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Alex Rivera
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I have a dataframe with ~300K rows and ~40 columns. I want to find out if any rows contain null values - and put these 'null'-rows into a separate dataframe so that I could explore them easily. I can create a mask explicitly: mask = False for col in df.columns: mask = mask | df[col].isnull() dfnulls = df[mask] Or I can do something like: df.ix[df.index[(df.T == np.nan).sum() > 1]] Is there a more elegant way of doing it (locating rows with nulls in them)?
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