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‎blog/all-about-actions/index.html‎

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‎blog/all-about-actions/index.md‎

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‎blog/index.html‎

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<tr data-index="0" data-listing-date-sort="1748995200000" data-listing-file-modified-sort="1776296206321" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="24" data-listing-word-count-sort="4706" data-listing-title-sort="Data Validation Libraries for Polars (2025 Edition)" data-listing-filename-sort="index.qmd">
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<tr data-index="1" data-listing-date-sort="1748908800000" data-listing-file-modified-sort="1776296206320" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="2" data-listing-word-count-sort="251" data-listing-title-sort="C'mon C'mon: Let's Do a Pointblank Workshop!" data-listing-filename-sort="index.qmd">
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<tr data-index="2" data-listing-date-sort="1747699200000" data-listing-file-modified-sort="1776296206321" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="5" data-listing-word-count-sort="814" data-listing-title-sort="Overhauling Pointblank's User Guide" data-listing-filename-sort="index.qmd">
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<tr data-index="3" data-listing-date-sort="1746144000000" data-listing-file-modified-sort="1776296206319" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="12" data-listing-word-count-sort="2204" data-listing-title-sort="Level Up Your Data Validation with `Actions` and `FinalActions`" data-listing-filename-sort="index.qmd">
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<tr data-index="4" data-listing-date-sort="1743724800000" data-listing-file-modified-sort="1776296206320" data-listing-date-modified-sort="NaN" data-listing-reading-time-sort="11" data-listing-word-count-sort="2026" data-listing-title-sort="Introducing Pointblank" data-listing-filename-sort="index.qmd">
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‎blog/intro-pointblank/index.html‎

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<h1>The Data Validation Workflow</h1>
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<p>Let’s get right to performing a basic check of a Polars DataFrame. We’ll make use of the included <code>small_table</code> dataset.</p>
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<div class="sourceCode cell-code" id="cb2"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb2-1"><a href="#cb2-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pointblank <span class="im">as</span> pb</span>
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<span id="cb2-3"><a href="#cb2-3" aria-hidden="true" tabindex="-1"></a>small_table <span class="op">=</span> pb.load_dataset(dataset<span class="op">=</span><span class="st">"small_table"</span>, tbl_type<span class="op">=</span><span class="st">"polars"</span>)</span>
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<p>All of those numbers under the <code>UNITS</code>, <code>PASS</code>, and <code>FAIL</code> columns have to do with test units, a measure of central importance in Pointblank. Each validation step will execute a type of validation test on the target table. For example, a <a href="../../reference/Validate.col_vals_lt.html#pointblank.Validate.col_vals_lt" class="gdls-link"><code>col_vals_lt()</code></a> validation step can test that each value in a column is less than a specified number. The key finding that’s reported as a result of this test is the number of test units that pass or fail. This little diagram explains what those numbers mean:</p>
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<p><img src="validation-test-units.png" class="img-fluid" data-text-align="center"></p>
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<p>Failing test units can be tied to threshold levels, which can provide a better indication of whether failures should raise some basic awareness or spur you into action. Here’s a validation workflow that sets three failure threshold levels that signal the severity of data quality problems:</p>
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<div class="sourceCode cell-code" id="cb3"><pre class="sourceCode python"><code class="sourceCode python"><span id="cb3-1"><a href="#cb3-1" aria-hidden="true" tabindex="-1"></a><span class="im">import</span> pointblank <span class="im">as</span> pb</span>
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<p>The second validation step of the previous data validation showed 18 failing test units. That translates to 18 spots in a 2,000 row DataFrame where a data quality assertion failed. We often would like to know exactly what that failing data is; it’s usually the next step toward addressing data quality issues.</p>
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<p>Pointblank offers a method that gives you a tabular report on a specific step: <a href="../../reference/Validate.get_step_report.html#pointblank.Validate.get_step_report" class="gdls-link"><code>get_step_report()</code></a>. The previous tables you’ve seen (the validation report table) dealt with providing a summary of all validation steps. In contrast, a focused report on a single step can help to get to the heart of a data quality issue. Here’s how that looks for Step 2:</p>
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<p>This report provides the 18 rows where the failure occurred. If you scroll the table to the right you’ll see the column that underwent testing (<code>session_duration</code>) is highlighted in red. All of these values are <code>5.0</code> or less, which is in violation of the assertion (in the header) that <code>session_duration &gt; 5</code>.</p>
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<p>These types of bespoke reports are useful for finding a needle in a haystack. Another good use for a step report is when validating a table schema. Using the <a href="../../reference/Validate.col_schema_match.html#pointblank.Validate.col_schema_match" class="gdls-link"><code>col_schema_match()</code></a> validation method with a table schema prepared with the <a href="../../reference/Schema.html#pointblank.Schema" class="gdls-link"><code>Schema</code></a> class allows us to verify our understanding of the table structure. Here is a validation that performs a schema validation with the <code>small_table</code> dataset prepared as a DuckDB table:</p>
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<p>This step fails, but the validation report table doesn’t tell us how (or where). Using <a href="../../reference/Validate.get_step_report.html#pointblank.Validate.get_step_report" class="gdls-link"><code>get_step_report()</code></a> will show us what the underlying issues are:</p>
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