- Python 99.7%
- C 0.2%
- Just 0.1%
Three ways a column containing nulls produced a wrong answer, all from
nulls not being accounted for.
1. The "single distinct value" shortcut. count(distinct col) skips
nulls in SQL, so a column holding nulls plus one other value reports
num_distinct == 1 and took the shortcut, which reports that one value
with a count of total_rows:
insert [{'c': None}, {'c': None}, {'c': 'a'}, {'c': 'a'}]
analyze_column('c').most_common # was [(None, 4)]
select c, count(*) group by c # is [(None, 2), ('a', 2)]
The shortcut now requires that the column has no nulls, so those
columns fall through to the group by like any other.
2. The tie-break sort compared the values directly, so a null and a
string with the same count raised TypeError:
analyze_column('c', common_limit=5)
# TypeError: '<' not supported between instances of 'NoneType' and 'str'
sort_key() keeps nulls out of the comparison and sorts them last,
which keeps the existing ordering of non-null ties - the test for
'Terryterryterry' ahead of 'Kumar' at equal counts still passes.
3. truncate() turned a null into the string 'None', so a null was
indistinguishable from a column really holding 'None'. That is the
path analyze-tables takes, since it passes value_truncate=80.
num_distinct itself is unchanged: it still follows SQL and reports 0 for
an all-null column, which test_analyze_table_column_all_nulls pins. But
num_distinct can therefore be lower than len(most_common) when a column
holds nulls alongside all-distinct values - {'c': None, 'a', 'b'} gives
num_distinct 2 and three most_common entries. Whether null should count
as a distinct value is a question about what the number means, so I have
left it alone rather than change an output the tests already pin.
Tests: 6 new cases in tests/test_analyze.py, all failing on
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| .github | ||
| docs | ||
| sqlite_utils | ||
| tests | ||
| .gitignore | ||
| .readthedocs.yaml | ||
| codecov.yml | ||
| Justfile | ||
| LICENSE | ||
| MANIFEST.in | ||
| mypy.ini | ||
| pyproject.toml | ||
| README.md | ||
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
- Configure SQLite full-text search against your database tables and run search queries against them, ordered by relevance
- Run transformations against your tables to make schema changes that SQLite
ALTER TABLEdoes not directly support, such as changing the type of a column - Extract columns into separate tables to better normalize your existing data
- Manage database migrations using Python migration files and the
sqlite-utils migratecommand - Install plugins to add custom SQL functions and additional features
Upgrading from sqlite-utils 3.x? See the 4.0 upgrade guide.
Read more on my blog, in this series of posts on New features in sqlite-utils and other entries tagged sqlite-utils.
Installation
pip install sqlite-utils
Or if you use Homebrew 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 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:
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 for everything else you can do with the Python library.
Related projects
- Datasette: A tool for exploring and publishing data
- csvs-to-sqlite: Convert CSV files into a SQLite database
- db-to-sqlite: CLI tool for exporting a MySQL or PostgreSQL database as a SQLite file
- dogsheep: A family of tools for personal analytics, built on top of
sqlite-utils