📚 Informatics Practices (IP) Learning Tool

Python | SQL | Pandas | NumPy | Data Visualization

🐍 Python Basics

# Variables & Data Types
name = "Student"
age = 16
marks = 95.5
is_pass = True

# Lists & Loops
subjects = ["Python", "SQL", "Pandas"]
for sub in subjects:
    print(sub)

📝 Functions & Conditionals

def check_grade(marks):
    if marks >= 90:
        return "A+"
    elif marks >= 75:
        return "A"
    else:
        return "B"

print(check_grade(85)) # Output: A

▶️ Python Code Runner (Simulated)

Try Python code here (simulated environment):

Output will appear here...

🗄️ SQL Commands

-- CREATE TABLE
CREATE TABLE students (
    id INT PRIMARY KEY,
    name VARCHAR(50),
    marks INT,
    grade CHAR(2)
);

-- INSERT DATA
INSERT INTO students VALUES
(1, 'Alice', 95, 'A+'),
(2, 'Bob', 82, 'A');

-- QUERY DATA
SELECT * FROM students
WHERE marks > 80;

📊 Sample Database

🔍 SQL Query Executor

Write SQL queries on our sample database:

SQL results will appear here...

📊 Pandas Operations

import pandas as pd
import numpy as np

# Create DataFrame
data = {
    'Name': ['Alice', 'Bob', 'Charlie'],
    'Math': [95, 82, 78],
    'Science': [90, 88, 85]
}
df = pd.DataFrame(data)

# Operations
print(df.head())
print(df['Math'].mean())
print(df.describe())

📈 Data Visualization

import matplotlib.pyplot as plt

# Sample plot
subjects = ['Python', 'SQL', 'Pandas']
scores = [85, 90, 78]

plt.bar(subjects, scores)
plt.title('Student Scores')
plt.show()

📊 DataFrame Viewer

DataFrame operations will appear here...

📝 Informatics Practices Quiz

📚 Important Topics

• Python Fundamentals
• SQL Queries (DDL, DML)
• Pandas DataFrame Operations
• NumPy Arrays
• Data Visualization with Matplotlib
• Database Connectivity

🎯 Key Formulas

# Mean (Average)
mean = sum(values) / len(values)

# Median
sorted_values = sorted(values)
median = sorted_values[len//2]

# Mode
from statistics import mode
mode_value = mode(values)

📥 Download Learning Resources

Get all important notes, code examples, and practice questions: