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Set method symmetric difference

In Python, the symmetric_difference() method is a built-in function of the set data type that returns the set of elements that are in one set or the other, but not in both sets.

Here's an example of how to use the symmetric_difference() method:

set1 = {1, 2, 3, 4, 5}
set2 = {3, 4, 5, 6, 7}
 
sym_diff_set = set1.symmetric_difference(set2)
 
print(sym_diff_set)

Output:

{1, 2, 6, 7}

In this example, we define two sets, set1 and set2, that have some overlapping elements. We use the symmetric_difference() method on set1 and pass in set2 as an argument to return the set of elements that are in set1 or set2, but not in both sets. The resulting set, sym_diff_set, contains the elements 1, 2, 6, and 7.

Note that the symmetric_difference() method does not modify the original set. Instead, it returns a new set that contains the result of the symmetric difference operation.

Also note that the order of the arguments passed to symmetric_difference() does not matter. The resulting set will contain the elements that are in either set but not in both sets.

Efficient Python code Pythonic building with builtins. Efficient code using Pythons built-in range() function. Pythonic efficiency with the built-in enumerate() function. Pythonic efficiency with the built-in map() function. Pythonic efficiency with the power of NumPy arrays. Broadcasting NumPy arrays for pythonic data manipulation. Boolean indexing for pythonic data manipulation with NumPy arrays. In Python How to use %timeit in Python for performance testing Interpreting the %timeit output in Python Python loop optimization: Specifying the number of loops Using %timeit in Pythons line magic mode for code optimization Measuring Python code performance with %timeit in cell magic mode Saving Python code output to a file How to compare Python code execution times Optimizing Python code with runtime code profiling Measuring Python code line by line with %lprun Optimizing Python code with memory usage code profiling Python code optimization with memory profiler %mprun Tips and tricks for efficient combining Efficient ways to combine objects in Python Using zip for efficient object combination in Python Python techniques for efficient counting with loops Efficient counting with collections.Counter() in Python Efficient iteration with the itertools module in Python Python techniques for efficient combination with loops Efficient combination with itertools.combinations() in Python Python techniques for efficient object comparison with loops Python techniques for efficient set differences using the difference() method Python techniques for efficient symmetric differences using the symmetric_difference() method Python techniques for efficient set unions using the union() method Python techniques for finding unique elements using sets Improving Python code performance with loop elimination techniques How NumPy can help eliminate loops in Python code The benefits of moving calculations above loops in Python Simplifying data transformations with holistic conversions in Python Exploring DataFrame Iteration in pandas Determining the success rate: Calculating win percentage Enhancing DataFrame with win percentage calculation Exploring data with iterative access using .iloc Traversing DataFrame rows with .iterrows() iterator Traversing DataFrame rows with .itertuples() iterator Traversing DataFrame rows using .itertuples() iterator Applying a function to pandas data with .apply()