pandaspgs.set_operation

bind 

bind(
    a: AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory,
    b: AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory,
) -> (
    AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory
)

Binds together PGS objects of the same object. Note that bind() preserves duplicates whereas union() does not.

Parameters:
Returns: 
from pandaspgs.get_cohort import get_cohorts
from pandaspgs.set_operation import bind


a = get_cohorts(cohort_symbol='100-plus')
b = get_cohorts(cohort_symbol='23andMe')
c = bind(a,b)

intersect 

intersect(
    a: AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory,
    b: AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory,
) -> (
    AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory
)

Returns the data common to both A and B, with no repetitions

Parameters:
Returns: 
from pandaspgs.get_cohort import get_cohorts
from pandaspgs.set_operation import intersect


a = get_cohorts(cohort_symbol='100-plus')
b = get_cohorts(cohort_symbol='23andMe')
c = intersect(a,b)

set_diff 

set_diff(
    a: AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory,
    b: AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory,
) -> (
    AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory
)

returns the data in A that is not in B, with no repetitions

Parameters:
Returns: 
from pandaspgs.get_cohort import get_cohorts
from pandaspgs.set_operation import set_diff


a = get_cohorts(cohort_symbol='23andMe')
b = get_cohorts(cohort_symbol='23andMe')
c = set_diff(a,b)

set_equal 

set_equal(
    a: AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory,
    b: AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory,
) -> bool

Check if the raw data of a and b are equal

Parameters:
Returns:
  • bool –

    An object of the same type as a.

 
from pandaspgs.get_cohort import get_cohorts
from pandaspgs.set_operation import set_equal


a = get_cohorts(cohort_symbol='100-plus')
b = get_cohorts(cohort_symbol='23andMe')
c = set_equal(a,b)

set_xor 

set_xor(
    a: AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory,
    b: AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory,
) -> (
    AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory
)

returns the data of A and B that are not in their intersection (the symmetric difference), with no repetitions

Parameters:
Returns: 
from pandaspgs.get_cohort import get_cohorts
from pandaspgs.set_operation import set_xor


a = get_cohorts(cohort_symbol='100-plus')
b = get_cohorts(cohort_symbol='23andMe')
c = set_xor(a,b)

union 

union(
    a: AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory,
    b: AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory,
) -> (
    AncestryCategory
    | Cohort
    | PerformanceMetric
    | Publication
    | Release
    | SampleSet
    | Score
    | Trait
    | TraitCategory
)

returns the combined data from A and B with no repetitions

Parameters:
Returns: 
from pandaspgs.get_cohort import get_cohorts
from pandaspgs.set_operation import union


a = get_cohorts(cohort_symbol='100-plus')
b = get_cohorts(cohort_symbol='23andMe')
c = union(a,b)