Consider the given NumPy arrays a and b. What will be the value of c after the following code is executed? <pre>import numpy as np a = np.arange(8) b = a[4:6] b[:] = 40 c = a[4] + a[6] </pre>

Since array slices are passed by reference, the values at indices 4 and 5 in a will also be modified after the values in the array slice b are changed to 40. Hence, after the code is run, a = array([ 0, 1, 2, 3, 40, 40, 6, 7]). As a[4] = 40 and a[6] = 6, c = 40 + 6 = 46.

Given the string s as shown below, which of the following expressions will be True? <pre>import re s = 'ABCAC'</pre>

re.match('A', s) return an re.match object. Since it will find a match for 'A' in 's', thus converting this object to bool will return True.

<img src="http://reaskly.com/wp-content/uploads/2022/11/1UYo3cAaQE6GKN3AGqBOkQ_027a84728fb5ab629d73e9f5f2002f29_quiz4_q4.png" alt="Reaskly"> Consider the following 4 expressions regarding the above pandas Series df. All of them have the same value except one expression. Can you identify which one it is?

df.index[0] = 'd', which is different from the other three which are equal to 4

<img src="http://reaskly.com/wp-content/uploads/2022/11/gJELy0bBT3yRC8tGwV98NA_ac02d3b2b91e0a4549362e42cd51fab5_quiz4_q5.png" alt="Reaskly"> Consider the two pandas Series objects shown above, representing the no. of items of different yogurt flavors that were sold in a day from two different stores, s1 and s2. Which of the following statements is True regarding the Series s3 defined below? <pre>s3 = s1.add(s2)</pre>

This is True because the values of both are the same. Notice that the comparison type is greater than equal to and not strictly greater than.

Consider the Series object S defined below. Which of the following is an incorrect way to slice S such that we obtain all data points corresponding to the indices 'b', 'c', and 'd'? <pre> S = pd.Series(np.arange(5), index=['a', 'b', 'c', 'd', 'e'])</pre>

This will incorrectly extract all the data for rows, 'b', 'c', 'd', and 'e'. We only need the data for 'b', 'c' and 'd'. Remember, slicing with labels behaves differently than normal Python slicing in that the endpoint is inclusive.

<img src="http://reaskly.com/wp-content/uploads/2022/11/FWfr2vfETs2n69r3xL7N2Q_3d13deb1f33d93982766879e681caa77_Screen-Shot-2020-07-06-at-5.23.40-PM.png" alt="Reaskly"> Consider the DataFrame df shown above with indexes 'R1', 'R2', 'R3', and 'R4'. In the following code, a new DataFrame df_new is created using df. What will be the value of df_new[1] after the below code is executed? <pre> f = lambda x: x.max() + x.min() df_new = df.apply(f) </pre>

The lambda function, when applied to df, will output a Series object with the sum of maximum and minimum values of each column 'a', 'b', and 'c'. df1[1] refers to the second row of df1, which has index 'b'. The maximum and minimum values of column 'b' are 82 and 6 respectively. Hence the correct answer is 82 + 6 = 88.

<img src="http://reaskly.com/wp-content/uploads/2022/11/CwfwjVchSa6H8I1XIemupA_d2120b4372a67fcb6324698ff469f66b_Screen-Shot-2020-07-23-at-1.33.13-PM.png" alt="Reaskly"> Consider the DataFrame named new_df shown above. Which of the following expressions will output the result (showing the head of a DataFrame) below? <img src="http://reaskly.com/wp-content/uploads/2022/11/b8B1QYGzRE-AdUGBs0RPPQ_700ad50f96b06fd269334a82f90d2737_wk3_05_q4_2.jpg" alt="Reaskly">

When we unstack the new_df first time, we will get a Series with triple row index, when we unstack this Series again, the inner most row, which is actually the previous country, will be moved to the column index again.