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+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Replace Query with Parameter\n",
+    "🎓 College: [Faculdade Metodista Granbery](http://granbery.edu.br/)\n",
+    "\n",
+    "👨‍🏫 Teacher: [Marco Antônio - Github](https://github.com/marcoaparaujo) | [Linkedin](https://www.linkedin.com/in/marco-ant%C3%B4nio-ara%C3%BAjo/)\n",
+    "\n",
+    "📗 Book: [Refatoração - Aperfeiçoando o design de códigos existentes - Martin Fowler](https://www.amazon.com/-/pt/dp/B087N8LKYB/ref=sr_1_1?__mk_pt_BR=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=M4T29CCKD30E&keywords=refatora%C3%A7%C3%A3o&qid=1651322207&sprefix=refatora%C3%A7%C3%A3o%2Caps%2C203&sr=8-1)\n",
+    "\n",
+    "> FOWLER, Martin. “Replace Query with Parameter” no código. *In*: REFATORAÇÃO: Aperfeiçoando o design de códigos existentes. 2. ed. [*S. l.*: *s. n.*], 2019. cap. 8.\n",
+    ">\n",
+    "\n",
+    "## Objetivo\n",
+    "\n",
+    "Objetivo desse método e trazer uma função que traga sempre o mesmo resultado  que se chama transparência referencial. A função que foi criada, não acessar um referencial transparente, a função não possuir transparência."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Código\n",
+    "O código que escolhi foi uma atividade dado em aula pelo Professor Ricardo. Essa função recebe um parâmetro de nomes de coluna do dataset, os dados são verificados pela sua severidade e depois ocorre sua substituição dos valores ausentes de acordo com o seu tipo.\n",
+    "\n",
+    "O código pode ser encontrado na plataforma [Kaggle](https://www.kaggle.com/code/edmilsoneddi/data-mining-discipline-exercise)\n",
+    "\n",
+    "## Ferramentas\n",
+    "### pytest\n",
+    "```sh\n",
+    "    pip install ipytest\n",
+    "```\n",
+    "### testbook\n",
+    "```sh\n",
+    "    pip install testbook\n",
+    "```\n",
+    "### jupyter notebook\n",
+    "```sh\n",
+    "    pip install jupyter\n",
+    "```\n",
+    "### pandas\n",
+    "```sh\n",
+    "    pip install pandas\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Código sem refatoração"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dataset = pd.read_csv('mamografia.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "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>BI-RADS</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>Forma</th>\n",
+       "      <th>Margem</th>\n",
+       "      <th>Densidade</th>\n",
+       "      <th>Severidade</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   BI-RADS  Idade  Forma   Margem   Densidade  Severidade\n",
+       "0      5.0   67.0    3.0      5.0         3.0           1\n",
+       "1      4.0   43.0    1.0      1.0         NaN           1\n",
+       "2      5.0   58.0    4.0      5.0         3.0           1\n",
+       "3      4.0   28.0    1.0      1.0         3.0           0\n",
+       "4      5.0   74.0    1.0      5.0         NaN           1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mamografia = dataset.copy()\n",
+    "mamografia.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS       float64\n",
+       "Idade         float64\n",
+       "Forma         float64\n",
+       " Margem       float64\n",
+       " Densidade    float64\n",
+       "Severidade      int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mamografia.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Análise de Valores Ausentes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      " Valores ausentes  \n",
+      "\n",
+      "Valores Benigno: \n",
+      "BI-RADS        1\n",
+      "Idade          0\n",
+      "Forma         19\n",
+      " Margem       37\n",
+      " Densidade    54\n",
+      "Severidade     0\n",
+      "dtype: int64\n",
+      "\n",
+      "Valores Maligno: \n",
+      "BI-RADS        1\n",
+      "Idade          5\n",
+      "Forma         12\n",
+      " Margem       11\n",
+      " Densidade    22\n",
+      "Severidade     0\n",
+      "dtype: int64\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "benigno = mamografia[mamografia['Severidade'] == 0].isnull().sum()\n",
+    "print(' Valores ausentes  ')\n",
+    "print()\n",
+    "print('Valores Benigno: ')\n",
+    "print(benigno)\n",
+    "print()\n",
+    "maligno = mamografia[mamografia['Severidade'] == 1].isnull().sum()\n",
+    "print('Valores Maligno: ')\n",
+    "print(maligno)\n",
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def tratar_ausentes():\n",
+    "    for lista in mamografia.columns:\n",
+    "        if lista != 'Idade':\n",
+    "            moda_benigno = mamografia[(mamografia['Severidade'] == 0)][lista].mode()[0]\n",
+    "            moda_maligno = mamografia[(mamografia['Severidade'] == 1)][lista].mode()[0]\n",
+    "            mamografia.loc[(mamografia[lista].isnull())&(mamografia['Severidade'] == 0), lista] = moda_benigno\n",
+    "            mamografia.loc[(mamografia[lista].isnull())&(mamografia['Severidade'] == 1), lista] = moda_maligno\n",
+    "            \n",
+    "        else:\n",
+    "            media_benigno = mamografia[(mamografia['Severidade'] == 0)][lista].mean()\n",
+    "            media_maligno = mamografia[(mamografia['Severidade'] == 1)][lista].mean()\n",
+    "            mamografia.loc[(mamografia[lista].isnull())&(mamografia['Severidade'] == 0), lista] = media_benigno\n",
+    "            mamografia.loc[(mamografia[lista].isnull())&(mamografia['Severidade'] == 1), lista] = media_maligno"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['BI-RADS', 'Idade', 'Forma', ' Margem', ' Densidade', 'Severidade'], dtype='object')"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mamografia.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tratar_ausentes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS       0\n",
