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/exercicio_casa_milena.ipynb b/exercicios/para-casa/exercicio_casa_milena.ipynb
new file mode 100644
index 0000000..0a0b9a5
--- /dev/null
+++ b/exercicios/para-casa/exercicio_casa_milena.ipynb
@@ -0,0 +1,455 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Gerando Dados Ficticios"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Conjunto de dados gerado e salvo como 'dados_vendas.csv'.\n"
+     ]
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "# Configurando a seed para reprodutibilidade\n",
+    "np.random.seed(42)\n",
+    "\n",
+    "# Gerando dados falsos\n",
+    "categorias = ['Eletrônicos', 'Roupas', 'Alimentos', 'Livros', 'Brinquedos']\n",
+    "datas = pd.date_range(start='2023-01-01', end='2023-12-31', freq='D')\n",
+    "num_registros = len(datas)\n",
+    "\n",
+    "# Criando DataFrame\n",
+    "data = {\n",
+    "    'Data': np.random.choice(datas, num_registros),\n",
+    "    'Categoria': np.random.choice(categorias, num_registros),\n",
+    "    'Quantidade_Vendida': np.random.randint(1, 100, num_registros),\n",
+    "    'Preco_Unitario': np.round(np.random.uniform(10, 500, num_registros), 2)\n",
+    "}\n",
+    "df = pd.DataFrame(data)\n",
+    "df['Valor_Total'] = df['Quantidade_Vendida'] * df['Preco_Unitario']\n",
+    "\n",
+    "# Salvando o DataFrame em um arquivo CSV\n",
+    "df.to_csv('dados_vendas.csv', index=False)\n",
+    "\n",
+    "print(\"Conjunto de dados gerado e salvo como 'dados_vendas.csv'.\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Consulta e Filtragem de Dados"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#Carregamento do csv\n",
+    "df = pd.read_csv('dados_vendas.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "O DataFrame tem (365, 5) linhas e colunas respectivamente\n"
+     ]
+    }
+   ],
+   "source": [
+    "share = df.shape\n",
+    "print(f'O DataFrame tem', share, 'linhas e colunas respectivamente')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "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": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Os tipos de dados de cada coluna\n",
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "         Data  Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+      "0  2023-04-13     Roupas                  74           60.43      4471.82\n",
+      "1  2023-12-15  Alimentos                  83          272.88     22649.04\n",
+      "2  2023-09-28     Roupas                  17          195.62      3325.54\n",
+      "3  2023-04-17     Roupas                  85          233.93     19884.05\n",
+      "4  2023-03-13     Roupas                  78          305.94     23863.32\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Exibindo as 5 primeiras linhas do DataFrame\n",
+    "print(df.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "array(['Roupas', 'Alimentos', 'Eletrônicos', 'Brinquedos', 'Livros'],\n",
+       "      dtype=object)"
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Os valores unicos da coluna categoria\n",
+    "df['Categoria'].unique()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Data    Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+      "5    2023-07-08  Eletrônicos                  73          256.12     18696.76\n",
+      "6    2023-01-21  Eletrônicos                   1          274.53       274.53\n",
+      "7    2023-04-13  Eletrônicos                  51          248.32     12664.32\n",
+      "14   2023-06-01  Eletrônicos                  34          303.52     10319.68\n",
+      "18   2023-09-15  Eletrônicos                  39          121.40      4734.60\n",
+      "..          ...          ...                 ...             ...          ...\n",
+      "347  2023-09-10  Eletrônicos                  33          431.46     14238.18\n",
+      "349  2023-09-13  Eletrônicos                  27          201.67      5445.09\n",
+      "351  2023-05-08  Eletrônicos                  29          243.57      7063.53\n",
+      "353  2023-10-08  Eletrônicos                  57          427.57     24371.49\n",
+      "356  2023-02-27  Eletrônicos                  55          466.48     25656.40\n",
+      "\n",
+      "[73 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Filtro para mostrar apenas a categoria eletrônicos\n",
+    "eletronicos = df[df['Categoria'] == 'Eletrônicos']\n",
+    "print(eletronicos)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "np.float64(12291.256301369864)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Média do valor total\n",
+    "eletronicos['Valor_Total'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "           Data   Categoria  Quantidade_Vendida  Preco_Unitario  Valor_Total\n",
+      "0    2023-04-13      Roupas                  74           60.43      4471.82\n",
+      "1    2023-12-15   Alimentos                  83          272.88     22649.04\n",
+      "2    2023-09-28      Roupas                  17          195.62      3325.54\n",
+      "3    2023-04-17      Roupas                  85          233.93     19884.05\n",
+      "4    2023-03-13      Roupas                  78          305.94     23863.32\n",
+      "..          ...         ...                 ...             ...          ...\n",
+      "358  2023-12-26  Brinquedos                  68          147.88     10055.84\n",
+      "359  2023-06-23   Alimentos                  86          133.80     11506.80\n",
+      "361  2023-04-24  Brinquedos                  10          350.90      3509.00\n",
+      "363  2023-12-08  Brinquedos                  74          270.98     20052.52\n",
+      "364  2023-05-31      Roupas                  97          481.44     46699.68\n",
+      "\n",
+      "[330 rows x 5 columns]\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Vendas acima de R$1000\n",
+    "vendas_acima_1000 = df[df['Valor_Total'] > 1000]\n",
+    "print(vendas_acima_1000)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(330, 5)"
+      ]
+     },
+     "execution_count": 22,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "#Quantidade de vendas acima de R$1000 que ocorreram\n",
