MaxCleanerDB 3.0.9.5

A data cleaning and quality solution for messy data, bug prevention, and leakage control.

Ideal for structured data storage where full database integration is overkill. Uses simple 1D/2D list logic with built-in validation, file management, backup and snapshot support. Dependency-free, designed by a Data Analyst/Engineer.

🚀 Features

• ✅ Safe read/write with structure validation (1D & 2D lists).
• ➕ Append support with consistency checks.
• ❌ Granular delete with filters (cutoff, keep, reverse, size).
• 💾 Backup and 📸 Snapshot support.
• 🧹 Debug and auto-clean corrupted files.
• 📚 Built-in mini-guide.
• 🎗️ Supports Multiple Console usage.
• 🗂️ Clean folder/files organization.
• 💪 Strong anti-corruption and Tamper mechanism.

Why Use This Package?

For Data Professionals (Analysts, Scientists, Engineers):

• Build quick dashboards and visualizations from real-time text data.
• Process and clean scraped or pipeline data without needing a database.
• Track, audit, and version small datasets during experimentation.
• Validate incoming data and auto-correct format issues on the fly (clean from the source).
• Run fast, repeatable data operations locally without SQL overhead.

For General Developers:

• Great for automation scripts, bots, or microservices.
• Ideal for prototyping features without setting up a database.
• Use as a flat-file backend for small apps or CLI tools.
• Snapshot and backup data safely without extra libraries.
• Handle concurrent access and reduce race-condition bugs automatically.

📦 Core Functions

w(txt_name: str, write_list: list) -> None

Write (or reset) the contents of a file, validating structure before saving to all backup locations.

r(txt_name: str, set_new: list | None = [], notify_new: bool = True) -> list | None

Read file contents. If the file is missing, return set_new and optionally notify user of new file creation.

a(txt_name: str, append_list: list) -> list

Append new rows to an existing file after validating structure. Returns the updated list.

d(txt_name: str, ...) -> tuple[int, list]

Delete matching rows with flexible options:

• del_list: values to delete
• index: column index for 2D deletion
• cutoff: max deletions per value
• keep: retain only N per value
• reverse: delete from end
• size: trim to max N items

backup(txt_name: str, display=True)

Create a manual backup of a file or all (txt_name="*").

snapshot(txt_name: str, unit, gap, begin=0, display=True)

Take time-based snapshots if eligible. Supports:

• unit: 'minute', 'hour', 'day', 'month', etc.
• gap: how much time must pass
• begin: used for daily-based triggers

debug(txt_name, is_2D=None, clean=None, length=None, display=True)

Scan and optionally auto-clean a file that fails validation. Great for corrupted data recovery.

help()

Opens the interactive mini-guide documentation tool.

📁 File Organization

Each file is saved as a .txt in a structured folder. All backups and snapshots are handled automatically.

Other folders:

• Backup 💾/ – Manual backups
• Snapshot 📸/ – A timed backup

Validation files:

• *_validation.txt – Schema registry per file

📦 Installation – MaxCleanerDB

pip install MaxCleanerDB

♻️ Upgrade

pip install --upgrade MaxCleanerDB

Usage Examples

**import MaxCleanerDB or import MaxCleanerDB as mdb

Write (w): Overwrite data

• Stores a simple (1D) list of student names:
  w("students", ["Alice", "Bob"])

• Stores a 2D list (like a table of people):
  Think of it like this:
  Headers = [name, sex, age]
  Rows = ["Eve", "female", 25], ["Adam", "male", 30]
  w("people", [["Eve", "female", 25], ["Adam", "male", 30]])

Read (r): Read data or set default if missing

• r("students") Reads the data from the "students" file.

• r("new_file", [], notify_new=True) If "new_file" doesn’t exist, it creates one with an empty list and optionally shows a notification.

Append (a): Add new entries

• a("students", ["Charlie"]) Adds "Charlie" to the list of students.

• a("people", [["Lucy", "female", 22], ["Mark", "male", 27]]) Adds new rows to the "people" table.

Delete (d): Remove entries with different options

For simple lists (like names):

• d("students", ["Bob"]) Deletes "Bob" from the student list.

• d("students", ["Bob", "Charlie"]) Deletes both "Bob" and "Charlie" if found.

For tables (like people list with name, sex, age):

• d("people", ["Eve"], index=0) Deletes rows where name is "Eve" (index 0 means the name column).

• d("people", ["female"], index=1, cutoff=1) Deletes only the first row where sex is "female".

• d("people", ["female"], index=1, keep=1) Keeps only one row with sex as "female", deletes any others.

• d("people", size=2) Keeps only the last 2 entries in the list.

• d("people", ["Lucy"], index=0, reverse=True) Deletes rows with name "Lucy", starting from the bottom.

Using multiple column conditions (like WHERE in SQL):

• d("people", [["Adam", "male"]], index=[0, 1]) Deletes the row where name is "Adam" AND sex is "male".

• d("people", [["Eve", "female", 25]], index="*") Deletes the exact row ["Eve", "female", 25].

• d("people", [["Adam", 30], ["Mark", 27]], index=[0, 2]) Deletes rows where (name is "Adam" AND age is 30) OR (name is "Mark" AND age is 27)

Backup and Snapshot

• backup("students") Makes a backup copy of the "students" file.

• backup("*") Backs up all files.

• snapshot("students", "day", 1) Takes a snapshot once a day if the last one was over a day ago.

• snapshot("*", "hour", 6) Snapshots all files every 6 hours if enough time has passed.

Debugging

• debug("students", is_2D=False, clean=True) Checks and cleans up simple list data in "students".

• debug("people", is_2D=True, length=3) Checks if each row in "people" has 3 columns (name, sex, age).

Help

• help() Shows this usage guide.