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Merge pull request #33 from rich-iannone/docs-examples-improve
docs: improve the Examples section
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‎docs/_quarto.yml‎

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@@ -21,7 +21,7 @@ website:
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- text: Get Started
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file: get-started/index.qmd
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- text: Examples
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file: examples/index.qmd
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file: demos/index.qmd
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- href: reference/index.qmd
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text: Reference
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right:

‎docs/demos/01-starter/index.qmd‎

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---
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pagetitle: Examples
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notebook-links: false
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page-navigation: false
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toc: false
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html-table-processing: none
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---
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### Starter Validation
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A validation with the basics.
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```{python}
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#| echo: false
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import pointblank as pb
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validation = (
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pb.Validate( # Use pb.Validate to start
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data=pb.load_dataset(dataset="small_table", tbl_type="polars")
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)
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.col_vals_gt(columns="d", value=1000) # STEP 1 |
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.col_vals_le(columns="c", value=5) # STEP 2 | <-- Build up a validation plan
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.col_exists(columns=["date", "date_time"]) # STEP 3 |
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.interrogate() # This will execute all validation steps and collect intel
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)
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validation
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```
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```python
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import pointblank as pb
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validation = (
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pb.Validate( # Use pb.Validate to start
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data=pb.load_dataset(dataset="small_table", tbl_type="polars")
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)
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.col_vals_gt(columns="d", value=1000) # STEP 1 |
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.col_vals_le(columns="c", value=5) # STEP 2 | <-- Build up a validation plan
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.col_exists(columns=["date", "date_time"]) # STEP 3 |
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.interrogate() # This will execute all validation steps and collect intel
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)
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validation
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```
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<details>
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<summary>Preview of Input Table</summary>
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```{python}
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# | echo: false
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pb.preview(pb.load_dataset(dataset="small_table"), n_head=20, n_tail=20)
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```
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</details>

‎docs/demos/02-advanced/index.qmd‎

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---
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pagetitle: "Examples: Advanced Validation"
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notebook-links: false
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page-navigation: false
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toc: false
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html-table-processing: none
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---
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### Advanced Validation
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A validation with a comprehensive set of rules.
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```{python}
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#| echo: false
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import pointblank as pb
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import polars as pl
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import narwhals as nw
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validation = (
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pb.Validate(
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data=pb.load_dataset(dataset="game_revenue", tbl_type="polars"),
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tbl_name="game_revenue",
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label="Comprehensive validation example",
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thresholds=pb.Thresholds(warn_at=0.10, stop_at=0.25, notify_at=0.35),
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)
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.col_vals_regex(columns="player_id", pattern=r"^[A-Z]{12}[0-9]{3}$") # STEP 1
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.col_vals_gt(columns="session_duration", value=5) # STEP 2
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.col_vals_ge(columns="item_revenue", value=0.02) # STEP 3
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.col_vals_in_set(columns="item_type", set=["iap", "ad"]) # STEP 4
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.col_vals_in_set( # STEP 5
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columns="acquisition",
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set=["google", "facebook", "organic", "crosspromo", "other_campaign"]
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)
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.col_vals_not_in_set(columns="country", set=["Mongolia", "Germany"]) # STEP 6
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.col_vals_expr(expr=nw.when( # STEP 7
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nw.col("item_type") == "iap")
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.then(nw.col("item_name").str.contains(r"^[a-z]*?\d$"))
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)
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.col_vals_between( # STEP 8
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columns="session_duration",
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left=10, right=50,
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pre = lambda df: df.select(pl.median("session_duration"))
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)
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.rows_distinct(columns_subset=["player_id", "session_id", "time"]) # STEP 9
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.row_count_match(count=2000) # STEP 10
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.col_count_match(count=11) # STEP 11
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.col_vals_not_null(columns=pb.starts_with("item")) # STEP 12-14
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.col_exists(columns="start_day") # STEP 15
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.interrogate()
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)
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validation
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```
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```python
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import pointblank as pb
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import polars as pl
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import narwhals as nw
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validation = (
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pb.Validate(
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data=pb.load_dataset(dataset="game_revenue", tbl_type="polars"),
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tbl_name="game_revenue",
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label="Comprehensive validation example",
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thresholds=pb.Thresholds(warn_at=0.10, stop_at=0.25, notify_at=0.35),
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)
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.col_vals_regex(columns="player_id", pattern=r"^[A-Z]{12}[0-9]{3}$") # STEP 1
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.col_vals_gt(columns="session_duration", value=5) # STEP 2
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.col_vals_ge(columns="item_revenue", value=0.02) # STEP 3
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.col_vals_in_set(columns="item_type", set=["iap", "ad"]) # STEP 4
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.col_vals_in_set( # STEP 5
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columns="acquisition",
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set=["google", "facebook", "organic", "crosspromo", "other_campaign"]
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)
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.col_vals_not_in_set(columns="country", set=["Mongolia", "Germany"]) # STEP 6
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.col_vals_expr(expr=nw.when( # STEP 7
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nw.col("item_type") == "iap")
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.then(nw.col("item_name").str.contains(r"^[a-z]*?\d$"))
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)
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.col_vals_between( # STEP 8
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columns="session_duration",
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left=10, right=50,
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pre = lambda df: df.select(pl.median("session_duration"))
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)
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.rows_distinct(columns_subset=["player_id", "session_id", "time"]) # STEP 9
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.row_count_match(count=2000) # STEP 10
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.col_count_match(count=11) # STEP 11
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.col_vals_not_null(columns=pb.starts_with("item")) # STEP 12-14
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.col_exists(columns="start_day") # STEP 15
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.interrogate()
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)
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validation
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```
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<details>
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<summary>Preview of Input Table</summary>
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```{python}
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# | echo: false
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pb.preview(pb.load_dataset(dataset="game_revenue"), n_head=10, n_tail=10)
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```
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</details>

