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
+2023-05-27,Eletrônicos,12,35.28,423.36
+2023-05-28,Brinquedos,47,338.26,15898.22
+2023-12-18,Livros,1,31.64,31.64
+2023-07-18,Livros,90,298.25,26842.5
+2023-11-04,Livros,14,499.68,6995.52
+2023-05-08,Alimentos,64,282.35,18070.4
+2023-02-08,Brinquedos,38,241.87,9191.06
+2023-12-04,Livros,37,163.3,6042.1
+2023-12-26,Alimentos,11,69.06,759.6600000000001
+2023-05-09,Roupas,77,368.2,28351.399999999998
+2023-09-24,Roupas,3,104.37,313.11
+2023-05-31,Alimentos,33,66.49,2194.1699999999996
+2023-10-25,Alimentos,6,216.66,1299.96
+2023-04-09,Brinquedos,50,399.72,19986.0
+2023-09-20,Brinquedos,10,375.08,3750.7999999999997
+2023-09-09,Roupas,5,36.89,184.45
+2023-05-24,Livros,23,233.02,5359.46
+2023-12-12,Roupas,10,265.99,2659.9
+2023-04-22,Livros,44,325.83,14336.519999999999
+2023-03-01,Livros,2,328.58,657.16
+2023-01-02,Brinquedos,13,188.7,2453.1
+2023-10-31,Eletrônicos,40,284.73,11389.2
+2023-09-11,Eletrônicos,2,245.72,491.44
+2023-05-20,Alimentos,84,443.65,37266.6
+2023-02-06,Brinquedos,65,269.83,17538.95
+2023-06-09,Livros,63,226.11,14244.93
+2023-01-09,Eletrônicos,73,208.2,15198.599999999999
+2023-08-21,Livros,17,290.49,4938.33
+2023-04-09,Eletrônicos,9,403.95,3635.5499999999997
+2023-05-27,Eletrônicos,75,273.82,20536.5
+2023-10-31,Eletrônicos,15,334.21,5013.15
+2023-07-27,Brinquedos,24,371.55,8917.2
+2023-05-11,Roupas,38,264.88,10065.44
+2023-06-01,Livros,35,219.14,7669.9
+2023-02-23,Brinquedos,94,439.57,41319.58
+2023-04-30,Brinquedos,95,214.58,20385.100000000002
+2023-06-10,Brinquedos,49,236.35,11581.15
+2023-04-26,Brinquedos,69,495.27,34173.63
+2023-03-16,Brinquedos,62,10.12,627.4399999999999
+2023-04-23,Alimentos,60,100.88,6052.799999999999
+2023-04-14,Livros,50,198.14,9907.0
+2023-12-06,Brinquedos,78,466.97,36423.66
+2023-09-11,Livros,75,44.31,3323.25
+2023-08-15,Alimentos,9,14.65,131.85
+2023-04-22,Alimentos,34,35.82,1217.88
+2023-04-09,Livros,76,53.39,4057.64
+2023-06-02,Eletrônicos,99,28.42,2813.5800000000004
+2023-12-15,Roupas,35,245.09,8578.15
+2023-12-04,Eletrônicos,1,294.57,294.57
+2023-07-13,Eletrônicos,40,143.13,5725.2
+2023-11-06,Eletrônicos,64,205.16,13130.24
+2023-06-12,Brinquedos,22,54.93,1208.46
+2023-07-27,Alimentos,60,174.82,10489.199999999999
+2023-06-18,Eletrônicos,64,266.03,17025.92
+2023-06-10,Alimentos,93,368.81,34299.33
+2023-03-09,Livros,72,11.64,838.08
+2023-10-16,Roupas,11,238.98,2628.7799999999997
+2023-10-04,Livros,14,155.53,2177.42
+2023-10-31,Livros,60,427.88,25672.8
+2023-05-15,Brinquedos,30,360.62,10818.6
+2023-07-14,Roupas,35,298.66,10453.1
+2023-05-08,Livros,85,145.86,12398.1
+2023-02-02,Livros,37,456.07,16874.59
+2023-06-25,Roupas,5,32.1,160.5
+2023-01-22,Roupas,83,63.64,5282.12
+2023-08-26,Livros,78,201.9,15748.2
+2023-06-07,Roupas,26,70.97,1845.22
+2023-02-07,Livros,62,478.64,29675.68
+2023-08-18,Livros,4,401.01,1604.04
+2023-12-31,Brinquedos,89,136.9,12184.1
+2023-02-20,Eletrônicos,42,298.39,12532.38
+2023-09-21,Livros,89,491.34,43729.259999999995
+2023-10-10,Alimentos,18,443.26,7978.68
+2023-01-27,Eletrônicos,40,304.39,12175.599999999999
+2023-08-14,Eletrônicos,72,452.82,32603.04
+2023-10-04,Eletrônicos,39,494.7,19293.3
+2023-10-13,Brinquedos,14,374.24,5239.360000000001
+2023-04-07,Livros,32,41.84,1338.88
+2023-10-11,Brinquedos,51,206.77,10545.27
+2023-11-13,Livros,38,420.53,15980.14
+2023-10-31,Brinquedos,97,122.98,11929.06
+2023-05-27,Brinquedos,23,417.01,9591.23
+2023-01-04,Alimentos,63,68.97,4345.11
+2023-02-04,Brinquedos,15,33.0,495.0
+2023-07-11,Roupas,97,198.2,19225.399999999998
+2023-02-18,Alimentos,25,28.0,700.0
+2023-01-17,Brinquedos,17,479.06,8144.02
+2023-06-21,Eletrônicos,97,414.84,40239.479999999996
+2023-08-08,Roupas,66,402.38,26557.079999999998
+2023-06-07,Roupas,78,318.59,24850.019999999997
+2023-02-15,Roupas,53,116.0,6148.0
+2023-01-06,Alimentos,51,263.95,13461.449999999999
+2023-04-09,Brinquedos,39,302.66,11803.740000000002
+2023-08-21,Brinquedos,51,267.27,13630.769999999999
+2023-02-06,Eletrônicos,70,137.51,9625.699999999999
+2023-10-07,Eletrônicos,6,262.47,1574.8200000000002
+2023-12-15,Roupas,67,250.68,16795.56
+2023-10-29,Eletrônicos,7,498.2,3487.4
+2023-06-30,Alimentos,51,449.74,22936.74
+2023-04-05,Brinquedos,72,237.06,17068.32
+2023-04-09,Roupas,42,315.24,13240.08
+2023-07-07,Eletrônicos,64,376.46,24093.44
+2023-04-26,Alimentos,15,27.08,406.2
+2023-07-10,Alimentos,29,448.51,13006.789999999999
+2023-09-10,Eletrônicos,33,431.46,14238.179999999998
+2023-06-10,Brinquedos,94,234.14,22009.16
+2023-09-13,Eletrônicos,27,201.67,5445.089999999999
+2023-11-19,Roupas,36,143.75,5175.0
+2023-05-08,Eletrônicos,29,243.57,7063.53
+2023-01-18,Alimentos,38,31.74,1206.12
+2023-10-08,Eletrônicos,57,427.57,24371.489999999998
+2023-08-11,Brinquedos,97,27.36,2653.92
+2023-02-23,Livros,27,171.24,4623.4800000000005
