diff --git a/exercicios/para-casa/Exercicio_casa.ipynb b/exercicios/para-casa/Exercicio_casa.ipynb new file mode 100644 index 0000000..91fc5c4 --- /dev/null +++ b/exercicios/para-casa/Exercicio_casa.ipynb @@ -0,0 +1,875 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Exercícios Casa " + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: pandas in c:\\users\\jenif\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.2.2)\n", + "Requirement already satisfied: numpy>=1.26.0 in c:\\users\\jenif\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas) (2.0.1)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\jenif\\appdata\\roaming\\python\\python312\\site-packages (from pandas) (2.9.0.post0)\n", + "Requirement already satisfied: pytz>=2020.1 in c:\\users\\jenif\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas) (2024.1)\n", + "Requirement already satisfied: tzdata>=2022.7 in c:\\users\\jenif\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from pandas) (2024.1)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\jenif\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 24.0 -> 24.2\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: numpy in c:\\users\\jenif\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (2.0.1)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 24.0 -> 24.2\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: matplotlib in c:\\users\\jenif\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (3.9.1.post1)\n", + "Requirement already satisfied: contourpy>=1.0.1 in c:\\users\\jenif\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.2.1)\n", + "Requirement already satisfied: cycler>=0.10 in c:\\users\\jenif\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in c:\\users\\jenif\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (4.53.1)\n", + "Requirement already satisfied: kiwisolver>=1.3.1 in c:\\users\\jenif\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (1.4.5)\n", + "Requirement already satisfied: numpy>=1.23 in c:\\users\\jenif\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (2.0.1)\n", + "Requirement already satisfied: packaging>=20.0 in c:\\users\\jenif\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (24.1)\n", + "Requirement already satisfied: pillow>=8 in c:\\users\\jenif\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (10.4.0)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in c:\\users\\jenif\\appdata\\local\\programs\\python\\python312\\lib\\site-packages (from matplotlib) (3.1.2)\n", + "Requirement already satisfied: python-dateutil>=2.7 in c:\\users\\jenif\\appdata\\roaming\\python\\python312\\site-packages (from matplotlib) (2.9.0.post0)\n", + "Requirement already satisfied: six>=1.5 in c:\\users\\jenif\\appdata\\roaming\\python\\python312\\site-packages (from python-dateutil>=2.7->matplotlib) (1.16.0)\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "\n", + "[notice] A new release of pip is available: 24.0 -> 24.2\n", + "[notice] To update, run: python.exe -m pip install --upgrade pip\n" + ] + } + ], + "source": [ + "!pip install pandas\n", + "!pip install numpy\n", + "!pip install matplotlib" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "metadata": {}, + "outputs": [], + "source": [ + "import pandas as pd\n", + "import numpy as np" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [], + "source": [ + "np.random.seed(42)" + ] + }, + { + "cell_type": "code", + "execution_count": 66, + "metadata": {}, + "outputs": [], + "source": [ + "categorias = ['Eletrônicos', 'Roupas', 'Alimentos', 'Livros', 'Brinquedos']\n", + "datas = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')\n", + "num_registros = len(datas)" + ] + }, + { + "cell_type": "code", + "execution_count": 67, + "metadata": {}, + "outputs": [], + "source": [ + "data = {\n", + " 'Data': np.random.choice(datas, num_registros),\n", + " 'Categoria': np.random.choice(categorias, num_registros),\n", + " 'Quantidade_Vendida': np.random.randint(1, 100, num_registros),\n", + " 'Preco_Unitario': np.round(np.random.uniform(10, 500, num_registros), 2)\n", + " }\n", + "df = pd.DataFrame(data)\n", + "df['Valor_Total'] = df['Quantidade_Vendida'] * df['Preco_Unitario']" + ] + }, + { + "cell_type": "code", + "execution_count": 68, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv('dados_vendas.csv', index=False)" + ] + }, + { + "cell_type": "code", + "execution_count": 69, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Data Categoria Quantidade_Vendida Preco_Unitario Valor_Total\n", + "0 2023-04-13 Roupas 74 60.43 4471.82\n", + "1 2023-12-15 Alimentos 83 272.88 22649.04\n", + "2 2023-09-28 Roupas 17 195.62 3325.54\n", + "3 2023-04-17 Roupas 85 233.93 19884.05\n", + "4 2023-03-13 Roupas 78 305.94 23863.32\n", + ".. ... ... ... ... ...\n", + "360 2023-10-07 Brinquedos 66 12.49 824.34\n", + "361 2023-04-24 Brinquedos 10 350.90 3509.00\n", + "362 2023-10-15 Brinquedos 5 33.90 169.50\n", + "363 2023-12-08 Brinquedos 74 270.98 20052.52\n", + "364 2023-05-31 Roupas 97 481.44 46699.68\n", + "\n", + "[365 rows x 5 columns]\n" + ] + } + ], + "source": [ + "print(df)" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DataCategoriaQuantidade_VendidaPreco_UnitarioValor_Total
02023-04-13Roupas7460.434471.82
12023-12-15Alimentos83272.8822649.04
22023-09-28Roupas17195.623325.54
32023-04-17Roupas85233.9319884.05
42023-03-13Roupas78305.9423863.32
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DataCategoriaQuantidade_VendidaPreco_UnitarioValor_Total
02023-04-13Roupas7460.434471.82
12023-12-15Alimentos83272.8822649.04
22023-09-28Roupas17195.623325.54
32023-04-17Roupas85233.9319884.05
42023-03-13Roupas78305.9423863.32
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" + ], + "text/plain": [ + " Data Categoria Quantidade_Vendida Preco_Unitario Valor_Total\n", + "0 2023-04-13 Roupas 74 60.43 4471.82\n", + "1 2023-12-15 Alimentos 83 272.88 22649.04\n", + "2 2023-09-28 Roupas 17 195.62 3325.54\n", + "3 2023-04-17 Roupas 85 233.93 19884.05\n", + "4 2023-03-13 Roupas 78 305.94 23863.32" + ] + }, + "execution_count": 74, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(5)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 75, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Data Categoria Quantidade_Vendida Preco_Unitario Valor_Total\n", + "5 2023-07-08 Eletrônicos 73 256.12 18696.76\n", + "6 2023-01-21 Eletrônicos 1 274.53 274.53\n", + "7 2023-04-13 Eletrônicos 51 248.32 12664.32\n", + "14 2023-06-01 Eletrônicos 34 303.52 10319.68\n", + "18 2023-09-15 Eletrônicos 39 121.40 4734.60\n", + ".. ... ... ... ... ...