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https://github.com/simonw/sqlite-transform

Tool for running transformations on columns in a SQLite database
https://github.com/simonw/sqlite-transform

datasette-io datasette-tool sqlite

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Tool for running transformations on columns in a SQLite database

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# sqlite-transform



![No longer maintained](https://img.shields.io/badge/no%20longer-maintained-red)

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Tool for running transformations on columns in a SQLite database.



> **:warning: This tool is no longer maintained**

>

> I added a new tool to [sqlite-utils](https://sqlite-utils.datasette.io/) called [sqlite-utils convert](https://sqlite-utils.datasette.io/en/stable/cli.html#converting-data-in-columns) which provides a super-set of the functionality originally provided here. `sqlite-transform` is no longer maintained, and I recommend switching to using `sqlite-utils convert` instead.



## How to install



    pip install sqlite-transform



## parsedate and parsedatetime



These subcommands will run all values in the specified column through `dateutils.parser.parse()` and replace them with the result, formatted as an ISO timestamp or ISO date.



For example, if a row in the database has an `opened` column which contains `10/10/2019 08:10:00 PM`, running the following command:



    sqlite-transform parsedatetime my.db mytable opened



Will result in that value being replaced by `2019-10-10T20:10:00`.



Using the `parsedate` subcommand here would result in `2019-10-10` instead.



In the case of ambiguous dates such as `03/04/05` these commands both default to assuming American-style `mm/dd/yy` format. You can pass `--dayfirst` to specify that the day should be assumed to be first, or `--yearfirst` for the year.



## jsonsplit



The `jsonsplit` subcommand takes columns that contain a comma-separated list, for example a `tags` column containing records like `"trees,park,dogs"` and converts it into a JSON array `["trees", "park", "dogs"]`.



This is useful for taking advantage of Datasette's [Facet by JSON array](https://docs.datasette.io/en/stable/facets.html#facet-by-json-array) feature.



    sqlite-transform jsonsplit my.db mytable tags



It defaults to splitting on commas, but you can specify a different delimiter character using the `--delimiter` option, for example:



    sqlite-transform jsonsplit \

        my.db mytable tags --delimiter ';'



Values within the array will be treated as strings, so a column containing `123,552,775` will be converted into the JSON array `["123", "552", "775"]`.



You can specify a different type for these values using `--type int` or `--type float`, for example:



    sqlite-transform jsonsplit \

        my.db mytable tags --type int



This will result in that column being converted into `[123, 552, 775]`.



## lambda for executing your own code



The `lambda` subcommand lets you specify Python code which will be executed against the column.



Here's how to convert a column to uppercase:



    sqlite-transform lambda my.db mytable mycolumn --code='str(value).upper()'



The code you provide will be compiled into a function that takes `value` as a single argument. You can break your function body into multiple lines, provided the last line is a `return` statement:



    sqlite-transform lambda my.db mytable mycolumn --code='value = str(value)

    return value.upper()'



You can also specify Python modules that should be imported and made available to your code using one or more `--import` options:



    sqlite-transform lambda my.db mytable mycolumn \

        --code='"\n".join(textwrap.wrap(value, 10))' \

        --import=textwrap



The `--dry-run` option will output a preview of the transformation against the first ten rows, without modifying the database.



## Saving the result to a separate column



Each of these commands accepts optional `--output` and `--output-type` options. These can be used to save the result of the transformation to a separate column, which will be created if the column does not already exist.



To save the result of `jsonsplit` to a new column called `json_tags`, use the following:



    sqlite-transform jsonsplit my.db mytable tags \

      --output json_tags



The type of the created column defaults to `text`, but a different column type can be specified using `--output-type`. This example will create a new floating point column called `float_id` with a copy of each item's ID increased by 0.5:



    sqlite-transform lambda my.db mytable id \

      --code 'float(value) + 0.5' \

      --output float_id \

      --output-type float



You can drop the original column at the end of the operation by adding `--drop`.



## Splitting a column into multiple columns



Sometimes you may wish to convert a single column into multiple derived columns. For example, you may have a `location` column containing `latitude,longitude` values which you wish to split out into separate `latitude` and `longitude` columns.



You can achieve this using the `--multi` option to `sqlite-transform lambda`. This option expects your `--code` function to return a Python dictionary: new columns well be created and populated for each of the keys in that dictionary.



For the `latitude,longitude` example you would use the following:



    sqlite-transform lambda demo.db places location \

      --code 'return {

        "latitude": float(value.split(",")[0]),

        "longitude": float(value.split(",")[1]),

      }' --multi



The type of the returned values will be taken into account when creating the new columns. In this example, the resulting database schema will look like this:



```sql

CREATE TABLE [places] (

    [location] TEXT,

    [latitude] FLOAT,

    [longitude] FLOAT

);

```

The code function can also return `None`, in which case its output will be ignored.



You can drop the original column at the end of the operation by adding `--drop`.



## Disabling the progress bar



By default each command will show a progress bar. Pass `-s` or `--silent` to hide that progress bar.