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I have a pandas Series object containing boolean values. How can I get a series containing the logical NOT of each value?
For example, consider a series containing:
True True True False The series I'd like to get would contain:
False False False True This seems like it should be reasonably simple, but apparently I've misplaced my mojo =(
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In order to access the series element refers to the index number. Use the index operator [ ] to access an element in a series. The index must be an integer. In order to access multiple elements from a series, we use Slice operation.
Series can only contain single list with index, whereas dataframe can be made of more than one series or we can say that a dataframe is a collection of series that can be used to analyse the data.
Element-wise means handling data element by element.
To invert a boolean Series, use ~s:
In [7]: s = pd.Series([True, True, False, True]) In [8]: ~s Out[8]: 0 False 1 False 2 True 3 False dtype: bool Using Python2.7, NumPy 1.8.0, Pandas 0.13.1:
In [119]: s = pd.Series([True, True, False, True]*10000) In [10]: %timeit np.invert(s) 10000 loops, best of 3: 91.8 µs per loop In [11]: %timeit ~s 10000 loops, best of 3: 73.5 µs per loop In [12]: %timeit (-s) 10000 loops, best of 3: 73.5 µs per loop As of Pandas 0.13.0, Series are no longer subclasses of numpy.ndarray; they are now subclasses of pd.NDFrame. This might have something to do with why np.invert(s) is no longer as fast as ~s or -s.
Caveat: timeit results may vary depending on many factors including hardware, compiler, OS, Python, NumPy and Pandas versions.
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