+       "Idade         0\n",
+       "Forma         0\n",
+       " Margem       0\n",
+       " Densidade    0\n",
+       "Severidade    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Objetivo da atividade\n",
+    "\n",
+    "1. Elabore ou escolha um código que a refatoração possa ser aplicada.\n",
+    "2. Identifique o mau cheiro associado.\n",
+    "3. Escreva os casos de teste e execute\n",
+    "4. Aplique a refatoração\n",
+    "5. Execute os casos de teste novamente\n",
+    "6. Documente cada etapa para a apresentação\n",
+    "\n",
+    "## Mau Cheiro\n",
+    "\n",
+    "### Lista longa de parâmetros\n",
+    "\n",
+    "O mau cheiro que encontrei dentro do código abaixo foi o lista longa de parâmetros, por possuir consultas longas e não claras. Esse mal cheiro, pode trazer dificuldades ou erros no código."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Procedimento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "refatoraMamografia = dataset.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS        2\n",
+       "Idade          5\n",
+       "Forma         31\n",
+       " Margem       48\n",
+       " Densidade    76\n",
+       "Severidade     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 11,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Use Extrair variável (Extract Variable) no código da consulta para separá-lo do restante do corpo da função."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def tratar_ausentes():\n",
+    "    colunas = refatoraMamografia.columns\n",
+    "    for lista in colunas:\n",
+    "        if lista != 'Idade':\n",
+    "            moda_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mode()[0]\n",
+    "            moda_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mode()[0]\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = moda_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = moda_maligno\n",
+    "        else:\n",
+    "            media_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mean()\n",
+    "            media_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mean()\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = media_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = media_maligno"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tratar_ausentes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS       0\n",
+       "Idade         0\n",
+       "Forma         0\n",
+       " Margem       0\n",
+       " Densidade    0\n",
+       "Severidade    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Aplique Extrair função (Extract Function) no código do corpo que não seja a chamada da consulta."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Dê à nova função um nome fácil de pesquisar, para poder renomeá-la depois."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS        2\n",
+       "Idade          5\n",
+       "Forma         31\n",
+       " Margem       48\n",
+       " Densidade    76\n",
+       "Severidade     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia = dataset.copy() #Apenas usei novamente para retorna o valor do dataset com os ausentes\n",
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def XXXNEWtratar_ausentes_refatora(listaColuna):\n",
+    "    for lista in listaColuna:\n",
+    "        if lista != 'Idade':\n",
+    "            moda_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mode()[0]\n",
+    "            moda_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mode()[0]\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = moda_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = moda_maligno\n",
+    "        else:\n",
+    "            media_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mean()\n",
+    "            media_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mean()\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = media_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = media_maligno"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def tratar_ausentes_refatora(): # tratar_ausentes_refatora() está escrito refatora, para não chamar a outra função tratar_ausentes()\n",
+    "    colunas = refatoraMamografia.columns\n",
+    "    return XXXNEWtratar_ausentes_refatora(colunas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tratar_ausentes_refatora()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS       0\n",
+       "Idade         0\n",
+       "Forma         0\n",
+       " Margem       0\n",
+       " Densidade    0\n",
+       "Severidade    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Use Internalizar variável (Inline Variable) para se livrar da variável que você acabou de criar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS        2\n",
+       "Idade          5\n",
+       "Forma         31\n",
+       " Margem       48\n",
+       " Densidade    76\n",
+       "Severidade     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia = dataset.copy()  #Apenas usei novamente para retorna o valor do dataset com os ausentes\n",
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def XXXNEWtratar_ausentes_refatora(listaColuna):\n",
+    "    for lista in listaColuna:\n",
+    "        if lista != 'Idade':\n",
+    "            moda_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mode()[0]\n",
+    "            moda_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mode()[0]\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = moda_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = moda_maligno\n",
