- #PYTHON COMBINE TWO DICTIONARIES PRO#
- #PYTHON COMBINE TWO DICTIONARIES SERIES#
- #PYTHON COMBINE TWO DICTIONARIES FREE#
Convert a String representation of a Dictionary to a dictionary. Extract specific element from each sublist. Find the intersection between two lists. Find the cumulative sum of numbers in a list. #PYTHON COMBINE TWO DICTIONARIES SERIES#
Get the Cartesian product of a series of lists. Get all possible combinations of a list's elements. Creare a flat list out of a nested list. Sort a list of objects by an attribute of the objects. 
Sort list based on values from another list. 
Convert string representation of list to a list. Check if string exists in a list of strings. Check if multiple strings exist in another string. Conbine items in a list to a single string. Split strings into words with multiple delimiters. String updating, replacing and deleting. Read specific lines from a file by line number. Convert between local time and UTC time. strftime(): from datetime to readable string. of 7 runs, 10 loops each) #: Method: havok_method #: 103 ms ± 1.31 ms per loop (mean ± std. of 7 runs, 10 loops each) #: Method: scott_update_method_static #: 136 ms ± 3.19 ms per loop (mean ± std. of 7 runs, 10 loops each) #: Method: scott_update_method #: 123 ms ± 1.94 ms per loop (mean ± std. of 7 runs, 10 loops each) #: Method: scott_method_nosum #: 289 ms ± 14.3 ms per loop (mean ± std. of 7 runs, 10 loops each) #: Method: scott_method #: 324 ms ± 16.8 ms per loop (mean ± std. of 7 runs, 10 loops each) #: Method: npe_method #: 119 ms ± 11 ms per loop (mean ± std. of 7 runs, 10 loops each) #: Method: georg_method_nosum #: 280 ms ± 4.97 ms per loop (mean ± std. Naturally, couldn't run anywhere near as many loops for name, method in ems(): print("Method:", name) %timeit -n10 method(large_tests) #: Method: georg_method #: 347 ms ± 20 ms per loop (mean ± std. 
of 7 runs, 10000 loops each) Results: Large Tests of 7 runs, 10000 loops each) #: Method: havok_method #: 3.09 µs ± 47.9 ns per loop (mean ± std. of 7 runs, 10000 loops each) #: Method: scott_update_method_static #: 14.4 µs ± 122 ns per loop (mean ± std. of 7 runs, 10000 loops each) #: Method: scott_update_method #: 9.1 µs ± 90.7 ns per loop (mean ± std. of 7 runs, 10000 loops each) #: Method: scott_method_nosum #: 18.9 µs ± 64.8 ns per loop (mean ± std. of 7 runs, 10000 loops each) #: Method: scott_method #: 24.9 µs ± 326 ns per loop (mean ± std. of 7 runs, 10000 loops each) #: Method: npe_method #: 3.2 µs ± 24.7 ns per loop (mean ± std. of 7 runs, 10000 loops each) #: Method: georg_method_nosum #: 4.6 µs ± 48.8 ns per loop (mean ± std.
#PYTHON COMBINE TWO DICTIONARIES PRO#
MacBook Pro (15-inch, Late 2016), 2.9 GHz Intel Core i7, 16 GB 2133 MHz LPDDR3 RAM, running macOS Mojave Version 10.14.5įinally, the results: Results: Small Tests for name, method in ems(): print("Method:", name) %timeit -n10000 method(small_tests) #: Method: georg_method #: 7.81 µs ± 321 ns per loop (mean ± std. Unfortunately, that's when I found the problem in Scott's method, namely, if you have dictionaries that total to 0, the dictionary won't be included at all because of how Counter() behaves when adding. I also wrote a quick function find whatever differences there were between the lists. I've updated it recently to include tests with MUCH larger dictionaries, and again to include Havok's and Scott's newer methods:įirstly I used the following data: import random x = #PYTHON COMBINE TWO DICTIONARIES FREE#
If anyone has better suggestions for them, feel free to edit. Because I wanted to do this on any number of dictionaries, I had to change some of the answers a bit. I was trying to perform this action on collections of 2 or more dictionaries and was interested in seeing the time it took for each. Additional notes based on the answers of georg, NPE, Scott and Havok.
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