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Ans. (a) print(df1.head(4))
(b) print(df1['Name'])
(c) df1['Subject'] = "IT"
(d) print(df1[df1['Class'] == 12][['TeacherName', 'Experience']])
(e) df1.rename(columns={'Experience': 'Experience(in years)'}, inplace=True)
37. Write suitable SQL queries for the following: [5]
(a) To calculate the average marks from marks column (attribute) in Practicals table.
(b) To display the first eight characters from Item_Name column (attribute) in Items table.
(c) To display Fabric_Type column (attribute) from Fabrics table after converting all characters to
lowercase.
(d) To find the maximum value in stock_qty column (attribute) of Stocks table.
(e) To count the total number of unique city in Projects table.
Or
(a) Round off the value 178.251 to two decimal points.
(b) Calculate the remainder when 154 is divided by 7.
(c) Display the string ‘CaptivatingPerformance’ in uppercase.
(d) Display the first 5 characters from the string ‘Festive Vibes’.
(e) Display the data from Recharge column (attribute) in Mobiles table after removing the leading
spaces.
Ans. (a) Select Avg(marks) from Practicals;
(b) Select Left(Item_Name, 8) from Items;
(c) Select Lcase(Fabric_Type) from Fabrics;
(d) Select Max(stock_qty) from Stocks;
(e) Select Count(Distinct city) from Projects;
Or
(a) Select Round(178.251,2);
(b) Select Mod(154,7);
(c) Select Upper('CaptivatingPerformanceypted');
(d) Select Left('CaptivatingPerformnce');
(e) Select Ltrim(Recharge) from Mobiles;

M.23 Informatics Practices with Python–XII
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