+    "        else:\n",
+    "            media_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mean()\n",
+    "            media_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mean()\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = media_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = media_maligno"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def tratar_ausentes_refatora(): # tratar_ausentes_refatora() está escrito refatora, para não chamar a outra função tratar_ausentes()\n",
+    "    return XXXNEWtratar_ausentes_refatora(refatoraMamografia.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS       0\n",
+       "Idade         0\n",
+       "Forma         0\n",
+       " Margem       0\n",
+       " Densidade    0\n",
+       "Severidade    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tratar_ausentes_refatora()\n",
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Aplique Internalizar função (Inline Function) na função original."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS        2\n",
+       "Idade          5\n",
+       "Forma         31\n",
+       " Margem       48\n",
+       " Densidade    76\n",
+       "Severidade     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 24,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia = dataset.copy()  #Apenas usei novamente para retorna o valor do dataset com os ausentes\n",
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def XXXNEWtratar_ausentes_refatora(listaColuna):\n",
+    "    for lista in listaColuna:\n",
+    "        if lista != 'Idade':\n",
+    "            moda_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mode()[0]\n",
+    "            moda_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mode()[0]\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = moda_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = moda_maligno\n",
+    "        else:\n",
+    "            media_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mean()\n",
+    "            media_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mean()\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = media_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = media_maligno"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#def tratar_ausentes_refatora():\n",
+    "    # tratar_ausentes_refatora() está escrito refatora, para não chamar a outra função tratar_ausentes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "XXXNEWtratar_ausentes_refatora(refatoraMamografia.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 28,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS       0\n",
+       "Idade         0\n",
+       "Forma         0\n",
+       " Margem       0\n",
+       " Densidade    0\n",
+       "Severidade    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 28,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Renomeie a nova função dando-lhe o nome da função original."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS        2\n",
+       "Idade          5\n",
+       "Forma         31\n",
+       " Margem       48\n",
+       " Densidade    76\n",
+       "Severidade     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 29,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia = dataset.copy()  #Apenas usei novamente para retorna o valor do dataset com os ausentes\n",
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def tratar_ausentes_refatora(listaColuna):\n",
+    "    for lista in listaColuna:\n",
+    "        if lista != 'Idade':\n",
+    "            moda_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mode()[0]\n",
+    "            moda_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mode()[0]\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = moda_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = moda_maligno\n",
+    "        else:\n",
+    "            media_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mean()\n",
+    "            media_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mean()\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = media_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = media_maligno"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tratar_ausentes_refatora(refatoraMamografia.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS       0\n",
+       "Idade         0\n",
+       "Forma         0\n",
+       " Margem       0\n",
+       " Densidade    0\n",
+       "Severidade    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 32,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia.isnull().sum()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.7"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
diff --git a/README.md b/README.md
new file mode 100644
index 0000000..81d8d93
--- /dev/null
+++ b/README.md
@@ -0,0 +1,91 @@
+# Replace Query with Parameter
+
+🎓 College: [Faculdade Metodista Granbery](http://granbery.edu.br/)
+
+👨‍🏫 Teacher: [Marco Antônio - Github](https://github.com/marcoaparaujo) | [Linkedin](https://www.linkedin.com/in/marco-ant%C3%B4nio-ara%C3%BAjo/)
+
+📗 Book: [Refatoração - Aperfeiçoando o design de códigos existentes - Martin Fowler](https://www.amazon.com/-/pt/dp/B087N8LKYB/ref=sr_1_1?__mk_pt_BR=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=M4T29CCKD30E&keywords=refatora%C3%A7%C3%A3o&qid=1651322207&sprefix=refatora%C3%A7%C3%A3o%2Caps%2C203&sr=8-1)
+
+> FOWLER, Martin. “Replace Query with Parameter” no código. *In*: REFATORAÇÃO: Aperfeiçoando o design de códigos existentes. 2. ed. [*S. l.*: *s. n.*], 2019. cap. 11.
