If I have:
x = np.array(([1,4], [2,5], [2,6], [3,4], [3,6], [3,7], [4,3], [4,5], [5,2]))
for item in range(3):
choice = random.choice(x)
How can I get the index number of the random choice taken from the array?
I tried:
indexNum = np.where(x == choice)
print(indexNum[0])
But it didn't work.
I want the output, for example, to be something like:
chosenIndices = [1 5 8]
Another possibility is using np.where and np.intersect1d. Here random choice without repetition.
x = np.array(([1,4], [2,5], [2,6], [3,4], [3,6], [3,7], [4,3], [4,5], [5,2]))
res=[]
cont = 0
while cont<3:
choice = random.choice(x)
ind = np.intersect1d(np.where(choice[0]==x[:,0]),np.where(choice[1]==x[:,1]))[0]
if ind not in res:
res.append(ind)
cont+=1
print (res)
# Output [8, 1, 5]
You can achieve this by converting the numpy array to list of tuples and then apply the index function.
This would work:
import random
import numpy as np
chosenIndices = []
x = np.array(([1,4], [2,5], [2,6], [3,4], [3,6], [3,7], [4,3], [4,5], [5,2]))
x = x.T
x = list(zip(x[0],x[1]))
item = 0
while len(chosenIndices)!=3:
choice = random.choice(x)
indexNum = x.index(choice)
if indexNum in chosenIndices: # if index already exist, then it will rerun that particular iteration again.
item-=1
else:
chosenIndices.append(indexNum)
print(chosenIndices) # Thus all different results.
Output:
[1, 3, 2]
Related
To find all possible combinations that can be added to make given sum.
Combinations can be formed with multiple elements and also if any single element exists.
Input:
l1 = [9,1, 2, 7, 6, 1, 5]
target = 8
**Constraints**
1<=(len(l1))<=500000
1<=each_list_element<=1000
Output:
Format : {index:element}
{1:1, 5:1, 4:6} #Indices : 1,5,4 Elements : 1,1,6
{1:1, 2:2, 6:5}
{5:1, 2:2, 6:5}
{1:1, 3:7}
{5:1, 3:7}
{2:2, 4:6}
More Scenarios:
Input = [4,6,8,5,3]
target = 3
Output {4:3}
Input = [4,6,8,3,5,3]
target = 3
Output {5:3,3:3}
Input = [1,2,3,15]
target = 15
Output {3:15}
Below code covers for all above scenarios.
Scenarios to be handled, along with above.
Input =[1,6,7,1,3]
target=5
Output={0:1,3:1,4:3} , {0:1,0:1,4:3}, {3:1,3:1,4:3}
Input=[9,6,8,1,7]
target=5
Output={3:1,3:1,3:1,3:1,3:1}
As suggested by #Chris Doyle in previous question, will be using that code.
(How to find indices and combinations that adds upto given sum?)
Code:
from itertools import combinations
def find_sum_with_index(l1, target):
index_vals = [iv for iv in enumerate(l1) if iv[1] < target]
for r in range(1, len(index_vals) + 1):
for perm in combinations(index_vals, r):
if sum([p[1] for p in perm]) == target:
yield perm
l1 = [9, 1, 2, 7, 6, 1, 5]
target = 8
for match in find_sum_with_index(l1, target):
print(dict(match))
You can use dictionary comprehension
from itertools import combinations
l1 = [9,1, 2, 7, 6, 1, 5]
target = 8
for i in range(len(l1)):
for c in combinations(l1,i):
if sum(c) == target:
res = { i:x for i,x in enumerate(c)}
print(res)
Simple question here:
I'm trying to get an array that alternates values (1, -1, 1, -1.....) for a given length. np.repeat just gives me (1, 1, 1, 1,-1, -1,-1, -1). Thoughts?
I like #Benjamin's solution. An alternative though is:
import numpy as np
a = np.empty((15,))
a[::2] = 1
a[1::2] = -1
This also allows for odd-length lists.
EDIT: Also just to note speeds, for a array of 10000 elements
import numpy as np
from timeit import Timer
if __name__ == '__main__':
setupstr="""
import numpy as np
N = 10000
"""
method1="""
a = np.empty((N,),int)
a[::2] = 1
a[1::2] = -1
"""
method2="""
a = np.tile([1,-1],N)
"""
method3="""
a = np.array([1,-1]*N)
"""
method4="""
a = np.array(list(itertools.islice(itertools.cycle((1,-1)), N)))
"""
nl = 1000
t1 = Timer(method1, setupstr).timeit(nl)
t2 = Timer(method2, setupstr).timeit(nl)
t3 = Timer(method3, setupstr).timeit(nl)
t4 = Timer(method4, setupstr).timeit(nl)
print 'method1', t1
print 'method2', t2
print 'method3', t3
print 'method4', t4
Results in timings of:
method1 0.0130500793457
method2 0.114426136017
method3 4.30518102646
method4 2.84446692467
If N = 100, things start to even out but starting with the empty numpy arrays is still significantly faster (nl changed to 10000)
method1 0.05735206604
method2 0.323992013931
method3 0.556654930115
method4 0.46702003479
Numpy arrays are special awesome objects and should not be treated like python lists.
