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Identifying the dependency relationship for python packages installed with pip
...ch displays dependencies as a tree structure e.g.:
$ pipdeptree
Lookupy==0.1
wsgiref==0.1.2
argparse==1.2.1
psycopg2==2.5.2
Flask-Script==0.6.6
- Flask [installed: 0.10.1]
- Werkzeug [required: >=0.7, installed: 0.9.4]
- Jinja2 [required: >=2.4, installed: 2.7.2]
- MarkupSafe...
Find nearest value in numpy array
...(array - value)).argmin()
return array[idx]
array = np.random.random(10)
print(array)
# [ 0.21069679 0.61290182 0.63425412 0.84635244 0.91599191 0.00213826
# 0.17104965 0.56874386 0.57319379 0.28719469]
value = 0.5
print(find_nearest(array, value))
# 0.568743859261
...
What is the difference between quiet NaN and signaling NaN?
...
70
When an operation results in a quiet NaN, there is no indication that anything is unusual until ...
Twitter oAuth callbackUrl - localhost development
...c/hosts file to point a live domain to your localhost IP. such as:
127.0.0.1 xyz.com
where xyz.com is your real domain.
Alternative 2.
Also, the article gives the tip to alternatively use a URL shortener service. Shorten your local URL and provide the result as callback.
Alternative 3.
Fur...
Getting Java version at runtime
...nical Articles
J2SE SDK/JRE Version String Naming Convention
Version 1.5.0 or 5.0?
"J2SE also keeps the version number 1.5.0 (or 1.5) in some places that are visible only to developers, or where the version number is parsed by programs"
"java.version system property"
Version 1.6.0 Used by De...
clearing a char array c
... to view this as a C/C++ null terminated string, setting the first byte to 0 will effectively clear the string.
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dropping infinite values from dataframes in pandas?
...nf, -np.inf])
In [12]: df.replace([np.inf, -np.inf], np.nan)
Out[12]:
0
0 1
1 2
2 NaN
3 NaN
The same method would work for a Series.
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Scala downwards or decreasing for loop?
...
230
scala> 10 to 1 by -1
res1: scala.collection.immutable.Range = Range(10, 9, 8, 7, 6, 5, 4, 3, ...
How does numpy.histogram() work?
...qual width) of each bar.
In this example:
np.histogram([1, 2, 1], bins=[0, 1, 2, 3])
There are 3 bins, for values ranging from 0 to 1 (excl 1.), 1 to 2 (excl. 2) and 2 to 3 (incl. 3), respectively. The way Numpy defines these bins if by giving a list of delimiters ([0, 1, 2, 3]) in this example...
Remap values in pandas column with a dict
...
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