Any comment on the relative performance of the two methods, considering that the first method (df['e'] = e.values) does not create a copy of the dataframe, while the second option (using df.assign) does?. Df = df.dropna () remove all rows wit null values from the dataframe. The boolean indexing operation [df['factor']] creates a boolean mask that is a.
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That means that if you set inplace = true , dropna will drop all missing values from your original dataset. The second df in df[df['factor']] refers to the dataframe on which the boolean indexing is being performed.
