PyMyForing 0.1.1

A simple, wrapper for effortless file reading and writing.

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foring is a lightweight, intuitive Python utility designed to take the friction out of file I/O operations. Inspired by the simplicity of pandas data loading routines, foring lets you read and write virtually any file format using just two clean, highly smart top-level functions: read() and write().

No more managing context managers (with open...), manually choosing between text or binary modes ('r', 'wb'), parsing formats, or handling complex serialization formats like pickle or plist manually. foring auto-detects the extension and takes care of the heavy lifting.

🚀 Installation

You can install foring directly from PyPI using pip:

pip install PyMyForing

Dependencies

foring relies entirely on Python's built-in standard library, with the single exception of PyYAML (which is automatically fetched and configured during the pip install process to parse YAML profiles cleanly).

🛠️ Quick Start & Core Usage

The entire power of the library resides in a highly focused import structure:

import foring.core as fc

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1. JSON Files

JSON is perfect for structured, human-readable data serialization.

Writing JSON:

import foring.core as fc

user_data = {"name": "Shayan Saha", "role": "Developer", "verified": True}
fc.write(user_data, "profile.json")

File content (profile.json):

{
    "name": "Shayan Saha",
    "role": "Developer",
    "verified": true
}

Reading JSON:

import foring.core as fc

data = fc.read("profile.json")
print(data["name"])  # Output: Shayan Saha

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2. CSV Files

CSV files are ideal for tabular data and spreadsheets. Automatically detects delimiters.

Writing CSV:

import foring.core as fc

matrix = [["ID", "Item"], ["101", "Laptop"], ["102", "Monitor"]]
fc.write(matrix, "inventory.csv")

File content (inventory.csv):

ID,Item
101,Laptop
102,Monitor

Reading CSV:

import foring.core as fc

rows = fc.read("inventory.csv")
print(rows[1])  # Output: ['101', 'Laptop']

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3. YAML Files

YAML is perfect for configuration files and human-readable hierarchical data. Supports both .yaml and .yml extensions.

Writing YAML:

import foring.core as fc

yaml_config = {
    "server": {
        "host": "localhost",
        "ports": [8000, 8080]
    },
    "database": {
        "enabled": True
    }
}
fc.write(yaml_config, "config.yaml")

File content (config.yaml):

database:
  enabled: true
server:
  host: localhost
  ports:
  - 8000
  - 8080

Reading YAML:

import foring.core as fc

config = fc.read("config.yaml")
print(config["server"]["ports"])  # Output: [8000, 8080]

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4. INI Files

INI files are commonly used for configuration with sections and key-value pairs.

Writing INI:

import foring.core as fc

ini_data = {
    "DATABASE": {"Host": "localhost", "User": "root", "Password": "secret"},
    "LOGGING": {"Level": "DEBUG", "File": "app.log"}
}
fc.write(ini_data, "settings.ini")

File content (settings.ini):

[DATABASE]
host = localhost
user = root
password = secret

[LOGGING]
level = DEBUG
file = app.log

Reading INI:

import foring.core as fc

settings = fc.read("settings.ini")
print(settings["DATABASE"]["User"])  # Output: root

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5. XML Files

XML is ideal for hierarchical, structured data with attributes and nested elements.

Writing XML:

import xml.etree.ElementTree as ET
import foring.core as fc

# Build standard XML tree structural elements
root = ET.Element("project")
child = ET.SubElement(root, "name")
child.text = "foring"
child.set("version", "1.0")

# Writes as an XML element hierarchy tree
fc.write(root, "meta.xml")

File content (meta.xml):

<?xml version='1.0' encoding='utf-8'?>
<project><name version="1.0">foring</name></project>

Reading XML:

import foring.core as fc

parsed_root = fc.read("meta.xml")
print(parsed_root.find("name").text)  # Output: foring
print(parsed_root.find("name").get("version"))  # Output: 1.0

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6. Text Files (TXT)

Plain text files for logs, notes, and unformatted content.

Writing Text:

import foring.core as fc

content = "This is a plain text file.\nLine 2 of content."
fc.write(content, "notes.txt")

File content (notes.txt):

This is a plain text file.
Line 2 of content.

