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/resolucao-casa.ipynb b/exercicios/para-casa/resolucao-casa.ipynb
new file mode 100644
index 0000000..cb05596
--- /dev/null
+++ b/exercicios/para-casa/resolucao-casa.ipynb
@@ -0,0 +1,1034 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Parte 1: Preparação do Ambiente"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "np.random.seed(42)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "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": 22,
+   "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",
+    "   \n",
+    "df = pd.DataFrame(data)\n",
+    "\n",
+    "df['Valor_Total'] = df['Quantidade_Vendida'] * df['Preco_Unitario']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 23,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conjunto de dados gerado e salvo como 'dados_vendas.csv'\n"
+     ]
+    }
+   ],
+   "source": [
+    "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": [
+    "#### Parte 2: Consulta e Filtragem de Dados"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 24,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = pd.read_csv('dados_vendas.csv')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 1: Quantas linhas e colunas existem no DataFrame carregado?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(365, 5)"
+      ]
+     },
+     "execution_count": 25,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "O DataFrame possui 365 linhas e 5 colunas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 2: Quais são os tipos de dados das colunas?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "Data                   object\n",
+       "Categoria              object\n",
+       "Quantidade_Vendida      int64\n",
+       "Preco_Unitario        float64\n",
+       "Valor_Total           float64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 26,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Exibir as primeiras 5 linhas do DataFrame:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 27,
+   "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": 27,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 3: Quantas categorias únicas de produtos estão presentes no DataFrame?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "O número de categorias únicas é igual a 5\n"
+     ]
+    }
+   ],
+   "source": [
+    "categorias_unicas = df['Categoria'].nunique()\n",
+    "print(\"O número de categorias únicas é igual a\", categorias_unicas)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Filtrar os dados para mostrar apenas as vendas da categoria 'Eletrônicos'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "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": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "eletronicos = df[df['Categoria'] == 'Eletrônicos']\n",
+    "eletronicos"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 4: Qual é a média do valor total das vendas na categoria 'Eletrônicos'?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 64,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'A média do valor total de vendas de eletrônicos é igual a 12291.26 reais.'"
+      ]
+     },
+     "execution_count": 64,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "media_valor_eletronicos = eletronicos['Valor_Total'].mean()\n",
+    "media_valor_eletronicos\n",
+    "\n",
+    "f'A média do valor total de vendas de eletrônicos é igual a {media_valor_eletronicos:.2f} reais.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Filtrar as vendas acima de R$ 1000,00**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "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": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vendas_acima_1000 = df[df['Valor_Total'] > 1000]\n",
+    "vendas_acima_1000"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 5: Quantas vendas acima de R$ 1000,00 ocorreram?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'Ocorreram 330 vendas acima de 1000 reais.'"
+      ]
+     },
+     "execution_count": 62,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_vendas_acima_1000 = vendas_acima_1000.shape[0]\n",
+    "total_vendas_acima_1000\n",
+    "\n",
+    "f'Ocorreram {total_vendas_acima_1000} vendas acima de 1000 reais.'"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Parte 3: Agrupamento e Agregação"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Agrupar os dados por categoria e calcular o total de vendas por categoria**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 65,
+   "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": 65,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "total_por_categoria = df.groupby('Categoria')['Valor_Total'].sum().reset_index()\n",
+    "total_por_categoria"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 6: Qual categoria teve o maior valor total de vendas?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 70,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(1187964.04)"
+      ]
+     },
+     "execution_count": 70,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "maior_vendas = total_por_categoria['Valor_Total'].max()\n",
+    "maior_vendas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A categoria 'Brinquedos' que obteve o maior valor total de vendas."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Calcular a quantidade média vendida por categoria:**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 69,
+   "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": 69,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "media_quantidade_categoria = df.groupby('Categoria')['Quantidade_Vendida'].mean().reset_index()\n",
+    "media_quantidade_categoria"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 7: Qual categoria teve a maior quantidade média vendida?**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 76,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(55.61818181818182)"
+      ]
+     },
+     "execution_count": 76,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "maior_media_vendidas = media_quantidade_categoria['Quantidade_Vendida'].max()\n",
+    "maior_media_vendidas"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "A categoria 'Alimentos' que obteve a maior quantidade média vendida."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "#### Parte 4: Visualização de Dados"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Criar um gráfico de barras mostrando o total de vendas por categoria**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 80,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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qOTlZ48aNs2kfNWqULBaLzR9ivL290wxDrq6uqUZTxo4dq+TkZIfrcXV1VXh4uJYsWaLY2Fhr+969e7VmzRqbvi+++KJcXV01cODAVPs3DEPnz5+3a5+3R5f69++v3bt3p/ruphYtWigmJibV/qVb05QnJSXZtZ/b8ufPrwoVKmjGjBk2l1utW7cu1b0wrq6uslgsNq/l0aNHU12GndZndIUKFSTd+oOZmZiYGJt7po4dO6alS5fqueeek6urq1xdXfXcc89p6dKlNveDnT59WrNmzdIzzzwjX19f0/2k5eWXX1b58uU1ePBgxcTEpFp+5coVffTRR5L+bzTvzvfbMAyNGTMm1Xq3vzfq3+dsixYtlJycrEGDBqVaJykpydq/bt26ypIliyZMmGDT59//V6Rb/4+2b99uU39CQoK+/vprFSlSJM1718w4cqwAcL8e6xGne4mNjdW0adMUGxurgIAASdL777+v1atXa9q0aRoyZIj++usv/f3335o/f76+/fZbJScn67333tPLL7+sH374wclHACC9NWnSRHXq1NFHH32ko0ePKiQkRGvXrtXSpUv17rvvWqccl25d1rZ+/XqNHDlSAQEBKlq0qKpWrarGjRvru+++k5+fn8qUKaOYmBitX79euXLluq+aBg4cqNWrV6tGjRrq3LmzkpKSNHbsWJUtW1a//vqrtV/x4sX16aefqm/fvjp69KiaNWumbNmy6ciRI1q8eLHeeOMNvf/++6b7K1q0qKpVq6alS5dKUqrg1KtXL/3vf/9T48aN1bZtW1WqVEkJCQn67bfftGDBAh09elS5c+d26BiHDh2qRo0a6ZlnnlH79u114cIF6zHGx8db+zVq1EgjR45U/fr19eqrr+rMmTMaP368goKCbF6LTz75RJs2bVKjRo1UuHBhnTlzRl999ZUKFiyY5ncj/Vu5cuUUHh5uMx25dOu9uO3TTz+1fldU586dlSVLFk2aNEk3btzQsGHDHDr+O2XNmlWLFi1SWFiYatasqRYtWqh69erKmjWr/vjjD82aNUs5cuTQ4MGDVapUKRUvXlzvv/++jh8/Ll9fXy1cuDDVCJAk62QX3bp1U3h4uHUykFq1aunNN9/U0KFDtXv3bj333HPKmjWrDh48qPnz52vMmDF6+eWXlTdvXnXv3l0jRoxQ06ZNVb9+fe3Zs0erVq1S7ty5bUa0+vTpo9mzZ6tBgwbq1q2bcubMqRkzZujIkSNauHChXFwc/5uuI8cKAPfNCTP5ZUqSjMWLF1ufL1++3JBkeHt72zyyZMlitGjRwjAMw+jUqZMhydi/f791vR07dhiSjH379j3sQwCQztKaovnKlSvGe++9ZwQEBBhZs2Y1goODjS+++MJISUmx6bdv3z6jZs2ahqenp810zBcvXjTatWtn5M6d2/Dx8THCw8ONffv2GYULF7aZstne6cgNwzA2btxoVKpUyXBzczOKFStmTJw40YiMjExzeumFCxcazzzzjPUzrVSpUsY777xj8zlmZvz48YYko0qVKmkuv3LlitG3b18jKCjIcHNzM3Lnzm1Uq1bNGD58uJGYmGgYxv9NR/7FF1+kWl+SERkZmaru0qVLG+7u7kaZMmWMRYsWGREREammI58yZYoRHBxsuLu7G6VKlTKmTZuW6rWIiooynn/+eSMgIMBwc3MzAgICjFatWqWaQj0tkox33nnH+P777637qVixYprv086dO43w8HDDx8fH8PLyMurUqWNs27bNps/t6ch