3.1. mbl.schema.RandomHeisenbergEDSchema

class mbl.schema.RandomHeisenbergEDSchema(*args, **kwargs)

Bases: pandera.model.SchemaModel

Check if all columns in a dataframe have a column in the Schema.

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.

Example

Return type

pandera.typing.common.DataFrameBase[pandera.model.TSchemaModel]

Calling schema.validate returns the dataframe.

>>> import pandas as pd
>>> import pandera as pa
>>>
>>> df = pd.DataFrame({
...     "probability": [0.1, 0.4, 0.52, 0.23, 0.8, 0.76],
...     "category": ["dog", "dog", "cat", "duck", "dog", "dog"]
... })
>>>
>>> schema_withchecks = pa.DataFrameSchema({
...     "probability": pa.Column(
...         float, pa.Check(lambda s: (s >= 0) & (s <= 1))),
...
...     # check that the "category" column contains a few discrete
...     # values, and the majority of the entries are dogs.
...     "category": pa.Column(
...         str, [
...             pa.Check(lambda s: s.isin(["dog", "cat", "duck"])),
...             pa.Check(lambda s: (s == "dog").mean() > 0.5),
...         ]),
... })
>>>
>>> schema_withchecks.validate(df)[["probability", "category"]]
   probability category
0         0.10      dog
1         0.40      dog
2         0.52      cat
3         0.23     duck
4         0.80      dog
5         0.76      dog

__init__()

Methods

__init__()
bipartite_entropy_bounded_in(df)
bounded_in(series)
close_to_integer(series)
example(*[, size])	Create a hypothesis strategy for generating a DataFrame.
monotonically_increasing(series)
strategy(*[, size])	Create a hypothesis strategy for generating a DataFrame.
to_schema()	Create DataFrameSchema from the SchemaModel.
to_yaml([stream])	Convert Schema to yaml using io.to_yaml.
validate(check_obj[, head, tail, sample, ...])	Check if all columns in a dataframe have a column in the Schema.

Attributes

bipartite_entropy
disorder
edge_entropy
en
level_id
offset
penalty
s_target
seed
system_size
total_sz
trial_id

level_id: pandera.typing.pandas.Series[int] = 'level_id'

en: pandera.typing.pandas.Series[float] = 'en'

total_sz: pandera.typing.pandas.Series[float] = 'total_sz'

edge_entropy: pandera.typing.pandas.Series[float] = 'edge_entropy'

bipartite_entropy: pandera.typing.pandas.Series[float] = 'bipartite_entropy'

system_size: pandera.typing.pandas.Series[int] = 'system_size'

disorder: pandera.typing.pandas.Series[float] = 'disorder'

trial_id: pandera.typing.pandas.Series[str] = 'trial_id'

seed: pandera.typing.pandas.Series[int] = 'seed'

penalty: pandera.typing.pandas.Series[float] = 'penalty'

s_target: pandera.typing.pandas.Series[int] = 's_target'

offset: pandera.typing.pandas.Series[float] = 'offset'

classmethod monotonically_increasing(series)

Parameters

series (pandera.typing.pandas.Series[float]) –

Return type

bool

classmethod close_to_integer(series)

Parameters

series (pandera.typing.pandas.Series[float]) –

Return type

pandera.typing.pandas.Series[bool]

classmethod bounded_in(series)

Parameters

series (pandera.typing.pandas.Series[float]) –

Return type

pandera.typing.pandas.Series[bool]

classmethod bipartite_entropy_bounded_in(df)

Parameters

df (pandas.core.frame.DataFrame) –

Return type

pandera.typing.pandas.Series[bool]

class Config

Bases: pandera.typing.config.BaseConfig

name: Optional[str] = 'RandomHeisenbergEDSchema'

name of schema