html_url,issue_url,id,node_id,user,created_at,updated_at,author_association,body,reactions,performed_via_github_app,issue
https://github.com/pydata/xarray/issues/2488#issuecomment-703222102,https://api.github.com/repos/pydata/xarray/issues/2488,703222102,MDEyOklzc3VlQ29tbWVudDcwMzIyMjEwMg==,26384082,2020-10-04T08:32:37Z,2020-10-04T08:32:37Z,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
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,370183554
https://github.com/pydata/xarray/issues/2488#issuecomment-430232155,https://api.github.com/repos/pydata/xarray/issues/2488,430232155,MDEyOklzc3VlQ29tbWVudDQzMDIzMjE1NQ==,16838898,2018-10-16T13:13:37Z,2018-10-16T13:13:37Z,NONE,I am open for suggestions to get the code running faster :D,"{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,370183554
https://github.com/pydata/xarray/issues/2488#issuecomment-430232027,https://api.github.com/repos/pydata/xarray/issues/2488,430232027,MDEyOklzc3VlQ29tbWVudDQzMDIzMjAyNw==,16838898,2018-10-16T13:13:13Z,2018-10-16T13:13:13Z,NONE,"%%time
def group_lat(x):
    # x is a DataFrame of group values
    # now find the value of the longitude box to append to the dictionary key
    value = np.ones(1)
    value[0] = x.lon.mean()
    idx = (np.abs(lon_cent - value)).argmin()
    lokey = lon_cent[idx] # longitude value of the box
    
    # compute groups for the latitude
    y = x.groupby_bins('lat', lat_bin, labels=lat_cent)
    y = dict(y)
    # replace the old key with the new key: lon,lat
    key = np.asarray((list(y.keys()))) # get dict keys as array
    newkey = np.stack((np.ones(len(key))*lokey,key),axis=1)
    newkey = tuple(newkey.tolist())
    key = tuple(y.keys()) # get dict keys as list
    
    for i in range(len(key)):
        y[tuple(newkey[i])] = y[key[i]]
        del y[key[i]]  
    return y

#geop_mean = geop1.groupby_bins('lon', lon_bin, labels=lon_cent).apply(group_lat)
geop_mean = geop1.groupby_bins('lon', lon_bin, labels=lon_cent)
geop_mean = dict(geop_mean) 

# group into lat boxes
l = 0
geo_grid = dict()

for x in list(geop_mean.keys()):
    y = group_lat(geop_mean[x])
    if l == 0:
        geo_grid = y
    else:
        geo_grid.update(y)
    l += 1

# Now the data is sorted into boxes and still contains all metadata

# Now get the mean values for each box
l = 0
m = np.zeros((len(tuple(geo_grid.keys())),4))
d = np.asarray(list(geo_grid.keys()))

gp = xr.Dataset({'geopot': (['lat','lon'],
                                np.ones((lat_cent.shape[0],
                                         lon_cent.shape[0]))),
                     'z': (['lat','lon'],
                           np.ones((lat_cent.shape[0],
                                    lon_cent.shape[0])))},
                           coords={'lon': (['lon'],lon_cent),
                                   'lat': (['lat'],lat_cent)})

for k in range(d.shape[0]):
    e = tuple(d[k])
    #m[l,2] = geo_grid[e].z.mean()
    gp['geopot'].loc[dict(lat=d[k][1], lon=d[k][0])] = geo_grid[e].geopot.mean()
    gp['z'].loc[dict(lat=d[k][1], lon=d[k][0])] = geo_grid[e].z.mean()
    #gp.loc[dict(lat=m[0,1], lon=m[0,0])]
    l +=1
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,370183554
https://github.com/pydata/xarray/issues/2488#issuecomment-430231535,https://api.github.com/repos/pydata/xarray/issues/2488,430231535,MDEyOklzc3VlQ29tbWVudDQzMDIzMTUzNQ==,16838898,2018-10-16T13:11:44Z,2018-10-16T13:12:36Z,NONE,"I wrote a work around for my purpose but I guess I could still be faster ...
","{""total_count"": 0, ""+1"": 0, ""-1"": 0, ""laugh"": 0, ""hooray"": 0, ""confused"": 0, ""heart"": 0, ""rocket"": 0, ""eyes"": 0}",,370183554