//vln033f6eLFi0b//v2N8uXLG15eXoaHh4dRrlw5o2/fvsbJkyet/f78808jLCzM8PHxMXLnzm106tTJ2LNnjyHJmDZtmrVfUlKS0bVrV8Pf39+wWCypzp2vv/7aqFSpkuHp6Wlky5bNKF++vNG7d2/jxIkTNtvo16+fkS9fPsPT09N49tlnjb179xq5cuUy3nrrLZvtHT582Hj55ZeN7NmzGx4eHkaVKlWM5cuX2/S5/X9g/vz5qY4/rf8f9h4rANwvi2Hcxx24jyCLxaLFixerWbNmkqS5c+eqdevW+uOPP1LdKOzj46N8+fIpMjJSQ4YM0c2bN63Lrl27Ji8vL61du1b16tV7mIcAAMhgFotF77zzTpqXoCG1S5cuKUeOHPr000+tlxACwH8Vl+rdRcWKFZWcnKwzZ85Yv1fk36pXr66kpCQdPnzYeonOgQMHJN3fza0AAPxXXbt2LdUEEqNHj5Yk66RKAPBf9lgHp/j4eB06dMj6/MiRI9q9e7dy5sypEiVKqHXr1mrTpo1GjBihihUr6uzZs4qKitITTzyhRo0aKSwsTE8++aTat2+v0aNHKyUlRe+8847q1av3QLNWAQDwXzN37lxNnz5dDRs2lI+Pj7Zs2aLZs2frueeeU/Xq1Z1dHgA8sMc6OP3yyy+qU6eO9XmPHj0k3fqW8unTp2vatGn69NNP1bNnTx0/fly5c+fW008/rcaNG0u6NZ3tsmXL1LVrV9WsWVPe3t5q0KCBzTTDAAA8Dp544gllyZJFw4YNU1xcnHXCiE8//dTZpQFAuuAeJwAAAAAwwfc4AQAAAIAJghMAAAAAmHjs7nFKSUnRiRMnlC1bNpsv5AMAAADweDEMQ1euXFFAQIDpF3A/dsHpxIkTCgwMdHYZAAAAADKJY8eOqWDBgvfs89gFp2zZskm69eL4+vo6uRoAAAAAzhIXF6fAwEBrRriXxy443b48z9fXl+AEAAAAwK5beJgcAgAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwIRTg9OmTZvUpEkTBQQEyGKxaMmSJffsv2jRItWrV0/+/v7y9fVVaGio1qxZ83CKBQAAAPDYcmpwSkhIUEhIiMaPH29X/02bNqlevXpauXKlduzYoTp16qhJkybatWtXBlcKAAAA4HFmMQzDcHYRkmSxWLR48WI1a9bMofXKli2rli1bqn///nb1j4uLk5+fny5fvixfX9/7qBQAAADAo8CRbJDlIdWUIVJSUnTlyhXlzJnzrn1u3LihGzduWJ/HxcU9jNIAAAAAPEL+05NDDB8+XPHx8WrRosVd+wwdOlR+fn7WR2Bg4EOsEAAAAMCj4D8bnGbNmqWBAwdq3rx5ypMnz1379e3bV5cvX7Y+jh079hCrBAAAAPAo+E9eqjdnzhx17NhR8+fPV1hY2D37uru7y93d/SFVBgAAAOBR9J8bcZo9e7batWun2bNnq1GjRs4uBwAAAMBjwKkjTvHx8Tp06JD1+ZEjR7R7927lzJlThQoVUt++fXX8+HF9++23km5dnhcREaExY8aoatWqOnXqlCTJ09NTfn5+TjkGAAAAAI8+p05HHh0drTp16qRqj4iI0PTp09W2bVsdPXpU0dHRkqTatWtr48aNd+1vD6Yjx6PKMtDi7BKQAYzITPGNEQAAPJIcyQaZ5nucHhaCEx5VBKdHE8EJAICM40g2+M/d4wQAAAAADxvBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwATBCQAAAABMEJwAAAAAwIRTg9OmTZvUpEkTBQQEyGKxaMmSJabrREdH68knn5S7u7uCgoI0ffr0DK8TAAAAwOPNqcEpISFBISEhGj9+vF39jxw5okaNGqlOnTravXu33n33XXXs2FFr1qzJ4EoBAAAAPM6yOHPnDRo0UIMGDezuP3HiRBUtWlQjRoyQJJUuXVpbtmzRqFGjFB4enlFlAgAAAHjM/afucYqJiVFYWJhNW3h4uGJiYu66zo0bNxQXF2fzAAAAAABH/KeC06lTp5Q3b16btrx58youLk7Xrl1Lc52hQ4fKz8/P+ggMDHwYpQIAAAB4hPyngtP96Nu3ry5fvmx9HDt2zNklAQAAAPiPceo9To7Kly+fTp8+bdN2+vRp+fr6ytPTM8113N3d5e7u/jDKAwAAAPCI+k+NOIWGhioqKsqmbd26dQoNDXVSRQAAAAAeB04dcYqPj9ehQ4esz48cOaLdu3crZ86cKlSokPr27avjx4/r22+/lSS99dZbGjdunHr37q327dvrhx9+0Lx587RixQpnHQIAAAAymGWgxdklIJ0ZkYazS3CYU0ecfvnlF1WsWFEVK1aUJPXo0UMVK1ZU//79JUknT55UbGystX/RokW1YsUKrVu3TiEhIRoxYoQmT57MVOQAAAAAMpRTR5xq164tw7h72pw+fXqa6+zatSsDqwIAAAAAW/+pe5wAAAAAwBkITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACac+j1OAADg0WUZaHF2CUhnRuTdv38TeNQx4gQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJrI4uwAAQOZiGWhxdglIZ0ak4ewSAOA/jxEnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADBBcAIAAAAAEwQnAAAAADDxQMHpxo0b6VUHAAAAAGRaDgWnVatWKSIiQsWKFVPWrFnl5eUlX19f1apVS4MHD9aJEycyqk4AAAAAcBq7gtPixYtVokQJtW/fXlmyZNEHH3ygRYsWac2aNZo8ebJq1aql9evXq1ixYnrrrbd09uzZjK4bAAAAAB6aLPZ0GjZsmEaNGqUGDRrIxSV11mrRooUk6fjx4xo7dqy+//57vffee+lbKQAAAAA4iV3BKSYmxq6NFShQQJ999tkDFQQAAAAAmQ2z6gEAAACACYeCU1JSkhITE23aJk+erIiICI0dO1aGYaRrcQAAAACQGTgUnFq3bq3IyEjr80mTJql79+5KSEjQJ598og8//DDdCwQAAAAAZ3MoOO3cuVP169e3Pp80aZJGjx6tBQsWaP78+Zo1a1a6FwgAAAAAzmbX5BDt2rWTJP3zzz/68ssvNWPGDBmGoT179mjVqlWKiYlRUlKSTpw4ofbt20uSpk6dmnFVAwAAAMBDZFdwmjZtmiTphx9+0LvvvqsaNWpoxYoV2rp1qxYtWiRJunz5spYuXUpgAgAAAPDIsSs43Va7dm298cYbatOmjaZNm6aWLVtal+3Zs0fBwcHpXiAAAAAAOJtD9ziNHDlSlStX1qxZs/Tss8/aTAaxZMkSvfbaa+leIAAAAAA4m0MjTrly5dJ3332X5rKRI0emS0EAAAAAkNmk6xfgLliwID03BwAAAACZgsNfgPv777/rwIEDNu1Lly5VSEiIWrduna7FAQAAAEBmYHdw+v333xUUFKSQkBCVLl1aL774ok6fPq1atWqpffv2atCggQ4fPpyRtQIAAACAU9h9j9MHH3ygoKAgjRs3TrNnz9bs2bO1d+9edejQQatXr5anp2dG1gkAAAAATmN3cPr555+1du1aVahQQTVq1NDs2bP14Ycf6vXXX8/I+gAAAADA6ey+VO/cuXMKCAiQJPn5+cnb21tPP/10hhUGAAAAAJmF3SNOFotFV65ckYeHhwzDkMVi0bVr1xQXF2fTz9fXN92LBAAAAABnsnvEyTAMlShRQjly5FDOnDkVHx+vihUrKkeOHMqRI4eyZ8+uHDlyOFzA+PHjVaRIEXl4eKhq1aravn37PfuPHj1aJUuWlKenpwIDA/Xee+/p+vXrDu8XAAAAAOxl94jThg0b0n3nc+fOVY8ePTRx4kRVrVpVo0ePVnh4uPbv3688efKk6j9r1iz16dNHU6dOVbVq1XTgwAG1bdtWFouFL+AFAAAAkGHsDk61atVK952PHDlSnTp1Urt27SRJEydO1IoVKzR16lT16dMnVf9t27apevXqevXVVyVJRYoUUatWrfTTTz/ddR83btzQjRs3rM//fWkhAAAAAJix61K9hIQEhzZqT//ExETt2LFDYWFh/1eMi4vCwsIUExOT5jrVqlXTjh07rJfz/fXXX1q5cqUaNmx41/0MHTpUfn5+1kdgYKBDxwIAAAAAdgWnoKAgffbZZzp58uRd+xiGoXXr1qlBgwb68ssvTbd57tw5JScnK2/evDbtefPm1alTp9Jc59VXX9Unn3yiZ555RlmzZlXx4sVVu3Ztffjhh3fdT9++fXX58mXr49ixY6a1AQAAAMCd7LpULzo6Wh9++KEGDBigkJAQVa5cWQEBAfLw8NDFixf1559/KiYmRlmyZFHfvn315ptvZkix0dHRGjJkiL766itVrVpVhw4dUvfu3TVo0CD169cvzXXc3d3l7u6eIfUAAAAAeDzYFZxKliyphQsXKjY2VvPnz9fmzZu1bds2Xbt2Tblz51bFihX1zTffqEGDBnJ1dbVrx7lz55arq6tOnz5t03769Gnly5cvzXX69eun119/XR07dpQklS9fXgkJCXrjjTf00UcfycXF7kkCAQAAAMBudk8OIUmFChVSz5491bNnzwfesZubmypVqqSoqCg1a9ZMkpSSkqKoqCh16dIlzXWuXr2aKhzdDmqGYTxwTQAAAACQFoeCU3rr0aOHIiIiVLlyZVWpUkWjR49WQkKCdZa9Nm3aqECBAho6dKgkqUmTJho5cqQqVqxovVSvX79+atKkid0jXQAAAADgKKcGp5YtW+rs2bPq37+/Tp06pQoVKmj16tXWCSNiY2NtRpg+/vhjWSwWffzxxzp+/Lj8/f3VpEkTDR482FmHAAAAAOAxYDEes2vc4uLi5Ofnp8uXL8vX19fZ5QDpxjLQ4uwSkAGMyIf/Ec259OhxxnkkcS49ijiXkF6cdS79myPZgNkUAAAAAMAEwQkAAAAATDgcnFavXq0tW7ZYn48fP14VKlTQq6++qosXL6ZrcQAAAACQGTgcnHr16qW4uDhJ0m+//aaePXuqYcOGOnLkiHr06JHuBQIAAACAszk8q96RI0dUpkwZSdLChQvVuHFjDRkyRDt37lTDhg3TvUAAAAAAcDaHR5zc3Nx09epVSdL69ev13HPPSZJy5sxpHYkCAAAAgEeJwyNOzzzzjHr06KHq1atr+/btmjt3riTpwIEDKliwYLoXCAAAAADO5vCI07hx45QlSxYtWLBAEyZMUIECBSRJq1atUv369dO9QAAAAABwNodHnAoVKqTly5enah81alS6FAQAAAAAmY3DwelO169fV2Jiok2b2TfuAgAAAMB/jcOX6iUkJKhLly7KkyePvL29lSNHDpsHAAAAADxqHA5OvXv31g8//KAJEybI3d1dkydP1sCBAxUQEKBvv/02I2oEAAAAAKdy+FK9ZcuW6dtvv1Xt2rXVrl071ahRQ0FBQSpcuLBmzpyp1q1bZ0SdAAAAAOA0Do84XbhwQcWKFZN0636mCxcuSLo1TfmmTZvStzoAAAAAyAQcHnEqVqyYjhw5okKFCqlUqVKaN2+eqlSpomXLlil79uwZUOKjzzLQ4uwSkM6MSMPZJQAAACAdOTzi1K5dO+3Zs0eS1KdPH40fP14eHh5677331KtXr3QvEAAAAACczeERp/fee8/677CwMO3bt087duxQUFCQnnjiiXQtDgAAAAAygwf6HidJKly4sAoXLpwetQAAAABApmRXcPryyy/t3mC3bt3uuxgAAAAAyIzsCk6jRo2yeX727FldvXrVOhnEpUuX5OXlpTx58hCcAAAAADxy7Joc4siRI9bH4MGDVaFCBe3du1cXLlzQhQsXtHfvXj355JMaNGhQRtcLAAAAAA+dw7Pq9evXT2PHjlXJkiWtbSVLltSoUaP08ccfp2txAAAAAJAZOBycTp48qaSkpFTtycnJOn36dLoUBQAAAACZicPBqW7dunrzzTe1c+dOa9uOHTv09ttvKywsLF2LAwAAAIDMwOHgNHXqVOXLl0+VK1eWu7u73N3dVaVKFeXNm1eTJ0/OiBoBAAAAwKkc/h4nf39/rVy5UgcOHNC+ffskSaVKlVKJEiXSvTgAAAAAyAzu+wtwS5QoQVgCAAAA8FhwODglJydr+vTpioqK0pkzZ5SSkmKz/Icffki34gAAAAAgM3A4OHXv3l3Tp09Xo0aNVK5cOVksloyoCwAAAAAyDYeD05w5czRv3jw1bNgwI+oBAAAAgEzH4Vn13NzcFBQUlBG1AAAAAECm5HBw6tmzp8aMGSPDMDKiHgAAAADIdBy+VG/Lli3asGGDVq1apbJlyypr1qw2yxctWpRuxQEAAABAZuBwcMqePbteeOGFjKgFAAAAADIlh4PTtGnTMqIOAAAAAMi0HL7HSZKSkpK0fv16TZo0SVeuXJEknThxQvHx8elaHAAAAABkBg6POP3999+qX7++YmNjdePGDdWrV0/ZsmXT559/rhs3bmjixIkZUScAAAAAOI3DI07du3dX5cqVdfHiRXl6elrbX3jhBUVFRaVrcQAAAACQGTg84rR582Zt27ZNbm5uNu1FihTR8ePH060wAAAAAMgsHB5xSklJUXJycqr2f/75R9myZUuXogAAAAAgM3E4OD333HMaPXq09bnFYlF8fLwiIyPVsGHD9KwNAAAAADIFhy/VGzFihMLDw1WmTBldv35dr776qg4ePKjcuXNr9uzZGVEjAAAAADiV3cFp3Lhxeu2111SwYEHt2bNHc+bM0a+//qr4+Hh16NBBrVu3tpksAgAAAAAeFXYHp48++ki9e/dWs2bN1LFjR7322msZWRcAAAAAZBp23+N06tQpTZw4USdPnlS9evVUtGhRDRo0SP/8809G1gcAAAAATmd3cPL09FSbNm20YcMGHTx4UK+//rqmTJmiIkWKqH79+po/f75u3ryZkbUCAAAAgFM4PKueJBUrVkyffPKJjhw5olWrVilXrlxq27atChQokN71AQAAAIDT3Vdwus1isShLliyyWCwyDIMRJwAAAACPpPsKTseOHdMnn3yiYsWKqV69ejpx4oS++eYbnTx5Mr3rAwAAAACns3tWvcTERC1atEhTp07VDz/8oPz58ysiIkLt27dXsWLFMrJGAAAAAHAqu4NTvnz5dPXqVTVu3FjLli1TeHi4XFwe6Eo/AAAAAPhPsDs4ffzxx3r99dfl7++fkfUAAAAAQKZjd3Dq0aNHRtYBAAAAAJkW19oBAAAAgAmCEwAAAACYIDgBAAAAgIn7Dk6JiYnav3+/kpKS0rMeAAAAAMh0HA5OV69eVYcOHeTl5aWyZcsqNjZWktS1a1d99tln6V4gAAAAADibw8Gpb9++2rNnj6Kjo+Xh4WFtDwsL09y5c9O1OAAAAADIDOyejvy2JUuWaO7cuXr66adlsVis7WXLltXhw4fTtTgAAAAAyAwcHnE6e/as8uTJk6o9ISHBJkgBAAAAwKPC4eBUuXJlrVixwvr8dliaPHmyQkND068yAAAAAMgkHL5Ub8iQIWrQoIH+/PNPJSUlacyYMfrzzz+1bds2bdy4MSNqBAAAAACncnjE6ZlnntHu3buVlJSk8uXLa+3atcqTJ49iYmJUqVIlhwsYP368ihQpIg8PD1WtWlXbt2+/Z/9Lly7pnXfeUf78+eXu7q4SJUpo5cqVDu8XAAAAAOzl8IiTJBUvXlzffPPNA+987ty56tGjhyZOnKiqVatq9OjRCg8P1/79+9O8jyoxMVH16tVTnjx5tGDBAhUoUEB///23smfP/sC1AAAAAMDd2BWc4uLi7N6gr6+v3X1HjhypTp06qV27dpKkiRMnasWKFZo6dar69OmTqv/UqVN14cIFbdu2TVmzZpUkFSlS5J77uHHjhm7cuGF97sixAAAAAIBk56V62bNnV44cOex62CsxMVE7duxQWFjY/xXj4qKwsDDFxMSkuc7//vc/hYaG6p133lHevHlVrlw5DRkyRMnJyXfdz9ChQ+Xn52d9BAYG2l0jAAAAAEh2jjht2LDB+u+jR4+qT58+atu2rXUWvZiYGM2YMUNDhw61e8fnzp1TcnKy8ubNa9OeN29e7du3L811/vrrL/3www9q3bq1Vq5cqUOHDqlz5866efOmIiMj01ynb9++6tGjh/V5XFwc4QkAAACAQ+wKTrVq1bL++5NPPtHIkSPVqlUra1vTpk1Vvnx5ff3114qIiEj/Kv+/lJQU5cmTR19//bVcXV1VqVIlHT9+XF988cVdg5O7u7vc3d0zrCYAAAAAjz6HZ9WLiYlR5cqVU7VXrlzZdEa8O+XOnVuurq46ffq0Tfvp06eVL1++NNfJnz+/SpQoIVdXV2