Validate.to_code() produces the `pb.Validate(...).col_vals_*()...` chain as a string
- Validate.to_yaml() produces a yaml_interrogate()-compatible YAML document
Both are ordinary, deterministic utilities: no network access, no API keys, no LLM. They're useful on their own for sharing plans, reviewing changes in a diff, and persisting a plan alongside its results. They're also the foundation for the [AI Validation Editor](../../user-guide/advanced-validation/ai-validation-editor.md), which sends the current plan to a model as editable code.
# Rendering a Plan as Python Code
Given any validation plan, to_code() returns a self-contained block of Python that reproduces it:
``` python
import pointblank as pb
validation = (
pb.Validate(
data=pb.load_dataset("small_table"),
tbl_name="small_table",
label="Small table checks",
thresholds=pb.Thresholds(warning=0.1, error=0.25),
)
.col_vals_gt(columns="d", value=100)
.col_vals_not_null(columns="a")
.col_vals_not_null(columns="b")
.col_vals_in_set(columns="f", set=["low", "mid", "high"])
.rows_distinct()
.row_count_match(count=13)
)
print(validation.to_code())
```
import pointblank as pb
validation = (
pb.Validate(
data=your_data, # Replace your_data with the actual data variable
tbl_name="small_table",
label="Small table checks",
thresholds=pb.Thresholds(warning=0.1, error=0.25),
)
.col_vals_gt(columns="d", value=100)
.col_vals_not_null(columns=["a", "b"])
.col_vals_in_set(columns="f", set=["low", "mid", "high"])
.rows_distinct()
.row_count_match(count=13)
)
validation
A few things to notice in the output:
- The table-level configuration (`tbl_name`, `label`, `thresholds`) is reproduced on the `pb.Validate(...)` call.
- The data source is rendered as the placeholder `your_data`, since the plan doesn't know the name of the variable your data lives in. Replace it with your actual data variable before running the code.
- Adjacent steps that differ only by column are collapsed into a single call: the two [col_vals_not_null()](../../reference/Validate.col_vals_not_null.md#pointblank.Validate.col_vals_not_null) steps above become `col_vals_not_null(columns=["a", "b"])`. This keeps the output compact and diffs minimal.
- Only non-default arguments are emitted. A step-level threshold that matches the table-level default is omitted, `na_pass=True` appears only where it was set, and so on.
The result is regular source you can paste into a script or notebook, commit to version control, or share with a colleague.
# Rendering a Plan as YAML
to_yaml() renders the same plan using the schema consumed by yaml_interrogate():
``` python
print(validation.to_yaml())
```
tbl: small_table
tbl_name: small_table
label: Small table checks
thresholds:
warning: 0.1
error: 0.25
steps:
- col_vals_gt:
columns: d
value: 100
- col_vals_not_null:
columns:
- a
- b
- col_vals_in_set:
columns: f
set:
- low
- mid
- high
- rows_distinct
- row_count_match:
count: 13
The `tbl` field is set from `tbl_name` when available, otherwise to the placeholder `your_data`. Set it to a loadable data source (a dataset name, file path, or Python expression) before handing the YAML to [yaml_interrogate()](../../reference/yaml_interrogate.md#pointblank.yaml_interrogate). See the [YAML Validation Workflows](../../user-guide/yaml/yaml-validation-workflows.md) page for the full YAML schema.
You can also write the YAML directly to a file by passing `path=`:
``` python
validation.to_yaml(path="plans/small_table.yaml")
```
# Round-Tripping a Plan
Because [to_yaml()](../../reference/Contract.md#pointblank.Contract.to_yaml) emits a [yaml_interrogate()](../../reference/yaml_interrogate.md#pointblank.yaml_interrogate)-compatible document, a plan round-trips through YAML. Here we serialize a plan, then rebuild and run it with [yaml_interrogate()](../../reference/yaml_interrogate.md#pointblank.yaml_interrogate):
``` python
yaml_str = validation.to_yaml()
rebuilt = pb.yaml_interrogate(yaml_str)
rebuilt
```
| Pointblank Validation | |||||||||||||
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
Small table checks
Polarssmall_tableWARNING0.1ERROR0.25CRITICAL--
|
|||||||||||||
| STEP | COLUMNS | VALUES | TBL | EVAL | UNITS | PASS | FAIL | W | E | C | EXT | ||
| #4CA64C | 1 | 
col_vals_gt()
|
 |
✓ | 13 | 13 1.00 |
0 0.00 |
○ | ○ | -- | -- | ||
| #4CA64C | 2 | 
col_vals_not_null()
|
|
✓ | 13 | 13 1.00 |
0 0.00 |
○ | ○ | -- | -- | ||
| #4CA64C | 3 |
col_vals_not_null()
|
|
✓ | 13 | 13 1.00 |
0 0.00 |
○ | ○ | -- | -- | ||
| #4CA64C | 4 | 
col_vals_in_set()
|
|
✓ | 13 | 13 1.00 |
0 0.00 |
○ | ○ | -- | -- | ||
| #AAAAAA | 5 | 
rows_distinct()
|
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" />
✓ | 13 | 11 0.85 |
2 0.15 |
● | ○ | -- | CSV | |||
| #4CA64C | 6 | 
row_count_match()
|
|
✓ | 1 | 1 1.00 |
0 0.00 |
○ | ○ | -- | -- | ||
| 2026-07-13 17:57:38 UTC< 1 s2026-07-13 17:57:38 UTC | |||||||||||||
col_vals_expr(), conjointly(), and specially()
For these, Pointblank emits a clearly-marked placeholder so the generated code still parses and runs, and raises a warning describing what was lost. The following plan uses a `pre=` callable that can't be serialized:
``` python
import warnings
flagged = pb.Validate(data=pb.load_dataset("small_table")).col_vals_gt(
columns="d", value=100, pre=lambda df: df
)
with warnings.catch_warnings(record=True) as caught:
warnings.simplefilter("always")
code = flagged.to_code()
for w in caught:
print("warning:", w.message)
```
warning: Some parts of the validation plan could not be fully serialized to code:
- Step 'col_vals_gt' has a `pre=` preprocessing callable that cannot be serialized; it was dropped from the generated plan.
When you see such a warning, review the generated plan and restore the affected step by hand. Plans built entirely from literal arguments (values, ranges, sets, patterns, counts, schemas) serialize without any loss.
# When to Use Serialization
Rendering a plan back to source is helpful whenever you want the plan to leave the current Python session:
- **Sharing.** Send a colleague the exact plan as code or YAML rather than describing it.
- **Code review.** Serialize a plan before and after a change and diff the two to see precisely what moved. You can produce this comparison directly with EditValidation.from_plans() (no LLM required), which is also how the [AI Validation Editor](../../user-guide/advanced-validation/ai-validation-editor.md) presents proposed edits.
- **Persistence.** Store the *plan* (not just the results) alongside a validation run, so you have a durable, human-readable record of what was checked.
- **Migrating between formats.** Move a plan from Python to YAML (or, via yaml_to_python(), from YAML to Python) to fit it into a configuration-driven workflow.
# Conclusion
to_code() and to_yaml() turn a live validation plan back into portable source. They're deterministic, require no external services, and round-trip through execution and yaml_interrogate() respectively. Beyond sharing and persistence, they provide the reviewable, diffable representation that powers the [AI Validation Editor](../../user-guide/advanced-validation/ai-validation-editor.md) covered in the next section.