acore.types.differential_analysis module

class AnovaSchema(*args, **kwargs)

Bases: DataFrameModel

Schema for the enrichment analysis results DataFrame.

group1: str = 'group1'

group2: str = 'group2'

mean_group1: float = 'mean(group1)'

std_group1: float = 'std(group1)'

mean_group2: float = 'mean(group2)'

std_group2: float = 'std(group2)'

t_statistics: float = 'T-Statistics'

pvalue: float = 'pvalue'

log2FC: float = 'log2FC'

FC: float = 'FC'

padj: float = 'padj'

correction: str = 'correction'

rejected: bool = 'rejected'

neg_log10_p_value: float = '-log10 pvalue'

Method: str = 'Method'

class Config

Bases: BaseConfig

add_missing_columns: bool = False

add columns to dataframe if they are missing

coerce: bool = False

coerce types of all schema components

description: str | None = None

arbitrary textual description

drop_invalid_rows: bool = False

drop invalid rows on validation

dtype: str | type | DataType | ExtensionDtype | dtype | None = None

datatype of the dataframe. This overrides the data types specified in any of the fields.

from_format: Literal[Formats.csv] | Literal[Formats.dict] | Literal[Formats.json] | Literal[Formats.feather] | Literal[Formats.parquet] | Literal[Formats.pickle] | Literal[Formats.json_normalize] | Callable | None = None

data format before validation. This option only applies to schemas used in the context of the pandera type constructor pa.typing.DataFrame[Schema](data). If None, assumes a data structure compatible with the pandas.DataFrame constructor.

from_format_kwargs: dict[str, Any] | None = None

a dictionary keyword arguments to pass into the reader function that converts the object of type from_format to a pandera-validate-able data structure. The reader function is implemented in the pandera.typing generic types via the from_format and to_format methods.

metadata: dict | None = None

a dictionary object to store key-value data at schema level

multiindex_coerce: bool = False

coerce types of all MultiIndex components

multiindex_name: str | None = None

name of multiindex

multiindex_ordered: bool = True

validate MultiIndex in order

multiindex_strict: bool | Literal['filter'] = False

make sure all specified columns are in validated MultiIndex - if "filter", removes indexes not specified in the schema

multiindex_unique = None

make sure the MultiIndex is unique along the list of columns

name: str | None = 'AnovaSchema'

name of schema

ordered: bool = False

validate columns order

strict: bool | Literal['filter'] = False

make sure all specified columns are in the validated dataframe - if "filter", removes columns not specified in the schema

title: str | None = None

human-readable label for schema

to_format: Literal[Formats.csv] | Literal[Formats.dict] | Literal[Formats.json] | Literal[Formats.feather] | Literal[Formats.parquet] | Literal[Formats.pickle] | Literal[Formats.json_normalize] | Callable | None = None

data format to serialize into after validation. This option only applies to schemas used in the context of the pandera type constructor pa.typing.DataFrame[Schema](data). If None, returns a dataframe.

to_format_buffer: str | Callable | None = None

Buffer to be provided when to_format is a custom callable. See docs for example of how to implement an example of a to format function.

to_format_kwargs: dict[str, Any] | None = None

a dictionary keyword arguments to pass into the writer function that converts the pandera-validate-able object to type to_format. The writer function is implemented in the pandera.typing generic types via the from_format and to_format methods.

unique: str | list[str] | None = None

make sure certain column combinations are unique

unique_column_names: bool = False

make sure dataframe column names are unique

classmethod build_schema_(**kwargs) → DataFrameSchema

classmethod empty(*_args) → DataFrame[Self]

Create an empty DataFrame with the schema of this model.

classmethod example(**kwargs) → DataFrameBase[Self]

Generate an example of this data model specification.

classmethod from_json(source)

Load a schema from JSON.

Parameters:

source – str, Path, or file stream with JSON content.

Returns:

the backend-specific schema object.

classmethod from_yaml(yaml_schema)

Load a schema from YAML.

Parameters:

yaml_schema – str, Path, or file stream with YAML content.

Returns:

the backend-specific schema object.

classmethod get_metadata() → dict | None

Provide metadata for columns and schema level

classmethod pydantic_validate(schema_model: Any) → DataFrameModel

Verify that the input is a compatible dataframe model.

classmethod strategy(**kwargs)

Create a data synthesis strategy.

classmethod to_json(target: PathLike | None = None, **kwargs)

Convert this model’s schema to JSON.

classmethod to_json_schema()

Serialize schema metadata into json-schema format.