+2023-02-27,Eletrônicos,55,466.48,25656.4
+2023-11-19,Brinquedos,33,370.25,12218.25
+2023-12-26,Brinquedos,68,147.88,10055.84
+2023-06-23,Alimentos,86,133.8,11506.800000000001
+2023-10-07,Brinquedos,66,12.49,824.34
+2023-04-24,Brinquedos,10,350.9,3509.0
+2023-10-15,Brinquedos,5,33.9,169.5
+2023-12-08,Brinquedos,74,270.98,20052.52
+2023-05-31,Roupas,97,481.44,46699.68
diff --git a/exercicios/para-casa/exerc.casa.ipynb b/exercicios/para-casa/exerc.casa.ipynb
new file mode 100644
index 0000000..f7301c1
--- /dev/null
+++ b/exercicios/para-casa/exerc.casa.ipynb
@@ -0,0 +1,1038 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conjunto de dados gerado e salvo como 'dados_vendas.csv'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Configurando a seed para reprodutibilidade\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Gerando dados falsos\n",
+    "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)\n",
+    "\n",
+    "# Criando DataFrame\n",
+    "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']\n",
+    "\n",
+    "# Salvando o DataFrame em um arquivo CSV\n",
+    "df.to_csv('dados_vendas.csv', index=False)\n",
+    "\n",
+    "print(\"Conjunto de dados gerado e salvo como 'dados_vendas.csv'.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Consulta e Filtragem de Dados"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Data</th>\n",
+       "      <th>Categoria</th>\n",
+       "      <th>Quantidade_Vendida</th>\n",
+       "      <th>Preco_Unitario</th>\n",
+       "      <th>Valor_Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-04-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>74</td>\n",
+       "      <td>60.43</td>\n",
+       "      <td>4471.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-12-15</td>\n",
+       "      <td>Alimentos</td>\n",
+       "      <td>83</td>\n",
+       "      <td>272.88</td>\n",
+       "      <td>22649.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-09-28</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>17</td>\n",
+       "      <td>195.62</td>\n",
+       "      <td>3325.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-04-17</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>85</td>\n",
+       "      <td>233.93</td>\n",
+       "      <td>19884.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-03-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>78</td>\n",
+       "      <td>305.94</td>\n",
+       "      <td>23863.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>360</th>\n",
+       "      <td>2023-10-07</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>66</td>\n",
+       "      <td>12.49</td>\n",
+       "      <td>824.34</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>2023-04-24</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>10</td>\n",
+       "      <td>350.90</td>\n",
+       "      <td>3509.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>362</th>\n",
+       "      <td>2023-10-15</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>5</td>\n",
+       "      <td>33.90</td>\n",
+       "      <td>169.50</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>2023-12-08</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>74</td>\n",
+       "      <td>270.98</td>\n",
+       "      <td>20052.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>2023-05-31</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>97</td>\n",
+       "      <td>481.44</td>\n",
+       "      <td>46699.68</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>365 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "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",
+       "..          ...         ...                 ...             ...          ...\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]"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Carregar os dados\n",
+    "pd.read_csv('dados_vendas.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Quantidade de linhas e coluna respectivamente: (365, 5)\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Quantas linhas e colunas existem no DataFrame carregado?\n",
+    "print(f'Quantidade de linhas e coluna respectivamente:',df.shape)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Data                  datetime64[ns]\n",
+       "Categoria                     object\n",
+       "Quantidade_Vendida             int32\n",
+       "Preco_Unitario               float64\n",
+       "Valor_Total                  float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 9,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Quais são os tipos de dados (dtypes) das colunas?\n",
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Data</th>\n",
+       "      <th>Categoria</th>\n",
+       "      <th>Quantidade_Vendida</th>\n",
