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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 have a list:
l = [1,2,3,4,6,7,9,10]
I want to multiply only adjacent numbers whose difference is 1 to get a final list.
The process in this example would be:
[1*2*3*4, 6*7, 9*10]
[24, 42, 90]
Convert to an array then split after taking the np.diff then use np.prod:
l = [1,2,3,4,6,7,9,10]
a = np.array(l)
outlist = [*map(np.prod,np.split(a,np.where(np.diff(a)!=1)[0]+1))]
print(outlist)
#[24, 42, 90]
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So let's say we have the list [2,2,3,4]
Then how would you find all possible sublists that don't have any gaps in it
The the outcome would be-
[]
[2]
[2]
[3]
[4]
[2,2]
[2,3]
[3,4]
[2,2,3]
[2,3,4]
[2,2,3,4]
This should do.
a = [2,2,3,4]
print([]) # print just empty list
for i in range(len(a)): # get the rest
for j in range(i+1, len(a)+1):
print(a[i:j])
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I have input like this:
y=[[array([ 0.12984648, 0.02116148, 0.08041889, ..., -0.11139846,
-0.0893152 , -0.05336994]), 1], [array([-0.11865588, -0.16726171, -0.06753636, ..., 0.00991138,
-0.11180532, -0.01146698]), 0] ]
I want to convert it into:
y=[
[[ 0.12984648, 0.02116148, 0.08041889, ..., -0.11139846,
-0.0893152 , -0.05336994], 1], [[-0.11865588, -0.16726171, -0.06753636, ..., 0.00991138,
-0.11180532, -0.01146698], 0]
]
You cannot have non-rectangular arrays. So your only option is to use lists and here is how you convert it to lists:
y = [[i[0].tolist(),i[1]] for i in y]
output:
[[[0.12984648, 0.02116148, 0.08041889, ..., -0.11139846, -0.0893152, -0.05336994], 1],
[[-0.11865588, -0.16726171, -0.06753636, ..., 0.00991138, -0.11180532, -0.01146698], 0]]
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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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a = [1,2,3,4,5,6,8]
b = [6,8,9,4,5,3,2,1]
final result should be
c = [6,8,4,5]
This array contains the same pair of numbers in both arrays - how to write this kind of code in python?
I only known how to create an array with duplicated values
a = [1,2,3,4,5,6,8]
b = [6,8,9,4,5,3,2,1]
c = [x for x in a if x in b]
print (c)
>>> [e for t in [t for t in zip(b,b[1:]) if t in zip(a,a[1:])] for e in t]
[6, 8, 4, 5]