# Validation Reports After interrogating your data with a validation plan, Pointblank automatically generates a *validation report*. That tabular report comprehensively summarizes the results of all validation steps. It'll be your primary tool for understanding data quality at a glance, identifying issues, and communicating results to stakeholders. Validation reports are [Great Tables](https://github.com/posit-dev/great-tables) objects that provide rich information about each validation step. It includes: identifying information for the step, pass/fail statistics, threshold exceedances, and visual status indicators. The report makes it easy to quickly assess overall data quality and pinpoint specific areas that need attention. # Viewing the Validation Report The most straightforward way to view a validation report is to simply print the [Validate](../../reference/Validate.md#pointblank.Validate) object after calling [interrogate()](../../reference/Validate.interrogate.md#pointblank.Validate.interrogate): ``` python import pointblank as pb import polars as pl # Sample data data = pl.DataFrame({ "id": range(1, 11), "value": [120, 85, 47, 210, 30, 155, 175, 95, 205, 140], "category": ["A", "B", "C", "A", "D", "B", "A", "E", "A", "C"], "ratio": [0.5, 0.7, 0.3, 1.2, 0.8, 0.9, 0.4, 1.5, 0.6, 0.2], }) # Create and interrogate a validation validation = ( pb.Validate(data=data, tbl_name="sales_data") .col_vals_gt(columns="value", value=50, brief=True) .col_vals_in_set(columns="category", set=["A", "B", "C"], brief=True) .col_exists(columns=["id", "value"], brief=True) .interrogate() ) # Display the validation report validation ```
Pointblank Validation
2026-07-22|23:24:52 Polarssales_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_vals_gt()

Expect that values in value should be > 50.

 value 50 ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C66 2
col_vals_in_set()

Expect that values in category should be in the set of A, B, C.

 category A, B, C ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C 3
col_exists()

Expect that column id exists.

 id -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_exists()

Expect that column value exists.

