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1. Introduction to Python Language

Python is a popular, high-level programming language known for its readability and simplicity. Created by Guido van Rossum and first released in 1991, Python's design philosophy emphasizes code readability with its notable use of significant indentation. It's a versatile language used for web development, data analysis, artificial intelligence, scientific computing, and more. Python's extensive standard library and supportive community make it a preferred choice for beginners and professionals alike.

Write a Python program to explore various data types including numeric types,Boolean types and compound types.
Python 
# Numeric types
num_int = 10
num_float = 3.14
num_complex = 2 + 3j

# Boolean types
bool_true = True
bool_false = False

# Compound types
list_example = [1, 2, 3, 4]
tuple_example = (1, 2, 3, 4)
dict_example = {'a': 1, 'b': 2, 'c': 3}

# Print the data types
print("Numeric types:")
print(type(num_int))
print(type(num_float))
print(type(num_complex))
print("\nBoolean types:")
print(type(bool_true))
print(type(bool_false))
print("\nCompound types:")
print(type(list_example))
print(type(tuple_example))
print(type(dict_example))
Python 
# Input and Output Operations

# Input
name = input("Enter your name: ")
age = int(input("Enter your age: "))

# Output
print("Hello, " + name + "! You are " + str(age) + " years old.")
Python 
# Looping with break and continue
numbers = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]

print("Loop with break:")
for num in numbers:
    if num == 5:
        break  # exit the loop when num equals 5
    print(num)

print("\nLoop with continue:")
for num in numbers:
    if num % 2 == 0:
        continue  # skip even numbers
    print(num)

2. Functions in Python.

Functions in Python are reusable blocks of code designed to perform a specific task. They help in organizing code into manageable sections, enhancing readability and reusability. A function is defined using the def keyword followed by the function name and parentheses. Functions can accept parameters and return values. They are crucial in breaking down complex problems into simpler, modular pieces, making the code easier to understand and maintain.

Write a Python program to define and use functions
Python 
# Function definition
def greet(name):
    """
    This function greets the user.
    """
    print("Hello,", name)

# Using the function
greet("Alice")
greet("Bob")
Python 
# Using built-in functions
numbers = [3, 1, 4, 1, 5, 9, 2, 6, 5]

# Finding the length of a list
print("Length of numbers:", len(numbers))

# Finding the maximum and minimum values
print("Maximum number:", max(numbers))
print("Minimum number:", min(numbers))

# Summing up the elements of the list
print("Sum of numbers:", sum(numbers))

# Sorting the list
sorted_numbers = sorted(numbers)
print("Sorted numbers:", sorted_numbers)
Python 
# Lambda function to calculate square of a number
square = lambda x: x ** 2

# Using the lambda function
print("Square of 5:", square(5))

# Lambda function to check if a number is even
is_even = lambda x: x % 2 == 0

# Using the lambda function
print("Is 10 even?", is_even(10))
print("Is 7 even?", is_even(7))

3. Array and String in Python.

In Python, arrays are used to store multiple values in a single variable, but the standard library does not include a built-in array data type; instead, lists or the array module are used. Strings are sequences of characters enclosed in quotes. Python provides various methods to manipulate strings, such as slicing, concatenation, and formatting. Strings are immutable, meaning their values cannot be changed once created.

Write a Python Program to implement arrays for storing homogeneous data items. Apply indexing and slicing operations to access elements of array.
Python 
# Importing the array module
import array

# Creating an array of integers
int_array = array.array('i', [1, 2, 3, 4, 5])

# Accessing elements using indexing
print("First element:", int_array[0])
print("Second element:", int_array[1])

# Accessing elements using slicing
print("First three elements:", int_array[:3])
print("Last two elements:", int_array[-2:])
Python 
# String declaration
my_string = "Hello, World!"

# String operations
print("Original string:", my_string)
print("Uppercase:", my_string.upper())
print("Lowercase:", my_string.lower())
print("Replace 'World' with 'Python':", my_string.replace("World", "Python"))

# String properties
print("Length of the string:", len(my_string))
print("Does the string start with 'Hello'? ", my_string.startswith("Hello"))
print("Does the string end with 'World!'? ", my_string.endswith("World!"))

