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https://chatgpt.com/c/673b6f9f-cde0-8004-841b-a57608ccbbee

1. Operators in Python

i) Arithmetic Operators

Arithmetic operators perform basic mathematical operations like addition, subtraction, etc.

python
a, b = 10, 3
print("Addition:", a + b) # Adds a and b
print("Subtraction:", a - b) # Subtracts b from a
print("Multiplication:", a * b) # Multiplies a and b
print("Division:", a / b) # Divides a by b
print("Modulus:", a % b) # Finds remainder of a/b
print("Exponent:", a ** b) # Computes a raised to b
print("Floor Division:", a // b) # Divides and truncates the result

Output:

yaml
Addition: 13
Subtraction: 7
Multiplication: 30
Division: 3.3333333333333335
Modulus: 1
Exponent: 1000
Floor Division: 3

ii) Relational Operators

Relational operators compare two values and return True or False.

python
a, b = 10, 3
print("Equal to:", a == b)
print("Not equal to:", a != b)
print("Greater than:", a > b)
print("Less than:", a < b)
print("Greater than or equal to:", a >= b)
print("Less than or equal to:", a <= b)

Output:

mathematica
Equal to: False
Not equal to: True
Greater than: True
Less than: False
Greater than or equal to: True
Less than or equal to: False

iii) Assignment Operators

Assignment operators are used to assign values to variables.

python
a = 5
a += 2 # Same as a = a + 2
print("a after += 2:", a)
a *= 3 # Same as a = a * 3
print("a after *= 3:", a)

Output:

less
a after += 2: 7
a after *= 3: 21

iv) Logical Operators

Logical operators (and, or, not) are used for boolean operations.

python
x, y = True, False
print("x and y:", x and y) # True if both are True
print("x or y:", x or y) # True if either is True
print("not x:", not x) # Negates x

Output:

yaml
x and y: False
x or y: True
not x: False

v) Bitwise Operators

Bitwise operators perform operations on binary representations of integers.

python
a, b = 5, 3 # Binary: a = 101, b = 011
print("Bitwise AND:", a & b) # Performs AND operation
print("Bitwise OR:", a | b) # Performs OR operation
print("Bitwise XOR:", a ^ b) # Performs XOR operation

Output:

yaml
Bitwise AND: 1
Bitwise OR: 7
Bitwise XOR: 6

vi) Ternary Operator

A ternary operator is a shorthand for an if-else statement.

python
a, b = 10, 20
max_value = a if a > b else b
print("Maximum value:", max_value)

Output:

yaml
Maximum value: 20

vii) Membership Operators

Membership operators check for the presence of a value in a sequence.

python
my_list = [1, 2, 3, 4]
print("3 in list:", 3 in my_list)
print("5 not in list:", 5 not in my_list)

Output:

yaml
3 in list: True
5 not in list: True

viii) Identity Operators

Identity operators check if two variables reference the same object.

python
a = 10
b = 10
print("a is b:", a is b)
print("a is not b:", a is not b)

Output:

less
a is b: True
a is not b: False

2. Add and Multiply Complex Numbers

Complex numbers are represented as a + bj in Python.

python
a = 2 + 3j
b = 1 + 4j
print("Addition:", a + b) # Adds the real and imaginary parts
print("Multiplication:", a * b) # Multiplies two complex numbers

Output:

makefile
Addition: (3+7j)
Multiplication: (-10+11j)

3. Find String Length Without Library Functions

The program iterates through the string to count the characters.

python
string = "Hello, World!"
length = 0
for _ in string:
length += 1
print("Length of string:", length)

Output:

c
Length of string: 13

4. Check if Substring is Present

The in operator checks if the substring exists in the string.

python
string = "Hello, World!"
substring = "World"
print("Substring found:", substring in string)

Output:

yaml
Substring found: True

5. Check if Key Exists in Dictionary

The in operator checks for the key in the dictionary.

python
my_dict = {"a": 1, "b": 2, "c": 3}
key = "b"
print("Key exists:", key in my_dict)

Output:

mathematica
Key exists: True

6. Add Key-Value Pair to Dictionary

This program adds a new key-value pair to a dictionary.

python
my_dict = {"a": 1, "b": 2}
my_dict["c"] = 3
print("Updated dictionary:", my_dict)

Output:

css
Updated dictionary: {'a': 1, 'b': 2, 'c': 3}

7. Array Operations

The array module allows creating and manipulating arrays.

python

from array import array

arr = array(‘i’, [1, 2, 3])
print(“Original array:”, arr)

arr.append(4)
print(“After append:”, arr)

arr.insert(2, 5)
print(“After insert:”, arr)

arr.reverse()
print(“Reversed array:”, arr)

