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Update api-docs.txt
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‎pointblank/data/api-docs.txt‎

Lines changed: 144 additions & 80 deletions
Original file line numberDiff line numberDiff line change
@@ -1445,12 +1445,16 @@ col_vals_gt(self, columns: 'str | list[str] | Column | ColumnSelector | ColumnSe
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(i.e., no validation steps will be created for them).
14461446

14471447
A list with a combination of column names and tuples can be provided as well. This allows
1448-
for more complex segmentation scenarios. The following inputs are all valid:
1448+
for more complex segmentation scenarios. The following inputs are both valid:
14491449

1450-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
1451-
in the `"region"` column and specific dates in the `"date"` column
1452-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
1453-
columns
1450+
```
1451+
# Segments from all unique values in the `region` column
1452+
# and specific dates in the `date` column
1453+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
1454+
1455+
# Segments from all unique values in the `region` and `date` columns
1456+
segments=["region", "date"]
1457+
```
14541458

14551459
The segmentation is performed during interrogation, and the resulting validation steps will
14561460
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -1666,12 +1670,16 @@ col_vals_lt(self, columns: 'str | list[str] | Column | ColumnSelector | ColumnSe
16661670
(i.e., no validation steps will be created for them).
16671671

16681672
A list with a combination of column names and tuples can be provided as well. This allows
1669-
for more complex segmentation scenarios. The following inputs are all valid:
1673+
for more complex segmentation scenarios. The following inputs are both valid:
16701674

1671-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
1672-
in the `"region"` column and specific dates in the `"date"` column
1673-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
1674-
columns
1675+
```
1676+
# Segments from all unique values in the `region` column
1677+
# and specific dates in the `date` column
1678+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
1679+
1680+
# Segments from all unique values in the `region` and `date` columns
1681+
segments=["region", "date"]
1682+
```
16751683

16761684
The segmentation is performed during interrogation, and the resulting validation steps will
16771685
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -1887,12 +1895,16 @@ col_vals_ge(self, columns: 'str | list[str] | Column | ColumnSelector | ColumnSe
18871895
(i.e., no validation steps will be created for them).
18881896

18891897
A list with a combination of column names and tuples can be provided as well. This allows
1890-
for more complex segmentation scenarios. The following inputs are all valid:
1898+
for more complex segmentation scenarios. The following inputs are both valid:
18911899

1892-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
1893-
in the `"region"` column and specific dates in the `"date"` column
1894-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
1895-
columns
1900+
```
1901+
# Segments from all unique values in the `region` column
1902+
# and specific dates in the `date` column
1903+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
1904+
1905+
# Segments from all unique values in the `region` and `date` columns
1906+
segments=["region", "date"]
1907+
```
18961908

18971909
The segmentation is performed during interrogation, and the resulting validation steps will
18981910
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -2108,12 +2120,16 @@ col_vals_le(self, columns: 'str | list[str] | Column | ColumnSelector | ColumnSe
21082120
(i.e., no validation steps will be created for them).
21092121

21102122
A list with a combination of column names and tuples can be provided as well. This allows
2111-
for more complex segmentation scenarios. The following inputs are all valid:
2123+
for more complex segmentation scenarios. The following inputs are both valid:
21122124

2113-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
2114-
in the `"region"` column and specific dates in the `"date"` column
2115-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
2116-
columns
2125+
```
2126+
# Segments from all unique values in the `region` column
2127+
# and specific dates in the `date` column
2128+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
2129+
2130+
# Segments from all unique values in the `region` and `date` columns
2131+
segments=["region", "date"]
2132+
```
21172133

21182134
The segmentation is performed during interrogation, and the resulting validation steps will
21192135
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -2329,12 +2345,16 @@ col_vals_eq(self, columns: 'str | list[str] | Column | ColumnSelector | ColumnSe
23292345
(i.e., no validation steps will be created for them).
23302346

23312347
A list with a combination of column names and tuples can be provided as well. This allows
2332-
for more complex segmentation scenarios. The following inputs are all valid:
2348+
for more complex segmentation scenarios. The following inputs are both valid:
23332349

2334-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
2335-
in the `"region"` column and specific dates in the `"date"` column
2336-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
2337-
columns
2350+
```
2351+
# Segments from all unique values in the `region` column
2352+
# and specific dates in the `date` column
2353+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
2354+
2355+
# Segments from all unique values in the `region` and `date` columns
2356+
segments=["region", "date"]
2357+
```
23382358

23392359
The segmentation is performed during interrogation, and the resulting validation steps will
23402360
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -2548,12 +2568,16 @@ col_vals_ne(self, columns: 'str | list[str] | Column | ColumnSelector | ColumnSe
25482568
(i.e., no validation steps will be created for them).
25492569

