Compute the Pearson correlation coefficient between two DataArray objects along a shared dimension. Parameters ---------- da_a : DataArray Array to compute. da_b : DataArray Array to compute. dim : str, iterable of hashable, "..." or None, optional
(
da_a: T_DataArray,
da_b: T_DataArray,
dim: Dims = None,
weights: T_DataArray | None = None,
)
| 150 | |
| 151 | |
| 152 | def corr( |
| 153 | da_a: T_DataArray, |
| 154 | da_b: T_DataArray, |
| 155 | dim: Dims = None, |
| 156 | weights: T_DataArray | None = None, |
| 157 | ) -> T_DataArray: |
| 158 | """ |
| 159 | Compute the Pearson correlation coefficient between |
| 160 | two DataArray objects along a shared dimension. |
| 161 | |
| 162 | Parameters |
| 163 | ---------- |
| 164 | da_a : DataArray |
| 165 | Array to compute. |
| 166 | da_b : DataArray |
| 167 | Array to compute. |
| 168 | dim : str, iterable of hashable, "..." or None, optional |
| 169 | The dimension along which the correlation will be computed |
| 170 | weights : DataArray, optional |
| 171 | Array of weights. |
| 172 | |
| 173 | Returns |
| 174 | ------- |
| 175 | correlation: DataArray |
| 176 | |
| 177 | See Also |
| 178 | -------- |
| 179 | pandas.Series.corr : corresponding pandas function |
| 180 | xarray.cov : underlying covariance function |
| 181 | |
| 182 | Examples |
| 183 | -------- |
| 184 | >>> from xarray import DataArray |
| 185 | >>> da_a = DataArray( |
| 186 | ... np.array([[1, 2, 3], [0.1, 0.2, 0.3], [3.2, 0.6, 1.8]]), |
| 187 | ... dims=("space", "time"), |
| 188 | ... coords=[ |
| 189 | ... ("space", ["IA", "IL", "IN"]), |
| 190 | ... ("time", pd.date_range("2000-01-01", freq="1D", periods=3)), |
| 191 | ... ], |
| 192 | ... ) |
| 193 | >>> da_a |
| 194 | <xarray.DataArray (space: 3, time: 3)> Size: 72B |
| 195 | array([[1. , 2. , 3. ], |
| 196 | [0.1, 0.2, 0.3], |
| 197 | [3.2, 0.6, 1.8]]) |
| 198 | Coordinates: |
| 199 | * space (space) <U2 24B 'IA' 'IL' 'IN' |
| 200 | * time (time) datetime64[us] 24B 2000-01-01 2000-01-02 2000-01-03 |
| 201 | >>> da_b = DataArray( |
| 202 | ... np.array([[0.2, 0.4, 0.6], [15, 10, 5], [3.2, 0.6, 1.8]]), |
| 203 | ... dims=("space", "time"), |
| 204 | ... coords=[ |
| 205 | ... ("space", ["IA", "IL", "IN"]), |
| 206 | ... ("time", pd.date_range("2000-01-01", freq="1D", periods=3)), |
| 207 | ... ], |
| 208 | ... ) |
| 209 | >>> da_b |