+> 
+
+## Método
+
+Objetivo desse método e retirar a consulta dentro função que está em uma dependência indesejada e trazer uma função que traga sempre o mesmo resultado  que se chama transparência referencial. 
+
+## Código
+
+O código que escolhi foi uma atividade dado em aula pelo Professor Ricardo. Essa função recebe um parâmetro de nomes de coluna do dataset, os dados são verificados pela sua severidade e depois ocorre sua substituição dos valores ausentes de acordo com o seu tipo.
+
+O código pode ser encontrado na plataforma [Kaggle](https://www.kaggle.com/code/edmilsoneddi/data-mining-discipline-exercise)
+
+## Ferramentas
+
+```bash
+    pip install ipytest
+```
+
+```bash
+    pip install testbook
+```
+
+```bash
+    pip install pandas
+```
+
+```bash
+    pip install jupyter
+```
+
+## Objetivo da atividade
+
+1. Elabore ou escolha um código que a refatoração possa ser aplicada.
+2. Identifique o mau cheiro associado.
+3. Escreva os casos de teste e execute
+4. Aplique a refatoração
+5. Execute os casos de teste novamente
+6. Documente cada etapa para a apresentação
+
+## Mau Cheiro
+
+### Lista longa de parâmetros
+
+O mau cheiro que encontrei dentro do código abaixo foi o lista longa de parâmetros, sendo que esse mal cheiro é para método ‘Replace Parameter with Query’, mas esse método e o inverso do ‘Replace Query with Paramater’, sendo assim o seu mal cheiro será o inverso. O mal cheiro será de **consultas longas** que trazem **confusão** para realizar a **compreensão do código**. 
+
+## Procedimento
+
+## Código sem refatoração
+
+![funcaoSemRefatoracao.png](images/funcaoSemRefatoracao.png)
+
+## Teste sem refatoração
+
+![testeSemRefatoracao.png](images/testeSemRefatoracao.png)
+
+## Código novo aplicando a refatoração
+
+## Use Extrair variável (Extract Variable) no código da consulta para separá-lo do restante do corpo da função.
+
+![extractVariable.png](images/extractVariable.png)
+
+## Aplique Extrair função (Extract Function) no código do corpo que não seja a chamada da consulta.
+
+## Dê à nova função um nome fácil de pesquisar, para poder renomeá-la depois.
+
+![extractFunction.png](images/extractFunction.png)
+
+## Use Internalizar variável (Inline Variable) para se livrar da variável que você acabou de criar.
+
+![inlineVariable.png](images/inlineVariable.png)
+
+## Aplique Internalizar função (Inline Function) na função original.
+
+![inlineFunction.png](images/inlineFunction.png)
+
+## Renomeie a nova função dando-lhe o nome da função original.
