![]() M = np.logical_and.reduce(, v) for op, c, v in zip(ops, cols, values)]) This is powerful, because it lets you build on top of this with more complex logic (for example, dynamically generating masks in a list comprehension and adding all of them): import operator However, an easier option is np.logical_and.reduce() For example, to AND masks m1 and m2 and m3 with &, you would have to do m1 & m2 & m3 This means it is easier to generalise with logical_and if you have multiple masks to AND. Np.logical_and is a ufunc (Universal Functions), and most ufuncs have a reduce method. Generalizing: np.logical_and (and logical_and.reduce)Īnother alternative is using np.logical_and, which also does not need parentheses grouping: np.logical_and(df 5) You won't usually need this, but it is useful to know. ![]() Internally calls Series._and_ which corresponds to the bitwise operator. I have extensively documented query and eval in Dynamic Expression Evaluation in pandas using pd.eval().Īllows you to perform this operation in a functional manner. If the individual masks are built up using functions instead of conditional operators, you will no longer need to group by parens to specify evaluation order: df.lt(5) Most operators have a corresponding bound method for DataFrames. Which throws ValueError: The truth value of a Series is ambiguous. Something_else_you_dont_want1 and something_else_you_dont_want2 Which becomes, # Both operands are Series. Which becomes (see the python docs on chained operator comparison), (df 5) For example, if you accidentally attempt something such as df 5 If you do not use parentheses, the expression is evaluated incorrectly. See the section of Operator Precedence in the python docs. The parentheses are used to override the default precedence order of bitwise operators, which have higher precedence over the conditional operators. Must be grouped by using parentheses, since by default Python willĮvaluate an expression such as df.A > 2 & df.B (2 &Īnd the subsequent filtering step is simply, df 5)] The operators are: | for or, & for and, and ~ for not. This is done by computing masks for each condition separately, and ANDing them.īefore continuing, please take note of this particular excerpt of the docs, which stateĪnother common operation is the use of boolean vectors to filter theĭata. Pandas provides three operators: & for logical AND, | for logical OR, and ~ for logical NOT.Ĭonsider the following setup: np.ed(0)ĭf = pd.DataFrame(np.random.choice(10, (5, 3)), columns=list('ABC'))įor df above, say you'd like to return all rows where A 5. If in the process of performing logical operation you get a ValueError, then you need to use parentheses for grouping: (exp1) op (exp2)įor example, (df = x) & (df = y)īoolean Indexing: A common operation is to compute boolean masks through logical conditions to filter the data. So the following in python ( exp1 and exp2 are expressions which evaluate to a boolean result). So Pandas had to do one better and override the bitwise operators to achieve vectorized (element-wise) version of this functionality. Python's and, or and not logical operators are designed to work with scalars. TLDR Logical Operators in Pandas are &, | and ~, and parentheses (.) is important! That's why the parentheses are mandatory. The use of and with two Series would again trigger the same ValueError as above. That is an expression of the form Series and Series. Without the parentheses, a=1 & a=10 would be evaluated as a = (1 & a) = 10 which would in turn be equivalent to the chained comparison (a = (1 & a)) and ((1 & a) = 10). The parentheses are mandatory since & has a higher operator precedence than =. ![]() That is what the & binary operator performs: (a=1) & (a=10) In this case, however, it looks like you do not want Boolean evaluation, you want element-wise logical-and. Instead, you must be explicit, by calling the empty(), all() or any() method to indicate which behavior you desire. ![]() Others might want it to be True if any of its elements are True.īecause there are so many conflicting expectations, the designers of NumPy and Pandas refuse to guess, and instead raise a ValueError. Others might desire for it to be True only if all its elements are True. Some users might assume they are True if they have non-zero length, like a Python list. That's because it's unclear when it should be True or False. ![]() ValueError: The truth value of an array is ambiguous. NumPy arrays (of length greater than 1) and Pandas objects such as Series do not have a Boolean value - in other words, they raise You are implicitly asking Python to convert (a=1) and (a=10) to Boolean values. ![]()
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