# How can I obtain the element-wise logical NOT of a pandas Series?

### Question

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 =(

1
183
9/13/2018 7:44:49 PM

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.

211
10/28/2016 6:58:59 PM

The inverse of an 'object' series won't throw an error, instead you'll get a garbage mask of ints that won't work as you expect.

``````In[1]: df = pd.DataFrame({'A':[True, False, np.nan], 'B':[True, False, True]})
In[2]: df.dropna(inplace=True)
In[3]: df['A']
Out[3]:
0    True
1   False
Name: A, dtype object
In[4]: ~df['A']
Out[4]:
0   -2
0   -1
Name: A, dtype object
``````

After speaking with colleagues about this one I have an explanation: It looks like pandas is reverting to the bitwise operator:

``````In [1]: ~True
Out[1]: -2
``````