+
+![renomear.png](images/renomear.png)
+
+## Realização do Teste
+
+![testeComRefatoracao.png](images/testeComRefatoracao.png)
\ No newline at end of file
diff --git a/Replace_Query_with_Parameter.pdf b/Replace_Query_with_Parameter.pdf
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diff --git a/mamografia.csv b/mamografia.csv
new file mode 100644
index 0000000..f1cdc51
--- /dev/null
+++ b/mamografia.csv
@@ -0,0 +1,962 @@
+BI-RADS,Idade,Forma, Margem, Densidade,Severidade
+5,67,3,5,3,1
+4,43,1,1,,1
+5,58,4,5,3,1
+4,28,1,1,3,0
+5,74,1,5,,1
+4,65,1,,3,0
+4,70,,,3,0
+5,42,1,,3,0
+5,57,1,5,3,1
+5,60,,5,1,1
+5,76,1,4,3,1
+3,42,2,1,3,1
+4,64,1,,3,0
+4,36,3,1,2,0
+4,60,2,1,2,0
+4,54,1,1,3,0
+3,52,3,4,3,0
+4,59,2,1,3,1
+4,54,1,1,3,1
+4,40,1,,,0
+,66,,,1,1
+5,56,4,3,1,1
+4,43,1,,,0
+5,42,4,4,3,1
+4,59,2,4,3,1
+5,75,4,5,3,1
+2,66,1,1,,0
+5,63,3,,3,0
+5,45,4,5,3,1
+5,55,4,4,3,0
+4,46,1,5,2,0
+5,54,4,4,3,1
+5,57,4,4,3,1
+4,39,1,1,2,0
+4,81,1,1,3,0
+4,77,3,,,0
+4,60,2,1,3,0
+5,67,3,4,2,1
+4,48,4,5,,1
+4,55,3,4,2,0
+4,59,2,1,,0
+4,78,1,1,1,0
+4,50,1,1,3,0
+4,61,2,1,,0
+5,62,3,5,2,1
+5,44,2,4,,1
+5,64,4,5,3,1
+4,23,1,1,,0
+2,42,,,4,0
+5,67,4,5,3,1
+4,74,2,1,2,0
+5,80,3,5,3,1
+4,23,1,1,,0
+4,63,2,1,,0
+4,53,,5,3,1
+4,43,3,4,,0
+4,49,2,1,1,0
+5,51,2,4,,0
+4,45,2,1,,0
+5,59,2,,,1
+5,52,4,3,3,1
+5,60,4,3,3,1
+4,57,2,5,3,0
+3,57,2,1,,0
+5,74,4,4,3,1
+4,25,2,1,,0
+4,49,1,1,3,0
+5,72,4,3,,1
+4,45,2,1,3,0
+4,64,2,1,3,0
+4,73,2,1,2,0
+5,68,4,3,3,1
+5,52,4,5,3,0
+5,66,4,4,3,1
+5,70,,4,,1
+4,25,1,1,3,0
+5,74,1,1,2,1
+4,64,1,1,3,0
+5,60,4,3,2,1
+5,67,2,4,1,0
+4,67,4,5,3,0
+5,44,4,4,2,1
+3,68,1,1,3,1
+4,57,,4,1,0
+5,51,4,,,1
+4,33,1,,,0
+5,58,4,4,3,1
+5,36,1,,,0
+4,63,1,1,,0
+5,62,1,5,3,1
+4,73,3,4,3,1
+4,80,4,4,3,1
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\ No newline at end of file
diff --git a/notebookApp.ipynb b/notebookApp.ipynb
new file mode 100644
index 0000000..c88f055
--- /dev/null
+++ b/notebookApp.ipynb
@@ -0,0 +1,946 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Replace Query with Parameter\n",
+    "🎓 College: [Faculdade Metodista Granbery](http://granbery.edu.br/)\n",
+    "\n",
+    "👨‍🏫 Teacher: [Marco Antônio - Github](https://github.com/marcoaparaujo) | [Linkedin](https://www.linkedin.com/in/marco-ant%C3%B4nio-ara%C3%BAjo/)\n",
+    "\n",
+    "📗 Book: [Refatoração - Aperfeiçoando o design de códigos existentes - Martin Fowler](https://www.amazon.com/-/pt/dp/B087N8LKYB/ref=sr_1_1?__mk_pt_BR=%C3%85M%C3%85%C5%BD%C3%95%C3%91&crid=M4T29CCKD30E&keywords=refatora%C3%A7%C3%A3o&qid=1651322207&sprefix=refatora%C3%A7%C3%A3o%2Caps%2C203&sr=8-1)\n",
+    "\n",
+    "> FOWLER, Martin. “Replace Query with Parameter” no código. *In*: REFATORAÇÃO: Aperfeiçoando o design de códigos existentes. 2. ed. [*S. l.*: *s. n.*], 2019. cap. 8.\n",
+    ">\n",
+    "\n",
+    "## Objetivo\n",
+    "\n",
+    "Objetivo desse método e trazer uma função que traga sempre o mesmo resultado  que se chama transparência referencial. A função que foi criada, não acessar um referencial transparente, a função não possuir transparência."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Código\n",
+    "O código que escolhi foi uma atividade dado em aula pelo Professor Ricardo. Essa função recebe um parâmetro de nomes de coluna do dataset, os dados são verificados pela sua severidade e depois ocorre sua substituição dos valores ausentes de acordo com o seu tipo.\n",
+    "\n",
+    "O código pode ser encontrado na plataforma [Kaggle](https://www.kaggle.com/code/edmilsoneddi/data-mining-discipline-exercise)\n",
+    "\n",
+    "## Ferramentas\n",
+    "### pytest\n",
+    "```sh\n",
+    "    pip install ipytest\n",
+    "```\n",
+    "### testbook\n",
+    "```sh\n",
+    "    pip install testbook\n",
+    "```\n",
+    "### jupyter notebook\n",
+    "```sh\n",
+    "    pip install jupyter\n",
+    "```\n",
+    "### pandas\n",
+    "```sh\n",
+    "    pip install pandas\n",
+    "```\n"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Código sem refatoração"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "dataset = pd.read_csv('mamografia.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "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>BI-RADS</th>\n",
+       "      <th>Idade</th>\n",
+       "      <th>Forma</th>\n",
+       "      <th>Margem</th>\n",
+       "      <th>Densidade</th>\n",
+       "      <th>Severidade</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>67.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>43.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>58.0</td>\n",
+       "      <td>4.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4.0</td>\n",
