leaspy.io.data.data
.Data
- class Data
Bases:
Iterable
Main data container for a collection of individuals
It can be iterated over and sliced, both of these operations being applied to the underlying individuals attribute.
- Attributes
- individualsDict[IDType, IndividualData]
Included individuals and their associated data
- iter_to_idxDict[int, IDType]
Maps an integer index to the associated individual ID
- headersList[FeatureType]
Feature names
dimension
intNumber of features
n_individuals
intNumber of individuals
n_visits
intTotal number of visits
cofactors
List[FeatureType]Feature names corresponding to cofactors
Methods
from_csv_file
(path, **kws)Create a Data object from a CSV file.
from_dataframe
(df, **kws)Create a Data object from a
pandas.DataFrame
.from_individual_values
(indices, timepoints, ...)Construct Data from a collection of individual data points
from_individuals
(individuals, headers)Construct Data from a list of individuals
load_cofactors
(df, *[, cofactors])Load cofactors from a pandas.DataFrame to the Data object
to_dataframe
(*[, cofactors])Convert the Data object to a
pandas.DataFrame
- static from_csv_file(path: str, **kws) Data
Create a Data object from a CSV file.
- Parameters
- pathstr
Path to the CSV file to load (with extension)
- **kws
Keyword arguments that are sent to
CSVDataReader
- Returns
- static from_dataframe(df: DataFrame, **kws) Data
Create a Data object from a
pandas.DataFrame
.- Parameters
- df
pandas.DataFrame
Dataframe containing ID, TIME and features.
- **kws
Keyword arguments that are sent to
DataframeDataReader
- df
- Returns
- static from_individual_values(indices: List[str], timepoints: List[List[float]], values: List[List[List[float]]], headers: List[str]) Data
Construct Data from a collection of individual data points
- Parameters
- indicesList[IDType]
List of the individuals’ unique ID
- timepointsList[List[float]]
For each individual
i
, list of timepoints associated with the observations. The number of such timepoints is notedn_timepoints_i
- valuesList[array-like[float, 2D]]
For each individual
i
, two-dimensional array-like object containing observed data points. Its expected shape is(n_timepoints_i, n_features)
- headersList[FeatureType]
Feature names. The number of features is noted
n_features
- Returns
- static from_individuals(individuals: List[IndividualData], headers: List[str]) Data
Construct Data from a list of individuals
- Parameters
- individualsList[IndividualData]
List of individuals
- headersList[FeatureType]
List of feature names
- Returns
- load_cofactors(df: DataFrame, *, cofactors: Optional[List[str]] = None) None
Load cofactors from a pandas.DataFrame to the Data object
- Parameters
- df
pandas.DataFrame
The dataframe where the cofactors are stored. Its index should be ID, the identifier of subjects and it should uniquely index the dataframe (i.e. one row per individual).
- cofactorsList[FeatureType] or None (default)
Names of the column(s) of df which shall be loaded as cofactors. If None, all the columns from the input dataframe will be loaded as cofactors.
- df
- Raises
- to_dataframe(*, cofactors: Optional[Union[List[str], str]] = None) DataFrame
Convert the Data object to a
pandas.DataFrame
- Parameters
- cofactorsList[FeatureType], ‘all’, or None (default None)
Cofactors to include in the DataFrame. If None (default), no cofactors are included. If “all”, all the available cofactors are included.
- Returns
pandas.DataFrame
A DataFrame containing the individuals’ ID, timepoints and associated observations (optional - and cofactors).
- Raises