+    "vendas_acima_1000.shape"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Agrupamento e Agregações"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 25,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Categoria  Valor_Total\n",
+      "0    Alimentos    742457.74\n",
+      "1   Brinquedos   1187964.04\n",
+      "2  Eletrônicos    897261.71\n",
+      "3       Livros    963685.46\n",
+      "4       Roupas    991215.29\n"
+     ]
+    }
+   ],
+   "source": [
+    "#Total de vendas por categoria\n",
+    "total_por_categoria = df.groupby('Categoria')['Valor_Total'].sum().reset_index()\n",
+    "print(total_por_categoria)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 29,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A categoria com maior numero de vendas é 1187964.04\n"
+     ]
+    }
+   ],
+   "source": [
+    "maior_valor_vendas = total_por_categoria['Valor_Total'].max()\n",
+    "#maior_categoria_vendas = maior_valor_vendas \n",
+    "print(f'A categoria com maior numero de vendas é', maior_valor_vendas)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 30,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "     Categoria  Quantidade_Vendida\n",
+      "0    Alimentos           55.618182\n",
+      "1   Brinquedos           47.247191\n",
+      "2  Eletrônicos           46.780822\n",
+      "3       Livros           50.917808\n",
+      "4       Roupas           47.200000\n"
+     ]
+    }
+   ],
+   "source": [
+    "#A quantidade média vendida por categoria\n",
+    "media_quantidade_categoria = df.groupby('Categoria')['Quantidade_Vendida'].mean().reset_index()\n",
+    "print(media_quantidade_categoria)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 32,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "A categoria com maior quantidade média vendida é 55.61818181818182\n"
+     ]
+    }
+   ],
+   "source": [
+    "#A categoria teve a maior quantidade média vendida\n",
+    "categoria_maior_vendas = media_quantidade_categoria['Quantidade_Vendida'].max()\n",
+    "print(f'A categoria com maior quantidade média vendida é', categoria_maior_vendas)"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "### Visualização de Dados"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "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='#725')\n",
+    "plt.xlabel('Categoria')\n",
+    "plt.ylabel('Valor Total de Vendas (R$)')\n",
+    "plt.title('Total de Vendas por Categoria')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Com base no gráfico, os brinquedos foi a categoria com o maior valor de vendas"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 34,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "vendas_diarias = eletronicos.groupby('Data')['Valor_Total'].sum().reset_index()\n",
+    "\n",
+    "plt.figure(figsize=(10, 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": [
+    "Existe algum padrão ou tendência observável nas vendas diárias de eletrônicos?\n",
+    "\n",
+    "Com base no gráfico, não é possível observar um padrão entre a data e o valor total de vendas."
+   ]
+  }
+ ],
+ "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-sala/dados_ficticios_1.csv b/exercicios/para-sala/dados_ficticios_1.csv
new file mode 100644
index 0000000..bead322
--- /dev/null
+++ b/exercicios/para-sala/dados_ficticios_1.csv
@@ -0,0 +1,1001 @@
+Categoria,Valor_1,Valor_2,Valor_3,Data
+C,53.41755975777159,126.03482578797596,1,2023-01-01
+D,68.76170839215887,131.23022393453707,2,2023-01-02
+A,59.5042383818605,100.64008298131533,6,2023-01-03
+C,44.23096344337597,84.93164259307633,5,2023-01-04
+C,41.01585328651642,109.19944285878233,9,2023-01-05
+D,54.919191715065054,86.44569260495788,5,2023-01-06
+A,36.797667929793576,140.26774495053246,2,2023-01-07
+A,68.31458765854353,102.73070662165475,4,2023-01-08
+C,61.79440120721287,92.69356897375782,9,2023-01-09
+B,45.30824347895295,103.69360611729817,1,2023-01-10
+C,32.868654709091224,73.05747420940607,1,2023-01-11
+C,63.538723741654124,80.56771922978697,7,2023-01-12
+C,48.85460154747382,124.00827815888852,3,2023-01-13
+C,62.378163119734616,86.86211442057208,2,2023-01-14
+D,34.05572341205633,79.0617803463612,9,2023-01-15
+A,44.00624977046227,110.73305505054572,3,2023-01-16
+D,50.05243699718183,123.71408309325994,2,2023-01-17
+D,50.46980593764742,114.37906621330134,8,2023-01-18
+D,45.49934528520756,119.92095371611633,6,2023-01-19
+C,56.228499323474985,84.86409822861079,6,2023-01-20
+B,39.32379570617405,71.56378666516312,7,2023-01-21
+A,48.576205149787064,130.02667303910974,7,2023-01-22
+B,51.20295631711899,93.54640322315728,6,2023-01-23
+D,55.14438834058749,94.98333967089906,4,2023-01-24
+D,57.1161487808889,126.56388283698115,9,2023-01-25
+B,38.75357908162131,111.1246001880132,6,2023-01-26
+B,34.65885829264378,109.11775544163216,4,2023-01-27
+B,62.77676821898509,143.30004689802166,3,2023-01-28
+D,53.323140119795916,87.12963539239715,4,2023-01-29
+D,42.51513463443446,118.55680256162475,3,2023-01-30
+A,65.51151975522524,101.14026249794169,2,2023-01-31
+A,51.156746342928585,105.37184557726513,5,2023-02-01
+D,61.792971840638266,130.56936852825064,2,2023-02-02
+B,50.67518481410109,110.15671512579247,6,2023-02-03
+B,70.60747924881987,110.76592158099265,3,2023-02-04
+A,67.55340842443204,121.45014674619604,6,2023-02-05
+D,47.51035851520926,92.70094540914229,9,2023-02-06
+A,59.71570950954356,83.21580665321987,1,2023-02-07
+A,56.45375949585147,79.10381611957934,6,2023-02-08
+C,63.68631557532349,60.67286821247996,9,2023-02-09
+C,40.35076539419896,141.12414259880643,4,2023-02-10
+C,56.86051459998439,77.93583268556634,2,2023-02-11
+B,60.58424486849588,95.57492752828456,4,2023-02-12
+D,32.41260513576886,94.46373400222159,1,2023-02-13
+D,38.16741487334225,106.14813395618202,3,2023-02-14
+D,29.607678222398995,116.31474425078594,9,2023-02-15
+D,47.305931655554424,117.2094697671949,5,2023-02-16
+C,57.17542255795962,88.33845122862124,6,2023-02-17
+B,65.02357052096028,96.65756572427178,3,2023-02-18
+B,50.74094780419775,105.65159900976599,5,2023-02-19
+C,66.28615545571292,95.02617747632905,8,2023-02-20
+B,36.198985417851084,132.14691152039984,6,2023-02-21
+C,32.96617560644845,109.81949903276502,4,2023-02-22
+D,49.44452301103381,114.69755572099635,2,2023-02-23
+C,53.840654489393074,113.25762537334825,9,2023-02-24