tb6dKlderUKSUmJtq9bwAAAABwhMPBKTAwMM0Z9SZPnuzQJXBubm6qVKmSoqKirG0pKSmKioq66xfpVq9eXYcOHVJKSoq17cCBA8qfP7/c3NwcOAoAAAAAsJ/D05GPGjVKL730klatWqWqVatKkrZv366DBw9q4cKFDm2rR48eioiIUOXKlVWlShWNHj1aCQkJ1ln22rRpowIFCljvnXr77bc1btw4de/eXV27dtXBgwc1ZMgQdevWzdHDAAAAAAC7ORycGjZsqIMHD2rChAnau3evJKlJkyZ66623HJ50oWXLljp79qz69++vU6dOqUKFClq9erV1wojY2Fi5uPzfoFhgYKDWrFmj9957T0888YQKFCig7t2764MPPnD0MAAAAADAbvf1BbgFCxbU4MGD06WALl26qEuXLmkui46OTtUWGhqqH3/8MV32DQAAAAD2cPgeJwAAAAB43BCcAAAAAMAEwQkAAAAATBCcAAAAAMAEwQkAAAAATNg1q17FihVlsVjs2uDOnTsfqCAAAAAAyGzsCk7NmjXL4DIAAAAAIPOyKzhFRkZmdB0AAAAAkGlxjxMAAAAAmLBrxOlOycnJGjVqlObNm6fY2FglJibaLL9w4UK6FQcAAAAAmYHDI04DBw7UyJEj1bJlS12+fFk9evTQiy++KBcXFw0YMCADSgQAAAAA53I4OM2cOVPffPONevbsqSxZsqhVq1aaPHmy+vfvrx9//DEjagQAAAAAp3I4OJ06dUrly5eXJPn4+Ojy5cuSpMaNG2vFihXpWx0AAAAAZAIOB6eCBQvq5MmTkqTixYtr7dq1kqSff/5Z7u7u6VsdAAAAAGQCDgenF154QVFRUZKkrl27ql+/fgoODlabNm3Uvn37dC8QAAAAAJzN4Vn1PvvsM+u/W7ZsqcKFC2vbtm0KDg5WkyZN0rU4AAAAAMgMHA5OmzZtUrVq1ZQly61Vn376aT399NNKSkrSpk2bVLNmzXQvEgAAAACcyeFL9erUqZPmdzVdvnxZderUSZeiAAAAACAzcTg4GYYhi8WSqv38+fPy9vZOl6IAAAAAIDOx+1K9F198UZJksVjUtm1bmxn0kpOT9euvv6patWrpXyEAAAAAOJndwcnPz0/SrRGnbNmyydPT07rMzc1NTz/9tDp16pT+FQIAAACAk9kdnKZNmyZJKlKkiN5//30uywMAAADw2HB4Vr3IyEhJ0tmzZ7V//35JUsmSJeXv75++lQEAAABAJuHw5BBXr15V+/btlT9/ftWsWVM1a9ZUQECAOnTooKtXr2ZEjQAAAADgVA4Hp/fee08bN27UsmXLdOnSJV26dElLly7Vxo0b1bNnz4yoEQAAAACcyuFL9RYuXKgFCxaodu3a1raGDRvK09NTLVq00IQJE9KzPgAAAABwuvu6VC9v3ryp2vPkycOlegAAAAAeSQ4Hp9DQUEVGRur69evWtmvXrmngwIEKDQ1N1+IAAAAAIDOw+1I9V1dXnTx5UqNHj1b9+vVVsGBBhYSESJL27NkjDw8PrVmzJsMKBQAAAABnsTs4GYYhSSpfvrwOHjyomTNnat++fZKkVq1aqXXr1jZfigsAAAAAjwqHJ4eQJC8vL3Xq1Cm9awEAAACATMmh4DR58mT5+Pjcs0+3bt0eqCAAAAAAyGwcCk4TJ06Uq6vrXZdbLBaCEwAAAIBHjkPB6ZdfflGePHkyqhYAAAAAyJTsno7cYrFkZB0AAAAAkGnZHZxuz6oHAAAAAI8bu4NTZGSk6cQQAAAAAPAosvsep8jIyIysAwAAAAAyLbtHnAAAAADgcUVwAgAAAAATDgUnwzAUGxur69evZ1Q9AAAAAJDpOBycgoKCdOzYsYyqBwAAAAAyHYeCk4uLi4KDg3X+/PmMqgcAAAAAMh2H73H67LPP1KtXL/3+++8ZUQ8AAAAAZDp2T0d+W5s2bXT16lWFhITIzc1Nnp6eNssvXLiQbsUBAAAAQGbgcHAaPXp0BpQBAAAAAJmXw8EpIiIiI+oAAAAAgEzL4eAkScnJyVqyZIn27t0rSSpbtqyaNm0qV1fXdC0OAAAAADIDh4PToUOH1LBhQx0/flwlS5aUJA0dOlSBgYFasWKFihcvnu5FAgAAAIAzOTyrXrdu3VS8eHEdO3ZMO3fu1M6dOxUbG6uiRYuqW7duGVEjAAAAADiVwyNOGzdu1I8//qicOXNa23LlyqXPPvtM1atXT9fiAAAAACAzcHjEyd3dXVeuXEnVHh8fLzc3t3QpCgAAAAAyE4eDU+PGjfXGG2/op59+kmEYMgxDP/74o9566y01bdo0I2oEAAAAAKdyODh9+eWXKl68uEJDQ+Xh4SEPDw9Vr15dQUFBGjNmTEbUCAAAAABO5fA9TtmzZ9fSpUt18OBB7du3T5JUunRpBQUFpXtxAAAAAJAZ3Nf3OElScHCwgoOD07MWAAAAAMiU7ApOPXr0sHuDI0eOvO9iAAAAACAzsis47dq1y66NWSyWByoGAAAAADIju4LThg0bMroOAAAAAMi0HJ5VDwAAAAAeN/c1OcQvv/yiefPmKTY2VomJiTbLFi1alC6FAQAAAEBm4fCI05w5c1StWjXt3btXixcv1s2bN/XHH3/ohx9+kJ+fX0bUCAAAAABO5XBwGjJkiEaNGqVly5bJzc1NY8aM0b59+9SiRQsVKlQoI2oEAAAAAKdyODgdPnxYjRo1kiS5ubkpISFBFotF7733nr7++uv7KmL8+PEqUqSIPDw8VLVqVW3fvt2u9ebMmSOLxaJmzZrd134BAAAAwB4OB6ccOXLoypUrkqQCBQro999/lyRdunRJV69edbiAuXPnqkePHoqMjNTOnTsVEhKi8PBwnTlz5p7rHT16VO+//75q1Kjh8D4BAAAAwBEOB6eaNWtq3bp1kqTmzZure/fu6tSpk1q1aqW6des6XMDIkSPVqVMntWvXTmXKlNHEiRPl5eWlqVOn3nWd5ORktW7dWgMHDlSxYsUc3icAAAAAOMLuWfV+//13lStXTuPGjdP169clSR999JGyZs2qbdu26aWXXtLHH3/s0M4TExO1Y8cO9e3b19rm4uKisLAwxcTE3HW9Tz75RHny5FGHDh20efPme+7jxo0bunHjhvV5XFycQzUCAAAAgN3B6YknntBTTz2ljh076pVXXpF0K+T06dPnvnd+7tw5JScnK2/evDbtefPm1b59+9JcZ8uWLZoyZYp2795t1z6GDh2qgQMH3neNAAAAAGD3pXobN25U2bJl1bNnT+XPn18RERGmoz3p7cqVK3r99df1zTffKHfu3Hat07dvX12+fNn6OHbsWAZXCQAAAOBRY/eIU40aNVSjRg2NHTtW8+bN0/Tp01WrVi0FBQWpQ4cOioiIUL58+Rzaee7cueXq6qrTp0/btJ8+fTrNbR0+fFhHjx5VkyZNrG0pKSm3DiRLFu3fv1/Fixe3Wcfd3V3u7u4O1QUAAAAAd3J4cghvb2+1a9dOGzdu1IEDB9S8eXONHz9ehQoVUtOmTR3alpubmypVqqSoqChrW0pKiqKiohQaGpqqf6lSpfTbb79p9+7d1kfTpk1Vp04d7d69W4GBgY4eDgAAAACYsnvEKS1BQUH68MMPVbhwYfXt21crVqxweBs9evRQRESEKleurCpVqmj06NFKSEhQu3btJElt2rRRgQIFNHToUHl4eKhcuXI262fPnl2SUrUDAAAAQHq57+C0adMmTZ06VQsXLpSLi4tatGihDh06OLydli1b6uzZs+rfv79OnTqlChUqaPXq1dYJI2JjY+Xi4vDAGAAAAACkG4eC04kTJzR9+nRNnz5dhw4dUrVq1fTll1+qRYsW8vb2vu8iunTpoi5duqS5LDo6+p7rTp8+/b73CwAAAAD2sDs4NWjQQOvXr1fu3LnVpk0btW/fXiVLlszI2gAAAAAgU7A7OGXNmlULFixQ48aN5erqmpE1AQAAAECmYndw+t///peRdQAAAABApsWsCwAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYyRXAaP368ihQpIg8PD1WtWlXbt2+/a99vvvlGNWrUUI4cOZQjRw6FhYXdsz8AAAAAPCinB6e5c+eqR48eioyM1M6dOxUSEqLw8HCdOXMmzf7R0dFq1aqVNmzYoJiYGAUGBuq5557T8ePHH3LlAAAAAB4XTg9OI0eOVKdOndSuXTuVKVNGEydOlJeXl6ZOnZpm/5kzZ6pz586qUKGCSpUqpcmTJyslJUVRUVEPuXIAAAAAjwunBqfExETt2LFDYWFh1jYXFxeFhYUpJibGrm1cvXpVN2/eVM6cOdNcfuPGDcXFxdk8AAAAAMARTg1O586dU3JysvLmzWvTnjdvXp06dcqubXzwwQcKCAiwCV93Gjp0qPz8/KyPwMDAB64bAAAAwOPF6ZfqPYjPPvtMc+bM0eLFi+Xh4ZFmn759++ry5cvWx7Fjxx5ylQAAAAD+67I4c+e5c+eWq6urTp8+bdN++vRp5cuX757rDh8+XJ999pnWr1+vJ5544q793N3d5e7uni71AgAAAHg8OXXEyc3NTZUqVbKZ2OH2RA+hoaF3XW/YsGEaNGiQVq9ercqVKz+MUgEAAAA8xpw64iRJPXr0UEREhCpXrqwqVapo9OjRSkhIULt27SRJbdq0UYECBTR06FBJ0ueff67+/ftr1qxZKlKkiPVeKB8fH/n4+DjtOAAAAAA8upwenFq2bKmzZ8+qf//+OnXqlCpUqKDVq1dbJ4yIjY2Vi8v/DYxNmDBBiYmJevnll222ExkZqQEDBjzM0gEAAAA8JpwenCSpS5cu6tKlS5rLoqOjbZ4fPXo04wsCAAAAgDv8p2fVAwAAAICHgeAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgguAEAAAAACYITgAAAABgIlMEp/Hjx6tIkSLy8PBQ1apVtX379nv2nz9/vkqVKiUPDw+VL19eK1eufEiVAgAAAHgcOT04zZ07Vz169FBkZKR27typkJAQhYeH68yZM2n237Ztm1q1aqUOHTpo165datasmZo1a6bff//9IVcOAAAA4HHh9OA0cuRIderUSe3atVOZMmU0ceJEeXl5aerUqWn2HzNmjOrXr69evXqpdOnSGjRokJ588kmNGzfuIVcOAAAA4HGRxZk7T0xM1I4dO9S3b19rm4uLi8LCwhQTE5PmOjExMerRo4dNW3h4uJYsWZJm/xs3bujGjRvW55cvX5YkxcXFPWD16ei6swtAenPK+cV59EjiXEJ6cNrPPM6lRw7nEtJLZvld/HYdhmGY9nVqcDp37pySk5OVN29em/a8efNq3759aa5z6tSpNPufOnUqzf5Dhw7VwIEDU7UHBgbeZ9WAOb/P/JxdAh4RnEtID5xHSC+cS0gvme1cunLlivz87l2TU4PTw9C3b1+bEaqUlBRduHBBuXLlksVicWJlj5e4uDgFBgbq2LFj8vX1dXY5+A/jXEJ64VxCeuFcQnrgPHIOwzB05coVBQQEmPZ1anDKnTu3XF1ddfr0aZv206dPK1++fGmuky9fPof6u7u7y93d3aYte/bs9180Hoivry8fBkgXnEtIL5xLSC+cS0gPnEcPn9lI021OnRzCzc1NlSpVUlRUlLUtJSVFUVFRCg0NTXOd0NBQm/6StG7durv2BwAAAIAH5fRL9Xr06KGIiAhVrlxZVapU0ejRo5WQkKB27dpJktq0aaMCBQpo6NChkqTu3burVq1aGjFihBo1aqQ5c+bol19+0ddff+3MwwAAAADwCHN6cGrZsqXOnj2r/v3769SpU6pQoYJWr15tnQAiNjZWLi7/NzBWrVo1zZo1Sx9//LE+/PBDBQcHa8mSJSpXrpyzDgF2cHd3V2RkZKrLJgFHcS4hvXAuIb1wLiE9cB5lfhbDnrn3AAAAAOAx5vQvwAUAAACAzI7gBAAAAAAmCE4AgMfG3r17NXjwYN28edPZpQAA/mMITpAkRUdHy2Kx6NKlS5Kk6dOn831XuG//tfOnbdu2atasmbPLeOxZLBYtWbIkw7afnJystm3basuWLfr0008dXv+/dl7j/mX0uQjgv4ng9JiJiYmRq6urGjVqdM9+LVu21IEDBx5SVfdWu3Ztvfvuu84u47HUtm1bWSwW6yNXrlyqX7++fv3113uul5nOH2Qe/z6fbj/q169/39tzJPB+8cUXCg8P19KlSxUVFaXdu3c7tD/O60fLvc6fkydPqkGDBg+3IPwn3Pk5ljVrVhUtWlS9e/fW9evXnV0aHgKnT0eOh2vKlCnq2rWrpkyZohMnTiggICDNfp6envL09HzI1SEzql+/vqZNmyZJOnXqlD7++GM1btxYsbGxafa/efMm5w/u6s7z6baMnnr35s2bypo1q/r06WNt27Jli8Pb4bx+fOTLl++B1k9MTJSbm1s6VYPM5vbn2M2bN7Vjxw5FRETIYrHo888/d3ZpyGCMOD1G4uPjNXfuXL399ttq1KiRpk+ffte+/74kZcCAAapQoYKmTp2qQoUKycfHR507d1ZycrKGDRumfPnyKU+ePBo8eLDNdi5duqSOHTvK399fvr6+evbZZ7Vnz55U2/3uu+9UpEgR+fn56ZVXXtGVK1ck3frLzsaNGzVmzBjrX3iOHj0qSdq4caOqVKkid3d35c+fX3369FFSUpJ12wsWLFD58uXl6empXLlyKSwsTAkJCQ/+Qj5m3N3dlS9fPuXLl08VKlRQnz59dOzYMZ09e1ZHjx6VxWLR3LlzVatWLXl4eGjmzJl3PX/u9j5LUkJCgtq0aSMfHx/lz59fI0aMSDXamNblM9mzZ7c5l48dO6YWLVooe/bsypkzp55//nnrOSPdulyrR48eyp49u3LlyqXevXvr39/KcOPGDXXr1k158uSRh4eHnnnmGf3888/W5RcvXlTr1q3l7+8vT09PBQcHpwoDSNud59PtR44cOdLse6/3csCAAZoxY4aWLl1q/WyIjo6+6zmZkpKiTz75RAULFpS7u7v1OwNvu73eokWLVKdOHXl5eSkkJEQxMTHWPmldqrds2TI99dRT8vDwUO7cufXCCy9Yl128eFFt2rRRjhw55OXlpQYNGujgwYPW5X///beaNGmiHDlyyNvbW2XLltXKlSvT4VXGg7rzs6ZatWr64IMPbJafPXtWWbNm1aZNmyRJRYoU0aBBg9SmTRv5+vrqjTfekCQtXLhQZcuWlbu7u4oUKaIRI0bYbOerr75ScHCwPDw8lDdvXr388ssZf3B4YLc/xwIDA9WsWTOFhYVp3bp1ksx/fqT1ObJkyRJZLBbr89s/MydNmqTAwEB5eXmpRYsWunz5srXPzz//rHr16il37tzy8/NTrVq1tHPnTutywzA0YMAAFSpUSO7u7goICFC3bt0y6BV5fBCcHiPz5s1TqVKlVLJkSb322muaOnVqql8Y7+Xw4cNatWqVVq9erdmzZ2vKlClq1KiR/vnnH23cuFGff/65Pv74Y/3000/WdZo3b64zZ85o1apV2rFjh5588knVrVtXFy5csNnukiVLtHz5ci1fvlwbN27UZ599JkkaM2aMQkND1alTJ508eVInT55UYGCgjh8/roYNG+qpp57Snj17NGHCBE2ZMsV638LJkyfVqlUrtW/fXnv37lV0dLRefPFFh44XqcXHx+v7779XUFCQcuXKZW3v06ePunfvrr179yo8PDzNde/1PktSr169tHHjRi1dulRr165VdHS0zQ8Be9y8eVPh4eHKli2bNm/erK1bt8rHx0f169dXYmKiJGnEiBGaPn26pk6dqi1btujChQtavHixzXZ69+6thQsXasaMGdq5c6eCgoIUHh5uPW/79eunP//8U6tWrdLevXs1YcIE5c6d26FacW9m7+X777+vFi1aqH79+tbPhmrVqlnX//c5OWbMGI0YMULDhw/Xr7/+qvDwcDVt2tQmyEjSRx99pPfff1+7d+9WiRIl1KpVK5s/yNxpxYoVeuGFF9SwYUPt2rVLUVFRqlKlinV527Zt9csvv+h///ufYmJiZBiGGjZsaJ2Y4p133tGNGze0adMm/fbbb/r888/l4+OTAa8mHkTr1q01Z84cm58fc+fOVUBAgGrUqGFtGz58uEJCQrRr1y7169dPO3bsUIsWLfTKK6/ot99+04ABA9SvXz/rH3p++eUXdevWTZ988on279+v1atXq2bNmg/78PCAfv/9d23bts06wmj288Nehw4d0rx587Rs2TKtXr1au3btUufOna3Lr1y5ooiICG3ZskU//vijgoOD1bBhQ+sfJBcuXKhRo0Zp0qRJOnjwoJYsWaLy5cun34E/rgw8NqpVq2aMHj3aMAzDuHnzppE7d25jw4YNhmEYxoYNGwxJxsWLFw3DMIxp06YZfn5