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DjonatanSDjonatanS
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Args Kwargs
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lines changed

3 files changed

+254
-2
lines changed

‎Data_Validation_TDD_and_APIS/main.ipynb‎

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@@ -9,11 +9,13 @@
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"from pydantic import BaseModel, validator, ValidationError"
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"from pydantic import BaseModel, validator, ValidationError\n",
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"import logging as logger\n",
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"\n"
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]
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},
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{
@@ -23,6 +25,15 @@
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"# 2 - Class Building "
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [],
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"source": [
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"logger.info('it works and is fully configured')"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,

‎python_intermediario/funcao_args.ipynb‎

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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import sys\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"1 2 [3, 4]\n"
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]
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}
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],
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"source": [
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"x, y , *resto = 1 ,2 ,3 ,4\n",
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"print(x, y, resto)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 15,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"var dados 15\n",
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"total de kbytes em memoria 0.75KB\n"
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]
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}
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],
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"source": [
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"def soma(*args):\n",
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" total = 0\n",
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" for numero in args:\n",
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" total += numero\n",
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" return total\n",
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"dados = soma(1,2,3,4,5)\n",
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"print(f\"var dados {dados}\")\n",
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"\n",
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"#É necessário realizar o desempacotamento após empacotar os argumentos em *args\n",
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"\n",
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"numeros = 1,2,3,4,5,6,7,8,9,10\n",
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"lista_de_numeros = [1,2,3,4,5]\n",
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"\n",
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"#enviando valores * para serem desempacotados\n",
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"soma(*numeros)\n",
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"#desempacotando valores de uma lista\n",
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"soma(*lista_de_numeros)\n",
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"#calcular valor de kbytes das variáveis em memoria\n",
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"variaveis = locals().copy()\n",
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"total = 0\n",
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"for nome, valor in variaveis.items():\n",
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" if nome != 'total':\n",
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" total = sys.getsizeof(valor)\n",
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"print(f\"total de kbytes em memoria {total/1024}KB\")"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

‎python_intermediario/funcao_args_e_kwargs.ipynb‎

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{
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 1 - Imports"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"import json\n",
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"import pandas as pd\n"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"### 2 - variables declarations"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dados_dict = {\n",
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" 'nome': 'Guilherme',\n",
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" 'sobrenome': 'Borges',\n",
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" 'idade': 20\n",
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"}\n",
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"\n",
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"x, y , *resto = 1 ,2 ,3 ,4\n",
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"numeros = 1,2,3,4,5,6,7,8,9,10\n",
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"lista_de_numeros = [1,2,3,4,5]"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dados_dict['nome']"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"def soma(*args):\n",
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" total = 0\n",
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" for numero in args:\n",
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" total += numero\n",
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" return total\n",
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"dados = soma(1,2,3,4,5)\n",
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"print(f\"var dados {dados}\")"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"json_data = json.dumps(dados_dict)\n",
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"print(json_data)"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.DataFrame(dados_dict, index=[0]);df"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dados_dict"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"#KWARGS\n",
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"def dados_pessoais(**kwargs):\n",
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" print(kwargs)\n",
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"\n",
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" #transformar dict em df\n",
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" df = pd.DataFrame(kwargs)\n",
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" return df\n",
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" \n",
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"\n",
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"#transformar df em dict\n",
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"df_dict = df.to_dict()\n",
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"dados_pessoais(**df.to_dict())\n",
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"\n"
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]
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": [
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"dados_pessoais(**dados_dict)\n"
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]
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "base",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.4"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}

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