Reading Text:

import foring.core as fc

text = fc.read("notes.txt")
print(text)  # Output: This is a plain text file. Line 2 of content.

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7. Markdown Files (MD)

Markdown files for documentation and formatted text content.

Writing Markdown:

import foring.core as fc

markdown_content = """# My Project

This is a **bold** statement.

- Item 1
- Item 2
"""
fc.write(markdown_content, "README.md")

File content (README.md):

# My Project

This is a **bold** statement.

- Item 1
- Item 2

Reading Markdown:

import foring.core as fc

content = fc.read("README.md")
print(content)

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8. Log Files (LOG)

Log files for application events and diagnostics.

Writing Logs:

import foring.core as fc

log_message = "2026-06-17 10:45:32: Operation completed successfully.\n2026-06-17 10:46:00: Process finished."
fc.write(log_message, "app.log")

File content (app.log):

2026-06-17 10:45:32: Operation completed successfully.
2026-06-17 10:46:00: Process finished.

Reading Logs:

import foring.core as fc

logs = fc.read("app.log")
print(logs)

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9. Pickle Files (PKL & PICKLE)

Python pickle format for serializing complex Python objects, including tuples, lists, and custom objects.

Writing Pickle:

import foring.core as fc

# Store a tuple with mixed data types
state_packet = (1, "active", [10, 20, 30], {"key": "value"})
fc.write(state_packet, "state.pkl")

File content (state.pkl):

(Binary pickled Python object - not human-readable)
Size: ~50 bytes (varies by content)

Reading Pickle:

import foring.core as fc

loaded_packet = fc.read("state.pkl")
print(loaded_packet[1])  # Output: active
print(loaded_packet[2])  # Output: [10, 20, 30]

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10. PList Files (PLIST)

Property list files commonly used on macOS for storing structured data.

Writing PList:

import foring.core as fc

plist_data = {
    "Name": "MyApp",
    "Version": "1.0",
    "Enabled": True,
    "Settings": {"Theme": "dark", "AutoSave": True}
}
fc.write(plist_data, "app.plist")

File content (app.plist):

<?xml version="1.0" encoding="UTF-8"?>
<!DOCTYPE plist PUBLIC "-//Apple//DTD PLIST 1.0//EN" "http://www.apple.com/DTDs/PropertyList-1.0.dtd">
<plist version="1.0">
<dict>
    <key>Name</key>
    <string>MyApp</string>
    <key>Version</key>
    <string>1.0</string>
    <key>Enabled</key>
    <true/>
    <key>Settings</key>
    <dict>
        <key>Theme</key>
        <string>dark</string>
        <key>AutoSave</key>
        <true/>
    </dict>
</dict>
</plist>

Reading PList:

import foring.core as fc

plist_content = fc.read("app.plist")
print(plist_content["Settings"]["Theme"])  # Output: dark

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11. HTML Files (HTML)

HTML markup for web content and structured markup.

Writing HTML:

import foring.core as fc

html_content = """<html>
    <body>
        <h1>Welcome to foring</h1>
        <p>A lightweight file I/O utility.</p>
    </body>
</html>"""
fc.write(html_content, "index.html")

File content (index.html):

<html>
    <body>
        <h1>Welcome to foring</h1>
        <p>A lightweight file I/O utility.</p>
    </body>
</html>

Reading HTML:

import foring.core as fc

html = fc.read("index.html")
print(html)

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12. Binary & Media Files (PNG, PDF, ZIP, etc.)

For unsupported extensions, foring automatically handles binary files. It tries text decoding first, then falls back to raw bytes for binary formats.

Writing Binary:

import foring.core as fc

# Read binary image
raw_image = open("original.png", "rb").read()

# Write as backup
fc.write(raw_image, "backup.png")

Reading Binary:

import foring.core as fc

# Read any binary file
raw_data = fc.read("image.png")
print(f"File size: {len(raw_data)} bytes")

# Works seamlessly with images, PDFs, ZIP archives, etc.

📄 License

This project is open-source software licensed under the terms of the MIT License. Feel free to use, modify, and distribute it as needed!

Attributions

Logo : Dragonfly icons created by Khadija Arif - Flaticon