import requests

# Simple GET request
response = requests.get('https://api.github.com/users/octocat')
print(f"Status: {response.status_code}")
print(f"Data: {response.json()}")

# POST request with data
data = {'name': 'John', 'age': 30}
response = requests.post('https://httpbin.org/post', json=data)
print(response.json())

# Handling errors gracefully
try:
    response = requests.get('https://api.github.com/users/nonexistentuser')
    response.raise_for_status()  # Raises exception for bad status codes
    except requests.exceptions.RequestException as e:
    print(f"Error: {e}")

import requests

def get_random_quote():
    """Fetch a random inspirational quote"""
    url = "https://zenquotes.io/api/random"
    
    try:
        response = requests.get(url, timeout=5)
        response.raise_for_status()
        
        data = response.json()
        # ZenQuotes returns an array with one quote
        quote_data = data[0]
        return f'"{quote_data["q"]}" - {quote_data["a"]}'
    except requests.exceptions.RequestException as e:
        return f"Error fetching quote: {e}"

# Usage
quote = get_random_quote()
print(quote)

import matplotlib.pyplot as plt

# Simple line plot
months = ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun']
sales = [1200, 1500, 1100, 1800, 2000, 1700]

plt.figure(figsize=(10, 6))
plt.plot(months, sales, marker='o')
plt.title('Monthly Sales')
plt.xlabel('Month')
plt.ylabel('Sales ($)')
plt.grid(True)
plt.show()

# Bar chart
categories = ['Product A', 'Product B', 'Product C', 'Product D']
values = [23, 45, 56, 78]

plt.figure(figsize=(8, 6))
plt.bar(categories, values, color=['red', 'blue', 'green', 'orange'])
plt.title('Product Sales Comparison')
plt.ylabel('Units Sold')
plt.xticks(rotation=45)
plt.tight_layout()
plt.show()

import matplotlib.pyplot as plt

# Create subplots
fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5))

# Left plot: Line chart
months = ['Q1', 'Q2', 'Q3', 'Q4']
revenue = [50000, 65000, 70000, 80000]
ax1.plot(months, revenue, marker='s', linewidth=2)
ax1.set_title('Quarterly Revenue')
ax1.set_ylabel('Revenue ($)')

# Right plot: Pie chart
expenses = [25000, 15000, 10000, 8000]
labels = ['Salaries', 'Rent', 'Marketing', 'Other']
ax2.pie(expenses, labels=labels, autopct='%1.1f%%')
ax2.set_title('Expense Breakdown')

plt.tight_layout()
plt.show()

import pandas as pd

# Creating a DataFrame
data = {
'Name': ['Alice', 'Bob', 'Charlie', 'Diana'],
'Age': [25, 30, 35, 28],
'Department': ['Sales', 'IT', 'Marketing', 'Sales'],
'Salary': [60000, 80000, 70000, 65000]
}
df = pd.DataFrame(data)
print(df)

print("Dataset Info:")
print(f"Shape: {df.shape}")
print(f"Columns: {list(df.columns)}")
print("\nFirst few rows:")
print(df.head())

# Basic statistics
print(f"\nAverage salary: ${df['Salary'].mean():,.2f}")
print(f"Youngest employee: {df['Age'].min()} years old")
print(f"Oldest employee: {df['Age'].max()} years old")

# Filtering data
sales_team = df[df['Department'] == 'Sales']
print(f"\nSales team members: {len(sales_team)}")
print(sales_team[['Name', 'Salary']])

# Grouping
dept_stats = df.groupby('Department')['Salary'].agg(['mean', 'count'])
print(f"\nDepartment salary statistics:")
print(dept_stats)

# Load from CSV (most common)
df = pd.read_csv('employees.csv')

# Load from Excel
df = pd.read_excel('data.xlsx') -->

print(df)

import requests
import pandas as pd
import matplotlib.pyplot as plt
from datetime import datetime, timedelta

class FinanceTracker:
    def __init__(self):
        self.transactions = []

    def add_transaction(self, amount, category, description, date=None):
        """Add a new transaction"""
        if date is None:
            date = datetime.now().date()
        
        transaction = {
            'date': date,
            'amount': amount,
            'category': category,
            'description': description
        }
        self.transactions.append(transaction)

    def get_exchange_rate(self, from_currency, to_currency):
        """Fetch current exchange rate"""
        url = f"https://api.exchangerate-api.com/v4/latest/{from_currency}"
        try:
            response = requests.get(url, timeout=5)
            response.raise_for_status()
            rates = response.json()['rates']
            return rates.get(to_currency, 1.0)
        except:
            return 1.0  # Fallback

    def analyze_spending(self):
        """Analyze spending patterns"""
        if not self.transactions:
            print("No transactions to analyze")
            return
        
        # Convert to DataFrame
        df = pd.DataFrame(self.transactions)
        
        # Basic statistics
        total_spent = df['amount'].sum()
        avg_transaction = df['amount'].mean()
        
        print(f"Total spent: ${total_spent:.2f}")
        print(f"Average transaction: ${avg_transaction:.2f}")
        print(f"Number of transactions: {len(df)}")
        
        # Category breakdown
        category_totals = df.groupby('category')['amount'].sum().sort_values(ascending=False)
        print(f"\nSpending by category:")
        for category, amount in category_totals.items():
            print(f"  {category}: ${amount:.2f}")
        
        return df, category_totals

    def visualize_spending(self):
        """Create spending visualizations"""
        df, category_totals = self.analyze_spending()
        
        if df is None:
            return
        
        # Create plots
        fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(15, 6))
        
        # Pie chart of categories
        ax1.pie(category_totals.values, labels=category_totals.index, autopct='%1.1f%%')
        ax1.set_title('Spending by Category')
        
        # Timeline of spending
        df['date'] = pd.to_datetime(df['date'])
        daily_spending = df.groupby('date')['amount'].sum()
        ax2.plot(daily_spending.index, daily_spending.values, marker='o')
        ax2.set_title('Daily Spending')
        ax2.set_xlabel('Date')
        ax2.set_ylabel('Amount ($)')
        ax2.tick_params(axis='x', rotation=45)
        
        plt.tight_layout()
        plt.show()

# Usage example
tracker = FinanceTracker()

# Add some sample transactions
tracker.add_transaction(50.00, 'Food', 'Grocery shopping', datetime(2024, 1, 15).date())
tracker.add_transaction(1200.00, 'Rent', 'Monthly rent', datetime(2024, 1, 1).date())
tracker.add_transaction(25.00, 'Food', 'Restaurant dinner', datetime(2024, 1, 20).date())
tracker.add_transaction(100.00, 'Utilities', 'Electric bill', datetime(2024, 1, 10).date())
tracker.add_transaction(30.00, 'Transportation', 'Gas', datetime(2024, 1, 18).date())

# Analyze and visualize
print("=== Personal Finance Analysis ===")
tracker.analyze_spending()
tracker.visualize_spending()

# Bonus: Currency conversion
usd_to_eur = tracker.get_exchange_rate('USD', 'EUR')
print(f"\nExchange rate USD to EUR: {usd_to_eur:.4f}")