dalio.validator package

Submodules

dalio.validator.array_val module

Definte validators applied to array-like inputs

class dalio.validator.array_val.HAS_DIMS(dims, comparisson='==')

Bases: dalio.validator.validator.Validator

Check if an array has a number of dimensions

_dims

number of dimensions

Type:int

_comparisson

which comparisson to perform

Type:str

validate(data)

Validate data

Check if data fits a certain description.

Returns:A description of any errors in the data according to this specific validation condition, and None if data is valid.

dalio.validator.base_val module

Define Validators used for general python objects

class dalio.validator.base_val.ELEMS_TYPE(t)

Bases: dalio.validator.base_val.HAS_ATTR

Checks if all elements of an iterator is of a certain type.

_t

type to check iterator’s elements for

Type:type, tuple

validate(data)

Validates data if it is an iterable with all elements of type self._t

class dalio.validator.base_val.HAS_ATTR(attr)

Bases: dalio.validator.validator.Validator

Checks if data has an attribute

_attr

attribute to check for

Type:str

validate(data)

Validates data if it contains attribute self._attr

class dalio.validator.base_val.IS_TYPE(t)

Bases: dalio.validator.validator.Validator

Checks if data is of a certain type

Attribute:

t (type): type of data to check for

validate(data)

Validates data if it is of type self._t

dalio.validator.pandas_val module

class dalio.validator.pandas_val.HAS_COLS(cols, level=None)

Bases: dalio.validator.pandas_val.IS_PD_DF

Checks if data has certain column names

_cols

list of column names to check

validate(data)

Validates data if all the columns in self._cols is present in the dataframe

class dalio.validator.pandas_val.HAS_INDEX_NAMES(names, axis=0)

Bases: dalio.validator.pandas_val.IS_PD_DF

Checks if an axis has specified names

_names

names to check for

_axis

axis to check for names

validate(data)

Validates data if specified axis has the specified names

class dalio.validator.pandas_val.HAS_IN_COLS(items, cols=None)

Bases: dalio.validator.pandas_val.HAS_COLS

Check if certain items are present in certain columns

_cols

See base class

_items

items that must be present in each of the specified columns

validate(data)

Validates data if items in self._items are not present in specified columns. Specified columns are all columns if self._cols is None.

class dalio.validator.pandas_val.HAS_LEVELS(levels, axis=0, comparisson='<=')

Bases: dalio.validator.pandas_val.IS_PD_DF

validate(data)

Validates data if it is of type self._t

class dalio.validator.pandas_val.IS_PD_DF

Bases: dalio.validator.base_val.IS_TYPE

Checks if data is a pandas dataframe

See base class

class dalio.validator.pandas_val.IS_PD_TS

Bases: dalio.validator.base_val.IS_TYPE

Checks if data is a pandas time series

validate(data)

Validates data if it’s index is of type pandas.DateTimeIndex

dalio.validator.presets module

Define Validator collection presets

These are useful to describe very specific data characteristics commonly used in some analysis.

dalio.validator.validator module

Define Validator class

Validators are the building blocks of data integrity in the graph. As modularity is key, validators ensure that the data that enters a node is what it is mean to be or that errors are targeted to make debugging easier.

class dalio.validator.validator.Validator(fatal=True)

Bases: object

Check for some characteristic of a piece of data

Validators can have any attribute needed, but functionality is stored in u the .validate function, which returns any errors in the data.

fatal

Whether if invalid data is fatal. Decides whether invalid data can still be passed on (with a warning) or if it is grounds to stop the execution of the graph. False by default.

Type:bool

test_desc

Description of tests performed on data

Type:str

fatal_off()

Turn fatal off and return self

fatal_on()

Turn fatal on and return self

validate(data)

Validate data

Check if data fits a certain description.

Returns:A description of any errors in the data according to this specific validation condition, and None if data is valid.

Module contents