simple-sqlite3 0.0.2

A simple SQLite3 wrapper for Python

Simple SQLite3

Simple SQLite3 is a lightweight, Pythonic wrapper for SQLite3 databases. It provides an intuitive and easy-to-use API for managing tables, inserting/querying data, and exporting results, with built-in support for JSON/CSV/TXT, schema evolution, and a convenient CLI.

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Table of Contents

• Features
• Installation
• Quick Start
  • Programmatic Usage
  • CLI Examples
• Advanced Features
• License

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Features

• Easy-to-use API for SQLite3 database and table management.
• Command-line interface (CLI) for database operations.
• Support for exporting data to JSON, CSV and TXT formats.
• Robust to both nested and non-nested data, with datetime support.
• Utilities for processing queried results.

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Installation

pip install simple-sqlite3

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Quick Start

Programmatic Usage

1. Insert a List of Dicts (Non-Nested Data)

from simple_sqlite3 import Database

db = Database("database.db")
table = db.table("people")

table.insert([
    {"name": "Amy", "age": 30, "city": "Helsinki"},
    {"name": "Bob", "age": 25, "city": "Cambridge"},
    {"name": "Cat", "age": 20, "city": "Paris"},
])

results = table.query("SELECT *")

print(results)

Example Output:

[
    {'name': 'Amy', 'age': 30, 'city': 'Helsinki'},
    {'name': 'Bob', 'age': 25, 'city': 'Cambridge'},
    {'name': 'Cat', 'age': 20, 'city': 'Paris'}
]

2. Insert a List of Dicts (Non-Nested Data)

from simple_sqlite3 import Database

db = Database("database.db")
table = db.table("nested")

table.insert([
  {
    "country": "Finland",
    "info": {
      "capital": "Helsinki",
      "latitude": 60.1699,
      "longitude": 24.9384
    }
  },
  {
    "country": "France",
    "info": {
      "capital": "Paris",
      "latitude": 48.8566,
      "longitude": 2.3522
    }
  },
  {
    "country": "Japan",
    "info": {
      "capital": "Tokyo",
      "latitude": 35.6895,
      "longitude": 139.6917
    }
  }
])

results = table.query("SELECT *")

print(results)

3. Insert Timeseries

from datetime import datetime as dt
from simple_sqlite3 import Database

db = Database("database.db")
table = db.table("timeseries")

table.insert([
  {"date": dt(2024, 6, 1), "value": 1.2345, "pair": "EURUSD"},
  {"date": dt(2024, 6, 2), "value": 1.2350, "pair": "EURUSD"},
  {"date": dt(2024, 6, 3), "value": 1.2360, "pair": "EURUSD"},
  {"date": dt(2024, 6, 4), "value": 1.2375, "pair": "EURUSD"},
  {"date": dt(2024, 6, 1), "value": 109.45, "pair": "USDJPY"},
  {"date": dt(2024, 6, 2), "value": 109.60, "pair": "USDJPY"},
  {"date": dt(2024, 6, 3), "value": 109.75, "pair": "USDJPY"},
  {"date": dt(2024, 6, 4), "value": 109.90, "pair": "USDJPY"},
])

results = table.query("SELECT * WHERE pair = 'EURUSD'", auto_parse_dates=True)

print(results)

4. Insert Mixed Data

from datetime import datetime as dt
from simple_sqlite3 import Database

db = Database("database.db")
table = db.table("mixed_data")

table.insert([
  {"date": dt(2024, 6, 1), "value": 1.2345, "pair": "EURUSD", "source": "ECB"},
  {"date": dt(2024, 6, 1), "value": 109.45, "pair": "USDJPY", "source": "BOJ"},
  {"date": dt(2024, 6, 2), "value": 0.8567, "pair": "EURGBP", "source": "ECB"},
  {"date": dt(2024, 6, 2), "value": 1.4200, "pair": "GBPUSD", "source": "FED"},
  {
    "date": dt(2024, 6, 2),
    "value": 1.2370,
    "pair": "EURUSD",
    "source": "ECB",
    "meta": {
      "confidence": 0.98,
      "contributors": ["ECB", "Bloomberg"],
      "valuation": {"buy": 0.4, "hold": 0.2, "sell": 0.4}
    }
  },
  {
    "date": dt(2024, 6, 3),
    "value": 109.80,
    "pair": "USDJPY",
    "source": "BOJ",
    "meta": {
      "confidence": 0.95,
      "contributors": ["BOJ"]
    }
  }
])

results = table.query("SELECT *", auto_parse_dates=True)

print(results)