 value -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
2026-07-22 23:24:52 UTC< 1 s2026-07-22 23:24:52 UTC
In a notebook or interactive environment, simply typing the validation object name displays the report automatically. In a script or REPL, you might need to explicitly call `validation.get_tabular_report().show()` to display the table. > **Note: Note** > > You can display a validation report even before calling [interrogate()](../../reference/Validate.interrogate.md#pointblank.Validate.interrogate). The report will show your validation plan with all the steps you've defined, but it won't contain any interrogation results. Additionally, validation steps that use column selection patterns (like validating multiple columns at once) won't be expanded into individual rows yet, as that expansion happens during interrogation. # Understanding Report Components The validation report table consists of several key components that work together to provide a complete picture of your data quality: ### Report Header The report header (title and subtitle area) contains important metadata about the validation: - **Title**: by default, shows "Pointblank Validation" but can be customized - **Label**: your custom label for the validation (if provided via the `label=` parameter) - **Table Information**: the table name and type (Polars, Pandas, DuckDB, etc.) - **Thresholds**: the warning, error, and critical threshold values used This header information provides essential context for interpreting the validation results, especially when sharing reports with stakeholders or reviewing historical validations. ### Report Footer The report footer contains several pieces of information that provide context and traceability: **Timestamps**: The footer shows when the interrogation was performed, including the start time, duration, and end time. This helps track when data quality checks were executed, which is especially useful when archiving reports or monitoring data quality over time. **Governance Metadata**: When you provide governance parameters to [Validate](../../reference/Validate.md#pointblank.Validate), they are displayed in the footer as well. This metadata helps document data ownership and dependencies: - **Owner**: who is responsible for the data quality (e.g., `"data-platform-team"`) - **Consumers**: who depends on this data (e.g., `["ml-team", "analytics"]`) - **Version**: the version of the validation plan or data contract (e.g., `"2.1.0"`) Here's an example showing governance metadata in a validation report: ``` python # Example with governance metadata governance_validation = ( pb.Validate( data=data, tbl_name="sales_data", label="Sales data validation", owner="data-platform-team", consumers=["ml-team", "analytics", "finance"], version="1.2.0", ) .col_vals_gt(columns="value", value=0) .col_vals_in_set(columns="category", set=["A", "B", "C", "D", "E"]) .interrogate() ) governance_validation ``` 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" />
Pointblank Validation
Sales data validation Polarssales_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C 1
col_vals_gt()
value 0 ✓ 10 10
1.00		 0
0.00		 -- -- -- --
#4CA64C 2
col_vals_in_set()
category A, B, C, D, E ✓ 10 10
1.00		 0
0.00		 -- -- -- --
2026-07-22 23:24:52 UTC< 1 s2026-07-22 23:24:52 UTC
Owner: data-platform-teamConsumers: ml-team, analytics, financeVersion: 1.2.0
The governance metadata appears below the timestamps in the footer, making it easy to identify who owns the data, who depends on it, and which version of the validation rules are being applied. > **Tip: Tip** > > Governance metadata is particularly useful in enterprise environments where data lineage and accountability are important. By including `owner`, `consumers`, and `version` in your validations, you create self-documenting reports that can be easily understood by anyone reviewing them. > **Note: Note** > > Throughout this documentation, the footer is hidden in example reports for brevity. This is controlled through a global option (see the section on controlling header and footer display later in this guide). In practice, including the footer provides valuable timestamp information for tracking when validations were executed. ## Report Columns The validation report table includes the following columns, each providing specific information about the validation steps: ### Status Indicator (first column, unlabeled) The first column is an unlabeled vertical colored bar that provides instant visual feedback about each step's status: - **Green**: all test units passed the validation - **Light green (semi-transparent)**: some test units failed but no thresholds were exceeded - **Gray**: the 'warning' threshold was exceeded - **Yellow**: the 'error' threshold was exceeded - **Red**: the 'critical' threshold was exceeded This visual indicator allows you to quickly scan the report and identify problem areas. ### Step Number (second column, unlabeled) The second column is unlabeled and contains the sequential step number, starting from 1. This number is used when referencing specific steps in other methods like `get_step_report(i=2)` or when extracting data from specific validation steps. ### TYPE The TYPE column displays the validation method name along with an icon that visually represents the type of validation being performed. The validation method indicates what aspect of data quality is being checked, such as: - [col_vals_gt()](../../reference/Validate.col_vals_gt.md#pointblank.Validate.col_vals_gt): column values greater than - [col_vals_in_set()](../../reference/Validate.col_vals_in_set.md#pointblank.Validate.col_vals_in_set): column values in a set - [col_exists()](../../reference/Validate.col_exists.md#pointblank.Validate.col_exists): column existence check - [rows_distinct()](../../reference/Validate.rows_distinct.md#pointblank.Validate.rows_distinct): row uniqueness check - and many others… When you provide a brief message (via `brief=True` for auto-generated briefs or `brief="custom text"` for custom messages), it appears within the TYPE column below the validation method name. These briefs provide human-readable explanations of what each validation step is checking, making the report more accessible to non-technical stakeholders. ``` python # Example showing brief messages in the TYPE column validation_with_briefs = ( pb.Validate(data=data, tbl_name="sales_data") .col_vals_gt( columns="value", value=50, brief="Sales values should always exceed the $50 threshold" ) .col_vals_in_set( columns="category", set=["A", "B", "C"], brief=True # Auto-generated brief ) .interrogate() ) validation_with_briefs ```
Pointblank Validation
2026-07-22|23:24:52 Polarssales_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_vals_gt()

Sales values should always exceed the $50 threshold

 value 50 ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C66 2
col_vals_in_set()

Expect that values in category should be in the set of A, B, C.