\n", + "347 2023-09-10 Eletrônicos 33 431.46 14238.18\n", + "349 2023-09-13 Eletrônicos 27 201.67 5445.09\n", + "351 2023-05-08 Eletrônicos 29 243.57 7063.53\n", + "353 2023-10-08 Eletrônicos 57 427.57 24371.49\n", + "356 2023-02-27 Eletrônicos 55 466.48 25656.40\n", + "\n", + "[73 rows x 5 columns]\n" + ] + } + ], + "source": [ + "eletronicos = df[df['Categoria'] == 'Eletrônicos']\n", + "print(eletronicos)" + ] + }, + { + "cell_type": "code", + "execution_count": 79, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "np.float64(13102.970520547946)" + ] + }, + "execution_count": 79, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "media_valor = df['Valor_Total']\n", + "media_valor.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": 80, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Data Categoria Quantidade_Vendida Preco_Unitario Valor_Total\n", + "0 2023-04-13 Roupas 74 60.43 4471.82\n", + "1 2023-12-15 Alimentos 83 272.88 22649.04\n", + "2 2023-09-28 Roupas 17 195.62 3325.54\n", + "3 2023-04-17 Roupas 85 233.93 19884.05\n", + "4 2023-03-13 Roupas 78 305.94 23863.32\n", + ".. ... ... ... ... ...\n", + "358 2023-12-26 Brinquedos 68 147.88 10055.84\n", + "359 2023-06-23 Alimentos 86 133.80 11506.80\n", + "361 2023-04-24 Brinquedos 10 350.90 3509.00\n", + "363 2023-12-08 Brinquedos 74 270.98 20052.52\n", + "364 2023-05-31 Roupas 97 481.44 46699.68\n", + "\n", + "[330 rows x 5 columns]\n" + ] + } + ], + "source": [ + "vendas_acima_1000 = df[df['Valor_Total'] > 1000]\n", + "print(vendas_acima_1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 81, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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DataCategoriaQuantidade_VendidaPreco_UnitarioValor_Total
02023-04-13Roupas7460.434471.82
12023-12-15Alimentos83272.8822649.04
22023-09-28Roupas17195.623325.54
32023-04-17Roupas85233.9319884.05
42023-03-13Roupas78305.9423863.32
52023-07-08Eletrônicos73256.1218696.76
62023-01-21Eletrônicos1274.53274.53
72023-04-13Eletrônicos51248.3212664.32
82023-05-02Alimentos45210.399467.55
92023-08-03Brinquedos77388.2229892.94
102023-11-27Roupas415.9863.92
112023-03-29Roupas62303.2418800.88
122023-04-10Alimentos65287.1018661.50
132023-12-26Roupas32360.9311549.76
142023-06-01Eletrônicos34303.5210319.68
152023-05-11Brinquedos92415.1338191.96
162023-05-30Livros95479.9545595.25
172023-11-05Roupas72177.8412804.48
182023-09-15Eletrônicos39121.404734.60
192023-12-10Livros26217.565656.56
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" + ], + "text/plain": [ + " Data Categoria Quantidade_Vendida Preco_Unitario Valor_Total\n", + "0 2023-04-13 Roupas 74 60.43 4471.82\n", + "1 2023-12-15 Alimentos 83 272.88 22649.04\n", + "2 2023-09-28 Roupas 17 195.62 3325.54\n", + "3 2023-04-17 Roupas 85 233.93 19884.05\n", + "4 2023-03-13 Roupas 78 305.94 23863.32\n", + "5 2023-07-08 Eletrônicos 73 256.12 18696.76\n", + "6 2023-01-21 Eletrônicos 1 274.53 274.53\n", + "7 2023-04-13 Eletrônicos 51 248.32 12664.32\n", + "8 2023-05-02 Alimentos 45 210.39 9467.55\n", + "9 2023-08-03 Brinquedos 77 388.22 29892.94\n", + "10 2023-11-27 Roupas 4 15.98 63.92\n", + "11 2023-03-29 Roupas 62 303.24 18800.88\n", + "12 2023-04-10 Alimentos 65 287.10 18661.50\n", + "13 2023-12-26 Roupas 32 360.93 11549.76\n", + "14 2023-06-01 Eletrônicos 34 