tbl_match()
1.00
0.00
Actions class should be used to define the actions.
`brief: str | bool | None = None`
An optional brief description of the validation step that will be displayed in the reporting table. You can use the templating elements like `"{step}"` to insert the step number, or `"{auto}"` to include an automatically generated brief. If `True` the entire brief will be automatically generated. If `None` (the default) then there won't be a brief.
`active: bool | Callable = ``True`
A boolean value or callable that determines whether the validation step should be active. Using `False` will make the validation step inactive (still reporting its presence and keeping indexes for the steps unchanged). A callable can also be provided; it will receive the data table as its single argument and must return a boolean value. The callable is evaluated *before* any `pre=` processing. Inspection functions like has_columns() and has_rows() can be used here to conditionally activate a step based on properties of the target table.
`dimension: str | None = None`
An optional data quality dimension to categorize this validation step for health scoring. One of `"completeness"`, `"validity"`, `"uniqueness"`, `"consistency"`, `"timeliness"`, or `"volume"` (or any custom string). If `None` (the default), the dimension is inferred automatically from the assertion type. This label appears in the validation report and feeds the overall and per-dimension health scores.
## Returns
`Validate`
The [Validate](Validate.md#pointblank.Validate) object with the added validation step.
## Preprocessing
The `pre=` argument allows for a preprocessing function or lambda to be applied to the data table during interrogation. This function should take a table as input and return a modified table. This is useful for performing any necessary transformations or filtering on the data before the validation step is applied.
The preprocessing function can be any callable that takes a table as input and returns a modified table. For example, you could use a lambda function to filter the table based on certain criteria or to apply a transformation to the data. Note that the same preprocessing is **not** applied to the comparison table; only the target table is preprocessed. Regarding the lifetime of the transformed table, it only exists during the validation step and is not stored in the [Validate](Validate.md#pointblank.Validate) object or used in subsequent validation steps.
## Thresholds
The `thresholds=` parameter is used to set the failure-condition levels for the validation step. If they are set here at the step level, these thresholds will override any thresholds set at the global level in `Validate(thresholds=...)`.
There are three threshold levels: 'warning', 'error', and 'critical'. The threshold values can either be set as a proportion failing of all test units (a value between `0` to `1`), or, the absolute number of failing test units (as integer that's `1` or greater).
Thresholds can be defined using one of these input schemes:
1. use the Thresholds class (the most direct way to create thresholds)
2. provide a tuple of 1-3 values, where position `0` is the 'warning' level, position `1` is the 'error' level, and position `2` is the 'critical' level
3. create a dictionary of 1-3 value entries; the valid keys: are 'warning', 'error', and 'critical'
4. a single integer/float value denoting absolute number or fraction of failing test units for the 'warning' level only
If the number of failing test units exceeds set thresholds, the validation step will be marked as 'warning', 'error', or 'critical'. All of the threshold levels don't need to be set, you're free to set any combination of them.
Aside from reporting failure conditions, thresholds can be used to determine the actions to take for each level of failure (using the `actions=` parameter).
## Cross-Backend Validation
The [tbl_match()](Validate.tbl_match.md#pointblank.Validate.tbl_match) method supports **automatic backend coercion** when comparing tables from different backends (e.g., comparing a Polars DataFrame against a Pandas DataFrame, or comparing database tables from DuckDB/SQLite against in-memory DataFrames). When tables with different backends are detected, the comparison table is automatically converted to match the data table's backend before validation proceeds.
**Certified Backend Combinations:**
All combinations of the following backends have been tested and certified to work (in both directions):
- Pandas DataFrame
- Polars DataFrame
- DuckDB (native)
- DuckDB (as Ibis table)
- SQLite (via Ibis)
Note that database backends (DuckDB, SQLite, PostgreSQL, MySQL, Snowflake, BigQuery) are automatically materialized during validation:
- if comparing **against Polars**: materialized to Polars
- if comparing **against Pandas**: materialized to Pandas
- if **both tables are database backends**: both materialized to Polars
This ensures optimal performance and type consistency.
**Data Types That Work Best in Cross-Backend Validation:**
- numeric types: int, float columns (including proper NaN handling)
- string types: text columns with consistent encodings
- boolean types: True/False values
- null values: `None` and `NaN` are treated as equivalent across backends
- list columns: nested list structures (with basic types)
**Known Limitations:**
While many data types work well in cross-backend validation, there are some known limitations to be aware of:
- date/datetime types: When converting between Polars and Pandas, date objects may be represented differently. For example, `datetime.date` objects in Pandas may become `pd.Timestamp` objects when converted from Polars, leading to false mismatches. To work around this, ensure both tables use the same datetime representation before comparison.
- custom types: User-defined types or complex nested structures may not convert cleanly between backends and could cause unexpected comparison failures.
- categorical types: Categorical/factor columns may have different internal representations across backends.
- timezone-aware datetimes: Timezone handling differs between backends and may cause comparison issues.
Here are some ideas to overcome such limitations:
- for date/datetime columns, consider using `pre=` preprocessing to normalize representations before comparison.
- when working with custom types, manually convert tables to the same backend before using [tbl_match()](Validate.tbl_match.md#pointblank.Validate.tbl_match).
- use the same datetime precision (e.g., milliseconds vs microseconds) in both tables.
## Examples
For the examples here, we'll create two simple tables to demonstrate the [tbl_match()](Validate.tbl_match.md#pointblank.Validate.tbl_match) validation.
``` python
import pointblank as pb
import polars as pl
# Create the first table
tbl_1 = pl.DataFrame({
"a": [1, 2, 3, 4],
"b": ["w", "x", "y", "z"],
"c": [4.0, 5.0, 6.0, 7.0]
})
# Create an identical table
tbl_2 = pl.DataFrame({
"a": [1, 2, 3, 4],
"b": ["w", "x", "y", "z"],
"c": [4.0, 5.0, 6.0, 7.0]
})
pb.preview(tbl_1)
```
PolarsRows4Columns3
|
|||
|---|---|---|---|
a
Int64
|
b
String
|
c
Float64
|
|
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | E | C | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #4CA64C | 1 |
tbl_match()
|
|
✓ | 1 | 1 1.00 |
0 0.00 |
-- | -- | -- | -- |
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | E | C | EXT | ||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| #4CA64C66 | 1 |
tbl_match()
|
|
✓ | 1 | 0 0.00 |
1 1.00 |
-- | -- | -- | -- |