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dateutil functions for Datasette


Install this plugin in the same environment as Datasette.

$ datasette install datasette-dateutil


This function adds custom SQL functions that expose functionality from the dateutil Python library.

Once installed, the following SQL functions become available:

Parsing date strings

  • dateutil_parse(text) - returns an ISO8601 date string parsed from the text, or null if the input could not be parsed. dateutil_parse("10 october 2020 3pm") returns 2020-10-10T15:00:00.
  • dateutil_parse_fuzzy(text) - same as dateutil_parse() but this also works against strings that contain a date somewhere within them - that date will be returned, or null if no dates could be found. dateutil_parse_fuzzy("This is due 10 september") returns 2020-09-10T00:00:00 (but will start returning the 2021 version of that if the year is 2021).

The dateutil_parse() and dateutil_parse_fuzzy() functions both follow the American convention of assuming that 1/2/2020 lists the month first, evaluating this example to the 2nd of January.

If you want to assume that the day comes first, use these two functions instead:

  • dateutil_parse_dayfirst(text)
  • dateutil_parse_fuzzy_dayfirst(text)

Here's a query demonstrating these functions:

  dateutil_parse("10 october 2020 3pm"),
  dateutil_parse_fuzzy("This is due 10 september"),

Try that query

Calculating Easter

  • dateutil_easter(year) - returns the date for Easter in that year, for example dateutil_easter("2020") returns 2020-04-12.

Example Easter query

JSON arrays of dates

Several functions return JSON arrays of date strings. These can be used with SQLite's json_each() function to perform joins against dates from a specific date range or recurrence rule.

These functions can return up to 10,000 results. They will return an error if more than 10,000 dates would be returned - this is to protect against denial of service attacks.

  • dateutil_dates_between('1 january 2020', '5 jan 2020') - given two dates (in any format that can be handled by dateutil_parse()) this function returns a JSON string containing the dates between those two days, inclusive. This example returns ["2020-01-01", "2020-01-02", "2020-01-03", "2020-01-04", "2020-01-05"].
  • dateutil_dates_between('1 january 2020', '5 jan 2020', 0) - set the optional third argument to 0 to specify that you would like this to be exclusive of the last day. This example returns ["2020-01-01", "2020-01-02", "2020-01-03", "2020-01-04"].

Try these queries

The dateutil_rrule() and dateutil_rrule_date() functions accept the iCalendar standard ``rrule` format - see the dateutil documentation for more examples.

This format lets you specify recurrence rules such as "the next four last mondays of the month".

  • dateutil_rrule(rrule, optional_dtsart) - given an rrule returns a JSON array of ISO datetimes. The second argument is optional and will be treated as the start date for the rule.
  • dateutil_rrule_date(rrule, optional_dtsart) - same as dateutil_rrule() but returns ISO dates.

Example query:

    '1st jan 2020'

Try the rrule example query

Joining data using json_each()

SQLite's json_each() function can be used to turn a JSON array of dates into a table that can be joined against other data. Here's a query that returns a table showing every day in January 2019:

  value as date
    dateutil_dates_between('1 Jan 2019', '31 Jan 2019')

Try that query

You can run joins against this table by assigning it a name using SQLite's support for Common Table Expressions (CTEs).

This example query uses substr(created, 0, 11) to retrieve the date portion of the created column in the facetable demo table, then joins that against the table of days in January to calculate the count of rows created on each day. The LEFT JOIN against days_in_january ensures that days which had no created records are still returned in the results, with a count of 0.

with created_dates as (
    substr(created, 0, 11) as date
days_in_january as (
    value as date
      dateutil_dates_between('1 Jan 2019', '31 Jan 2019')
  count( as total
  left join created_dates on =
group by;

Try that query with a bar chart rendered using the datasette-vega plugin.


To set up this plugin locally, first checkout the code. Then create a new virtual environment:

cd datasette-dateutil
python3 -mvenv venv
source venv/bin/activate

Or if you are using pipenv:

pipenv shell

Now install the dependencies and tests:

pip install -e '.[test]'

To run the tests: