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How to format strings in Java
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140
In addition to String.format, also take a look java.text.MessageFormat. The format less terse a...
How do multiple clients connect simultaneously to one port, say 80, on a server? [duplicate]
... don't get is how multiple clients can simultaneously connect to say port 80. I know each client has a unique (for their machine) port. Does the server reply back from an available port to the client, and simply state the reply came from 80? How does this work?
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Convert string[] to int[] in one line of code using LINQ
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answered Aug 19 '09 at 0:15
Ahmad MageedAhmad Mageed
85.1k1717 gold badges148148 silver badges167167 bronze badges
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Text border using css (border around text)
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Use multiple text shadows:
text-shadow: 2px 0 0 #fff, -2px 0 0 #fff, 0 2px 0 #fff, 0 -2px 0 #fff, 1px 1px #fff, -1px -1px 0 #fff, 1px -1px 0 #fff, -1px 1px 0 #fff;
body {
font-family: sans-serif;
background: #222;
color: darkred;
}
h1 {
text-sha...
Are there any O(1/n) algorithms?
...he following one:
def get_faster(list):
how_long = (1 / len(list)) * 100000
sleep(how_long)
Clearly, this function spends less time as the input size grows … at least until some limit, enforced by the hardware (precision of the numbers, minimum of time that sleep can wait, time to proce...
Which method performs better: .Any() vs .Count() > 0?
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edited Nov 20 '08 at 12:51
answered Nov 20 '08 at 12:37
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Creating a zero-filled pandas data frame
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140
You can try this:
d = pd.DataFrame(0, index=np.arange(len(data)), columns=feature_list)
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Is it safe to check floating point values for equality to 0?
...ality between double or decimal type values normally, but I'm wondering if 0 is a special case.
9 Answers
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Reading an Excel file in python using pandas
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Tid dummy1 dummy2 dummy3 dummy4 dummy5 \
0 2006-09-01 00:00:00 0 5.894611 0.605211 3.842871 8.265307
1 2006-09-01 01:00:00 0 5.712107 0.605211 3.416617 8.301360
2 2006-09-01 02:00:00 0 5.105300 0.605211 3.090865 8.335395
3 2006...
dplyr summarise: Equivalent of “.drop=FALSE” to keep groups with zero length in output
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Since dplyr 0.8 group_by gained the .drop argument that does just what you asked for:
df = data.frame(a=rep(1:3,4), b=rep(1:2,6))
df$b = factor(df$b, levels=1:3)
df %>%
group_by(b, .drop=FALSE) %>%
summarise(count_a=length(a)...