pandas casting issues #1734
pandas casting issues #1734
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Thanks for the PR! Doc improvements are always appreciated. Maybe this would be better placed in the computations section? (http://xarray.pydata.org/en/latest/computation.html) Also, I don't understand what you mean with |
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In a nutshell, x = xr.DataArray([1,2,3])
pd.Series({'mean': x.mean()})does not work as expected. You'll want one of pd.Series({'mean': float(x.mean())})
pd.Series({'mean': x.mean()}, dtype=float)
pd.Series({'mean': x.mean()}).astype(float) |
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Can you merge master? #1747 should fix the build failure |
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@shoyer all good |
doc/faq.rst
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| @@ -58,6 +58,15 @@ fundamentally multi-dimensional. If your data is unstructured or | |||
| one-dimensional, stick with pandas. | |||
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| Gotchas | |||
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Can you frame this as a question, so it looks more similar to the other headings?
e.g., "Why don't aggregations return Python scalars?"
doc/faq.rst
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| xarray tries hard to be self-consistent. Operations on xarray objects return | ||
| other xarray objects. In particular, operations like `mean` or `sum` - even |
doc/faq.rst
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| xarray tries hard to be self-consistent. Operations on xarray objects return | ||
| other xarray objects. In particular, operations like `mean` or `sum` - even | ||
| when applied to all axes - will return xarray objects. Constructing `pandas` | ||
| objects sometimes require explicit casting. |
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I don't quite understand what this last comment means, maybe an example would help?
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I'll add something similar to my reply to @fmaussion above
doc/faq.rst
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| Gotchas | ||
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| xarray tries hard to be self-consistent. Operations on xarray objects return |
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I would note that the self-consistency is that the DataArray methods already return DataArrays (and the same for Dataset), i.e., it preserves the type. This isn't true for NumPy or pandas, which return scalars.
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I just noticed but numpy aggregation funcs return boxed objects that look like an ndarray
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Yes, NumPy and pandas return boxed scalar types (subclasses of np.generic).
doc/faq.rst
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| other xarray objects. In particular, operations like `mean` or `sum` - even | ||
| when applied to all axes - will return xarray objects. Constructing `pandas` | ||
| objects sometimes require explicit casting. | ||
| pd.Series({'x': arr[0], 'mean': arr.mean(), 'std': arr.std()}).astype(float) | ||
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Can you also note float(arr[0]) and arr[0].item() as two others ways to convert scalars to builtin types?
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@shoyer Thanks for the help for the writing, most of these are your own words ^^ |
doc/whats-new.rst
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| Documentation | ||
| ~~~~~~~~~~~~~ | ||
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| - New entry `Why don’t aggregations return Python scalars?` in the :doc:`faq`. |
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Consider giving yourself credit here :)
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@0x0L - I gave you credit on the whats new page and synced with master. I'll merge once the tests pass here again. |
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@jhamman Thank you |
Added a comment about constructing
pandasobjects fromxarrayobjects