 category A, B, C ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
2026-07-22 23:24:52 UTC< 1 s2026-07-22 23:24:52 UTC
In the above report, you'll see the custom brief message appear below the [col_vals_gt](../../reference/Validate.col_vals_gt.md#pointblank.Validate.col_vals_gt) method name in the first step, and an automatically generated brief below [col_vals_in_set](../../reference/Validate.col_vals_in_set.md#pointblank.Validate.col_vals_in_set) in the second step. ### COLUMNS The column(s) being validated in this step. For validation methods that don't target specific columns (like [row_count_match](../../reference/Validate.row_count_match.md#pointblank.Validate.row_count_match)), this will show an em dash (--). ### VALUES The comparison value(s) or criteria used in the validation. For example: - for `col_vals_gt(value=100)`, this shows `100` - for `col_vals_in_set(set=["A", "B", "C"])`, this shows `A | B | C` - for existence checks, this shows an em dash (--) ### TBL Icons indicating whether any preprocessing or segmentation was applied: - **Table icon**: standard validation on the original data - **Transformation icon**: preprocessing function was applied via `pre=` - **Segmentation icon**: data was segmented via `segments=` These icons help you understand if you're validating transformed or segmented data. ### EVAL Indicates whether the validation step was evaluated: - **Checkmark**: step was successfully evaluated - **Error icon**: an evaluation error occurred (e.g., column not found) - **Inactive icon**: step was marked as inactive This column is crucial for identifying validation steps that couldn't be executed properly. ### UNITS The number of units tested in this validation step. A 'test unit' is the atomic unit being validated, which varies by validation type: - for column value checks: each cell in the target column(s) - for row checks: each row - for table checks: typically 1 (the table itself) This number is formatted with locale-appropriate thousand separators for readability. Also, since space is limited, values are often abbreviated so a figure like 43,534 will appear as `43.5K`. ### PASS The number and fraction of test units that passed the validation, displayed as: n_passed f_passed For example, the cell with 8 0.80 means 8 test units passed out of the total, representing an 80% success rate (though [f_passed](../../reference/Validate.f_passed.md#pointblank.Validate.f_passed) is always expressed as a fractional value from `0` to `1`). ### FAIL The number and fraction of test units that failed the validation, displayed similarly to PASS: n_failed f_failed For example, the cell with 2 0.20 means 2 test units failed, representing a 20% failure rate from a fractional value of `0.20`. Note that this fractional [f_failed](../../reference/Validate.f_failed.md#pointblank.Validate.f_failed) value is what's used to set failure thresholds for 'warning', 'error', and 'critical' states. ### W, E, C (Warning, Error, Critical) Three columns showing whether each threshold level was exceeded for the three different states. - **Long dash**: threshold wasn't set for a state - **Empty colored circle**: threshold was set but wasn't exceeded for a given state - **Filled colored circle**: threshold was set and exceeded In terms of colors, the 'warning' state is gray, the 'error' state is yellow, and the 'critical' state is red. Having visual indicators makes it easy to identify which validation steps have crossed into warning, error, or critical territory. ### EXT Indicates whether failing row data was extracted for this step: - **Em dash (--)**: no extract available - **Download button**: click to download failing rows as CSV When extracts are available, you can download them directly from the report for further analysis or to share with data stewards who need to fix the issues. # Understanding Validation Status The validation report helps you quickly understand the overall status of your data: - **All green status indicators**: all validations passed completely - **Light green indicators**: minor failures below warning threshold - **Gray, yellow, or red indicators**: threshold exceedances requiring attention - **Error icons in EVAL column**: validation steps that couldn't be evaluated By scanning the status indicators column, you can immediately identify which validation steps need attention and prioritize your data quality efforts accordingly. # Customizing the Report Title You can customize the validation report's title using the `title=` parameter in [get_tabular_report()](../../reference/Validate.get_tabular_report.md#pointblank.Validate.get_tabular_report). This is particularly useful when generating multiple reports or when you want to provide more context: ``` python # Default title validation.get_tabular_report() ```
Pointblank Validation
2026-07-22|23:24:52 Polarssales_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_vals_gt()

Expect that values in value should be > 50.

 value 50 ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C66 2
col_vals_in_set()

Expect that values in category should be in the set of A, B, C.

 category A, B, C ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C 3
col_exists()

Expect that column id exists.

 id -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_exists()

Expect that column value exists.

