Python Kernel Example¶

This notebook demonstrates using the Python 3 kernel for data science and general programming tasks.

Kernel Information¶

This notebook uses the Python 3 kernel, which is the default kernel for Jupyter notebooks.

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# Display Python version and kernel information
import platform
import sys

print(f"Python version: {sys.version}")
print(f"Python implementation: {platform.python_implementation()}")
print(f"Platform: {platform.platform()}")

# Display Python version and kernel information import platform import sys print(f"Python version: {sys.version}") print(f"Python implementation: {platform.python_implementation()}") print(f"Platform: {platform.platform()}")

Python Features¶

Python is excellent for:

• Data Science and Machine Learning
• Web Development
• Automation and Scripting
• Scientific Computing

In [ ]:

import numpy as np

# Example: Data manipulation with pandas
import pandas as pd

# Create a sample dataset
data = {
    "Language": ["Python", "R", "Julia", "Ruby"],
    "Type": ["General", "Statistical", "Scientific", "General"],
    "Performance": [7, 6, 9, 5],
    "Ease of Use": [9, 7, 6, 8],
}

df = pd.DataFrame(data)
print("Programming Languages Comparison:")
df

import numpy as np # Example: Data manipulation with pandas import pandas as pd # Create a sample dataset data = { "Language": ["Python", "R", "Julia", "Ruby"], "Type": ["General", "Statistical", "Scientific", "General"], "Performance": [7, 6, 9, 5], "Ease of Use": [9, 7, 6, 8], } df = pd.DataFrame(data) print("Programming Languages Comparison:") df

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# Visualization example
import matplotlib.pyplot as plt

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

# Performance comparison
ax1.bar(
    df["Language"],
    df["Performance"],
    color=["#3776ab", "#276DC3", "#9558B2", "#CC342D"],
)
ax1.set_title("Performance Comparison")
ax1.set_ylabel("Performance Score")
ax1.set_ylim(0, 10)

# Ease of use comparison
ax2.bar(
    df["Language"],
    df["Ease of Use"],
    color=["#3776ab", "#276DC3", "#9558B2", "#CC342D"],
)
ax2.set_title("Ease of Use Comparison")
ax2.set_ylabel("Ease of Use Score")
ax2.set_ylim(0, 10)

plt.tight_layout()
plt.show()

# Visualization example import matplotlib.pyplot as plt fig, (ax1, ax2) = plt.subplots(1, 2, figsize=(12, 5)) # Performance comparison ax1.bar( df["Language"], df["Performance"], color=["#3776ab", "#276DC3", "#9558B2", "#CC342D"], ) ax1.set_title("Performance Comparison") ax1.set_ylabel("Performance Score") ax1.set_ylim(0, 10) # Ease of use comparison ax2.bar( df["Language"], df["Ease of Use"], color=["#3776ab", "#276DC3", "#9558B2", "#CC342D"], ) ax2.set_title("Ease of Use Comparison") ax2.set_ylabel("Ease of Use Score") ax2.set_ylim(0, 10) plt.tight_layout() plt.show()

Advanced Python Features¶

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import time

# Example: Decorators and context managers
from functools import wraps


def timer_decorator(func):
    """Measure execution time of a function"""

    @wraps(func)
    def wrapper(*args, **kwargs):
        start = time.time()
        result = func(*args, **kwargs)
        end = time.time()
        print(f"{func.__name__} took {end - start:.4f} seconds")
        return result

    return wrapper


@timer_decorator
def calculate_fibonacci(n):
    """Calculate Fibonacci sequence up to n terms"""
    fib = [0, 1]
    for i in range(2, n):
        fib.append(fib[-1] + fib[-2])
    return fib


# Test the decorated function
fib_sequence = calculate_fibonacci(20)
print(f"First 20 Fibonacci numbers: {fib_sequence}")

import time # Example: Decorators and context managers from functools import wraps def timer_decorator(func): """Measure execution time of a function""" @wraps(func) def wrapper(*args, **kwargs): start = time.time() result = func(*args, **kwargs) end = time.time() print(f"{func.__name__} took {end - start:.4f} seconds") return result return wrapper @timer_decorator def calculate_fibonacci(n): """Calculate Fibonacci sequence up to n terms""" fib = [0, 1] for i in range(2, n): fib.append(fib[-1] + fib[-2]) return fib # Test the decorated function fib_sequence = calculate_fibonacci(20) print(f"First 20 Fibonacci numbers: {fib_sequence}")

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# Example: Async/await (for demonstration - won't execute in sync context)
import asyncio


async def fetch_data(delay, data_id):
    """Simulate async data fetching"""
    print(f"Fetching data {data_id}...")
    await asyncio.sleep(delay)
    return f"Data {data_id} retrieved"


# Note: This is just for demonstration
# In a real async context, you would use:
# asyncio.run(fetch_data(1, "A"))

print("Python supports async/await for concurrent programming!")

# Example: Async/await (for demonstration - won't execute in sync context) import asyncio async def fetch_data(delay, data_id): """Simulate async data fetching""" print(f"Fetching data {data_id}...") await asyncio.sleep(delay) return f"Data {data_id} retrieved" # Note: This is just for demonstration # In a real async context, you would use: # asyncio.run(fetch_data(1, "A")) print("Python supports async/await for concurrent programming!")

Installing Additional Python Packages¶

You can install additional packages directly from a notebook cell:

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# Example of installing packages (commented out to avoid actual installation)
# !pip install scikit-learn
# !pip install tensorflow
# !pip install requests

print("Use !pip install <package> to install Python packages")

# Example of installing packages (commented out to avoid actual installation) # !pip install scikit-learn # !pip install tensorflow # !pip install requests print("Use !pip install to install Python packages")

Summary¶

The Python kernel provides:

• Full access to the Python ecosystem
• Excellent data science libraries (pandas, numpy, scikit-learn)
• Visualization tools (matplotlib, seaborn, plotly)
• Web frameworks and automation tools
• Strong community support and documentation

Python remains the most popular kernel for Jupyter notebooks due to its versatility and extensive library ecosystem.