From https://www.continuum.io/content/xray-dask-out-core-labeled-arrays-python
In [47]:
%matplotlib inline
import numpy as np
import matplotlib.pyplot as plt
import xarray as xr
import pandas as pd
import seaborn as sns # pandas aware plotting library
ds = xr.open_mfdataset('C:\\Users\\Norman\\EOData\\Ozone-CCI\\*.nc', concat_dim='time')
In [40]:
u = ds.mean(['latitude', 'longitude'])
ozone = u['atmosphere_mole_content_of_ozone']
error = u['atmosphere_mole_content_of_ozone_standard_error']
In [41]:
ozone.plot()
Out[41]:
In [42]:
error.plot()
Out[42]:
In [60]:
sns.pairplot(u.to_dataframe().reset_index(), vars=('atmosphere_mole_content_of_ozone', 'atmosphere_mole_content_of_ozone_standard_error'))
Out[60]:
In [46]:
ozone.to_pandas().corr(error.to_pandas())
Out[46]:
In [63]:
error.to_series()
Out[63]:
In [50]:
ds.to_dataframe()
Out[50]:
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