# String indexing and slicing
print("First character:", my_string[0])
print("Last character:", my_string[-1])
print("Substring (first 5 characters):", my_string[:5])
Python 
# Membership operators
fruits = ["apple", "banana", "cherry"]
print("Is 'apple' in the list of fruits?", "apple" in fruits)
print("Is 'orange' not in the list of fruits?", "orange" not in fruits)

# Identity operators
a = 10
b = 10
c = a

print("Is 'a' identical to 'b'? ", a is b)
print("Is 'a' not identical to 'b'? ", a is not b)

# Changing the value of b
b = 20
print("After changing b, is 'a' identical to 'b'? ", a is b)
print("Is 'a' identical to 'c'? ", a is c)
Python 
# Importing the numpy module
import numpy as np

# Creating a 1-dimensional array
one_d_array = np.array([1, 2, 3, 4, 5])

# Creating a 2-dimensional array
two_d_array = np.array([[1, 2, 3], [4, 5, 6]])

# Accessing elements in a 1-dimensional array
print("Element at index 2 in 1D array:", one_d_array[2])

# Accessing elements in a 2-dimensional array
print("Element at row 1, column 2 in 2D array:", two_d_array[1, 2])

# Slicing in 1-dimensional array
print("First three elements in 1D array:", one_d_array[:3])

# Slicing in 2-dimensional array
print("First row in 2D array:", two_d_array[0, :])
print("Second column in 2D array:", two_d_array[:, 1])

4. List and Tuples in Python.

Lists and tuples are data structures in Python used to store collections of items. Lists are mutable, meaning their elements can be changed, added, or removed. They are defined using square brackets. Tuples, on the other hand, are immutable and defined using parentheses. Both can store heterogeneous data types, and are widely used for organizing data.

Write a Python Program to create list, apply various functions to it.
Python 
# Creating a list
my_list = [10, 20, 30, 40, 50]

# Applying various functions to the list
print("Original list:", my_list)

# Appending an element to the list
my_list.append(60)
print("After appending 60:", my_list)

# Inserting an element at a specific position
my_list.insert(2, 25)
print("After inserting 25 at index 2:", my_list)

# Removing an element from the list
my_list.remove(40)
print("After removing 40:", my_list)

# Popping an element from the list
popped_element = my_list.pop()
print("After popping the last element:", my_list)
print("Popped element:", popped_element)

# Sorting the list
my_list.sort()
print("After sorting:", my_list)

# Reversing the list
my_list.reverse()
print("After reversing:", my_list)

# Finding the index of an element
index_of_30 = my_list.index(30)
print("Index of 30:", index_of_30)

# Counting occurrences of an element
count_of_20 = my_list.count(20)
print("Count of 20:", count_of_20)
Python 
# Aliasing
original_list = [1, 2, 3, 4, 5]
alias_list = original_list  # aliasing the list

# Modifying the alias list
alias_list.append(6)
print("Original list after modifying alias:", original_list)
print("Alias list:", alias_list)

# Cloning
cloned_list = original_list[:]  # cloning the list

# Modifying the cloned list
cloned_list.append(7)
print("Original list after modifying clone:", original_list)
print("Cloned list:", cloned_list)
Python 
# Creating a tuple
my_tuple = (10, 20, 30, 40, 50)

# Accessing elements in the tuple
print("Original tuple:", my_tuple)
print("First element:", my_tuple[0])
print("Last element:", my_tuple[-1])

# Trying to modify an element (should raise an error)
try:
    my_tuple[0] = 100  # This will raise a TypeError
except TypeError as e:
    print("Error:", e)

# Demonstrating immutability
# We cannot modify the elements, but we can create a new tuple
new_tuple = my_tuple + (60, 70)
print("New tuple after concatenation:", new_tuple)

5. Dictionaries and Sets.

Dictionaries and sets are Python data structures used to store collections of data. Dictionaries store data in key-value pairs, allowing for fast retrieval based on the key. They are defined using curly braces. Sets are unordered collections of unique elements, useful for membership testing and eliminating duplicates. They are defined using the set() function or curly braces with comma-separated elements.

