Assume that numpy has been import with the alias np . Consider the 5 by 7 array of maximum temperatures of five cities (Sydney, Melbourne, Perth, Adelaide, Brisbane) in Australia: [[23.8, 24.9, 27. [30.5, 22.4, 28. [29.9, 28.4, 33. (3740 2370y 1505 " ~N NN - [np.nan , 32.5, 38. Use the array to create a dataframe 28.6, 27.4, 28.4, 30.8], 24,7, 24.e, 22.1, 19.4], 32.4, np.nan, 27.3, 32.8], 25.6, 26.8, 21.9, 20.5], 3, 28.9, 29.7, 30.2, 31.0]] df with the city name as the index (use the city names in the given order) and the days March 8, March 9, ... , March 14 as the columns. Your code has to handle missing values by not including them in any of the computations. Note that you have to include the non-missing values, for example, you cannot erase a column or row that contains a missing value. In separate code cells create from df : 1. A pandas series with the number of days with maximum temperature greater than 25 degrees for each city. 2. A pandas series with the average of the temperatures of the last three days for each city. 3. A pandas series with the maximum of the recorded temperatures for each day, across the cities. 4. Add a new row to df with the minimum of the recorded temperatures for each day, across the cities. Label the index for this row as 'min across cities'