1. NumPy (Numerical Python)

What is NumPy?

NumPy is the foundational library for numerical computing in Python. It provides:

• N-dimensional arrays (ndarray) for efficient storage and operations
• Mathematical functions (linear algebra, statistics, trigonometry)
• Broadcasting for vectorized operations (avoiding loops)

Creating Arrays

                        import numpy as np
                        
                        # 1D array
                        arr1 = np.array([1, 2, 3])
                        
                        # 2D array (matrix)
                        arr2 = np.array([[1, 2], [3, 4]])
                        
                        # Special arrays
                        zeros = np.zeros((3, 3))  # 3x3 matrix of zeros
                        ones = np.ones((2, 2))    # 2x2 matrix of ones
                        range_arr = np.arange(0, 10, 2)  # [0, 2, 4, 6, 8]
                        linspace = np.linspace(0, 1, 5)  # 5 evenly spaced numbers between 0 and 1
                        
                        print("1D Array:", arr1)
                        print("2D Array:\n", arr2)
                        print("Zeros:\n", zeros)
                        print("Linspace:", linspace)

Array Operations (Vectorization & Broadcasting)

                        a = np.array([1, 2, 3])
                        b = np.array([4, 5, 6])
                        
                        # Element-wise operations
                        print("Addition:", a + b)  # [5, 7, 9]
                        print("Multiplication:", a * b)  # [4, 10, 18]
                        
                        # Broadcasting (applying operations to mismatched shapes)
                        print("Array + Scalar:", a + 5)  # [6, 7, 8]

Mathematical & Linear Algebra Functions

                        # Trigonometry
                        angles = np.array([0, 30, 45, 60, 90]) * np.pi / 180
                        print("Sine:", np.sin(angles))
                        
                        # Statistics
                        data = np.array([1, 2, 3, 4, 5])
                        print("Mean:", np.mean(data))
                        print("Standard Deviation:", np.std(data))
                        
                        # Matrix operations
                        A = np.array([[1, 2], [3, 4]])
                        B = np.array([[5, 6], [7, 8]])
                        print("Matrix Multiplication:\n", np.matmul(A, B))
                        print("Inverse of A:\n", np.linalg.inv(A))

2. Pandas (Data Analysis & Manipulation)

What is Pandas?

Pandas provides DataFrames (tables) and Series (columns) for:

• Data cleaning (handling missing values)
• Data manipulation (filtering, grouping, merging)
• Statistical analysis (mean, median, correlation)

Creating DataFrames & Series

                        import pandas as pd
                        
                        # Series (1D array with labels)
                        series = pd.Series([10, 20, 30], index=["A", "B", "C"])
                        
                        # DataFrame (2D table)
                        data = {"Name": ["Alice", "Bob"], "Age": [25, 30]}
                        df = pd.DataFrame(data)
                        
                        print("Series:\n", series)
                        print("DataFrame:\n", df)

Data Manipulation (Filtering, Grouping, Aggregation)

                        # Filtering
                        print("Age > 25:\n", df[df["Age"] > 25])
                        
                        # Grouping & Aggregation
                        sales = pd.DataFrame({
                            "Region": ["East", "West", "East", "West"],
                            "Sales": [200, 300, 150, 400]
                        })
                        print("Average Sales by Region:\n", sales.groupby("Region").mean())

Handling Missing Data

                        df = pd.DataFrame({"A": [1, None, 3], "B": [4, 5, None]})
                        print("Original:\n", df)
                        print("Filled NA:\n", df.fillna(0))  # Replace missing values with 0

3. Matplotlib & Seaborn (Data Visualization)

What are Matplotlib & Seaborn?

• Matplotlib: Basic plotting (line, bar, scatter plots)
• Seaborn: High-level statistical visualizations (violin plots, heatmaps)

Line & Bar Plots (Matplotlib)

                        import matplotlib.pyplot as plt
                        
                        x = [1, 2, 3, 4]
                        y = [10, 20, 15, 25]
                        
                        plt.plot(x, y, label="Line Plot")
                        plt.bar(x, y, label="Bar Plot")
                        plt.xlabel("X-axis")
                        plt.ylabel("Y-axis")
                        plt.legend()
                        plt.show()

Seaborn Statistical Plots

                        import seaborn as sns
                        
                        tips = sns.load_dataset("tips")
                        sns.boxplot(x="day", y="total_bill", data=tips)
                        plt.title("Bill Distribution by Day")
                        plt.show()

4. Requests (HTTP Requests & Web Scraping)

What is the Requests Library?

• Used for HTTP requests (GET, POST, API calls)
• Helps in web scraping (extracting data from websites)

GET & POST Requests

                        import requests
                        
                        # GET request
                        response = requests.get("https://api.github.com")
                        print("Status Code:", response.status_code)
                        print("JSON Data:", response.json())
                        
                        # POST request
                        data = {"key": "value"}
                        response = requests.post("https://httpbin.org/post", data=data)
                        print("POST Response:", response.json())

5. Flask & Django (Web Development)

Flask (Microframework)

                        from flask import Flask
                        
                        app = Flask(__name__)
                        
                        @app.route("/")
                        def home():
                            return "Hello, World!"
                        
                        if __name__ == "__main__":
                            app.run(debug=True)

Django (Full-stack Framework)

• Used for larger web apps (admin panel, authentication, ORM)
• Requires django-admin startproject to create a project

6. SQL & Database Interactions

SQLite (Lightweight Database)

                        import sqlite3
                        
                        conn = sqlite3.connect("example.db")
                        cursor = conn.cursor()
                        
                        # Create table
                        cursor.execute("CREATE TABLE IF NOT EXISTS users (id INTEGER, name TEXT)")
                        
                        # Insert data
                        cursor.execute("INSERT INTO users VALUES (1, 'Alice')")
                        
                        # Query data
                        cursor.execute("SELECT * FROM users")
                        print(cursor.fetchall())  # [(1, 'Alice')]
                        
                        conn.commit()
                        conn.close()

This guide covered:

• ✅ NumPy (numerical computing)
• ✅ Pandas (data analysis)
• ✅ Matplotlib/Seaborn (visualization)
• ✅ Requests (HTTP/web scraping)
• ✅ Flask/Django (web development)
• ✅ SQL/Databases (data storage)

Each library serves a specific purpose in Python development, from data science to web apps. 🚀