![]() Mylist = list('abcdefghijklmnopqrstuvwxyz') So, for example, you could use a listcomp like this: How can you do this? One way is to access the item by index. Say you have a list of animals and you want to access each item one at a time. In this part, I will describe and compare approaches for the following: In Part 2, I will compare Python 2 and Python 3 to see if there is credence to the claim that Python 3 is indeed faster. In Part 1, I will compare two approaches commensurate with the “do this, not that” logic to see if there is a substantial difference and, if so, which approach is better. The plots contained in this article depict the results of my experiments for Python 3 and are labeled as such.įor detailed information including methodology and quantitative values for timings as well as details about Python 2, please review the notebooks linked above. All of my experiments as well as timings and corresponding plots can be found here: Py27 Notebook and P圓5 Notebook. So I plied the exact same experimental methods in Python 2.7 (Py27) and Python 3.5 (P圓5), affecting the necessary changes like using xrange for Py27 and and range for P圓5. However, as I collected experimental data on the various methods, I became curious about another claim I often hear: Python 3 is faster than Python 2. My initital intent was just to test the assertions. There exist many “do this, not that” strategies but I decided to focus on just a few, which I discovered in the Links section below. An example of the latter is using vectorized implementations instead of for loops. Some stategies revolve around leveraging libraries like Cython whereas others propose a “do this, not that” coding approach. ![]() There is a plethora of information about how to speed up Python code.
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