Write a Python Program to implement Dictionary and operations on dictionaries.
Python 
# Creating a dictionary
my_dict = {
    "name": "Alice",
    "age": 25,
    "city": "New York"
}

# Printing the original dictionary
print("Original dictionary:", my_dict)

# Accessing elements by key
print("Name:", my_dict["name"])
print("Age:", my_dict["age"])

# Adding a new key-value pair
my_dict["email"] = "alice@example.com"
print("After adding email:", my_dict)

# Updating the value of an existing key
my_dict["age"] = 26
print("After updating age:", my_dict)

# Removing a key-value pair using pop
removed_value = my_dict.pop("city")
print("After removing city:", my_dict)
print("Removed value:", removed_value)

# Checking if a key exists
if "name" in my_dict:
    print("The key 'name' exists in the dictionary.")

# Getting all keys
keys = my_dict.keys()
print("All keys:", keys)

# Getting all values
values = my_dict.values()
print("All values:", values)

# Getting all key-value pairs
items = my_dict.items()
print("All key-value pairs:", items)

# Clearing the dictionary
my_dict.clear()
print("After clearing:", my_dict)
Python 
# Creating sets
set_a = {1, 2, 3, 4, 5}
set_b = {4, 5, 6, 7, 8}

# Printing the original sets
print("Set A:", set_a)
print("Set B:", set_b)

# Adding an element to a set
set_a.add(6)
print("Set A after adding 6:", set_a)

# Removing an element from a set
set_a.remove(2)
print("Set A after removing 2:", set_a)

# Checking if an element is in a set
print("Is 3 in Set A?", 3 in set_a)
print("Is 2 in Set A?", 2 in set_a)

# Set operations
# Union
union_set = set_a | set_b
print("Union of Set A and Set B:", union_set)

# Intersection
intersection_set = set_a & set_b
print("Intersection of Set A and Set B:", intersection_set)

# Difference
difference_set = set_a - set_b
print("Difference of Set A and Set B:", difference_set)

# Symmetric difference
symmetric_difference_set = set_a ^ set_b
print("Symmetric difference of Set A and Set B:", symmetric_difference_set)

# Checking subsets and supersets
print("Is Set A a subset of Set B?", set_a.issubset(set_b))
print("Is Set A a superset of Set B?", set_a.issuperset(set_b))

6. Regular Expression.

Regular expressions (regex) are sequences of characters that form search patterns, primarily used for string matching and manipulation. Python's re module provides support for regex operations. Regex is powerful for tasks like validating input, searching for patterns within text, and replacing text based on patterns. They are essential for text processing and data validation.

Write a Python Program for implementing various methods for searching and replacing operations
Python 
# String for searching and replacing operations
text = "Python is a powerful programming language. Python is widely used for various purposes."

# Searching operations
# Find the index of the first occurrence of a substring
index = text.find("Python")
print("Index of 'Python':", index)

# Count the number of occurrences of a substring
count = text.count("Python")
print("Number of occurrences of 'Python':", count)

# Replacing operations
# Replace all occurrences of a substring with another substring
new_text = text.replace("Python", "Java")
print("After replacing 'Python' with 'Java':", new_text)
Python 
# Importing the BeautifulSoup module for parsing HTML
from bs4 import BeautifulSoup

# HTML content
html_content = """


Sample HTML Page



Welcome to my website


This is a sample HTML page.