‎docs/demos/03-data-extracts/index.qmd‎

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---
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pagetitle: "Examples: Data Extracts"
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notebook-links: false
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page-navigation: false
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toc: false
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html-table-processing: none
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---
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### Data Extracts
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Pulling out data extracts that highlight rows with validation failures.
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Validation with failures at *Step 3*:
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```{python}
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#| echo: false
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import pointblank as pb
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validation = (
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pb.Validate(data=pb.load_dataset(dataset="game_revenue"))
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.col_vals_lt(columns="item_revenue", value=200)
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.col_vals_gt(columns="item_revenue", value=0)
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.col_vals_ge(columns="session_duration", value=5)
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.col_vals_in_set(columns="item_type", set=["iap", "ad"])
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.col_vals_regex(columns="player_id", pattern=r"[A-Z]{12}\d{3}")
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.interrogate()
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)
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validation
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```
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<br>
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Extract from *Step 3* (`.col_vals_ge(columns="session_duration", value=5)`):
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```{python}
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#| echo: false
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pb.preview(validation.get_data_extracts(i=3, frame=True), n_head=10, n_tail=10)
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```
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```python
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import pointblank as pb
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validation = (
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pb.Validate(data=pb.load_dataset(dataset="game_revenue"))
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.col_vals_lt(columns="item_revenue", value=200)
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.col_vals_gt(columns="item_revenue", value=0)
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.col_vals_ge(columns="session_duration", value=5)
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.col_vals_in_set(columns="item_type", set=["iap", "ad"])
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.col_vals_regex(columns="player_id", pattern=r"[A-Z]{12}\d{3}")
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.interrogate()
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)
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```
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```python
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pb.preview(validation.get_data_extracts(i=3, frame=True), n_head=20, n_tail=20)
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```
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<details>
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<summary>Preview of Input Table</summary>
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```{python}
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# | echo: false
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pb.preview(pb.load_dataset(dataset="game_revenue"), n_head=10, n_tail=10)
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```
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</details>

‎docs/demos/04-sundered-data/index.qmd‎

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---
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pagetitle: "Examples: Sundered Data"
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notebook-links: false
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page-navigation: false
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toc: false
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html-table-processing: none
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---
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### Sundered Data
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Splitting your data into 'pass' and 'fail' subsets.
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```{python}
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#| echo: false
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import pointblank as pb
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validation = (
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pb.Validate( # Use pb.Validate to start
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data=pb.load_dataset(dataset="small_table", tbl_type="pandas")
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)
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.col_vals_gt(columns="d", value=1000)
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.col_vals_le(columns="c", value=5)
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.interrogate()
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)
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validation
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```
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```{python}
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#| echo: false
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pb.preview(validation.get_sundered_data(type="pass"))
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```
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```python
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import pointblank as pb
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import polars as pl
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validation = (
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pb.Validate( # Use pb.Validate to start
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data=pb.load_dataset(dataset="small_table", tbl_type="pandas")
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)
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.col_vals_gt(columns="d", value=1000)
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.col_vals_le(columns="c", value=5)
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.interrogate()
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)
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validation
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```
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```python
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pb.preview(validation.get_sundered_data(type="pass"))
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```
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<details>
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<summary>Preview of Input Table</summary>
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```{python}
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# | echo: false
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pb.preview(pb.load_dataset(dataset="small_table", tbl_type="pandas"), n_head=20, n_tail=20)
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```
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</details>

‎docs/demos/apply-checks-to-several-columns/index.qmd‎

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---
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pagetitle: "Examples: Apply Validation Rules to Multiple Columns"
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notebook-links: false
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page-navigation: false
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toc: false
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html-table-processing: none
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---
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### Apply Validation Rules to Multiple Columns
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Create multiple validation steps by supplying a list of column names to the `columns=` argument.
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```{python}
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#| echo: false
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import pointblank as pb
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validation = (
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pb.Validate(
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data=pb.load_dataset(dataset="small_table", tbl_type="polars")
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)
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.col_vals_ge(columns=["a", "c", "d"], value=0) # check values in 'a', 'c', and 'd'
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.col_exists(columns=["date_time", "date"]) # check for the existence of two columns
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.interrogate()
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)
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validation
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```
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```python
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import pointblank as pb
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validation = (
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pb.Validate(
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data=pb.load_dataset(dataset="small_table", tbl_type="polars")
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)
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.col_vals_ge(columns=["a", "c", "d"], value=0) # check values in 'a', 'c', and 'd'
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.col_exists(columns=["date_time", "date"]) # check for the existence of two columns
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.interrogate()
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)
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validation
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```
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<details>
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<summary>Preview of Input Table</summary>
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```{python}
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# | echo: false
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pb.preview(pb.load_dataset(dataset="small_table", tbl_type="polars"), n_head=20, n_tail=20)
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```
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</details>

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