "output_type": "error",
      "traceback": [
-      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
-      "\u001b[1;32m<ipython-input-75-7d8af068c742>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdict_table\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'11027211101.0'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
-      "\u001b[1;31mKeyError\u001b[0m: '11027211101.0'"
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-25-7d8af068c742>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdict_table\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'11027211101.0'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
+      "\u001b[0;31mKeyError\u001b[0m: '11027211101.0'"
      ]
     }
    ],
@@ -727,7 +875,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 77,
+   "execution_count": 26,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -741,30 +889,30 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 78,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'m': 2,\n",
-       " 'p': 2,\n",
-       " 'r': 4,\n",
+       "{'t': 3,\n",
+       " 'd': 2,\n",
+       " 'c': 1,\n",
+       " 'e': 4,\n",
        " 'g': 1,\n",
        " 's': 3,\n",
        " 'a': 6,\n",
-       " 'c': 1,\n",
-       " 'e': 4,\n",
-       " 't': 3,\n",
-       " 'i': 2,\n",
-       " 'h': 1,\n",
-       " 'd': 2,\n",
-       " ' ': 5,\n",
        " 'o': 2,\n",
-       " 'f': 1}"
+       " 'p': 2,\n",
+       " 'f': 1,\n",
+       " 'h': 1,\n",
+       " 'i': 2,\n",
+       " 'm': 2,\n",
+       " 'r': 4,\n",
+       " ' ': 5}"
       ]
      },
-     "execution_count": 78,
+     "execution_count": 27,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -775,7 +923,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 79,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -794,7 +942,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 83,
+   "execution_count": 29,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -803,7 +951,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 84,
+   "execution_count": 30,
    "metadata": {},
    "outputs": [
     {
@@ -812,7 +960,7 @@
        "dict"
       ]
      },
-     "execution_count": 84,
+     "execution_count": 30,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -823,56 +971,56 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 86,
+   "execution_count": 31,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "{'27': 2017,\n",
-       " '36': 1723,\n",
+       "{'50': 1461,\n",
+       " '49': 1429,\n",
+       " '32': 1852,\n",
+       " '27': 2017,\n",
+       " '45': 1627,\n",
        " '60': 951,\n",
-       " '37': 1712,\n",
-       " '24': 1892,\n",
        " '31': 1796,\n",
-       " '45': 1627,\n",
-       " '43': 1699,\n",
-       " '41': 1552,\n",
-       " '34': 1737,\n",
-       " '50': 1461,\n",
-       " '39': 1759,\n",
-       " '20': 2104,\n",
+       " '56': 1087,\n",
+       " '28': 2056,\n",
+       " '47': 1446,\n",
+       " '23': 2014,\n",
        " '59': 999,\n",
-       " '42': 1673,\n",
-       " '52': 1244,\n",
-       " '32': 1852,\n",
-       " '25': 2014,\n",
-       " '38': 1727,\n",
        " '30': 1996,\n",
+       " '22': 2034,\n",
+       " '42': 1673,\n",
+       " '33': 1812,\n",
+       " '26': 2040,\n",
+       " '39': 1759,\n",
        " '44': 1688,\n",
-       " '53': 1249,\n",
-       " '51': 1260,\n",
-       " '23': 2014,\n",
-       " '48': 1505,\n",
-       " '54': 1221,\n",
-       " '29': 1943,\n",
-       " '21': 1987,\n",
-       " '28': 2056,\n",
        " '57': 1092,\n",
-       " '26': 2040,\n",
+       " '41': 1552,\n",
+       " '48': 1505,\n",
+       " '43': 1699,\n",
+       " '58': 969,\n",
        " '35': 1672,\n",
-       " '56': 1087,\n",
        " '55': 1133,\n",
-       " '22': 2034,\n",
-       " '40': 1732,\n",
-       " '58': 969,\n",
+       " '21': 1987,\n",
+       " '36': 1723,\n",
+       " '38': 1727,\n",
        " '46': 1566,\n",
-       " '47': 1446,\n",
-       " '49': 1429,\n",
-       " '33': 1812}"
+       " '20': 2104,\n",
+       " '54': 1221,\n",
+       " '29': 1943,\n",
+       " '25': 2014,\n",
+       " '51': 1260,\n",
+       " '53': 1249,\n",
+       " '34': 1737,\n",
+       " '52': 1244,\n",
+       " '40': 1732,\n",
+       " '37': 1712,\n",
+       " '24': 1892}"
       ]
      },
-     "execution_count": 86,
+     "execution_count": 31,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -883,16 +1031,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 87,