    
  
  • Item 1
    • 
  
  • Item 2
    • 
  
  • Item 3
    • 




"""

# Parsing the HTML content
soup = BeautifulSoup(html_content, 'html.parser')

# Retrieving information from the HTML
# Getting the title of the page
title = soup.title.string
print("Title of the page:", title)

# Getting the text of the first heading
heading = soup.h1.string
print("Heading of the page:", heading)

# Getting the text of the paragraph
paragraph = soup.p.string
print("Paragraph content:", paragraph)

# Getting all list items
list_items = soup.find_all('li')
print("List items:")
for item in list_items:
    print("-", item.string)


7. Date and Time in Python.

Python's datetime module provides classes for manipulating dates and times. It allows for date arithmetic, formatting, and parsing. The datetime module supports various functionalities like getting the current date and time, converting between different date formats, and performing time zone conversions. This is crucial for applications requiring date and time manipulations, such as logging and scheduling.

Write a Python Program to compare dates and implement calendar module
Python 
# Importing necessary modules
import datetime
import calendar

# Comparing Dates
date1 = datetime.date(2023, 5, 15)
date2 = datetime.date(2023, 5, 20)

# Comparing dates
if date1 < date2:
    print("Date 1 is before Date 2")
elif date1 > date2:
    print("Date 1 is after Date 2")
else:
    print("Both dates are equal")

# Implementing Calendar Module
# Creating a calendar for a specific month
year = 2023
month = 5
print("\nCalendar for May 2023:")
print(calendar.month(year, month))

# Determining the first weekday of a month (0 - Monday, 6 - Sunday)
first_weekday = calendar.weekday(year, month, 1)
print("First weekday of May 2023:", first_weekday)

# Determining the number of days in a month
num_days = calendar.monthrange(year, month)[1]
print("Number of days in May 2023:", num_days)


8. Using Ipython.

IPython is an enhanced interactive Python shell that provides a rich toolkit to help you make the most out of using Python interactively. It offers powerful introspection, additional shell syntax, tab completion, and rich history. IPython is widely used in data science for interactive data analysis, providing an efficient environment for exploratory computing.

Using IPython and Jupyter notebook
Python 

  
Header

  

	

		
Using IPython, Jupyter Notebook & Debugging IPython:

	


	
	

		
Steps to Use IPython:

	


	
	

		
1.Install IPython if you haven't already
			
Use the code provide below in your terminal OR Open cmd propmt or bash to use the below provided code.

	

	
	          

                

                  bash or cmd
                  
                

                

                  
pip install ipython

                

              

			  
			  
	

		
2.Open a terminal or command prompt.

	

	
	

		
3.Type ipython and press Enter to start the IPython interactive shell.

	

	
	

		
4.You can now execute Python code directly in the IPython shell..

	

	

	

	
		

		
Steps to Debugging Errors in IPython:

	


	
	

		
1.Identify the error message displayed in the IPython shell.

	

			  
	

		
2.Determine which line of your code is causing the error.

	

	
	

		
3.Use IPython's %debug magic command to enter the interactive debugger:

	

	
	    

            

                Python
                  
            

                

                  
%debug

                

              

	
	

		
4.Once in the debugger, you can inspect variables, navigate through the code, and identify the source of the error.

	

	

	

	
	

		
Steps to Use Jupyter Notebook:

	


	
	

		
1.Install Jupyter Notebook if you haven't already
			
Use the code provide below in your terminal OR Open cmd propmt or bash to use the below provided code.

	

	
	
	
	              

                

                  bash or cmd
                  
                

                

                  
pip install jupyterlab

                

              

			  
			  
	

		
2.Open a terminal or command prompt.

	

	
	

		
3.Navigate to the directory where you want to store your Jupyter Notebook files.

	

	
	

		
4.Type jupyter notebook and press Enter.

	

	
	

		
5.Your default web browser will open, showing the Jupyter Notebook dashboard.

	
	  
  
	

		
6.Click on New and select Python 3 to create a new Jupyter Notebook.

	
	
	
	

		
7.You can now write and execute Python code in cells within the notebook.

	
	
  
  
  

	
Footer

9. Using the NumPy Package.

NumPy (Numerical Python) is a fundamental package for scientific computing in Python. It provides support for arrays, matrices, and many mathematical functions to operate on these data structures. NumPy enables efficient numerical computations and is the foundation for many other scientific computing packages in Python, making it essential for data analysis and machine learning.

