Python Programming Examples

import numpy as np

# Create arrays
arr1 = np.array([1, 2, 3, 4, 5])
arr2 = np.array([10, 20, 30, 40, 50])

# Array operations
print("Array 1:", arr1)
print("Array 2:", arr2)
print("Sum:", arr1 + arr2)
print("Product:", arr1 * arr2)
print("Dot product:", np.dot(arr1, arr2))

# Matrix operations
matrix_a = np.array([[1, 2], [3, 4]])
matrix_b = np.array([[5, 6], [7, 8]])

print("\nMatrix A:")
print(matrix_a)
print("Matrix B:")
print(matrix_b)
print("Matrix multiplication:")
print(np.matmul(matrix_a, matrix_b))

# Statistical operations
data = np.random.normal(100, 15, 1000)  # 1000 samples, mean=100, std=15
print(f"\nGenerated data statistics:")
print(f"Mean: {np.mean(data):.2f}")
print(f"Standard deviation: {np.std(data):.2f}")
print(f"Min: {np.min(data):.2f}")
print(f"Max: {np.max(data):.2f}")

import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np

# Create sample data
np.random.seed(42)
data = pd.DataFrame({
    'x': np.random.randn(100),
    'y': np.random.randn(100),
    'category': np.random.choice(['A', 'B', 'C'], 100)
})

# Set up the plotting area
fig, axes = plt.subplots(2, 2, figsize=(12, 10))
fig.suptitle('Python Data Visualization Examples', fontsize=16)

# 1. Scatter plot
axes[0, 0].scatter(data['x'], data['y'], c=data['category'].astype('category').cat.codes, alpha=0.6)
axes[0, 0].set_title('Scatter Plot')
axes[0, 0].set_xlabel('X values')
axes[0, 0].set_ylabel('Y values')

# 2. Histogram
axes[0, 1].hist(data['x'], bins=20, alpha=0.7, color='skyblue')
axes[0, 1].set_title('Histogram')
axes[0, 1].set_xlabel('X values')
axes[0, 1].set_ylabel('Frequency')

# 3. Box plot
data.boxplot(column='y', by='category', ax=axes[1, 0])
axes[1, 0].set_title('Box Plot by Category')

# 4. Line plot
x_line = np.linspace(0, 10, 100)
y_line = np.sin(x_line)
axes[1, 1].plot(x_line, y_line, 'b-', linewidth=2)
axes[1, 1].set_title('Sine Wave')
axes[1, 1].set_xlabel('X')
axes[1, 1].set_ylabel('sin(X)')

plt.tight_layout()
plt.show()

import requests
from bs4 import BeautifulSoup
import pandas as pd
import time

def scrape_quotes():
    """Scrape quotes from quotes.toscrape.com"""
    quotes_data = []
    base_url = "http://quotes.toscrape.com/page/{}"

    for page in range(1, 4):  # Scrape first 3 pages
        url = base_url.format(page)
        response = requests.get(url)

        if response.status_code == 200:
            soup = BeautifulSoup(response.content, 'html.parser')
            quotes = soup.find_all('div', class_='quote')

            for quote in quotes:
                text = quote.find('span', class_='text').text
                author = quote.find('small', class_='author').text
                tags = [tag.text for tag in quote.find_all('a', class_='tag')]

                quotes_data.append({
                    'quote': text,
                    'author': author,
                    'tags': ', '.join(tags)
                })

        # Be respectful - add delay between requests
        time.sleep(1)

    return quotes_data

# Scrape and save data
quotes = scrape_quotes()
df = pd.DataFrame(quotes)

print(f"Scraped {len(quotes)} quotes")
print(df.head())

# Save to CSV
df.to_csv('quotes.csv', index=False)
print("Data saved to quotes.csv")

import requests
import json

# 1. Simple GET request
def get_user_info(username):
    """Get GitHub user information"""
    url = f"https://api.github.com/users/{username}"
    response = requests.get(url)

    if response.status_code == 200:
        return response.json()
    else:
        return {"error": f"User {username} not found"}

# 2. POST request with authentication
def create_gist(token, description, files):
    """Create a GitHub gist"""
    url = "https://api.github.com/gists"
    headers = {
        "Authorization": f"token {token}",
        "Accept": "application/vnd.github.v3+json"
    }

    data = {
        "description": description,
        "public": True,
        "files": files
    }

    response = requests.post(url, headers=headers, json=data)
    return response.json()

# 3. Handle different response types
def fetch_json_data(url):
    """Fetch and handle JSON data with error handling"""
    try:
        response = requests.get(url, timeout=10)
        response.raise_for_status()  # Raises HTTPError for bad responses

        return {
            "success": True,
            "data": response.json(),
            "status_code": response.status_code
        }
    except requests.exceptions.Timeout:
        return {"success": False, "error": "Request timed out"}
    except requests.exceptions.HTTPError as e:
        return {"success": False, "error": f"HTTP Error: {e}"}
    except requests.exceptions.RequestException as e:
        return {"success": False, "error": f"Request failed: {e}"}

# Example usage
user_data = get_user_info("octocat")
print(json.dumps(user_data, indent=2))