+D,49.67305251905907,123.4694771497065,1,2023-02-25
+D,29.325578999601234,103.62043117407414,1,2023-02-26
+A,49.10879960487212,74.06336104088307,4,2023-02-27
+C,36.95530499495147,107.99375903505283,7,2023-02-28
+A,56.69672548830039,86.97286212416341,1,2023-03-01
+C,53.66598246096848,89.42766636500487,9,2023-03-02
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diff --git a/exercicios/para-sala/ex_extra.ipynb b/exercicios/para-sala/ex_extra.ipynb
index 600614b..0861796 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": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -34,7 +34,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -151,7 +151,7 @@
        "4                   8  "
       ]
      },
-     "execution_count": 6,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -628,7 +628,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,
diff --git a/exercicios/para-sala/exercicio.ipynb b/exercicios/para-sala/exercicio.ipynb
index 224a55d..e793e6b 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": 1,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -13,7 +13,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 14,
+   "execution_count": 2,
    "metadata": {},
    "outputs": [
     {
@@ -143,7 +143,7 @@
        "9         B  45.308243  103.693606        1  2023-01-10"
       ]
      },
-     "execution_count": 14,
+     "execution_count": 2,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -154,7 +154,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 15,
+   "execution_count": 3,
    "metadata": {},
    "outputs": [
     {
@@ -176,7 +176,7 @@
        "[1000 rows x 5 columns]>"
       ]
      },
-     "execution_count": 15,
+     "execution_count": 3,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -187,7 +187,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 16,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -281,7 +281,7 @@
        "max      80.788808   163.862151     9.000000"
       ]
      },
-     "execution_count": 16,
+     "execution_count": 4,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -292,7 +292,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 17,
+   "execution_count": 6,
    "metadata": {},
    "outputs": [
     {
@@ -512,7 +512,7 @@
        "461         A  74.553001   82.676503        7  2024-04-06"
       ]
      },
-     "execution_count": 17,
+     "execution_count": 6,
      "metadata": {},
      "output_type": "execute_result"
     }
@@ -1635,7 +1635,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.12"
+   "version": "3.10.0"
   }
  },
  "nbformat": 4,
diff --git a/exercicios/para-sala/exercicio_milena.ipynb b/exercicios/para-sala/exercicio_milena.ipynb
new file mode 100644
index 0000000..12e0c55
--- /dev/null
+++ b/exercicios/para-sala/exercicio_milena.ipynb
@@ -0,0 +1,1130 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "\n",
+    "df = pd.read_csv('dados_ficticios_1.csv')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "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_1</th>\n",
+       "      <th>Valor_2</th>\n",
+       "      <th>Valor_3</th>\n",
+       "      <th>Data</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>C</td>\n",
+       "      <td>53.417560</td>\n",
+       "      <td>126.034826</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2023-01-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>D</td>\n",
+       "      <td>68.761708</td>\n",
+       "      <td>131.230224</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-01-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>A</td>\n",
+       "      <td>59.504238</td>\n",
+       "      <td>100.640083</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-01-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>C</td>\n",
+       "      <td>44.230963</td>\n",
+       "      <td>84.931643</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-01-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>C</td>\n",
+       "      <td>41.015853</td>\n",
+       "      <td>109.199443</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-01-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>5</th>\n",
+       "      <td>D</td>\n",
+       "      <td>54.919192</td>\n",
+       "      <td>86.445693</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-01-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>6</th>\n",
+       "      <td>A</td>\n",
+       "      <td>36.797668</td>\n",
+       "      <td>140.267745</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-01-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>A</td>\n",
+       "      <td>68.314588</td>\n",
+       "      <td>102.730707</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2023-01-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>8</th>\n",
+       "      <td>C</td>\n",
+       "      <td>61.794401</td>\n",
+       "      <td>92.693569</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-01-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>9</th>\n",
+       "      <td>B</td>\n",
+       "      <td>45.308243</td>\n",
+       "      <td>103.693606</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2023-01-10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "  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",
+       "5         D  54.919192   86.445693        5  2023-01-06\n",
+       "6         A  36.797668  140.267745        2  2023-01-07\n",
+       "7         A  68.314588  102.730707        4  2023-01-08\n",
+       "8         C  61.794401   92.693569        9  2023-01-09\n",
+       "9         B  45.308243  103.693606        1  2023-01-10"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "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": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.info"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "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>Valor_1</th>\n",
+       "      <th>Valor_2</th>\n",
+       "      <th>Valor_3</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>count</th>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "      <td>1000.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>mean</th>\n",
+       "      <td>50.401664</td>\n",