Programs using NumPy Package and different functions available in it.
Python 
# Importing NumPy
import numpy as np

# Creating arrays
# 1D array
arr_1d = np.array([1, 2, 3, 4, 5])
print("1D Array:", arr_1d)

# 2D array
arr_2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])
print("\n2D Array:")
print(arr_2d)

# Array properties
print("\nShape of 2D Array:", arr_2d.shape)
print("Number of dimensions of 2D Array:", arr_2d.ndim)
print("Number of elements in 2D Array:", arr_2d.size)
print("Data type of elements in 2D Array:", arr_2d.dtype)

# Reshaping arrays
arr_reshaped = arr_1d.reshape(5, 1)
print("\nReshaped Array:")
print(arr_reshaped)

# Slicing arrays
print("\nSliced Array:")
print(arr_2d[:2, 1:])

# Array operations
arr1 = np.array([[1, 2], [3, 4]])
arr2 = np.array([[5, 6], [7, 8]])

# Element-wise addition
print("\nElement-wise Addition:")
print(np.add(arr1, arr2))

# Element-wise subtraction
print("\nElement-wise Subtraction:")
print(np.subtract(arr1, arr2))

# Element-wise multiplication
print("\nElement-wise Multiplication:")
print(np.multiply(arr1, arr2))

# Element-wise division
print("\nElement-wise Division:")
print(np.divide(arr1, arr2))

# Dot product
print("\nDot Product:")
print(np.dot(arr1, arr2))

# Transpose of an array
print("\nTranspose of 2D Array:")
print(np.transpose(arr_2d))

# Generating random numbers
# Random integers
random_integers = np.random.randint(low=1, high=100, size=(3, 3))
print("\nRandom Integers:")
print(random_integers)

# Random numbers from a normal distribution
random_normal = np.random.normal(loc=0, scale=1, size=(3, 3))
print("\nRandom Numbers from Normal Distribution:")
print(random_normal)

10. Using the Pandas package.

Pandas is a powerful data manipulation and analysis library for Python. It provides data structures like Series and DataFrame to handle structured data intuitively. Pandas offers numerous functions for data cleaning, transformation, and visualization. It's an indispensable tool for data analysis in Python, especially for handling large datasets and performing complex operations efficiently.

Programs using Pandas Package and different functions available in it.
Python 
# Importing Pandas
import pandas as pd

# Creating a DataFrame
data = {'Name': ['Alice', 'Bob', 'Charlie', 'David', 'Emma'],
        'Age': [25, 30, 35, 40, 45],
        'City': ['New York', 'Los Angeles', 'Chicago', 'Houston', 'Philadelphia']}

df = pd.DataFrame(data)
print("DataFrame:")
print(df)

# Accessing columns
print("\nColumn 'Name':")
print(df['Name'])

# Accessing rows
print("\nRow 1:")
print(df.iloc[0])

# Adding a new column
df['Gender'] = ['Female', 'Male', 'Male', 'Male', 'Female']
print("\nDataFrame after adding 'Gender' column:")
print(df)

# Descriptive statistics
print("\nDescriptive Statistics:")
print(df.describe())

# Sorting by a column
sorted_df = df.sort_values(by='Age')
print("\nSorted DataFrame by 'Age':")
print(sorted_df)

# Filtering rows based on a condition
filtered_df = df[df['Age'] > 30]
print("\nFiltered DataFrame with Age > 30:")
print(filtered_df)

# Grouping data
grouped_df = df.groupby('Gender').mean()
print("\nGrouped DataFrame by 'Gender':")
print(grouped_df)

# Reading data from a CSV file
csv_file_path = 'data.csv'
csv_df = pd.read_csv(csv_file_path)
print("\nDataFrame from CSV file:")
print(csv_df)

# Writing data to a CSV file
output_csv_file_path = 'output_data.csv'
df.to_csv(output_csv_file_path, index=False)
print("\nDataFrame written to CSV file:", output_csv_file_path)


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