issue_comments: 500165601

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https://github.com/pydata/xarray/issues/3004#issuecomment-500165601 https://api.github.com/repos/pydata/xarray/issues/3004 500165601 MDEyOklzc3VlQ29tbWVudDUwMDE2NTYwMQ== 21049064 2019-06-08T21:28:34Z 2019-06-08T21:28:34Z NONE

The best way I have found so far is: df = rank_norm.to_dataframe() bins = pd.qcut(df['rank_norm'], 5, labels=[1, 2, 3, 4, 5]) output = bins.to_xarray().to_dataset().rename({'rank_norm':'rank_quantile'})

Which returns: <xarray.Dataset> Dimensions: (lat: 10, lon: 10, time: 70) Coordinates: * lat (lat) float64 -5.175 -5.125 -5.075 ... -4.825 -4.775 -4.725 * lon (lon) float64 33.52 33.57 33.62 33.67 ... 33.87 33.92 33.97 * time (time) datetime64[ns] 2010-02-28 2010-03-31 ... 2015-11-30 Data variables: rank_quantile (lat, lon, time) int64 2 1 1 1 2 2 1 1 1 ... 1 1 2 2 1 4 2 2

 
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