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| 219372133 | MDEwOlJlcG9zaXRvcnkyMTkzNzIxMzM= | sqlite-transform | simonw/sqlite-transform | 0 | 9599 | https://github.com/simonw/sqlite-transform | Tool for running transformations on columns in a SQLite database | 0 | 2019-11-03T22:07:53Z | 2021-08-02T22:06:23Z | 2021-08-02T22:07:57Z | 64 | 29 | 29 | Python | 1 | 1 | 1 | 1 | 0 | 1 | 0 | 0 | 0 | apache-2.0 | ["sqlite", "datasette-io", "datasette-tool"] | 1 | 0 | 29 | main | {"admin": false, "push": false, "pull": false} | 1 | 1 | # sqlite-transform  [](https://pypi.org/project/sqlite-transform/) [](https://github.com/simonw/sqlite-transform/releases) [](https://github.com/simonw/sqlite-transform/actions?query=workflow%3ATest) [](https://github.com/dogsheep/sqlite-transform/blob/main/LICENSE) 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 **:warning: This tool is no longer maintained** 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 `sqlite-transform` is no longer maintained, and I recommend switching to using `sqlite-utils convert` instead.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. | <div id="readme" class="md" data-path="README.md"><article class="markdown-body entry-content container-lg" itemprop="text"><h1><a id="user-content-sqlite-transform" class="anchor" aria-hidden="true" href="#user-content-sqlite-transform"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>sqlite-transform</h1> <p><a target="_blank" rel="noopener noreferrer" href="https://camo.githubusercontent.com/818df98789ea0f246ed427c6efefc9450fdab68f50d69b83ecbbda8dda1d82b8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e6f2532306c6f6e6765722d6d61696e7461696e65642d726564"><img src="https://camo.githubusercontent.com/818df98789ea0f246ed427c6efefc9450fdab68f50d69b83ecbbda8dda1d82b8/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6e6f2532306c6f6e6765722d6d61696e7461696e65642d726564" alt="No longer maintained" data-canonical-src="https://img.shields.io/badge/no%20longer-maintained-red" style="max-width:100%;"></a> <a href="https://pypi.org/project/sqlite-transform/" rel="nofollow"><img src="https://camo.githubusercontent.com/607faf62b18abed6c31fc21c85d2e93f7800da5d89495d6df6355b2bb0b11a38/68747470733a2f2f696d672e736869656c64732e696f2f707970692f762f73716c6974652d7472616e73666f726d2e737667" alt="PyPI" data-canonical-src="https://img.shields.io/pypi/v/sqlite-transform.svg" style="max-width:100%;"></a> <a href="https://github.com/simonw/sqlite-transform/releases"><img src="https://camo.githubusercontent.com/351675bc73115e1f71304704ec82e7402dd85f44a866cd2580a9e1ce4194384f/68747470733a2f2f696d672e736869656c64732e696f2f6769746875622f762f72656c656173652f73696d6f6e772f73716c6974652d7472616e73666f726d3f696e636c7564655f70726572656c6561736573266c6162656c3d6368616e67656c6f67" alt="Changelog" data-canonical-src="https://img.shields.io/github/v/release/simonw/sqlite-transform?include_prereleases&label=changelog" style="max-width:100%;"></a> <a href="https://github.com/simonw/sqlite-transform/actions?query=workflow%3ATest"><img src="https://github.com/simonw/sqlite-transform/workflows/Test/badge.svg" alt="Tests" style="max-width:100%;"></a> <a href="https://github.com/dogsheep/sqlite-transform/blob/main/LICENSE"><img src="https://camo.githubusercontent.com/1698104e976c681143eb0841f9675c6f802bb7aa832afc0c7a4e719b1f3cf955/68747470733a2f2f696d672e736869656c64732e696f2f62616467652f6c6963656e73652d417061636865253230322e302d626c75652e737667" alt="License" data-canonical-src="https://img.shields.io/badge/license-Apache%202.0-blue.svg" style="max-width:100%;"></a></p> <p>Tool for running transformations on columns in a SQLite database.</p> <blockquote> <p><strong><g-emoji class="g-emoji" alias="warning" fallback-src="https://github.githubassets.com/images/icons/emoji/unicode/26a0.png">⚠️</g-emoji> This tool is no longer maintained</strong></p> <p>I added a new tool to <a href="https://sqlite-utils.datasette.io/" rel="nofollow">sqlite-utils</a> called <a href="https://sqlite-utils.datasette.io/en/stable/cli.html#converting-data-in-columns" rel="nofollow">sqlite-utils convert</a> which provides a super-set of the functionality originally provided here. <code>sqlite-transform</code> is no longer maintained, and I recommend switching to using <code>sqlite-utils convert</code> instead.