Parameters:

dataframe_schema – schema to write to json-schema format.

Note

This function is currently does not fully specify a pandera schema, and is primarily used internally to render OpenAPI docs via the FastAPI integration.

classmethod to_schema() → TSchema

Create DataFrameSchema from the DataFrameModel.

classmethod to_yaml(stream: PathLike | None = None)

Convert this model’s schema to YAML.

classmethod validate(check_obj: DataFrame, head: int | None = None, tail: int | None = None, sample: int | None = None, random_state: int | None = None, lazy: bool = False, inplace: bool = False) → DataFrame[Self]

Validate a DataFrame based on the schema specification.

Parameters:

• check_obj (pd.DataFrame) – the dataframe to be validated.

• head – validate the first n rows. Rows overlapping with tail or sample are de-duplicated.

• tail – validate the last n rows. Rows overlapping with head or sample are de-duplicated.

• sample – validate a random sample of n rows. Rows overlapping with head or tail are de-duplicated.

• random_state – random seed for the sample argument.

• lazy – if True, lazily evaluates dataframe against all validation checks and raises a SchemaErrors. Otherwise, raise SchemaError as soon as one occurs.

• inplace – if True, applies coercion to the object of validation, otherwise creates a copy of the data.

Returns:

validated DataFrame

Raises:

SchemaError – when DataFrame violates built-in or custom checks.

class AnovaSchemaMultiGroup(*args, **kwargs)

Bases: AnovaSchema

Schema for more than two groups

t_statistics: float = 'posthoc T-Statistics'

posthoc_pvalue: float = 'posthoc pvalue'

f_statistics: float = 'F-statistics'

posthoc_padj: float = 'posthoc padj'

posthoc_paired: bool = 'posthoc Paired'

posthoc_parametric: bool = 'posthoc Parametric'

posthoc_dof: float = 'posthoc dof'

posthoc_tail: str = 'posthoc tail'

posthoc_BF10: str = 'posthoc BF10'

posthoc_effsize: float = 'posthoc effsize'

efftype: str = 'efftype'

class Config

Bases: Config

add_missing_columns: bool = False

add columns to dataframe if they are missing

coerce: bool = False

coerce types of all schema components

description: str | None = None

arbitrary textual description

drop_invalid_rows: bool = False

drop invalid rows on validation

dtype: str | type | DataType | ExtensionDtype | dtype | None = None

datatype of the dataframe. This overrides the data types specified in any of the fields.

from_format: Literal[Formats.csv] | Literal[Formats.dict] | Literal[Formats.json] | Literal[Formats.feather] | Literal[Formats.parquet] | Literal[Formats.pickle] | Literal[Formats.json_normalize] | Callable | None = None

data format before validation. This option only applies to schemas used in the context of the pandera type constructor pa.typing.DataFrame[Schema](data). If None, assumes a data structure compatible with the pandas.DataFrame constructor.

from_format_kwargs: dict[str, Any] | None = None

a dictionary keyword arguments to pass into the reader function that converts the object of type from_format to a pandera-validate-able data structure. The reader function is implemented in the pandera.typing generic types via the from_format and to_format methods.

metadata: dict | None = None

a dictionary object to store key-value data at schema level

multiindex_coerce: bool = False

coerce types of all MultiIndex components

multiindex_name: str | None = None

name of multiindex

multiindex_ordered: bool = True

validate MultiIndex in order

multiindex_strict: bool | Literal['filter'] = False

make sure all specified columns are in validated MultiIndex - if "filter", removes indexes not specified in the schema

multiindex_unique = None

make sure the MultiIndex is unique along the list of columns

name: str | None = 'AnovaSchemaMultiGroup'

name of schema

ordered: bool = False

validate columns order

strict: bool | Literal['filter'] = False

make sure all specified columns are in the validated dataframe - if "filter", removes columns not specified in the schema

title: str | None = None

human-readable label for schema

to_format: Literal[Formats.csv] | Literal[Formats.dict] | Literal[Formats.json] | Literal[Formats.feather] | Literal[Formats.parquet] | Literal[Formats.pickle] | Literal[Formats.json_normalize] | Callable | None = None

data format to serialize into after validation. This option only applies to schemas used in the context of the pandera type constructor pa.typing.DataFrame[Schema](data). If None, returns a dataframe.

to_format_buffer: str | Callable | None = None

Buffer to be provided when to_format is a custom callable. See docs for example of how to implement an example of a to format function.