+       "      <td>28.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>3.0</td>\n",
+       "      <td>0</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5.0</td>\n",
+       "      <td>74.0</td>\n",
+       "      <td>1.0</td>\n",
+       "      <td>5.0</td>\n",
+       "      <td>NaN</td>\n",
+       "      <td>1</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   BI-RADS  Idade  Forma   Margem   Densidade  Severidade\n",
+       "0      5.0   67.0    3.0      5.0         3.0           1\n",
+       "1      4.0   43.0    1.0      1.0         NaN           1\n",
+       "2      5.0   58.0    4.0      5.0         3.0           1\n",
+       "3      4.0   28.0    1.0      1.0         3.0           0\n",
+       "4      5.0   74.0    1.0      5.0         NaN           1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mamografia = dataset.copy()\n",
+    "mamografia.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS       float64\n",
+       "Idade         float64\n",
+       "Forma         float64\n",
+       " Margem       float64\n",
+       " Densidade    float64\n",
+       "Severidade      int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mamografia.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "# Análise de Valores Ausentes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "benigno = mamografia[mamografia['Severidade'] == 0].isnull().sum()\n",
+    "print(' Valores ausentes  ')\n",
+    "print()\n",
+    "print('Valores Benigno: ')\n",
+    "print(benigno)\n",
+    "print()\n",
+    "maligno = mamografia[mamografia['Severidade'] == 1].isnull().sum()\n",
+    "print('Valores Maligno: ')\n",
+    "print(maligno)\n",
+    "print()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def tratar_ausentes():\n",
+    "    for lista in mamografia.columns:\n",
+    "        if lista != 'Idade':\n",
+    "            moda_benigno = mamografia[(mamografia['Severidade'] == 0)][lista].mode()[0]\n",
+    "            moda_maligno = mamografia[(mamografia['Severidade'] == 1)][lista].mode()[0]\n",
+    "            mamografia.loc[(mamografia[lista].isnull())&(mamografia['Severidade'] == 0), lista] = moda_benigno\n",
+    "            mamografia.loc[(mamografia[lista].isnull())&(mamografia['Severidade'] == 1), lista] = moda_maligno\n",
+    "            \n",
+    "        else:\n",
+    "            media_benigno = mamografia[(mamografia['Severidade'] == 0)][lista].mean()\n",
+    "            media_maligno = mamografia[(mamografia['Severidade'] == 1)][lista].mean()\n",
+    "            mamografia.loc[(mamografia[lista].isnull())&(mamografia['Severidade'] == 0), lista] = media_benigno\n",
+    "            mamografia.loc[(mamografia[lista].isnull())&(mamografia['Severidade'] == 1), lista] = media_maligno"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Index(['BI-RADS', 'Idade', 'Forma', ' Margem', ' Densidade', 'Severidade'], dtype='object')"
+      ]
+     },
+     "execution_count": 6,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mamografia.columns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tratar_ausentes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS       0\n",
+       "Idade         0\n",
+       "Forma         0\n",
+       " Margem       0\n",
+       " Densidade    0\n",
+       "Severidade    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "mamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Objetivo da atividade\n",
+    "\n",
+    "1. Elabore ou escolha um código que a refatoração possa ser aplicada.\n",
+    "2. Identifique o mau cheiro associado.\n",
+    "3. Escreva os casos de teste e execute\n",
+    "4. Aplique a refatoração\n",
+    "5. Execute os casos de teste novamente\n",
+    "6. Documente cada etapa para a apresentação\n",
+    "\n",
+    "## Mau Cheiro\n",
+    "\n",
+    "### Lista longa de parâmetros\n",
+    "\n",
+    "O mau cheiro que encontrei dentro do código abaixo foi o lista longa de parâmetros, por possuir consultas longas e não claras. Esse mal cheiro, pode trazer dificuldades ou erros no código."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "## Procedimento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "refatoraMamografia = dataset.copy()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS        2\n",
+       "Idade          5\n",
+       "Forma         31\n",
+       " Margem       48\n",
+       " Densidade    76\n",
+       "Severidade     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Use Extrair variável (Extract Variable) no código da consulta para separá-lo do restante do corpo da função."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def tratar_ausentes():\n",
