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what does np.c means in this code. Learning it from Udemy
df_cancer = pd.DataFrame(np.c_[cancer['data'], cancer['target'], columns=np.append(cancer ['feature_names'],['target;]))
According to official NumPy documentation,
numpy.c_ translates slice objects to concatenation along the second axis.
Example 1:
>>> np.c_[np.array([1,2,3]), np.array([4,5,6])]
array([[1, 4],
[2, 5],
[3, 6]])
Example 2:
>>> np.c_[np.array([[1,2,3]]), 0, 0, np.array([[4,5,6]])]
array([[1, 2, 3, 0, 0, 4, 5, 6]])
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Consider,
tp = (2, 3, 5, 7) # tuple
ls = [2, 3, 5, 7] # list
st = {2, 3, 5, 7} # set
Python has twin delimiters for different types of ensembles. As shown above, we have () for tuples, [] for lists and {} for sets. Is it possible to define new ones for something one wrote themselves or pre-written? E. g., say we wanted to define a numpy array np.array([2, 3, 5, 7]) as <2, 3, 5, 7>?
Edit: Not quite looking to define new infix relations. That's why I used the term twin delimiters.
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Is it ok / good style to use python built in functions like map() in unit tests?
for example instead of writing an individual assert for all test cases something like:
def double_x(x):
return x*2
def test_double_x():
orig_vals = [1, 2, 3, 4, 5, 6]
expected_vals = [2, 4, 6, 8, 10, 12]
assert list(map(double_x, orig_vals)) == expected_vals
There's no problem, but like anywhere else, a list comprehension may be preferable.
assert [double_x(x) for x in orig-vals] == expected_vals
Individual assertions, though, may make it easier to identify the failed test.
for x, y in zip(orig_vals, expected_vals):
z = double_x(x)
assert z == y, f'double_x({x}) returned {z}, not {y} as expected'
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Can someone help me writing a code in order to increasingly iterate on multiple iterators.
As an example imagine that I have several lists (for example : [1, 9] and [2, 3, 4]) and I want to get an iterable from those lists yielding 1, 2, 3, 4 and 9. Of course the idea is to use more big and complex iterators (otherwise it is easy to just merge and sort).
Thank you !
heapq is exactly designed for what you want : It creates an iterator, so you don't have to handle huge lists. Here is the code for a simple example :
import heapq
a = [1, 4, 7, 10]
b = [2, 5, 6, 11]
for c in heapq.merge(a, b):
print(c)
Of course, it works only if your lists are sorted, you have to sort them before if they are not sorted.
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a=np.array([[[[0,0],[0,1]],[[1,1],[1,1]]],
[[[1,0],[1,1]],[[0,1],[1,1]]]])
how can I get the intersection of this array?
This is the expected output:
array([[[0, 0],
[0, 1]],
[[0, 1],
[1, 1]]]
For case provided you can use
a[0] & a[1]
or, alternatively:
np.logical_and(a[0], a[1]).astype(int)
In general, if length of a is not defined, you can use:
np.logical_and.reduce(a).astype(int)
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I want the following output: [[1,2,1,2],[1,2,3,4],[3,4,1,2],[3,4,3,4]] but my code does not work out properly.
flag_2=[[1,2],[3,4]]
for i in flag_2:
icopy = copy.copy(i)
print "icopy",icopy
for j in flag_2:
temp4=[]
jcopy = copy.copy(j)
print "jcopy",jcopy
temp4=copy.copy(list_extend(icopy,jcopy))
print temp4
temp4=[]
print temp4
Use itertools' product to get the combinations you need and chain to flatten the result:
from itertools import product,chain
[list(chain.from_iterable(c)) for c in product(flag_2,repeat=2)]
Out[7]: [[1, 2, 1, 2], [1, 2, 3, 4], [3, 4, 1, 2], [3, 4, 3, 4]]