</p> </blockquote> <h2><a id="user-content-how-to-install" class="anchor" aria-hidden="true" href="#user-content-how-to-install"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>How to install</h2> <div class="snippet-clipboard-content position-relative" data-snippet-clipboard-copy-content="pip install sqlite-transform "><pre><code>pip install sqlite-transform </code></pre></div> <h2><a id="user-content-parsedate-and-parsedatetime" class="anchor" aria-hidden="true" href="#user-content-parsedate-and-parsedatetime"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>parsedate and parsedatetime</h2> <p>These subcommands will run all values in the specified column through <code>dateutils.parser.parse()</code> and replace them with the result, formatted as an ISO timestamp or ISO date.</p> <p>For example, if a row in the database has an <code>opened</code> column which contains <code>10/10/2019 08:10:00 PM</code>, running the following command:</p> <div class="snippet-clipboard-content position-relative" data-snippet-clipboard-copy-content="sqlite-transform parsedatetime my.db mytable opened "><pre><code>sqlite-transform parsedatetime my.db mytable opened </code></pre></div> <p>Will result in that value being replaced by <code>2019-10-10T20:10:00</code>.</p> <p>Using the <code>parsedate</code> subcommand here would result in <code>2019-10-10</code> instead.</p> <p>In the case of ambiguous dates such as <code>03/04/05</code> these commands both default to assuming American-style <code>mm/dd/yy</code> format. You can pass <code>--dayfirst</code> to specify that the day should be assumed to be first, or <code>--yearfirst</code> for the year.</p> <h2><a id="user-content-jsonsplit" class="anchor" aria-hidden="true" href="#user-content-jsonsplit"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>jsonsplit</h2> <p>The <code>jsonsplit</code> subcommand takes columns that contain a comma-separated list, for example a <code>tags</code> column containing records like <code>"trees,park,dogs"</code> and converts it into a JSON array <code>["trees", "park", "dogs"]</code>.</p> <p>This is useful for taking advantage of Datasette's <a href="https://docs.datasette.io/en/stable/facets.html#facet-by-json-array" rel="nofollow">Facet by JSON array</a> feature.</p> <div class="snippet-clipboard-content position-relative" data-snippet-clipboard-copy-content="sqlite-transform jsonsplit my.db mytable tags "><pre><code>sqlite-transform jsonsplit my.db mytable tags </code></pre></div> <p>It defaults to splitting on commas, but you can specify a different delimiter character using the <code>--delimiter</code> option, for example:</p> <div class="snippet-clipboard-content position-relative" data-snippet-clipboard-copy-content="sqlite-transform jsonsplit \ my.db mytable tags --delimiter ';' "><pre><code>sqlite-transform jsonsplit \ my.db mytable tags --delimiter ';' </code></pre></div> <p>Values within the array will be treated as strings, so a column containing <code>123,552,775</code> will be converted into the JSON array <code>["123", "552", "775"]</code>.</p> <p>You can specify a different type for these values using <code>--type int</code> or <code>--type float</code>, for example:</p> <div class="snippet-clipboard-content position-relative" data-snippet-clipboard-copy-content="sqlite-transform jsonsplit \ my.db mytable tags --type int "><pre><code>sqlite-transform jsonsplit \ my.db mytable tags --type int </code></pre></div> <p>This will result in that column being converted into <code>[123, 552, 775]</code>.</p> <h2><a id="user-content-lambda-for-executing-your-own-code" class="anchor" aria-hidden="true" href="#user-content-lambda-for-executing-your-own-code"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>lambda for executing your own code</h2> <p>The <code>lambda</code> subcommand lets you specify Python code which will be executed against the column.</p> <p>Here's how to convert a column to uppercase:</p> <div class="snippet-clipboard-content position-relative" data-snippet-clipboard-copy-content="sqlite-transform lambda my.db mytable mycolumn --code='str(value).upper()' "><pre><code>sqlite-transform lambda my.db mytable mycolumn --code='str(value).upper()' </code></pre></div> <p>The code you provide will be compiled into a function that takes <code>value</code> as a single argument. You can break your function body into multiple lines, provided the last line is a <code>return</code> statement:</p> <div class="snippet-clipboard-content position-relative" data-snippet-clipboard-copy-content="sqlite-transform lambda my.db mytable mycolumn --code='value = str(value) return value.upper()' "><pre><code>sqlite-transform lambda my.db mytable mycolumn --code='value = str(value) return value.upper()' </code></pre></div> <p>You can also specify Python modules that should be imported and made available to your code using one or more <code>--import</code> options:</p> <div class="snippet-clipboard-content position-relative" data-snippet-clipboard-copy-content="sqlite-transform lambda my.db mytable mycolumn \ --code='"\n".join(textwrap.wrap(value, 10))' \ --import=textwrap "><pre><code>sqlite-transform lambda my.db mytable mycolumn \ --code='"\n".join(textwrap.wrap(value, 10))' \ --import=textwrap </code></pre></div> <p>The <code>--dry-run</code> option will output a preview of the transformation against the first ten rows, without modifying the database.