use resize():
In [38]: np.resize([1,-1], 10) # 10 is the length of result array
Out[38]: array([ 1, -1, 1, -1, 1, -1, 1, -1, 1, -1])
it can produce odd-length array:
In [39]: np.resize([1,-1], 11)
Out[39]: array([ 1, -1, 1, -1, 1, -1, 1, -1, 1, -1, 1])
Use numpy.tile!
import numpy
a = numpy.tile([1,-1], 15)
use multiplication:
[1,-1] * n
If you want a memory efficient solution, try this:
def alternator(n):
for i in xrange(n):
if i % 2 == 0:
yield 1
else:
yield -1
Then you can iterate over the answers like so:
for i in alternator(n):
# do something with i
Maybe you're looking for itertools.cycle?
list_ = (1,-1,2,-2) # ,3,-3, ...
for n, item in enumerate(itertools.cycle(list_)):
if n==30:
break
print item
I'll just throw these out there because they could be more useful in some circumstances.
If you just want to alternate between positive and negative:
[(-1)**i for i in range(n)]
or for a more general solution
nums = [1, -1, 2]
[nums[i % len(nums)] for i in range(n)]
I have a 2d array, how can I delete certain element(s) from it?
x = [[2,3,4,5,2],[5,3,6,7,9,2],[34,5,7],[2,46,7,4,36]]
for i in range(len(x)):
for j in range(len(x[i])):
if x[i][j] == 2:
del x[i][j]
This will destroy the array and returns error "list index out of range".
you can use pop on the list item. For example -
>>> array = [[1,2,3,4], [6,7,8,9]]
>>> array [1].pop(3)
>>> array
[[1, 2, 3, 4], [6, 7, 8]]
I think this can solve your problem.
x = [[2,3,4,5,2],[5,3,6,7,9,2],[34,5,7],[2,46,7,4,36]]
for i in range(len(x)):
for j in range(len(x[i])):
if j<len(x[i]):
if x[i][j] == 2:
del x[i][j]
I have tested it locally and working as expected.Hope it will help.
Mutating a list while iterating over it is always a bad idea. Just make a new list and add everything except those items you want to exclude. Such as:
x = [[2,3,4,5,2],[5,3,6,7,9,2],[34,5,7],[2,46,7,4,36]]
new_array = []
temp = []
delete_val = 2
for list_ in x:
for element in list_:
if element != delete_val:
temp.append(element)
new_array.append(temp)
temp = []
x = new_array
print(x)
Edit: made it a little more pythonic by omitting list indices.
I think this is more readable at the cost of temporarily more memory usage (making a new list) compared to the solution that Sai prateek has offered.
When I run this code it returns that the numpy.ndarray object has no attributes. I'm trying to write a function that in case the number given is in the array will return with the position of that number in the array.
a = np.c_[np.array([1, 2, 3, 4, 5])]
x = int(input('Type a number'))
def findelement(x, a):
if x in a:
print (a.index(x))
else:
print (-1)
print(findelement(x, a))
Please use np.where instead of list.index.
import numpy as np
a = np.c_[np.array([1, 2, 3, 4, 5])]
x = int(input('Type a number: '))
def findelement(x, a):
if x in a:
print(np.where(a == x)[0][0])
else:
print(-1)
print(findelement(x, a))
Result:
Type a number: 3
2
None
Note np.where returns the indices of elements in an input array where
the given condition is satisfied.
You should check out np.where and np.argwhere.
I am having list which as follows
input_list= [2, 3, 5, 2, 5, 1, 5]
I want to get all the indexes of maximum value. Need efficient solution. The output will be as follows.
output = [2,4,6] (The above list 5 is maximum value in a list)
I have tried by using below code
m = max(input_list)
output = [i for i, j in enumerate(a) if j == m]
I need to find any other optimum solution.
from collections import defaultdict
dic=defaultdict(list)
input_list=[]
for i in range(len(input_list)):
dic[input_list[i]]+=[i]
max_value = max(input_list)
Sol = dic[max_value]
You can use numpy (numpy arrays are very fast):
import numpy as np
input_list= np.array([2, 3, 5, 2, 5, 1, 5])
i, = np.where(input_list == np.max(input_list))
print(i)
Output:
[2 4 6]
Here's the approach which is described in comments. Even if you use some library, fundamentally you need to traverse at least once to solve this problem (considering input list is unsorted). So even lower bound for the algorithm would be Omega(size_of_list). If list is sorted we can leverage binary_search to solve the problem.
def max_indexes(l):
try:
assert l != []
max_element = l[0]
indexes = [0]
for index, element in enumerate(l[1:]):
if element > max_element:
max_element = element
indexes = [index + 1]
elif element == max_element:
indexes.append(index + 1)
return indexes
except AssertionError:
print ('input_list in empty')
Use a for loop for O(n) and iterating just once over the list resolution:
from itertools import islice
input_list= [2, 3, 5, 2, 5, 1, 5]
def max_indexes(l):
max_item = input_list[0]
indexes = [0]
for i, item in enumerate(islice(l, 1, None), 1):
if item < max_item:
continue
elif item > max_item:
max_item = item
indexes = [i]
elif item == max_item:
indexes.append(i)
return indexes
Here you have the live example
Think of it in this way, unless you iterate through the whole list once, which is O(n), n being the length of the list, you won't be able to compare the maximum with all values in the list, so the best you can do is O(n), which you already seems to be doing in your example.
So I am not sure you can do it faster than O(n) with the list approach.