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6 | 6 | "source": [
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7 | 7 | "# Xarray's Data structures\n",
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8 | 8 | "\n",
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9 |
| - "In this lesson, we cover the basics of Xarray data structures. Our\n", |
10 |
| - "learning goals are as follows. By the end of the lesson, we will be able to:\n", |
| 9 | + "In this lesson, we cover the basics of Xarray data structures. By the end of the lesson, we will be able to:\n", |
11 | 10 | "\n",
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12 | 11 | ":::{admonition} Learning Goals\n",
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13 |
| - "- Understand the basic data structures (`DataArray` and `Dataset` objects) in Xarray\n", |
14 |
| - "- Customize the display of Xarray objects\n", |
15 |
| - "- Access variables, coordinates, and arbitrary metadata\n", |
16 |
| - "- Transform to tabular Pandas data structures\n", |
| 12 | + "- Understand the basic Xarray data structures `DataArray` and `Dataset` \n", |
| 13 | + "- Customize the display of Xarray data structures\n", |
| 14 | + "- The connection between Pandas and Xarray data structures\n", |
17 | 15 | ":::"
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18 | 16 | ]
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19 | 17 | },
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130 | 128 | "cell_type": "markdown",
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131 | 129 | "metadata": {},
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132 | 130 | "source": [
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133 |
| - "The output consists of:\n", |
| 131 | + "☝️ From top to bottom the output consists of:\n", |
134 | 132 | "\n",
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135 |
| - "- a summary of all *dimensions* of the `Dataset` `(lat: 25, time: 2920, lon: 53)`: this tells us that the first\n", |
136 |
| - " dimension is named `lat` and has a size of `25`, the second dimension is named\n", |
137 |
| - " `time` and has a size of `2920`, and the third dimension is named `lon` and has a size\n", |
138 |
| - " of `53`. Because we will access the dimensions by name, the order doesn't matter.\n", |
139 |
| - "- an unordered list of *coordinates* or dimensions with coordinates with one item\n", |
140 |
| - " per line. Each item has a name, one or more dimensions in parentheses, a dtype\n", |
141 |
| - " and a preview of the values. Also, if it is a dimension coordinate, it will be\n", |
142 |
| - " printed in **bold** font.\n", |
143 |
| - "- an alphabetically sorted list of *dimensions without coordinates* (if there are any)\n", |
144 |
| - "- an unordered list of *attributes*, or metadata" |
| 133 | + "- **Dimensions**: summary of all *dimensions* of the `Dataset` `(lat: 25, time: 2920, lon: 53)`: this tells us that the first dimension is named `lat` and has a size of `25`, the second dimension is named `time` and has a size of `2920`, and the third dimension is named `lon` and has a size of `53`. Because we will access the dimensions by name, the order doesn't matter.\n", |
| 134 | + "- **Coordinates**: an unordered list of *coordinates* or dimensions with coordinates with one item per line. Each item has a name, one or more dimensions in parentheses, a dtype and a preview of the values. Also, if it is a dimension coordinate, it will be printed in **bold** font. *dimensions without coordinates* appear in plain font (there are none in this example, but you might imagine a 'mask' coordinate that has a value assigned at every point).\n", |
| 135 | + "- **Data variables**: names of each nD *measurement* in the dataset, followed by its dimensions `(time, lat, lon)`, dtype, and a preview of values.\n", |
| 136 | + "- **Indexes**: Each dimension with coordinates is backed by an \"Index\". In this example, each dimension is backed by a `PandasIndex`\n", |
| 137 | + "- **Attributes**: an unordered list of metadata (for example, a paragraph describing the dataset)" |
145 | 138 | ]
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146 | 139 | },
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147 | 140 | {
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