Merge branch 'posit-dev:main' into add-get_dataframe

Author: Meghansaha

CITATION.cff

@@ -3,7 +3,7 @@ message: 'If you wish to cite the "Pointblank" package use:'
 type: software
 license: MIT
 title: "Pointblank: data validation toolkit for assessing and monitoring data quality."
-version: 0.16.0
+version: 0.21.0
 abstract: Validate data in Polars and Pandas DataFrames and database tables.
   Validation pipelines can be made using easily-readable, consecutive validation
   steps. Upon execution of the validation plan, several reporting options are available.

README.md

@@ -392,6 +392,7 @@ data
 The generator supports sophisticated data generation with these capabilities:
 
 - **Realistic data with presets**: Use built-in presets like `"name"`, `"email"`, `"address"`, `"phone"`, etc.
+- **User agent strings**: Generate highly varied, realistic browser user agent strings from 17 browser categories with over 42,000 unique combinations
 - **50+ country support**: Generate locale-specific data (e.g., `country="DE"` for German addresses)
 - **Field constraints**: Control ranges, patterns, uniqueness, and allowed values
 - **Multiple output formats**: Returns Polars DataFrames by default, but also supports Pandas (`output="pandas"`) or dictionaries (`output="dict"`)
@@ -404,6 +405,7 @@ This makes it easy to generate test data that matches your validation rules, hel
 - **Built for collaboration**: Share results with colleagues through beautiful interactive reports
 - **Practical outputs**: Get exactly what you need: counts, extracts, summaries, or full reports
 - **Flexible deployment**: Use in notebooks, scripts, or data pipelines
+- **Synthetic data generation**: Create realistic test data with 30+ presets, user agent strings, locale-aware formatting, and 50+ country support
 - **Customizable**: Tailor validation steps and reporting to your specific needs
 - **Internationalization**: Reports can be generated in 40 languages, including English, Spanish, French, and German
 

docs/_quarto.yml

@@ -274,6 +274,16 @@ quartodoc:
         - name: get_data_path
         - name: connect_to_table
         - name: print_database_tables
+    - title: Table Pre-checks
+      desc: >
+        The *Table Pre-checks* group contains helper functions that are designed for use with the
+        `active=` parameter of validation methods. These callables inspect the target table before
+        a validation step runs and conditionally skip the step when a precondition is not met
+        (e.g., a required column is missing or the table does not have enough rows). A descriptive,
+        locale-aware note is automatically attached to any step that is skipped.
+      contents:
+        - name: has_columns
+        - name: has_rows
     - title: YAML
       desc: >
         The *YAML* group contains functions that allow for the use of YAML to orchestrate validation
@@ -304,7 +314,8 @@ quartodoc:
       desc: >
         Generate synthetic test data based on schema definitions. Use `generate_dataset()` to
         create data from a `Schema` object. The helper functions define typed fields with
-        constraints for realistic test data generation.
+        constraints for realistic test data generation. The `profile_fields()` helper creates a
+        complete person-profile schema (name, email, address, phone, etc.) in a single call.
       contents:
         - name: generate_dataset
         - name: int_field
@@ -315,6 +326,7 @@ quartodoc:
         - name: datetime_field
         - name: time_field
         - name: duration_field
+        - name: profile_fields
     - title: Prebuilt Actions
       desc: >
         The *Prebuilt Actions* group contains a function that can be used to send a Slack

docs/demos/datetime-validations/index.qmd

@@ -16,7 +16,7 @@ html-table-processing: none
 import pointblank as pb
 import polars as pl
 from datetime import date, datetime
-import pytz
+from zoneinfo import ZoneInfo
 
 # Create sample data with various temporal data types
 temporal_data = pl.DataFrame({
@@ -33,10 +33,10 @@ temporal_data = pl.DataFrame({
         datetime(2024, 3, 20, 17, 22, 45)
     ],
     "event_time_tz": [
-        datetime(2023, 1, 15, 9, 0, tzinfo=pytz.timezone("America/New_York")),
-        datetime(2023, 6, 10, 12, 30, tzinfo=pytz.timezone("America/New_York")),
-        datetime(2023, 12, 5, 15, 45, tzinfo=pytz.timezone("America/New_York")),
-        datetime(2024, 3, 20, 18, 15, tzinfo=pytz.timezone("America/New_York"))
+        datetime(2023, 1, 15, 9, 0, tzinfo=ZoneInfo("America/New_York")),
+        datetime(2023, 6, 10, 12, 30, tzinfo=ZoneInfo("America/New_York")),
+        datetime(2023, 12, 5, 15, 45, tzinfo=ZoneInfo("America/New_York")),
+        datetime(2024, 3, 20, 18, 15, tzinfo=ZoneInfo("America/New_York"))
     ],
     "order_id": [1001, 1002, 1003, 1004],
     "amount": [150.0, 275.5, 89.99, 420.00]
@@ -57,7 +57,7 @@ validation = (
     )
     .col_vals_ge(
         columns="event_time_tz",
-        value=datetime(2023, 1, 1, 8, 0, tzinfo=pytz.timezone("America/New_York")),
+        value=datetime(2023, 1, 1, 8, 0, tzinfo=ZoneInfo("America/New_York")),
         brief="Timezone-aware events after 8 AM Eastern"
     )
     .col_schema_match(
@@ -82,7 +82,7 @@ validation
 import pointblank as pb
 import polars as pl
 from datetime import date, datetime
-import pytz
+from zoneinfo import ZoneInfo
 
 # Create sample data with various temporal data types
 temporal_data = pl.DataFrame({
@@ -99,10 +99,10 @@ temporal_data = pl.DataFrame({
         datetime(2024, 3, 20, 17, 22, 45)
     ],
     "event_time_tz": [
-        datetime(2023, 1, 15, 9, 0, tzinfo=pytz.timezone("America/New_York")),
-        datetime(2023, 6, 10, 12, 30, tzinfo=pytz.timezone("America/New_York")),
-        datetime(2023, 12, 5, 15, 45, tzinfo=pytz.timezone("America/New_York")),
-        datetime(2024, 3, 20, 18, 15, tzinfo=pytz.timezone("America/New_York"))
+        datetime(2023, 1, 15, 9, 0, tzinfo=ZoneInfo("America/New_York")),
+        datetime(2023, 6, 10, 12, 30, tzinfo=ZoneInfo("America/New_York")),
+        datetime(2023, 12, 5, 15, 45, tzinfo=ZoneInfo("America/New_York")),
+        datetime(2024, 3, 20, 18, 15, tzinfo=ZoneInfo("America/New_York"))
     ],
     "order_id": [1001, 1002, 1003, 1004],
     "amount": [150.0, 275.5, 89.99, 420.00]
@@ -123,7 +123,7 @@ validation = (
     )
     .col_vals_ge(
         columns="event_time_tz",
-        value=datetime(2023, 1, 1, 8, 0, tzinfo=pytz.timezone("America/New_York")),
+        value=datetime(2023, 1, 1, 8, 0, tzinfo=ZoneInfo("America/New_York")),
         brief="Timezone-aware events after 8 AM Eastern"
     )
     .col_schema_match(