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  • keewis · 2 ✖

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  • .to_xarray(): a 9Mb dataframe requires 30Gb ram · 2 ✖

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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
654688921 https://github.com/pydata/xarray/issues/4203#issuecomment-654688921 https://api.github.com/repos/pydata/xarray/issues/4203 MDEyOklzc3VlQ29tbWVudDY1NDY4ODkyMQ== keewis 14808389 2020-07-07T08:30:35Z 2020-07-07T15:38:11Z MEMBER

that's only the short repr, the values are not modified: python In [5]: da.lat Out[5]: <xarray.DataArray 'lat' (lat: 16100)> array([37.49944, 37.5004 , 37.50135, ..., 43.1014 , 43.10143, 43.10144]) Coordinates: * lat (lat) float64 37.5 37.5 37.5 37.5 37.5 ... 43.1 43.1 43.1 43.1 43.1

 
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   .to_xarray(): a 9Mb dataframe requires 30Gb ram  651101286
654210829 https://github.com/pydata/xarray/issues/4203#issuecomment-654210829 https://api.github.com/repos/pydata/xarray/issues/4203 MDEyOklzc3VlQ29tbWVudDY1NDIxMDgyOQ== keewis 14808389 2020-07-06T12:42:43Z 2020-07-06T12:42:43Z MEMBER

thanks, that helps. First of all (unless I did something wrong with the read_csv call), there's a Unnamed: 0 column that has to be removed.

Other than that, your data seems to be quite sparse so that's an ideal fit for sparse: python In [38]: %%time ...: df = pd.read_csv("/tmp/data.csv") ...: a = df.drop("Unnamed: 0", axis=1).set_index(["lat", "lon"]) ...: a = a.stack() ...: a.index.names = ["lat", "lon", "time"] ...: a = a.sort_index() ...: a.name = "T" ...: xr.DataArray.from_series(a, sparse=True) ...: ...: CPU times: user 606 ms, sys: 63.9 ms, total: 670 ms Wall time: 670 ms Out[38]: <xarray.DataArray 'T' (lat: 16100, lon: 29959, time: 31)> <COO: shape=(16100, 29959, 31), dtype=float64, nnz=1003191, fill_value=nan> Coordinates: * lat (lat) float64 37.5 37.5 37.5 37.5 37.5 ... 43.1 43.1 43.1 43.1 43.1 * lon (lon) float64 96.46 96.46 96.46 96.47 ... 102.6 102.6 102.6 102.6 * time (time) object '2011-01-01 00:00:00' ... '2011-01-31 00:00:00'

 
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   .to_xarray(): a 9Mb dataframe requires 30Gb ram  651101286

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