+       "      <td>100.759179</td>\n",
+       "      <td>5.014000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>std</th>\n",
+       "      <td>10.012925</td>\n",
+       "      <td>19.673028</td>\n",
+       "      <td>2.588533</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>min</th>\n",
+       "      <td>21.037446</td>\n",
+       "      <td>39.609757</td>\n",
+       "      <td>1.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>25%</th>\n",
+       "      <td>43.749954</td>\n",
+       "      <td>87.668313</td>\n",
+       "      <td>3.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>50%</th>\n",
+       "      <td>50.366335</td>\n",
+       "      <td>100.416802</td>\n",
+       "      <td>5.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>75%</th>\n",
+       "      <td>56.815984</td>\n",
+       "      <td>113.760305</td>\n",
+       "      <td>7.000000</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>max</th>\n",
+       "      <td>80.788808</td>\n",
+       "      <td>163.862151</td>\n",
+       "      <td>9.000000</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Valor_1      Valor_2      Valor_3\n",
+       "count  1000.000000  1000.000000  1000.000000\n",
+       "mean     50.401664   100.759179     5.014000\n",
+       "std      10.012925    19.673028     2.588533\n",
+       "min      21.037446    39.609757     1.000000\n",
+       "25%      43.749954    87.668313     3.000000\n",
+       "50%      50.366335   100.416802     5.000000\n",
+       "75%      56.815984   113.760305     7.000000\n",
+       "max      80.788808   163.862151     9.000000"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.describe()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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_1</th>\n",
+       "      <th>Valor_2</th>\n",
+       "      <th>Valor_3</th>\n",
+       "      <th>Data</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>7</th>\n",
+       "      <td>A</td>\n",
+       "      <td>68.314588</td>\n",
+       "      <td>102.730707</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2023-01-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>30</th>\n",
+       "      <td>A</td>\n",
+       "      <td>65.511520</td>\n",
+       "      <td>101.140262</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-01-31</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>35</th>\n",
+       "      <td>A</td>\n",
+       "      <td>67.553408</td>\n",
+       "      <td>121.450147</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-02-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>74</th>\n",
+       "      <td>A</td>\n",
+       "      <td>69.647251</td>\n",
+       "      <td>97.230880</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-03-16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>87</th>\n",
+       "      <td>A</td>\n",
+       "      <td>63.668743</td>\n",
+       "      <td>100.293764</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2023-03-29</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>152</th>\n",
+       "      <td>A</td>\n",
+       "      <td>60.062928</td>\n",
+       "      <td>76.601663</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-06-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>178</th>\n",
+       "      <td>A</td>\n",
+       "      <td>62.012139</td>\n",
+       "      <td>114.736878</td>\n",
+       "      <td>1</td>\n",
+       "      <td>2023-06-28</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>185</th>\n",
+       "      <td>A</td>\n",
+       "      <td>66.764373</td>\n",
+       "      <td>74.914212</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2023-07-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>217</th>\n",
+       "      <td>A</td>\n",
+       "      <td>63.556379</td>\n",
+       "      <td>84.016160</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-08-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>228</th>\n",
+       "      <td>A</td>\n",
+       "      <td>74.457520</td>\n",
+       "      <td>96.390402</td>\n",
+       "      <td>3</td>\n",
+       "      <td>2023-08-17</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>243</th>\n",
+       "      <td>A</td>\n",
+       "      <td>60.941915</td>\n",
+       "      <td>117.055474</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2023-09-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>305</th>\n",
+       "      <td>A</td>\n",
+       "      <td>61.593298</td>\n",
+       "      <td>90.501927</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-11-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>321</th>\n",
+       "      <td>A</td>\n",
+       "      <td>67.547942</td>\n",
+       "      <td>137.997639</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-11-18</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>352</th>\n",
+       "      <td>A</td>\n",
+       "      <td>66.654744</td>\n",
+       "      <td>119.006152</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-12-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>383</th>\n",
+       "      <td>A</td>\n",
+       "      <td>68.124486</td>\n",
+       "      <td>111.029708</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2024-01-19</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>399</th>\n",
+       "      <td>A</td>\n",
+       "      <td>60.807807</td>\n",
+       "      <td>70.848970</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2024-02-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>426</th>\n",
+       "      <td>A</td>\n",
+       "      <td>64.469779</td>\n",
+       "      <td>132.244413</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2024-03-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>428</th>\n",
+       "      <td>A</td>\n",
+       "      <td>60.318445</td>\n",
+       "      <td>94.629387</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2024-03-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>440</th>\n",
+       "      <td>A</td>\n",
+       "      <td>65.795721</td>\n",
+       "      <td>78.192018</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2024-03-16</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>461</th>\n",
