speedy-utils 0.1.1

Fast and easy-to-use package for data science

Speedy Utils

Speedy Utils is a Python utility library designed to streamline common programming tasks such as caching, parallel processing, file I/O, and data manipulation. It provides a collection of decorators, functions, and classes to enhance productivity and performance in your Python projects.

Table of Contents

• Features
• Installation
• Usage
  • Caching
  • Parallel Processing
  • File I/O
  • Data Manipulation
  • Utility Functions
• Testing
• Deployment
• Contributing
• License

Features

• Caching Mechanisms: Disk-based and in-memory caching to optimize function calls.
• Parallel Processing: Multi-threading, multi-processing, and asynchronous multi-threading utilities.
• File I/O: Simplified JSON, JSONL, and pickle file handling with support for various file extensions.
• Data Manipulation: Utilities for flattening lists and dictionaries, converting data types, and more.
• Timing Utilities: Tools to measure and log execution time of functions and processes.
• Pretty Printing: Enhanced printing functions for structured data, including HTML tables for Jupyter notebooks.

Installation

You can install Speedy Utils via PyPI using pip:

pip install speedy-utils

Alternatively, install directly from the repository:

git clone https://github.com/yourusername/speedy-utils.git
cd speedy-utils
pip install .

Usage

Below are examples demonstrating how to utilize various features of Speedy Utils.

Caching

Memoize Decorator

Cache the results of function calls to disk to avoid redundant computations.

from speedy_utils import memoize

@memoize
def expensive_function(x):
    # Simulate an expensive computation
    import time
    time.sleep(2)
    return x * x

result = expensive_function(4)  # Takes ~2 seconds
result = expensive_function(4)  # Retrieved from cache instantly

In-Memory Memoization

Cache function results in memory for faster access within the same runtime.

from speedy_utils import imemoize

@imemoize
def compute_sum(a, b):
    return a + b

result = compute_sum(5, 7)  # Computed and cached
result = compute_sum(5, 7)  # Retrieved from in-memory cache

Parallel Processing

Multi-threading

Execute functions concurrently using multiple threads.

from speedy_utils import multi_thread

def process_item(item):
    # Your processing logic
    return item * 2

items = [1, 2, 3, 4, 5]
results = multi_thread(process_item, items, workers=3)
print(results)  # [2, 4, 6, 8, 10]

Multi-processing

Leverage multiple CPU cores for parallel execution.

from speedy_utils import multi_process

def compute_square(n):
    return n * n

numbers = list(range(10))
squares = multi_process(compute_square, numbers, workers=4)
print(squares)  # [0, 1, 4, 9, ..., 81]

Asynchronous Multi-threading

Combine asynchronous programming with multi-threading for efficient I/O-bound operations.

import asyncio
from speedy_utils import async_multi_thread

def fetch_data(url):
    import requests
    response = requests.get(url)
    return response.text

urls = [
    "https://example.com",
    "https://openai.com",
    "https://github.com",
]

async def main():
    results = await async_multi_thread(fetch_data, urls, desc="Fetching URLs")
    for content in results:
        print(len(content))

asyncio.run(main())

File I/O

Dumping Data

Save data in JSON, JSONL, or pickle formats.

from speedy_utils import dump_json_or_pickle, dump_jsonl

data = {"name": "Alice", "age": 30}

# Save as JSON
dump_json_or_pickle(data, "data.json")

# Save as JSONL
dump_jsonl([data, {"name": "Bob", "age": 25}], "data.jsonl")

# Save as Pickle
dump_json_or_pickle(data, "data.pkl")

Loading Data

Load data based on file extensions.

from speedy_utils import load_json_or_pickle, load_by_ext

# Load JSON
data = load_json_or_pickle("data.json")

# Load JSONL
data_list = load_json_or_pickle("data.jsonl")

# Load Pickle
data = load_json_or_pickle("data.pkl")

# Load based on extension with parallel processing
loaded_data = load_by_ext(["data.json", "data.pkl"])

Data Manipulation

Flattening Lists and Dictionaries

from speedy_utils import flatten_list, flatten_dict

nested_list = [[1, 2], [3, 4], [5]]
flat_list = flatten_list(nested_list)
print(flat_list)  # [1, 2, 3, 4, 5]

nested_dict = {"a": {"b": 1, "c": 2}, "d": 3}
flat_dict = flatten_dict(nested_dict)
print(flat_dict)  # {'a.b': 1, 'a.c': 2, 'd': 3}

Converting to Built-in Python Types

from speedy_utils import convert_to_builtin_python
from pydantic import BaseModel

class User(BaseModel):
    name: str
    age: int

user = User(name="Charlie", age=28)
builtin_user = convert_to_builtin_python(user)
print(builtin_user)  # {'name': 'Charlie', 'age': 28}

Utility Functions

Pretty Printing

from speedy_utils import fprint, print_table

data = {"name": "Dana", "age": 22, "city": "New York"}

# Pretty print as table
fprint(data)

# Print as table using tabulate
print_table(data)

Timing Utilities

from speedy_utils import timef, Clock

@timef
def slow_function():
    import time
    time.sleep(3)
    return "Done"

result = slow_function()  # Prints execution time

# Using Clock
clock = Clock()
# ... your code ...
clock.log()

Testing

The project includes a comprehensive test suite using unittest. To run the tests, execute the following command in the project root directory:

python test.py

Ensure all dependencies are installed before running tests:

pip install -r requirements.txt

Deployment

The project is configured to publish releases to PyPI using GitHub Actions. To publish a new version:

1. Create a Git Tag: Follow semantic versioning (e.g., v0.1.0).
2. Push to Repository: Push the tag to trigger the GitHub Actions workflow.

The workflow defined in .github/workflows/publish.yml will handle building and uploading the package to PyPI. Ensure you have set the PYPI_API_TOKEN in your repository secrets.

Contributing

Contributions are welcome! Please follow these steps to contribute:

1. Fork the Repository: Click the "Fork" button at the top right of the repository page.
2. Create a Branch:

   git checkout -b feature/YourFeature
   

3. Commit Changes:

   git commit -m "Add your feature"
   

4. Push to Fork:

   git push origin feature/YourFeature
   

5. Create a Pull Request: Navigate to the repository and create a pull request from your fork.

Please ensure your code adheres to the project's coding standards and includes appropriate tests.

License

This project is licensed under the MIT License.

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Happy Coding! 🚀