+    "    colunas = refatoraMamografia.columns\n",
+    "    for lista in colunas:\n",
+    "        if lista != 'Idade':\n",
+    "            moda_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mode()[0]\n",
+    "            moda_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mode()[0]\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = moda_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = moda_maligno\n",
+    "        else:\n",
+    "            media_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mean()\n",
+    "            media_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mean()\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = media_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = media_maligno"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tratar_ausentes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS       0\n",
+       "Idade         0\n",
+       "Forma         0\n",
+       " Margem       0\n",
+       " Densidade    0\n",
+       "Severidade    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Aplique Extrair função (Extract Function) no código do corpo que não seja a chamada da consulta."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Dê à nova função um nome fácil de pesquisar, para poder renomeá-la depois."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS        2\n",
+       "Idade          5\n",
+       "Forma         31\n",
+       " Margem       48\n",
+       " Densidade    76\n",
+       "Severidade     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia = dataset.copy() #Apenas usei novamente para retorna o valor do dataset com os ausentes\n",
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def XXXNEWtratar_ausentes_refatora(listaColuna):\n",
+    "    for lista in listaColuna:\n",
+    "        if lista != 'Idade':\n",
+    "            moda_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mode()[0]\n",
+    "            moda_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mode()[0]\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = moda_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = moda_maligno\n",
+    "        else:\n",
+    "            media_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mean()\n",
+    "            media_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mean()\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = media_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = media_maligno"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def tratar_ausentes_refatora(): # tratar_ausentes_refatora() está escrito refatora, para não chamar a outra função tratar_ausentes()\n",
+    "    colunas = refatoraMamografia.columns\n",
+    "    return XXXNEWtratar_ausentes_refatora(colunas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tratar_ausentes_refatora()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS       0\n",
+       "Idade         0\n",
+       "Forma         0\n",
+       " Margem       0\n",
+       " Densidade    0\n",
+       "Severidade    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Use Internalizar variável (Inline Variable) para se livrar da variável que você acabou de criar."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS        2\n",
+       "Idade          5\n",
+       "Forma         31\n",
+       " Margem       48\n",
+       " Densidade    76\n",
+       "Severidade     0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "refatoraMamografia = dataset.copy()  #Apenas usei novamente para retorna o valor do dataset com os ausentes\n",
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def XXXNEWtratar_ausentes_refatora(listaColuna):\n",
+    "    for lista in listaColuna:\n",
+    "        if lista != 'Idade':\n",
+    "            moda_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mode()[0]\n",
+    "            moda_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mode()[0]\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = moda_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = moda_maligno\n",
+    "        else:\n",
+    "            media_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mean()\n",
+    "            media_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mean()\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = media_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = media_maligno"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def tratar_ausentes_refatora(): # tratar_ausentes_refatora() está escrito refatora, para não chamar a outra função tratar_ausentes()\n",
+    "    return XXXNEWtratar_ausentes_refatora(refatoraMamografia.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "BI-RADS       0\n",
