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• Create a series named heights A with values 176.2, 158.4, 167.6, 156.2, and 161.4. These values represent the heights of 5 students of class A.
• Label each student as s1, s2, s3, s4, and s5.
. Print the second element of series heights_A.
Note: Use index number or the value corresponding to the second element inside square brackets.
Task 2
• Print the middle three elements of Series heights A.
Note: Use slice objects corresponding to index numbers or index values.
Task 3
• Create another series named weights_A with values 85.1, 90.2, 76.8, 80.4, and 78.9. These values represent the weights of 5 students of class A.
• Label each student as s1, s2, s3, s4, and s5.
• Create a dataframe named df_A, which contains the height and weight of five students namely s1, s2, s3, s4 and s5.
• Label the columns as Student_height and Student_weight, respectively.
• Select the column df_A referring to student heights, and store it in the variable height. Note: Specify the required column name inside. square brackets.
• Print the type of object of the variable height.
Task 4
• Select the rows corresponding to students s1, s2 of df_A, and capture them in another dataframe df_s1s2. Note: Use either .loc or .iloc methods.
• Print the dataframe df_s1s2.
Task 5
• Select the rows corresponding to students s1, s2 and s5 of df_A in the order s2, s5, s1, and capture them in another dataframe df_s2s5s1.
Note: Make use of either .loc or .iloc methods.
• Print the dataframe df_s2s5s1
Task 6
• Select those rows of df A whose index values end with 1 or 4, and capture them in another dataframe df_s1s4. Note: Pass a boolean function which checks if an index string ends with 1 or 4 to .loc or .iloc methods.
• Print the dataframe df_s1s4.
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