5. Insert Data Into Memory and Export as JSON, CSV and TXT

from datetime import datetime as dt
from simple_sqlite3 import Database

db = Database(":memory:")
table = db.table("timeseries")

table.insert([
  {"date": dt(2025, 5, 22), "value": 5328, "idx": "S&P 500"},
  {"date": dt(2025, 5, 21), "value": 5421, "idx": "S&P 500"},
  {"date": dt(2025, 5, 22), "value": 5448, "idx": "EURO STOXX 50"},
  {"date": dt(2025, 5, 21), "value": 5452, "idx": "EURO STOXX 50"},
])

table.export_to_json("timeseries.json")
table.export_to_csv("timeseries.csv")
table.export_to_txt("timeseries.txt")

6. Exporting Queried Results

from datetime import datetime as dt
from simple_sqlite3 import Database
from simple_sqlite3.utils import QueryResultsProcessor

db = Database(":memory:")
table = db.table("timeseries")

table.insert([
  {"date": dt(2025, 5, 22), "value": 5328, "idx": "S&P 500"},
  {"date": dt(2025, 5, 21), "value": 5421, "idx": "S&P 500"},
  {"date": dt(2025, 5, 22), "value": 5448, "idx": "EURO STOXX 50"},
  {"date": dt(2025, 5, 21), "value": 5452, "idx": "EURO STOXX 50"},
])

results = table.query("SELECT * WHERE idx = 'EURO STOXX 50'")

processor = QueryResultsProcessor(results)

processor.to_json("timeseries.json")
processor.to_csv("timeseries.csv")
processor.to_txt("timeseries.txt")

7. Vacuuming and Cleaning Up

from datetime import datetime as dt
from simple_sqlite3 import Database

db = Database("database.db")

table = db.table("stocks")

table.insert([
  {"date": dt(2025, 5, 22), "value": 5328, "idx": "S&P 500"},
  {"date": dt(2025, 5, 21), "value": 5421, "idx": "S&P 500"},
  {"date": dt(2025, 5, 22), "value": 5448, "idx": "EURO STOXX 50"},
  {"date": dt(2025, 5, 21), "value": 5451, "idx": "EURO STOXX 50"},
] * 2) # Duplicated

table.delete_duplicates()

table = db.table("currencies")

table.insert([
  {"date": dt(2024, 6, 1), "value": 1.2345, "pair": "EURUSD"},
  {"date": dt(2024, 6, 2), "value": 1.2350, "pair": "EURUSD"},
  {"date": dt(2024, 6, 1), "value": 109.45, "pair": "USDJPY"},
  {"date": dt(2024, 6, 2), "value": 109.60, "pair": "USDJPY"},
])

table.delete(confirm=True)

db.vacuum()

db.delete(confirm=True)

CLI Examples

Note: The CLI can be run using the db command after installation (e.g., db insert ...).
If installed as a module, you can also use python -m simple_sqlite3.cli ....

Insert data from a JSON file into a table

db insert -database database.db -table timeseries -file timeseries.json

Query all rows from a table

db query -database database.db -table timeseries -sql "SELECT *"

Remove duplicate rows from a table

db delete_duplicates -database database.db -table timeseries

Export a table to CSV format

db export -database database.db -table timeseries -output timeseries.csv

Delete a table from the database

db delete -database database.db -table timeseries

Delete the entire database file

db delete -database database.db

Advanced Features

• Automatic WAL Mode: Write-Ahead Logging for better concurrency (default).
• Schema Evolution: New columns are added automatically on insert if force=True (default).
• Batch Export: Efficiently export large tables in batches to avoid memory issues.

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License

This project is developed by Rob Suomi and licensed under the MIT License.
See the LICENSE file for details.