Page 337 - Informatics_Practices_Fliipbook_Class12
P. 337

26.  Write programs to display the following patterns:












             27.   Write a program to accept a list of integers, reverse the order of elements in the list, and display the reversed
                 list.  Do  NOT  use  any  built-in  function  or  apply    slicing  in  your  program.  For  example,  for  the  given  list
                 [1, 4, 2, 6], the program should output the list [6, 2, 4, 1]
             28.   Consider the table Athlete given below:
                 Table: Athlete














                 Write SQL statements to perform the following tasks
                   Display the records of category Sub Junior

                   Change the category of Piyush to Junior
                   Display the records in alphabetical order of names.
                   Delete the records of students who are not participating in Event2.
             29.   Consider  the DataFrame  weatherDF created given below and  write the code snippet for the following
                 queries:

                 weatherData = {
                     'Date': ['2023-01-01', '2023-01-02', '2023-01-03', '2023-01-04', '2023-01-05'],
                     'Temperature': [15.0, 14.5, 16.2, 13.8, 15.5],
                     'Humidity': [50, 52, 48, 55, 51],
                     'Wind_Speed': [10, 12, 9, 11, 13]
                 }
                 weatherDF = pd.DataFrame(weatherData)
                 (i)   Retrieve the columns Date and Temperature.
                 (ii)  Retrieve rows with Temperature greater than 15.
                 (iii)  Retrieve rows where Humidity is greater than 50.
                 (iv)  Use the iloc function to select the rows from index 1 to index 3.
                 (v)  Set row labels to ['A', 'B', 'C', 'D', 'E']. Also, display the updated DataFrame.
                 (vi)  Retrieve the summary statistics of the DataFrame.
                 (vii)  Determine the maximum wind speed.
                 (viii)  Retrieve the number of occurrences for each unique value in the Temperature column.

                 (ix)   Add a new column Heat Index to the DataFrame, calculated as a function of temperature and humidity.


                                                                                                      Practical  323
   332   333   334   335   336   337   338   339   340   341   342