values

This module gathers all classes and utilities used to work with sequence of values (1D data).

class CommensurableValues(data, scale=None, stype=None)

Bases: Values[S], Generic[S]

This class describes values with a single scale.

Instances are also immutable mapping of labels and Value objects and can be accessed, iterated over, as such.

Parameters

  • data (Series) –

  • scale (S | None) –

  • stype (Union[Type[S], None]) – scales type used

Raises

  • KeyError – if some labels are duplicated

  • TypeError – if stype and scale type mismatch

Attr data

internal representation of data

property bounds: S

Infer one common scale from the data.

It uses stype and call its mcda.core.scales.Scale.fit() method.

Returns

inferred scale

copy()

Return a copy of the object.

Return type

Self

dominate(other)

Check whether the ranking dominates an other one.

Parameters

other (Values) –

Returns

True if this object dominates other, False otherwise

Raises

TypeError – if values are not ordinal

Return type

bool

Note

this is done according to this object scales

dominate_strongly(other)

Check whether the ranking dominates strongly an other one.

Parameters

other (Values) –

Returns

True if this object dominates strongly other, False otherwise

Raises

TypeError – if values are not ordinal

Return type

bool

Note

this is done according to this object scales

get(k[, d]) → D[k] if k in D, else d.  d defaults to None.
property is_numeric: bool

Check whether values are numeric.

Returns

Return type

bool

property is_ordinal: bool

Check whether scales are all ordinal.

Returns

property is_within_scales: bool

Check whether all values are within their respective scales.

Returns

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
property labels: List

Return the data labels.

property name: Any

Return the name of the data attribute.

sort(ascending=False)

Return sorted data in new instance.

Numeric values are used to determine the sorting order.

Parameters

ascending (bool) – if True, will sort in ascending order

Returns

Raises

TypeError – if values are not ordinal

Return type

Self

Note

in case of equal values, original order is conserved

Warning

scales are not taken into account

Changed in version 1.1.0: original order between equal values is conserved (less surprising)

sum()

Return the sum of the data.

Returns

Return type

float

Warning

it will raise a TypeError if data contains numeric and non-numeric values

property to_numeric: CommensurableValues[QuantitativeScale]

Return numeric conversion of ordinal values.

Raises

TypeError – if scales are not ordinal

values() → an object providing a view on D's values
property within_scales: Series

Return a series indicating which values are within their respective scale.

Returns

class Value(value, scale)

Bases: Generic[S]

This class define a single value along its scale.

Those instances are comparable with each other, as long as they share a common scale, and can be compared to raw values. In the latter case, the scale is assumed as beign the scale of the raw value.

Parameters

  • value (Any) –

  • scale (S) –

Note

This class is not intended for general use, it is only intended for the inspection of Values cells.

class Values(data, scales=None, stype=None)

Bases: IVector, Mapping[Any, Value[S]], Generic[S]

This class associates a data pandas.Series with their multiple mcda.core.Scale.

Instances are also immutable mapping of labels and Value objects and can be accessed, iterated over, as such.

Parameters

  • data (Series) – series containing the data

  • scales (Union[S, Mapping[Any, S], None]) – data scale(s) (one per value or one shared, will be inferred from data if absent using Scale.fit())

  • stype (Union[Type[S], None]) – scales type used

Raises

  • KeyError – if some labels are duplicated

  • TypeError – if stype and scales types mismatch

Attr data

internal representation of data

property bounds: S

Infer one common scale from the data.

It uses stype and call its mcda.core.scales.Scale.fit() method.

Returns

inferred scale

copy()

Return a copy of the object.

Return type

Self

dominate(other)

Check whether the ranking dominates an other one.

Parameters

other (Values) –

Returns

True if this object dominates other, False otherwise

Raises

TypeError – if values are not ordinal

Return type

bool

Note

this is done according to this object scales

dominate_strongly(other)

Check whether the ranking dominates strongly an other one.

Parameters

other (Values) –

Returns

True if this object dominates strongly other, False otherwise

Raises

TypeError – if values are not ordinal

Return type

bool

Note

this is done according to this object scales

get(k[, d]) → D[k] if k in D, else d.  d defaults to None.
property is_numeric: bool

Check whether values are numeric.

Returns

Return type

bool

property is_ordinal: bool

Check whether scales are all ordinal.

Returns

property is_within_scales: bool

Check whether all values are within their respective scales.

Returns

items() → a set-like object providing a view on D's items
keys() → a set-like object providing a view on D's keys
property labels: List

Return the data labels.

property name: Any

Return the name of the data attribute.

sort(ascending=False)

Return sorted data in new instance.

Numeric values are used to determine the sorting order.

Parameters

ascending (bool) – if True, will sort in ascending order

Returns

Raises

TypeError – if values are not ordinal

Return type

Self

Warning

scales are not taken into account

sum()

Return the sum of the data.

Returns

Return type

float

Warning

it will raise a TypeError if data contains numeric and non-numeric values

property to_numeric: Values[QuantitativeScale]

Return numeric conversion of ordinal values.

Raises

TypeError – if scales are not ordinal

values() → an object providing a view on D's values
property within_scales: Series

Return a series indicating which values are within their respective scale.

Returns

series_equals(s1, s2)

Check if two series have the same values.

It will realign the indexes if they are ordered differently.

Parameters

  • s1 (Series) –

  • s2 (Series) –

Returns

Return type

bool