When working with earthquake data, it is common to encounter NaN values. NaNs can occur in earthquake data due to incomplete or missing data. It is often necessary to aggregate data while ignoring NaNs.
In dask, we can use the nansum(), nanmean(), nanmax(), nanmin(), nanvar(), and nanstd() functions to aggregate data while ignoring NaNs. These functions work in a similar way to their NumPy counterparts, but they ignore NaN values.
For example, to compute the mean magnitude of the earthquakes while ignoring NaNs, we can use the nanmean() function as follows:
import dask.array as dawith h5py.File('earthquake_data.h5', 'r') as f: magnitude = da.from_array(f['magnitude'], chunks='auto')mean_magnitude = da.nanmean(magnitude).compute() |
In this example, we use da.nanmean() to compute the mean magnitude of the earthquakes while ignoring NaNs. We pass the magnitude dask array to the da.nanmean() function, which computes the mean of the non-NaN values in the array. We then call .compute() to compute the result in parallel.
We can use the same approach to compute other aggregates, such as the maximum or minimum magnitude, while ignoring NaNs. For example, to compute the maximum magnitude while ignoring NaNs, we can use the nanmax() function as follows:
max_magnitude = da.nanmax(magnitude).compute() |
Note that the nansum() function can be used to compute the sum of the values while ignoring NaNs. The nanvar() and nanstd() functions can be used to compute the variance and standard deviation while ignoring NaNs.