</p> <h2><a id="user-content-saving-the-result-to-a-separate-column" class="anchor" aria-hidden="true" href="#user-content-saving-the-result-to-a-separate-column"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Saving the result to a separate column</h2> <p>Each of these commands accepts optional <code>--output</code> and <code>--output-type</code> 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.</p> <p>To save the result of <code>jsonsplit</code> to a new column called <code>json_tags</code>, use the following:</p> <div class="snippet-clipboard-content position-relative" data-snippet-clipboard-copy-content="sqlite-transform jsonsplit my.db mytable tags \ --output json_tags "><pre><code>sqlite-transform jsonsplit my.db mytable tags \ --output json_tags </code></pre></div> <p>The type of the created column defaults to <code>text</code>, but a different column type can be specified using <code>--output-type</code>. This example will create a new floating point column called <code>float_id</code> with a copy of each item's ID increased by 0.5:</p> <div class="snippet-clipboard-content position-relative" data-snippet-clipboard-copy-content="sqlite-transform lambda my.db mytable id \ --code 'float(value) + 0.5' \ --output float_id \ --output-type float "><pre><code>sqlite-transform lambda my.db mytable id \ --code 'float(value) + 0.5' \ --output float_id \ --output-type float </code></pre></div> <p>You can drop the original column at the end of the operation by adding <code>--drop</code>.</p> <h2><a id="user-content-splitting-a-column-into-multiple-columns" class="anchor" aria-hidden="true" href="#user-content-splitting-a-column-into-multiple-columns"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Splitting a column into multiple columns</h2> <p>Sometimes you may wish to convert a single column into multiple derived columns. For example, you may have a <code>location</code> column containing <code>latitude,longitude</code> values which you wish to split out into separate <code>latitude</code> and <code>longitude</code> columns.</p> <p>You can achieve this using the <code>--multi</code> option to <code>sqlite-transform lambda</code>. This option expects your <code>--code</code> function to return a Python dictionary: new columns well be created and populated for each of the keys in that dictionary.</p> <p>For the <code>latitude,longitude</code> example you would use the following:</p> <div class="snippet-clipboard-content position-relative" data-snippet-clipboard-copy-content="sqlite-transform lambda demo.db places location \ --code 'return { "latitude": float(value.split(",")[0]), "longitude": float(value.split(",")[1]), }' --multi "><pre><code>sqlite-transform lambda demo.db places location \ --code 'return { "latitude": float(value.split(",")[0]), "longitude": float(value.split(",")[1]), }' --multi </code></pre></div> <p>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:</p> <div class="highlight highlight-source-sql position-relative" data-snippet-clipboard-copy-content="CREATE TABLE [places] ( [location] TEXT, [latitude] FLOAT, [longitude] FLOAT ); "><pre>CREATE TABLE [places] ( [location] <span class="pl-k">TEXT</span>, [latitude] FLOAT, [longitude] FLOAT );</pre></div> <p>The code function can also return <code>None</code>, in which case its output will be ignored.</p> <p>You can drop the original column at the end of the operation by adding <code>--drop</code>.</p> <h2><a id="user-content-disabling-the-progress-bar" class="anchor" aria-hidden="true" href="#user-content-disabling-the-progress-bar"><svg class="octicon octicon-link" viewBox="0 0 16 16" version="1.1" width="16" height="16" aria-hidden="true"><path fill-rule="evenodd" d="M7.775 3.275a.75.75 0 001.06 1.06l1.25-1.25a2 2 0 112.83 2.83l-2.5 2.5a2 2 0 01-2.83 0 .75.75 0 00-1.06 1.06 3.5 3.5 0 004.95 0l2.5-2.5a3.5 3.5 0 00-4.95-4.95l-1.25 1.25zm-4.69 9.64a2 2 0 010-2.83l2.5-2.5a2 2 0 012.83 0 .75.75 0 001.06-1.06 3.5 3.5 0 00-4.95 0l-2.5 2.5a3.5 3.5 0 004.95 4.95l1.25-1.25a.75.75 0 00-1.06-1.06l-1.25 1.25a2 2 0 01-2.83 0z"></path></svg></a>Disabling the progress bar</h2> <p>By default each command will show a progress bar. Pass <code>-s</code> or <code>--silent</code> to hide that progress bar.</p> </article></div> |
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- 10 rows from repo in releases