Validate.row_count_match

Validate.row_count_match(
    count,
    inverse=False,
    pre=None,
    thresholds=None,
    active=True,
)

Validate whether the row count of the table matches a specified count.

The row_count_match() method checks whether the row count of the target table matches a specified count. This validation will operate over a single test unit, which is whether the row count matches the specified count.

We also have the option to invert the validation step by setting inverse=True. This will make the expectation that the row count of the target table does not match the specified count.

Parameters

count : int | FrameT | Any

The expected row count of the table. This can be an integer value, a Polars or Pandas DataFrame object, or an Ibis backend table. If a DataFrame/table is provided, the row count of that object will be used as the expected count.

inverse : bool = False

Should the validation step be inverted? If True, then the expectation is that the row count of the target table should not match the specified count= value.

pre : Callable | None = None

A pre-processing function or lambda to apply to the data table for the validation step.

thresholds : int | float | bool | tuple | dict | Thresholds = None

Failure threshold levels so that the validation step can react accordingly when exceeding the set levels for different states (warn, stop, and notify). This can be created simply as an integer or float denoting the absolute number or fraction of failing test units for the ‘warn’ level. Otherwise, you can use a tuple of 1-3 values, a dictionary of 1-3 entries, or a Thresholds object.

active : bool = True

A boolean value indicating 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).

Returns

: Validate

The Validate object with the added validation step.

Examples

For the examples here, we’ll use the built in dataset "small_table". The table can be obtained by calling load_dataset("small_table").

import pointblank as pb

small_table = pb.load_dataset("small_table")

pb.preview(small_table)
PolarsRows13Columns8
date_time
Datetime
date
Date
a
Int64
b
String
c
Int64
d
Float64
e
Boolean
f
String
1 2016-01-04 11:00:00 2016-01-04 2 1-bcd-345 3 3423.29 True high
2 2016-01-04 00:32:00 2016-01-04 3 5-egh-163 8 9999.99 True low
3 2016-01-05 13:32:00 2016-01-05 6 8-kdg-938 3 2343.23 True high
4 2016-01-06 17:23:00 2016-01-06 2 5-jdo-903 None 3892.4 False mid
5 2016-01-09 12:36:00 2016-01-09 8 3-ldm-038 7 283.94 True low
9 2016-01-20 04:30:00 2016-01-20 3 5-bce-642 9 837.93 False high
10 2016-01-20 04:30:00 2016-01-20 3 5-bce-642 9 837.93 False high
11 2016-01-26 20:07:00 2016-01-26 4 2-dmx-010 7 833.98 True low
12 2016-01-28 02:51:00 2016-01-28 2 7-dmx-010 8 108.34 False low
13 2016-01-30 11:23:00 2016-01-30 1 3-dka-303 None 2230.09 True high

Let’s validate that the number of rows in the table matches a fixed value. In this case, we will use the value 13 as the expected row count.

validation = (
    pb.Validate(data=small_table)
    .row_count_match(count=13)
    .interrogate()
)

validation
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W S N EXT
#4CA64C 1
row_count_match
row_count_match() 		— 13 ✓ 1 1
1.00		 0
0.00		 — — — —

The validation table shows that the expectation value of 13 matches the actual count of rows in the target table. So, the single test unit passed.