 value -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
2026-07-22 23:24:52 UTC< 1 s2026-07-22 23:24:52 UTC
``` python # Use the table name as the title validation.get_tabular_report(title=":tbl_name:") ```
sales_data
2026-07-22|23:24:52 Polarssales_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_vals_gt()

Expect that values in value should be > 50.

 value 50 ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C66 2
col_vals_in_set()

Expect that values in category should be in the set of A, B, C.

 category A, B, C ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C 3
col_exists()

Expect that column id exists.

 id -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_exists()

Expect that column value exists.

 value -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
2026-07-22 23:24:52 UTC< 1 s2026-07-22 23:24:52 UTC
``` python # Provide a custom title (supports Markdown) validation.get_tabular_report(title="**Sales Data** Quality Report") ```

Sales Data Quality Report
2026-07-22|23:24:52 Polarssales_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_vals_gt()

Expect that values in value should be > 50.

 value 50 ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C66 2
col_vals_in_set()

Expect that values in category should be in the set of A, B, C.

 category A, B, C ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C 3
col_exists()

Expect that column id exists.

 id -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_exists()

Expect that column value exists.

 value -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
2026-07-22 23:24:52 UTC< 1 s2026-07-22 23:24:52 UTC
``` python # No title validation.get_tabular_report(title=":none:") ```
2026-07-22|23:24:52 Polarssales_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_vals_gt()

Expect that values in value should be > 50.

 value 50 ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C66 2
col_vals_in_set()

Expect that values in category should be in the set of A, B, C.

 category A, B, C ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C 3
col_exists()

Expect that column id exists.

 id -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_exists()

Expect that column value exists.

 value -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
2026-07-22 23:24:52 UTC< 1 s2026-07-22 23:24:52 UTC
The title customization options are: - `":default:"` (default): shows `"Pointblank Validation"` - `":tbl_name:"`: uses the table name from `tbl_name=` parameter - `":none:"`: hides the title completely - Any string: custom title text (Markdown is supported) # Customizing with Great Tables Since the validation report is a Great Tables object, you can leverage the full power of Great Tables to customize its appearance. This allows you to match your organization's branding, highlight specific information, or adjust the presentation for different audiences. ## Guide to Internal Column Names When working with Great Tables methods to customize the validation report, you'll need to use the *internal column names* rather than the display labels you see in the rendered table. This is because Great Tables operates on the underlying data table structure, where columns have technical names that differ from their user-facing labels. For example, the column labeled `"STEP"` in the report is actually stored internally as `"i"`, and the `"TYPE"` column is internally named `"type_upd"`. Most Great Tables methods that target specific columns (like `tab_style()`, `cols_width()`, `cols_hide()`, etc.) require these internal names. Here's the complete mapping from display labels to internal column names: 1. Status indicator (no label): `"status_color"` 2. Step number (no label): `"i"` 3. `TYPE`: `"type_upd"` 4. `COLUMNS`: `"columns_upd"` 5. `VALUES`: `"values_upd"` 6. `TBL`: `"tbl"` 7. `EVAL`: `"eval"` 8. `UNITS`: `"test_units"` 9. `PASS`: `"pass"` 10. `FAIL`: `"fail"` 11. `W`: `"w_upd"` 12. `E`: `"e_upd"` 13. `C`: `"c_upd"` 14. `EXT`: `"extract_upd"` Always use these internal names when calling Great Tables methods. Using the display labels (like `"STEP"` or `"TYPE"`) will result in errors since these labels only exist in the rendered output, not in the underlying data structure. In the examples that follow, you'll see how to use these internal column names to customize various aspects of the validation report. ## Adding Custom Styling You can apply custom styles to the report table: ``` python from great_tables import style, loc # Get the report as a Great Tables object report = validation.get_tabular_report() # Add custom styling using internal column names report = ( report .tab_style( style=style.fill(color="#F0F8FF"), locations=loc.body(columns="i") # Internal name for step number ) .tab_style( style=style.text(weight="bold"), locations=loc.body(columns="type_upd") # Internal name for TYPE ) ) report ```
Pointblank Validation
2026-07-22|23:24:52 Polarssales_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_vals_gt()

Expect that values in value should be > 50.

 value 50 ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C66 2
