CODEWITHSUNDEEP
pythonpandaslogical-operators
Jamie Bull
Jamie Bull

Reputation: 13529

Using logical operators in building a Pandas DataFrame

I have two snippets of pandas code which I think should be equivalent, but the second one doesn't do what I expect.

# snippet 1
    data = all_data[[((np.isfinite(all_data[self.design_metric][i]) 
                    and all_data['Source'][i] == 2)) 
                    or ((np.isfinite(all_data[self.actual_metric][i]) 
                    and all_data['Source'][i] != 2))
                    for i in range(len(all_data))]]


# snippet 2
    data = all_data[(all_data['Source'] == 2 &
                    np.isfinite(all_data[self.design_metric])) |
                    (all_data['Source'] != 2 &
                    np.isfinite(all_data[self.actual_metric]))]

Each section (e.g. all_data['Source'] == 2 ) does what I expect on its own but it seems that I'm doing something wrong with the logical operators as the final result is coming out with a different result to the list comprehension version.

Upvotes: 2

Views: 14785

Answers (2)

alko
alko

Reputation: 48317

Along with priority, there is a difference between AND and & operators, first one being boolean and the latter being binary bitwise. Also, you must be aware of boolead expressions.

See examples in the following snippet:

logical expressions

>>> 1 and 2
1

>>> '1' and '2'
'1'

>>> 0 == 1 and 2 == 0 or 0
0

bitwise operators

>>> 1 & 2
0

>>> '1' & '2'
Traceback (most recent call last):
  ...
TypeError: unsupported operand type(s) for &: 'str' and 'str'

>>> 0 == 1 & 2 == 0 | 0
True

Upvotes: 1

BrenBarn
BrenBarn

Reputation: 251408

The & operator binds more tightly than == (or any comparison operator). See the documentation. A simpler example is:

>>> 2 == 2 & 3 == 3
False

This is because it is grouped as 2 == (2 & 3) == 3, and then comparison chaining is invoked. This is what is happening in your case. You need to put parentheses around each comparison.

 data = all_data[((all_data['Source'] == 2) &
                np.isfinite(all_data[self.design_metric])) |
                ((all_data['Source'] != 2) &
                np.isfinite(all_data[self.actual_metric]))]

Note the extra parentheses around the == and != comparisons.

Upvotes: 10

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