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Unicode equivalents for \w and \b in Java regular expressions?
...s Regex Unicode Problems
The problem with Java regexes is that the Perl 1.0 charclass escapes — meaning \w, \b, \s, \d and their complements — are not in Java extended to work with Unicode. Alone amongst these, \b enjoys certain extended semantics, but these map neither to \w, nor to Unicode i...
BestPractice - Transform first character of a string into lower case
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240
I would use simple concatenation:
Char.ToLowerInvariant(name[0]) + name.Substring(1)
The firs...
Why is SSE scalar sqrt(x) slower than rsqrt(x) * x?
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edited Oct 7 '09 at 0:02
answered Oct 6 '09 at 23:52
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Append value to empty vector in R?
...et.seed(21)
values <- sample(letters, 1e4, TRUE)
vector <- character(0)
# slow
system.time( for (i in 1:length(values)) vector[i] <- values[i] )
# user system elapsed
# 0.340 0.000 0.343
vector <- character(length(values))
# fast(er)
system.time( for (i in 1:length(values)) vec...
How to calculate the SVG Path for an arc (of a circle)
Given a circle centered at (200,200), radius 25, how do I draw an arc from 270 degree to 135 degree and one that goes from 270 to 45 degree?
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Avoid trailing zeroes in printf()
...ormat specifiers. The closest you could get would be:
printf("%.6g", 359.013); // 359.013
printf("%.6g", 359.01); // 359.01
but the ".6" is the total numeric width so
printf("%.6g", 3.01357); // 3.01357
breaks it.
What you can do is to sprintf("%.20g") the number to a string buffer then man...
Add one row to pandas DataFrame
...n range(5):
>>> df.loc[i] = ['name' + str(i)] + list(randint(10, size=2))
>>> df
lib qty1 qty2
0 name0 3 3
1 name1 2 4
2 name2 2 8
3 name3 2 1
4 name4 9 6
share...
Regular expression for a hexadecimal number?
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How about the following?
0[xX][0-9a-fA-F]+
Matches expression starting with a 0, following by either a lower or uppercase x, followed by one or more characters in the ranges 0-9, or a-f, or A-F
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Is it worth using Python's re.compile?
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I've had a lot of experience running a compiled regex 1000s of times versus compiling on-the-fly, and have not noticed any perceivable difference. Obviously, this is anecdotal, and certainly not a great argument against compiling, but I've found the difference to be negligible.
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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':
...