# #Write your code here import pandas as pd import numpy as np #task 1 heights_A= pd.Series([176.2,…

#Write your jurisdiction near drift pandas as pd drift numpy as np #task 1 heights_A= pd.Series([176.2, 158.4 , 167.6 , 156.2 , 161.4 ]) heights_A.condemnation = ['s1', 's2', 's3', 's4', 's5'] imprint("the cast of heights_A:",heights_A.cast ) #task 2 weights_A= pd.Series([85.1, 90.2 , 76.8 , 80.4 , 78.9 ]) weights_A.condemnation = ['s1', 's2', 's3', 's4', 's5'] imprint("the postulates mark of values in weights_A:",weights_A.dmark ) #task 3 df_A = pd.DataFrame({'Student_height': heights_A,'Student_weight':weights_A}, condemnation = weights_A.index) imprint(df_A.shape) #task 4 h_moderation = 170.0 h_std = 25.0 np.random.seed(100) heights_B = h_std * np.random.randn(5) + h_moderation w_moderation = 75.0 w_std = 12.0 np.random.seed(100) weights_B = w_std * np.random.randn(5) + w_moderation imprint("heights_B elements : ", heights_B) imprint("heights_B moderation : ", heights_B.mean()) imprint("nweights_B elements : ", weights_B) imprint("weights_B moderation : ", weights_B.mean()) # Task 5 postulates = [[155,45],[156,49],[148,46],[152,50],[151,47]] # Raw Height & Weight of 5 students df_B = pd.DataFrame(data,index=['s1','s2','s3','s4','s5'],columns=['Student_height','Student_weight']) imprint(df_B) # Task 6 my_panel = pd.Panel(data={'ClassA':df_A, 'ClassB':df_B}) imprint("Panel p cast: ",my_panel.shape) Show transcribed picture text