Nano DB

A lightweight, shell-based database management system that stores all data as plain CSV files. No SQL, no setup, no external database server — just Python and a clean command language.

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Features

• Plain CSV storage — every table is a .csv file you can open in any spreadsheet app
• Rich query language — filtering, sorting, limiting, joining, and aggregates
• Coloured output — green for success, red for errors, yellow for warnings, cyan for info
• Robust error handling — every command validates input and reports problems clearly
• No dependencies beyond Python — only colorama and tabulate required

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Installation

git clone [https://github.com/ciada-3301/NanoDB]
cd nano-db
pip install colorama tabulate
python NanoDB.py

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Quick Start

>>> create employees(id,name,age,city,department,salary)
>>> use employees
>>> add record((1,alice,30,london,engineering,85000),(2,bob,25,paris,marketing,45000))
>>> get all
>>> get all where(city=london)
>>> exit

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Command Reference

create — Create a new table

create <table>(field1,field2,...)

>>> create employees(id,name,age,city,department,salary)
>>> create products(product_id,name,category,price,stock)

Table and field names may only contain lowercase letters, digits, and underscores.

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use — Select a table

use <table>

>>> use employees

The active table name is shown in the prompt. Most commands operate on the active table by default.

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current — Show active table info

current

>>> current
ℹ  Database : employees
ℹ  Fields   : id, name, age, city, department, salary
ℹ  Rows     : 8

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list — List all tables

list

>>> list
ℹ  Databases found: 3
   ► employees  (current)
   • projects
   • archive

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add record — Insert rows

add record(<v1>,<v2>,...)                    # single row
add record((<v1>,<v2>),(<v3>,<v4>),...)     # multiple rows

>>> add record(1,alice,30,london,engineering,85000)
>>> add record((1,alice,30,london,engineering,85000),(2,bob,17,paris,marketing,45000))

All records are validated before any are written — a mismatch on one record cancels all of them.

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add column — Add new columns

add column(<col1>,<col2>,...)

>>> add column(bonus)
>>> add column(bonus,notes,rating)

New columns are added with empty values for all existing rows.

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delete database — Delete a table

delete database <table>

>>> delete database archive

⚠️ Irreversible. Consider using copy to back up first.

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delete column — Remove a column

delete column(<column>)

>>> delete column(bonus)

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delete where — Remove matching rows

delete where(<conditions>)

>>> delete where(city=paris)
>>> delete where(department=hr and city=berlin)

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change — Update matching rows

change <field>=<value> where(<conditions>)

>>> change city=berlin where(name=alice)
>>> change salary=90000 where(department=engineering and city=london)

Only one field can be updated per command.

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rename table — Rename a table

rename database <old> to <new>

>>> rename database employees to staff

If the renamed table is currently selected, the prompt updates automatically.

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rename column — Rename a column

rename column(<old> to <new>)
rename column(<old> to <new>) in <table>

>>> rename column(salary to income)
>>> rename column(bonus to reward) in employees

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copy — Duplicate a table

copy <table> to <new_table>

>>> copy employees to employees_backup

Useful before destructive operations like delete where or change.

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get — Query rows

get all
get all from (<table>)
get (<field1>,<field2>,...) from (<table>)
get all where(<conditions>)
get all sort(<column> asc|desc) limit(<n>)
get all from (<t1>,<t2>) join(<t1.col>=<t2.col>)
get (count(<col>),sum(<col>),avg(<col>),min(<col>),max(<col>))

>>> get all
>>> get all from (employees)
>>> get (name,salary) from (employees)
>>> get all where(city=london)
>>> get all where(city=london or city=berlin)
>>> get all where(not department=hr)
>>> get all where(name~ali)
>>> get all sort(salary desc) limit(5)
>>> get all from (projects,employees) join(projects.empid=employees.id)
>>> get (count(id),avg(salary),min(salary),max(salary)) from (employees)

from() is optional when a table is already selected. Both from(db) and from (db) spacing styles are supported.

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export — Create a table from a join or filter

export <newdb>(<table1.col>=<table2.col>)    # join two tables
export <newdb>(<table.col>=<value>)          # filter one table

>>> export merged(projects.empid=employees.id)
>>> export londonteam(employees.city=london)

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Where-clause Operators

Operator	Meaning	Example
=	Exact match	where(city=london)
!=	Not equal	where(city!=paris)
~	Substring match (case-insensitive)	where(name~ali)
and	Both conditions must be true	where(city=london and department=engineering)
or	Either condition must be true	where(city=london or city=berlin)
not	Negates the following condition	where(not age=17)

Conditions can be combined freely:

>>> get all where(city=london and not name~ca)
>>> get all where(not department=hr or salary!=45000)

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Aggregate Functions

Function	Description
count(col)	Number of rows
sum(col)	Sum of numeric values
avg(col)	Average of numeric values
min(col)	Minimum numeric value
max(col)	Maximum numeric value

>>> get (count(id),avg(salary),min(salary),max(salary)) from (employees)
>>> get (count(id),avg(salary)) from (employees) where(department=engineering)

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Common Patterns

Safe bulk update

>>> copy employees to employees_backup
>>> change salary=90000 where(department=engineering)
>>> get all where(department=engineering)
>>> delete database employees_backup

Join two tables

>>> get all from (projects,employees) join(projects.empid=employees.id)

Export a filtered subset

>>> export londoneng(employees.city=london)
>>> use londoneng
>>> get all where(department=engineering)

Aggregate by running a filter first

>>> get (count(id),avg(salary)) from (employees) where(department=engineering)

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Output Colours

Colour	Meaning
🟢 Green ✔	Success — operation completed
🔴 Red ✖	Error — invalid input, missing table, etc.
🟡 Yellow ⚠	Warning — 0 rows matched, field skipped
🔵 Cyan ℹ	Info — table details, list output

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Project Structure

nano-db/
├── db_shell.py       # main shell — run this
└── README.md

All tables are stored as .csv files in the same directory as db_shell.py.

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Requirements

• Python 3.7+
• colorama — terminal colours
• tabulate — grid table output

pip install colorama tabulate

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License

Free to use but with proper credits to source