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98 | 98 | "source": [ |
99 | 99 | "**Pointwise** or **Vectorized indexing**, shown on the left, selects specific elements at given coordinates, resulting in an array of those individual elements. In the example shown, the indices `[0, 2, 4]`, `[0, 2, 4]` select the elements at positions `(0, 0)`, `(2, 2)`, and `(4, 4)`, resulting in the values `[1, 13, 25]`. This is the default behavior of NumPy arrays.\n", |
100 | 100 | " \n", |
101 | | - "In contrast, **orthogonal indexing** uses the same indices to select entire rows and columns, forming a cross-product of the specified indices. This method results in sub-arrays that include all combinations of the selected rows and columns. The example demonstrates this by selecting rows 0, 2, and 4 and columns 0, 2, and 4, resulting in a subarray containing `[[1, 3, 5], [11, 13, 15], [21, 23, 25]]`. This is Xarray DataArray's default behavior.\n", |
| 101 | + "In contrast, **orthogonal indexing** uses the same indices to select entire rows and columns, forming the Cartesian product of the specified indices. This method results in sub-arrays that include all combinations of the selected rows and columns. The example demonstrates this by selecting rows 0, 2, and 4 and columns 0, 2, and 4, resulting in a subarray containing `[[1, 3, 5], [11, 13, 15], [21, 23, 25]]`. This is Xarray DataArray's default behavior.\n", |
102 | 102 | " \n", |
103 | 103 | "The output of vectorized indexing is a `1D array`, while the output of orthogonal indexing is a `3x3` array. \n", |
104 | 104 | "\n", |
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