+       "      <th>Preco_Unitario</th>\n",
+       "      <th>Valor_Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-04-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>74</td>\n",
+       "      <td>60.43</td>\n",
+       "      <td>4471.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-12-15</td>\n",
+       "      <td>Alimentos</td>\n",
+       "      <td>83</td>\n",
+       "      <td>272.88</td>\n",
+       "      <td>22649.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-09-28</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>17</td>\n",
+       "      <td>195.62</td>\n",
+       "      <td>3325.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-04-17</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>85</td>\n",
+       "      <td>233.93</td>\n",
+       "      <td>19884.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-03-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>78</td>\n",
+       "      <td>305.94</td>\n",
+       "      <td>23863.32</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "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": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Exibir as primeiras 5 linhas do DataFrame:\n",
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<bound method Series.unique of 0          Roupas\n",
+       "1       Alimentos\n",
+       "2          Roupas\n",
+       "3          Roupas\n",
+       "4          Roupas\n",
+       "          ...    \n",
+       "360    Brinquedos\n",
+       "361    Brinquedos\n",
+       "362    Brinquedos\n",
+       "363    Brinquedos\n",
+       "364        Roupas\n",
+       "Name: Categoria, Length: 365, dtype: object>"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Quantas categorias únicas de produtos estão presentes no DataFrame?\n",
+    "df['Categoria'].unique"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Data</th>\n",
+       "      <th>Categoria</th>\n",
+       "      <th>Quantidade_Vendida</th>\n",
+       "      <th>Preco_Unitario</th>\n",
+       "      <th>Valor_Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>2023-07-08</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>73</td>\n",
+       "      <td>256.12</td>\n",
+       "      <td>18696.76</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>2023-01-21</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>1</td>\n",
+       "      <td>274.53</td>\n",
+       "      <td>274.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>2023-04-13</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>51</td>\n",
+       "      <td>248.32</td>\n",
+       "      <td>12664.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>14</th>\n",
+       "      <td>2023-06-01</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>34</td>\n",
+       "      <td>303.52</td>\n",
+       "      <td>10319.68</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>18</th>\n",
+       "      <td>2023-09-15</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>39</td>\n",
+       "      <td>121.40</td>\n",
+       "      <td>4734.60</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>347</th>\n",
+       "      <td>2023-09-10</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>33</td>\n",
+       "      <td>431.46</td>\n",
+       "      <td>14238.18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>349</th>\n",
+       "      <td>2023-09-13</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>27</td>\n",
+       "      <td>201.67</td>\n",
+       "      <td>5445.09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>351</th>\n",
+       "      <td>2023-05-08</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>29</td>\n",
+       "      <td>243.57</td>\n",
+       "      <td>7063.53</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>353</th>\n",
+       "      <td>2023-10-08</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>57</td>\n",
+       "      <td>427.57</td>\n",
+       "      <td>24371.49</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>356</th>\n",
+       "      <td>2023-02-27</td>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>55</td>\n",
+       "      <td>466.48</td>\n",
+       "      <td>25656.40</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>73 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "          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]"
+      ]
+     },
+     "execution_count": 31,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Filtrar os dados para mostrar apenas as vendas da categoria 'Eletrônicos':\n",
+    "vendas_eletronicos=df[df['Categoria'] == 'Eletrônicos']\n",
+    "vendas_eletronicos"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(12291.256301369864)"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Qual é a média do valor total das vendas na categoria 'Eletrônicos'?\n",
