issue_comments

Table actions

5 rows where user = 1478822 sorted by updated_at descending

✎ View and edit SQL

This data as json, CSV (advanced)

Suggested facets: issue_url, reactions, created_at (date), updated_at (date)

user 1

  • Marston · 5 ✖

author_association 1

id html_url issue_url node_id user created_at updated_at ▲ author_association body reactions performed_via_github_app issue
680138594 https://github.com/pydata/xarray/issues/4374#issuecomment-680138594 https://api.github.com/repos/pydata/xarray/issues/4374 MDEyOklzc3VlQ29tbWVudDY4MDEzODU5NA== Marston 1478822 2020-08-25T16:39:26Z 2020-08-25T16:39:26Z NONE

I stand corrected. I missed that flag in the open_dataset() method. I appreciate the edification.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   Xarray improper handling of Fillvalues when converting to Numpy array 685613931
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.

 
{
    "total_count": 1,
    "+1": 1,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   time dimension 217485308
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 :-) 

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   time dimension 217485308
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. 

{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   time dimension 217485308
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.

 
{
    "total_count": 0,
    "+1": 0,
    "-1": 0,
    "laugh": 0,
    "hooray": 0,
    "confused": 0,
    "heart": 0,
    "rocket": 0,
    "eyes": 0
}
   time dimension 217485308

Advanced export

JSON shape: default, array, newline-delimited, object

CSV options: 

CREATE TABLE [issue_comments] (
   [html_url] TEXT,
   [issue_url] TEXT,
   [id] INTEGER PRIMARY KEY,
   [node_id] TEXT,
   [user] INTEGER REFERENCES [users]([id]),
   [created_at] TEXT,
   [updated_at] TEXT,
   [author_association] TEXT,
   [body] TEXT,
   [reactions] TEXT,
   [performed_via_github_app] TEXT,
   [issue] INTEGER REFERENCES [issues]([id])
);
CREATE INDEX [idx_issue_comments_issue]
    ON [issue_comments] ([issue]);
CREATE INDEX [idx_issue_comments_user]
    ON [issue_comments] ([user]);