col_vals_in_set()

Expect that values in category should be in the set of A, B, C.

 category A, B, C ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C 3
MzQuMDYyMzU1NiA0Ny4xODY4NTIxLDM0LjU2NTkzODkgQzQ2Ljk0MTQwODEsMzUuMDY5NTIyMSA0Ni44MTg2ODgsMzUuNzQ4NzE0NiA0Ni44MTg2ODgsMzYuNjAzNTM2NSBMNDYuODE4Njg4LDM4LjAwMDAyMDkgTDQ0LjMxNzcxMTQsMzguMDAwMDIwOSBaIiBpZD0iPyIgZmlsbD0iIzAwMDAwMCIgLz4KICAgICAgICA8L2c+CiAgICA8L2c+Cjwvc3ZnPg==" /> col_exists()

Expect that column id exists.

 id -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_exists()

Expect that column value exists.

 value -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
2026-07-22 23:24:52 UTC< 1 s2026-07-22 23:24:52 UTC
## Modifying Column Widths Adjust column widths to optimize the layout: ``` python report = ( validation .get_tabular_report() .cols_width( cases={ "status_color": "20px", # Status indicator column "i": "40px", # Step number column "type_upd": "170px", # TYPE column "columns_upd": "100px", # COLUMNS column } ) ) report ```
Pointblank Validation
2026-07-22|23:24:52 Polarssales_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_vals_gt()

Expect that values in value should be > 50.

 value 50 ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C66 2
col_vals_in_set()

Expect that values in category should be in the set of A, B, C.

 category A, B, C ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C 3
col_exists()

Expect that column id exists.

 id -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_exists()

Expect that column value exists.

 value -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
2026-07-22 23:24:52 UTC< 1 s2026-07-22 23:24:52 UTC
## Hiding Columns Hide specific columns that aren't relevant for your audience: ``` python # Hide the TBL and EVAL columns for a cleaner presentation (using internal names) report = ( validation .get_tabular_report() .cols_hide(columns=["tbl", "eval"]) # Use internal column names ) report ```
Pointblank Validation
2026-07-22|23:24:52 Polarssales_data
STEP COLUMNS VALUES UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_vals_gt()

Expect that values in value should be > 50.

 value 50 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C66 2
col_vals_in_set()

Expect that values in category should be in the set of A, B, C.

 category A, B, C 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C 3
col_exists()

Expect that column id exists.

 id -- 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_exists()

Expect that column value exists.

 value -- 1 1
1.00		 0
0.00		 -- -- -- --
2026-07-22 23:24:52 UTC< 1 s2026-07-22 23:24:52 UTC
## Adding a Source Note Add information about data source or validation context: ``` python report = ( validation .get_tabular_report() .tab_source_note( source_note="Data validated on 2025-10-10 | Production database snapshot" ) ) report ```
Pointblank Validation
2026-07-22|23:24:52 Polarssales_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_vals_gt()

Expect that values in value should be > 50.

 value 50 ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C66 2
col_vals_in_set()

Expect that values in category should be in the set of A, B, C.

 category A, B, C ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C 3
col_exists()

Expect that column id exists.

 id -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_exists()

Expect that column value exists.

 value -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
2026-07-22 23:24:52 UTC< 1 s2026-07-22 23:24:52 UTC
Data validated on 2025-10-10 | Production database snapshot
# Exporting the Report Great Tables provides multiple export options for sharing validation reports: ``` python # Save as a standalone HTML file validation.get_tabular_report().write_raw_html("validation_report.html") # Save as a PNG image validation.get_tabular_report().save("validation_report.png") # Open in browser validation.get_tabular_report().show("browser") ``` # Controlling Header and Footer Display You can control whether the header and footer appear in the validation report: ``` python # Hide the footer validation.get_tabular_report(incl_footer=False) ```
Pointblank Validation
2026-07-22|23:24:52 Polarssales_data
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_vals_gt()

Expect that values in value should be > 50.

 value 50 ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C66 2
col_vals_in_set()

Expect that values in category should be in the set of A, B, C.

 category A, B, C ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C 3
col_exists()

Expect that column id exists.

 id -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_exists()

Expect that column value exists.

 value -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
``` python # Hide the header validation.get_tabular_report(incl_header=False) ``` 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" />
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_vals_gt()

Expect that values in value should be > 50.

 value 50 ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C66 2
col_vals_in_set()

