fetch_weather

skfda.datasets.fetch_weather(*, return_X_y: Literal[False] = False, as_frame: bool = False) → Bunch

skfda.datasets.fetch_weather(*, return_X_y: Literal[True], as_frame: Literal[False] = False) → Tuple[FDataGrid, ndarray[tuple[int, ...], dtype[integer[Any]]]]

skfda.datasets.fetch_weather(*, return_X_y: Literal[True], as_frame: Literal[True]) → Tuple[DataFrame, Series]

Load the Canadian Weather dataset.

The data is obtained from the R package ‘fda’ from CRAN.

Daily temperature and precipitation at 35 different locations in Canada averaged over 1960 to 1994.

References

Ramsay, James O., and Silverman, Bernard W. (2006), Functional Data Analysis, 2nd ed. , Springer, New York.

Ramsay, James O., and Silverman, Bernard W. (2002), Applied Functional Data Analysis, Springer, New York

Parameters:

• return_X_y – Return only the data and target as a tuple.

• as_frame – Return the data in a Pandas Dataframe or Series.

Examples using skfda.datasets.fetch_weather

Mixed data structure and visualization.

Mixed data structure and visualization.

Mixed effects model for irregular data

Mixed effects model for irregular data

Mixed effects model for irregular data: robustness of the conversion

Mixed effects model for irregular data: robustness of the conversion

Boxplot

Boxplot

Magnitude-Shape Plot

Magnitude-Shape Plot

Clustering

Clustering

Functional Linear Regression with multivariate covariates.

Functional Linear Regression with multivariate covariates.

Neighbors Functional Regression

Neighbors Functional Regression

Neighbors Scalar Regression

Neighbors Scalar Regression

Creating a new basis

Creating a new basis

Introduction

Introduction

Getting the data

Getting the data

Basis representation

Basis representation

Scikit-fda and scikit-learn

Scikit-fda and scikit-learn