+       "      <td>A</td>\n",
+       "      <td>74.553001</td>\n",
+       "      <td>82.676503</td>\n",
+       "      <td>7</td>\n",
+       "      <td>2024-04-06</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "    Categoria    Valor_1     Valor_2  Valor_3        Data\n",
+       "7           A  68.314588  102.730707        4  2023-01-08\n",
+       "30          A  65.511520  101.140262        2  2023-01-31\n",
+       "35          A  67.553408  121.450147        6  2023-02-05\n",
+       "74          A  69.647251   97.230880        5  2023-03-16\n",
+       "87          A  63.668743  100.293764        1  2023-03-29\n",
+       "152         A  60.062928   76.601663        5  2023-06-02\n",
+       "178         A  62.012139  114.736878        1  2023-06-28\n",
+       "185         A  66.764373   74.914212        7  2023-07-05\n",
+       "217         A  63.556379   84.016160        9  2023-08-06\n",
+       "228         A  74.457520   96.390402        3  2023-08-17\n",
+       "243         A  60.941915  117.055474        7  2023-09-01\n",
+       "305         A  61.593298   90.501927        9  2023-11-02\n",
+       "321         A  67.547942  137.997639        6  2023-11-18\n",
+       "352         A  66.654744  119.006152        6  2023-12-19\n",
+       "383         A  68.124486  111.029708        2  2024-01-19\n",
+       "399         A  60.807807   70.848970        6  2024-02-04\n",
+       "426         A  64.469779  132.244413        2  2024-03-02\n",
+       "428         A  60.318445   94.629387        6  2024-03-04\n",
+       "440         A  65.795721   78.192018        5  2024-03-16\n",
+       "461         A  74.553001   82.676503        7  2024-04-06"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "filter = df[(df['Categoria'] == 'A') & (df['Valor_1'] > 60)]\n",
+    "filter.head(20)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "<class 'pandas.core.frame.DataFrame'>\n",
+      "RangeIndex: 1000 entries, 0 to 999\n",
+      "Data columns (total 5 columns):\n",
+      " #   Column     Non-Null Count  Dtype  \n",
+      "---  ------     --------------  -----  \n",
+      " 0   Categoria  1000 non-null   object \n",
+      " 1   Valor_1    1000 non-null   float64\n",
+      " 2   Valor_2    1000 non-null   float64\n",
+      " 3   Valor_3    1000 non-null   int64  \n",
+      " 4   Data       1000 non-null   object \n",
+      "dtypes: float64(2), int64(1), object(2)\n",
+      "memory usage: 39.2+ KB\n"
+     ]
+    }
+   ],
+   "source": [
+    "df.info()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "#df_data = pd.to_numeric(df['Data'])\n",
+    "df['Data'] = pd.to_datetime(df['Data'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "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>Estoque da loja1</th>\n",
+       "      <th>Estoque da loja2</th>\n",
+       "      <th>Vendas</th>\n",
+       "      <th>Data</th>\n",
+       "    </tr>\n",
+       "  </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",
+       "      <td>119.106104</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>B</td>\n",
+       "      <td>50.485216</td>\n",
+       "      <td>101.768138</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-05-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>C</td>\n",
+       "      <td>41.690499</td>\n",
+       "      <td>129.550602</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-05-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>A</td>\n",
+       "      <td>52.704568</td>\n",
+       "      <td>77.166218</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2023-05-05</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>125</th>\n",
+       "      <td>D</td>\n",
+       "      <td>49.497619</td>\n",
+       "      <td>96.126811</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-05-06</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>126</th>\n",
+       "      <td>B</td>\n",
+       "      <td>47.610520</td>\n",
+       "      <td>85.663554</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-05-07</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>127</th>\n",
+       "      <td>A</td>\n",
+       "      <td>40.924363</td>\n",
+       "      <td>62.669268</td>\n",
+       "      <td>6</td>\n",
+       "      <td>2023-05-08</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>128</th>\n",
+       "      <td>D</td>\n",
+       "      <td>44.232287</td>\n",
+       "      <td>98.346386</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2023-05-09</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>129</th>\n",
+       "      <td>D</td>\n",
+       "      <td>57.553912</td>\n",
+       "      <td>97.565050</td>\n",
+       "      <td>4</td>\n",
+       "      <td>2023-05-10</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",
+       "124         A         52.704568         77.166218       8 2023-05-05\n",
+       "125         D         49.497619         96.126811       5 2023-05-06\n",
+       "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"
+      ]
+     },
+     "execution_count": 56,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df = df[(df['Data'] >= '2023-05-01') & (df['Data'] <= '2023-05-31')]\n",
+    "df.head(10)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.rename(columns={'estoque da loja1':'Estoque da loja1', 'estoque da loja2': 'Estoque da loja2', 'vendas': 'Vendas'})"
+   ]
+  },
+  {
+   "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>Categoria</th>\n",
+       "      <th>Estoque da loja1</th>\n",
+       "      <th>Estoque da loja2</th>\n",
+       "      <th>Vendas</th>\n",
+       "      <th>Data</th>\n",
+       "    </tr>\n",
+       "  </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",
+       "      <td>119.106104</td>\n",
+       "      <td>5</td>\n",
+       "      <td>2023-05-02</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>122</th>\n",
+       "      <td>B</td>\n",
+       "      <td>50.485216</td>\n",
+       "      <td>101.768138</td>\n",
+       "      <td>9</td>\n",
+       "      <td>2023-05-03</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>123</th>\n",
+       "      <td>C</td>\n",