+1nUjIyMNLy8vIy4uztoWHh5uFClSxEhOTra2lSxZ0hg6dKhhGIaxefNmw9fX17h+/bpNHcWLFzcmTZp01+326tXLqFq1qvV5rVq1jO7du9ts48MPPzRKlixppKSkWNvGjx9v+Pj4GMnJycaOHTsMScbRo0cdfJVwp4iICMPV1dXw9vY2vL29DUlG/vz5jR07dhiGYRhHjhwxJFnPq9vsOX/ufJ+vXLliuLm5GfPmzbMuP3/+vOHp6Wnz3ksyFi9ebLMvPz8/Y9q0aYZhGMZ3332X6ry4ceOG4enpaaxZs8YwDMPInz+/MWzYMOvymzdvGgULFjSef/55wzAMIz4+3siaNasxc+ZMa5/ExEQjICDAul6TJk2Mdu3a2fMS4g7/Pp9uPwYPHmwYhu37a897GRERYX3fbrvbORkQEGDdz21PPfWU0blzZ5v1Jk+ebF3+xx9/GJKMvXv3GoaR+rwODQ01WrduneaxHjhwwJBkbN261dp27tw5w9PT03qely9f3hgwYMA9XzNknLTOn9vuPBfPnDljZMmSxdi0aZN1eWhoqPHBBx9YnxcuXNho1qyZzTZeffVVo169ejZtvXr1MsqUKWMYhmEsXLjQ8PX1tflcROZ35+eYu7u7IclwcXExFixYYNfPj39/jhiGYSxevNi481fyyMhIw9XV1fjnn3+sbatWrTJcXFyMkydPpllXcnKykS1bNmPZsmWGYRjGiBEjjBIlShiJiYnpdegwDIMRp8fE/v37tX37drVq1UqSlCVLFrVs2VJTpkyxextFihRRtmzZrM/z5s2rMmXKyMXFxabtzJkzkqQ9e/YoPj5euXLlko+Pj/Vx5MgRHT58+K7bzZ8/v3Ubd7N3716FhobaDG1Xr15d8fHx+ueffxQSEqK6deuqfPnyat68ub755htdvHjR7mPF/6lTp452796t3bt3a/v27QoPD1eDBg30999/W/tUrlzZdDv3ep8PHz6sxMREVa1a1bo8Z86cKlmypEO17tmzR4cOHVK2bNms51vOnDl1/fp1HT58WJcvX9bJkydt9pMlSxab+g8fPqybN2+qevXq1rasWbOqSpUq2rt3ryTp7bff1pw5c1ShQgX17t1b27Ztc6jOx9md59Ptx1tvvZWqn9l7aebO9zQuLk4nTpyweU+lW58Zt9/T25544gnrv/Pnzy9Jd/082r17t+rWrZvmsr179ypLliw251quXLlUsmRJ6z67deumTz/9VNWrV1dkZKTppCtwDn9/fz333HOaOXOmJOnIkSOKiYlR69atbfr9+3Nw7969aZ5zBw8eVHJysurVq6fChQurWLFiev311zVz5kxdvXo1Yw8G6eL259hPP/2kiIgItWvXTi+99JJdPz/sVahQIRUoUMD6PDQ0VCkpKdq/f78k6fTp0+rUqZOCg4Pl5+cnX19fxcfHW+8/bt68ua5du6ZixYqpU6dOWrx48V1Hz2E/Jod4TEyZMkVJSUk2k0EYhiF3d3eNGzfOrm1kzZrV5vntGWX+3ZaSkiLp1mVd+fPnV3R0dKpt3Xl97722cb9cXV21bt06bdu2TWvXrtXYsWP10Ucf6aefflLRokUfaNuPG29vbwUFBVmfT548WX5+fvrmm2/UsWNHax8z6fE+WyyWVJdb3vl9PPHx8apUqZL1F5w7+fv7O7Sve7kdHFeuXKl169apbt26eueddzR8+PB028ej6t/n09086HtpzzmZljvP09t/mLnbefqgE0V07NhR4eHhWrFihdauXauhQ4dqxIgR6tq16wNtF+mvdevW6tatm8aOHatZs2apfPnyqS57cvScy5Ytm3bu3Kno6GitXbtW/fv314ABA/Tzzz8z7X0md+fn2NSpUxUSEqIpU6boqaeeMl3XxcXlnj/H7BUREaHz589rzJgxKly4sNzd3RUaGmq9LD0wMFD79+/X+vXrtW7dOnXu3FlffPGFNm7cmOrnMezHiNNjICkpSd9++61GjBhh81fePXv2KCAgQLNnz86Q/T755JM6deqUsmTJoqCgIJuHI/eDuLm5KTk52aatdOnS1nsGbtu6dauyZcumggULSrr1S0/16tU1cOBA7dq1S25ubqnuZYHjLBaLXFxcdO3atXTbZvHixZU1a1ab++MuXryYaupnf39/nTx50vr84MGDNn+hffLJJ3Xw4EHlyZMn1Tnn5+cnPz8/5c+f32Y/SUlJ2rFjh00tbm5u2rp1q7Xt5s2b+vnnn1WmTBmbWiIiIvT9999r9OjR+vrrr9PnxYAk8/dSSvuzIS2+vr4KCAiweU+lW58Zd76njnriiScUFRWV5rLSpUsrKSnJ5lw7f/689u/fb7PPwMBAvfXWW1q0aJF69uypb7755r7rQcZ5/vnndf36da1evVqzZs1KNdqUltKlS6d5zpUoUUKurq6Sbo14h4WFadiwYfr111919OhR/fDDDxlyDMgYLi4u+vDDD/Xxxx/b9fPD399fV65csZmsKq2vRoiNjdWJEyesz3/88Ue5uLhYr8TYunWrunXrpoYNG1onIDl37pzNNjw9PdWkSRN9+eWXio6OVkxMjH777bf0PPzHDiNOj4Hly5fr4sWL6tChg/UXjtteeuklTZkyRV988UW67zcsLEyhoaFq1qyZhg0bphIlSujEiRPWG6rtubxLunWJ108//aSjR49aL9fp3LmzRo8era5du6pLly7av3+/IiMj1aNHD7m4uOinn35SVFSUnnvuOeXJk0c//fSTzp49q9KlS6f7cT7qbty4oVOnTkm6FWbGjRun+Ph4NWnSJN324ePjow4dOqhXr17KlSuX8uTJo48++sjmMlBJevbZZzVu3DiFhoYqOTlZH3zwgc1fzlq3bq0vvvhCzz//vHUGtb///luLFi1S7969VbBgQXXv3l2fffaZgoODVapUKY0cOdL6xc/Srb8kvv322+rVq5dy5sypQoUKadiwYbp69ao6dOggSerfv78qVaqksmXL6saNG1q+fDnnlp3uPJ9uy5IlS6o/ptjzXhYpUkRr1qzR/v37lStXrlSfb3fq1auXIiMjVbx4cVWoUEHTpk3T7t270xzRsldkZKTq1q2r4sWL65VXXlFSUpJWrlypDz74QMHBwXr++efVqVMnTZo0SdmyZVOfPn1UoEABPf/885Kkd999Vw0aNFCJEiV08eJFbdiwgfPoIbt8+XKqX1rvnPjmNm9vbzVr1kz9+vXT3r17rZe930vPnj311FNPadCgQWrZsqViYmI0btw4ffXVV5Ju/Wz+66+/VLNmTeXIkUMrV65USkqKw5cow/maN2+uXr16acKECaY/P6pWrSovLy99+OGH6tatm3766ac0Zzn28PBQRESEhg8frri4OHXr1k0tWrSwTpUfHBys7777TpUrV1ZcXJx69eplMwo+ffp0JScnW/f3/fffy9PTU4ULF34or8kjy7m3WOFhaNy4sdGwYcM0l/3000+GJGPMmDGmk0OEhITYrJvWjbX/nsghLi7O6Nq1qxEQEGBkzZrVCAwMNFq3bm3ExsbedbujRo0yChcubH2+f/9+4+mnnzY8PT0NScaRI0cMwzCM6Oho46mnnjLc3NyMfPnyGR988IFx8+ZNwzAM488//zTCw8MNf39/w93d3ShRooQxduxYu14v/J+IiAhDkvWRLVs246mnnjIWLFhgGMb/3VC/a9cum/XsOX/+/T5fuXLFeO211wwvLy8jb968xrBhw1KdT8ePHzeee+45w9vb2wgODjZWrlxpMzmEYRjGyZMnjTZt2hi5c+c23N3djWLFihmdOnUyLl++bBjGrckgunfvbvj6+hrZs2c3evToYbRp08bmXL527ZrRtWtX6zaqV69ubN++3bp80KBBRunSpQ1PT08jZ86cxvPPP2/89ddf9/UaP07+fT7dfpQsWdIwjNSTf5i9l2fOnDHq1atn+Pj4GJKMDRs23PWcTE5ONgYMGGAUKFDAyJo1qxESEmKsWrXKujyt9S5evGjdrmGkfVP3woULjQoVKhhubm5G7ty5jRdffNG67MKFC8brr79u+Pn5GZ6enkZ4eLhx4MAB6/IuXboYxYsXN9zd3Q1/f3/j9ddfN86dO/cArzAccbfzsUOHDmlORLNy5UpDklGzZs1U2ypcuLAxatSoVO0LFiwwypQpY2TNmtUoVKiQ8cUXX1iXbd682ahVq5aRI0cOw9PT03jiiSeMuXPnpvdhIp3dbVKRoUOHGv7+/kZ8fPw9f34Yxq3JIIKCggxPT0+jcePGxtdff51qcoiQkBDjq6++MgICAgwPDw/j5ZdfNi5cuGDts3PnTqNy5cqGh4eHERwcbMyfP9/mPFy8eLFRtWpVw9fX1/D29jaefvppY/369RnymjxOLIbB/MwAMqfatWurQoUKGj16tLNLAQDgoRgwYICWLFmS5iV8cC7ucQIAAAAAEwQnAAAAADDBpXoAAAAAYIIRJwAAAAAwQXACAAAAABMEJwAAAAAwQXACAAAAABMEJwAAAAAwQXACACCdRUdHy2Kx6NKlS84uBQCQTghOAACnOnXqlLp27apixYrJ3d1dgYGBatKkiaKiouxaf/r06cqePXvGFumgatWq6eTJk/Lz83N2KQCAdJLF2QUAAB5fR48eVfXq1ZU9e3Z98cUXKl++vG7evKk1a9bonXfe0b59+5xdosNu3rwpNzc35cuXz9mlAADSESNOAACn6dy5sywWi7Zv366XXnpJJUqUUNmyZdWjRw/9+OOPkqSRI0eqfPny8vb2VmBgoDp37qz4+HhJty6Ja9eunS5fviyLxSKLxaIBAwZIkm7cuKH3339fBQoUkLe3t6pWraro6Gib/X/zzTcKDAyUl5eXXnjhBY0cOTLV6NWECRNUvHhxubm5qWTJkvruu+9sllssFk2YMEFNmzaVt7e3Bg8enOpSvfPnz6tVq1YqUKCAvLy8VL58ec2ePTvdX08AQMYhOAEAnOLChQtavXq13nnnHXl7e6dafjvAuLi46Msvv9Qff/yhGTNm6IcfflDv3r0l3bokbvTo0fL19dXJkyd18uRJvf/++5KkLl26KCYmRnPmzNGvv/6q5s2bq379+jp48KAkaevWrXrrrbfUvXt37d69W/Xq1dPgwYNtali8eLG6d++unj176vfff9ebb76pdu3aacOGDTb9BgwYoBdeeEG//fab2rdvn+pYrl+/rkqVKmnFihX6/fff9cYbb+j111/X9u3bH/h1BAA8HBbDMAxnFwEAePxs375dVatW1aJFi/TCCy/Yvd6CBQv01ltv6dy5c5Ju3eP07rvv2kzEEBsbq2LFiik2NlYBAQHW9rCwMFWpUkVDhgzRK6+8ovj4eC1fvty6/LXXXtPy5cut26pevbrKli2rr7/+2tqnRYsWSkhI0IoVKyTdGnF69913NWrUKGuf6Oho1alTRxcvXrzr/VeNGzdWqVKlNHz4cLuPHQDgPIw4AQCcwt6/261fv15169ZVgQIFlC1bNr3++us6f/68rl69etd1fvvtNyUnJ6tEiRLy8fGxPjZu3KjDhw9Lkvbv368qVarYrPfv53v37lX16tVt2qpXr669e/fatFWuXPmex5CcnKxBgwapfPnyypkzp3x8fLRmzRrFxsaaHj8AIHNgcggAgFMEBwfLYrHccwKIo0ePqnHjxnr77bc1ePBg5cyZU1u2bFGHDh2UmJgoLy+vNNeLj4+Xq6urduzYIVdXV5tlPj4+6XocktK81PBOX3zxhcaMGaPRo0db79d69913lZiYmO61AAAyBiNOAACnyJkzp8LDwzV+/HglJCSkWn7p0iXt2LFDKSkpGjFihJ5++mmVKFFCJ06csOnn5uam5ORkm7aKFSsqOTlZZ86cUVBQkM3j9mx3JUuW1M8//2yz3r+fly5dWlu3brVp27p1q8qUKePQsW7dulXPP/+8XnvtNYWEhKhYsWI6cOCAQ9sAADgXwQkA4DTjx49XcnKyqlSpooULF+rgwYPau3evvvzyS4WGhiooKEg3b97U2LFj9ddff+m7777TxIkTbbZRpEgRxcfHKyoqSufOndPVq1dVokQJtW7dWm3atNGiRYt05MgRbd++XUOHDrXem9S1a1etXLlSI0eO1MGDBzVp0iStWrVKFovFuu1evXpp+vTpmjBhgg4ePKiRI0dq0aJF1gko7BUcHKx169Zp27Zt2rt3r958802dPn36wV9AAMBDQ3ACADhNsWLFtHPnTtWpU0c9e/ZUuXLlVK9ePUVFRWnChAkKCQnRyJEj9fnnn6tcuXKaOXOmhg4darONatWq6a233lLLli3l7++vYcOGSZKmTZumNm3aqGfPnipZsqSaNWumn3/+WYUKFZJ0616liRMnauTIkQoJCdHq1av13nvvycPDw7rtZs2aacyYMRo+fLjKli2rSZMmadq0aapdu7ZDx/nxxx/rySefVHh4uGrXrq18+fKpWbNmD/TaAQAeLmbVAwDg/+vUqZP27dunzZs3O7sUAEAmw+QQAIDH1vDhw1WvXj15e3tr1apVmjFjhr766itnlwUAyIQYcQIAPLZatGih6OhoXblyRcWKFVPXrl311ltvObssAEAmRHACAAAAABNMDgEAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGCC4AQAAAAAJghOAAAAAGDi/wGFDHvoXDCOwwAAAABJRU5ErkJggg==",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.bar(total_por_categoria['Categoria'], total_por_categoria['Valor_Total'], color='green')\n",
+    "plt.xlabel('Categoria')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Total de Vendas por Categoria')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 8: Com base no gráfico, qual categoria visualmente se destaca em termos de vendas totais?**\n",
+    "\n",
+    "Pelo gráfico gerado observa-se que a categoria 'Brinquedos' que teve um maior resuldado de vendas totais."
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Criar um gráfico de linha mostrando a variação diária das vendas de 'Eletrônicos'**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 86,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 2200x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vendas_diarias = eletronicos.groupby('Data')['Valor_Total'].sum().reset_index()\n",
+    "\n",
+    "plt.figure(figsize=(22, 6))\n",
+    "plt.plot(vendas_diarias['Data'], vendas_diarias['Valor_Total'], color='purple')\n",
+    "plt.xlabel('Data')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Variação Diária das Vendas de Eletrônicos')\n",
+    "plt.xticks(rotation=45)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Pergunta 9: Existe algum padrão ou tendência observável nas vendas diárias de eletrônicos?**\n",
+    "\n",
+    "Pela análise do gráfico é observado que no primeiro semestre do ano foram obtidos os maiores valor de venda de eletrônicos dessa empresa com seu pico de vendas encontrado no dia 06/02/2023.\n",
+    "Também é visto que assim que a empresa encontra um máximo de vendas ela obtém uma quebra brusca e encontra um valor mínimo abaixo de 1000 reais."
