|
| 1 | +--- |
| 2 | +jupytext: |
| 3 | + text_representation: |
| 4 | + format_name: myst |
| 5 | +kernelspec: |
| 6 | + display_name: Python 3 |
| 7 | + name: python |
| 8 | +--- |
| 9 | + |
| 10 | +# pandas: IntervalIndex |
| 11 | + |
| 12 | +````{grid} |
| 13 | +```{grid-item} |
| 14 | +:columns: 3 |
| 15 | +```{image} https://pandas.pydata.org/docs/_static/pandas.svg |
| 16 | +--- |
| 17 | +alt: Alt text |
| 18 | +width: 200px |
| 19 | +align: center |
| 20 | +--- |
| 21 | +``` |
| 22 | +```{grid-item} |
| 23 | +:columns: 9 |
| 24 | +```{seealso} |
| 25 | +Learn more at the [Pandas](https://pandas.pydata.org/pandas-docs/stable/user_guide/advanced.html#intervalindex) documentation. |
| 26 | +``` |
| 27 | +```` |
| 28 | + |
| 29 | +# Highlights |
| 30 | + |
| 31 | +1. Xarray's built-in support for pandas Index classes extends to more sophisticated classes like {py:class}`pandas.IntervalIndex`. |
| 32 | +1. Xarray now generates such indexes automatically when using {py:meth}`xarray.DataArray.groupby_bins` or {py:meth}`xarray.Dataset.groupby_bins`. |
| 33 | +1. Sadly {py:class}`pandas.IntervalIndex` supports numpy datetimes but not cftime. |
| 34 | + |
| 35 | +## Example |
| 36 | + |
| 37 | +### Assigning |
| 38 | + |
| 39 | +```{code-cell} |
| 40 | +%xmode minimal |
| 41 | +
|
| 42 | +import pandas as pd |
| 43 | +import xarray as xr |
| 44 | +
|
| 45 | +xr.set_options(display_expand_indexes=True, display_expand_attrs=False) |
| 46 | +pd.set_option('display.max_seq_items', 10) |
| 47 | +
|
| 48 | +orig = xr.tutorial.open_dataset("air_temperature") |
| 49 | +orig |
| 50 | +``` |
| 51 | + |
| 52 | +Let's replace the `time` vector with an IntervalIndex, assuming that the data represent averages over 6 hour periods centered at 00h, 06h, 12h, 18h |
| 53 | + |
| 54 | +```{code-cell} |
| 55 | +left = orig.time.data - pd.Timedelta("3h") |
| 56 | +right = orig.time.data + pd.Timedelta("3h") |
| 57 | +time_bounds = pd.IntervalIndex.from_arrays(left, right, closed="left") |
| 58 | +time_bounds |
| 59 | +``` |
| 60 | + |
| 61 | +```{code-cell} |
| 62 | +indexed = orig.copy(deep=True) |
| 63 | +indexed["time"] = time_bounds |
| 64 | +indexed |
| 65 | +``` |
| 66 | + |
| 67 | +### Indexing |
| 68 | + |
| 69 | +Let's index out a representative value for 2013-05-01 02:00. |
| 70 | + |
| 71 | +```{code-cell} |
| 72 | +--- |
| 73 | +tags: [raises-exception] |
| 74 | +--- |
| 75 | +orig.sel(time="2013-05-01 02:00") |
| 76 | +``` |
| 77 | + |
| 78 | +Indexing the original dataset required specifying `method="nearest"` |
| 79 | + |
| 80 | +```{code-cell} |
| 81 | +orig.sel(time="2013-05-01 02:00", method="nearest").time |
| 82 | +``` |
| 83 | + |
| 84 | +With an IntervalIndex, however, that is unnecessary |
| 85 | + |
| 86 | +```{code-cell} |
| 87 | +indexed.sel(time="2013-05-01 02:00").time |
| 88 | +``` |
| 89 | + |
| 90 | +### Binned grouping |
| 91 | + |
| 92 | +Xarray now creates IntervalIndex by default for binned grouping operations |
| 93 | + |
| 94 | +```{code-cell} |
| 95 | +orig.groupby_bins("lat", bins=[25, 35, 45, 55]).mean() |
| 96 | +``` |
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