+       "      <td>41.690499</td>\n",
+       "      <td>129.550602</td>\n",
+       "      <td>2</td>\n",
+       "      <td>2023-05-04</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>124</th>\n",
+       "      <td>A</td>\n",
+       "      <td>52.704568</td>\n",
+       "      <td>77.166218</td>\n",
+       "      <td>8</td>\n",
+       "      <td>2023-05-05</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",
+       "124         A         52.704568         77.166218       8 2023-05-05"
+      ]
+     },
+     "execution_count": 57,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 79,
+   "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>Estoque da loja1</th>\n",
+       "      <th>Estoque da loja2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Categoria</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>47.012307</td>\n",
+       "      <td>101.616545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>49.047868</td>\n",
+       "      <td>93.715846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>39.910509</td>\n",
+       "      <td>103.563094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>48.450760</td>\n",
+       "      <td>101.268104</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Estoque da loja1  Estoque da loja2\n",
+       "Categoria                                    \n",
+       "A                 47.012307        101.616545\n",
+       "B                 49.047868         93.715846\n",
+       "C                 39.910509        103.563094\n",
+       "D                 48.450760        101.268104"
+      ]
+     },
+     "execution_count": 79,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agrupado_media = df.groupby('Categoria')[['Estoque da loja1', 'Estoque da loja2']].mean()\n",
+    "\n",
+    "agrupado_media"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 59,
+   "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>Estoque da loja1</th>\n",
+       "      <th>Estoque da loja2</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Categoria</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>47.012307</td>\n",
+       "      <td>101.616545</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>49.047868</td>\n",
+       "      <td>93.715846</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>39.910509</td>\n",
+       "      <td>103.563094</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>48.450760</td>\n",
+       "      <td>101.268104</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Estoque da loja1  Estoque da loja2\n",
+       "Categoria                                    \n",
+       "A                 47.012307        101.616545\n",
+       "B                 49.047868         93.715846\n",
+       "C                 39.910509        103.563094\n",
+       "D                 48.450760        101.268104"
+      ]
+     },
+     "execution_count": 59,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "agrupado_max = df.groupby('Categoria')[['Estoque da loja1', 'Estoque da loja2']].mean()\n",
+    "\n",
+    "agrupado_max"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 60,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df_agrupamento = df.groupby('Categoria').agg({\n",
+    "    'Vendas': 'sum',\n",
+    "    'Categoria': 'count'\n",
+    "}).rename(columns={'Categoria': 'Contagem'})\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 61,
+   "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>Vendas</th>\n",
+       "      <th>Contagem</th>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>Categoria</th>\n",
+       "      <th></th>\n",
+       "      <th></th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>A</th>\n",
+       "      <td>58</td>\n",
+       "      <td>12</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>B</th>\n",
+       "      <td>11</td>\n",
+       "      <td>2</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>C</th>\n",
+       "      <td>38</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>D</th>\n",
+       "      <td>50</td>\n",
+       "      <td>10</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "           Vendas  Contagem\n",
+       "Categoria                  \n",
+       "A              58        12\n",
+       "B              11         2\n",
+       "C              38         7\n",
+       "D              50        10"
+      ]
+     },
+     "execution_count": 61,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df_agrupamento"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 62,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 63,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "media_valor_1 = df.groupby('Categoria')['Estoque da loja1'].mean()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 78,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "media_valor_1.plot(kind='bar', color='#636', title='Média de Estoque da loja1 por Categoria')\n",
+    "plt.xlabel('Categoria')\n",
+    "plt.ylabel('Média de Estoque da loja1')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/exercicios/para-sala/extra_meu.ipynb b/exercicios/para-sala/extra_meu.ipynb
new file mode 100644
index 0000000..f28638b
--- /dev/null
+++ b/exercicios/para-sala/extra_meu.ipynb
@@ -0,0 +1,636 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Dados fictícios para exemplo\n",
+    "dados = {\n",
+    "'ID_Consulta': [1, 2, 3, 4, 5],\n",
+    "'Nome_Paciente': ['João Silva', 'Maria Oliveira', 'Carlos Souza', 'Ana Paula', 'Marcos Lima'],\n",
+    "'Data_Consulta': ['2024-08-01', '2024-08-02', '2024-08-03', '2024-08-04', '2024-08-05'],\n",
+    "'Sintomas': ['Febre, Tosse', 'Dor de Cabeça', 'Náusea, Vômito', 'Dor de Garganta', 'Cansaço, Falta de Ar'],\n",
+    "'Diagnostico': ['Gripe', 'Enxaqueca', 'Gastrite', 'Amigdalite', 'Bronquite'],\n",
+    "'Prescricao': ['Antitérmico, Repouso', 'Analgésico', 'Antiácido, Dieta', 'Antibiótico', 'Broncodilatador'],\n",
+    "'Duracao_Consulta_Minutos': [30, 45, 25, 40, 35], # Duração em minutos\n",
+    "'Gravidade_Sintomas': [4, 6, 5, 7, 8] # Gravidade dos sintomas em uma escala de 1 a 10\n",
+    "}\n",
+    "df = pd.DataFrame(dados)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "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>ID_Consulta</th>\n",