+   ]
+  }
+ ],
+ "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": "3.12.5"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-sala/ex_extra.ipynb b/exercicios/para-sala/ex_extra.ipynb
index 93224fd..94f32ff 100644
--- a/exercicios/para-sala/ex_extra.ipynb
+++ b/exercicios/para-sala/ex_extra.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 27,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 28,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
@@ -151,7 +151,7 @@
        "4                   8  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 16,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -169,7 +169,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 7,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
     {
@@ -186,7 +186,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 7,
+     "execution_count": 17,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -197,7 +197,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 8,
+   "execution_count": 18,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -206,7 +206,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 9,
+   "execution_count": 19,
    "metadata": {},
    "outputs": [
     {
@@ -223,7 +223,7 @@
        "dtype: object"
       ]
      },
-     "execution_count": 9,
+     "execution_count": 19,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -234,7 +234,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 10,
+   "execution_count": 20,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -243,7 +243,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 11,
+   "execution_count": 21,
    "metadata": {},
    "outputs": [
     {
@@ -360,7 +360,7 @@
        "4                   8  "
       ]
      },
-     "execution_count": 11,
+     "execution_count": 21,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -371,7 +371,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 12,
+   "execution_count": 22,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -381,7 +381,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 23,
    "metadata": {},
    "outputs": [
     {
@@ -398,7 +398,7 @@
        "dtype: int64"
       ]
      },
-     "execution_count": 13,
+     "execution_count": 23,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -409,7 +409,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 24,
    "metadata": {},
    "outputs": [
     {
@@ -526,7 +526,7 @@
        "4                   8  "
       ]
      },
-     "execution_count": 14,
+     "execution_count": 24,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -544,12 +544,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 25,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
        "<Figure size 1000x600 with 1 Axes>"
       ]
@@ -572,7 +572,27 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "# Análise "
+    "# Análise \n",
+    "\n",
+    "Estamos analisando a duração da consulta x gravidade dos sintomas dos atendimentos de uma clinica médica, observando a forma que a triagem faz o processo de divisão levando em conta a gravidade dos sintomas:\n",
+    "\n",
+    "Considerando que na triagem a pessoa que chegar vai receber uma pulseira nas cores de identificação, sendo:\n",
+    "\n",
+    "Gravidade de Sintomas 1 - 4: Pulseira Verde\n",
+    "\n",
+    "Gravidade de Sintomas 5 - 7: Pulseira Amarela\n",
+    "\n",
+    "Gravidade de Sintomas 8 - 10: Pulseira Vermelha\n",
+    "\n",
+    "Consultas 25min: A grande maioria demonstra ser de gravidade relativamente baixa e recebe a pulseira amarela.\n",
+    "\n",
+    "Consultas 30min: A grande maioria demostra ser de gravidade baixa e recebe a pulseira verde. Ponto de atenção para o tempo destinado para esse processo. Existe alguma falha triagem que prolonga o atendimento em 5 min a mais que atendimentos de riscos mais altos? Esse tempo de diferença pode ser importante para atendimentos de gravidade mais alta.\n",
+    "\n",
+    "Consultas 35min: A grande maioria demostra ser de gravidade alta, recebe pulseira vermelha. Acreditamos que isso pode estar relacionado com os sintomas, como são sintomas mais expressivos, são identificados como graves mais rápidos do que o caso das consultas de 40min, por exemplo.\n",
+    "\n",
+    "Consultas 40min: A grande maioria demostra ser de gravidade média, acreditamos que isso acontece porque demora mais para entender se esses sintomas são graves ou não, necessitando de mais exames e exploração por parte da triagem. Esses casos são estados de alerta, precisam ser observados com atenção para caso necessário o atendimento ser o mais rápido possível.\n",
+    "\n",
+    "Consultas 45min: A grande maioria demostra ser de gravidade média, acreditamos que isso acontece porque demora mais para entender se esses sintomas são graves ou não, necessitando de mais exames e exploração por parte da triagem. Esses casos são estados de alerta, precisam ser observados com atenção para caso necessário o atendimento ser o mais rápido possível."
    ]
   },
   {
@@ -581,6 +601,34 @@
    "source": [
     "# Storylling"
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "colors = df['Gravidade_Sintomas']\n",
+    "plt.figure(figsize=(10, 6))\n",
+    "scatter = plt.scatter(df['Duracao_Consulta_Minutos'], df['Gravidade_Sintomas'], c=colors, cmap='viridis')\n",
+    "plt.title('Duração da Consulta x Gravidade dos Sintomas')\n",
+    "plt.xlabel('Duração da Consulta (minutos)')\n",
+    "plt.ylabel('Gravidade dos Sintomas (1 a 10)')\n",
+    "plt.colorbar(scatter, label='Gravidade dos Sintomas')\n",
+    "plt.grid(True)\n",
+    "plt.show()"
+   ]
   }
  ],
  "metadata": {
@@ -599,7 +647,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,
diff --git a/exercicios/para-sala/exercicio.ipynb b/exercicios/para-sala/exercicio.ipynb
index 1503234..4c5b878 100644
--- a/exercicios/para-sala/exercicio.ipynb
+++ b/exercicios/para-sala/exercicio.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 13,
+   "execution_count": 49,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -11,9 +11,16 @@
     "df = pd.read_csv(\"dados_ficticios.csv\")"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 1. Exibir as 10 primeiras linhas do DataFrame gerado e as informações gerais sobre o DataFrame, como tipos de dados e estatísticas descritivas."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 50,
    "metadata": {},
    "outputs": [
     {
@@ -143,7 +150,7 @@
        "9         B  45.308243  103.693606        1  2023-01-10"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 50,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -154,40 +161,35 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 70,
    "metadata": {},
    "outputs": [
     {
-     "data": {
-      "text/plain": [
-       "<bound method DataFrame.info of     Categoria    Valor_1     Valor_2  Valor_3        Data\n",
-       "0           C  53.417560  126.034826        1  2023-01-01\n",
-       "1           D  68.761708  131.230224        2  2023-01-02\n",
-       "2           A  59.504238  100.640083        6  2023-01-03\n",
-       "3           C  44.230963   84.931643        5  2023-01-04\n",
-       "4           C  41.015853  109.199443        9  2023-01-05\n",
-       "..        ...        ...         ...      ...         ...\n",
-       "995         A  45.120886   80.856982        4  2025-09-22\n",
-       "996         A  71.573082  106.875758        7  2025-09-23\n",
-       "997         D  43.942851   99.026953        6  2025-09-24\n",
-       "998         D  57.420954  100.655940        2  2025-09-25\n",
-       "999         C  52.992926   84.830093        8  2025-09-26\n",
-       "\n",
-       "[1000 rows x 5 columns]>"
-      ]
-     },
-     "execution_count": 15,
-     "metadata": {},
-     "output_type": "execute_result"
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "Index: 31 entries, 120 to 150\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column            Non-Null Count  Dtype  \n",
+      "---  ------            --------------  -----  \n",
+      " 0   Categoria         31 non-null     object \n",
+      " 1   estoque da loja1  31 non-null     float64\n",
+      " 2   estoque da loja2  31 non-null     float64\n",
+      " 3   vendas            31 non-null     int64  \n",
+      " 4   Data              31 non-null     object \n",
+      "dtypes: float64(2), int64(1), object(2)\n",
+      "memory usage: 1.5+ KB\n"
+     ]
     }
    ],
    "source": [
-    "df.info"
+    "df.info()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 52,
    "metadata": {},
    "outputs": [
     {
@@ -281,7 +283,7 @@
        "max      80.788808   163.862151     9.000000"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 52,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -290,9 +292,16 @@
     "df.describe()"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 2. Filtrar o DataFrame para mostrar apenas os registros onde `Categoria` é 'A' e `Valor_1` é maior que 60."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 53,
    "metadata": {},
    "outputs": [
     {
@@ -512,7 +521,7 @@
        "461         A  74.553001   82.676503        7  2024-04-06"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 53,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -526,13 +535,12 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "### 3. **Pandas: Consultas e Filtros com Datas**\n",
-    "**Exercício**: Filtre os registros para mostrar apenas aqueles com `Data` entre '2023-05-01' e '2023-05-31'.\n"
+    "##### 3. Filtrar os registros para mostrar apenas aqueles com `Data` entre '2023-05-01' e '2023-05-31'."
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 27,
+   "execution_count": 54,
    "metadata": {},
    "outputs": [
     {
@@ -565,6 +573,14 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>D</td>\n",
+       "      <td>44.076061</td>\n",
+       "      <td>113.927749</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-05-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>121</th>\n",
        "      <td>A</td>\n",
        "      <td>41.360092</td>\n",
@@ -636,20 +652,13 @@
        "      <td>4</td>\n",
        "      <td>2023-05-10</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>130</th>\n",
-       "      <td>D</td>\n",
-       "      <td>55.009172</td>\n",
-       "      <td>130.268995</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2023-05-11</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "    Categoria    Valor_1     Valor_2  Valor_3        Data\n",
+       "120         D  44.076061  113.927749        6  2023-05-01\n",
        "121         A  41.360092  119.106104        5  2023-05-02\n",
        "122         B  50.485216  101.768138        9  2023-05-03\n",
        "123         C  41.690499  129.550602        2  2023-05-04\n",
@@ -658,11 +667,10 @@
        "126         B  47.610520   85.663554        2  2023-05-07\n",
        "127         A  40.924363   62.669268        6  2023-05-08\n",
        "128         D  44.232287   98.346386        8  2023-05-09\n",
-       "129         D  57.553912   97.565050        4  2023-05-10\n",
-       "130         D  55.009172  130.268995        2  2023-05-11"
+       "129         D  57.553912   97.565050        4  2023-05-10"
       ]
      },
-     "execution_count": 27,
+     "execution_count": 54,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -672,9 +680,16 @@
     "df.head(10)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 4. Renomear as colunas"
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 28,
+   "execution_count": 55,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -683,7 +698,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 29,
+   "execution_count": 56,
    "metadata": {},
    "outputs": [
     {
@@ -716,6 +731,14 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>D</td>\n",
+       "      <td>44.076061</td>\n",
+       "      <td>113.927749</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-05-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>121</th>\n",
        "      <td>A</td>\n",
        "      <td>41.360092</td>\n",
@@ -787,20 +810,13 @@
        "      <td>4</td>\n",
        "      <td>2023-05-10</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>130</th>\n",
-       "      <td>D</td>\n",
-       "      <td>55.009172</td>\n",
-       "      <td>130.268995</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2023-05-11</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "    Categoria  estoque da loja1  Estoque da loja2  vendas        Data\n",
+       "120         D         44.076061        113.927749       6  2023-05-01\n",
        "121         A         41.360092        119.106104       5  2023-05-02\n",
        "122         B         50.485216        101.768138       9  2023-05-03\n",
        "123         C         41.690499        129.550602       2  2023-05-04\n",
@@ -809,11 +825,10 @@
        "126         B         47.610520         85.663554       2  2023-05-07\n",
        "127         A         40.924363         62.669268       6  2023-05-08\n",
        "128         D         44.232287         98.346386       8  2023-05-09\n",
-       "129         D         57.553912         97.565050       4  2023-05-10\n",
-       "130         D         55.009172        130.268995       2  2023-05-11"
+       "129         D         57.553912         97.565050       4  2023-05-10"
       ]
      },
-     "execution_count": 29,
+     "execution_count": 56,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -822,18 +837,39 @@
     "df.head(10)"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 5. Agrupar o DataFrame pela coluna `Categoria` e calcular a média de `Estoque Loja 1` e `Estoque Loja 2` para cada grupo."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 33,
+   "execution_count": 80,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "ename": "KeyError",
+     "evalue": "\"Columns not found: 'Estoque da loja2'\"",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
+      "Cell \u001b[1;32mIn[80], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m agrupado_media \u001b[38;5;241m=\u001b[39m \u001b[43mdf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgroupby\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mCategoria\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mestoque da loja1\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mEstoque da loja2\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\u001b[38;5;241m.\u001b[39mmean()\n",
+      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\pandas\\core\\groupby\\generic.py:1951\u001b[0m, in \u001b[0;36mDataFrameGroupBy.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m   1944\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(key, \u001b[38;5;28mtuple\u001b[39m) \u001b[38;5;129;01mand\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(key) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[0;32m   1945\u001b[0m     \u001b[38;5;66;03m# if len == 1, then it becomes a SeriesGroupBy and this is actually\u001b[39;00m\n\u001b[0;32m   1946\u001b[0m     \u001b[38;5;66;03m# valid syntax, so don't raise\u001b[39;00m\n\u001b[0;32m   1947\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[0;32m   1948\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mCannot subset columns with a tuple with more than one element. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1949\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mUse a list instead.\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m   1950\u001b[0m     )\n\u001b[1;32m-> 1951\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43msuper\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[38;5;21;43m__getitem__\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43mkey\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[1;32m~\\AppData\\Local\\Packages\\PythonSoftwareFoundation.Python.3.12_qbz5n2kfra8p0\\LocalCache\\local-packages\\Python312\\site-packages\\pandas\\core\\base.py:239\u001b[0m, in \u001b[0;36mSelectionMixin.__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m    237\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39mcolumns\u001b[38;5;241m.\u001b[39mintersection(key)) \u001b[38;5;241m!=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mset\u001b[39m(key)):\n\u001b[0;32m    238\u001b[0m         bad_keys \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlist\u001b[39m(\u001b[38;5;28mset\u001b[39m(key)\u001b[38;5;241m.\u001b[39mdifference(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mobj\u001b[38;5;241m.\u001b[39mcolumns))\n\u001b[1;32m--> 239\u001b[0m         \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mColumns not found: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mstr\u001b[39m(bad_keys)[\u001b[38;5;241m1\u001b[39m:\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m    240\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_gotitem(\u001b[38;5;28mlist\u001b[39m(key), ndim\u001b[38;5;241m=\u001b[39m\u001b[38;5;241m2\u001b[39m)\n\u001b[0;32m    242\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n",
+      "\u001b[1;31mKeyError\u001b[0m: \"Columns not found: 'Estoque da loja2'\""
+     ]
+    }
+   ],
    "source": [
     "agrupado_media = df.groupby('Categoria')[['estoque da loja1', 'Estoque da loja2']].mean()"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 34,
+   "execution_count": 81,
    "metadata": {},
    "outputs": [
     {
@@ -879,13 +915,13 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
-       "      <td>39.662949</td>\n",
-       "      <td>102.444943</td>\n",
+       "      <td>39.910509</td>\n",
+       "      <td>103.563094</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>D</th>\n",
-       "      <td>48.936837</td>\n",
-       "      <td>99.861477</td>\n",
+       "      <td>48.450760</td>\n",
+       "      <td>101.268104</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -896,11 +932,11 @@
        "Categoria                                    \n",
        "A                 47.012307        101.616545\n",
        "B                 49.047868         93.715846\n",
-       "C                 39.662949        102.444943\n",
-       "D                 48.936837         99.861477"
+       "C                 39.910509        103.563094\n",
+       "D                 48.450760        101.268104"
       ]
      },
-     "execution_count": 34,
+     "execution_count": 81,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -911,7 +947,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 44,
+   "execution_count": 59,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -920,7 +956,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 45,
+   "execution_count": 60,
    "metadata": {},
    "outputs": [
     {
@@ -953,6 +989,14 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>D</td>\n",
+       "      <td>44.076061</td>\n",
+       "      <td>113.927749</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-05-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>121</th>\n",
        "      <td>A</td>\n",
        "      <td>41.360092</td>\n",
@@ -1024,20 +1068,13 @@
        "      <td>4</td>\n",
        "      <td>2023-05-10</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>130</th>\n",
-       "      <td>D</td>\n",
-       "      <td>55.009172</td>\n",
-       "      <td>130.268995</td>\n",
-       "      <td>2</td>\n",
-       "      <td>2023-05-11</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "    Categoria  estoque da loja1  estoque da loja2  vendas        Data\n",
+       "120         D         44.076061        113.927749       6  2023-05-01\n",
        "121         A         41.360092        119.106104       5  2023-05-02\n",
        "122         B         50.485216        101.768138       9  2023-05-03\n",
        "123         C         41.690499        129.550602       2  2023-05-04\n",
@@ -1046,11 +1083,10 @@
        "126         B         47.610520         85.663554       2  2023-05-07\n",
        "127         A         40.924363         62.669268       6  2023-05-08\n",
        "128         D         44.232287         98.346386       8  2023-05-09\n",
-       "129         D         57.553912         97.565050       4  2023-05-10\n",
-       "130         D         55.009172        130.268995       2  2023-05-11"
+       "129         D         57.553912         97.565050       4  2023-05-10"
       ]
      },
-     "execution_count": 45,
+     "execution_count": 60,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1061,7 +1097,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 46,
+   "execution_count": 61,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1070,7 +1106,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 47,
+   "execution_count": 62,
    "metadata": {},
    "outputs": [
     {
@@ -1137,7 +1173,7 @@
        "D                 57.553912        137.691726"
       ]
      },
-     "execution_count": 47,
+     "execution_count": 62,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1146,9 +1182,16 @@
     "agrupado_max"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 6. Agrupar o DataFrame pela coluna `Categoria` e calcule a soma de `Vendas` para cada grupo. Além disso, contar o número de registros em cada grupo."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 48,
+   "execution_count": 79,
    "metadata": {},
    "outputs": [
     {
@@ -1181,6 +1224,14 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
+       "      <th>120</th>\n",
+       "      <td>D</td>\n",
+       "      <td>44.076061</td>\n",
+       "      <td>113.927749</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-05-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>121</th>\n",
        "      <td>A</td>\n",
        "      <td>41.360092</td>\n",
@@ -1412,12 +1463,21 @@
        "      <td>6</td>\n",
        "      <td>2023-05-30</td>\n",
        "    </tr>\n",
+       "    <tr>\n",
+       "      <th>150</th>\n",
+       "      <td>C</td>\n",
+       "      <td>41.395866</td>\n",
+       "      <td>110.272002</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-05-31</td>\n",
+       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
        "</div>"
       ],
       "text/plain": [
        "    Categoria  estoque da loja1  estoque da loja2  vendas        Data\n",
+       "120         D         44.076061        113.927749       6  2023-05-01\n",
        "121         A         41.360092        119.106104       5  2023-05-02\n",
        "122         B         50.485216        101.768138       9  2023-05-03\n",
        "123         C         41.690499        129.550602       2  2023-05-04\n",
@@ -1446,10 +1506,11 @@
        "146         C         49.339202        106.463354       2  2023-05-27\n",
        "147         C         37.889838         97.047948       9  2023-05-28\n",
        "148         A         43.481639         90.679270       1  2023-05-29\n",
-       "149         D         50.473987         68.105939       6  2023-05-30"
+       "149         D         50.473987         68.105939       6  2023-05-30\n",
+       "150         C         41.395866        110.272002       6  2023-05-31"
       ]
      },
-     "execution_count": 48,
+     "execution_count": 79,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1460,7 +1521,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 56,
+   "execution_count": 64,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1469,7 +1530,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 55,
+   "execution_count": 65,
    "metadata": {},
    "outputs": [
     {
@@ -1515,13 +1576,13 @@
        "    </tr>\n",
        "    <tr>\n",
        "      <th>C</th>\n",
-       "      <td>32</td>\n",
-       "      <td>6</td>\n",
+       "      <td>38</td>\n",
+       "      <td>7</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>D</th>\n",
-       "      <td>44</td>\n",
-       "      <td>9</td>\n",
+       "      <td>50</td>\n",
+       "      <td>10</td>\n",
        "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
@@ -1532,11 +1593,11 @@
        "Categoria                                 \n",
        "A              58                       12\n",
        "B              11                        2\n",
-       "C              32                        6\n",
-       "D              44                        9"
+       "C              38                        7\n",
+       "D              50                       10"
       ]
      },
-     "execution_count": 55,
+     "execution_count": 65,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1545,9 +1606,16 @@
     "df_agrupamento"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "##### 7. Criar um gráfico de barras mostrando a média de `Estoque Loja 1` para cada `Categoria`."
