PYTHON & SQL

Python Set Operations: •Set can be performed mathematical operation such as union, intersection, difference, and symmetric difference. Python provides the facility to carry out these operations with operators or methods. We describe these operations as follows. Union of two Sets: •The union of two sets is calculated by using the pipe (|) operator. The union of the two sets contains all the items that are present in both the sets. •Consider the following example to calculate the union of two sets. Example 1: using union | operator Days1 = {"Monday","Tuesday","Wednesday","Thursday", "Sunday"} Days2 = {"Friday","Saturday","Sunday"} print(Days1|Days2) or print(Days1.union(Days2)) #printing the union of the sets Output: {'Friday', 'Sunday', 'Saturday', 'Tuesday', 'Wednesday', 'Monday', 'Thursday'} Intersection of two sets: •The intersection of two sets can be performed by the and & operator or the intersection() function. The intersection of the two sets is given as the set of the elements that common in both sets. •Consider the following example. Example 1: Using & operator Days1 = {"Monday","Tuesday", "Wednesday", "Thursday"} Days2 = {"Monday","Tuesday","Sunday", "Friday"} print(Days1&Days2) or print(set1.intersection(set2)) #prints the intersection of the two sets Output: {‘Monday’,’Tuesday’} Set Difference •Difference of the set B from set A(A - B) is a set of elements that are only in A but not in B. • Similarly, B - A is a set of elements in B but not in A. •Difference is performed using - operator. Same can be accomplished using the difference() method. Example: A = {1, 2, 3, 4, 5} B = {4, 5, 6, 7, 8} print(A - B) # Output: {1, 2, 3} Set Symmetric Difference •Symmetric Difference of A and B is a set of elements in A and B but not in both (excluding the intersection). •Symmetric difference is performed using ^ operator. Same can be accomplished using the method symmetric_difference(). Example: A = {1, 2, 3, 4, 5} B = {4, 5, 6, 7, 8} # use ^ operator print(A ^ B) Output: {1, 2, 3, 6, 7, 8}

•A loop statement allows us to execute a statement or group of statements multiple times. •Python has two primitive loop commands: •while loops •for loops For Loops •A for loop is used for iterating over a sequence (that is either a list, a tuple, a dictionary, a set, or a string). Syntax: for var in iterable: # statements •Here the iterable is a collection of objects like list, tuple. Example: •#Program to print the multiplication table of the given number . list = [1,2,3,4,5,6,7,8,9,10] n = int(input(“Enter the number :”)) for i in list: c = n*i print(c, end=" ") Output: Enter the number : 5 5 10 15 20 25 30 35 40 45 50 While Loops •In Python, While Loops is used to execute a block of statements repeatedly until a given condition is satisfied. •And when the condition becomes false, the line immediately after the loop in the program is executed. Syntax: while expression: statement(s) Example: #Program to print 1 to 10 using while loop i=1 #The while loop will iterate until condition becomes false. while(i<=10): print(i,end=" ") i=i+1 Output: 1 2 3 4 5 6 7 8 9 10 #Print i as long as i is less than 6: i = 1 while (i < 6): print(i,end=" ") i += 1 Output: 1 2 3 4 5 The break Statement •With the break statement we can stop the loop even if the while condition is true: •Example: #Exit the loop when i is 3: i = 1 while i < 6: print(i,end=" ") if i == 3: break i += 1 Output: 1 2 3 The continue Statement •With the continue statement we can stop the current iteration, and continue with the next: Example: #Continue to the next iteration if i is 3: i = 0 while i < 6: i += 1 if i == 3: continue print(i,end=" ") Output: 1 2 4 5 6

Functions in Python are blocks of reusable code that perform a specific task. They are important in data science for code modularity and reusability. example: def calculate_mean(numbers): total = sum(numbers) count = len(numbers) mean = total / count return mean data = [10, 20, 30, 40, 50] result = calculate_mean(data) print(f"The mean of the data is {result}") In this example, the calculate_mean function calculates the mean of a list of numbers, making the code more organized and maintainable.