+       "      <th>Nome_Paciente</th>\n",
+       "      <th>Data_Consulta</th>\n",
+       "      <th>Sintomas</th>\n",
+       "      <th>Diagnostico</th>\n",
+       "      <th>Prescricao</th>\n",
+       "      <th>Duracao_Consulta_Minutos</th>\n",
+       "      <th>Gravidade_Sintomas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>João Silva</td>\n",
+       "      <td>2024-08-01</td>\n",
+       "      <td>Febre, Tosse</td>\n",
+       "      <td>Gripe</td>\n",
+       "      <td>Antitérmico, Repouso</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Maria Oliveira</td>\n",
+       "      <td>2024-08-02</td>\n",
+       "      <td>Dor de Cabeça</td>\n",
+       "      <td>Enxaqueca</td>\n",
+       "      <td>Analgésico</td>\n",
+       "      <td>45</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Carlos Souza</td>\n",
+       "      <td>2024-08-03</td>\n",
+       "      <td>Náusea, Vômito</td>\n",
+       "      <td>Gastrite</td>\n",
+       "      <td>Antiácido, Dieta</td>\n",
+       "      <td>25</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Ana Paula</td>\n",
+       "      <td>2024-08-04</td>\n",
+       "      <td>Dor de Garganta</td>\n",
+       "      <td>Amigdalite</td>\n",
+       "      <td>Antibiótico</td>\n",
+       "      <td>40</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Marcos Lima</td>\n",
+       "      <td>2024-08-05</td>\n",
+       "      <td>Cansaço, Falta de Ar</td>\n",
+       "      <td>Bronquite</td>\n",
+       "      <td>Broncodilatador</td>\n",
+       "      <td>35</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID_Consulta   Nome_Paciente Data_Consulta              Sintomas  \\\n",
+       "0            1      João Silva    2024-08-01          Febre, Tosse   \n",
+       "1            2  Maria Oliveira    2024-08-02         Dor de Cabeça   \n",
+       "2            3    Carlos Souza    2024-08-03        Náusea, Vômito   \n",
+       "3            4       Ana Paula    2024-08-04       Dor de Garganta   \n",
+       "4            5     Marcos Lima    2024-08-05  Cansaço, Falta de Ar   \n",
+       "\n",
+       "  Diagnostico            Prescricao  Duracao_Consulta_Minutos  \\\n",
+       "0       Gripe  Antitérmico, Repouso                        30   \n",
+       "1   Enxaqueca            Analgésico                        45   \n",
+       "2    Gastrite      Antiácido, Dieta                        25   \n",
+       "3  Amigdalite           Antibiótico                        40   \n",
+       "4   Bronquite       Broncodilatador                        35   \n",
+       "\n",
+       "   Gravidade_Sintomas  \n",
+       "0                   4  \n",
+       "1                   6  \n",
+       "2                   5  \n",
+       "3                   7  \n",
+       "4                   8  "
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Limpeza**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID_Consulta                 0\n",
+       "Nome_Paciente               0\n",
+       "Data_Consulta               0\n",
+       "Sintomas                    0\n",
+       "Diagnostico                 0\n",
+       "Prescricao                  0\n",
+       "Duracao_Consulta_Minutos    0\n",
+       "Gravidade_Sintomas          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 8,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df = df.drop_duplicates()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID_Consulta                  int64\n",
+       "Nome_Paciente               object\n",
+       "Data_Consulta               object\n",
+       "Sintomas                    object\n",
+       "Diagnostico                 object\n",
+       "Prescricao                  object\n",
+       "Duracao_Consulta_Minutos     int64\n",
+       "Gravidade_Sintomas           int64\n",
+       "dtype: object"
+      ]
+     },
+     "execution_count": 10,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.dtypes"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Data_Consulta'] = pd.to_datetime(df['Data_Consulta'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "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>ID_Consulta</th>\n",
+       "      <th>Nome_Paciente</th>\n",
+       "      <th>Data_Consulta</th>\n",
+       "      <th>Sintomas</th>\n",
+       "      <th>Diagnostico</th>\n",
+       "      <th>Prescricao</th>\n",
+       "      <th>Duracao_Consulta_Minutos</th>\n",
+       "      <th>Gravidade_Sintomas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>João Silva</td>\n",
+       "      <td>2024-08-01</td>\n",
+       "      <td>Febre, Tosse</td>\n",
+       "      <td>Gripe</td>\n",
+       "      <td>Antitérmico, Repouso</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Maria Oliveira</td>\n",
+       "      <td>2024-08-02</td>\n",
+       "      <td>Dor de Cabeça</td>\n",
+       "      <td>Enxaqueca</td>\n",
+       "      <td>Analgésico</td>\n",
+       "      <td>45</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Carlos Souza</td>\n",
+       "      <td>2024-08-03</td>\n",
+       "      <td>Náusea, Vômito</td>\n",
+       "      <td>Gastrite</td>\n",
+       "      <td>Antiácido, Dieta</td>\n",
+       "      <td>25</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Ana Paula</td>\n",
+       "      <td>2024-08-04</td>\n",
+       "      <td>Dor de Garganta</td>\n",
+       "      <td>Amigdalite</td>\n",
+       "      <td>Antibiótico</td>\n",
+       "      <td>40</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Marcos Lima</td>\n",
+       "      <td>2024-08-05</td>\n",
+       "      <td>Cansaço, Falta de Ar</td>\n",
+       "      <td>Bronquite</td>\n",
+       "      <td>Broncodilatador</td>\n",
+       "      <td>35</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID_Consulta   Nome_Paciente Data_Consulta              Sintomas  \\\n",
+       "0            1      João Silva    2024-08-01          Febre, Tosse   \n",
+       "1            2  Maria Oliveira    2024-08-02         Dor de Cabeça   \n",
+       "2            3    Carlos Souza    2024-08-03        Náusea, Vômito   \n",
