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import numpy as np # get dimensions of df nrows, ncols = len(df.index), 30 volume = nrows * ncols # total number of entries in df volume_to_be_nan = int(volume * 0.1) # number of entries to turn to NaN (10 %) # randomly generate index locations for the new NaNs indices = np.random.randint(volume, size=volume_to_be_nan) row_indices = indices % nrows col_indices = (indices / nrows).astype(int) # assign NaN to each of the indices in df for ri, ci in zip(row_indices, col_indices): df.iloc[ri, ci] = np.nan
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