+   ]
+  },
   {
    "cell_type": "code",
-   "execution_count": 58,
+   "execution_count": 76,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1556,7 +1624,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 60,
+   "execution_count": 77,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -1565,12 +1633,12 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 61,
+   "execution_count": 78,
    "metadata": {},
    "outputs": [
     {
      "data": {
-      "image/png": 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",
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s2dPantPTNGXLlrX2KtzuzntK3BqgGBQUpFatWuVo3Xcun5iYmKNlPT091bFjR3Xs2FFpaWkaOHCgPvjgA7322msZ9l7cvi23ar99P6akpOjkyZPWz759f9/e03Tjxg2dPHlSDz74oLXtVk/KnVdD3frGe/v2STd7qOzZP2XLltUvv/wiY4zN9t15HPJ6vDP63Fufc2ev2+HDh63TM7J8+XIlJyfrm2++UZkyZazt2Z1ytFdGv6dHjhxRwYIFrV8MChYsmOH9UA4dOiQ3Nze7B3dXrFhR27dv140bN6yDyO+0dOlSeXt7a9WqVTaXMc+fPz/dvBn9DhcvXlz+/v5KTU3N9neobNmyOnDgQLrfl2PHjqWbT7Lv+GYlN9uKu4sxM7Cbn5+fZs6cqddff10dO3bMdL5u3bopNTVVb775Zrppf/75p/UPZKtWreTh4aH3339fxhjrPLeu8MnKrfP9ty9njLFeypyddu3aadu2bdqxY4e17ffff0/XmxQeHq6AgABNmDBBN27cSLeeW938menWrZu2bt2qVatWpZsWGxtrvXfP5cuXbaa5ublZv43e6sr29fW1Lne7Vq1aydPTU1OnTrXZH3PnzlVcXJz1Cot69eqpePHimjVrls3VYgsWLEi3zlsh5fZLgFNTU9OdBggNDVXFihX17rvvKjExMd02Zrd/2rVrp/Pnz9tc2nr16tV0n5PX432nevXqKSgoSLNmzbI5VbBixQodPHgwy6tSMqolLi7urv1B27p1q814jDNnzujrr79WmzZt5O7uLnd3d7Vp00Zff/21zaX0MTExWrRokR5++OFsT4dmpmvXrrp06ZKmTZuWbtqt7Xd3d5fFYrHptfvtt98yvDmer69vut81d3d3de3aVUuXLtUvv/ySbpnbf4fCw8N17tw5ffPNN9a269eva86cOTbL5OX4ZiU324q7i54Z5ElWYyNuadasmfr376/IyEjt3btXbdq0kYeHh44ePaolS5ZoypQpeuKJJ1S8eHGNGDFCkZGR6tChg9q1a6c9e/ZoxYoVKlasWJafUa1aNVWsWFEjRozQuXPnFBAQoKVLl2bbo3PLyJEjtXDhQj366KMaOnSo9dLssmXLat++fdb5AgICNHPmTP3jH//Q3/72Nz399NMqXry4Tp8+re+++06NGzfO8H/0t7z88sv65ptv1KFDB/Xu3VuhoaFKSkrS/v379cUXX+i3335TsWLF9Pzzz+vKlStq0aKFHnjgAZ06dUrvv/++6tata738um7dunJ3d9fEiRMVFxcnLy8v671ORo8erXHjxunRRx9Vp06ddPjwYc2YMUN///vf9eyzz0q6OTZm/Pjx6t+/v1q0aKGnnnpKJ0+e1Pz589ONmalZs6YaNmyo0aNH68qVKypSpIgWL16c7saJbm5u+vDDD9W2bVvVrFlTffr0UenSpXXu3DmtW7dOAQEBWr58eab7p1+/fpo2bZp69uypXbt2qWTJklq4cGG6S2Pzerzv5OHhoYkTJ6pPnz5q1qyZunfvbr10t1y5clk+eqNNmzbWXrT+/fsrMTFRc+bMUVBQkC5cuGAz76lTp7Rw4UJJNwelStL48eMl3ew9+Mc//pFtrbVq1VJ4eLjNpdmSNG7cOOs848eP1+rVq/Xwww9r4MCBKlCggD744AMlJyfr7bffzt3OuU3Pnj318ccfKyIiQjt27FCTJk2UlJSkH374QQMHDlTnzp3Vvn17RUVF6dFHH9Uzzzyjixcvavr06apUqZLNvyXpZvj94YcfFBUVpVKlSql8+fJq0KCB3nrrLa1bt04NGjRQv379VKNGDV25ckW7d+/WDz/8oCtXrki6ecuHadOmqXv37ho6dKhKliypTz/91DrI91ZvTV6Ob1Zys624y5xxCRVc0+2XZmflzkuzb5k9e7YJDQ01Pj4+xt/f39SuXduMHDnSnD9/3jpPamqqGTdunClZsqTx8fExzZs3N7/88ospW7ZstpdmHzhwwLRq1cr4+fmZYsWKmX79+pmff/7ZSDLz58/Pdvv27dtnmjVrZry9vU3p0qXNm2++aebOnZvpJcnh4eEmMDDQeHt7m4oVK5revXvbXDKbmYSEBDN69GhTqVIl4+npaYoVK2YaNWpk3n33XZOSkmKMMeaLL74wbdq0MUFBQcbT09OUKVPG9O/f31y4cMFmXXPmzDEVKlQw7u7u6fbHtGnTTLVq1YyHh4cpUaKEGTBggPnjjz/S1TNjxgxTvnx54+XlZerVq2c2btxomjVrZnNptjHGHD9+3LRq1cp4eXmZEiVKmH//+99m9erVGV4evmfPHvP444+bokWLGi8vL1O2bFnTrVs3s2bNmmz3z6lTp0ynTp1MwYIFTbFixczQoUOtl/E76nhn9PtjjDGff/65eeihh4yXl5cpUqSI6dGjhzl79qzNPBldmv3NN9+YOnXqGG9vb1OuXDkzceJE62XMt//u3PrcjF537u+MSDKDBg0yn3zyialcubLx8vIyDz30ULrtMMaY3bt3m/DwcOPn52cKFixoHnnkEfPjjz/azJPTf9O3u3r1qnnllVdM+fLljYeHhwkODjZPPPGEzWXgc+fOtdZXrVo1M3/+/Az326FDh0zTpk2Nj49PutsvxMTEmEGDBpmQkBDr57Rs2dLMnj3bZh0nTpww7du3Nz4+PqZ48eLmpZdeMkuXLjWSzLZt22zmzcnx7dWrl/H19c1w2zO6NDun24q7y2LMbX2jACBZ7/57t59K7opee+01RUZGpuuVuhcsFosGDRqUZe8fbp6aHj58uM6ePavSpUs7uxzcA4yZAYBcuHDhQranPXHv3Pn4iuvXr+uDDz5Q5cqVCTJ/IYyZAYAcOHHihL788kstWbJEHTp0cHY5+D+PP/64ypQpo7p16youLk6ffPKJDh06lOmtIHB/IswAQA5s3LhR48aNU/PmzRUVFeXscvB/wsPD9eGHH+rTTz9VamqqatSoocWLF+upp55ydmm4hxgzAwAAXBpjZgAAgEsjzAAAAJd234+ZSUtL0/nz5+Xv78+DvwAAcBHGGCUkJKhUqVLZPivrvg8z58+ft/s5JAAAwLnOnDmT7VPM7/sw4+/vL+nmzrD3eSQAAODeio+PV0hIiPXveFbu+zBz69RSQEAAYQYAABeTkyEiDAAGAAAujTADAABcGmEGAAC4NMIMAABwaU4NM6+//rosFovNq1q1atbp169f16BBg1S0aFH5+fmpa9euiomJcWLFAAAgv3F6z0zNmjV14cIF62vz5s3WacOHD9fy5cu1ZMkSbdiwQefPn9fjjz/uxGoBAEB+4/RLswsUKKDg4OB07XFxcZo7d64WLVqkFi1aSJLmz5+v6tWra9u2bWrYsOG9LhUAAORDTu+ZOXr0qEqVKqUKFSqoR48eOn36tCRp165dunHjhlq1amWdt1q1aipTpoy2bt2a6fqSk5MVHx9v8wIAAPcvp4aZBg0aaMGCBVq5cqVmzpypkydPqkmTJkpISFB0dLQ8PT1VqFAhm2VKlCih6OjoTNcZGRmpwMBA64tHGQAAcH9z6mmmtm3bWn+uU6eOGjRooLJly+q///2vfHx87Frn6NGjFRERYX1/63bIAADg/uT000y3K1SokKpUqaJjx44pODhYKSkpio2NtZknJiYmwzE2t3h5eVkfXcAjDAAAuP/lqzCTmJio48ePq2TJkgoNDZWHh4fWrFljnX748GGdPn1aYWFhTqwSAADkJ049zTRixAh17NhRZcuW1fnz5zV27Fi5u7ure/fuCgwMVN++fRUREaEiRYooICBAgwcPVlhYGFcyAQAAK6eGmbNnz6p79+66fPmyihcvrocffljbtm1T8eLFJUmTJk2Sm5ubunbtquTkZIWHh2vGjBnOLBkAAOQzFmOMcXYRd1N8fLwCAwMVFxfH+BkAAFxEbv5+O/2mecD9pPWuUc4uwSlWh77l7BIA/IXlqwHAAAAAuUWYAQAALo0wAwAAXBpjZgAAyAHGxOVf9MwAAACXRpgBAAAujTADAABcGmEGAAC4NMIMAABwaYQZAADg0ggzAADApRFmAACASyPMAAAAl0aYAQAALo3HGdxl3P4aAIC7i54ZAADg0ggzAADApRFmAACASyPMAAAAl0aYAQAALo0wAwAAXBphBgAAuDTCDAAAcGmEGQAA4NIIMwAAwKURZgAAgEsjzAAAAJdGmAEAAC6NMAMAAFwaYQYAALg0wgwAAHBphBkAAODSCDMAAMClEWYAAIBLI8wAAACXRpgBAAAujTADAABcGmEGAAC4NMIMAABwaYQZAADg0ggzAADApRFmAACASyPMAAAAl0aYAQAALo0wAwAAXBphBgAAuDTCDAAAcGmEGQAA4NIIMwAAwKURZgAAgEsjzAAAAJdGmAEAAC6NMAMAAFwaYQYAALg0wgwAAHBphBkAAODSCDMAAMClEWYAAIBLI8wAAACXRpgBAAAuLV+FmbfeeksWi0XDhg2ztl2/fl2DBg1S0aJF5efnp65duyomJsZ5RQIAgHwl34SZn376SR988IHq1Klj0z58+HAtX75cS5Ys0YYNG3T+/Hk9/vjjTqoSAADkN/kizCQmJqpHjx6aM2eOChcubG2Pi4vT3LlzFRUVpRYtWig0NFTz58/Xjz/+qG3btjmxYgAAkF/kizAzaNAgtW/fXq1atbJp37Vrl27cuGHTXq1aNZUpU0Zbt27NcF3JycmKj4+3eQEAgPtXAWcXsHjxYu3evVs//fRTumnR0dHy9PRUoUKFbNpLlCih6OjoDNcXGRmpcePG3Y1SAQBAPuTUMHPmzBkNHTpUq1evlre3t0PWOXr0aEVERFjfx8fHKyQkxCHrBoDbtd41ytklOMXq0LecXQJgw6mnmXbt2qWLFy/qb3/7mwoUKKACBQpow4YNmjp1qgoUKKASJUooJSVFsbGxNsvFxMQoODg4w3V6eXkpICDA5gUAAO5fTu2Zadmypfbv32/T1qdPH1WrVk3/+te/FBISIg8PD61Zs0Zdu3aVJB0+fFinT59WWFiYM0oGAAD5jFPDjL+/v2rVqmXT5uvrq6JFi1rb+/btq4iICBUpUkQBAQEaPHiwwsLC1LBhQ2eUDAAA8hmnDwDOzqRJk+Tm5qauXbsqOTlZ4eHhmjFjhrPLAgAA+US+CzPr16+3ee/t7a3p06dr+vTpzikIAADka/niPjMAAAD2IswAAACXRpgBAAAujTADAABcGmEGAAC4NMIMAABwaQ4NM2fOnNFzzz3nyFUCAABkyaFh5sqVK/roo48cuUoAAIAs5eqmed98802W00+cOJGnYgAAAHIrV2GmS5cuslgsMsZkOo/FYslzUQAAADmVq9NMJUuW1LJly5SWlpbha/fu3XerTgAAgAzlKsyEhoZq165dmU7PrtcGAADA0XJ1munll19WUlJSptMrVaqkdevW5bkoAACAnMpVmGnSpEmW0319fdWsWbM8FQQAAJAb3DQPAAC4tFz1zNxu586d+u9//6vTp08rJSXFZtqyZcvyXBgAAEBO2NUzs3jxYjVq1EgHDx7Ul19+qRs3bujXX3/V2rVrFRgY6OgaAQAAMmVXmJkwYYImTZqk5cuXy9PTU1OmTNGhQ4fUrVs3lSlTxtE1AgAAZMquMHP8+HG1b99ekuTp6amkpCRZLBYNHz5cs2fPdmiBAAAAWbErzBQuXFgJCQmSpNKlS+uXX36RJMXGxurq1auOqw4AACAbdg0Abtq0qVavXq3atWvrySef1NChQ7V27VqtXr1aLVu2dHSNAAAAmbIrzEybNk3Xr1+XJL3yyivy8PDQjz/+qK5du+rVV191aIEAAABZsSvMFClSxPqzm5ubRo0a5bCCAAAAciPHYSY+Pl4BAQHWn7NSsGBBFShg9y1sAAAAcizHA4ALFy6sixcvSpIKFSqkwoULZ/ry9vZW9erVeU4TAAC463LcfbJ27Vrr6aXsQkpycrK++uorDRgwQIcOHcpbhQAAAFnIcZi5/QGSOXmYZN26dbVjxw77qgIAAMghuwe2pKam6quvvtLBgwclSTVr1lSnTp3k7u4uSQoKCtLOnTsdUyUAAEAm7Aozx44dU7t27XTu3DlVrVpVkhQZGamQkBB99913qlixokOLBAAAyIxddwAeMmSIKlasqDNnzmj37t3avXu3Tp8+rfLly2vIkCGOrhEAACBTdvXMbNiwQdu2bbO530zRokX11ltvqXHjxg4rDgAAIDt29cx4eXlZn810u8TERHl6eua5KAAAgJyyK8x06NBBL7zwgrZv3y5jjIwx2rZtm1588UV16tTJ0TUCAABkyq4wM3XqVFWsWFFhYWHy9vaWt7e3GjdurEqVKmnKlCmOrhEAACBTdo2ZKVSokL7++msdPXrUelO86tWrq1KlSg4tDgAAIDt5eoBS5cqVVblyZUfVAgAAkGs5DjMRERE5XmlUVJRdxQAAAORWjsPMnj17cjSfxWKxuxgAAIDcynGY4QnYAAAgP7LraiYAAID8gjADAABcGmEGAAC4NMIMAABwaYQZAADg0vJ007wDBw7o9OnTSklJsWnn+UwAAOBesSvMnDhxQo899pj2798vi8UiY4yk/3+PmdTUVMdVCAAAkAW7TjMNHTpU5cuX18WLF1WwYEH9+uuv2rhxo+rVq6f169c7uEQAAIDM2dUzs3XrVq1du1bFihWTm5ub3Nzc9PDDDysyMlJDhgzJ8d2CAQAA8squnpnU1FT5+/tLkooVK6bz589LksqWLavDhw87rjoAAIBs2NUzU6tWLf38888qX768GjRooLfffluenp6aPXu2KlSo4OgaAQAAMmVXmHn11VeVlJQkSXrjjTfUoUMHNWnSREWLFtXnn3/u0AIBAACyYleYCQ8Pt/5cqVIlHTp0SFeuXFHhwoV5ajYAALin8nSfmdsVKVLEUasCAADIsRyHmccffzzHK122bJldxQAAAORWjq9mCgwMtL4CAgK0Zs0a7dy50zp9165dWrNmjQIDA+9KoQAAABnJcc/M/PnzrT//61//Urdu3TRr1iy5u7tLunm59sCBAxUQEOD4KgEAADJh131m5s2bpxEjRliDjCS5u7srIiJC8+bNc1hxAAAA2bErzPz55586dOhQuvZDhw4pLS0tz0UBAADklF1XM/Xp00d9+/bV8ePHVb9+fUnS9u3b9dZbb6lPnz4OLRAAACArdoWZd999V8HBwXrvvfd04cIFSVLJkiX18ssv66WXXnJogQAAAFmxK8y4ublp5MiRGjlypOLj4yWJgb8AAMAp7Bozc7uAgAC7g8zMmTNVp04d6zrCwsK0YsUK6/Tr169r0KBBKlq0qPz8/NS1a1fFxMTktWQAAHAfyXOYyYsHHnhAb731lnbt2qWdO3eqRYsW6ty5s3799VdJ0vDhw7V8+XItWbJEGzZs0Pnz53N18z4AAHD/c9jjDOzRsWNHm/f/+c9/NHPmTG3btk0PPPCA5s6dq0WLFqlFixaSbt7rpnr16tq2bZsaNmzojJIBAEA+49SemdulpqZq8eLFSkpKUlhYmHbt2qUbN26oVatW1nmqVaumMmXKaOvWrZmuJzk5WfHx8TYvAABw/8pzmLl+/Xqelt+/f7/8/Pzk5eWlF198UV9++aVq1Kih6OhoeXp6qlChQjbzlyhRQtHR0ZmuLzIy0ubRCyEhIXmqDwAA5G92hZm0tDS9+eabKl26tPz8/HTixAlJ0muvvaa5c+fmal1Vq1bV3r17tX37dg0YMEC9evXSgQMH7ClLkjR69GjFxcVZX2fOnLF7XQAAIP+zK8yMHz9eCxYs0Ntvvy1PT09re61atfThhx/mal2enp6qVKmSQkNDFRkZqQcffFBTpkxRcHCwUlJSFBsbazN/TEyMgoODM12fl5eX9eqovFxpBQAAXINdYebjjz/W7Nmz1aNHD5vnMz344IMZPuYgN9LS0pScnKzQ0FB5eHhozZo11mmHDx/W6dOnFRYWlqfPAAAA9w+7rmY6d+6cKlWqlK49LS1NN27cyPF6Ro8erbZt26pMmTJKSEjQokWLtH79eq1atUqBgYHq27evIiIiVKRIEQUEBGjw4MEKCwvjSiYAAGBlV5ipUaOGNm3apLJly9q0f/HFF3rooYdyvJ6LFy+qZ8+eunDhggIDA1WnTh2tWrVKrVu3liRNmjRJbm5u6tq1q5KTkxUeHq4ZM2bYUzIAALhP2RVmxowZo169euncuXNKS0vTsmXLdPjwYX388cf69ttvc7ye7AYLe3t7a/r06Zo+fbo9ZQIAgL8Au8bMdO7cWcuXL9cPP/wgX19fjRkzRgcPHtTy5cutvSoAAAD3gt13AG7SpIlWr17tyFoAAAByLd/cARgAAMAedvXMuLm5yWKxZDo9NTXV7oIAAAByw64w8+WXX9q8v3Hjhvbs2aOPPvpI48aNc0hhAAAAOWFXmOncuXO6tieeeEI1a9bU559/rr59++a5MAAAgJxw6JiZhg0b2tyxFwAA4G5zWJi5du2apk6dqtKlSztqlQAAANmy6zRT4cKFbQYAG2OUkJCgggUL6pNPPnFYcQAAANmxK8xMmjTJJsy4ubmpePHiatCggQoXLuyw4gAAALJjV5jp3bu3g8sAAACwj11hZt++fTmet06dOvZ8BAAAQI7YFWbq1q2b5U3zpJvjaCwWCzfQAwAAd5VdVzMtW7ZM5cuX14wZM7Rnzx7t2bNHM2bMUMWKFbV06VKdOHFCJ0+e1IkTJxxdLwAAgA27emYmTJigqVOnql27dta2OnXqKCQkRK+99pp27drlsAIBAACyYlfPzP79+1W+fPl07eXLl9eBAwfyXBQAAEBO2RVmqlevrsjISKWkpFjbUlJSFBkZqerVqzusOAAAgOzYdZpp1qxZ6tixox544AHr1Ur79u2TxWLR8uXLHVogAABAVuwKM/Xr19eJEyf06aef6tChQ5Kkp556Ss8884x8fX0dWiAAAEBW7AozkuTr66sXXnjBkbUAAADkmt1h5vjx45o8ebIOHjwoSapZs6aGDBmiihUrOqw4AACA7Ng1AHjVqlWqUaOGduzYoTp16qhOnTratm2batasqdWrVzu6RgAAgEzZ1TMzatQoDR8+XG+99Va69n/9619q3bq1Q4oDAADIjl09MwcPHlTfvn3TtT/33HPcZwYAANxTdoWZ4sWLa+/evena9+7dq6CgoLzWBAAAkGN2nWbq16+fXnjhBZ04cUKNGjWSJG3ZskUTJ05URESEQwsEAADIil1h5rXXXpO/v7/ee+89jR49WpJUqlQpvf766xoyZIhDCwQAAMiKXWHGYrFo+PDhGj58uBISEiRJ/v7+Di0MAAAgJ+waM9OiRQvFxsZKuhlibgWZ+Ph4tWjRwmHFAQAAZMeuMLN+/Xqbh0zecv36dW3atCnPRQEAAORUrk4z7du3z/rzgQMHFB0dbX2fmpqqlStXqnTp0o6rDgAAIBu5CjN169aVxWKRxWLJ8HSSj4+P3n//fYcVBwAAkJ1chZmTJ0/KGKMKFSpox44dKl68uHWap6engoKC5O7u7vAiAQAAMpOrMFO2bFlJUlpa2l0pBgAAILfsGgD80Ucf6bvvvrO+HzlypAoVKqRGjRrp1KlTDisOAAAgO3aFmQkTJsjHx0eStHXrVk2bNk1vv/22ihUrpuHDhzu0QAAAgKzYddO8M2fOqFKlSpKkr776Sk888YReeOEFNW7cWM2bN3dkfQAAAFmyq2fGz89Ply9fliT973//U+vWrSVJ3t7eunbtmuOqAwAAyIZdPTOtW7fW888/r4ceekhHjhxRu3btJEm//vqrypUr58j6AAAAsmRXz8z06dMVFham33//XUuXLlXRokUlSbt27VL37t0dWiAAAEBW7OqZKVSokKZNm5aufdy4cXkuCAAAIDfs6pmRpE2bNunZZ59Vo0aNdO7cOUnSwoULtXnzZocVBwAAkB27wszSpUsVHh4uHx8f7d69W8nJyZKkuLg4TZgwwaEFAgAAZMWuMDN+/HjNmjVLc+bMkYeHh7W9cePG2r17t8OKAwAAyI5dYebw4cNq2rRpuvbAwEDFxsbmtSYAAIAcsyvMBAcH69ixY+naN2/erAoVKuS5KAAAgJyyK8z069dPQ4cO1fbt22WxWHT+/Hl9+umnGjFihAYMGODoGgEAADJl16XZo0aNUlpamlq2bKmrV6+qadOm8vLy0ogRIzR48GBH1wgAAJApu8KMxWLRK6+8opdfflnHjh1TYmKiatSoIT8/P0fXBwAAkCW7wswtnp6eqlGjhqNqAQAAyDW7b5oHAACQHxBmAACASyPMAAAAl0aYAQAALs3uMLNw4UI1btxYpUqV0qlTpyRJkydP1tdff+2w4gAAALJjV5iZOXOmIiIi1K5dO8XGxio1NVWSVKhQIU2ePNmR9QEAAGTJrjDz/vvva86cOXrllVfk7u5uba9Xr57279/vsOIAAACyY1eYOXnypB566KF07V5eXkpKSspzUQAAADllV5gpX7689u7dm6595cqVql69el5rAgAAyDG77gAcERGhQYMG6fr16zLGaMeOHfrss88UGRmpDz/80NE1AgAAZMqunpnnn39eEydO1KuvvqqrV6/qmWee0cyZMzVlyhQ9/fTTOV5PZGSk/v73v8vf319BQUHq0qWLDh8+bDPP9evXNWjQIBUtWlR+fn7q2rWrYmJi7CkbAADch+y+NLtHjx46evSoEhMTFR0drbNnz6pv3765WseGDRs0aNAgbdu2TatXr9aNGzfUpk0bm3E3w4cP1/Lly7VkyRJt2LBB58+f1+OPP25v2QAA4D6TpwdNSlLBggVVsGBBu5ZduXKlzfsFCxYoKChIu3btUtOmTRUXF6e5c+dq0aJFatGihSRp/vz5ql69urZt26aGDRvmtXwAAODichxmHnroIVkslhzNu3v3bruKiYuLkyQVKVJEkrRr1y7duHFDrVq1ss5TrVo1lSlTRlu3bs0wzCQnJys5Odn6Pj4+3q5aAACAa8hxmOnSpYv15+vXr2vGjBmqUaOGwsLCJEnbtm3Tr7/+qoEDB9pVSFpamoYNG6bGjRurVq1akqTo6Gh5enqqUKFCNvOWKFFC0dHRGa4nMjJS48aNs6sGAADgenIcZsaOHWv9+fnnn9eQIUP05ptvppvnzJkzdhUyaNAg/fLLL9q8ebNdy98yevRoRUREWN/Hx8crJCQkT+sEAAD5l11jZpYsWaKdO3ema3/22WdVr149zZs3L1fr++c//6lvv/1WGzdu1AMPPGBtDw4OVkpKimJjY216Z2JiYhQcHJzhury8vOTl5ZWrzwcAAK7LrquZfHx8tGXLlnTtW7Zskbe3d47XY4zRP//5T3355Zdau3atypcvbzM9NDRUHh4eWrNmjbXt8OHDOn36tPX0FgAA+Guzq2dm2LBhGjBggHbv3q369etLkrZv36558+bptddey/F6Bg0apEWLFunrr7+Wv7+/dRxMYGCgfHx8FBgYqL59+yoiIkJFihRRQECABg8erLCwMK5kAgAAkuwMM6NGjVKFChU0ZcoUffLJJ5Kk6tWra/78+erWrVuO1zNz5kxJUvPmzW3a58+fr969e0uSJk2aJDc3N3Xt2lXJyckKDw/XjBkz7CkbAADch+y+z0y3bt1yFVwyYozJdh5vb29Nnz5d06dPz9NnAQCA+5PddwAGAADIDwgzAADApRFmAACASyPMAAAAl0aYAQAALs3uq5nOnj2rb775RqdPn1ZKSorNtKioqDwXBgAAkBN2hZk1a9aoU6dOqlChgg4dOqRatWrpt99+kzFGf/vb3xxdIwAAQKbsOs00evRojRgxQvv375e3t7eWLl2qM2fOqFmzZnryyScdXSMAAECm7AozBw8eVM+ePSVJBQoU0LVr1+Tn56c33nhDEydOdGiBAAAAWbErzPj6+lrHyZQsWVLHjx+3Trt06ZJjKgMAAMgBu8bMNGzYUJs3b1b16tXVrl07vfTSS9q/f7+WLVvGAyABAMA9ZVeYiYqKUmJioiRp3LhxSkxM1Oeff67KlStzJRMAALin7AozFSpUsP7s6+urWbNmOawgAACA3OCmeQAAwKXluGemSJEiOnLkiIoVK6bChQvLYrFkOu+VK1ccUhwAAEB2chxmJk2aJH9/f0nS5MmT71Y9AAAAuZLjMNOrV68MfwYAAHCmHIeZ+Pj4HK80ICDArmIAAAByK8dhplChQlmOk7ldamqq3QUBAADkRo7DzLp166w///bbbxo1apR69+6tsLAwSdLWrVv10UcfKTIy0vFVAgAAZCLHYaZZs2bWn9944w1FRUWpe/fu1rZOnTqpdu3amj17NmNqAADAPWPXfWa2bt2qevXqpWuvV6+eduzYkeeiAAAAcsquMBMSEqI5c+aka//www8VEhKS56IAAAByyq7HGUyaNEldu3bVihUr1KBBA0nSjh07dPToUS1dutShBQIAAGTFrp6Zdu3a6ciRI+rYsaOuXLmiK1euqGPHjjpy5IjatWvn6BoBAAAyZVfPjHTzVNOECRMcWQsAAECu2f2gyU2bNunZZ59Vo0aNdO7cOUnSwoULtXnzZocVBwAAkJ0chZnt27frxo0b1vdLly5VeHi4fHx8tHv3biUnJ0uS