I'm trying to find and print the most common number in a matrix, using numpy if possible.
This list was given (made it a matrix using numpy.matrix(list)):
import numpy as np
list = [[2,4,1,6,3], [2,4,1,8,4], [6,5,4,3,2], [6,5,4,3,4], [1,2,3,4,5]]
matrix=np.matrix(list)
for this example i should be getting: 4 (as it is the most common number)
Given:
>>> import numpy as np
>>> LoL = [[2,4,1,6,3], [2,4,1,8,4], [6,5,4,3,2], [6,5,4,3,4], [1,2,3,4,5]]
>>> matrix=np.array(LoL)
>>> matrix
[[2 4 1 6 3]
[2 4 1 8 4]
[6 5 4 3 2]
[6 5 4 3 4]
[1 2 3 4 5]]
You can do:
>>> np.argmax(np.bincount(matrix.flat))
4
Or,
u, c = np.unique(your_lst, return_counts=True)
u[c.argmax()]
# 4
If you wanted to do this without numpy or any import to count the most frequent entry in a list of lists, you can use a dictionary to count each element from a generator that is flattening your list of lists:
cnts={}
for e in (x for sl in LoL for x in sl):
cnts[e]=cnts.get(e, 0)+1
Then sort by most frequent:
>>> sorted(cnts.items(), key=lambda t: t[1], reverse=True)
[(4, 7), (2, 4), (3, 4), (1, 3), (5, 3), (6, 3), (8, 1)]
Or, just use max if you only want the largest:
>>> max(cnts.items(), key=lambda t: t[1])
np.matrix is being deprecated and, in this case, np.array produces the matrix you are looking for.You don't need an intermediate matrix. You can directly flatten your list to get a single list and use bincount. It returns a list where the frequency of each number is given at the index position which corresponds to the number. That's why you use argmax to get the corresponding index
import numpy as np
listt = [[2,4,1,6,3], [2,4,1,8,4], [6,5,4,3,2], [6,5,4,3,4], [1,2,3,4,5]]
flat = [i for j in listt for i in j]
counts = np.bincount(flat)
print (np.argmax(counts))
# 4
sum(listt, []) is one of the worst possible ways to flatten a list, and it doesn't work at all with a NumPy matrix. Also, bincount does not return a list.
sum part with a list comprehension. And what is this argument about bincount not returning alist? What is its relevance to sum?[] instances and sums them. If you have two lists a = [1,2,3] and b = [4,5,6] then a+b merges the two lists to give [1,2,3,4,5,6]. This is what sum(listt, []) was doingsum argument being inefficient which I took care of in my edit. As coldspeed also said, numpy matrix was not needed here.
np.matrix. It's got all sorts of weird compatibility issues, it's deprecated, and people often find that they want the exact opposite of the behavior they asked for by using it.numpy.unique. You can return counts.