datasette-dateutil 0.3
dateutil functions for Datasette
License: Apache License, Version 2.0

# datasette-dateutil

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dateutil functions for Datasette

## Installation

Install this plugin in the same environment as Datasette.

    $ datasette install datasette-dateutil

## Usage

This function adds custom SQL functions that expose functionality from the [dateutil](https://dateutil.readthedocs.io/) 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:

```sql
select
  dateutil_parse("10 october 2020 3pm"),
  dateutil_parse_fuzzy("This is due 10 september"),
  dateutil_parse("1/2/2020"),
  dateutil_parse("2020-03-04"),
  dateutil_parse_dayfirst("2020-03-04");
```

[Try that query](https://latest-with-plugins.datasette.io/fixtures?sql=select%0D%0A++dateutil_parse%28%2210+october+2020+3pm%22%29%2C%0D%0A++dateutil_parse_fuzzy%28%22This+is+due+10+september%22%29%2C%0D%0A++dateutil_parse%28%221%2F2%2F2020%22%29%2C%0D%0A++dateutil_parse%28%222020-03-04%22%29%2C%0D%0A++dateutil_parse_dayfirst%28%222020-03-04%22%29%3B)

### Optional default dates

The `dateutil_parse()`, `dateutil_parse_fuzzy()`, `dateutil_parse_dayfirst()` and `dateutil_parse_fuzzy_dayfirst()` functions all accept an optional second argument specifying a "default" datetime to consider if some of the details are missing. For example, the following:
```sql
select dateutil_parse('1st october', '1985-01-01')
```
Will return `1985-10-01T00:00:00` - the missing year is replaced with the year from the default date.

[Example query demonstrating the default date argument](https://latest-with-plugins.datasette.io/fixtures?sql=with+times+as+%28%0D%0A++select%0D%0A++++datetime%28%27now%27%29+as+t%0D%0A++union%0D%0A++select%0D%0A++++datetime%28%27now%27%2C+%27-1+year%27%29%0D%0A++union%0D%0A++select%0D%0A++++datetime%28%27now%27%2C+%27-3+years%27%29%0D%0A%29%0D%0Aselect+t%2C+dateutil_parse_fuzzy%28%22This+is+due+10+september%22%2C+t%29+from+times)

### 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](https://latest-with-plugins.datasette.io/fixtures?sql=select%0D%0A++dateutil_easter%282019%29%2C%0D%0A++dateutil_easter%282020%29%2C%0D%0A++dateutil_easter%282021%29)

### 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](https://latest-with-plugins.datasette.io/fixtures?sql=select%0D%0A++dateutil_dates_between%28%271+january+2020%27%2C+%275+jan+2020%27%29%2C%0D%0A++dateutil_dates_between%28%271+january+2020%27%2C+%275+jan+2020%27%2C+0%29)

The `dateutil_rrule()` and `dateutil_rrule_date()` functions accept the iCalendar standard ``rrule` format - see [the dateutil documentation](https://dateutil.readthedocs.io/en/stable/rrule.html#rrulestr-examples) 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:

```sql
select
  dateutil_rrule('FREQ=HOURLY;COUNT=5'),
  dateutil_rrule_date(
    'FREQ=DAILY;COUNT=3',
    '1st jan 2020'
  );
```
[Try the rrule example query](https://latest-with-plugins.datasette.io/fixtures?sql=select%0D%0A++dateutil_rrule('FREQ%3DHOURLY%3BCOUNT%3D5')%2C%0D%0A++dateutil_rrule_date(%0D%0A++++'FREQ%3DDAILY%3BCOUNT%3D3'%2C%0D%0A++++'1st+jan+2020'%0D%0A++)%3B)

### Joining data using json_each()

SQLite's [json_each() function](https://www.sqlite.org/json1.html#jeach) 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:

```sql
select
  value as date
from
  json_each(
    dateutil_dates_between('1 Jan 2019', '31 Jan 2019')
  )
```
[Try that query](https://latest-with-plugins.datasette.io/fixtures?sql=select%0D%0A++value+as+date%0D%0Afrom%0D%0A++json_each%28%0D%0A++++dateutil_dates_between%28%271+Jan+2019%27%2C+%2731+Jan+2019%27%29%0D%0A++%29)

You can run joins against this table by assigning it a name using SQLite's [support for Common Table Expressions (CTEs)](https://sqlite.org/lang_with.html).

This example query uses `substr(created, 0, 11)` to retrieve the date portion of the `created` column in the [facetable demo table](https://latest-with-plugins.datasette.io/fixtures/facetable), 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.

```sql
with created_dates as (
  select
    substr(created, 0, 11) as date
  from
    facetable
),
days_in_january as (
  select
    value as date
  from
    json_each(
      dateutil_dates_between('1 Jan 2019', '31 Jan 2019')
    )
)
select
  days_in_january.date,
  count(created_dates.date) as total
from
  days_in_january
  left join created_dates on days_in_january.date = created_dates.date
group by
  days_in_january.date;
```
[Try that query](https://latest-with-plugins.datasette.io/fixtures?sql=with+created_dates+as+%28%0D%0A++select%0D%0A++++substr%28created%2C+0%2C+11%29+as+date%0D%0A++from%0D%0A++++facetable%0D%0A%29%2C%0D%0Adays_in_january+as+%28%0D%0A++select%0D%0A++++value+as+date%0D%0A++from%0D%0A++++json_each%28%0D%0A++++++dateutil_dates_between%28%271+Jan+2019%27%2C+%2731+Jan+2019%27%29%0D%0A++++%29%0D%0A%29%0D%0Aselect%0D%0A++days_in_january.date%2C%0D%0A++count%28created_dates.date%29+as+total%0D%0Afrom%0D%0A++days_in_january%0D%0A++left+join+created_dates+on+days_in_january.date+%3D+created_dates.date%0D%0Agroup+by%0D%0A++days_in_january.date%3B#g.mark=bar&g.x_column=date&g.x_type=ordinal&g.y_column=total&g.y_type=quantitative) with a bar chart rendered using the [datasette-vega](https://github.com/simonw/datasette-vega) plugin.

## Development

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:

    pytest