+   "execution_count": 32,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_keys(['27', '36', '60', '37', '24', '31', '45', '43', '41', '34', '50', '39', '20', '59', '42', '52', '32', '25', '38', '30', '44', '53', '51', '23', '48', '54', '29', '21', '28', '57', '26', '35', '56', '55', '22', '40', '58', '46', '47', '49', '33'])"
+       "dict_keys(['50', '49', '32', '27', '45', '60', '31', '56', '28', '47', '23', '59', '30', '22', '42', '33', '26', '39', '44', '57', '41', '48', '43', '58', '35', '55', '21', '36', '38', '46', '20', '54', '29', '25', '51', '53', '34', '52', '40', '37', '24'])"
       ]
      },
-     "execution_count": 87,
+     "execution_count": 32,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -903,16 +1051,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 88,
+   "execution_count": 33,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_values([2017, 1723, 951, 1712, 1892, 1796, 1627, 1699, 1552, 1737, 1461, 1759, 2104, 999, 1673, 1244, 1852, 2014, 1727, 1996, 1688, 1249, 1260, 2014, 1505, 1221, 1943, 1987, 2056, 1092, 2040, 1672, 1087, 1133, 2034, 1732, 969, 1566, 1446, 1429, 1812])"
+       "dict_values([1461, 1429, 1852, 2017, 1627, 951, 1796, 1087, 2056, 1446, 2014, 999, 1996, 2034, 1673, 1812, 2040, 1759, 1688, 1092, 1552, 1505, 1699, 969, 1672, 1133, 1987, 1723, 1727, 1566, 2104, 1221, 1943, 2014, 1260, 1249, 1737, 1244, 1732, 1712, 1892])"
       ]
      },
-     "execution_count": 88,
+     "execution_count": 33,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -923,16 +1071,16 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 89,
+   "execution_count": 34,
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "dict_items([('27', 2017), ('36', 1723), ('60', 951), ('37', 1712), ('24', 1892), ('31', 1796), ('45', 1627), ('43', 1699), ('41', 1552), ('34', 1737), ('50', 1461), ('39', 1759), ('20', 2104), ('59', 999), ('42', 1673), ('52', 1244), ('32', 1852), ('25', 2014), ('38', 1727), ('30', 1996), ('44', 1688), ('53', 1249), ('51', 1260), ('23', 2014), ('48', 1505), ('54', 1221), ('29', 1943), ('21', 1987), ('28', 2056), ('57', 1092), ('26', 2040), ('35', 1672), ('56', 1087), ('55', 1133), ('22', 2034), ('40', 1732), ('58', 969), ('46', 1566), ('47', 1446), ('49', 1429), ('33', 1812)])"
+       "dict_items([('50', 1461), ('49', 1429), ('32', 1852), ('27', 2017), ('45', 1627), ('60', 951), ('31', 1796), ('56', 1087), ('28', 2056), ('47', 1446), ('23', 2014), ('59', 999), ('30', 1996), ('22', 2034), ('42', 1673), ('33', 1812), ('26', 2040), ('39', 1759), ('44', 1688), ('57', 1092), ('41', 1552), ('48', 1505), ('43', 1699), ('58', 969), ('35', 1672), ('55', 1133), ('21', 1987), ('36', 1723), ('38', 1727), ('46', 1566), ('20', 2104), ('54', 1221), ('29', 1943), ('25', 2014), ('51', 1260), ('53', 1249), ('34', 1737), ('52', 1244), ('40', 1732), ('37', 1712), ('24', 1892)])"
       ]
      },
-     "execution_count": 89,
+     "execution_count": 34,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -943,7 +1091,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 92,
+   "execution_count": 35,
    "metadata": {},
    "outputs": [
     {
@@ -1000,18 +1148,11 @@
    ]
   },
   {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "cell_type": "markdown",
    "metadata": {},
-   "outputs": [],
-   "source": []
+   "source": [
+    "#### Calculando a moda"
+   ]
   },
   {
    "cell_type": "code",
@@ -1020,13 +1161,6 @@
    "outputs": [],
    "source": []
   },
-  {
-   "cell_type": "code",
-   "execution_count": 28,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1045,95 +1179,73 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### Moda\n",
-    "\n",
-    "\n",
-    "Em estatística, moda é uma das medidas de tendência central de um conjunto de dados, assim como a média e a mediana. Ela pode ser definida em moda amostral e populacional.\n",
-    "\n",
-    "Em relação à primeira delas, a moda amostral de um conjunto de dados trata do valor que ocorre com maior frequência ou o valor mais comum em um conjunto de dados. Moda é especialmente útil quando os valores ou as observações não são numéricos, casos em que a média e a mediana não podem ser definidas. \n",
-    "\n",
-    "Moda amostral não é necessariamente única como média ou mediana. Amostras que possuem uma moda são chamadas unimodais. Por exemplo, a amostra {1, 2, 3, 5, 5, 6, 7} tem moda 5. Amostras que possuem duas modas são chamadas bimodais. Por exemplo, a amostra {1, 2, 3, 5, 5, 6, 6} tem modas 5 e 6. Amostras que possuem várias modas são chamadas multimodais. Por exemplo, a amostra {1, 2 3, 5, 5, 6, 6, 7, 7} tem modas 5, 6 e 7. Amostras que não possuem moda são chamadas amodais. Por exemplo, a amostra {1, 3, 2, 5, 7, 6} não tem moda. \n",
-    "\n",
-    "Por exemplo, a moda de [0,1,1,2,2,2,2,3,3,4,4,4,5] é 2.\n",
-    "\n",
-    "A moda não é necessariamente única. Pode ser que existam dois valores diferentes que sejam os mais frequentes. Por exemplo, no caso de [10, 13, 13, 20, 20], tanto 13 como 20 são a moda.\n",
-    "\n",
-    "<br>\n",
-    "<img src=\"img/moda2.png\">\n",
-    "<br>"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "Vamos criar um looping para procurar a pessoa mais velha da base"
+    "## Prévia da resposta para o nosso problema. As homens ganham mais do que as mulheres? Quanto?"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 93,
+   "execution_count": 36,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "A pessoa mais velha na base é o id 11002710101.0, que tem 60 anos.\n"
+      "Média salário homem: 58358.4\n"
      ]
     }
    ],
    "source": [
-    "max_age = None\n",
-    "oldest_person = None\n",
-    "\n",
-    "input_file = csv.DictReader(open(r'data/data.csv', 'r'))\n",
+    "with open(r'data/data.csv', 'r') as data_csv:\n",
+    "    data = csv.reader(data_csv)\n",
+    "    \n",
+    "    lista_homem = []\n",
+    "    somatorio = 0.0\n",
+    "    for i, line in enumerate(data):\n",
+    "        if i < 10 and i>0:\n",
+    "            if line[3] == 'homem':\n",
+    "                somatorio +=  float(line[6])\n",
+    "                lista_homem.append(line[6])\n",
+    "            \n",
     "\n",
-    "for row in input_file:\n",
-    "    age = int(row[\"idade\"])\n",
-    "    if max_age == None or max_age < age:\n",
-    "        max_age = age\n",
-    "        oldest_person = row[\"id\"]\n",
+    "media = round(somatorio/len(lista_homem),2)\n",
     "\n",
-    "if max_age != None:\n",
-    "    print(\"A pessoa mais velha na base é o id %s, que tem %d anos.\" % (oldest_person, max_age))\n",
-    "else:\n",
-    "    print(\"Não tem ninguém nesse arquivo\")"
+    "print(f'Média salário homem: {media}')"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
+   "execution_count": 37,
    "metadata": {},
+   "outputs": [
+    {
+     "ename": "ValueError",
+     "evalue": "could not convert string to float: ",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
+      "\u001b[0;32m<ipython-input-37-555e8bef9cf1>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      7\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mi\u001b[0m \u001b[0;34m<\u001b[0m \u001b[0;36m10\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0mi\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mline\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m'mulher'\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m                 \u001b[0msomatorio\u001b[0m \u001b[0;34m+=\u001b[0m  \u001b[0mfloat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m                 \u001b[0mlista_mulher\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mline\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
+      "\u001b[0;31mValueError\u001b[0m: could not convert string to float: "
+     ]
+    }
+   ],
    "source": [
-    "## Prévia da resposta para o nosso problema. As homens ganham mais do que as mulheres? Quanto?"
+    "with open(r'data/data.csv', 'r') as data_csv:\n",
+    "    data = csv.reader(data_csv)\n",
+    "    \n",
+    "    lista_mulher = []\n",
+    "    somatorio = 0.0\n",
+    "    for i, line in enumerate(data):\n",
+    "        if i < 10 and i>0:\n",
+    "            if line[3] == 'mulher':\n",
+    "                somatorio +=  float(line[6])\n",
+    "                lista_mulher.append(line[6])\n",
+    "            \n",
+    "\n",
+    "media = round(somatorio/len(lista_mulher),2)\n",
+    "\n",
+    "print(f'Média salário mulher: {media}')"
    ]
   },
   {
@@ -1143,13 +1255,6 @@
    "outputs": [],
    "source": []
   },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
   {
    "cell_type": "code",
    "execution_count": null,
@@ -1175,7 +1280,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.1"
+   "version": "3.6.8"
   },
   "toc": {
    "base_numbering": 1,