id,node_id,name,full_name,private,owner,html_url,description,fork,created_at,updated_at,pushed_at,homepage,size,stargazers_count,watchers_count,language,has_issues,has_projects,has_downloads,has_wiki,has_pages,forks_count,archived,disabled,open_issues_count,license,topics,forks,open_issues,watchers,default_branch,permissions,temp_clone_token,organization,network_count,subscribers_count,readme,readme_html,allow_forking,visibility,is_template,template_repository,web_commit_signoff_required,has_discussions
140912432,MDEwOlJlcG9zaXRvcnkxNDA5MTI0MzI=,sqlite-utils,simonw/sqlite-utils,0,9599,https://github.com/simonw/sqlite-utils,Python CLI utility and library for manipulating SQLite databases,0,2018-07-14T03:21:46Z,2022-11-15T18:12:16Z,2022-11-15T15:53:38Z,https://sqlite-utils.datasette.io,1437,1029,1029,Python,1,1,1,1,0,79,0,0,72,apache-2.0,"[""cli"", ""click"", ""datasette"", ""datasette-io"", ""datasette-tool"", ""python"", ""sqlite"", ""sqlite-database""]",79,72,1029,main,"{""admin"": false, ""maintain"": false, ""push"": false, ""triage"": false, ""pull"": false}",,,79,16,"# sqlite-utils
[](https://pypi.org/project/sqlite-utils/)
[](https://sqlite-utils.datasette.io/en/stable/changelog.html)
[](https://pypi.org/project/sqlite-utils/)
[](https://github.com/simonw/sqlite-utils/actions?query=workflow%3ATest)
[](http://sqlite-utils.datasette.io/en/stable/?badge=stable)
[](https://codecov.io/gh/simonw/sqlite-utils)
[](https://github.com/simonw/sqlite-utils/blob/main/LICENSE)
[](https://discord.gg/Ass7bCAMDw)
Python CLI utility and library for manipulating SQLite databases.
## Some feature highlights
- [Pipe JSON](https://sqlite-utils.datasette.io/en/stable/cli.html#inserting-json-data) (or [CSV or TSV](https://sqlite-utils.datasette.io/en/stable/cli.html#inserting-csv-or-tsv-data)) directly into a new SQLite database file, automatically creating a table with the appropriate schema
- [Run in-memory SQL queries](https://sqlite-utils.datasette.io/en/stable/cli.html#querying-data-directly-using-an-in-memory-database), including joins, directly against data in CSV, TSV or JSON files and view the results
- [Configure SQLite full-text search](https://sqlite-utils.datasette.io/en/stable/cli.html#configuring-full-text-search) against your database tables and run search queries against them, ordered by relevance
- Run [transformations against your tables](https://sqlite-utils.datasette.io/en/stable/cli.html#transforming-tables) to make schema changes that SQLite `ALTER TABLE` does not directly support, such as changing the type of a column
- [Extract columns](https://sqlite-utils.datasette.io/en/stable/cli.html#extracting-columns-into-a-separate-table) into separate tables to better normalize your existing data
Read more on my blog, in this series of posts on [New features in sqlite-utils](https://simonwillison.net/series/sqlite-utils-features/) and other [entries tagged sqliteutils](https://simonwillison.net/tags/sqliteutils/).
## Installation
pip install sqlite-utils
Or if you use [Homebrew](https://brew.sh/) for macOS:
brew install sqlite-utils
## Using as a CLI tool
Now you can do things with the CLI utility like this:
$ sqlite-utils memory dogs.csv ""select * from t""
[{""id"": 1, ""age"": 4, ""name"": ""Cleo""},
{""id"": 2, ""age"": 2, ""name"": ""Pancakes""}]
$ sqlite-utils insert dogs.db dogs dogs.csv --csv
[####################################] 100%
$ sqlite-utils tables dogs.db --counts
[{""table"": ""dogs"", ""count"": 2}]
$ sqlite-utils dogs.db ""select id, name from dogs""
[{""id"": 1, ""name"": ""Cleo""},
{""id"": 2, ""name"": ""Pancakes""}]
$ sqlite-utils dogs.db ""select * from dogs"" --csv
id,age,name
1,4,Cleo
2,2,Pancakes
$ sqlite-utils dogs.db ""select * from dogs"" --table
id age name
---- ----- --------
1 4 Cleo
2 2 Pancakes
You can import JSON data into a new database table like this:
$ curl https://api.github.com/repos/simonw/sqlite-utils/releases \
| sqlite-utils insert releases.db releases - --pk id
Or for data in a CSV file:
$ sqlite-utils insert dogs.db dogs dogs.csv --csv
`sqlite-utils memory` lets you import CSV or JSON data into an in-memory database and run SQL queries against it in a single command:
$ cat dogs.csv | sqlite-utils memory - ""select name, age from stdin""
See the [full CLI documentation](https://sqlite-utils.datasette.io/en/stable/cli.html) for comprehensive coverage of many more commands.
## Using as a library
You can also `import sqlite_utils` and use it as a Python library like this:
```python
import sqlite_utils
db = sqlite_utils.Database(""demo_database.db"")
# This line creates a ""dogs"" table if one does not already exist:
db[""dogs""].insert_all([
{""id"": 1, ""age"": 4, ""name"": ""Cleo""},
{""id"": 2, ""age"": 2, ""name"": ""Pancakes""}
], pk=""id"")
```
Check out the [full library documentation](https://sqlite-utils.datasette.io/en/stable/python-api.html) for everything else you can do with the Python library.
## Related projects
* [Datasette](https://datasette.io/): A tool for exploring and publishing data
* [csvs-to-sqlite](https://github.com/simonw/csvs-to-sqlite): Convert CSV files into a SQLite database
* [db-to-sqlite](https://github.com/simonw/db-to-sqlite): CLI tool for exporting a MySQL or PostgreSQL database as a SQLite file
* [dogsheep](https://dogsheep.github.io/): A family of tools for personal analytics, built on top of `sqlite-utils`
","
sqlite-utils
Python CLI utility and library for manipulating SQLite databases.
Some feature highlights
Pipe JSON (or CSV or TSV) directly into a new SQLite database file, automatically creating a table with the appropriate schema
Run in-memory SQL queries, including joins, directly against data in CSV, TSV or JSON files and view the results
Run transformations against your tables to make schema changes that SQLite ALTER TABLE does not directly support, such as changing the type of a column
Extract columns into separate tables to better normalize your existing data
You can also import sqlite_utils and use it as a Python library like this:
importsqlite_utilsdb=sqlite_utils.Database(""demo_database.db"")
# This line creates a ""dogs"" table if one does not already exist:db[""dogs""].insert_all([
{""id"": 1, ""age"": 4, ""name"": ""Cleo""},
{""id"": 2, ""age"": 2, ""name"": ""Pancakes""}
], pk=""id"")