+       "Idade         0\n",
+       "Forma         0\n",
+       " Margem       0\n",
+       " Densidade    0\n",
+       "Severidade    0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 23,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tratar_ausentes_refatora()\n",
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Aplique Internalizar função (Inline Function) na função original."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "refatoraMamografia = dataset.copy()  #Apenas usei novamente para retorna o valor do dataset com os ausentes\n",
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def XXXNEWtratar_ausentes_refatora(listaColuna):\n",
+    "    for lista in listaColuna:\n",
+    "        if lista != 'Idade':\n",
+    "            moda_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mode()[0]\n",
+    "            moda_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mode()[0]\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = moda_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = moda_maligno\n",
+    "        else:\n",
+    "            media_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mean()\n",
+    "            media_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mean()\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = media_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = media_maligno"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "#def tratar_ausentes_refatora():\n",
+    "    # tratar_ausentes_refatora() está escrito refatora, para não chamar a outra função tratar_ausentes()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "XXXNEWtratar_ausentes_refatora(refatoraMamografia.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {
+    "pycharm": {
+     "name": "#%% md\n"
+    }
+   },
+   "source": [
+    "### Renomeie a nova função dando-lhe o nome da função original."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "refatoraMamografia = dataset.copy()  #Apenas usei novamente para retorna o valor do dataset com os ausentes\n",
+    "refatoraMamografia.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def tratar_ausentes_refatora(listaColuna):\n",
+    "    for lista in listaColuna:\n",
+    "        if lista != 'Idade':\n",
+    "            moda_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mode()[0]\n",
+    "            moda_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mode()[0]\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = moda_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = moda_maligno\n",
+    "        else:\n",
+    "            media_benigno = refatoraMamografia[(refatoraMamografia['Severidade'] == 0)][lista].mean()\n",
+    "            media_maligno = refatoraMamografia[(refatoraMamografia['Severidade'] == 1)][lista].mean()\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 0), lista] = media_benigno\n",
+    "            refatoraMamografia.loc[(refatoraMamografia[lista].isnull())&(refatoraMamografia['Severidade'] == 1), lista] = media_maligno"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "tratar_ausentes_refatora(refatoraMamografia.columns)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "refatoraMamografia.isnull().sum()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.10.4"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 1
+}
\ No newline at end of file
diff --git a/replace_query_with_parameter_atividade_edmilson_silva_7_periodo_FMG.zip b/replace_query_with_parameter_atividade_edmilson_silva_7_periodo_FMG.zip
new file mode 100644
index 0000000..689cbff
Binary files /dev/null and b/replace_query_with_parameter_atividade_edmilson_silva_7_periodo_FMG.zip differ
diff --git a/testComRefatoracao.py b/testComRefatoracao.py
new file mode 100644
index 0000000..2ee4813
--- /dev/null
+++ b/testComRefatoracao.py
@@ -0,0 +1,10 @@
+import pytest
+from testbook import testbook
+
+@testbook('./notebookApp.ipynb', execute=True)
+def test_tratar_ausentes(tb):
+    tratar_ausentes = tb.ref("tratar_ausentes")
+    lista = ['BI-RADS', 'Idade', 'Forma', ' Margem', ' Densidade', 'Severidade']
+    assert tratar_ausentes() == None
+
+
diff --git a/testSemRefatoracao.py b/testSemRefatoracao.py
new file mode 100644
index 0000000..3d03559
--- /dev/null
+++ b/testSemRefatoracao.py
@@ -0,0 +1,12 @@
+import pytest
+from testbook import testbook
+
+@testbook('./notebookApp.ipynb', execute=True)
+def test_tratar_ausentes_refatora(tb):
+    tratar_ausentes = tb.ref("tratar_ausentes_refatora")
+    lista = ['BI-RADS', 'Idade', 'Forma', ' Margem', ' Densidade', 'Severidade']
+    assert tratar_ausentes(lista) == None
+
+
+
+