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CSS div element - how to show horizontal scroll bars only?
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10 Answers
10
Active
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How to initialize a vector in C++ [duplicate]
...is with an array:
int vv[2] = { 12,43 };
std::vector<int> v(&vv[0], &vv[0]+2);
Or, for the case of assignment to an existing vector:
int vv[2] = { 12,43 };
v.assign(&vv[0], &vv[0]+2);
Like James Kanze suggested, it's more robust to have functions that give you the beginni...
promise already under evaluation: recursive default argument reference or earlier problems?
...g the two instances where they occur we get:
f <- function(x, T) {
10 * sin(0.3 * x) * sin(1.3 * x^2) + 0.001 * x^3 + 0.2 * x + 80
}
g <- function(x, T, f. = f) { ## 1. note f.
exp(-f.(x)/T)
}
test<- function(g. = g, T = 1) { ## 2. note g.
g.(1,T)
}
test()
## [1] 8.560335e...
Passing a URL with brackets to curl
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edited Jul 2 '19 at 20:14
moveson
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answered Nov...
Populate data table from data reader
I'm doing a basic thing in C# (MS VS2008) and have a question more about proper design than specific code.
5 Answers
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Prepend a level to a pandas MultiIndex
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cs95
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answered Feb 7 '17 at 16:11
okartalokartal
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What does ** (double star/asterisk) and * (star/asterisk) do for parameters?
...s the other way around:
def foo(a, b, c):
print(a, b, c)
obj = {'b':10, 'c':'lee'}
foo(100,**obj)
# 100 10 lee
Another usage of the *l idiom is to unpack argument lists when calling a function.
def foo(bar, lee):
print(bar, lee)
l = [1,2]
foo(*l)
# 1 2
In Python 3 it is possible to...
In Scala, what exactly does 'val a: A = _' (underscore) mean?
...he variable to a default value. From the Scala Language Specification:
0 if T is Int or one of its subrange types,
0L if T is Long,
0.0f if T is Float,
0.0d if T is Double,
false if T is Boolean,
() if T is Unit,
null for all other types T.
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Capturing Groups From a Grep RegEx
I've got this little script in sh (Mac OSX 10.6) to look through an array of files. Google has stopped being helpful at this point:
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Compare two DataFrames and output their differences side-by-side
... rows are empty*:
In [21]: ne = (df1 != df2).any(1)
In [22]: ne
Out[22]:
0 False
1 True
2 True
dtype: bool
Then we can see which entries have changed:
In [23]: ne_stacked = (df1 != df2).stack()
In [24]: changed = ne_stacked[ne_stacked]
In [25]: changed.index.names = ['id', 'col']
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