+    "eletronicos=df[df['Categoria'] == 'Eletrônicos']\n",
+    "media_vendas = eletronicos['Valor_Total'].mean()\n",
+    "media_vendas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Data</th>\n",
+       "      <th>Categoria</th>\n",
+       "      <th>Quantidade_Vendida</th>\n",
+       "      <th>Preco_Unitario</th>\n",
+       "      <th>Valor_Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>2023-04-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>74</td>\n",
+       "      <td>60.43</td>\n",
+       "      <td>4471.82</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2023-12-15</td>\n",
+       "      <td>Alimentos</td>\n",
+       "      <td>83</td>\n",
+       "      <td>272.88</td>\n",
+       "      <td>22649.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>2023-09-28</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>17</td>\n",
+       "      <td>195.62</td>\n",
+       "      <td>3325.54</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>2023-04-17</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>85</td>\n",
+       "      <td>233.93</td>\n",
+       "      <td>19884.05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>2023-03-13</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>78</td>\n",
+       "      <td>305.94</td>\n",
+       "      <td>23863.32</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>...</th>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "      <td>...</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>358</th>\n",
+       "      <td>2023-12-26</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>68</td>\n",
+       "      <td>147.88</td>\n",
+       "      <td>10055.84</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>359</th>\n",
+       "      <td>2023-06-23</td>\n",
+       "      <td>Alimentos</td>\n",
+       "      <td>86</td>\n",
+       "      <td>133.80</td>\n",
+       "      <td>11506.80</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>361</th>\n",
+       "      <td>2023-04-24</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>10</td>\n",
+       "      <td>350.90</td>\n",
+       "      <td>3509.00</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>363</th>\n",
+       "      <td>2023-12-08</td>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>74</td>\n",
+       "      <td>270.98</td>\n",
+       "      <td>20052.52</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>364</th>\n",
+       "      <td>2023-05-31</td>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>97</td>\n",
+       "      <td>481.44</td>\n",
+       "      <td>46699.68</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "<p>330 rows × 5 columns</p>\n",
+       "</div>"
+      ],
+      "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",
+       "..         ...         ...                 ...             ...          ...\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]"
+      ]
+     },
+     "execution_count": 53,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Filtrar as vendas acima de R$ 1000\n",
+    "vendas_acima_1000=df[df['Valor_Total']> 1000]\n",
+    "vendas_acima_1000"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 68,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Quantidade de vendas acima de 1000: 330\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Quantas vendas acima de R$ 1000 ocorreram?\n",
+    "acima_1000=len(vendas_acima_1000)\n",
+    "print(f\"Quantidade de vendas acima de 1000:\", acima_1000)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Agrupamento e Agregação"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Categoria</th>\n",
+       "      <th>Valor_Total</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alimentos</td>\n",
+       "      <td>742457.74</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>1187964.04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>897261.71</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Livros</td>\n",
+       "      <td>963685.46</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>991215.29</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     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"
+      ]
+     },
+     "execution_count": 80,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Agrupar os dados por categoria e calcular o total de vendas por categoria\n",
+    "#Agrupa os dados da coluna categoria, soma o valor de cada categoria pela coluna 'Valor_Total' e cria um novo índice\n",
+    "total_categoria=df.groupby('Categoria')['Valor_Total'].sum().reset_index()\n",