Expect that values in category should be in the set of A, B, C.

 category A, B, C ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C 3
col_exists()

Expect that column id exists.

 id -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_exists()

Expect that column value exists.

 value -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
2026-07-22 23:24:52 UTC< 1 s2026-07-22 23:24:52 UTC
``` python # Hide both validation.get_tabular_report(incl_header=False, incl_footer=False) ```
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
col_vals_gt()

Expect that values in value should be > 50.

 value 50 ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C66 2
col_vals_in_set()

Expect that values in category should be in the set of A, B, C.

 category A, B, C ✓ 10 8
0.80		 2
0.20		 -- -- -- CSV
#4CA64C 3
col_exists()

Expect that column id exists.

 id -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
#4CA64C 4
col_exists()

Expect that column value exists.

 value -- ✓ 1 1
1.00		 0
0.00		 -- -- -- --
You can also set these preferences globally using `pb.config()`: ``` python # Set global preferences pb.config(report_incl_header=True, report_incl_footer_timings=False) ``` # Best Practices for Validation Reports Here are some guidelines for creating effective validation reports: ### 1. Use Descriptive Table Names and Labels Provide meaningful names and labels to make reports self-documenting: ``` python validation = pb.Validate( data=sales_df, tbl_name="Q3_2025_sales", label="Quarterly sales data validation for financial reporting" ) ``` ### 2. Include Governance Metadata for Accountability Add ownership and dependency information for enterprise data governance: ``` python validation = pb.Validate( data=sales_df, tbl_name="Q3_2025_sales", label="Quarterly sales data validation", owner="data-platform-team", consumers=["ml-team", "analytics", "finance"], version="2.1.0" ) ``` This creates a clear record of who is responsible for the data, who depends on it, and which version of the validation rules are being applied. ### 3. Add Brief Messages for Stakeholder Reports When sharing reports with non-technical stakeholders, always include briefs: ``` python .col_vals_between( columns="price", left=0, right=10000, brief="Product prices must be between $0 and $10,000" ) ``` ### 4. Set Appropriate Thresholds Configure thresholds that align with your data quality requirements: ``` python validation = pb.Validate( data=data, tbl_name="customer_data", thresholds=pb.Thresholds( warning=0.01, # 1% failure triggers warning error=0.05, # 5% failure triggers error critical=0.10 # 10% failure triggers critical ) ) ``` ### 5. Customize for Your Audience Tailor the report presentation to your audience: - **Technical teams**: include all columns, show preprocessing indicators - **Management**: hide technical columns, emphasize status indicators - **Data stewards**: include extract download buttons, detailed briefs ### 6. Combine with Other Reporting Tools Use validation reports alongside other Pointblank features: - **Step reports**: drill down into specific failing steps with [get_step_report()](../../reference/Validate.get_step_report.md#pointblank.Validate.get_step_report) - **Extracts**: use [get_data_extracts()](../../reference/Validate.get_data_extracts.md#pointblank.Validate.get_data_extracts) to get all failing data for analysis - **Sundered data**: use [get_sundered_data()](../../reference/Validate.get_sundered_data.md#pointblank.Validate.get_sundered_data) to split data into passing/failing sets ### 7. Archive Reports for Trend Analysis Save validation reports over time to track data quality trends: ``` python from datetime import datetime # Save with timestamp timestamp = datetime.now().strftime("%Y%m%d_%H%M%S") validation.get_tabular_report().write_raw_html(f"validation_report_{timestamp}.html") ``` # Conclusion The validation report is your primary interface for understanding data quality after running a validation. By providing a comprehensive overview of all validation steps, visual status indicators, and detailed statistics, it enables you to: - quickly assess overall data quality across multiple dimensions - identify specific validation steps that need attention - communicate data quality status to technical and non-technical stakeholders - track threshold exceedances and their severity levels - access failing data through extract downloads Combined with customization options from Great Tables, you can create reports that perfectly match your organization's needs and workflows. Whether you're validating data in an interactive notebook, generating automated quality reports, or presenting findings to stakeholders, the validation report provides the clarity and detail you need to maintain high data quality standards.