to_format_kwargs: dict[str, Any] | None = None

a dictionary keyword arguments to pass into the writer function that converts the pandera-validate-able object to type to_format. The writer function is implemented in the pandera.typing generic types via the from_format and to_format methods.

unique: str | list[str] | None = None

make sure certain column combinations are unique

unique_column_names: bool = False

make sure dataframe column names are unique

FC: float = 'FC'

Method: str = 'Method'

classmethod build_schema_(**kwargs) → DataFrameSchema

correction: str = 'correction'

classmethod empty(*_args) → DataFrame[Self]

Create an empty DataFrame with the schema of this model.

classmethod example(**kwargs) → DataFrameBase[Self]

Generate an example of this data model specification.

classmethod from_json(source)

Load a schema from JSON.

Parameters:

source – str, Path, or file stream with JSON content.

Returns:

the backend-specific schema object.

classmethod from_yaml(yaml_schema)

Load a schema from YAML.

Parameters:

yaml_schema – str, Path, or file stream with YAML content.

Returns:

the backend-specific schema object.

classmethod get_metadata() → dict | None

Provide metadata for columns and schema level

group1: str = 'group1'

group2: str = 'group2'

log2FC: float = 'log2FC'

mean_group1: float = 'mean(group1)'

mean_group2: float = 'mean(group2)'

neg_log10_p_value: float = '-log10 pvalue'

padj: float = 'padj'

pvalue: float = 'pvalue'

classmethod pydantic_validate(schema_model: Any) → DataFrameModel

Verify that the input is a compatible dataframe model.

rejected: bool = 'rejected'

std_group1: float = 'std(group1)'

std_group2: float = 'std(group2)'

classmethod strategy(**kwargs)

Create a data synthesis strategy.

classmethod to_json(target: PathLike | None = None, **kwargs)

Convert this model’s schema to JSON.

classmethod to_json_schema()

Serialize schema metadata into json-schema format.

Parameters:

dataframe_schema – schema to write to json-schema format.

Note

This function is currently does not fully specify a pandera schema, and is primarily used internally to render OpenAPI docs via the FastAPI integration.

classmethod to_schema() → TSchema

Create DataFrameSchema from the DataFrameModel.

classmethod to_yaml(stream: PathLike | None = None)

Convert this model’s schema to YAML.

classmethod validate(check_obj: DataFrame, head: int | None = None, tail: int | None = None, sample: int | None = None, random_state: int | None = None, lazy: bool = False, inplace: bool = False) → DataFrame[Self]

Validate a DataFrame based on the schema specification.

Parameters:

• check_obj (pd.DataFrame) – the dataframe to be validated.

• head – validate the first n rows. Rows overlapping with tail or sample are de-duplicated.

• tail – validate the last n rows. Rows overlapping with head or sample are de-duplicated.

• sample – validate a random sample of n rows. Rows overlapping with head or tail are de-duplicated.

• random_state – random seed for the sample argument.

• lazy – if True, lazily evaluates dataframe against all validation checks and raises a SchemaErrors. Otherwise, raise SchemaError as soon as one occurs.

• inplace – if True, applies coercion to the object of validation, otherwise creates a copy of the data.

Returns:

validated DataFrame

Raises:

SchemaError – when DataFrame violates built-in or custom checks.

class AncovaSchema(*args, **kwargs)

Bases: DataFrameModel

Schema for the enrichment analysis results DataFrame.

t_statistics: float = 'posthoc T-Statistics'

posthoc_pvalue: float = 'posthoc pvalue'

coef: float = 'coef'

std_err: float = 'std err'

conf_int_low: float = 'Conf. Int. Low'

conf_int_upp: float = 'Conf. Int. Upp.'

f_statistics: float = 'F-statistics'

posthoc_padj: float = 'posthoc padj'

class Config

Bases: BaseConfig

add_missing_columns: bool = False

add columns to dataframe if they are missing

coerce: bool = False

coerce types of all schema components

description: str | None = None

arbitrary textual description

drop_invalid_rows: bool = False

drop invalid rows on validation

dtype: str | type | DataType | ExtensionDtype | dtype | None = None

datatype of the dataframe. This overrides the data types specified in any of the fields.

from_format: Literal[Formats.csv] | Literal[Formats.dict] | Literal[Formats.json] | Literal[Formats.feather] | Literal[Formats.parquet] | Literal[Formats.pickle] | Literal[Formats.json_normalize] | Callable | None = None

data format before validation. This option only applies to schemas used in the context of the pandera type constructor pa.typing.DataFrame[Schema](data). If None, assumes a data structure compatible with the pandas.DataFrame constructor.