4uLi6K0BAAD3VI7DTJs2bZSQkCBJGj9+vGbNmqU5c+bIw8PDOl/jxo21e/fuu1MpAABABnI0ZmbIkCG6ceOGmjVrpt27d+vw4cNq2rRpuvkCAwMVGxvr6BoBAAAyleMBwC+99JL10QXBwcE6duyYypUrZzPP5s2bVaFCBYcWCAAAkJVcDQBu1KiRJKlfv34aOnSotm/fLovFovPnz+vTTz/ViBEjNGDAgLtSKAAAQEbsujR71KhRSktLU8uWLXX16lU1bdpUXl5eGjFihAYPHuzoGgEAADJlV5ixWCx65ZVX9PLLL+vYsWNKTExUjRo15Ofn5+j6AAAAsmT3TfMkydPTUzVq1HBULQAAALmWqzDz3HPP5Wi+efPm2VUMAABAbuUqzCxYsEBly5bVQw89JGPM3aoJAAAgx3IVZgYMGKDPPvtMJ0+eVJ8+ffTss8+qSJEid6s2AACAbOXq0uzp06frwoULGjlypJYvX66QkBB169ZNq1atoqcGAAA4Ra4fNOnl5aXu3btr9erVOnDggGrWrKmBAweqXLlySkxMvBs1AgAAZMrup2ZLkpubmywWi4wxSk1NdVRNAAAAOZbrMJOcnKzPPvtMrVu3VpUqVbR//35NmzZNp0+f5j4zAADgnsvVAOCBAwdq8eLFCgkJ0XPPPafPPvtMxYoVu1u1AQAAZCtXYWbWrFkqU6aMKlSooA0bNmjDhg0Zzrds2TKHFAcAAJCdXIWZnj17ymKx3K1aAAAAci3XN80DAADIT/J0NRMAAICzEWYAAIBLI8wAAACXRpgBAAAujTADAABcGmEGAAC4NMIMAABwaYQZAADg0ggzAADApRFmAACASyPMAAAAl0aYAQAALo0wAwAAXBphBgAAuDTCDAAAcGlODzMbN25Ux44dVapUKVksFn311Vc2040xGjNmjEqWLCkfHx+1atVKR48edU6xAAAg33F6mElKStKDDz6o6dOnZzj97bff1tSpUzVr1ixt375dvr6+Cg8P1/Xr1+9xpQAAID8q4OwC2rZtq7Zt22Y4zRijyZMn69VXX1Xnzp0lSR9//LFKlCihr776Sk8//fS9LBUAAORDTu+ZycrJkycVHR2tVq1aWdsCAwPVoEEDbd26NcNlkpOTFR8fb/MCAAD3r3wdZqKjoyVJJUqUsGkvUaKEddqdIiMjFRgYaH2FhITc9ToBAIDz5OswY4/Ro0crLi7O+jpz5oyzSwIAAHdRvg4zwcHBkqSYmBib9piYGOu0O3l5eSkgIMDmBQAA7l/5OsyUL19ewcHBWrNmjbUtPj5e27dvV1hYmBMrAwAA+YXTr2ZKTEzUsWPHrO9PnjypvXv3qkiRIipTpoyGDRum8ePHq3Llyipfvrxee+01lSpVSl26dHFe0QAAIN9wepjZuXOnHnnkEev7iIgISVKvXr20YMECjRw5UklJSXrhhRcUGxurhx9+WCtXrpS3t7ezSgYAAPmI08NM8+bNZYzJdLrFYtEbb7yhN9544x5WBQAAXEW+HjMDAACQHcIMAABwaYQZAADg0ggzAADApRFmAACASyPMAAAAl0aYAQAALo0wAwAAXBphBgAAuDTCDAAAcGmEGQAA4NIIMwAAwKURZgAAgEsjzAAAAJdGmAEAAC6NMAMAAFwaYQYAALg0wgwAAHBphBkAAODSCDMAAMClEWYAAIBLI8wAAACXRpgBAAAujTADAABcGmEGAAC4NMIMAABwaYQZAADg0ggzAADApRFmAACASyPMAAAAl0aYAQAALo0wAwAAXBphBgAAuDTCDAAAcGmEGQAA4NIIMwAAwKURZgAAgEsjzAAAAJdGmAEAAC6NMAMAAFwaYQYAALg0wgwAAHBphBkAAODSCDMAAMClEWYAAIBLI8wAAACXRpgBAAAujTADAABcGmEGAAC4NMIMAABwaYQZAADg0ggzAADApRFmAACASyPMAAAAl0aYAQAALo0wAwAAXBphBgAAuDTCDAAAcGmEGQAA4NIIMwAAwKURZgAAgEtzmTAzffp0lStXTt7e3mrQoIF27Njh7JIAAEA+4BJh5vPPP1dERITGjh2r3bt368EHH1R4eLguXrzo7NIAAICTuUSYiYqKUr9+/dSnTx/VqFFDs2bNUsGCBTVv3jxnlwYAAJysgLMLyE5KSop27dql0aNHW9vc3NzUqlUrbd26Nd38ycnJSk5Otr6Pi4uTJMXHx9/9YjPwZ2Jy9jPdh5y1v52N4/3XwvH+a+F4O+dzjTHZzpvvw8ylS5eUmpqqEiVK2LSXKFFChw4dSjd/ZGSkxo0bl649JCTkrtWI9AI12dkl4B7ieP+1cLz/Wpx9vBMSEhQYGJjlPPk+zOTW6NGjFRERYX2flpamK1euqGjRorJYLE6s7N6Kj49XSEiIzpw5o4CAAGeXg7uM4/3XwvH+a/mrHm9jjBISElSqVKls5833YaZYsWJyd3dXTEyMTXtMTIyCg4PTze/l5SUvLy+btkKFCt3NEvO1gICAv9Qv/18dx/uvheP91/JXPN7Z9cjcku8HAHt6eio0NFRr1qyxtqWlpWnNmjUKCwtzYmUAACA/yPc9M5IUERGhXr16qV69eqpfv74mT56spKQk9enTx9mlAQAAJ3OJMPPUU0/p999/15gxYxQdHa26detq5cqV6QYF4//z8vLS2LFj051yw/2J4/3XwvH+a+F4Z89icnLNEwAAQD6V78fMAAAAZIUwAwAAXBphBgAAuDTCDAAAcGmEmb+IX375xdklAABwVxBm7mMJCQmaPXu26tevrwcffNDZ5QCww9q1a1WjRo0MH/YXFxenmjVratOmTU6oDMg/CDP3oY0bN6pXr14qWbKk3n33XbVo0ULbtm1zdlm4Cy5fvmz9+cyZMxozZoxefvll/rjdRyZPnqx+/fpleBv7wMBA9e/fX1FRUU6oDHdTWlqa5s2bpw4dOqhWrVqqXbu2OnXqpI8//jhHT5H+q+E+M/eJ6OhoLViwQHPnzlV8fLy6deumWbNm6eeff1aNGjWcXR4cbP/+/erYsaPOnDmjypUra/HixXr00UeVlJQkNzc3JSUl6YsvvlCXLl2cXSryqGzZslq5cqWqV6+e4fRDhw6pTZs2On369D2uDHeLMUYdO3bU999/rwcffFDVqlWTMUYHDx7U/v371alTJ3311VfOLjNfoWfmPtCxY0dVrVpV+/bt0+TJk3X+/Hm9//77zi4Ld9HIkSNVu3Ztbdy4Uc2bN1eHDh3Uvn17xcXF6Y8//lD//v311ltvObtMOEBMTIw8PDwynV6gQAH9/vvv97Ai3G0LFizQxo0btWbNGu3Zs0efffaZFi9erJ9//lk//PCD1q5dq48//tjZZeYr9MzcBwoUKKAhQ4ZowIABqly5srXdw8ODnpn7VLFixbR27VrVqVNHiYmJCggI0E8//aTQ0FBJN7+tN2zYULGxsc4tFHlWsWJFvffee5n2si1btkwjRozQiRMn7m1huGvatGmjFi1aaNSoURlOnzBhgjZs2KBVq1bd48ryL3pm7gObN29WQkKCQkND1aBBA02bNk2XLl1ydlm4i65cuaLg4GBJkp+fn3x9fVW4cGHr9MKFCyshIcFZ5cGB2rVrp9dee03Xr19PN+3atWsaO3asOnTo4ITKcLfs27dPjz76aKbT27Ztq59//vkeVpT/0TNzH0lKStLnn3+uefPmaceOHUpNTVVUVJSee+45+fv7O7s8OJCbm5tiYmJUvHhxSZK/v7/27dun8uXLS7p5aqJUqVJKTU11ZplwgJiYGP3tb3+Tu7u7/vnPf6pq1aqSbva+TZ8+Xampqdq9ezcP3r2PeHp66tSpUypZsmSG08+fP6/y5csrOTn5HleWfxFm7lOHDx/W3LlztXDhQsXGxqp169b65ptvnF0WHMTNzU1t27a1PkV3+fLlatGihXx9fSVJycnJWrlyJWHmPnHq1CkNGDBAq1atsl7JYrFYFB4erunTp1tDLO4P7u7uio6Otn5ZuRNfVtIjzNznUlNTtXz5cs2bN48wcx/p06dPjuabP3/+Xa4E99Iff/yhY8eOyRijypUr25xaxP3jzi8rd+LLSnqEGQAA8hG+rOQeYQYAALg0rmYCAAAujTADAABcGmEGAAC4NMIMAGRg/fr1slgs3EUZcAGEGQB5Fh0drcGDB6tChQry8vJSSEiIOnbsqDVr1uRo+QULFqhQoUJ3t8hcatSokS5cuKDAwEBnlwIgGwWcXQAA1/bbb7+pcePGKlSokN555x3Vrl1bN27c0KpVqzRo0CAdOnTI2SXm2o0bN+Tp6Wl9ZASA/I2eGQB5MnDgQFksFu3YsUNdu3ZVlSpVVLNmTUVERGjbtm2SpKioKNWuXVu+vr4KCQnRwIEDlZiYKOnm6Zw+ffooLi5OFotFFotFr7/+uqSbNwcbMWKESpcuLV9fXzVo0EDr16+3+fw5c+YoJCREBQsW1GOPPaaoqKh0vTwzZ85UxYoV5enpqapVq2rhwoU20y0Wi2bOnKlOnTrJ19dX//nPf9KdZrp8+bK6d++u0qVLq2DBgqpdu7Y+++wzh+9PAHYwAGCny5cvG4vFYiZMmJDlfJMmTTJr1641J0+eNGvWrDFVq1Y1AwYMMMYYk5ycbCZPnmwCAgLMhQsXzIULF0xCQoIxxpjnn3/eNGrUyGzcuNEcO3bMvPPOO8bLy8scOXLEGGPM5s2bjZubm3nnnXfM4cOHzfTp002RIkVMYGCg9bOXLVtmPDw8zPTp083hw4fNe++9Z9zd3c3atWut80gyQUFBZt68eeb48ePm1KlTZt26dUaS+eOPP4wxxpw9e9a88847Zs+ePeb48eNm6tSpxt3d3Wzfvt2BexSAPQgzAOy2fft2I8ksW7YsV8stWbLEFC1a1Pp+/vz5NgHEGGNOnTpl3N3dzblz52zaW7ZsaUaPHm2MMeapp54y7du3t5neo0cPm3U1atTI9OvXz2aeJ5980rRr1876XpIZNmyYzTx3hpmMtG/f3rz00kuZTgdwb3CaCYDdTA5vIP7DDz+oZcuWKl26tPz9/fWPf/xDly9f1tWrVzNdZv/+/UpNTVWVKlXk5+dnfW3YsEHHjx+XdPOBqvXr17dZ7s73Bw8eVOPGjW3aGjdurIMHD9q01atXL8ttSE1N1ZtvvqnatWurSJEi8vPz06pVq3T69Olstx/A3cUAYAB2q1y5siwWS5aDfH/77Td16NBBAwYM0H/+8x8VKVJEmzdvVt++fZWSkqKCBQtmuFxiYqLc3d21a9cuubu720zz8/Nz6HZIsj5xPDPvvPOOpkyZosmTJ1vH/wwbNkwpKSkOrwVA7tAzA8BuRYoUUXh4uKZPn66kpKR002NjY7Vr1y6lpaXpvffeU8OGDVWlShWdP3/eZj5PT890TwB+6KGHlJqaqosXL6pSpUo2r1tXGVWtWlU//fSTzXJ3vq9evbq2bNli07ZlyxbVqFEjV9u6ZcsWde7cWc8++6wefPBBVahQQUeOHMnVOgDcHYQZAHkyffp0paamqn79+lq6dKmOHj2qgwcPaurUqQoLC1OlSpV048YNvf/++zpx4oQWLlyoWbNm2ayjXLlySkxM1Jo1a3Tp0iVdvXpVVapUUY8ePdSzZ08tW7ZMJ0+e1I4dOxQZGanvvvtOkjR48GB9//33ioqK0tGjR/XBBx9oxYoVslgs1nW//PLLWrBggWbOnKmjR48qKipKy5Yt04gRI3K1nZUrV9bq1av1448/6uDBg+rfv79iYmLyvgMB5J2zB+0AcH3nz583gwYNMmXLljWenp6mdOnSplOnTmbdunXGGGOioqJMyZIljY+PjwkPDzcff/xxusG1L774oilatKiRZMaOHWuMMSYlJcWMGTPGlCtXznh4eJiSJUuaxx57zOzbt8+63OzZs03p0qWNj4+P6dKlixk/frwJDg62qW/GjBmmQoUKxsPDw1SpUsV8/PHHNtMlmS+//NKm7c4BwJcvXzadO3c2fn5+JigoyLz66qumZ8+epnPnzo7YhQDywGJMDkfwAYAL6Nevnw4dOqRNmzY5uxQA9wgDgAG4tHfffVetW7eWr6+vVqxYoY8++kgzZsxwdlkA7iF6ZgC4tG7dumn9+vVKSEhQhQoVNHjwYL344ovOLgvAPUSYAQAALo2rmQAAgEsjzAAAAJdGmAEAAC6NMAMAAFwaYQYAALg0wgwAAHBphBkAAODSCDMAAMClEWYAAIBL+3/a8TE0RHNyjwAAAABJRU5ErkJggg==",
       "text/plain": [
        "<Figure size 640x480 with 1 Axes>"
       ]
@@ -1585,11 +1653,6 @@
     "plt.ylabel('Média de estoque da loja1')\n",
     "plt.show()"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": []
   }
  ],
  "metadata": {
@@ -1608,7 +1671,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.12.5"
   }
  },
  "nbformat": 4,