303.52 10319.68\n", + "15 2023-05-11 Brinquedos 92 415.13 38191.96\n", + "16 2023-05-30 Livros 95 479.95 45595.25\n", + "17 2023-11-05 Roupas 72 177.84 12804.48\n", + "18 2023-09-15 Eletrônicos 39 121.40 4734.60\n", + "19 2023-12-10 Livros 26 217.56 5656.56" + ] + }, + "execution_count": 81, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head(20)" + ] + }, + { + "cell_type": "code", + "execution_count": 84, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(365, 5)" + ] + }, + "execution_count": 84, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": 85, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Categoria Valor_Total\n", + "0 Alimentos 742457.74\n", + "1 Brinquedos 1187964.04\n", + "2 Eletrônicos 897261.71\n", + "3 Livros 963685.46\n", + "4 Roupas 991215.29\n" + ] + } + ], + "source": [ + "total_por_categoria = df.groupby('Categoria')['Valor_Total'].sum().reset_index()\n", + "print(total_por_categoria)" + ] + }, + { + "cell_type": "code", + "execution_count": 86, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + " Categoria Quantidade_Vendida\n", + "0 Alimentos 55.618182\n", + "1 Brinquedos 47.247191\n", + "2 Eletrônicos 46.780822\n", + "3 Livros 50.917808\n", + "4 Roupas 47.200000\n" + ] + } + ], + "source": [ + "media_quantidade_categoria = df.groupby('Categoria')['Quantidade_Vendida'].mean().reset_index()\n", + "print(media_quantidade_categoria)" + ] + }, + { + "cell_type": "code", + "execution_count": 91, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'plt' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[91], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241m.\u001b[39mfigure(figsize\u001b[38;5;241m=\u001b[39m(\u001b[38;5;241m10\u001b[39m, \u001b[38;5;241m6\u001b[39m))\n\u001b[0;32m 2\u001b[0m plt\u001b[38;5;241m.\u001b[39mbar(total_por_categoria[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCategoria\u001b[39m\u001b[38;5;124m'\u001b[39m], total_por_categoria[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mValor_Total\u001b[39m\u001b[38;5;124m'\u001b[39m], color\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mskyblue\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m 3\u001b[0m plt\u001b[38;5;241m.\u001b[39mxlabel(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mCategoria\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'plt' is not defined" + ] + } + ], + "source": [ + "plt.figure(figsize=(10, 6))\n", + "plt.bar(total_por_categoria['Categoria'], total_por_categoria['Valor_Total'], color='skyblue')\n", + "plt.xlabel('Categoria')\n", + "plt.ylabel('Valor Total de Vendas (R$)')\n", + "plt.title('Total de Vendas por Categoria')\n", + "plt.show()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.12.3" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/exercicios/para-casa/dados_vendas.csv b/exercicios/para-casa/dados_vendas.csv new file mode 100644 index 0000000..c98fa34 --- /dev/null +++ b/exercicios/para-casa/dados_vendas.csv @@ -0,0 +1,366 @@ +Data,Categoria,Quantidade_Vendida,Preco_Unitario,Valor_Total +2023-04-13,Roupas,74,60.43,4471.82 +2023-12-15,Alimentos,83,272.88,22649.04 +2023-09-28,Roupas,17,195.62,3325.54 +2023-04-17,Roupas,85,233.93,19884.05 +2023-03-13,Roupas,78,305.94,23863.32 +2023-07-08,Eletrônicos,73,256.12,18696.760000000002 +2023-01-21,Eletrônicos,1,274.53,274.53 +2023-04-13,Eletrônicos,51,248.32,12664.32 +2023-05-02,Alimentos,45,210.39,9467.55 +2023-08-03,Brinquedos,77,388.22,29892.940000000002 +2023-11-27,Roupas,4,15.98,63.92 +2023-03-29,Roupas,62,303.24,18800.88 +2023-04-10,Alimentos,65,287.1,18661.5 +2023-12-26,Roupas,32,360.93,11549.76 +2023-06-01,Eletrônicos,34,303.52,10319.68 +2023-05-11,Brinquedos,92,415.13,38191.96 +2023-05-30,Livros,95,479.95,45595.25 +2023-11-05,Roupas,72,177.84,12804.48 +2023-09-15,Eletrônicos,39,121.4,4734.6 +2023-12-10,Livros,26,217.56,5656.56 +2023-10-21,Brinquedos,34,151.09,5137.06 +2023-07-11,Livros,54,311.33,16811.82 +2023-10-04,Eletrônicos,3,456.81,1370.43 +2023-06-10,Livros,50,78.17,3908.5 +2023-11-10,Alimentos,12,59.39,712.6800000000001 +2023-01-22,Livros,65,135.45,8804.25 +2023-09-10,Roupas,54,365.79,19752.66 +2023-08-24,Roupas,5,300.55,1502.75 +2023-12-11,Alimentos,94,60.08,5647.5199999999995 +2023-02-18,Eletrônicos,94,460.19,43257.86 +2023-02-28,Roupas,57,397.14,22636.98 +2023-06-19,Brinquedos,17,21.28,361.76 +2023-07-07,Roupas,47,329.17,15470.990000000002 +2023-09-28,Roupas,23,387.91,8921.93 +2023-07-09,Eletrônicos,79,193.47,15284.13 +2023-06-24,Livros,85,43.77,3720.4500000000003 +2023-02-20,Roupas,14,47.89,670.46 +2023-12-30,Alimentos,66,61.08,4031.2799999999997 +2023-02-24,Livros,75,421.82,31636.5 +2023-09-01,Brinquedos,51,456.24,23268.24 +2023-11-16,Eletrônicos,38,70.18,2666.84 +2023-05-11,Brinquedos,64,125.59,8037.76 +2023-11-03,Livros,98,91.11,8928.78 +2023-05-15,Livros,38,101.3,3849.4 +2023-01-21,Livros,50,420.37,21018.5 +2023-11-25,Brinquedos,98,172.75,16929.5 +2023-06-16,Livros,82,162.61,13334.02 +2023-10-01,Brinquedos,30,121.42,3642.6 +2023-03-30,Livros,79,307.87,24321.73 +2023-11-12,Alimentos,91,195.86,17823.260000000002 +2023-01-14,Livros,51,374.68,19108.68 +2023-08-30,Brinquedos,63,110.73,6975.990000000001 +2023-09-22,Roupas,98,396.02,38809.96 +2023-12-12,Livros,52,305.82,15902.64 +2023-02-22,Roupas,38,65.99,2507.62 +2023-12-06,Alimentos,97,213.11,20671.670000000002 +2023-04-02,Eletrônicos,88,433.12,38114.56 +2023-09-21,Alimentos,79,462.25,36517.75 +2023-02-04,Livros,30,238.19,7145.7 +2023-07-25,Roupas,51,245.61,12526.11 +2023-03-22,Roupas,81,460.04,37263.240000000005 +2023-02-19,Brinquedos,5,297.66,1488.3000000000002 +2023-12-26,Roupas,29,26.09,756.61 +2023-01-02,Brinquedos,4,457.25,1829.0 +2023-02-23,Eletrônicos,10,131.63,1316.3 +2023-04-16,Livros,56,293.04,16410.24 +2023-09-17,Brinquedos,17,91.1,1548.6999999999998 +2023-11-06,Eletrônicos,74,26.6,1968.4 +2023-07-10,Roupas,17,162.62,2764.54 +2023-08-06,Roupas,84,392.44,32964.96 +2023-02-13,Eletrônicos,88,146.02,12849.76 +2023-06-11,Roupas,69,117.85,8131.65 +2023-07-21,Eletrônicos,34,114.21,3883.14 +2023-09-27,Brinquedos,6,262.44,1574.6399999999999 +2023-12-17,Brinquedos,53,488.02,25865.059999999998 +2023-10-31,Eletrônicos,66,234.9,15503.4 +2023-09-28,Brinquedos,77,283.08,21797.16 +2023-08-03,Brinquedos,43,431.71,18563.53 +2023-09-09,Brinquedos,75,272.18,20413.5 +2023-07-09,Alimentos,23,100.39,2308.97 +2023-10-23,Livros,55,156.8,8624.0 +2023-08-01,Roupas,80,161.87,12949.6 +2023-07-27,Alimentos,95,204.69,19445.55 +2023-08-25,Brinquedos,75,219.13,16434.75 +2023-12-04,Eletrônicos,16,401.89,6430.24 +2023-02-22,Brinquedos,8,181.2,1449.6 +2023-10-07,Livros,4,239.43,957.72 +2023-08-05,Brinquedos,4,316.24,1264.96 +2023-09-09,Eletrônicos,56,195.09,10925.04 +2023-07-07,Livros,25,419.92,10498.0 +2023-02-10,Brinquedos,67,297.85,19955.95 +2023-06-06,Livros,96,154.08,14791.68 +2023-01-15,Roupas,67,359.89,24112.629999999997 +2023-10-28,Roupas,27,268.55,7250.85 +2023-03-06,Brinquedos,93,271.88,25284.84 +2023-12-11,Livros,32,245.73,7863.36 +2023-11-23,Eletrônicos,50,253.54,12677.0 +2023-01-09,Brinquedos,61,385.02,23486.219999999998 +2023-12-10,Roupas,51,60.46,3083.46 +2023-05-09,Roupas,19,173.84,3302.96 +2023-05-16,Brinquedos,21,47.0,987.0 +2023-03-04,Livros,5,379.09,1895.4499999999998 +2023-05-19,Roupas,82,143.43,11761.26 +2023-03-22,Livros,92,449.74,41376.08 +2023-06-12,Roupas,42,268.02,11256.84 +2023-10-16,Roupas,61,402.37,24544.57 +2023-09-18,Alimentos,22,489.68,10772.960000000001 +2023-08-19,Roupas,21,421.5,8851.5 +2023-02-10,Eletrônicos,70,434.83,30438.1 +2023-01-28,Brinquedos,1,209.91,209.91 +2023-05-15,Brinquedos,5,280.34,1401.6999999999998 +2023-07-20,Livros,12,134.41,1612.92 +2023-11-24,Roupas,90,106.1,9549.0 +2023-09-25,Eletrônicos,46,257.7,11854.199999999999 +2023-02-02,Livros,34,301.57,10253.38 +2023-02-17,Alimentos,49,176.24,8635.76 +2023-03-03,Livros,78,289.03,22544.339999999997 +2023-08-04,Livros,90,444.86,40037.4 +2023-10-20,Roupas,45,282.79,12725.550000000001 +2023-04-09,Alimentos,27,363.2,9806.4 +2023-06-21,Livros,73,404.6,29535.800000000003 +2023-12-26,Eletrônicos,26,494.48,12856.48 +2023-08-02,Eletrônicos,47,305.57,14361.789999999999 +2023-02-04,Brinquedos,86,405.36,34860.96 +2023-08-15,Alimentos,56,481.7,26975.2 +2023-04-11,Alimentos,94,472.76,44439.44 +2023-05-11,Brinquedos,63,79.13,4985.19 +2023-09-14,Livros,48,209.13,10038.24 +2023-01-05,Alimentos,61,168.76,10294.359999999999 +2023-08-06,Eletrônicos,81,52.59,4259.79 +2023-09-12,Eletrônicos,26,320.29,8327.54 +2023-12-25,Roupas,36,370.59,13341.24 +2023-10-10,Alimentos,1,425.58,425.58 +2023-07-26,Livros,8,70.16,561.28 +2023-01-15,Brinquedos,99,439.46,43506.54 +2023-12-12,Brinquedos,52,325.03,16901.559999999998 +2023-02-11,Livros,79,354.93,28039.47 +2023-06-28,Roupas,47,456.21,21441.87 +2023-03-04,Brinquedos,56,316.13,17703.28 +2023-12-18,Alimentos,86,174.57,15013.019999999999 +2023-08-19,Roupas,14,414.3,5800.2 +2023-08-29,Alimentos,90,187.9,16911.0 +2023-02-21,Eletrônicos,28,26.77,749.56 +2023-04-06,Roupas,87,417.02,36280.74 +2023-08-10,Brinquedos,78,179.14,13972.919999999998 +2023-08-19,Roupas,88,389.18,34247.840000000004 +2023-08-25,Roupas,2,187.75,375.5 +2023-05-23,Roupas,26,431.92,11229.92 +2023-06-20,Roupas,14,117.56,1645.8400000000001 +2023-01-29,Alimentos,59,487.53,28764.269999999997 +2023-02-05,Eletrônicos,56,392.08,21956.48 +2023-01-13,Livros,7,65.98,461.86 +2023-06-09,Roupas,3,287.23,861.69 +2023-11-23,Brinquedos,23,492.83,11335.09 +2023-07-06,Roupas,18,240.82,4334.76 +2023-08-31,Brinquedos,38,99.23,3770.7400000000002 +2023-03-27,Alimentos,99,247.54,24506.46 +2023-10-11,Brinquedos,15,261.1,3916.5000000000005 +2023-03-07,Livros,64,373.43,23899.52 +2023-06-19,Eletrônicos,89,352.43,31366.27 +2023-02-14,Brinquedos,28,207.25,5803.0 +2023-03-03,Brinquedos,74,116.83,8645.42 +2023-05-14,Eletrônicos,39,326.08,12717.119999999999 +2023-10-11,Livros,57,216.63,12347.91 +2023-01-28,Roupas,17,74.82,1271.9399999999998 +2023-04-18,Brinquedos,86,451.28,38810.079999999994 +2023-02-13,Eletrônicos,90,348.7,31383.0 +2023-12-06,Alimentos,44,344.76,15169.439999999999 +2023-10-13,Eletrônicos,25,413.07,10326.75 +2023-11-27,Alimentos,17,269.53,4582.009999999999 +2023-05-08,Livros,13,409.38,5321.94 +2023-12-14,Roupas,84,254.06,21341.04 +2023-08-19,Eletrônicos,25,42.92,1073.0 +2023-07-09,Brinquedos,68,208.57,14182.76 +2023-08-13,Livros,10,253.76,2537.6 +2023-10-10,Eletrônicos,67,362.9,24314.3 +2023-05-01,Brinquedos,18,62.22,1119.96 +2023-04-26,Eletrônicos,86,78.61,6760.46 +2023-08-21,Alimentos,34,139.21,4733.14 +2023-09-16,Eletrônicos,8,140.58,1124.64 +2023-12-25,Eletrônicos,40,368.77,14750.8 +2023-07-17,Eletrônicos,83,132.86,11027.380000000001 +2023-05-17,Livros,42,320.22,13449.240000000002 +2023-11-14,Eletrônicos,41,252.19,10339.789999999999 +2023-06-14,Brinquedos,6,290.91,1745.46 +2023-08-13,Alimentos,52,420.67,21874.84 +2023-11-03,Alimentos,26,208.15,5411.900000000001 +2023-08-22,Eletrônicos,64,387.75,24816.0 +2023-06-21,Livros,98,216.8,21246.4 +2023-06-01,Livros,59,183.4,10820.6 +2023-11-11,Brinquedos,56,479.73,26864.88 +2023-06-09,Eletrônicos,59,100.34,5920.06 +2023-04-06,Alimentos,70,54.27,3798.9 +2023-08-21,Livros,33,387.78,12796.74 +2023-06-29,Eletrônicos,53,43.44,2302.3199999999997 +2023-04-23,Livros,22,420.17,9243.74 +2023-11-14,Livros,21,224.22,4708.62 +2023-02-21,Alimentos,70,458.6,32102.0 +2023-09-25,Roupas,70,363.55,25448.5 +2023-10-22,Brinquedos,4,309.1,1236.4 +2023-04-23,Brinquedos,94,475.16,44665.04 +2023-04-11,Alimentos,75,204.36,15327.000000000002 +2023-04-23,Livros,62,477.5,29605.0 +2023-03-22,Eletrônicos,62,76.15,4721.3 +2023-07-06,Livros,94,247.04,23221.76 +2023-04-23,Alimentos,95,142.26,13514.699999999999 +2023-01-02,Brinquedos,24,274.52,6588.48 +2023-05-10,Livros,55,89.39,4916.45 +2023-08-08,Brinquedos,9,422.35,3801.15 +2023-02-23,Eletrônicos,3,424.74,1274.22 +2023-12-09,Brinquedos,31,478.07,14820.17 +2023-08-12,Brinquedos,40,85.79,3431.6000000000004 +2023-08-13,Roupas,36,315.43,11355.48 +2023-05-06,Roupas,24,242.38,5817.12 +2023-05-10,Roupas,95,178.75,16981.25 +2023-02-22,Brinquedos,6,182.25,1093.5 +2023-06-21,Alimentos,66,212.27,14009.820000000002 +2023-08-06,Brinquedos,84,355.41,29854.440000000002 +2023-06-09,Alimentos,92,303.16,27890.72 +2023-07-17,Alimentos,75,235.24,17643.0 +2023-09-04,Roupas,4,47.21,188.84 +2023-11-20,Livros,79,48.11,3800.69 +2023-07-22,Eletrônicos,6,11.15,66.9 +2023-07-03,Roupas,94,484.17,45511.98 +2023-05-03,Roupas,51,12.57,641.07 +2023-09-12,Livros,62,60.52,3752.2400000000002 +2023-10-21,Eletrônicos,57,164.15,9356.550000000001 +2023-10-07,Brinquedos,66,405.64,26772.239999999998 +2023-11-21,Brinquedos,79,481.43,38032.97 +2023-04-08,Roupas,75,397.11,29783.25 +2023-07-17,Eletrônicos,8,348.37,2786.96 +2023-08-28,Roupas,26,265.44,6901.44 +2023-05-24,Alimentos,51,52.49,2676.9900000000002 +2023-04-07,Roupas,45,479.5,21577.5 +2023-07-20,Roupas,44,381.61,16790.84 +2023-05-04,Brinquedos,5,323.09,1615.4499999999998 +2023-07-06,Brinquedos,70,381.93,26735.100000000002 +2023-11-22,Brinquedos,26,364.81,9485.06 +2023-12-15,Alimentos,68,322.24,21912.32 +2023-09-16,Brinquedos,19,490.48,9319.12 +2023-05-28,Eletrônicos,84,452.65,38022.6 +2023-09-09,Livros,97,326.87,31706.39 +2023-12-12,Eletrônicos,20,349.66,6993.200000000001 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