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Iterating over a numpy array
...>>> for (x,y), value in numpy.ndenumerate(a):
... print x,y
...
0 0
0 1
1 0
1 1
2 0
2 1
Regarding the performance. It is a bit slower than a list comprehension.
X = np.zeros((100, 100, 100))
%timeit list([((i,j,k), X[i,j,k]) for i in range(X.shape[0]) for j in range(X.shape[1]) for k...
Django gives Bad Request (400) when DEBUG = False
...names, not urls. Leave out the port and the protocol. If you are using 127.0.0.1, I would add localhost to the list too:
ALLOWED_HOSTS = ['127.0.0.1', 'localhost']
You could also use * to match any host:
ALLOWED_HOSTS = ['*']
Quoting the documentation:
Values in this list can be fully qual...
Append a NumPy array to a NumPy array
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answered Mar 19 '12 at 18:01
endolithendolith
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Does MSTest have an equivalent to NUnit's TestCase?
... note on an older version of the TestAdapter, which was removed from the 2.0.0's description page:
Note that it doesn't work with VS Express
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Maven dependency for Servlet 3.0 API?
How can I tell Maven 2 to load the Servlet 3.0 API?
10 Answers
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How to iterate a loop with index and element in Swift
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Yes. As of Swift 3.0, if you need the index for each element along with its value, you can use the enumerated() method to iterate over the array. It returns a sequence of pairs composed of the index and the value for each item in the array. For...
Height equal to dynamic width (CSS fluid layout) [duplicate]
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edited Dec 5 '14 at 16:05
chridam
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COUNT DISTINCT with CONDITIONS
...count(distinct tag) as tag_count,
count(distinct (case when entryId > 0 then tag end)) as positive_tag_count
from
your_table_name;
The first count(distinct...) is easy.
The second one, looks somewhat complex, is actually the same as the first one, except that you use case...when clause. In ...
Get loop count inside a Python FOR loop
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605
The pythonic way is to use enumerate:
for idx,item in enumerate(list):
...
Adding an arbitrary line to a matplotlib plot in ipython notebook
...as np
import matplotlib.pyplot as plt
np.random.seed(5)
x = np.arange(1, 101)
y = 20 + 3 * x + np.random.normal(0, 60, 100)
plt.plot(x, y, "o")
# draw vertical line from (70,100) to (70, 250)
plt.plot([70, 70], [100, 250], 'k-', lw=2)
# draw diagonal line from (70, 90) to (90, 200)
plt.plot([70,...