from_format_kwargs: dict[str, Any] | None = None

a dictionary keyword arguments to pass into the reader function that converts the object of type from_format to a pandera-validate-able data structure. The reader function is implemented in the pandera.typing generic types via the from_format and to_format methods.

metadata: dict | None = None

a dictionary object to store key-value data at schema level

multiindex_coerce: bool = False

coerce types of all MultiIndex components

multiindex_name: str | None = None

name of multiindex

multiindex_ordered: bool = True

validate MultiIndex in order

multiindex_strict: bool | Literal['filter'] = False

make sure all specified columns are in validated MultiIndex - if "filter", removes indexes not specified in the schema

multiindex_unique = None

make sure the MultiIndex is unique along the list of columns

name: str | None = 'AncovaSchema'

name of schema

ordered: bool = False

validate columns order

strict: bool | Literal['filter'] = False

make sure all specified columns are in the validated dataframe - if "filter", removes columns not specified in the schema

title: str | None = None

human-readable label for schema

to_format: Literal[Formats.csv] | Literal[Formats.dict] | Literal[Formats.json] | Literal[Formats.feather] | Literal[Formats.parquet] | Literal[Formats.pickle] | Literal[Formats.json_normalize] | Callable | None = None

data format to serialize into after validation. This option only applies to schemas used in the context of the pandera type constructor pa.typing.DataFrame[Schema](data). If None, returns a dataframe.

to_format_buffer: str | Callable | None = None

Buffer to be provided when to_format is a custom callable. See docs for example of how to implement an example of a to format function.

to_format_kwargs: dict[str, Any] | None = None

a dictionary keyword arguments to pass into the writer function that converts the pandera-validate-able object to type to_format. The writer function is implemented in the pandera.typing generic types via the from_format and to_format methods.

unique: str | list[str] | None = None

make sure certain column combinations are unique

unique_column_names: bool = False

make sure dataframe column names are unique

classmethod build_schema_(**kwargs) → DataFrameSchema

classmethod empty(*_args) → DataFrame[Self]

Create an empty DataFrame with the schema of this model.

classmethod example(**kwargs) → DataFrameBase[Self]

Generate an example of this data model specification.

classmethod from_json(source)

Load a schema from JSON.

Parameters:

source – str, Path, or file stream with JSON content.

Returns:

the backend-specific schema object.

classmethod from_yaml(yaml_schema)

Load a schema from YAML.

Parameters:

yaml_schema – str, Path, or file stream with YAML content.

Returns:

the backend-specific schema object.

classmethod get_metadata() → dict | None

Provide metadata for columns and schema level

classmethod pydantic_validate(schema_model: Any) → DataFrameModel

Verify that the input is a compatible dataframe model.

classmethod strategy(**kwargs)

Create a data synthesis strategy.

classmethod to_json(target: PathLike | None = None, **kwargs)

Convert this model’s schema to JSON.

classmethod to_json_schema()

Serialize schema metadata into json-schema format.

Parameters:

dataframe_schema – schema to write to json-schema format.

Note

This function is currently does not fully specify a pandera schema, and is primarily used internally to render OpenAPI docs via the FastAPI integration.

classmethod to_schema() → TSchema

Create DataFrameSchema from the DataFrameModel.

classmethod to_yaml(stream: PathLike | None = None)

Convert this model’s schema to YAML.

classmethod validate(check_obj: DataFrame, head: int | None = None, tail: int | None = None, sample: int | None = None, random_state: int | None = None, lazy: bool = False, inplace: bool = False) → DataFrame[Self]

Validate a DataFrame based on the schema specification.

Parameters:

• check_obj (pd.DataFrame) – the dataframe to be validated.

• head – validate the first n rows. Rows overlapping with tail or sample are de-duplicated.

• tail – validate the last n rows. Rows overlapping with head or sample are de-duplicated.

• sample – validate a random sample of n rows. Rows overlapping with head or tail are de-duplicated.

• random_state – random seed for the sample argument.

• lazy – if True, lazily evaluates dataframe against all validation checks and raises a SchemaErrors. Otherwise, raise SchemaError as soon as one occurs.

• inplace – if True, applies coercion to the object of validation, otherwise creates a copy of the data.

Returns:

validated DataFrame

Raises:

SchemaError – when DataFrame violates built-in or custom checks.