When aggregating data in NumPy or Pandas, NaN (Not a Number) values can be problematic because they propagate through calculations and can lead to incorrect results. In such cases, it is often desirable to ignore the NaN values during the aggregation.
Here is an example of how to aggregate data while ignoring NaNs in Pandas:
import pandas as pdimport numpy as np# Create a DataFrame with some NaN valuesdf = pd.DataFrame({'A': [1, 2, np.nan, 4], 'B': [5, np.nan, 7, 8], 'C': [9, 10, 11, 12]})# Compute the mean while ignoring NaNsmean = df.mean(skipna=True) |
In this example, we create a Pandas DataFrame with some NaN values in columns A and B. We then compute the mean of each column using the mean() method, with the skipna parameter set to True to ignore the NaN values.
Here is an example of how to aggregate data while ignoring NaNs in Dask:
import dask.array as daimport numpy as np# Create a Dask array with some NaN valuesarr = da.from_array(np.array([[1, 2, np.nan], [4, np.nan, 6], [7, 8, 9]]), chunks=2)# Compute the mean while ignoring NaNsmean = da.nanmean(arr) |
In this example, we create a Dask array with some NaN values. We then compute the mean of the array using the nanmean() function from Dask, which ignores NaN values during the aggregation. Note that nanmean() is similar to the regular mean() function in NumPy and Dask, but with the added functionality of ignoring NaN values.