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How to get row from R data.frame
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answered Aug 20 '13 at 0:30
IRTFMIRTFM
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Use CSS to automatically add 'required field' asterisk to form inputs
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edited Dec 20 '19 at 2:25
abranhe
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DateTime.ToString(“MM/dd/yyyy HH:mm:ss.fff”) resulted in something like “09/14/2013 07.20.31.371”
...-MM-ddTHH:mm:ss.fff"));
}
}
That produces output (on September 18th 2013) of:
11/12/1434 15:04:31.750
My guess is that your web service would be surprised by that!
I'd actually suggest not only using the invariant culture, but also changing to an ISO-8601 date format:
string text = dateT...
Can PostgreSQL index array columns?
...TE TABLE "Test"("Column1" int[]);
INSERT INTO "Test" VALUES ('{10, 15, 20}');
INSERT INTO "Test" VALUES ('{10, 20, 30}');
CREATE INDEX idx_test on "Test" USING GIN ("Column1");
-- To enforce index usage because we have only 2 records for this test...
SET enable_seqscan TO off;...
How to use R's ellipsis feature when writing your own function?
...he unevaluated expression".
When you call my_ellipsis_function(a=1:10,b=11:20,c=21:30) then ... "creates" a list of arguments: list(a=1:10,b=11:20,c=21:30) and substitute make it a list of four elements:
List of 4
$ : symbol list
$ a: language 1:10
$ b: language 11:20
$ c: language 21:30
First e...
How to align content of a div to the bottom
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edited Sep 20 '17 at 11:21
GKFX
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an...
How can I reverse a list in Python?
...n make use of the reversed function for this as:
>>> array=[0,10,20,40]
>>> for i in reversed(array):
... print(i)
Note that reversed(...) does not return a list. You can get a reversed list using list(reversed(array)).
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pandas: How do I split text in a column into multiple rows?
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This sp...
Canvas width and height in HTML5
...keStyle = '#f00';
ctx.fillStyle = '#eff';
ctx.fillRect( 10.5, 10.5, 20, 20 );
ctx.strokeRect( 10.5, 10.5, 20, 20 );
ctx.fillRect( 40, 10.5, 20, 20 );
ctx.strokeRect( 40, 10.5, 20, 20 );
ctx.fillRect( 70, 10, 20, 20 );
ctx.strokeRect( 70, 10, 20, 20 );
ctx.strokeStyle = '#fff';
ct...
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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