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id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
467467599 https://github.com/pydata/xarray/issues/1334#issuecomment-467467599 https://api.github.com/repos/pydata/xarray/issues/1334 MDEyOklzc3VlQ29tbWVudDQ2NzQ2NzU5OQ== stale[bot] 26384082 2019-02-26T14:51:16Z 2019-02-26T14:51:16Z NONE

In order to maintain a list of currently relevant issues, we mark issues as stale after a period of inactivity

If this issue remains relevant, please comment here or remove the stale label; otherwise it will be marked as closed automatically

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289790484 https://github.com/pydata/xarray/issues/1334#issuecomment-289790484 https://api.github.com/repos/pydata/xarray/issues/1334 MDEyOklzc3VlQ29tbWVudDI4OTc5MDQ4NA== Marston 1478822 2017-03-28T14:36:05Z 2017-03-28T14:36:05Z NONE

This is true. I find that the Pandas creators assume too many things about the user. Not wanting to imply dumbing down, but users in science come to a module with a specific problem in mind, not to learn the module from scratch. Most of us are quick learners that can handle a steep learning curve if the docs/examples are rich in relevant info (intensive), which need not be long and drawn out. It would have been a better assumption to think that users such as myself are familiar with netCDF4, HDF4, Numpy, rather than the younger modules such as dask, xarray, and pandas, and draw associations from there.

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289778657 https://github.com/pydata/xarray/issues/1334#issuecomment-289778657 https://api.github.com/repos/pydata/xarray/issues/1334 MDEyOklzc3VlQ29tbWVudDI4OTc3ODY1Nw== rabernat 1197350 2017-03-28T13:59:21Z 2017-03-28T13:59:21Z MEMBER

I think this is related to #1282: docs frequently assume users are fluent pandas users, which is not always the case.

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289746180 https://github.com/pydata/xarray/issues/1334#issuecomment-289746180 https://api.github.com/repos/pydata/xarray/issues/1334 MDEyOklzc3VlQ29tbWVudDI4OTc0NjE4MA== Marston 1478822 2017-03-28T11:54:28Z 2017-03-28T11:54:28Z NONE

Ok. Concrete examples goes a far way. Maybe including this example into the docs might clarify how a user might access the data. I will use xr in the later stages of my work to analyse the data. Appreciate the clarification :-)

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289715756 https://github.com/pydata/xarray/issues/1334#issuecomment-289715756 https://api.github.com/repos/pydata/xarray/issues/1334 MDEyOklzc3VlQ29tbWVudDI4OTcxNTc1Ng== fmaussion 10050469 2017-03-28T09:33:30Z 2017-03-28T09:33:30Z MEMBER

What I want is to access the array [0,1,2,3....] as integers

ds['time.hour'].values gives you access to the underlying numpy arrays. However, it is needed only in rare case since xarray data structures behave like numpy arrays (one reason why you would want to access the numpy arrays instead of xarrays DataArrays is for performance).

Perhaps xr is it something more to plot data, and the objects within xr are only capable of manipulation only within a limited framework.

I strongly disagree with that one of course ;-) Let us now if we can do anything about the documentation in order to improve it.

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289710102 https://github.com/pydata/xarray/issues/1334#issuecomment-289710102 https://api.github.com/repos/pydata/xarray/issues/1334 MDEyOklzc3VlQ29tbWVudDI4OTcxMDEwMg== Marston 1478822 2017-03-28T09:10:42Z 2017-03-28T09:12:05Z NONE

apologies for these rookie questions. What I want is to access the array [0,1,2,3....] as integers, for example. Hope this is a bit clearer. I'm just checking if xarray can be use within my processing chain. Perhaps xr is it something more to plot data, and the objects within xr are only capable of manipulation only within a limited framework.

You can close this issue. I can ask this on stackoverflow as this is not truly a bug or the like.

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289708753 https://github.com/pydata/xarray/issues/1334#issuecomment-289708753 https://api.github.com/repos/pydata/xarray/issues/1334 MDEyOklzc3VlQ29tbWVudDI4OTcwODc1Mw== fmaussion 10050469 2017-03-28T09:05:21Z 2017-03-28T09:05:21Z MEMBER

I don't really understand what you mean. Here is the example from the docs again:

python import xarray as xr import pandas as pd import numpy as np time = pd.date_range('2000-01-01', freq='H', periods=24) ds = xr.Dataset({'foo': ('time', np.arange(24)), 'time': time}) ds['time.hour']

which prints:

<xarray.DataArray 'hour' (time: 24)> array([ 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23], dtype=int32) Coordinates: * time (time) datetime64[ns] 2000-01-01 2000-01-01T01:00:00 ...

What is it exactly what you are expecting?

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289707126 https://github.com/pydata/xarray/issues/1334#issuecomment-289707126 https://api.github.com/repos/pydata/xarray/issues/1334 MDEyOklzc3VlQ29tbWVudDI4OTcwNzEyNg== Marston 1478822 2017-03-28T08:59:00Z 2017-03-28T08:59:00Z NONE

Aha! I see, but how do I access the data as an integer? I apologise, I'm new and it is not clear from the link how to access this as standard types:

hour.time <xarray.DataArray 'time' ()> array(1167620400000000000, dtype='datetime64[ns]') Coordinates: time datetime64[ns] 2007-01-01T03:00:00 Attributes: standard_name: time long_name: time axis: T

hour.time give the same as above.

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289704003 https://github.com/pydata/xarray/issues/1334#issuecomment-289704003 https://api.github.com/repos/pydata/xarray/issues/1334 MDEyOklzc3VlQ29tbWVudDI4OTcwNDAwMw== fmaussion 10050469 2017-03-28T08:46:14Z 2017-03-28T08:46:14Z MEMBER

Yes, many new users of xarray are confused by this one: ds['time.hour'] is probably what you want. It is documented here:

http://xarray.pydata.org/en/latest/time-series.html#datetime-components

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  time dimension 217485308

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