Pythonで学ぶDaskによる並列プログラミング
James Fulton
Climate Informatics Researcher
import xarray as xr ds = xr.open_zarr("data/era_eu.zarr") print(ds)
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), ... >
# 特定の日付を選択 df.loc['2020-01-01'] # 行番号で選択 df.iloc[0] # 列を選択 df['column1']
# 特定の日付を選択 ds.sel(time='2020-01-01') # インデックス番号で選択 ds.isel(time=0) # 変数を選択 ds['variable1']
# 数値演算 df.mean() # groupby と平均 df.groupby(df['time'].dt.year).mean() # 移動平均 rolling_mean = df.rolling(5).mean()
# 数値演算 df.mean()
# groupby と平均 df.groupby(df['time'].dt.year).mean()
# 移動平均 rolling_mean = df.rolling(5).mean()
# 数値演算 ds.mean() ds.mean(dim='dim1') ds.mean(dim=('dim1', 'dim2')) # groupby と平均 ds.groupby(ds['time'].dt.year).mean() # 移動平均 rolling_mean = ds.rolling(dim1=5).mean() rolling_mean.compute()
# 数値演算 ds.mean() ds.mean(dim='dim1') ds.mean(dim=('dim1', 'dim2'))
# groupby と平均 ds.groupby(ds['time'].dt.year).mean()
# 移動平均 rolling_mean = ds.rolling(dim1=5).mean()
rolling_mean.compute()
ds['variable'].plot()
例
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