# xarray.DataArray.polyfit¶

DataArray.polyfit(dim, deg, skipna=None, rcond=None, w=None, full=False, cov=False)

Least squares polynomial fit.

This replicates the behaviour of numpy.polyfit but differs by skipping invalid values when skipna = True.

Parameters
• dim (hashable) – Coordinate along which to fit the polynomials.

• deg (int) – Degree of the fitting polynomial.

• skipna (bool, optional) – If True, removes all invalid values before fitting each 1D slices of the array. Default is True if data is stored in a dask.array or if there is any invalid values, False otherwise.

• rcond (float, optional) – Relative condition number to the fit.

• w (Union[Hashable, Any], optional) – Weights to apply to the y-coordinate of the sample points. Can be an array-like object or the name of a coordinate in the dataset.

• full (bool, optional) – Whether to return the residuals, matrix rank and singular values in addition to the coefficients.

• cov (Union[bool, str], optional) – Whether to return to the covariance matrix in addition to the coefficients. The matrix is not scaled if cov=’unscaled’.

Returns

polyfit_results – A single dataset which contains:

polyfit_coefficients

The coefficients of the best fit.

polyfit_residuals

The residuals of the least-square computation (only included if full=True)

[dim]_matrix_rank

The effective rank of the scaled Vandermonde coefficient matrix (only included if full=True)

[dim]_singular_value

The singular values of the scaled Vandermonde coefficient matrix (only included if full=True)

polyfit_covariance

The covariance matrix of the polynomial coefficient estimates (only included if full=False and cov=True)

Return type

Dataset

numpy.polyfit()