Pemrograman Paralel dengan Dask di Python
James Fulton
Climate Informatics Researcher


import xarray as xr ds = xr.open_zarr("data/era_eu.zarr")print(ds)
<xarray.Dataset>
Dimensions: (lat: 30, lon: 45, time: 504)
Coordinates:
* lat (lat) float64 35.5 36.5 37.5 38.5 39.5 ... 60.5 61.5 62.5 63.5 64.5
* lon (lon) float64 -14.5 -13.5 -12.5 -11.5 -10.5 ... 26.5 27.5 28.5 29.5
* time (time) datetime64[ns] 1979-05-31 1979-06-30 ... 2021-04-30
Data variables:
precip (time, lat, lon) float32 dask.array<chunksize=(12, 15, 15), ... >
temp (time, lat, lon) float32 dask.array<chunksize=(12, 15, 15), ... >
# Pilih tanggal tertentu
df.loc['2020-01-01']
# Pilih berdasarkan nomor indeks
df.iloc[0]
# Pilih kolom
df['column1']
# Pilih tanggal tertentu
ds.sel(time='2020-01-01')
# Pilih berdasarkan nomor indeks
ds.isel(time=0)
# Pilih variabel
ds['variable1']
# Lakukan operasi matematis df.mean()# Groupby dan mean df.groupby(df['time'].dt.year).mean()# Rolling mean rolling_mean = df.rolling(5).mean()
# Lakukan operasi matematis ds.mean() ds.mean(dim='dim1') ds.mean(dim=('dim1', 'dim2'))# Groupby dan mean ds.groupby(ds['time'].dt.year).mean()# Rolling mean rolling_mean = ds.rolling(dim1=5).mean()rolling_mean.compute()
ds['variable'].plot()
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ds['variable'].plot()
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ds['variable'].plot()
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Pemrograman Paralel dengan Dask di Python