Output:

sql
Original array: array('i', [1, 2, 3])
After append: array('i', [1, 2, 3, 4])
After insert: array('i', [1, 2, 5, 3, 4])
Reversed array: array('i', [4, 3, 5, 2, 1])

8. Matrix Operations

Using NumPy for matrix operations like addition, transpose, and multiplication.

python

import numpy as np

A = np.array([[1, 2], [3, 4]])
B = np.array([[5, 6], [7, 8]])

print(“Matrix Addition:\n”, A + B)
print(“Transpose of A:\n”, A.T)
print(“Matrix Multiplication:\n”, np.dot(A, B))

Output:

lua
Matrix Addition:
[[ 6 8]
[10 12]]
Transpose of A:
[[1 3]
[2 4]]
Matrix Multiplication:
[[19 22]
[43 50]]
 
 
 
 

Here are the solutions with explanations, code, and expected outputs:

9. Python program to find min, max, sum, and cumulative sum of an array

In Python, we can use the numpy library to efficiently compute the minimum, maximum, sum, and cumulative sum of an array.

Code:

python
import numpy as np # Create an array arr = np.array([10, 20, 30, 40, 50]) # Perform operations print("Array:", arr) print("Minimum:", np.min(arr)) # Find minimum value print("Maximum:", np.max(arr)) # Find maximum value print("Sum:", np.sum(arr)) # Compute sum of elements print("Cumulative Sum:", np.cumsum(arr)) # Compute cumulative sum

Explanation:

  • np.min(arr) returns the smallest value in the array.
  • np.max(arr) returns the largest value in the array.
  • np.sum(arr) computes the sum of all elements.
  • np.cumsum(arr) computes the cumulative sum, where each element is the sum of itself and all previous elements.

Output:

yaml
Array: [10 20 30 40 50] Minimum: 10 Maximum: 50 Sum: 150 Cumulative Sum: [ 10 30 60 100 150]

10. Create a dictionary and explore it as a pandas DataFrame

We will create a dictionary where each key maps to a list of ten values, convert it into a pandas DataFrame, and then explore the DataFrame.

Code:

python
import pandas as pd # Create a dictionary with five keys and lists of ten values each data = { "Column1": [1, 2, 3, 4, 5, 6, 7, 8, 9, 10], "Column2": [11, 12, 13, 14, 15, 16, 17, 18, 19, 20], "Column3": [21, 22, 23, 24, 25, 26, 27, 28, 29, 30], "Column4": [31, 32, 33, 34, 35, 36, 37, 38, 39, 40], "Column5": [41, 42, 43, 44, 45, 46, 47, 48, 49, 50], } # Convert the dictionary into a pandas DataFrame df = pd.DataFrame(data) # Explore the DataFrame print("DataFrame:\n", df) # a) Apply head() function to display the first 5 rows print("\nFirst 5 rows using head():\n", df.head()) # b) Perform data selection operations # Select a specific column print("\nSelect 'Column1':\n", df["Column1"]) # Select specific rows print("\nSelect first 3 rows:\n", df.iloc[:3]) # Select specific rows and columns print("\nSelect first 3 rows of 'Column1' and 'Column2':\n", df.loc[:2, ["Column1", "Column2"]])

Explanation:

  1. We create a dictionary with five keys, each containing a list of ten values.
  2. Using pd.DataFrame(data), we convert the dictionary into a DataFrame.
  3. Head Operation:
    • df.head() displays the first five rows of the DataFrame.
  4. Data Selection Operations:
    • df["Column1"] selects a single column.
    • df.iloc[:3] selects the first three rows by index.
    • df.loc[:2, ["Column1", "Column2"]] selects specific rows and columns using labels.

Output:

sql
DataFrame: Column1 Column2 Column3 Column4 Column5 0 1 11 21 31 41 1 2 12 22 32 42 2 3 13 23 33 43 3 4 14 24 34 44 4 5 15 25 35 45 5 6 16 26 36 46 6 7 17 27 37 47 7 8 18 28 38 48 8 9 19 29 39 49 9 10 20 30 40 50 First 5 rows using head(): Column1 Column2 Column3 Column4 Column5 0 1 11 21 31 41 1 2 12 22 32 42 2 3 13 23 33 43 3 4 14 24 34 44 4 5 15 25 35 45 Select 'Column1': 0 1 1 2 2 3 3 4 4 5 5 6 6 7 7 8 8 9 9 10 Name: Column1, dtype: int64 Select first 3 rows: Column1 Column2 Column3 Column4 Column5 0 1 11 21 31 41 1 2 12 22 32 42 2 3 13 23 33 43 Select first 3 rows of 'Column1' and 'Column2': Column1 Column2 0 1 11 1 2 12 2 3 13