To fix the issue you can do You should checkout numpy's apply_along_axis method: http://docs.scipy.org/doc/numpy/reference/generated/numpy.apply_along_axis.html code :
>>> def my_func(a):
... """Average first and last element of a 1D array"""
... return (a[0] + a[1]) * 0.5
>>> b = np.array([[1,2,3], [4,5,6], [7,8,9]])
>>> np.apply_along_axis(my_func, 0, b)
array([ 4., 5., 6.])
>>> np.apply_along_axis(my_func, 1, b)
array([ 2., 5., 8.])
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Removing duplicates from a list of numPy arrays
By : clairecai
Date : March 29 2020, 07:55 AM
it fixes the issue Using the solutions here: Most efficient property to hash for numpy array we see that hashing works best with a.tostring() if a is an numpy array. So: code :
import numpy as np
arraylist = [np.array([1,2,3,4]), np.array([1,2,3,4]), np.array([1,3,2,4])]
L = {array.tostring(): array for array in arraylist}
L.values() # [array([1, 3, 2, 4]), array([1, 2, 3, 4])]

Even / Odd list comps two ways
By : Melike Keskin
Date : March 29 2020, 07:55 AM
hope this fix your issue I'm trying to make the same list two different ways (just for practice) code :
def even_odd(L):
return ['even' if x % 2 == 0 else 'odd' for x in L]
def even_odd2(L):
return list(map(lambda x: 'even' if x % 2 == 0 else 'odd', L))
print(even_odd([6, 4, 1, 3, 8, 5]))
print(even_odd2([6, 4, 1, 3, 8, 5]))
['even', 'even', 'odd', 'odd', 'even', 'odd']
['even', 'even', 'odd', 'odd', 'even', 'odd']

Removing a Numpy object from Python list
By : Abdul Haleem
Date : March 29 2020, 07:55 AM
should help you out Python remove function is optimized to first check for identity check and then check for equality. So for alist.remove(item) first item is alist[0] is checked (identity check, looking for memory location) and then checking item == alist[0] (equality check, looking at the actual value(s)) For numpy arrays however, equality is overridden by numpy to return a per item check. (A vectorized check. np.array([1, 2]) == np.array([2, 2]) returns np.array([False, True]).) This cannot be processed by the remove function, because it expects just a single boolean. code :
ind = min(range(len(centers)), key=lambda ind: sum(centers[ind]))
summed_centers = centers.sum(axis=1)
mask = np.ones(len(summed_centers), np.bool_)
mask[[summed_centers.argmax(), summed_centers.argmin()]] = np.bool_(False)
new_centers = centers[mask]

Laravel  React component Does not like importing comps in other comps
By : user1914330
Date : March 29 2020, 07:55 AM
I think the issue was by ths following , Looks like my error was having styled components within. If anyone has this issue I fixed it by downlaoding babelpluginstyledcomponents then adding "babelpluginstyledcomponents", { "displayName": false } to my babel.rc file Salman for helping out

Removing list in list by criteria with numpy
By : user2600684
Date : March 29 2020, 07:55 AM
around this issue Modifying a list you are iterating on is a bad idea. Instead of removing arrays that do not satisfy your condition, simply return a list whose arrays satisfy your condition: code :
def toss_non_G2(potential_list):
return [l for l in potential_list if all(x in [0,1] for x in l)]

