Logical Operators

Logical operators are used for comparisons and logical operations.

• == is used to check if two values are equal.

• # Example of ==
  x = 5
  y = 10
  is_equal = x == y  # is_equal will be False
  print("Is x equal to y?", is_equal)

• < is used to check if one value is less than another.

• # Example of <
  x = 5
  y = 10
  is_less = x < y  # is_less will be True
  print("Is x less than y?", is_less)

• > is used to check if one value is greater than another.

• # Example of >
  x = 5
  y = 10
  is_greater = x > y  # is_greater will be False
  print("Is x greater than y?", is_greater)

• or is used for logical OR operations.

• # Example of or
  has_apple = True
  has_banana = False
  want_fruit = has_apple or has_banana  # want_fruit will be True
  print("Do you want fruit?", want_fruit)

• and is used for logical AND operations.

• # Example of and
  is_adult = True
  has_id = True
  can_enter_club = is_adult and has_id  # can_enter_club will be True
  print("Can you enter the club?", can_enter_club)

Arithmetic Operations

String Operations

greeting = "Hello, "
name = "Alice"
full_greeting = greeting + name
print(full_greeting)

Data Structures

Lists

numbers = [1, 2, 3, 4, 5]
print(numbers)

# Access the first element

fruits = ["apple", "banana", "cherry"]
first_fruit = fruits[0]
print(first_fruit)

Tuples

coordinates = (3, 4)
print(coordinates)

# Access the second element
y_coordinate = coordinates[1]
print(y_coordinate)

Dictionaries

student = {
    "name": "Alice",
    "age": 20,
    "grade": "A"
}
print(student)

# Access the age
student_age = student["age"]
print(student_age)

Sets

unique_numbers = {1, 2, 3, 4, 5}
print(unique_numbers)

Control Flow

Loops

for loop

fruits = ["apple", "banana", "cherry"]
for fruit in fruits:
    print(fruit)

while loop

count = 0
while count < 5:
    print(count)
    count += 1

Conditional Statements (if, elif, else)

age = 20
if age < 18:
    print("You are a minor")
elif age >= 18 and age < 65:
    print("You are an adult")
else:
    print("You are a senior citizen")

Functions

In Built Functions

• Functions in Python are blocks of code that perform specific tasks.
• We use functions by calling them with parentheses ().

• # Example of using a built-in function
  numbers = [1, 2, 3, 4, 5]
  result = sum(numbers)  # Calls the sum function to calculate the sum
  print("Sum of numbers:", result)

• Functions can also take arguments, perform actions, and return values.

User Defined Functions

def greet(name):
    return "Hello, " + name

# Call the function
greeting = greet("John")
print(greeting)

Function Arguments

def add_numbers(a, b):
    return a + b

result = add_numbers(5, 3)
print(result)

Return Statements

def is_even(number):
    if number % 2 == 0:
        return True
    else:
        return False

# Check if a number is even
even = is_even(6)
print(even)

–> –> –> –> –>

Modules and Libraries

Installation

• Install once per machine

• 
  pip install `package_name`

• Load once per session

• 
  import `package_name`

Importing Modules

import math
print(math.sqrt(25))

Using Built-in Modules (e.g., math, random)

import random
random_number = random.randint(1, 10)
print(random_number)

What is Pandas?

• Pandas is a powerful Python library for data manipulation and analysis.
• It provides easy-to-use data structures and data analysis tools.

Why Pandas?

• Pandas is widely used in data science and data analysis due to its efficiency and versatility.
• It excels at handling structured data, such as spreadsheets or databases.

Pandas Basics

• Installing Pandas

• # How to install Pandas using pip.

• Importing Pandas

• import pandas as pd

Pandas Data Structures

Series

data = [10, 20, 30, 40, 50]
series = pd.Series(data)
print(series)

DataFrame

data = {'Name': ['Alice', 'Bob', 'Charlie'],
        'Age': [25, 30, 35]}
df = pd.DataFrame(data)
print(df)

Loading Data

Reading CSV Files

df = pd.read_csv('data.csv')
print(df)

Viewing Data

#Display the first few rows of the DataFrame 
 print(df.head()) 

# Display the last few rows of the DataFrame 
print(df.tail) 

Introduction

• Data visualization is essential for understanding data and presenting insights.
• We’ll explore two popular Python libraries for data visualization: Matplotlib and Seaborn.

Matplotlib

• Powerful library for creating visualizations.
• Supports static, animated, and interactive plots.
• Wide range of customization options.

Installing Matplotlib

You can install Matplotlib using pip:

pip install matplotlib

Basic Matplotlib Example

import matplotlib.pyplot as plt

# Sample data
x = [1, 2, 3, 4, 5]
y = [2, 4, 1, 7, 5]

# Create a line plot
plt.plot(x, y)

# Add labels and a title
plt.xlabel('X-axis')
plt.ylabel('Y-axis')
plt.title('Simple Line Plot')

# Display the plot
plt.show()

Seaborn

• Built on top of Matplotlib.
• Simplifies creating informative and attractive visualizations.
• Ideal for statistical graphics.

Installing Seaborn

You can install Seaborn using pip:

pip install seaborn

Basic Seaborn Example

import seaborn as sns
import matplotlib.pyplot as plt

# Sample data
categories = ['A', 'B', 'C', 'D']
values = [10, 5, 12, 7]

# Create a bar plot
sns.barplot(x=categories, y=values)

# Add labels and a title
plt.xlabel('Categories')
plt.ylabel('Values')
plt.title('Simple Bar Plot')

# Display the plot
plt.show()