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What is the difference between CMD and ENTRYPOINT in a Dockerfile?
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edited May 9 '18 at 20:46
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.toArray(new MyClass[0]) or .toArray(new MyClass[myList.size()])?
...est version, on Hotspot 8, is:
MyClass[] arr = myList.toArray(new MyClass[0]);
I have run a micro benchmark using jmh the results and code are below, showing that the version with an empty array consistently outperforms the version with a presized array. Note that if you can reuse an existing arr...
Get ffmpeg information in friendly way
... would yield something like the following:
{
"streams": [{
"index": 0,
"codec_name": "wmv3",
"codec_long_name": "Windows Media Video 9",
"codec_type": "video",
"codec_time_base": "1/1000",
"codec_tag_string": "WMV3",
"codec_tag": "0x33564d57",
"width": 320,
"he...
What is IP address '::1'?
...1 is the loopback address in IPv6. Think of it as the IPv6 version of 127.0.0.1.
See http://en.wikipedia.org/wiki/Localhost
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How to format a float in javascript?
... string, how can I get just 2 digits after the decimal point? For example, 0.34 instead of 0.3445434.
14 Answers
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Enable remote MySQL connection: ERROR 1045 (28000): Access denied for user
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answered Oct 11 '12 at 17:02
OctavioOctavio
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String output: format or concat in C#?
...ded the result by some iterations was wrong. See what happens if you have 1000 milliseconds and 100 milliseconds. In both situations, you will get 0 ms after dividing it by 1000000.
Stopwatch s = new Stopwatch();
var p = new { FirstName = "Bill", LastName = "Gates" };
int n = 1000000;
long fElaps...
Peak signal detection in realtime timeseries data
...core at which the algorithm signals and influence = the influence (between 0 and 1) of new signals on the mean and standard deviation. For example, a lag of 5 will use the last 5 observations to smooth the data. A threshold of 3.5 will signal if a datapoint is 3.5 standard deviations away from the m...
Get program execution time in the shell
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Multiple aggregations of the same column using pandas GroupBy.agg()
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You can simply pass the functions as a list:
In [20]: df.groupby("dummy").agg({"returns": [np.mean, np.sum]})
Out[20]:
mean sum
dummy
1 0.036901 0.369012
or as a dictionary:
In [21]: df.groupby('dummy').agg({'returns':
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