25502570
A list with a combination of column names and tuples can be provided as well. This allows
2551-
for more complex segmentation scenarios. The following inputs are all valid:
2571+
for more complex segmentation scenarios. The following inputs are both valid:
25522572

2553-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
2554-
in the `"region"` column and specific dates in the `"date"` column
2555-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
2556-
columns
2573+
```
2574+
# Segments from all unique values in the `region` column
2575+
# and specific dates in the `date` column
2576+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
2577+
2578+
# Segments from all unique values in the `region` and `date` columns
2579+
segments=["region", "date"]
2580+
```
25572581

25582582
The segmentation is performed during interrogation, and the resulting validation steps will
25592583
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -2777,12 +2801,16 @@ col_vals_between(self, columns: 'str | list[str] | Column | ColumnSelector | Col
27772801
(i.e., no validation steps will be created for them).
27782802

27792803
A list with a combination of column names and tuples can be provided as well. This allows
2780-
for more complex segmentation scenarios. The following inputs are all valid:
2804+
for more complex segmentation scenarios. The following inputs are both valid:
27812805

2782-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
2783-
in the `"region"` column and specific dates in the `"date"` column
2784-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
2785-
columns
2806+
```
2807+
# Segments from all unique values in the `region` column
2808+
# and specific dates in the `date` column
2809+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
2810+
2811+
# Segments from all unique values in the `region` and `date` columns
2812+
segments=["region", "date"]
2813+
```
27862814

27872815
The segmentation is performed during interrogation, and the resulting validation steps will
27882816
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -3018,12 +3046,16 @@ col_vals_outside(self, columns: 'str | list[str] | Column | ColumnSelector | Col
30183046
(i.e., no validation steps will be created for them).
30193047

30203048
A list with a combination of column names and tuples can be provided as well. This allows
3021-
for more complex segmentation scenarios. The following inputs are all valid:
3049+
for more complex segmentation scenarios. The following inputs are both valid:
30223050

3023-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
3024-
in the `"region"` column and specific dates in the `"date"` column
3025-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
3026-
columns
3051+
```
3052+
# Segments from all unique values in the `region` column
3053+
# and specific dates in the `date` column
3054+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
3055+
3056+
# Segments from all unique values in the `region` and `date` columns
3057+
segments=["region", "date"]
3058+
```
30273059

30283060
The segmentation is performed during interrogation, and the resulting validation steps will
30293061
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -3218,12 +3250,16 @@ col_vals_in_set(self, columns: 'str | list[str] | Column | ColumnSelector | Colu
32183250
(i.e., no validation steps will be created for them).
32193251

32203252
A list with a combination of column names and tuples can be provided as well. This allows
3221-
for more complex segmentation scenarios. The following inputs are all valid:
3253+
for more complex segmentation scenarios. The following inputs are both valid:
32223254

3223-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
3224-
in the `"region"` column and specific dates in the `"date"` column
3225-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
3226-
columns
3255+
```
3256+
# Segments from all unique values in the `region` column
3257+
# and specific dates in the `date` column
3258+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
3259+
3260+
# Segments from all unique values in the `region` and `date` columns
3261+
segments=["region", "date"]
3262+
```
32273263

32283264
The segmentation is performed during interrogation, and the resulting validation steps will
32293265
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -3404,12 +3440,16 @@ col_vals_not_in_set(self, columns: 'str | list[str] | Column | ColumnSelector |
34043440
(i.e., no validation steps will be created for them).
34053441

34063442
A list with a combination of column names and tuples can be provided as well. This allows
3407-
for more complex segmentation scenarios. The following inputs are all valid:
3443+
for more complex segmentation scenarios. The following inputs are both valid:
34083444

3409-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
3410-
in the `"region"` column and specific dates in the `"date"` column
3411-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
3412-
columns
3445+
```
3446+
# Segments from all unique values in the `region` column
3447+
# and specific dates in the `date` column
3448+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
3449+
3450+
# Segments from all unique values in the `region` and `date` columns
3451+
segments=["region", "date"]
3452+
```
34133453

34143454
The segmentation is performed during interrogation, and the resulting validation steps will
34153455
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -3588,12 +3628,16 @@ col_vals_null(self, columns: 'str | list[str] | Column | ColumnSelector | Column
35883628
(i.e., no validation steps will be created for them).
35893629

35903630
A list with a combination of column names and tuples can be provided as well. This allows
3591-
for more complex segmentation scenarios. The following inputs are all valid:
3631+
for more complex segmentation scenarios. The following inputs are both valid:
35923632

3593-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
3594-
in the `"region"` column and specific dates in the `"date"` column
3595-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
3596-
columns
3633+
```
3634+
# Segments from all unique values in the `region` column
3635+
# and specific dates in the `date` column
3636+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
3637+
3638+
# Segments from all unique values in the `region` and `date` columns
3639+
segments=["region", "date"]
3640+
```
35973641

