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Difference between DirectCast() and CType() in VB.NET
...ut with CType a mistake just might cause occasional wrong behaviour at run-time - maybe on some user machine with different regional settings.
– MarkJ
Jun 17 '10 at 18:25
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Insert an element at a specific index in a list and return the updated list
...t;> b
[1, 2, 3, 4]
Performance comparison of all solutions
Here's the timeit comparison of all the answers with list of 1000 elements for Python 3.4.5:
Mine answer using sliced insertion - Fastest (3.08 µsec per loop)
mquadri$ python3 -m timeit -s "a = list(range(1000))" "b = a[:]; b[500:500...
(Mac) -bash: __git_ps1: command not found
... && /bin/bash" bash: __docker_machine_ps1: command not found every time I enter Docker CLI.
– IgorGanapolsky
Dec 22 '16 at 18:24
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An error occurred while validating. HRESULT = '8000000A'
...roj).
We've worked around this issue by triggering the MSI build a second time when it fails a first time. Not nice, but it works most of the time (error rate is down from ~ 10% to ~ 1%).
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Is there a built in function for string natural sort?
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Just by doing a timed test compared to all the others on this forum, this solution is by far the fastest and most efficient for the type of data @snakile is trying to process
– S. R. Colledge
Mar 22 at ...
Why does calling a function in the Node.js REPL with )( work?
... to see it start a culture and become a feature in all languages. How many times have I typed )( instead of () in a hurry... :))
– geomagas
Oct 11 '13 at 13:48
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Is there a difference between using a dict literal and a dict constructor?
...nd STORE_MAP opcodes rather than generic CALL_FUNCTION:
> python2.7 -m timeit "d = dict(a=1, b=2, c=3, d=4, e=5)"
1000000 loops, best of 3: 0.958 usec per loop
> python2.7 -m timeit "d = {'a':1, 'b':2, 'c':3, 'd':4, 'e':5}"
1000000 loops, best of 3: 0.479 usec per loop
> python3.2 -m tim...
Creating a zero-filled pandas data frame
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Testing this I find %timeit temp = np.zeros((10, 11)); d = pd.DataFrame(temp, columns = ['col1', 'col2',...'col11']) takes 156 us. But %timeit d = pd.DataFrame(0, index = np.arange(10), columns = ['col1', 'col2',...'col11']) takes 171 us. I'm sur...
How to remove illegal characters from path and filenames?
...he forseeable future, so all you're really doing is doubling the amount of time this function takes to run because you're worried that the definition of a valid path will change sometime soon. Which it won't.
– Warren Rumak
Jan 27 '14 at 19:09
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Why would iterating over a List be faster than indexing through it?
...item2 -> item3 print item3
This is horribly inefficient because every time you are indexing it restarts from the beginning of the list and goes through every item. This means that your complexity is effectively O(N^2) just to traverse the list!
If instead I did this:
for(String s: list) {
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