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