I am trying to use switches to turn on and off conditionals in a pandas dataframe. The switches are just boolean variables that will be True or False. The problem is that ~True does not evaluate the same as False as I expected it to. Why does this not work?
>>> dataframe = pd.DataFrame({'col1': [3, 4, 5, 6], 'col2': [6, 5, 4, 3]})
>>> dataframe
col1 col2
0 3 6
1 4 5
2 5 4
3 6 3
>>> dataframe.loc[dataframe.col1 <= dataframe.col2]
col1 col2
0 3 6
1 4 5
>>> dataframe.loc[(True) | (dataframe.col1 <= dataframe.col2)]
col1 col2
0 3 6
1 4 5
2 5 4
3 6 3
>>> dataframe.loc[(False) | (dataframe.col1 <= dataframe.col2)]
col1 col2
0 3 6
1 4 5
>>> dataframe.loc[(~True) | (dataframe.col1 <= dataframe.col2)]
col1 col2
0 3 6
1 4 5
2 5 4
3 6 3
>>> dataframe.loc[(~(True)) | (dataframe.col1 <= dataframe.col2)]
col1 col2
0 3 6
1 4 5
2 5 4
3 6 3
>>>
>>> dataframe = pd.DataFrame({'col1': [1, 2, 3, 4], 'col2': [True, False, False, True]})
>>> dataframe
col1 col2
0 1 True
1 2 False
2 3 False
3 4 True
>>> dataframe.loc[dataframe.col2]
col1 col2
0 1 True
3 4 True
>>> dataframe.loc[not dataframe.col2]
Traceback (most recent call last):
File "<stdin>", line 1, in <module>
File "/usr/lib/python3/dist-packages/pandas/core/generic.py", line 1537, in __nonzero__
raise ValueError(
ValueError: The truth value of a Series is ambiguous. Use a.empty, a.bool(), a.item(), a.any() or a.all().
>>> dataframe.loc[dataframe.col2 == False]
col1 col2
1 2 False
2 3 False
This is a pandas operator behavior (implemented from Numpy).
True
is not a pandas object. Instead it's a boolean. So obviously, the ~
operator isn't meant to reverse booleans, only in Pandas.
As you can see:
>>> ~True
-2
>>>
It gives -2
, which is the regular __invert__
magic method behavior.
Therefore:
>>> bool(-2)
True
>>>
Gives True
.
Don't mix up Pandas and Python behavior, Pandas implements it's on __invert__
usage, example:
>>> ~pd.Series([True])
0 False
dtype: bool
>>>
As you can see, in pandas (also Numpy), it inverts the booleans. Therefor if you write:
>>> dataframe.loc[~pd.Series([True]).any() | (dataframe.col1 <= dataframe.col2)]
col1 col2
0 3 6
1 4 5
>>>
You can clearly see that it behaves equivalently as False
.
The best way here is with not
:
>>> dataframe.loc[(not True) | (dataframe.col1 <= dataframe.col2)]
col1 col2
0 3 6
1 4 5
>>>
I think '~' is not what you want, maybe you want to use 'not':
>>> dataframe.loc[(not True) | (dataframe.col1 <= dataframe.col2)]
col1 col2
0 3 6
1 4 5
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