pandas_genomics.arrays.GenotypeArray

class pandas_genomics.arrays.GenotypeArray(values: Union[List[pandas_genomics.scalars.Genotype], pandas_genomics.arrays.genotype_array.GenotypeArray, numpy.ndarray], dtype: Optional[pandas_genomics.arrays.genotype_array.GenotypeDtype] = None, copy: bool = False)

Holder for genotypes

Variant information is stored as part of the type, and the genotype is stored as a pair of integer arrays

Parameters

valueslist-like

The values of the genotypes.

dtypeGenotypeDtype

The specific parametized type. Optional (if possible to infer from values)

Attributes

dtype: GenotypeDtype

The specific parametized type

data: np.dtype(“u8”) with shape (<genotypes>, <ploidy>)

The genotype values encoded as indices into the allele list of the dtype

__init__(values: Union[List[pandas_genomics.scalars.Genotype], pandas_genomics.arrays.genotype_array.GenotypeArray, numpy.ndarray], dtype: Optional[pandas_genomics.arrays.genotype_array.GenotypeDtype] = None, copy: bool = False)

Initialize assuming values is a GenotypeArray or a numpy array with the correct underlying shape

Methods

__init__(values[, dtype, copy])	Initialize assuming values is a GenotypeArray or a numpy array with the correct underlying shape
argmax([skipna])	Return the index of maximum value.
argmin([skipna])	Return the index of minimum value.
argsort([ascending, kind, na_position])	Return the indices that would sort this array.
astype(dtype[, copy])	Cast to a NumPy array with ‘dtype’.
copy()	Return a copy of the array.
delete(loc)
dropna()	Return ExtensionArray without NA values.
encode_additive()	Additive Encoding
encode_codominant()	This encodes the genotype into three categories.
encode_dominant()	Dominant Encoding
encode_edge(alpha_value, ref_allele, …)	Perform EDGE (weighted) encoding.
encode_recessive()	Recessive Encoding
equals(other)	Return if another array is equivalent to this array.
factorize([na_sentinel])	Return an array of ints indexing unique values
fillna([value, method, limit])	Fill NA/NaN values using the specified method.
is_genotype_array(other)
isin(values)	Pointwise comparison for set containment in the given values.
isna()	A 1-D array indicating if each value is missing
ravel([order])	Return a flattened view on this array.
repeat(repeats[, axis])	Repeat elements of a ExtensionArray.
searchsorted(value[, side, sorter])	Find indices where elements should be inserted to maintain order.
set_reference(allele)	Change the reference allele (in-place) by specifying an allele index value or an allele string
shift([periods, fill_value])	Shift values by desired number.
take(indexer[, allow_fill, fill_value])	Take elements from an array.
to_numpy([dtype, copy, na_value])	Convert to a NumPy ndarray.
transpose(*axes)	Return a transposed view on this array.
unique()	Return a GenotypeArray of unique values
value_counts([dropna])	Return a Series of unique counts with a GenotypeArray index
view([dtype])	Return a view on the array.

Attributes

T
allele_idxs	Return the allele indices for each genotype
dtype	The specific parametized type
gt_scores	Return the genotype score for each genotype (as a float)
hwe_pval	Calculate the probability that the samples are in HWE for diploid variants
is_heterozygous	Boolean array: True if the sample is heterozygous for any alleles
is_homozygous	Boolean array: True if the sample is homozygous for any allele
is_homozygous_alt	Boolean array: True if the sample is homozygous for any non-reference allele
is_homozygous_ref	Boolean array: True if the sample is homozygous for the reference allele
is_missing	Boolean array: True if the sample is missing all alleles
maf	Calculate the Minor Allele Frequency (MAF) for the most-frequent alternate allele.
nbytes	How many bytes to store this object in memory
ndim	Extension Arrays are only allowed to be 1-dimensional.
shape	Return a tuple of the array dimensions.
size	The number of elements in the array.
variant	Return the variant identifier