+       "3            4       Ana Paula    2024-08-04       Dor de Garganta   \n",
+       "4            5     Marcos Lima    2024-08-05  Cansaço, Falta de Ar   \n",
+       "\n",
+       "  Diagnostico            Prescricao  Duracao_Consulta_Minutos  \\\n",
+       "0       Gripe  Antitérmico, Repouso                        30   \n",
+       "1   Enxaqueca            Analgésico                        45   \n",
+       "2    Gastrite      Antiácido, Dieta                        25   \n",
+       "3  Amigdalite           Antibiótico                        40   \n",
+       "4   Bronquite       Broncodilatador                        35   \n",
+       "\n",
+       "   Gravidade_Sintomas  \n",
+       "0                   4  \n",
+       "1                   6  \n",
+       "2                   5  \n",
+       "3                   7  \n",
+       "4                   8  "
+      ]
+     },
+     "execution_count": 12,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "df['Duracao_Consulta_Minutos'] = pd.to_numeric(df['Duracao_Consulta_Minutos'], errors='coerce')\n",
+    "df['Gravidade_Sintomas'] = pd.to_numeric(df['Gravidade_Sintomas'], errors='coerce')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "ID_Consulta                 0\n",
+       "Nome_Paciente               0\n",
+       "Data_Consulta               0\n",
+       "Sintomas                    0\n",
+       "Diagnostico                 0\n",
+       "Prescricao                  0\n",
+       "Duracao_Consulta_Minutos    0\n",
+       "Gravidade_Sintomas          0\n",
+       "dtype: int64"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.isnull().sum()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "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>ID_Consulta</th>\n",
+       "      <th>Nome_Paciente</th>\n",
+       "      <th>Data_Consulta</th>\n",
+       "      <th>Sintomas</th>\n",
+       "      <th>Diagnostico</th>\n",
+       "      <th>Prescricao</th>\n",
+       "      <th>Duracao_Consulta_Minutos</th>\n",
+       "      <th>Gravidade_Sintomas</th>\n",
+       "    </tr>\n",
+       "  </thead>\n",
+       "  <tbody>\n",
+       "    <tr>\n",
+       "      <th>0</th>\n",
+       "      <td>1</td>\n",
+       "      <td>João Silva</td>\n",
+       "      <td>2024-08-01</td>\n",
+       "      <td>Febre, Tosse</td>\n",
+       "      <td>Gripe</td>\n",
+       "      <td>Antitérmico, Repouso</td>\n",
+       "      <td>30</td>\n",
+       "      <td>4</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>1</th>\n",
+       "      <td>2</td>\n",
+       "      <td>Maria Oliveira</td>\n",
+       "      <td>2024-08-02</td>\n",
+       "      <td>Dor de Cabeça</td>\n",
+       "      <td>Enxaqueca</td>\n",
+       "      <td>Analgésico</td>\n",
+       "      <td>45</td>\n",
+       "      <td>6</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>2</th>\n",
+       "      <td>3</td>\n",
+       "      <td>Carlos Souza</td>\n",
+       "      <td>2024-08-03</td>\n",
+       "      <td>Náusea, Vômito</td>\n",
+       "      <td>Gastrite</td>\n",
+       "      <td>Antiácido, Dieta</td>\n",
+       "      <td>25</td>\n",
+       "      <td>5</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>3</th>\n",
+       "      <td>4</td>\n",
+       "      <td>Ana Paula</td>\n",
+       "      <td>2024-08-04</td>\n",
+       "      <td>Dor de Garganta</td>\n",
+       "      <td>Amigdalite</td>\n",
+       "      <td>Antibiótico</td>\n",
+       "      <td>40</td>\n",
+       "      <td>7</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
+       "      <th>4</th>\n",
+       "      <td>5</td>\n",
+       "      <td>Marcos Lima</td>\n",
+       "      <td>2024-08-05</td>\n",
+       "      <td>Cansaço, Falta de Ar</td>\n",
+       "      <td>Bronquite</td>\n",
+       "      <td>Broncodilatador</td>\n",
+       "      <td>35</td>\n",
+       "      <td>8</td>\n",
+       "    </tr>\n",
+       "  </tbody>\n",
+       "</table>\n",
+       "</div>"
+      ],
+      "text/plain": [
+       "   ID_Consulta   Nome_Paciente Data_Consulta              Sintomas  \\\n",
+       "0            1      João Silva    2024-08-01          Febre, Tosse   \n",
+       "1            2  Maria Oliveira    2024-08-02         Dor de Cabeça   \n",
+       "2            3    Carlos Souza    2024-08-03        Náusea, Vômito   \n",
+       "3            4       Ana Paula    2024-08-04       Dor de Garganta   \n",
+       "4            5     Marcos Lima    2024-08-05  Cansaço, Falta de Ar   \n",
+       "\n",
+       "  Diagnostico            Prescricao  Duracao_Consulta_Minutos  \\\n",
+       "0       Gripe  Antitérmico, Repouso                        30   \n",
+       "1   Enxaqueca            Analgésico                        45   \n",
+       "2    Gastrite      Antiácido, Dieta                        25   \n",
+       "3  Amigdalite           Antibiótico                        40   \n",
+       "4   Bronquite       Broncodilatador                        35   \n",
+       "\n",
+       "   Gravidade_Sintomas  \n",
+       "0                   4  \n",
+       "1                   6  \n",
+       "2                   5  \n",
+       "3                   7  \n",
+       "4                   8  "
+      ]
+     },
+     "execution_count": 15,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "df.head()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Gráfico de Dispersão**"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x600 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.figure(figsize=(10, 6))\n",
+    "plt.scatter(df['Duracao_Consulta_Minutos'], df['Gravidade_Sintomas'], color='blue')\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.grid(True)\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "**Análise**"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "Correlação fraca\n",
+    "Não relação das duas variaveis\n",
+    "A partir deste gráfico não conseguimos perceber correlação entre a gravidade dos sintomas e duração da consulta."
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "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": {
+  "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": "3.10.0"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}