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

User · Answer

In support to the excellent answers here, and for future convenience, there may be a case where you want to flip the truth values in the columns and have other values remain the same (nan values for instance)

In[1]: series = pd.Series([True, np.nan, False, np.nan])
In[2]: series = series[series.notna()] #remove nan values
 
In[3]: series # without nan                                            
Out[3]: 
0     True
2    False
dtype: object

# Out[4] expected to be inverse of Out[3], pandas applies bitwise complement 
# operator instead as in `lambda x : (-1*x)-1`

In[4]: ~series
Out[4]: 
0    -2
2    -1
dtype: object

as a simple non-vectorized solution you can just, 1. check types2. inverse bools

In[1]: series = pd.Series([True, np.nan, False, np.nan])

In[2]: series = series.apply(lambda x : not x if x is bool else x)
Out[2]: 
Out[2]: 
0     True
1      NaN
2    False
3      NaN
dtype: object

User · Answer

NumPy is slower because it casts the input to boolean values  so None and 0 becomes False and everything else becomes True     import pandas as pd import numpy as np s   pd Series  True  None  False  True   np logical not s    gives you   0    False 1     True 2     True 3    False dtype  object   whereas  s would crash  In most cases tilde would be a safer choice than NumPy      Pandas 0 25  NumPy 1 17

User · Answer

I just give it a shot   In  9   s   Series  True  True  True  False    In  10   s Out 10    0     True 1     True 2     True 3    False  In  11   -s Out 11    0    False 1    False 2    False 3     True

User · Answer

You can also use numpy invert   In  1   import numpy as np  In  2   import pandas as pd  In  3   s   pd Series  True  True  False  True    In  4   np invert s  Out 4    0    False 1    False 2     True 3    False   EDIT  The difference in performance appears on Ubuntu 12 04  Python 2 7  NumPy 1 7 0 - doesn t seem to exist using NumPy 1 6 2 though   In  5    timeit  -s  10000 loops  best of 3  26 8 us per loop  In  6    timeit np invert s  100000 loops  best of 3  7 85 us per loop  In  7    timeit  s 10000 loops  best of 3  27 3 us per loop

User · Answer

unutbu s answer is spot on  just wanted to add a warning that your mask needs to be dtype bool  not  object    Ie your mask can t have ever had any nan s  See here - even if your mask is nan-free now  it will remain  object  type   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   As  geher says  you can convert it to bool with astype before you inverse with     df  A   astype bool  0    False 1     True Name  A  dtype  bool   df  A    astype bool  0    True 1    True Name  A  dtype  bool

User · Answer

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   s per loop  In  11    timeit  s 10000 loops  best of 3  73 5   s per loop  In  12    timeit  -s  10000 loops  best of 3  73 5   s 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

[python] How can I obtain the element-wise logical NOT of a pandas Series?

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