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Find MongoDB records where array field is not empty
... the key, you can use:
ME.find({ pictures: { $exists: true, $not: {$size: 0} } })
MongoDB don't use indexes if $size is involved, so here is a better solution:
ME.find({ pictures: { $exists: true, $ne: [] } })
Since MongoDB 2.6 release, you can compare with the operator $gt but could lead to u...
How to sort an array in descending order in Ruby
...Here's what I found out:
#!/usr/bin/ruby
require 'benchmark'
ary = []
1000.times {
ary << {:bar => rand(1000)}
}
n = 500
Benchmark.bm(20) do |x|
x.report("sort") { n.times { ary.sort{ |a,b| b[:bar] <=> a[:bar] } } }
x.report("sort reverse") { n.times {...
Get statistics for each group (such as count, mean, etc) using pandas GroupBy?
... counts
– alvitawa
Jun 24 '19 at 16:04
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Map Tiling Algorithm
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+50
The basic idea of this algorithm is to use a pre-processing step to find all edges and then select the correct smoothing tile accordin...
How to compare software version number using js? (only number)
...at if we have version strings that are not made up of just digits (e.g. "1.0a")?
What should happen if one version string has more parts than the other? Most likely "1.0" should be considered less than "1.0.1", but what about "1.0.0"?
Here's the code for an implementation that you can use directly...
Apply multiple functions to multiple groupby columns
...f = pd.DataFrame(np.random.rand(4,4), columns=list('abcd'))
df['group'] = [0, 0, 1, 1]
df
a b c d group
0 0.418500 0.030955 0.874869 0.145641 0
1 0.446069 0.901153 0.095052 0.487040 0
2 0.843026 0.936169 0.926090 0.041722 1
3 0.635846 ...
How to increase the gap between text and underlining in CSS
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370
No, but you could go with something like border-bottom: 1px solid #000 and padding-bottom: 3px.
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Regular expression for matching HH:MM time format
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Your original regular expression has flaws: it wouldn't match 04:00 for example.
This may work better:
^([0-1]?[0-9]|2[0-3]):[0-5][0-9]$
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Convert pandas dataframe to NumPy array
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409
To convert a pandas dataframe (df) to a numpy ndarray, use this code:
df.values
array([[nan, ...
Normalize data in pandas
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In [92]: df
Out[92]:
a b c d
A -0.488816 0.863769 4.325608 -4.721202
B -11.937097 2.993993 -12.916784 -1.086236
C -5.569493 4.672679 -2.168464 -9.315900
D 8.892368 0.932785 4.535396 0.598124
In [93]: df_norm = (df - df.mean()) / (df.max() - df...