Validate.row_count_match

Validate.row_count_match(
    count,
    tol=0,
    inverse=False,
    pre=None,
    thresholds=None,
    actions=None,
    brief=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.

tol : Tolerance = 0

The tolerance allowable for the row count match. This can be specified as a single numeric value (integer or float) or as a tuple of two integers representing the lower and upper bounds of the tolerance range. If a single integer value (greater than 1) is provided, it represents the absolute bounds of the tolerance, ie. plus or minus the value. If a float value (between 0-1) is provided, it represents the relative tolerance, ie. plus or minus the relative percentage of the target. If a tuple is provided, it represents the lower and upper absolute bounds of the tolerance range. See the examples for more.

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 optional preprocessing function or lambda to apply to the data table during interrogation. This function should take a table as input and return a modified table. Have a look at the Preprocessing section for more information on how to use this argument.

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

Set threshold failure levels for reporting and reacting to exceedences of the levels. The thresholds are set at the step level and will override any global thresholds set in Validate(thresholds=...). The default is None, which means that no thresholds will be set locally and global thresholds (if any) will take effect. Look at the Thresholds section for information on how to set threshold levels.

actions : Actions | None = None

Optional actions to take when the validation step meets or exceeds any set threshold levels. If provided, the Actions class should be used to define the actions.

brief : str | bool | None = None

An optional brief description of the validation step that will be displayed in the reporting table. You can use the templating elements like "{step}" to insert the step number, or "{auto}" to include an automatically generated brief. If True the entire brief will be automatically generated. If None (the default) then there won’t be a brief.

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.

Preprocessing

The pre= argument allows for a preprocessing function or lambda to be applied to the data table during interrogation. This function should take a table as input and return a modified table. This is useful for performing any necessary transformations or filtering on the data before the validation step is applied.

The preprocessing function can be any callable that takes a table as input and returns a modified table. For example, you could use a lambda function to filter the table based on certain criteria or to apply a transformation to the data. Regarding the lifetime of the transformed table, it only exists during the validation step and is not stored in the Validate object or used in subsequent validation steps.

Thresholds

The thresholds= parameter is used to set the failure-condition levels for the validation step. If they are set here at the step level, these thresholds will override any thresholds set at the global level in Validate(thresholds=...).

There are three threshold levels: ‘warning’, ‘error’, and ‘critical’. The threshold values can either be set as a proportion failing of all test units (a value between 0 to 1), or, the absolute number of failing test units (as integer that’s 1 or greater).

Thresholds can be defined using one of these input schemes:

  1. use the Thresholds class (the most direct way to create thresholds)
  2. provide a tuple of 1-3 values, where position 0 is the ‘warning’ level, position 1 is the ‘error’ level, and position 2 is the ‘critical’ level
  3. create a dictionary of 1-3 value entries; the valid keys: are ‘warning’, ‘error’, and ‘critical’
  4. a single integer/float value denoting absolute number or fraction of failing test units for the ‘warning’ level only

If the number of failing test units exceeds set thresholds, the validation step will be marked as ‘warning’, ‘error’, or ‘critical’. All of the threshold levels don’t need to be set, you’re free to set any combination of them.

Aside from reporting failure conditions, thresholds can be used to determine the actions to take for each level of failure (using the actions= parameter).

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 E C 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.

Let’s modify our example to show the different ways we can allow some tolerance to our validation by using the tol argument.

smaller_small_table = small_table.sample(n = 12) # within the lower bound
validation = (
    pb.Validate(data=smaller_small_table)
    .row_count_match(count=13,tol=(2, 0)) # minus 2 but plus 0, ie. 11-13
    .interrogate()
)

validation

validation = (
    pb.Validate(data=smaller_small_table)
    .row_count_match(count=13,tol=.05) # .05% tolerance of 13
    .interrogate()
)

even_smaller_table = small_table.sample(n = 2)
validation = (
    pb.Validate(data=even_smaller_table)
    .row_count_match(count=13,tol=5) # plus or minus 5; this test will fail
    .interrogate()
)

validation
STEP COLUMNS VALUES TBL EVAL UNITS PASS FAIL W E C EXT
#4CA64C66 1
row_count_match
row_count_match()
 — 13 ✓ 1 0
0.00		 1
1.00		 — — — —