35983642
The segmentation is performed during interrogation, and the resulting validation steps will
35993643
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -3770,12 +3814,16 @@ col_vals_not_null(self, columns: 'str | list[str] | Column | ColumnSelector | Co
37703814
(i.e., no validation steps will be created for them).
37713815

37723816
A list with a combination of column names and tuples can be provided as well. This allows
3773-
for more complex segmentation scenarios. The following inputs are all valid:
3817+
for more complex segmentation scenarios. The following inputs are both valid:
37743818

3775-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
3776-
in the `"region"` column and specific dates in the `"date"` column
3777-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
3778-
columns
3819+
```
3820+
# Segments from all unique values in the `region` column
3821+
# and specific dates in the `date` column
3822+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
3823+
3824+
# Segments from all unique values in the `region` and `date` columns
3825+
segments=["region", "date"]
3826+
```
37793827

37803828
The segmentation is performed during interrogation, and the resulting validation steps will
37813829
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -3958,12 +4006,16 @@ col_vals_regex(self, columns: 'str | list[str] | Column | ColumnSelector | Colum
39584006
(i.e., no validation steps will be created for them).
39594007

39604008
A list with a combination of column names and tuples can be provided as well. This allows
3961-
for more complex segmentation scenarios. The following inputs are all valid:
4009+
for more complex segmentation scenarios. The following inputs are both valid:
39624010

3963-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
3964-
in the `"region"` column and specific dates in the `"date"` column
3965-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
3966-
columns
4011+
```
4012+
# Segments from all unique values in the `region` column
4013+
# and specific dates in the `date` column
4014+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
4015+
4016+
# Segments from all unique values in the `region` and `date` columns
4017+
segments=["region", "date"]
4018+
```
39674019

39684020
The segmentation is performed during interrogation, and the resulting validation steps will
39694021
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -4140,12 +4192,16 @@ col_vals_expr(self, expr: 'any', pre: 'Callable | None' = None, segments: 'Segme
41404192
(i.e., no validation steps will be created for them).
41414193

41424194
A list with a combination of column names and tuples can be provided as well. This allows
4143-
for more complex segmentation scenarios. The following inputs are all valid:
4195+
for more complex segmentation scenarios. The following inputs are both valid:
41444196

4145-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
4146-
in the `"region"` column and specific dates in the `"date"` column
4147-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
4148-
columns
4197+
```
4198+
# Segments from all unique values in the `region` column
4199+
# and specific dates in the `date` column
4200+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
4201+
4202+
# Segments from all unique values in the `region` and `date` columns
4203+
segments=["region", "date"]
4204+
```
41494205

41504206
The segmentation is performed during interrogation, and the resulting validation steps will
41514207
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -4430,12 +4486,16 @@ rows_distinct(self, columns_subset: 'str | list[str] | None' = None, pre: 'Calla
44304486
(i.e., no validation steps will be created for them).
44314487

44324488
A list with a combination of column names and tuples can be provided as well. This allows
4433-
for more complex segmentation scenarios. The following inputs are all valid:
4489+
for more complex segmentation scenarios. The following inputs are both valid:
44344490

4435-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
4436-
in the `"region"` column and specific dates in the `"date"` column
4437-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
4438-
columns
4491+
```
4492+
# Segments from all unique values in the `region` column
4493+
# and specific dates in the `date` column
4494+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
4495+
4496+
# Segments from all unique values in the `region` and `date` columns
4497+
segments=["region", "date"]
4498+
```
44394499

44404500
The segmentation is performed during interrogation, and the resulting validation steps will
44414501
be numbered sequentially. Each segment will have its own validation step, and the results
@@ -4616,12 +4676,16 @@ rows_complete(self, columns_subset: 'str | list[str] | None' = None, pre: 'Calla
46164676
(i.e., no validation steps will be created for them).
46174677

46184678
A list with a combination of column names and tuples can be provided as well. This allows
4619-
for more complex segmentation scenarios. The following inputs are all valid:
4679+
for more complex segmentation scenarios. The following inputs are both valid:
46204680

4621-
- `segments=["region", ("date", ["2023-01-01", "2023-01-02"])]`: segments on unique values
4622-
in the `"region"` column and specific dates in the `"date"` column
4623-
- `segments=["region", "date"]`: segments on unique values in the `"region"` and `"date"`
4624-
columns
4681+
```
4682+
# Segments from all unique values in the `region` column
4683+
# and specific dates in the `date` column
4684+
segments=["region", ("date", ["2023-01-01", "2023-01-02"])]
4685+
4686+
# Segments from all unique values in the `region` and `date` columns
4687+
segments=["region", "date"]
4688+
```
46254689

46264690
The segmentation is performed during interrogation, and the resulting validation steps will
46274691
be numbered sequentially. Each segment will have its own validation step, and the results

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