NumPy performs array oriented computing. It efficiently implements the multidimensional arrays. It performs scientific computations. It is capable of performing Fourier Transform and reshaping the data stored in multidimensional arrays. NumPy provides the in built functions for linear algebra and random number generation. Create an array with datatype integer and shape (3,4) import numpy as np a=np.array(([3,2,1,4],[1,2,3,4],[4,5,3,2]),int) Find the dimension, size of the array a.ndim a.size Reshape the array to (4,3) np.reshape(a,(4,3)) Find the minimum value along column np.min(a,axis=0) Find the maximum value along row np.max(a,axis=1) Print the first row a[0] Print the first two values of second row print(a[1,:2]) For the given array A= [[12, 13, 14, 15],[17, 18, 19, 20],[22, 23, 24, 25]],write the code which provides the following output A= np.array(([11,12, 13, 14, 15],[16,17, 18, 19, 20],[21,22, 23, 24, 25])) A 20 print(A[1,-1:]) array([21, 22, 23, 24, 25]) A[2] array([[16, 17, 18, 19, 20],[21, 22, 23, 24, 25]]) A[1:3]

To find out the similarity among words, we use word similarity. We evaluate the similarity with the help of a number that lies between 0 and 1. We use the spacy library to implement the technique of word similarity. import spacy nlp = spacy.load('en_core_web_md') print("Enter the words") input_words = input() tokens = nlp(input_words) for i in tokens: print(i.text, i.has_vector, i.vector_norm, i.is_oov) token_1, token_2 = tokens[0], tokens[1] print("Similarity between words:", token_1.similarity(token_2)) Output: hot True 5.6898586 False cold True6.5396233 False Similarity: 0.597265

The database is a collection of inter-related data which is used to retrieve, insert and delete the data efficiently. It is also used to organize the data in the form of a table, schema, views, and reports, etc. Example: The college Database organizes the data about the admin, staff, students and faculty etc. Using the database, you can easily retrieve, insert, and delete the information.

---DDL: Create a new table to store book information CREATE TABLE Books ( BookID INT PRIMARY KEY, Title VARCHAR(100), Author VARCHAR(50), ISBN VARCHAR(13) UNIQUE, AvailableCopies INT, TotalCopies INT ); -- DML: Insert sample data into the Books table INSERT INTO Books (BookID, Title, Author, ISBN, AvailableCopies, TotalCopies) VALUES (1, 'The Great Gatsby', 'F. Scott Fitzgerald', '978-0743273565', 5, 10), (2, 'To Kill a Mockingbird', 'Harper Lee', '978-0061120084', 8, 8), (3, '1984', 'George Orwell', '978-0451524935', 3, 5), (4, 'Pride and Prejudice', 'Jane Austen', '978-0141439518', 6, 6); -- DQL: Retrieve a list of books with available copies SELECT BookID, Title, Author FROM Books WHERE AvailableCopies > 0; Expected Outcome: 1. DDL (Data Definition Language): The CREATE TABLE statement creates a new table named "Books" with columns for book information, including BookID, Title, Author, ISBN, AvailableCopies, and TotalCopies. The ISBN column is defined as unique to ensure each book has a distinct ISBN. 2. DML (Data Manipulation Language): The INSERT INTO statement inserts sample data into the "Books" table. Four books with their details are added to the table, specifying the BookID, Title, Author, ISBN, AvailableCopies, and TotalCopies for each book. 3. DQL (Data Query Language): The SELECT statement retrieves a list of books from the "Books" table where the number of available copies (AvailableCopies) is greater than 0. This query returns a list of books that are currently available for borrowing. The expected outcome of the combined SQL statements is a result set showing the BookID, Title, and Author of books that are currently available for borrowing based on the condition in the WHERE clause of the SELECT statement.