cleandb 5.0.2.1

easy to set up alternative to SQl, bugs and leak proof by design and also support multiple console interactions

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🧾 CleanDB

A Lightweight Data Cleaning and Quality Solution for Text-Based Structured Data

CleanDB is a Python-native, zero-dependency utility for managing structured .txt files. Perfect when full database integration is overkill using simple 1D/2D list logic.

Ideal for microservices, pipelines, automation scripts, bots, or lightweight data auditing.

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📦 Features

• ✅ Structured Data Enforcement (1D or 2D)
• ✍️ Write, Read, Append, and Delete with built-in validation
• 🔍 SQL-style read filtering by column index
• 🧹 Selective delete, file trimming, and cleanup
• 🎗️ Multi-console safe (handles concurrent access)
• 💾 Manual backups and timed snapshots
• 🧪 Debug tool with auto-fix for invalid rows
• 🗂️ Organized folder structure with versioned files
• 🔐 File hiding/unhiding with access controls
• 🗃️ Zero external dependencies
• 💪 Tamper-resistant and structure-locked

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⚙️ Installation

pip install cleandb

♻️ Upgrade

pip install --upgrade cleandb

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🚀 Quick Start

import cleandb as db

db.w("tasks", [["Read", "Done"], ["Code", "Pending"]])
db.a("tasks", [["Test", "Pending"]])
print(db.r("tasks"))

db.d("tasks", del_list=["Done"], index=1)
db.backup("tasks")
db.snapshot("tasks", unit="h", gap=6)

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🧰 Function Overview

🔄 Write

w(txt_name, write_list, is2d=None)

• Overwrites file content with a validated structure.
• Use is2d=True for 2D data, False for 1D.
• Use [] to reset file and structure lock.

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📖 Read

r(txt_name, index=None, set_new=[], notify_new=True)

• Reads file contents.
• Optional filtering using index (single or list).
• If file doesn’t exist, creates and returns set_new.

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➕ Append

a(txt_name, append_list, is2d=None)

• Appends rows to the file.
• Must match existing structure.
• is2d required if appending to a new file.

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❌ Delete

d(txt_name, del_list=[], index=None, cutoff=None, keep=None, reverse=False, size=None)

• Deletes matching rows using flexible criteria:

  • index: single, list, or "*" (match entire row)
  • cutoff: max deletions per value
  • keep: retain only N matches
  • reverse: delete from end
  • size: trim file to last N rows

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💾 Backup

backup(txt_name, display=True)

• Manual backup of a file.
• Use * to back up all files.

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⏱ Snapshot

snapshot(txt_name, unit, gap, trim=None, begin=0, display=True)

• Time-based snapshot after gap duration.
• Units: 's', 'm', 'h', 'd', 'mo', 'y'

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🧹 Debug

debug(txt_name, is2d=None, clean=None, length=None, display=True)

• Finds and optionally fixes structural issues (e.g., wrong row lengths).

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🧨 Remove File

remove(txt_name, display=True)

• Deletes file and all its backups.

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🙈 Hide / Unhide

hide(txt_name, display=True)
unhide(txt_name, display=True)

• Hide or unhide files.
• Use * for all files.

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📋 List Files

listdir(display=True)

• Lists all stored file names.

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ℹ️ File Info

info(txt_name, display=True)
describe(txt_name, display=True)

• Shows metadata: type, shape, row count, etc.

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👉 1D List Example

# Create a 1D file
db.w("shopping_list", ["Apples", "Bread", "Milk"], is2d=False)
# is2d=False is a must only for 1D list on first write / append or after a validation reset.

# Append a new item
db.a("shopping_list", ["Eggs"])

# Read all items
print(db.r("shopping_list"))           # ['Apples', 'Bread', 'Milk', 'Eggs']
print(db.r("shopping_list", index=1))  # 'Bread'

# Delete items
db.d("shopping_list", "Milk")
db.d("shopping_list", ["Bread", "Eggs"])

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👉 2D List Example

tasks = [
    ["Read Docs", "Done", "Low"],
    ["Fix Bug", "Pending", "High"],
    ["Write Tests", "Pending", "Medium"],
    ["Deploy", "In Progress", "High"],
    ["Fix Bug", "Done", "Low"]
]

db.w("task_board", tasks)

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🧹 Delete Workflow (Structured & Clear)

1️⃣ Delete rows where Priority is "Low"

db.d("task_board", del_list=["Low"], index=2)

2️⃣ Delete an exact row

db.d("task_board", del_list=[["Fix Bug", "Pending", "High"]], index="*")

3️⃣ Delete by partial match on Task

db.d("task_board", del_list=[["Deploy"], ["Write Tests"]], index=0)

4️⃣ Delete rows where Status = "Done" and Priority = "High"

db.d("task_board", del_list=[["Done", "High"]], index=[1, 2])

5️⃣ Trim file to last 2 rows

db.d("task_board", size=2)

6️⃣ Clean invalid rows

db.debug("task_board", is2d=True, clean=True, length=3)

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💾 Backup & Snapshot

db.backup("task_board")
db.snapshot("task_board", unit="h", gap=6, trim=10)

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🧪 Advanced Delete: cutoff, keep, size

tasks = [
    ["Read Docs", "Done"],
    ["Fix Bug", "Pending"],
    ["Write Tests", "Pending"],
    ["Deploy", "Pending"],
    ["Fix Bug", "Done"],
    ["Write Tests", "Done"],
    ["Fix Bug", "Pending"],
    ["Deploy", "Done"]
]

db.w("task_board", tasks)

# 1️⃣ Delete up to 2 rows where Status == "Pending"
db.d("task_board", del_list=["Pending"], index=1, cutoff=2)

# 2️⃣ Keep only 1 "Fix Bug" row
db.d("task_board", del_list=["Fix Bug"], index=0, keep=1)

# 3️⃣ Keep only last 4 rows
db.d("task_board", size=4)

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📂 Backup & Recovery

• 🔄 Backups are stored in: Backup 💾/
• 📸 Snapshots are stored in: Snapshot 📸/
• 🔁 Restore: Just copy desired file back to the main data directory.

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🧠 Notes

• Structure Locking: On first write/append, shape (1D/2D) and length are saved.
• Use w("file", []) to reset structure.
• Only list data is supported.
• All operations auto-sync with backups.

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🛡 Best Practices

• Always use CleanDB methods (w(), a(), d(), etc.).
• Avoid manual file edits — validation will fail.
• Use debug() when operations fail unexpectedly.
• Automate snapshot() for long-running apps.

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📜 License

This project is free to use, modify, and distribute. No warranties are provided.

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🙋 Contribution

Pull requests, issues, and forks are welcome!

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