In Python, merging dictionaries can be done in several ways, but to do it safely and idiomatically, you should consider the effects of overwriting keys and ensure compatibility among your data structures. Below are some common methods to merge dictionaries in Python:
The update()
method allows you to merge one dictionary into another. It will overwrite the keys from the first dictionary if they exist in the second.
# Example of merging with update
dict1 = {'a': 1, 'b': 2}
dict2 = {'b': 3, 'c': 4}
dict1.update(dict2)
print(dict1) # Output: {'a': 1, 'b': 3, 'c': 4}
Starting in Python 3.9, you can use the |
operator to merge dictionaries. This method does not modify the original dictionaries, but instead returns a new one.
# Example of merging using the pipe operator
dict1 = {'a': 1, 'b': 2}
dict2 = {'b': 3, 'c': 4}
merged_dict = dict1 | dict2
print(merged_dict) # Output: {'a': 1, 'b': 3, 'c': 4}
You can also use dictionary comprehension for a more manual but flexible approach.
# Example of merging using dictionary comprehension
dict1 = {'a': 1, 'b': 2}
dict2 = {'b': 3, 'c': 4}
merged_dict = {k: v for d in [dict1, dict2] for k, v in d.items()}
print(merged_dict) # Output: {'a': 1, 'b': 3, 'c': 4}
How do I avoid rehashing overhead with std::set in multithreaded code?
How do I find elements with custom comparators with std::set for embedded targets?
How do I erase elements while iterating with std::set for embedded targets?
How do I provide stable iteration order with std::unordered_map for large datasets?
How do I reserve capacity ahead of time with std::unordered_map for large datasets?
How do I erase elements while iterating with std::unordered_map in multithreaded code?
How do I provide stable iteration order with std::map for embedded targets?
How do I provide stable iteration order with std::map in multithreaded code?
How do I avoid rehashing overhead with std::map in performance-sensitive code?
How do I merge two containers efficiently with std::map for embedded targets?