+    "total_categoria"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 101,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A categoria mais vendida foi: Brinquedos -- Com um valor total de: 1187964.04\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Qual categoria teve o maior valor total de vendas?\n",
+    "#Encontra o índice onde o valor é o maior baseado na coluna \"Valor_Total\", e retorna a categoria onde o índice foi o maior\n",
+    "mais_vendida = total_categoria.loc[total_categoria['Valor_Total'].idxmax(), 'Categoria']\n",
+    "valor_maximo = total_categoria['Valor_Total'].max()\n",
+    "print(f'A categoria mais vendida foi:',mais_vendida,\"-- Com um valor total de:\",valor_maximo)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 107,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<div>\n",
+       "<style scoped>\n",
+       "    .dataframe tbody tr th:only-of-type {\n",
+       "        vertical-align: middle;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe tbody tr th {\n",
+       "        vertical-align: top;\n",
+       "    }\n",
+       "\n",
+       "    .dataframe thead th {\n",
+       "        text-align: right;\n",
+       "    }\n",
+       "</style>\n",
+       "<table border=\"1\" class=\"dataframe\">\n",
+       "  <thead>\n",
+       "    <tr style=\"text-align: right;\">\n",
+       "      <th></th>\n",
+       "      <th>Categoria</th>\n",
+       "      <th>Quantidade_Vendida</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>Alimentos</td>\n",
+       "      <td>55.618182</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>Brinquedos</td>\n",
+       "      <td>47.247191</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>Eletrônicos</td>\n",
+       "      <td>46.780822</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>Livros</td>\n",
+       "      <td>50.917808</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>Roupas</td>\n",
+       "      <td>47.200000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "     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"
+      ]
+     },
+     "execution_count": 107,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Calcular a quantidade média vendida por categoria\n",
+    "#Filtra a categoria e, calcula médida da quantidade vendida e cria um novo índice\n",
+    "media_quantidade_vendida=df.groupby('Categoria')['Quantidade_Vendida'].mean().reset_index()\n",
+    "media_quantidade_vendida"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 111,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A categoria com maior quantidade vendida foi: Alimentos -- Com um total de: 55.61818181818182\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Qual categoria teve a maior quantidade média vendida?\n",
+    "#Localiza no df media_quantidade_vendida o id com maior valor na coluna 'Quantidade_Vendida' e retorna a categoria que está na linha.\n",
+    "maior_quantidade = media_quantidade_vendida.loc[media_quantidade_vendida['Quantidade_Vendida'].idxmax(), 'Categoria']\n",
+    "media_maxima = media_quantidade_vendida['Quantidade_Vendida'].max()\n",
+    "print(f'A categoria com maior quantidade vendida foi:',maior_quantidade,\"-- Com um total de:\",media_maxima)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 147,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 600x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Criar um gráfico de barras mostrando o total de vendas por categoria\n",
+    "plt.figure(figsize=(6, 5))\n",
+    "plt.bar(total_categoria['Categoria'], total_categoria['Valor_Total'], color='gray')\n",
+    "plt.xlabel('-------Categoria-------')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Total de Vendas por Categoria')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Com base no gráfico, qual categoria visualmente se destaca em termos de vendas totais?\n",
+    "\n",
+    "-BRINQUEDOS"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 153,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x500 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "#Criar um gráfico de linha mostrando a variação diária das vendas de 'Eletrônicos':\n",
+    "\n",
+    "vendas_diarias = eletronicos.groupby('Data')['Valor_Total'].sum().reset_index()\n",
+    "\n",
+    "plt.figure(figsize=(8, 5))\n",
+    "plt.plot(vendas_diarias['Data'], vendas_diarias['Valor_Total'], color='gray')\n",
+    "plt.xlabel('Data')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Variação Diária das Vendas de Eletrônicos')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Existe algum padrão ou tendência observável nas vendas diárias de eletrônicos?\n",
+    "\n",
+    "Com base na observação de que as vendas são melhores nos primeiros 7 dias do mês e que, após esse período, há uma oscilação."
+   ]
+  }
+ ],
+ "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
+}