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html-table-processing: none
---

### Advanced Validation

A validation with a comprehensive set of rules.

```{python}
#| echo: false

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### Starter Validation

A validation with the basics.

```{python}
#| echo: false

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### Data Extracts

Pulling out data extracts that highlight rows with validation failures.

<p style="text-transform: uppercase;">Validation with failures at *Step 2*:</p>

```{python}
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### Step Report: Column Data Checks

A step report for column checks shows what went wrong.

```{python}
#| echo: false

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### Step Report: Schema Check

When a schema doesn't match, a step report gives you the details.

```{python}
# | echo: false

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### Sundered Data

Splitting your data into 'pass' and 'fail' subsets.

```{python}
# | echo: false

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### Set Failure Threshold Levels

Set threshold levels to better gauge adverse data quality.

```{python}
#| echo: false

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### Validation with Actions

Configure actions to trigger when validation thresholds are exceeded, such as logging warnings or errors.

```{python}
#| echo: false

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### Validation with Final Actions

Execute actions after validation completes, such as sending alerts or generating summary reports.

```{python}
#| echo: false

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### Apply Validation Rules to Multiple Columns

Create multiple validation steps by using a list of column names with `columns=`.

```{python}
#| echo: false

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### Checks for Missing Values

Perform validations that check whether missing/NA/Null values are present.

```{python}
#| echo: false

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### Custom Expression for Checking Column Values

A column expression can be used to check column values. Just use `col_vals_expr()` for this.

```{python}
#| echo: false

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### Comparison Checks Across Columns

Perform comparisons of values in columns to values in other columns.

```{python}
#| echo: false

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### Custom Validation with `specially()`

Create bespoke validations using `specially()` to implement domain-specific business rules.

```{python}
#| echo: false

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### Expect No Duplicate Rows

We can check for duplicate rows in the table with `rows_distinct()`.

```{python}
#| echo: false

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### Checking for Duplicate Values

To check for duplicate values down a column, use `rows_distinct()` with a `columns_subset=` value.

```{python}
#| echo: false

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### Expectations with a Text Pattern

With the `col_vals_regex()`, check for conformance to a regular expression.

```{python}
#| echo: false

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### Numeric Comparisons

Perform comparisons of values in columns to fixed values.

```{python}
#| echo: false

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### Set Membership

Perform validations that check whether values are part of a set (or *not* part of one).

```{python}
#| echo: false

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### Validating Data Freshness

Use date/datetime-based validations to ensure your data is current and recent. This is critical for applications that depend on timely data updates.

```{python}
#| echo: false

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### Verifying Row and Column Counts

Check the dimensions of the table with the `*_count_match()` validation methods.

```{python}
#| echo: false

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### Column Selector Functions: Easily Pick Columns

Use column selector functions in the `columns=` argument to conveniently choose columns.

```{python}
#| echo: false

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### Date and Datetime Validations

**pointblank** provides comprehensive support for validating date and datetime values, including timezone-aware comparisons. This ensures temporal data quality in applications that handle time-sensitive information.

```{python}
#| echo: false

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### Mutate the Table in a Validation Step

For far more specialized validations, modify the table with the `pre=` argument before checking it.

```{python}
#| echo: false

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### Check the Schema of a Table

The schema of a table can be flexibly defined with `Schema` and verified with `col_schema_match()`.

```{python}
#| echo: false

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### Using Parquet Data

A Parquet dataset can be used for data validation, thanks to Ibis.

```{python}
# | echo: false

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