Sorting Arrays in Ascending

[vino689]

[1.4472136]
[1.28]
[1.98994949]
[0.00211989]
[1.8]
[0.63960072]
[0.85857864]
[0.4]
[0.01005051]

Hi , I just want to Sort the above in Ascending order , All are 1D arrays

[ndc85430]

What have you tried or at least thought about?

[micseydel]

Something like

from operator import itemgetter

your_list = ... # list of 1-D lists

your_list.sort(key=itemgetter(0))

That said, as ndc85430 said, your posts should generally show your attempt.

[Marbelous]

There are MANY ways to do this but you haven't given us any idea what your actual goal is. There are no base "arrays" in Python. You have shown us 9 python lists, each with 1 float number in them. If you want to sort them, put them all into a list and use the built-in sorted() or list.sort() function.

https://docs.python.org/2/howto/sorting....rtinghowto

[perfringo]

Maybe I am stupid but what is wrong with just:

>>> sorted([[1.1], [1.0], [1.05]])
[[1.0], [1.05], [1.1]]

[micseydel]

Maybe I am stupid

For the OP's purposes, yours is probably equivalent to mine, and as you implied, simpler. In retrospect I should have used a lambda instead of a mysterious built-in library too.

[vino689]

Thanks
Actually my problem is to list the smallest Cosine distance in Graph
S= [(1,2),(3,4),(-1,1),(6,-7),(0, 6),(-5,-8),(-1,-1),(6,0),(1,-1)] # these are the list of x & y axix
P= (3,-4) # so here i have to find the distance between each tuple in S with P & find the top 5 lowest distance axis

below is the code i have written to find the cosine distance between each Tuple in S & P .


import math
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

S= [(1,2),(3,4),(-1,1),(6,-7),(0, 6),(-5,-8),(-1,-1),(6,0),(1,-1)]
for i in range (0,len(S)):
#print(S[i])
#print(np.ndim(S[i]))
J=np.reshape(S[i],(1,2))
#print(J)
#print(np.ndim(J))
P=[(3,-4)]
#print(np.ndim(P))
A=cosine_similarity(J,P)
#print(A)
C=1-A # Cosine Distance = 1-cosine similarity
#print©
#print(np.ndim©)
D=np.reshape(C,(1))
#print(np.ndim(D))
print(D)

# if i print D , i am getting the below Cosine distance
[1.4472136]
[1.28]
[1.98994949]
[0.00211989]
[1.8]
[0.63960072]
[0.85857864]
[0.4]
[0.01005051]
now i want to Sort 5 distance in ascending order (Less distance) & to print the respective X & Y axis

[vino689]

Actually my problem is to list the smallest Cosine distance in Graph
S= [(1,2),(3,4),(-1,1),(6,-7),(0, 6),(-5,-8),(-1,-1),(6,0),(1,-1)] # these are the list of x & y axix
P= (3,-4) # so here i have to find the distance between each tuple in S with P & find the top 5 lowest distance axis

below is the code i have written to find the cosine distance between each Tuple in S & P .


import math
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

S= [(1,2),(3,4),(-1,1),(6,-7),(0, 6),(-5,-8),(-1,-1),(6,0),(1,-1)]
for i in range (0,len(S)):
#print(S[i])
#print(np.ndim(S[i]))
J=np.reshape(S[i],(1,2))
#print(J)
#print(np.ndim(J))
P=[(3,-4)]
#print(np.ndim(P))
A=cosine_similarity(J,P)
#print(A)
C=1-A # Cosine Distance = 1-cosine similarity
#print©
#print(np.ndim©)
D=np.reshape(C,(1))
#print(np.ndim(D))
print(D)

# if i print D , i am getting the below Cosine distance
[1.4472136]
[1.28]
[1.98994949]
[0.00211989]
[1.8]
[0.63960072]
[0.85857864]
[0.4]
[0.01005051]
now i want to Sort 5 distance in ascending order (Less distance) & to print the respective X & Y axis

[vino689]

There are MANY ways to do this but you haven't given us any idea what your actual goal is. There are no base "arrays" in Python. You have shown us 9 python lists, each with 1 float number in them. If you want to sort them, put them all into a list and use the built-in sorted() or list.sort() function.

https://docs.python.org/2/howto/sorting....rtinghowto

Thanks
Actually my problem is to list the smallest Cosine distance in Graph
S= [(1,2),(3,4),(-1,1),(6,-7),(0, 6),(-5,-8),(-1,-1),(6,0),(1,-1)] # these are the list of x & y axix
P= (3,-4) # so here i have to find the distance between each tuple in S with P & find the top 5 lowest distance axis

below is the code i have written to find the cosine distance between each Tuple in S & P .


import math
import numpy as np
from sklearn.metrics.pairwise import cosine_similarity

S= [(1,2),(3,4),(-1,1),(6,-7),(0, 6),(-5,-8),(-1,-1),(6,0),(1,-1)]
for i in range (0,len(S)):
#print(S[i])
#print(np.ndim(S[i]))
J=np.reshape(S[i],(1,2))
#print(J)
#print(np.ndim(J))
P=[(3,-4)]
#print(np.ndim(P))
A=cosine_similarity(J,P)
#print(A)
C=1-A # Cosine Distance = 1-cosine similarity
#print©
#print(np.ndim©)
D=np.reshape(C,(1))
#print(np.ndim(D))
print(D)

# if i print D , i am getting the below Cosine distance
[1.4472136]
[1.28]
[1.98994949]
[0.00211989]
[1.8]
[0.63960072]
[0.85857864]
[0.4]
[0.01005051]
now i want to Sort 5 distance in ascending order (Less distance) & to print the respective X & Y axis